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International Journal of Emerging Electric Power Systems

Editor-in-Chief: Tarlochan Sidhu
Edited by: Fei Gao and Y. Liao

Language: English

First published: January 1, 2004

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Objective
International Journal of Emerging Electric Power Systems (IJEEPS) publishes significant research and scholarship related to latest and up-and-coming developments in power systems. The mandate of the journal is to assemble high quality papers from the recent research and development efforts in new technologies and techniques for generation, transmission, distribution and utilization of electric power.

Topics
The range of topics includes:

electric power generation sources
integration of unconventional sources into existing power systems
generation planning and control
new technologies and techniques for power transmission, distribution, protection, control and measurement
power system analysis, economics, operation and stability
deregulated power systems
power system communication
metering technologies
demand-side management
industrial electric power distribution and utilization systems

Article formats
Research articles, reviews

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Cutting-edge research on new developments
Novel methodologies
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Rapid ahead-of-print publication
High quality peer-review

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Impact of integrating large scale solar photovoltaic on the voltage stability of the Nigeria power network

December 3, 2025

Richard Oladayo Olarewaju, Ayodeji Samson Ogunjuyigbe, Temitope Raphael Ayodele, Adedayo Ademola Yusuff, Thapelo Mosetlhe, Feng Yuming, Chaoran Liu

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Integration of electricity based on intermittent renewable sources such as solar power to a grid can have adverse effect on electric power grid. In this work, we investigated the impact of integration of solar photovoltaic (SPV) on voltage stability. Six transmission buses (Kano, Kaduna, Gwagwalada, New Haven, Birnin Kebbi and Lokoja) with shortest distance to each of the 13 proposed locations have been identified and each of the Solar PV farms was integrated to the transmission bus closest to the proposed solar farm sites. The effects of SPV integration on Transmission lines loading have been performed and the Nigeria 56-bus transmission network was used for the investigation. Voltage stability analysis was carried out using the load margin obtained from the PV curve at each of the six identified buses and effect of SPV integration on the system voltage profile was identified. Sensitivity analysis was also performed in order to obtain the impact of increasing penetration on the voltage stability. The investigation was conducted using DigSilent Power Factory and MATLAB. The result shows that the safe region of integration for the six identified transmission buses is between 10 % (365.8 MW) at Gwagwalada bus and 19 % (695 MW) of base load power at Kaduna. 1 % of SPV was integrated simultaneously at Egbin, Ikeja West, Akangba, Sakete, Kano, Aja, Alagbon, and Osogbo with voltages lesser than 0.95 pu at the base case and the result reveals that all the buses in the system are within acceptable voltage level of 0.95–1.05 pu. Highest improvement in load margin is achieved when 1 % SPV is integrated at Kaduna bus among the six transmission buses considered. Different locations affect system load margins and voltage stability differently. 1 % integration of SPV at different buses significantly improve the load margin from 1,107.7 MW (Birnin Kebbi) to 1,448.9 MW (Kaduna).
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A novel power flow control unit for V2G and G2V mode of electric vehicles

June 12, 2025

Devika Harikumar, Narendramudra Narendran Geetha, Rajeev Thankappan Nair

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The increasing need for renewable energy and storage solutions, driven by the depletion of traditional energy sources, highlights the importance of integrating electric vehicles (EVs) as part of the energy storage system. The failure of distribution transformers during peak EV charging times in Kerala, India, highlights a significant issue related to judicious management of charging and discharging of Electric vehicles during peak time. The paper presents a novel control unit for EVs that supports both electric vehicle modes. The work focuses on designing and analyzing a Dual Active Bridge (DAB) based advanced charging system for G2V and V2G mode operation. A control scheme for managing power flow with bidirectional capability is developed based on the battery’s State-of-Charge (SoC) and the charging time slot during the day. The novelty of the work is the combined Time-of-Use (ToU) and State-of-Charge (SoC) based control algorithm for power flow management. The performance of the controller in both operating modes is thoroughly evaluated and validated, providing a robust foundation for its implementation. The system model is created in MATLAB/Simulink, and the effectiveness of the power flow control algorithm is tested through hardware-in-the-loop (HIL) experiments using OPAL-RT and TMS320F28379D microcontrollers. The proposed SoC and ToU-based power management strategy is economically efficient because it capitalizes on the pricing difference between off-peak and peak periods. With high charging/discharging efficiency, the combination of SoC and ToU-based tariffs proves profitable and sustainable, offering significant cost savings and financial returns in the long run. The intelligent power flow control unit will offer a practical solution to the peak demand challenges in utility due to uncoordinated EV charging.
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Wavelet-based fault detection and classification in MV grids using neural models: case study of Moroccan distribution system

