International Journal of Applied Power Engineering (IJAPE)
International Journal of Applied Power Engineering (IJAPE), p-ISSN 2252-8792, e-ISSN 2722-2624 focuses on the applied works in the areas of power generation, transmission, and distribution, sustainable energy, applications of power control in large power systems, etc. The main objective of IJAPE is to bring out the latest practices in research in the above-mentioned areas for efficient and cost-effective operations of power systems and electrical energy sources. The journal covers, but is not limited to, the following scope: electric power generation, transmission and distribution, utilization of electric power, energy conversion, electrical machinery, sustainable energy, insulation, solar energy, high-power semiconductors, power quality, power economic, FACTS, renewable energy, electromagnetic compatibility, electrical engineering materials, high voltage insulation technologies, high voltage apparatuses, lightning, protection system, power system analysis, SCADA, electrical measurements, and the electrical apparatus connected to such systems. The journal is published by the Intelektual Pustaka Media Utama (IPMU) in collaboration with Institute of Advanced Engineering and Science (IAES). Beginning with issue 1 of volume 10 (2021), the IJAPE will be published as a three-monthly journal (4 issues/year). We are happy to announce that the International Journal of Applied Power Engineering has been accepted for inclusion in Scopus, the world's largest abstract and citation database of peer-reviewed literature, and got Q2 in the journal ranking by ScimagoJR.
Institute of Advanced Engineering and Science
Optimal design of three-phase solar PV integrated unified power quality conditioner (UPQC)
This research investigates the utilization of a unified power quality conditioner (UPQC) to address power quality issues in the electrical grid and mitigate harmonics introduced by non-linear loads. The UPQC system is augmented by a combination of photovoltaic (PV) and battery energy storage system (BESS). Typically, the PV system supplies active power to the load. However, in cases where the PV system cannot provide sufficient power, the BESS is activated to ensure a continuous power supply, particularly during prolonged voltage interruptions. To enhance system reliability and reduce dependency on environmental conditions, a hybrid PV-BESS system is proposed. The inclusion of the BESS improves long-term voltage support capabilities, simplifies the DC-link voltage regulation algorithm, and facilitates the production of clean energy. For efficient phase synchronization operation of the UPQC controller under unbalanced and distorted grid voltage conditions, a self-tuning filter (STF) integrated with the unit vector generator (UVG) technique is employed.
Copyright (c) 2026 Yogesh S. Pawar, Mahesh Kadu, Pawan C. Tapre, Dinesh S. Wankhede, Rajendra M. Rewatkar, Swapna M. Choudhary, Rakesh G. Shriwastava http://creativecommons.org/licenses/by-sa/4.0
Extending battery life and reducing charging costs in electric vehicles through converter selection for on-board chargers
The electric vehicle (EV) sector is among the quickly expanding industries today. Global commitment to reducing pollution levels promotes interest in EVs. Fuel combustion engines emit around 10% of the globe's greenhouse gas emissions, which exacerbate the greenhouse effect. The emissions from electric vehicles are 17–27% less than those from internal combustion engines. The short battery life, high cost of charging, and lack of charging stations are some disadvantages of electric vehicles. The goal of this study is to suggest the ideal converter for the on-board charger (OBC), one that can extend battery life by lowering charging current at extremes of state of charge (SOC) and lower charging costs by increasing power factor (PF). The current control range than the isolated converter using transformers. Lastly, an analysis of the MATLAB/Simulink output findings is conducted to verify the effectiveness of the suggested OBC design with a non-isolated converter.
Copyright (c) 2026 Jangam Kishore Babu, Ganney Poorna Chandra Rao, Puvvula Venkata Rama Krishna, Swathi Karike, Sailaja Kethireddy, Sareddy Venkata Rami Reddy, B. Nagi Reddy, Rekha Rangam http://creativecommons.org/licenses/by-sa/4.0
Integration of renewable energy for sustainable electric vehicle charging in Sidoarjo, Indonesia: a techno-economic perspective
This study explores the techno-economic feasibility of integrating solar photovoltaic (PV) systems for electric vehicle (EV) charging infrastructure in Sidoarjo, Indonesia. Through simulation using HOMER Pro software, both standalone PV and PV-grid hybrid configurations were evaluated under real-world EV load profiles. The analysis reveals that the PV-grid hybrid system demonstrates superior economic performance, achieving an annual energy production of 39,214 kWh, a levelized cost of energy (LCOE) of $0.3975/kWh, and a return on investment (ROI) of 62.01% over 20 years, despite a payback period exceeding 30 years. Sensitivity analysis confirms that the system remains moderately resilient under a 10% reduction in solar irradiance or a 20% increase in EV demand. The study also compares the standalone PV configuration, which, while suitable for off-grid applications, results in significant energy underutilization. Moreover, the PV-grid model supports surplus electricity sales, enhancing financial viability. These findings provide actionable insights for stakeholders, energy planners, and policymakers aiming to scale EV charging infrastructure sustainably in Indonesia’s urban environments.
Copyright (c) 2026 Mochamad Choifin, Yuki Trisnoaji, Zainal Arifin, Mochamad Subchan Mauludin, Marsya Aulia Rizkita, Singgih Dwi Prasetyo, Dony Perdana http://creativecommons.org/licenses/by-sa/4.0
Current state of production of аlternative energy on the Absheron Peninsula
The article is devoted to the study of the relationship between sustainable development and the introduction of innovative technologies, and the formation of smart cities. The Azerbaijan Republic is a land-poor country and has exhausted most of its natural resources. Therefore, the use of renewable energy sources and scientific research in this direction are important and topical issues for the country's scientists. Wind demand: in 10 months (from January to October) showed 3.000 GWh-4.000 GWh in Absheron (2020-2024 years). Since bioenergy can be produced in any weather, it is more reliable than solar and wind energy in Azerbaijan's regions. Seasonal variations in the availability of agricultural residues can lead to uneven energy production and create difficulties in ensuring a constant supply. The study is innovative given the importance of non-competition with food production, as well as the unique environmental, economic, and technological implications of each biofuel production method.
