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International Journal of Applied Power Engineering (IJAPE)
Published by Institute of Advanced Engineering and Science
ISSN : 22528792     EISSN : 27222624     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
International Journal of Applied Power Engineering (IJAPE) 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. The journal covers, but not limited to, the following scope: electric power generation, transmission and distribution, 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, and electrical measurements.
Arjuna Subject : -
Articles 658 Documents
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Performance evaluation of a GA-tuned PID controller for a buck-boost converter based on integral error metrics Mahabaleshwara Bhat P.; Subramanya Bhat
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1168-1179

Abstract

DC-DC buck-boost converters are widely used in photovoltaic (PV) systems to maintain a regulated output voltage under varying source and load conditions. Although genetic algorithms (GA) based proportional-integral-derivative (PID) controllers are commonly used, the influence of different integral error performance indices on controller behavior has not been systematically examined. This paper presents a detailed study of GA-based PID tuning for a buck-boost converter operating in both buck and boost modes. Four integral error metrics-integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE)-are employed as fitness functions to obtain optimal controller gains. A detailed MATLAB/Simulink model of the converter working in continuous conduction mode (CCM) is developed to evaluate controller performance under step input, source transients, and load transients. The results demonstrate that the selected error metric significantly affects transient characteristics, including rise time, settling time, overshoot, and steady-state error. Controllers tuned using ITAE and ITSE provide better transient performance compared to IAE and ISE based tuning. The results highlight the critical role of objective function selection in GA based PID optimization and provide practical guidelines for buck-boost converters in PV applications.
Cost-effective hardware solutions for experimental validation in renewable energy emulation and storage systems Yassine El Asri; Abdellah Lassioui; Hassan El Fadil; Anwar Hasni; Marouane El Ancary; Hafsa Abbade; Mohammed Chiheb; Mohamed Koundi
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1287-1298

Abstract

Experimental validation is essential in renewable energy emulators and energy storage systems to ensure reliability, accuracy, and practical implementation. However, the high cost of AC/DC converters, measurement circuits, and microcontroller-based control platforms limits access to experimental validation, particularly in developing regions. This paper proposes cost-effective hardware and software solutions for validating renewable energy systems and storage management. The proposed approach is based on affordable power converters, low-cost voltage and current measurement circuits, data acquisition tools, and open-source control platforms. The methodology includes the design, implementation, and experimental testing of these low-cost solutions under different operating conditions. The obtained results demonstrate satisfactory measurement accuracy, stable behavior, and acceptable error margins, confirming the feasibility of the proposed setup for experimental validation. These solutions provide a scalable and accessible alternative to expensive laboratory platforms, enabling researchers and institutions with limited resources to perform reliable experimental studies and contribute to the advancement of sustainable energy technologies.
A novel MVVR controlled solar-PV fed MF-DVR for compensation of islanding and PQ issues in utility-grid integrated distribution system Tharinaematam Bhavani; Durgam Rajababu; Md Mujahid Irfan
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1023-1035

Abstract

The depletion of fossil fuels, planning of new industries, and increased population are considered significant motivations for the expansion of new power generation in line with the requisite load demand. In recent days, the solar-PV-based distribution generation is the most suitable power generation in a utility-grid-integrated distribution system. The main intention of this work is to present the effective DG scheme; it delivers the required active power during sudden interruptions, grid-islanding, and sudden block-outs. And also enhancing the voltage stability during voltage-harmonics, voltage sags/swells, and unsymmetrical fault conditions occurred in the utility-grid integrated distribution system through solar-PV fed multi-functional dynamic-voltage restorer (MF-DVR) device. The effective compensation performance of MF-DVR relies on viable reference voltage signals, which are produced by well-known control schemes reported in literature studies. But these regular schemes have reported that the major problems are highlighted and have been eliminated by proposing the novel modified voltage vector reference (MVVR) control scheme. The proposed MVVR controller perfectly produces the unique reference voltage signals for delivering a feasible switching pattern to the MF-DVR device. In this work, the design and performance of the proposed MVVR-controlled solar-PV-fed MF-DVR have been verified to enhance power quality and grid-islanding issues through MATLAB/Simulation software tool. The extracted simulation findings are presented with appealing interpretations complying with IEEE-519/2022 standards.
Near-zero NDZ islanding detection for multi-source DGs via hybrid ANFIS and adaptive fuzzy classification Madamaneri Ramya; Thangellamudi Devaraju
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1117-1131

