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29,734 Article Results

Improving photovoltaic efficiency: a systematic study of P&O and INC MPPT techniques

10.11591/ijpeds.v17.i1.pp728-739
Abdelkbir Jamaa , Ahmed Moutabir , Rachid Marrakh , Abderrahmane Ouchatti
Achieving high efficiency in photovoltaic (PV) systems under fluctuating irradiance and temperature conditions relies on effective maximum power point tracking (MPPT) techniques. Among the most commonly adopted approaches, perturb and observe (P&O) and incremental conductance (INC) are favored for their ease of implementation and operational flexibility. Nevertheless, a systematic comparison of their performance under dynamic conditions remains limited. This study conducts a comparative evaluation of P&O and INC algorithms using MATLAB/Simulink, with emphasis on tracking accuracy, convergence speed, and overall efficiency. A standard PV module is exposed to rapid variations in irradiance and temperature to examine algorithm robustness. The results indicate that although P&O achieves fast convergence in steady-state operation, it exhibits noticeable oscillations around the maximum power point, resulting in efficiency losses of up to 3%. Conversely, the INC method offers improved tracking precision and reduced oscillations, yielding efficiency gains of 2-4% over P&O in dynamic environments. These findings underline the trade-off between algorithmic simplicity and tracking accuracy, and provide practical guidance for selecting MPPT strategies in both grid-connected and standalone PV applications. 
Volume: 17
Issue: 1
Page: 728-739
Publish at: 2026-03-01

A framework for robust PID controller design: an optimization-based approach for inductive loads

10.11591/ijpeds.v17.i1.pp359-369
Ali Abderrazak Tadjeddine , Miloud Kamline , Latifa Smail , Soumia Djelaila , Hafidha Reriballah
This paper presents a comprehensive comparative study of proportional-integral-derivative (PID) controller tuning methodologies for inductive load applications across three representative scenarios. We systematically evaluate classical methods (Ziegler-Nichols, internal model control) against global optimization algorithms (genetic algorithm (GA), particle swarm optimization (PSO)) applied to resistor-resistor-inductor (RRL) circuit models. Results demonstrate that PSO achieves superior performance for moderate-to-slow systems, reducing settling time by 84% while completely eliminating overshoot compared to Ziegler-Nichols. The algorithm automatically discovers optimal PI controller structures, simplifying implementation. However, for ultra-fast systems (time constants < 1 ms), internal model control proves more reliable, achieving 0.84 ms settling with only 0.16% overshoot. Optimized controllers demonstrate exceptional robustness, maintaining stability under ±50% parameter variations and effectively rejecting disturbances. This research provides engineers with a scenario-based framework for method selection, moving beyond heuristic tuning to achieve previously unattainable performance levels. The findings establish optimization-based tuning as a systematic, reliable approach for high-performance control system design in industrial applications.
Volume: 17
Issue: 1
Page: 359-369
Publish at: 2026-03-01

Voltage compensation using fuel cell fed dynamic voltage restorer

10.11591/ijpeds.v17.i1.pp663-673
Ryma Berbaoui , Rachid Dehini
One of the basic tasks of the dynamic voltage restorer (DVR) is to maintain voltage stability in distribution systems by correcting any deviations or disturbances in the three-phase supply. Whether they are increases or decreases. However, one of its disadvantages is its power source, as it cannot supply itself with power from the electrical grid like parallel compensators, which obtain power directly from the grid. This article presents an energy study of a dynamic voltage regulator (DVR) when operated using a power source represented by fuel cells, which are considered a clean and renewable source. On the other hand, excess energy from the regenerator or fuel cells can be output and injected into the distribution network for utilization via a parallel compensator (CP). The parallel compensator also compensates for reactive energy on the reactive load side to increase the power factor measured at the source side of the distribution system. This integrated system also uses neural networks to identify voltage disturbances and determine the voltages (modules/arguments) that must be added to the voltages in the power grid for correction. This analytical study was completed using a simulation system to confirm the effectiveness of this integrated system. The distinctive feature of this study is the integration of fuel cells and neural network-based control in the DVR system, providing a sustainable and intelligent alternative to conventional configurations, which makes it different from traditional DVRs that operate with batteries and supercapacitors. Its efficiency in compensating for voltage drops and surges is evident, and it also improves the power factor and ensures reliable operation of voltage-sensitive devices.
Volume: 17
Issue: 1
Page: 663-673
Publish at: 2026-03-01