Copyright (c) 2026 Ramil Sadigov Ali, Nazila Alverdiyeva Farman, Gunay Mammadova Israphil, Vusala Isaqova Gudrat, Turkan Hasanova Allahverdi, Muhammad Madnee http://creativecommons.org/licenses/by-sa/4.0
Robust SOC estimation for lithium-ion batteries under faulty charging scenarios using sliding mode observer techniques
With the growing demand for electric vehicles, embedded electronics, and renewable energy applications, lithium-ion batteries have become an essential component in modern energy storage systems. Accurate state of charge (SOC) estimation is crucial for ensuring battery reliability, longevity, and safety, particularly under faulty charging conditions—a challenge where many conventional estimation techniques fall short due to model limitations or lack of robustness. In this study, we propose an advanced SOC estimation approach based on a sliding mode observer (SMO) integrated with a third-order equivalent circuit model (ECM). Unlike conventional methods, which either focus on SOC estimation without considering battery voltage or apply SMO techniques only to second-order models, our approach enhances estimation accuracy by incorporating a higher-order model that better captures the complex battery dynamics. The proposed methodology is tested under both normal and faulty charging conditions, demonstrating superior performance in estimating both SOC and terminal voltage over extended periods. The simulation results confirm the robustness of the method, with accurate SOC tracking even in the presence of charging current faults, making it a viable solution for real-world applications in battery management systems (BMS). This work contributes to improving fault-tolerant SOC estimation strategies, advancing the development of safer and more efficient energy storage technologies.
Copyright (c) 2026 Soulef Mahiddine, Abdelghani Djeddi, Dib Djalel http://creativecommons.org/licenses/by-sa/4.0
Hybrid system energy simulation for housing
Fossil fuel power plants are still used as a source of energy around the world and have a significant impact on emissions and environmental pollution. To reduce these emissions, renewable energy offers a solution that can be applied in future housing. This study proposes a simulation using HOMER to determine the most cost-effective composition of hybrid renewable energy systems in housing. This simulation can combine photovoltaic (PV) systems, wind power (WP), and a converter that functions to change DC to AC from PV to obtain an alternating current (AC) system. The hybrid combination of PV and WP proves to be the most appropriate and economical choice at the research location. The results of the study showed that the installation of a hybrid system in housing, with an initial investment cost of IDR 107,474.43 million and an annual operating cost of IDR 22,540.23 million, is 41% lower than conventional fossil fuel-based systems. Research data shows that the project's payback period is estimated to be around 11 years. These findings can be recommendations for similar systems in regions with similar contours and geography. Apart from that, positive monetary impacts can provide incentives for policymakers to implement similar hybrid systems, thereby contributing to the goal of global emissions neutralization.
Copyright (c) 2026 Agus Junaidi, Rahmaniar, Suwarno, Catra Indra Cahyadi, Wanapri Pangaribuan, Hot Marindo Hutabarat, Arsita Devi Tambunan, Albert Panjaitan http://creativecommons.org/licenses/by-sa/4.0
Enhancing electrolyzer performance for hydrogen production in a solar system using a buck converter with sliding mode control
As the world increasingly turns to renewable energy, green hydrogen produced through water electrolysis has emerged as a clean and promising alternative to fossil fuels. In this work, we explore a solar-powered hydrogen production system that uses real data from an operational photovoltaic (PV) installation, ensuring accurate and realistic modeling of environmental conditions. A DC-DC buck converter is used to regulate the fluctuating PV output, supplying the precise voltage needed by a PEM electrolyzer. Sliding mode control (SMC) strategy is applied to maintain voltage stability, and its performance is compared with a traditional proportional-integral (PI) controller. Simulations in MATLAB/Simulink demonstrate that SMC offers better dynamic performance, including minimal overshoot, faster response, and an impressive hydrogen production rate of 0.98 L/min (98% efficiency). By providing more consistent voltage to the electrolyzer, SMC significantly boosts overall system performance. These findings underline the potential of advanced control strategies, supported by real-world data, to make renewable hydrogen production more reliable and efficient.
Copyright (c) 2026 Abdellah El Idrissi, Belkasem Imodane, M’hand Oubella, Hatim Ameziane, Mohamed Benydir, Kaoutar Dahmane, Driss Belkhiri, Mohamed Ajaamoum http://creativecommons.org/licenses/by-sa/4.0
Enhanced intentional controlled islanding with BESS integration
Severe power system outages can lead to uncontrolled failures and system instability. Intentional controlled islanding is a strategy that deliberately splits the power system into balanced, stand-alone islands to ensure continuous electricity supply until full restoration. However, the execution of islanding may result in certain islands being unbalanced in terms of generation and load. In such cases, load shedding is implemented to achieve balanced stand-alone islands. Nevertheless, load shedding is not the best option as it will result in more users experiencing power disruptions. Therefore, this study explores the integration of battery energy storage systems (BESS) to enhance intentional controlled islanding, with the aim to form balance islands without the need to execute load shedding. This study evaluates the effectiveness of BESS in forming balanced islands and optimizing islanding strategies. The IEEE 30-bus and IEEE 118-bus test systems were used to validate the effectiveness of BESS in enhancing the intentional controlled islanding implementation. The results demonstrated the role of BESS in facilitating intentional controlled islanding, forming stable and balanced island operations without the need for a load shedding scheme. These findings highlight the potential of BESS to enhance the reliability and effectiveness of intentional controlled islanding.
Copyright (c) 2026 Amar Saufi Nasir, Nur Zawani Saharuddin, Izham Zainal Abidin, Ezreen Farina Shair, Sharin Ab Ghani http://creativecommons.org/licenses/by-sa/4.0
Trapezoidal PWM scheme for voltage gain inverter
The trapezoidal modulating wave-based high voltage gain 9-level inverter (HVG9LI) addresses significant difficulties related to the growing usage of capacitors, DC sources, and semiconductor switches. The proposed HVG9LI generates a nine-level resultant voltage with few components, exhibiting the capacity to double the output voltage gain. Furthermore, the HVG9LI utilizes a trapezoidal modulating wave and variable frequency carrier (TM-VFC) pulse width modulation method to increase the resulting voltage and enhance the voltage output quality. The performance and practicability of the HVG9LI with TM-VFC are evaluated across several modulation techniques and indices implemented by using MATLAB/SIMULINK and tested experimentally.
Copyright (c) 2026 Harikrishna Naraboyana, I. Kumaraswamy http://creativecommons.org/licenses/by-sa/4.0
High impedance fault discrimination in microgrid power system using stacking ensemble approach
High impedance (HI) faults in microgrid (MG) power systems are non-linear, intermittent, and have low fault current magnitudes, making them challenging to detect by typical protective systems. Consequently, it is imperative to implement a sophisticated protection system that is dependent on the precision of fault detection. In this study, a stacking ensemble classifier (SEC) is proposed to discriminate HI fault from other transients within a photovoltaic (PV) generated MG power system. The MG model is simulated with the introduction of faults and transients. The features of data set from event signals are generated using the discrete wavelet transform (DWT) technique. The dataset is used to train the individual classifiers (Naïve Bayes (NB), decision tree J48 (DTJ), and K-nearest neighbors (KNN)) at initial and meta learner in the final stage of SEC. The SEC outperforms other classification methods with respect to accuracy of classification, rate of success in detecting HI fault, and performance measures. The outcomes of the classification study conducted under standard test conditions (STC) of solar PV and the noisy environment of event signals clearly demonstrate that the SEC is more dependable and performs better than the individual base classification approaches.