Abstract

Increased penetration of distributed generation (DG) increases the likelihood of inadvertent islanding, in which traditional active/passive approaches are plagued with large non-detection zones (NDZ) and power-quality trade-offs. This article introduces a hybrid islanding detector that combines an adaptive neuro-fuzzy inference system (ANFIS) and an adaptive fuzzy classifier, concurrently benefiting from active frequency drift and rate-of-change-of-frequency (RoCoF) while consuming multi-signal features-RMS/THD of voltage and current, frequency, and active/reactive power sensed at the PCC. This architecture eliminates fixed-threshold brittleness and reduces the NDZ to zero without compromising power quality. Innovative aspects are i) a stacked, real-time sampling approach (Ts = 5 ms) that supplies per-signal ANFIS modules and a main decision ANFIS, ii) subtractive clustering for generating fuzzy rules data-driven, and iii) low iq perturbation to maintain unity power factor when querying doubtful NDZ examples. MATLAB/Simulink experimentation on a seven-case, seven-stage multi-source PV-interfaced microgrid (including power-matched NDZ) demonstrates fast, robust trips at disconnection with retention of IEEE-1547 voltage/frequency envelopes; the structure achieves minimum/ideal detection times of 0.04 s and reliably indicates islanding at ~0.4 s in matched and mismatched conditions, achieving normal breaker trip expectations (
Recurrent neural network-based electrical modeling and simulation of solar photovoltaic cell Bambang Purwahyudi; Agus Kiswantono; Hasti Afianti; Saidah Saidah
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1223-1232

Abstract

Photovoltaics (PV) are an important renewable energy (RE) source and a key component of solar power plants. Accurate modeling of their electrical output characteristics is crucial for efficient system design, such as the current-voltage (I-V) and power-voltage (P-V) curves. These characteristic curves depend on the photovoltaic cell (PVC) parameters such as short circuit current (ISC), open circuit voltage (VOC), and maximum power (PMAX). Previous studies have explored the artificial neural network (ANN) in photovoltaics generation (PVG) systems to estimate the output power of the PVC. Nevertheless, most ANN-based approaches are limited to estimating output power only from the daily power generation data of the PVG system. In this paper, a PVC model was developed using a recurrent neural network (RNN) to increase the exactness estimation of electrical parameters. The proposed RNN-based PVC model employs solar irradiance (S), temperature (T), and series resistance (RS) as input variables, whereas the output variables are the power and current of PVC. The contributions of the PVC model is evaluated through simulations under changing physical and environmental conditions. The simulation yield of the RNN-based PVC model successfully reproduces I-V and P-V curves and also fundamental parameters consistent with the behavior of real PVC.
Comparative study of MPPT algorithm on photovoltaic string under partial shading Dikpride Despa; Gigih Forda Nama; Zulmiftahul Huda; Stefanus Debiarto Marudut Sagala
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1422-1438

Abstract

Partial shading significantly degrades the photovoltaic (PV) performance value by introducing multiple peaks in power voltage (P-V) curve, complicating maximum power point tracking (MPPT). This research aims to presents a systematic comparative study of 4 MPPT algorithms, that are i) perturb and observe (P&O), ii) incremental conductance (InC), iii) particle swarm optimization (PSO), and iv) flower pollination algorithm (FPA), under 6 systematically testbeds modeled irradiance scenarios. The evaluation focused on tracking accuracy, convergence speed, and also robustness against local maxima entrapment. The findings indicated that slope-based algorithms (P&O and InC algorithm) achieved rapid convergence (
MPPT control of a PV-battery system using a gain-scheduled adaptive PI controller and dual active bridge converter Khalid Sabhi; Mohamed Talea; Hicham Bahri
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1299-1313

Abstract

Standalone photovoltaic-battery systems face significant challenges in maintaining optimal power extraction under rapidly varying irradiance conditions. Conventional MPPT controllers based on fixed-gain PI approaches suffer from poor dynamic response, oscillations around the maximum power point, and inability to adapt to the nonlinear behavior of dual active bridge (DAB) converters across different operating points. To address these limitations, this work proposes an innovative adaptive control strategy for maximum power point tracking (MPPT) in a stand-alone photovoltaic-battery system connected via a DAB converter. The gain-scheduled PI controller is recalculated at each 1 µs sample by local linearization of the complete nonlinear model of the PV-DAB-battery system, with explicit compensation for measurable disturbances (derived from irradiance G and temperature T). MATLAB/Simulink simulations under realistic irradiance profiles (slow ramps simulating the passage of clouds) demonstrate a clear superiority over a classic PI with fixed gains: ultra-fast response (approximately 1 ms), total absence of oscillations and overshoots, and strict maintenance of a constant second-order dynamic over the entire operating range. To our knowledge, this gain-scheduled analytical approach with explicit perturbation compensation has never before been applied to DAB topology in PV-battery systems. It is distinguished by its ease of implementation, low computational load, and robustness without the need for complex observers. This work lays the groundwork for future experimental validation and extensions to multi-source hybrid systems.
Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications Alvaro Yassif Marca Yucra; Gastón Orlando Suvire; John Armando Morales
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1200-1211