December 8, 2025

Saad Sarih, Samira Chabaa, Zakaria Boulghasoul, Abdelouahed Tajer, El Bacha Abdelhadi

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The increasing complexity of modern power distribution systems, driven by renewable integration, evolving load patterns, and aging infrastructure, has accentuated the need for advanced fault detection and classification mechanisms, particularly in emerging medium-voltage (MV) networks such as Moroccan distribution grid. However, traditional protection schemes, often based on centralized logic and fixed thresholds, tend to underperform in complex or high-impedance fault conditions. Furthermore, global signal features such as RMS or frequency components are insufficient to capture the localized and phase-dependent behavior of faults. These limitations have prompted a growing interest in intelligent, data-driven approaches combining signal processing and machine learning to achieve high-resolution fault diagnosis and improved system reliability. The present study proposes and evaluates an intelligent fault classification framework tailored to MV distribution networks. It explores the comparative performance of three neural architectures and supervised learning classifiers, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN), applied to both globally extracted features and phase-localized wavelet descriptors. In addition to the baseline classification framework, a novel phase-based analysis method is introduced to enhance diagnostic performance. This method processes each phase and neutral current independently, based on the hypothesis that fault signatures emerge more distinctly when analysed separately. The experimental results demonstrate that neural models can reliably identify fault types across both approaches and underscores the potential of wavelet-enhanced AI models for smart and localized protection in emerging power distribution systems and prove its readiness for real-world deployment in SCADA-integrated protection systems.
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Leveraging extra high voltage grid supplies for the dynamic performance analysis of large power transformer in short-circuit scenario

September 24, 2025

Narendra Kotta, Jayaram Nakka

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The current study introduces a novel approach to evaluating the short-circuit (SC) withstand capability of 3-phase, 400/220/33 kV, 500 MVA power transformer through dynamic short-circuit (DSC) tests conducted under actual grid conditions. Departing from conventional lab-based methods, it utilizes a 765 kV grid supply for high-voltage to medium-voltage (HV-MV) windings (i.e., 400kV–220kV) and a 400 kV grid supply for medium-voltage to low-voltage (MV-LV) windings (i.e., 220kV–33kV), replicating operational conditions with greater accuracy. The actual-grid energization technique highlights the feasibility of performing high-fidelity DSC testing without the need for specialized test facilities. The research establishes a detailed correlation between transformer design parameters and short-circuit performance, providing essential insights into mechanical and thermal robustness during fault events. Additionally, it identifies the equipment and setup required for field-based DSC tests across different supply configurations, presenting a viable testing framework for utilities and manufacturers. The results confirm the transformer’s ability to withstand SC stresses across varying dynamic conditions, including those involving auxiliary LV supplies. This grid-based testing methodology enhances the reliability assessment process and sets a new benchmark for realistic, cost-effective validation of transformer short-circuit performance in high-capacity transmission systems.
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Fault location in distribution networks based on an Improved Black-winged Kite Algorithm

December 5, 2025

Ke Xu, Jiaxin Shi

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With the large-scale integration of distributed generations (DGs) and the expansion of distribution networks, the complexity of fault location has significantly increased. Traditional fault location methods struggle to meet the demands of modern power systems. This paper explores the impact of distributed generation access on fault location in distribution networks and constructs switching functions and evaluation functions suitable for multi-state distributed generation based on fault characteristic information from automated monitoring devices. To augment the optimization performance of fault localization algorithms, this paper proposes an Improved Black Kite Algorithm (IBKA) that incorporates an Elite Learning Strategy along with Lens Imaging Reverse Learning Strategy and Golden Sine Strategy. This enhances the ability to escape local optima effectively. Simulation results demonstrate that the IBKA exhibits high localization accuracy and fault tolerance in complex scenarios, including single-section faults, multiple-section faults, and Feeder Terminal Unit (FTU) information distortion in active distribution networks, significantly improvs convergence speed and solution efficiency.
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Design analysis of matrix converter for wireless Vehicle-to-Vehicle charging system and ecological footprint assessment of electric vehicle charging station