Copyright (c) 2026 Arangarajan Vinayagam, Raman Mohandas, Meyyappan Chindamani, Bhadravathi Gavirangapa Sujatha, Soumya Mishra, Arivoli Sundaramurthy http://creativecommons.org/licenses/by-sa/4.0
The current status of the hydrogen value chain in India: a critical review
The Bharat is the largest economy with a humongous population that has increasing energy demands day by day. Clean energy sources like green hydrogen are necessary to balance climate change and meet energy demand, which also reduce carbon footprints in related energy sectors. This paper critically reviews the need of green hydrogen, production, storage and transportation strategies, the role of government schemes, and prominent private corporations working in the Indian green hydrogen sector. Efforts are made to analyze available data and current advisory regulations pertaining to the green hydrogen ecosystem in India. Based on this, suggestions are made for a research and development roadmap for establishing a green hydrogen value chain. This research paper suggests salt caverns as potential geological structures for hydrogen storage chains and also sheds light on potential collaborative initiatives and pilot projects for improving the efficiency and sustainability of the green hydrogen value chain across developing countries like India.
Copyright (c) 2026 Shyamsing Thakur, Lalitrao Amrutsagar, Dipankar Kakati, Vijaykumar Kisan Javanjal, Kuldeep A. Mahajan, Dipali B. Tawar http://creativecommons.org/licenses/by-sa/4.0
Enhancing security in portable solar power supply design for alternative energy applications
Access to reliable electricity remains a challenge in remote and off-grid areas, where conventional power sources are often unreliable or unavailable. This paper presents the design and development of an internet of things (IoT) system for monitoring and securing a portable solar power station tailored for alternative energy applications. The system, which can be recharged using photovoltaic energy sources, employs a coulomb counting method to accurately estimate the battery's state of charge (SoC) and prevent overcharging and overdischarging. The portable power supply provides stable direct current (DC) outputs (5 V, 12 V, 24 V) and an alternating current (AC) output for various remote area applications, including telecommunications and household use. A dual-relay mechanism is used for battery protection: one relay disconnects charging at 100% SoC and reactivates at 70%, while the other disconnects the load at 20% SoC to avoid deep discharge. IoT connectivity enables real-time monitoring and remote control via smartphone. This development promotes efficient energy management, battery longevity, and improved access to sustainable electricity in underserved regions.
Copyright (c) 2026 Syafii, Benny Dwika Leonanda, Novizon, Rindina Armysa http://creativecommons.org/licenses/by-sa/4.0
A novel 9-level fourfold-boost switched capacitor inverter (N9L-FBSCI) configuration utilizing fewer components and optimized active switches
Multilevel inverter (MLI) topologies are more important in high-voltage applications where the most common design tends to have significant disadvantages, including being very component when it comes to the switch voltage stress, control scheme, and also not self-voltage balanced. These problems lead to higher cost, lower efficiency, and lower reliability. This paper will therefore develop a new nine-level fourfold-boost switched capacitor inverter (N9L-FBSCI) without increasing the number of components but ensures greater voltage gains and ease of use. It uses only one DC source, eight active switches, and two capacitors with a self-balancing mechanism of the voltage, avoiding extra balancing of the voltage. A four fold voltage gain is achieved using fewer switching devices per stage and less blocking voltage to control across switches. An efficient control is achieved by a level-shifted phase disposition PWM (LS-PDPWM) technique. Analytical and comparative testing against recent MLI design proves that the topology proposed has better voltage boosting and efficiency using the least number of components. Simulation and experimental verification prove the practical efficiency of the N9L-FBSCI, which can achieve a 400 V peak output with low total harmonic distortion. The topology has a high potential in renewable and industrial fields with cost effective high performance. Experimental and simulation data support an output voltage of 400 V at an output load current of 2 A with RL loading (100 Ω, 100 mH) delivering 400 W power output. The efficiency in the case of the inverter reaches its peak at 97.84% and voltage and current total harmonic distortion (THD) of 16% and 6%, correspondingly. The present proposed N9L-FBSCI has a better voltage gain and fewer components than available nine-level topologies without altering the delight of the wave position.
Copyright (c) 2026 N. Subhashchandrabose, I. Kumaraswamy http://creativecommons.org/licenses/by-sa/4.0
ANFIS-MPPT based PMSG-wind turbine interfaced with water pumping and battery management systems for optimal power flow and energy management
This paper presents the adaptive neuro-fuzzy inference system-maximum power point tracking (ANFIS-MPPT) approach for optimizing power flow in a water system powered by a permanent magnet synchronous generator (PMSG)-wind turbine. The system uses a PMSG-based wind energy conversion system (WECS) with an ANFIS for MPPT, enabling efficient power extraction under variable wind conditions. A bidirectional SEPIC-Zeta converter interfaces a battery energy storage system (BESS) to regulate the DC-bus voltage and maintain continuous power supply to a three-phase induction motor driving the water pump. An artificial neural network (ANN)-based controller is used to manage the charging and discharging of the battery based on real-time voltage deviation. The entire system, including wind turbine, PMSG, converters, and intelligent control algorithms, is modeled and simulated in MATLAB/Simulink. Comparative analysis with conventional MPPT techniques highlights the superior performance of the proposed hybrid ANFIS-based control in terms of power flow regulation, voltage stability, and operational reliability. The results confirm that the proposed approach significantly enhances energy management and system resilience, making it suitable for standalone or remote water pumping applications powered by renewable energy sources.
Copyright (c) 2026 Saritha Kandukuri, Ram Dulare Nirala, Sivaprasad Kollati, Tata Himaja, Adireddy Durga Bhavani http://creativecommons.org/licenses/by-sa/4.0
A novel analytical hybrid optimization methodology for maximizing renewable energy integration in radial distribution networks
Integrating distributed generation (DG) units into radial distribution systems (RDS) presents significant challenges, including voltage instability, power losses, and compliance with modern grid standards. To address these limitations, this study proposes a novel hybrid optimization methodology that combines advanced mathematical models with iterative power flow analysis. The approach introduces a multi-objective optimization framework that integrates voltage sensitivity factors, power loss indices, and voltage stability measures. A key innovation is the use of voltage stability indices (VSIs) as dynamic weighting factors to guide the optimization process, ensuring a balanced trade-off between minimizing power losses and enhancing network stability. This framework provides a precise and scalable solution for optimizing DG placement and sizing simultaneously. The methodology is validated on the IEEE 33-bus distribution system, demonstrating a 68% reduction in power losses, a 4.88% improvement in voltage stability, and a 70.4% DG integration rate, all achieved without altering the network configuration. These results highlight the proposed framework’s potential to enhance the resilience, efficiency, and reliability of RDS, offering a robust and standards-compliant solution for DG integration.
