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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 40 Documents
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Search results for , issue "Vol 15, No 1: March 2026" : 40 Documents clear
Enhancing power grid reliability: a hybrid blockchain and machine learning approach Angadi, Ravi V.; Kumar, Suresh; Vijayalakshmi, A. K.; Shree, G. N. Vidya
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp421-429

Abstract

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.
Optimal battery selection for electric vehicles: a comparative ranking approach Pilla, Ramana; Sasidhar, Rebba; Sreedhar, Malleti; Naidu, Tentu Papi; Kiran, Shaik Rafi; Manoj, Vasupalli; Kumar, Kalyana Kiran
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp319-327

Abstract

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.
Modeling H2-enriched dual fuel engine performance and emissions Narayanan, Jayagopal; Murthy, Y. V. V. Satyanarayana; Kumar, Sandeep; Surendra, Talari; Madaka, Ram Mohan Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp211-227

Abstract

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.
The current status of the hydrogen value chain in India: a critical review Thakur, Shyamsing; Amrutsagar, Lalitrao; Kakati, Dipankar; Javanjal, Vijaykumar Kisan; Mahajan, Kuldeep A.; Tawar, Dipali B.
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp110-119

Abstract

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.
Extending battery life and reducing charging costs in electric vehicles through converter selection for on-board chargers Babu, Jangam Kishore; Rao, Ganney Poorna Chandra; Krishna, Puvvula Venkata Rama; Karike, Swathi; Kethireddy, Sailaja; Reddy, Sareddy Venkata Rami; Reddy, B. Nagi; Rangam, Rekha
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp14-22

Abstract

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.
Robust hall sensor signal conditioning for BLDC motor control using RC filters and optocoupler isolation Anwar, Hasni; Abdessamad, Intidam; Hassan, El Fadil; Abdellah, Lassioui; Marouane, El Ancary; Yassine, El Asri
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp373-382

Abstract

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.
Simulation of three phase grid interconnections with HVDC link with three level MMC converter Thiruveedula, Madhubabu; Babu, Nenavath Ramesh; Akash, Penagonda; Bhavana, Guthula Sravya; Arjun, Devasoth; Chethan, Gavvala
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp289-297

Abstract

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.
Preserving non-minimum phase dynamics in model order reduction of fifth-order DC-DC boost converters Rani, Neha; Ganguli, Souvik; Singh, Manjeet; Saini, Sundeep Singh
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp165-176

Abstract

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.
Enhancing electrolyzer performance for hydrogen production in a solar system using a buck converter with sliding mode control Idrissi, Abdellah El; Imodane, Belkasem; Oubella, M’hand; Ameziane, Hatim; Benydir, Mohamed; Dahmane, Kaoutar; Belkhiri, Driss; Ajaamoum, Mohamed
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp69-79

Abstract

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.
Elk herd optimizer for cost-efficient hybrid energy systems under renewable uncertainty Pham, Ly Huu; Nguyen, Hung Duc; Truong, Chi Trung; Nguyen, Quoc Trung
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp430-439

Abstract

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.

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