Abstract

This article presents a multi-objective optimization methodology for the optimal tuning of passive power filters in steelmaking facilities that operate with electric arc furnaces (EAFs). These industrial loads are well-known for introducing severe harmonic distortion, voltage unbalance, and flicker into the electrical network, significantly degrading power quality and equipment performance. To address these challenges, a multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), which simultaneously minimizes three key power quality indices: total harmonic distortion (THD), total demand distortion (TDD), and voltage unbalance factor (VUF). In addition, a multi-criteria decision analysis (MCDA) technique is applied to rank and select the most balanced and robust solution in different EAF operating scenarios. Unlike conventional filter design methods that prioritize a single performance criterion or rely on static harmonic assumptions, the proposed approach accounts for the nonlinear and time-varying behavior of EAFs, ensuring robust performance under diverse operating conditions. A comprehensive case study based on a Bolivian steel plant illustrates the effectiveness of the optimization strategy. Results indicate reductions of 47.6% in THD, 33.5% in TDD, and 63.6% in VUF, clearly outperforming conventional design approaches and significantly improving overall power quality. This work highlights the potential of evolutionary multi-objective algorithms for enhancing passive filter performance in complex industrial environments with highly distorted and unbalanced power conditions.
Novel approach for assessing the maximum load capacity of buses within a power transmission network Moussa Gonda; Arouna Oloulade; Richard Gilles Agbokpanzo; Maurel Richy Aza-Gnandji; Hassane Ousseyni Ibrahim; Cossi Télesphore Nounangnonhou; François-Xavier Fifatin; Adolphe Moukengue Imano
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1105-1116

Abstract

Achieving a balance between the satisfaction of energy requirements and adherence to environmental and social regulations stipulated in international agreements necessitates the rigorous management of existing electricity systems by electricity network operators, thereby ensuring that thermal and stability limits are not exceeded. Consequently, the assessment of load bus maximum capacity (LBMC) at any given load bus (LB) becomes imperative for ensuring the reliability and efficiency of electricity transmission networks. The extant literature on the subject of assessing LBMC in electrical networks either fails to take into account the combined effects on such networks of their various load points or, if it does, it is computationally intensive. The approach proposed in this paper involves a simplified method for assessing the LBMC for each of the LBs in the network. This is achieved to ensure that the combined effect of increasing the load on each of these buses does not compromise the load planning determined by a given network performance index, either during normal operation or in the event of a malfunction. The findings substantiate the efficacy of the proposed methodology, which facilitates the reliable estimation of LBMC. The reliability of this approach is ensured by the selection of a reliable performance indicator, which in our case is the complex stability index for transmission lines (CSITL).
Explainable AI for harmonic fingerprinting and voltage sag diagnosis in decentralized power grids: trends, challenges and future directions Mohd Hatta Jopri; Tole Sutikno; Yacine Djeghader; Mohd Riduan Mohd Shariff; Wan Azlan Wan Zainal Abidin
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1484-1498

Abstract

The evolution of decentralized power grids has increased the complexity of power-quality monitoring, particularly harmonic fingerprinting and voltage sag diagnosis. Artificial intelligence improves disturbance detection and classification, yet black-box models limit transparency, engineering validation, and operator trust. This review synthesizes 108 selected studies on explainable artificial intelligence (XAI) for power-system diagnostics, focusing on SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), attention-based interpretability, visual analytics, and physics-informed learning. The review integrates harmonic fingerprinting with voltage sag diagnosis through their shared requirements for source attribution, temporal interpretation, physical consistency, and operator-oriented explanation. Four major deployment gaps are identified: data quality, computational latency, physical grounding, and trustworthiness. Future priorities include real-time embedded XAI, physics-informed neural networks, federated learning, standardized trustworthiness metrics, and adaptive model lifecycle management. The findings indicate that reliable autonomous diagnosis requires explainability to be integrated with predictive performance, electrical-system physics, computational efficiency, and field validation. This integration provides a stronger foundation for transparent, resilient, and trustworthy diagnostic systems in decentralized power grids.

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