Copyright (c) 2026 Fateh Ouali, Narimen Aouzellag Lahaçani, Yanis Hamoudi http://creativecommons.org/licenses/by-sa/4.0
Preserving non-minimum phase dynamics in model order reduction of fifth-order DC-DC boost converters
In this work, a unified modelling approach is developed for the model order reduction of non-minimum phase systems. An optimized approach is adopted to address the problem. The coordinated hunting behavior of Cuban boa snake is made use of to develop a new optimization strategy. A constrained optimization method is developed to reduce a 5th order boost converter in the unified domain. Comparison is carried out with multiple classical techniques as well as some of the widely known nature inspired algorithms. The step and Bode responses using the proposed method offers closeness to the original responses as compared to the existing techniques. The pole zero mapping reveals the non-minimum nature of the reduced system. The stability of the reduced system is reflected through the Nyquist plot. A second-order proportional-integral-derivative (PID) controller is also synthesized using approximate model matching and Cuban boa snake optimization algorithm (CBSOA), which demonstrates superior transient performance, minimal steady-state error, and enhanced robustness.
Copyright (c) 2026 Neha Rani, Souvik Ganguli, Manjeet Singh, Sundeep Singh Saini http://creativecommons.org/licenses/by-sa/4.0
Optimization of load frequency control systems using PSO technique
This paper investigates the improvement of low-frequency load control (LFC) by optimizing integral part (PID) control using particle swarm optimization (PSO). Load frequency control is important to ensure energy stability by maintaining the balance between production and consumption. Conventional proportional integral derivative controllers are widely used for this purpose; however, their performance can be further improved through optimization. This work uses particle swarm optimization, a nature-inspired algorithm, to set the parameters of the proportional integral derivative controller. PSO was chosen because it can search for good solution space and find a good agreement between control parameters, thus improving the dynamic and stable response of the system. This article provides a comprehensive evaluation of the proposed approach, including simulation results and comparisons with standard PID controllers. The effectiveness of the optimized PID controllers in reducing the frequency difference and improving the overall efficiency of the power plant under different conditions is demonstrated. This study provides insight into the use of artificial intelligence to improve control parameters in the power grid, providing a promising way to improve the efficiency and reliability of frequency controllers.
Copyright (c) 2026 Debani Prasad Mishra, Rudranarayan Senapati, Lingam Yashwanth, Peesodi Uday, Surender Reddy Salkuti http://creativecommons.org/licenses/by-sa/4.0
Modulation and performance analysis of two-wheeler electric vehicle
When compared to traditional cars, electric vehicles (EVs) have less pollution, better fuel efficiency, and are better for the environment. This essay explores the evolution of EVs in great detail, emphasizing their vital role in lowering CO2 emissions and promoting sustainability. It builds a dynamic model for EVs using MATLAB/Simulink, which explains the state of charge (SOC) and range prediction. The study emphasizes the importance of EVs in promoting a sustainable future by thoroughly covering design details, modeling, and a scientific methodology. Through the use of modeling to clarify technical aspects and highlight the significance of EV adoption, this study highlights the vital role that EVs play in reducing environmental impact and advancing environmentally friendly transportation. It highlights EVs' potential to revolutionize the automobile sector while promoting cleaner modes of transportation. It offers a thorough overview of EV production and usage and fervently promotes their wider acceptance as a means of laying the groundwork for a more sustainable and clean future.
Copyright (c) 2026 Debani Prasad Mishra, Rudranarayan Senapati, Pavan Kumar, Lakshay Bhardwaj, Surender Reddy Salkuti http://creativecommons.org/licenses/by-sa/4.0
Induction motor simultaneous fault diagnosis based on Takagi-Sugeno models
This article proposes a model-based approach to the concurrent diagnosis of stator and rotor faults in induction motors (IMs) using Takagi-Sugeno (TS) fuzzy models. Fault-free detection is essential to prevent unexpected downtime and economic loss in industrial applications. The study first develops a dynamic model of the IM in the synchronized reference frame with the rotor under healthy and faulty operations. Different fault conditions like stator inter-turn short circuits, defective rotor bars, and combination thereof are considered. A TS model for every case is developed based on the precise nonlinear model. Simulation outcomes prove the validity of the new models in simulating the dynamic response of the motor under faulty operating modes. The residual signals are used to compare the performance of the model in fault isolation. The proposed method offers a classification that inherently separates between fault types. Such a contribution presents an efficient real-time fault detection and predictive maintenance facility, which renders it suitable for hardware-in-the-loop application in intelligent drive systems.
Copyright (c) 2026 Samira Souri, Mohamed Lakhdar Louazene, Abdelghani Djeddi, Youcef Soufi http://creativecommons.org/licenses/by-sa/4.0
Modeling H2-enriched dual fuel engine performance and emissions
This study utilizes a validated GT-Power simulation model to evaluate hydrogen (H2) enrichment effects on the performance and emissions of a four-cylinder, 86 kW dual-fuel diesel engine. The primary goal is identifying operating strategies that enhance efficiency while maintaining nitrogen oxide (NOx) emissions at or below baseline levels, termed "NOx neutral" operation. The methodology involves adjusting engine load between 2 and 16 bar brake mean effective pressure (BMEP) and varying H2 energy substitution from 10% to 70% at 1500 rpm. To analyse complex non-linear relationships, this research employed response surface methodology (RSM) and a random forest (RF) machine learning algorithm. Results indicate optimal H2 substitution lies in the 20-30% range, yielding a 2-3% improvement in brake thermal efficiency (BTE) and a significant decrease in brake specific fuel consumption (BSFC) from 200-220 g/kWh to 160-180 g/kWh. While CO2, HC, and CO emissions decreased, NOx remained stable only up to 25% substitution, increasing sharply thereafter. Consequently, H2 energy contribution should be limited to 25% to effectively control NOx. The combined use of simulation with RSM and RF models proves an efficient, accurate method for engine analysis, minimizing extensive physical testing requirements.