Solar power forecasting using a SARIMA approach for Indonesia's grid integration

10.11591/ijpeds.v17.i1.pp293-302
Ricky Maulana , Syafii Syafii , Aulia Aulia
Indonesia’s transition toward a renewable energy-dominated power grid is progressing to meet increasing energy demands while reducing dependence on fossil fuels. According to the National Energy General Plan, their goal is to have 23% of the energy mix come from renewables by 2025 and 31% by 2050. Accurate forecasting of photovoltaic (PV) power output is crucial to address the intermittent nature of solar energy and ensure grid stability. A seasonal autoregressive integrated moving average (SARIMA) model was developed to estimate day-ahead photovoltaic power output in Padang City, Indonesia. Using NASA solar irradiance data from March 1-31, 2024, the SARIMA(1,0,1)(4,0,3)24 model achieved high accuracy with an NRMSE of 4.19%. To evaluate its performance, a comparative evaluation was conducted between the SARIMA model and two machine learning methods, namely artificial neural network (ANN) and long short-term memory (LSTM), in which SARIMA achieved the lowest forecasting error. These findings indicate that SARIMA remains an effective and interpretable statistical method for short-term PV forecasting, supporting reliable energy planning and power grid operations towards Indonesia's renewable energy goals.
Volume: 17
Issue: 1
Page: 293-302
Publish at: 2026-03-01

A novel high-gain DC-DC converter with fuzzy logic control for hydrogen fuel cell vehicle applications

10.11591/ijpeds.v17.i1.pp617-628
Gaddala Anusha , A. V. V. Sudhakar , Shaik Rafikiran , Ram Ragotham Deshmukh , C. H. Hussaian Basha
Hydrogen fuel cell vehicles (HFCVs) are emerging as a sustainable alternative to conventional internal combustion engines due to their zero-emission characteristics and high energy efficiency. However, the low output voltage of fuel cells poses a significant challenge in meeting the high-voltage requirements of electric traction systems. To address this, this paper proposes a high-gain non-isolated switched-capacitor (SC) DC-DC converter integrated with a fuzzy logic controller (FLC) for efficient power management in hydrogen fuel cell vehicle applications. The proposed converter topology achieves a significant voltage step-up without the use of bulky magnetic components, making it lightweight and compact for automotive integration. A maximum power point tracking (MPPT) controller using fuzzy logic is used to recover optimum energy out of the fuel cell stack during different loads and conditions of the environment. MATLAB/Simulink simulation results validate the high voltage gain, stable operation, and improved dynamic response of the proposed converter under FLC control. The proposed intelligent control strategy enhances fuel cell utilization and ensures effective operation of HFCV powertrains.
Volume: 17
Issue: 1
Page: 617-628
Publish at: 2026-03-01

Hybrid renewable energy for cold chain in Indonesia: technical and economic evaluation

10.11591/ijpeds.v17.i1.pp674-682
I Made Aditya Nugraha , I Gusti Made Ngurah Desnanjaya , Anis Khairunnisa , Mahaldika Cesrany
Cold storage plays a crucial role in preserving temperature-sensitive products, particularly in the fisheries and food sectors. However, its operation is highly energy-intensive and often constrained by unstable electricity supply in many Indonesian regions. This study quantitatively evaluates a hybrid renewable energy system integrating photovoltaic (PV) panels, diesel generators, batteries, and the utility grid to ensure sustainable cold storage operations. Using measured load profiles, solar irradiation data, and annual operating costs, the system achieved a 60% reduction in diesel fuel consumption, 30-50% lower CO₂ emissions, and annual savings exceeding IDR 100 million compared to conventional generator-based systems. The system demonstrated 83.5% overall efficiency, with a payback period of 4.4 years and a positive net present value (NPV), confirming its economic viability. The novelty of this research lies in presenting the first comprehensive techno-economic analysis of a PV-diesel-battery-grid hybrid system specifically designed for fisheries-based cold storage facilities in Indonesia, considering local solar potential and grid reliability. Despite its feasibility, implementation challenges remain, including a lack of skilled technicians, limited financial incentives, and bureaucratic constraints. To overcome this, the study recommends PV subsidies, low-interest green loans, and public–private partnerships aligned with Indonesia's energy transition roadmap and cold chain development goals.
Volume: 17
Issue: 1
Page: 674-682
Publish at: 2026-03-01