Copyright (c) 2026 Jayagopal Narayanan, Y. V.V. Satyanarayana Murthy, Sandeep Kumar, Talari Surendra, Ram Mohan Rao Madaka http://creativecommons.org/licenses/by-sa/4.0
ANFIS controller and DQ frame-based power optimization for grid-tied three phases converter
The study proposes an effective optimization technique aimed at reducing power loss and improving energy conversion efficiency in a three-phase electrically linked converter by employing an adaptive neuro-fuzzy inference system (ANFIS) controller. The control strategy integrates ANFIS with Fourier direct-quadrature (DQ) cycle translation based on the synchronous reference frame theory (SRFT). This combination significantly enhances converter performance by ensuring precise line synchronization, efficient voltage regulation, and effective harmonic suppression, all while delivering faster dynamic responses. The approach also simplifies the control framework and enables independent regulation of reactive and active power, offering greater operational flexibility. Such converters play a critical role in transforming DC or AC power from the grid or renewable sources into compatible electrical energy suitable for distribution. The proposed ANFIS-based DQ frame control method provides an advanced solution for optimizing the operation of electrically interconnected three-phase power converters. The research demonstrates the method’s effectiveness through detailed MATLAB simulations, confirming improved stability, reduced losses, and superior overall system performance.
Copyright (c) 2026 Nagasridhar Arise, Madhubabu Thiruveedula, Guguloth Divya, Bhukiya Dheeraj Naik, Komaram Sravan Kumar, Ogulapu Kiran Kumar http://creativecommons.org/licenses/by-sa/4.0
Artificial neural network-optimized bridgeless Landsman converter for enhanced power factor correction in electric vehicle applications
Electric vehicles (EVs) are gaining popularity globally due to their energy-efficient battery storage systems, low carbon emissions, and eco-friendly operation. By transforming both the transportation and electrical sectors, EVs could create a synergistic relationship that reduces fossil fuel use and improves renewable energy integration. However, this convergence emphasizes the necessity for appropriate power factor correction (PFC) methods, especially in EV battery charging systems, to alleviate supply-end PQ concerns. Use of a bridgeless Landsman converter (BLC), noted for its efficiency and link voltage monitoring, is innovative in this research. A proportional-integral (PI) controller tuned by an artificial neural network (ANN) improves prediction and classification, especially response time. The ANN-based PI controller optimises system performance in real time using adaptive control. Using a hysteresis controller attached to a pulse width modulation (PWM) generator regulates the converter's steady-state switching frequency for accurate and consistent output. The proposed approach reduces harmonic distortions and improves operating efficiency. This comprehensive architecture improves power factor and addresses significant PQ concerns in EV charging infrastructure. Integrating improved control tactics and converter design shows that this approach may support electric car technology developments. MATLAB simulations show that power factor correction (PFC) charges EV batteries quickly and effectively. Findings suggest the technique could increase power quality, system efficiency, and EV uptake.
Copyright (c) 2026 Podila Purna Chandra Rao, Radhakrishnan Anandhakumar, T. Vijay Muni, L. Shanmukha Rao http://creativecommons.org/licenses/by-sa/4.0
Machine learning-based real-time power stability optimization for photovoltaic systems using hybrid inductor-capacitor patterns
Photovoltaic (PV) systems often face real-time power stability challenges due to rapid fluctuations in solar irradiance and varying load conditions, which conventional control strategies struggle to manage effectively. Addressing this limitation, the present study proposes a novel machine learning-based control framework integrated with a hybrid inductor-capacitor (LC) network to enhance dynamic power regulation. The proposed system employs predictive algorithms to adjust LC parameters in real time, enabling adaptive voltage and current stabilization during transient conditions. Simulation results validate the model's effectiveness, showing a 58% reduction in power fluctuation (from 12% to 5%) and consistent improvement in voltage stability index (VSI), maintaining values above 0.95 compared to 0.88-0.93 in traditional systems. Moreover, the approach reduces computation time by 66% (150 ms versus 450 ms for PID-based systems), supporting faster and more efficient control actions. These outcomes demonstrate that the proposed intelligent control strategy significantly improves energy efficiency, voltage stability, and responsiveness in PV systems, offering a scalable solution for reliable grid integration of renewable energy sources.
Copyright (c) 2026 Jayashree Kathirvel, S. Pushpa, P. Kavitha, Sathya Sureshkumar, Kannan Andi, Prabakaran Pramasivam http://creativecommons.org/licenses/by-sa/4.0
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems
In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
Copyright (c) 2026 Bodapati Venkata Rajanna, Aaluri Seenu, Kondragunta Rama Krishnaiah, Anantha Sravanthi Peddinti, Nelaturi Nanda Prakash, Bandreddi Venkata Seshukumari, Giriprasad Ambati, Shaik Hasane Ahammad, Chakrapani Srivardhan Kumar, Allamraju Shubhangi Rao http://creativecommons.org/licenses/by-sa/4.0
Artificial intelligence for optimizing renewable energy systems: techniques, applications, and future directions
The integration of artificial intelligence (AI) is critically transforming the renewable energy sector. This review synthesizes AI's role in optimizing solar and wind energy systems, focusing on power forecasting, system optimization, and predictive maintenance. The research goal was to systematically analyze how diverse AI techniques enhance these critical aspects. Key findings indicate AI's capacity to substantially improve short-term solar irradiance and wind power forecasts (e.g., via SARIMAX, long short-term memory (LSTM), and hybrid deep learning models), dynamically manage energy flow in smart grids and microgrids, optimize maximum power point tracking (MPPT) in photovoltaic (PV) systems, and enable proactive maintenance through anomaly detection in wind turbines using IoT-integrated AI. Key conclusions reveal that AI significantly enhances the efficiency, reliability, and economic viability of solar photovoltaic and wind power generation, offering superior adaptability and predictive capabilities over traditional methods. While AI is important for the global transition to cleaner energy, persistent challenges related to data quality and availability, model interpretability, and cybersecurity must be addressed to fully unlock its potential in practical renewable energy applications.
Copyright (c) 2026 Ian B. Benitez, Edwin C. Cuizon, Jose Carlo R. Dizon, Kristina P. Badec, Daryl Anne B. Varela http://creativecommons.org/licenses/by-sa/4.0
Simulation of three phase grid interconnections with HVDC link with three level MMC converter
This paper presents the simulation and analysis of a three-phase grid interconnection system using a high voltage direct current (HVDC) link with a three-level modular multilevel converter (MMC). The HVDC link enhances modern power transmission by reducing losses, increasing transfer capacity, and improving grid stability. The three-level MMC, known for its modular design, scalability, and low harmonic distortion, is employed for efficient grid integration. The system, modeled in MATLAB/Simulink, includes a three-phase alternating current (AC) grid, HVDC link, and MMC operating in both rectification and inversion modes to enable bidirectional power transfer. Proportional-integral (PI) controllers synchronize the MMC with the grid, ensuring stable operation under varying conditions such as load changes and disturbances. Simulation results indicate high efficiency, low harmonic distortion, reduced switching losses, and decreased voltage stress on components. The HVDC link also improves reliability by damping power oscillations and providing reactive power support. Overall, the integration of HVDC and MMC offers a robust, efficient, and sustainable solution for future high-performance grid interconnections, serving as a strong basis for further advancements in HVDC transmission systems.
Copyright (c) 2026 Madhubabu Thiruveedula, Nenavath Ramesh Babu, Penagonda Akash, Guthula Sravya Bhavana, Devasoth Arjun, Gavvala Chethan http://creativecommons.org/licenses/by-sa/4.0
Feature transformation with ensemble learning for power grid stability in sustainable energy and industry systems
Power grids today operate under unpredictable and rapidly changing conditions, making reliable stability prediction increasingly important. This study evaluates two hybrid learning frameworks that integrate deep feature transformation with ensemble classification. In the first framework, an autoencoder (AE) is used for feature encoding before classification with extreme gradient boosting (XGBoost), while the second applies a TabTransformer (TT) followed by the same classifier. For comparison, conventional ensemble models, including random forest and standalone LightGBM, are also assessed. The models are tested on a large public dataset using stratified cross-validation and standard performance metrics. Results show that the AE-XGBoost hybrid achieves the highest performance, with a test accuracy of 97.73% and an F1-score of 0.98 for both stable and unstable states. LightGBM also performs strongly, offering consistent accuracy (95.8%) and good interpretability. In contrast, TT-XGBoost, despite its architectural novelty, achieves lower accuracy (89.4%) and struggles with unstable states. These findings highlight that model effectiveness depends not only on architectural complexity but also on the synergy between feature transformation and classification. The results provide practical insights for building dependable, confidence-aware predictive systems to support smart grid decision-making.
Copyright (c) 2026 Sirish Kumar Pagoti, Kavitha Kapala, Thikka Rama Kanaka Durga Vara Prasad, Chukka Rajasekhar, Krishna Rao Pedada, Sai Kiran Oruganti http://creativecommons.org/licenses/by-sa/4.0
Implementation of the soil health monitoring system to achieve better yield
Agriculture is a fundamental pillar of the economy, particularly in developing countries where a significant proportion of the population depends on farming for their livelihood. Crop productivity is influenced by soil type and its changing chemical properties. A lack of understanding of soil health, crop-specific nutrient requirements, and the interaction between water and the environment often leads to inappropriate irrigation and fertilizer use. As a result, crops receive either excessive or insufficient nutrients, leading to reduced yields and the waste of water, energy, and other valuable resources. To address these issues, this paper presents an IoT-based soil health monitoring system that supports sustainable crop management. The proposed system integrates sensors to monitor key soil parameters such as temperature, humidity, soil moisture, and pH levels in real time. Based on the collected data, the system autonomously adjusts irrigation and environmental conditions to maintain soil health. This approach improves crop productivity, optimizes resource utilization, and promotes energy conservation in agricultural systems.
Copyright (c) 2026 S. R. Bhagyashree, Guddappa Halligudra, Anupama Sindagi, Madhu Nagaraj, C. Shyamala, Shaista Tarannum, R. Thailagavathy, T. R. Yashavantha Kumar http://creativecommons.org/licenses/by-sa/4.0
Optimal battery selection for electric vehicles: a comparative ranking approach
Electric vehicles (EVs) have emerged as an eco-friendly alternative to traditional internal combustion engines, with battery technology playing a pivotal role in their success. Key factors like energy density, power output, charging speed, durability, cost, safety, and environmental impact hinge on the choice of battery. Various technologies in lithium-ion batteries are assessed for their suitability in EVs. The right battery is essential for optimal performance, extended range, and sustainability. This paper offers an in-depth look at battery selection in EVs, examining different types in lithium-ion and their pros and cons. Additionally, it explores into three prominent decision-making methods: fuzzy analytic hierarchy process (FAHP), evaluation based on distance from average solution (EDAS), and preference ranking organization method for enrichment evaluation-II (PROMETHEE II). FAHP ranks batteries based on their relevance to specific EV requirements, while EDAS and PROMETHEE II provide a multi-criteria framework. These methods offer valuable insights into choosing the most suitable lithium-ion battery for EVs. The study underscores the importance of meticulous battery selection and highlights the efficacy of decision-making approaches like FAHP, EDAS, and PROMETHEE II. As battery tech advances, future research on alternatives like solid-state and sodium-ion batteries could revolutionize the EV industry.
Copyright (c) 2026 Ramana Pilla, Rebba Sasidhar, Malleti Sreedhar, Tentu Papi Naidu, Shaik Rafi Kiran, Vasupalli Manoj, Kalyana Kiran Kumar http://creativecommons.org/licenses/by-sa/4.0
Constrained multi-objective optimization of high frequency transformer design for dual active bridge converter in solid state transformers using genetic algorithms
This study presents a novel multi-constraint and multi-objective optimization based approach that applies genetic algorithms (GAs) for developing high-frequency transformer (HFT) designs for dual active bridge converters (DABs) in solid-state transformers (SSTs). SSTs are increasingly adopted in modern power systems due to their higher efficiency, compact structure, and improved operational reliability when compared with conventional transformers. Developing HFTs for SSTs involves several challenges, particularly the need to balance competing objectives such as improving efficiency, limiting losses, and reducing the area product while satisfying multiple design constraints. To address these challenges, this work applies a constrained multi-objective GA implemented in MATLAB to optimize the design of an HFT for a DAB converter. The methodology allows for the simultaneous optimization of multiple design objectives while taking into consideration restrictions like efficiency, leakage inductance, temperature limits, core winding area, and sizes. Our comparison with particle swarm optimization (PSO) indicates that the GA achieves more consistent convergence and consistently lower total losses. The case studies reinforce this observation, giving compact and high-performance HFT designs tailored for SST applications. The optimization approach provides a reliable and scalable method for developing thermally robust and space-efficient HFTs suitable for next-generation SST platforms and renewable-energy applications.