Performance evaluation of a trapezoidal solar pond using magnesium sulphate (MgSO₄)

10.11591/ijape.v15.i1.pp403-411
P. Dineshkumar , M. Raja , M. Venkatesan , M. Dineshkumar
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.
Volume: 15
Issue: 1
Page: 403-411
Publish at: 2026-03-01

Feature transformation with ensemble learning for power grid stability in sustainable energy and industry systems

10.11591/ijape.v15.i1.pp298-307
Sirish Kumar Pagoti , Kavitha Kapala , Thikka Rama Kanaka Durga Vara Prasad , Chukka Rajasekhar , Krishna Rao Pedada , Sai Kiran Oruganti
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.
Volume: 15
Issue: 1
Page: 298-307
Publish at: 2026-03-01

A novel 9-level fourfold-boost switched capacitor inverter (N9L-FBSCI) configuration utilizing fewer components and optimized active switches

10.11591/ijape.v15.i1.pp132-140
N. Subhashchandrabose , I. Kumaraswamy
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.
Volume: 15
Issue: 1
Page: 132-140
Publish at: 2026-03-01

Constrained multi-objective optimization of high frequency transformer design for dual active bridge converter in solid state transformers using genetic algorithms

10.11591/ijape.v15.i1.pp328-351
Jayrajsinh B. Solanki , Kalpesh J. Chudasama
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.
Volume: 15
Issue: 1
Page: 328-351
Publish at: 2026-03-01

Current state of production of аlternative energy on the Absheron Peninsula

10.11591/ijape.v15.i1.pp37-45
Ramil Sadigov Ali , Nazila Alverdiyeva Farman , Gunay Mammadova Israphil , Vusala Isaqova Gudrat , Turkan Hasanova Allahverdi , Muhammad Madnee
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.
Volume: 15
Issue: 1
Page: 37-45
Publish at: 2026-03-01

Optimization of load frequency control systems using PSO technique

10.11591/ijape.v15.i1.pp177-185
Debani Prasad Mishra , Rudranarayan Senapati , Lingam Yashwanth , Peesodi Uday , Surender Reddy Salkuti
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.
Volume: 15
Issue: 1
Page: 177-185
Publish at: 2026-03-01

Simulation of three phase grid interconnections with HVDC link with three level MMC converter

10.11591/ijape.v15.i1.pp289-297
Madhubabu Thiruveedula , Nenavath Ramesh Babu , Penagonda Akash , Guthula Sravya Bhavana , Devasoth Arjun , Gavvala Chethan
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.
Volume: 15
Issue: 1
Page: 289-297
Publish at: 2026-03-01

Robust hall sensor signal conditioning for BLDC motor control using RC filters and optocoupler isolation

10.11591/ijape.v15.i1.pp373-382
Hasni Anwar , Intidam Abdessamad , El Fadil Hassan , Lassioui Abdellah , El Ancary Marouane , El Asri Yassine
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.
Volume: 15
Issue: 1
Page: 373-382
Publish at: 2026-03-01

Elk herd optimizer for cost-efficient hybrid energy systems under renewable uncertainty

10.11591/ijape.v15.i1.pp430-439
Ly Huu Pham , Hung Duc Nguyen , Chi Trung Truong , Quoc Trung Nguyen
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.
Volume: 15
Issue: 1
Page: 430-439
Publish at: 2026-03-01
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