Copyright (c) 2026 Jayrajsinh B. Solanki, Kalpesh J. Chudasama http://creativecommons.org/licenses/by-sa/4.0
473 kV lightning impulse test of an insulator embedded in pressurized and heated liquid nitrogen
Liquid nitrogen is the most common fluid for cooling superconducting power engineering devices. The dielectric strength of an insulator rod embedded in liquid nitrogen at a pressure of 0.3 MPa was investigated with lightning impulse voltage series of 20 impulses of ±473 kV for gap lengths up to 50 mm between a grounded plane and a high voltage electrode in the shape of a bell. The influence of boiling due to quenching of the superconductor was simulated by heating impulses with a duration of 10.1 s. Before triggering the heater impulse, the liquid nitrogen was in the subcooled state i.e., a pure liquid. Transient bubble generation due to the heater impulse was confirmed by video recording through an observation window of the cryostat. The voltage of 473 kV was kept by a gap length of 18 mm in case of impulses of positive polarity. A gap of 30 mm was necessary in case of negative polarity. Hence, a strong polarity effect was found. Calculated field values based on the experimental results do not exceed limits used for the high voltage design study for a support insulator of a superconducting fault current limiter.
Copyright (c) 2026 Stefan Fink, Sven Lautensack, Volker Zwecker http://creativecommons.org/licenses/by-sa/4.0
Impact of ferrite materials on wireless power transfer efficiency for electric vehicles battery chargers
This paper investigates the impact of ferrite materials on the efficiency of wireless power transfer (WPT) systems designed for electric vehicle (EV) and E-bike battery chargers. The study employs 3D full-wave electromagnetic simulations in CST Studio Suite 2024 to evaluate how Laird Performance Materials 33P2098-0M0 ferrite influences magnetic coupling, field confinement, and overall transfer efficiency. Two configurations were analyzed: coil-only and coil-with-ferrite plates, under a fixed 20 mm air gap and an operating range of 30–50 kHz. The inclusion of ferrite materials significantly improved magnetic-flux directivity and coupling strength, resulting in a peak efficiency of 99.21% at 41.3 kHz, compared to 99.09% at 38.1 kHz for the coil-only design. The enhanced configuration also reduced magnetic leakage and improved resonance stability, as verified through mesh-independent simulations and analytical validation with less than 2% error. The proposed model correlates ferrite permeability with mutual inductance and resonant-frequency tuning, confirming the theoretical basis of the efficiency gain. This work bridges a gap in small-scale EV and E-bike WPT research by quantifying the measurable benefits of ferrite integration and providing design guidelines for compact, thermally stable, and high-efficiency wireless charging systems.
Copyright (c) 2026 Wan Muhamad Hakimi Wan Bunyamin, Rahimi Baharom http://creativecommons.org/licenses/by-sa/4.0
Robust hall sensor signal conditioning for BLDC motor control using RC filters and optocoupler isolation
Brushless DC (BLDC) motors require accurate rotor position feedback to guarantee reliable electronic commutation. However, hall-effect sensor signals are often degraded by high-frequency switching noise from the inverter, which can cause false commutations and control errors. Moreover, a direct connection to control hardware may introduce ground loops and jeopardize sensitive electronics. This study proposes a hardware-based hall signal conditioning method that integrates RC low-pass filters, designed with a 1.59 kHz cutoff frequency, to attenuate inverter-induced noise, and 4N35 optocouplers to provide galvanic isolation. Unlike existing approaches that rely primarily on algorithmic noise rejection or digital filtering, the proposed solution offers a compact, low-latency hardware implementation suitable for real-time embedded control. Experimental validation using a dSPACE DS1104 board shows a 14.7 dB improvement in signal-to-noise ratio (SNR) and a 36% reduction in timing jitter, ensuring clean and isolated hall signals for stable six-step commutation. These improvements directly translate into smoother torque production, enhanced speed stability, and increased protection of control electronics, making the method applicable to both research and industrial BLDC motor systems operating in noisy environments.
Copyright (c) 2026 Hasni Anwar, Intidam Abdessamad, El Fadil Hassan, Lassioui Abdellah, El Ancary Marouane, El Asri Yassine http://creativecommons.org/licenses/by-sa/4.0
Sensorless control strategy for brushless doubly fed reluctance generator under voltage flickering at point of common coupling
The brushless doubly-fed reluctance generator (BDFRG) is widely used in grid-connected wind energy conversion systems (WECS). It has been observed that there is a continuous voltage flickering at the point of common coupling (PCC) between the BDFRG power terminals and the alternating current (AC) microgrids due to either the load variations or wind turbine output variations. Under such circumstances, sensorless control of BDFRG using the existing model reference adaptive system (MRAS) models exhibits erroneous active power output. This is because the variables selected in these models are directly or indirectly dependent on the voltage at the PCC. In this paper, a sensorless control mechanism for the BDFRG is proposed, which provides better performance in terms of control accuracy. Moreover, the planned scheme is insensitive to the parameter variations of the BDFRG. The performance of the planned system has been tested with a voltage flickering of 50% for 1 ms at the PCC. The stability test presented in this paper reveals that the model is robust and error-free against the noise disturbances. The planned system is implemented using proper simulations and a hardware platform with a practical BDFRG of 2.5 kW, and a dSPACE CP1104 module.
Copyright (c) 2026 Manish Paul, Adikanda Parida, Anu Kumar Das http://creativecommons.org/licenses/by-sa/4.0
Blade number and angle effect the archimedes spiral wind turbine performance
The efficiency and performance of Archimedes spiral wind turbine (ASWT) are affected by the design and number of turbine blades which can convert the kinetic energy of the wind into mechanical energy to turn a generator that can produce electricity as much as possible in low wind speed. This study aims to obtain the optimal ASWT design in low wind speed in terms of aerodynamic performance. The method is conducted by numerically computational fluid dynamics (CFD) simulation on the fixed-opening angle and the blades number variations. The results show that the smallest C_D value is -2.18 at the 65° of opening angle, the largest C_L value at the 45° of opening angle is 0.37, and the largest C_M value is 0.61 at the 65° of opening angle and 4 blades. Therefore, it can be concluded that the Archimedes wind turbine with 4 blades and 65° pitch is the optimal.
Copyright (c) 2026 Rosadila Febritasari, Muhammad Ibnul Abidin http://creativecommons.org/licenses/by-sa/4.0
Performance evaluation of a trapezoidal solar pond using magnesium sulphate (MgSO₄)
Emerging global demand for clean and sustainable energy has intensified research into efficient methods of solar energy capture and storage. Among various renewable energy storage technologies, salt gradient solar ponds (SGSPs) have emerged as a reliable and cost-effective solution. This study presents an advanced experimental evaluation of a trapezoidal SGSP using magnesium sulphate (MgSO₄) as the salinity medium to enhance heat storage performance and system stability. A laboratory-scale trapezoidal pond with a depth of 30 cm was constructed using 18 mm thick plywood and an optimized 16% MgSO₄ concentration (SGSP-M16) was employed to maintain thermal stratification. Experiments conducted over a four-month period in Salem, Tamil Nadu, India, involved detailed energy and temperature analysis across upper convective zone (UCZ), non-convective zone (NCZ), and lower convective zone (LCZ). Results revealed maximum temperature difference of 28 °C among UCZ and LCZ, with LCZ achieving peak energy efficiencies of 25.24%, 26.80%, 28%, and 32.09% from January to April, respectively. These findings confirm the effectiveness of the trapezoidal MgSO₄ based SGSP as a sustainable and scalable system for renewable energy storage and efficient thermal management, suitable for applications such as desalination, greenhouse heating, and industrial preheating.
Copyright (c) 2026 P. Dineshkumar, M. Raja, M. Venkatesan, M. Dineshkumar http://creativecommons.org/licenses/by-sa/4.0
A technical review of implemented pulsed electric field generators with different topologies
Pulsed electric field technology (PEF) seeks application in a variety of industries, such as food processing, wastewater treatment, and biomedical engineering, as it provides a non-thermal substitute for conventional thermal pasteurization techniques. The PEF generators are an increasingly important component of this technology since it necessitates high voltage in the range of 2 kV/cm to 100 kV/cm in food processing to inactivate the microorganisms. Different PEF profiles are required based on different foods and the type of microorganisms present in it. The size of existing PEF producers and space limitations are the major challenges in this technology. Hence, there is a growing need to develop laboratory-scale PEF generators to study and analyze the PEF electrical profile for the specified applications. While the single MOSFET PEF generator is appropriate for high frequency applications, the series linked MOSFET PEF generator, one of the PEFs produced in our lab, is found economical. The voltage boosting concept is used to develop 1.62 kV pulses at 52 kHz from 120 V DC input. This paper majorly studies the circuit topologies, switching strategies, and output performances of PEF generators implemented in the laboratory.
Copyright (c) 2026 Krishnaveni Subramani, S. Jeroline Mary http://creativecommons.org/licenses/by-sa/4.0
Enhancing power grid reliability: a hybrid blockchain and machine learning approach
As contemporary power grids are becoming more complex with the integration of renewable energy sources, distributed generation, and smart grid technologies. Conventional contingency analysis techniques, based on centralized architectures and static rule-based evaluations, tend to be inadequate in real-time fault detection, automated response, and cybersecurity. This paper suggests a hybrid approach that combines machine learning algorithms with blockchain technology to improve both predictive intelligence and security of contingency analysis. For the IEEE 30-bus test case, different line outage and generator failure cases were simulated. Different machine learning models, such as random forest (RF), support vector machine (SVM), and gradient boosting (GB), were trained to classify and predict these contingencies. In parallel, cryptographic primitives like advanced encryption standard (AES), Rivest–Shamir–Adleman (RSA), and elliptic curve cryptography (ECC) were tested in a blockchain setting to provide security for event data and enable automatic recovery steps through smart contracts. Outcomes illustrate that the GB showed the maximum fault classification rate (93.4%), and ECC ensured light yet robust data protection for blockchain activities. Against the conventional system, the designed model enhanced the response time in case of faults, accuracy, and system fault tolerance. This two-layer mechanism presents a scalable, proactive, and cyber-safe mechanism for the power grid in the future.
Copyright (c) 2026 Ravi V. Angadi, Suresh Kumar, A. K. Vijayalakshmi, G. N. Vidya Shree http://creativecommons.org/licenses/by-sa/4.0
Elk herd optimizer for cost-efficient hybrid energy systems under renewable uncertainty
This paper suggests a new method, called elk herd optimizer (EHO), for effectively addressing the optimal generation cooperation problem involving thermal, hydro, solar, and wind power plants (WPPs), in which the uncertainty of wind speed and solar radiation from renewable power plants is considered. The primary goal of this study is to minimize the costs from thermal, wind, and solar power plants (SPPs) while adhering to all operational constraints associated with these power plants and the overall power system. Two systems were tested to evaluate the performance of EHO method alongside two other techniques: the coot optimization algorithm (COOT) and the tunicate swarm algorithm (TSA). Both systems were optimally scheduled over a 24-hour period; however, the second system accounted for uncertainties in generation and cost from solar and WPPs. From the result analysis, EHO method was able to achieve a lower cost compared to COOT, TSA, and other previously employed methods for optimizing generation across all plants. Therefore, EHO is recommended as an effective optimization tool for addressing the uncertainties associated with solar radiation and wind speed.
Copyright (c) 2026 Ly Huu Pham, Hung Duc Nguyen, Chi Trung Truong, Quoc Trung Nguyen http://creativecommons.org/licenses/by-sa/4.0
Hydrothermal synthesis and defect-driven optical characterization of CdS nanoparticles for semiconductor and solar applications
Nanoparticles (NPs) play a crucial role in advancing technology, particularly by enhancing the performance of energy storage in semiconductor applications. The synthesis of NPs with reduced particle size and increased surface area, along with a higher number of active sites, facilitates improved ion diffusion, making them highly suitable for such applications. Various methods have been employed to reduce the size of NPs, depending on factors such as purity and controlled composition. The present study focuses on controlling both the size and composition of cadmium sulfide (CdS) NPs, aiming to achieve a high surface-to-volume ratio. These NPs were synthesized using a hydrothermal method in a high-pressure autoclave. The evaluation of the synthesized inorganic CdS-NPs for technological applications requires experimental validation of their characteristics, including particle size, energy band gap, thermal stability, temperature response, as well as optical and electronic properties. The results obtained using the proposed methods reveal a bandgap of 2.28 eV, a hexagonal wurtzite structure with an average crystallite size of 10.26 nm, reduced effective mass, and an intense absorption peak at a higher wavelength. These characteristics indicate that the synthesized CdS nanoparticles are suitable for various applications, including high-power semiconductors, solar energy harvesting, optoelectronic devices, and materials for energy and electrical engineering.
Copyright (c) 2026 Deepti Bhargava, R. K. N. R. Manepalli, M. C. Rao, P. Venkata Ramana Rao, N. S. Subba Rao, A. Narendra Babu, P. Sree Brahmanandam http://creativecommons.org/licenses/by-sa/4.0