October 23, 2025

Shah Faisal, Govind Rai Goyal, Bhanu Pratap Soni, Dilawar Husain

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Electric vehicles (EVs) are increasingly being adopted as a sustainable transportation due to their lower dependency on fossil fuels and low emissions. However, charging time and range anxiety issues in battery technology are affecting the development and growth of EVs. The proposed study examined the system performance of wireless power transmission between two vehicles using a single-phase phase charging matrix converter system. Thus, besides its capability to convert the voltage level, the proposed matrix converter is designed to be an active power filter that helps enhance the power factor and the quality of the wave shape of the input current. A transmitter and receiver shape of coil is also recommended for a wireless Vehicle-to-Vehicle (V2V) charging system with consideration of misalignment and geometrical matching between the transmitters coils are mounted on the front side while the receiver coils are fixed on the rear side. The currently proposed core elements were seen to be effective in transferring power by super-thick silicon steel laminated core (current drawn up to 1.64 A) and a super-thick ferrite core (current drawn up to 1.89 A), both have very less hysteresis loss, which improves system performance. The ecological footprint (EF) associated with electric vehicle (EV) battery charging is estimated to be approximately 3.1 × 10 −4  gha/kWh. New research shows V2V wireless electrical vehicle charging can play a critical role in lowering greenhouse gas emissions combined with other air pollutants compared to traditional battery charging systems. The proposed methodology contains comprehensive modelling of the switching behaviour of the matrix converter as well as control strategies and equations of power flow. The experimental validation is conducted with alternative core materials, and the analysis of the efficiency of the proposed wireless V2V charging system is made with the results that justify the possibility and functionality of the proposed system.
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Partial discharge analysis in Class F and H epoxy resin insulators: void size impact on high-voltage performance

June 5, 2025

Mahesh Karre, Joshi Manohar

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Partial discharge (PD) describes localized electrical discharges that occur within insulating materials. Owing to its exceptional electrical and thermal properties, epoxy resin is widely utilized as an insulation medium in electrical systems. Insulation classes F and H are categorized based on their thermal endurance ratings, with insulation degradation remaining a primary cause of power equipment failures. PD-based diagnostic tools enable precise condition monitoring of insulation systems, critical for reliability. Voids inherent in solid dielectric materials often initiate insulation breakdown, particularly when PD activity originates within enclosed cavities. This paper investigates the relationship between void dimensions and insulation lifespan in dry-type transformers, focusing on PD-induced degradation within internal voids. Class-F and Class-H epoxy resin samples, incorporating artificial circular voids of 2 mm, 3.5 mm, and 4 mm diameters, were analyzed in collaboration with ESSENNAR R&D Center using IEC60270-compliant testing protocols. Key findings reveal that increased void size correlates with reduced partial discharge inception voltage (PDIV) and heightened discharge magnitudes and repetition rates. While observed discharges remained below the IEC60270 threshold of 140 pC, PDIV values approached the critical 10 pC limit in larger voids, underscoring the impact of void geometry on insulation performance. These insights emphasize the need for stringent quality control in epoxy resin applications to mitigate PD risks.
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A simplified multi-mode model predictive current control method for a novel neutral-point-connected open-end winding induction motor driven system

September 24, 2025

Leilei Guo, Jiaming Liu, Yanyan Li, Nan Jin

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This paper presents a simplified multi-mode model predictive current control (MPCC) scheme for a novel NPC-OEWIM powered by dual three-phase four-leg inverters with a 2:1 DC-link voltage ratio. Conventional MPCC requires evaluating 91 effective voltage vectors (VVs), resulting in high computational complexity. To address this, a cascaded split-sequence strategy is proposed that projects the 3D VVs onto the αβ plane, selects candidate VVs for the first inverter, refines the candidate set for the second inverter, and finally chooses the optimal VVs based on zero-sequence components. This method reduces predictive iterations from 91 to 23 – a 75 % reduction – and eliminates the requirement for weight coefficients. Additionally, a multi-mode switching strategy is integrated to reduce the switching frequency over a wide speed range: under low-speed conditions, only the low-voltage side inverter is active; during medium-speed operation, the high-voltage side inverter is engaged; and at high speeds, both inverters operate concurrently. Experimental results demonstrate significant computational efficiency and robust performance, making the proposed MPCC be well suited for high-performance industrial applications.
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A robust seven-level fault tolerant hybrid cascaded H-bridge inverter

August 1, 2025

Naga Venkata Vamshi Kumar Vanapalli, Nakka Jayaram, Jami Rajesh

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The most commonly used inverters for renewable energy applications are cascaded H-bridge (CHB) inverters. Fault tolerance is required for semi-conductor switches and DC supplies to safe guard the system under fault conditions for continuous operation. A novel seven-level hybrid CHB inverter with fault tolerance is introduced through the incorporation of a reduced switch seven-level boost (RSSLB) inverter module in conjunction with the H-bridge modules. This inverter configuration guarantees uninterrupted operation even in the presence of faults. Alongside its fault-tolerant features, the RSSLB module within the proposed system facilitates a voltage boost of three and ensures self-balancing of the switched capacitors with its implemented control scheme. Extensive simulation results under various operating scenarios validate the concept of the unique fault-tolerant inverter topology that has been developed. The proposed configuration is validated by the experimental findings by the prototype system developed in the lab.
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Assessment of the accuracy of Chinese and English transfer of circulating energy storage equipment

July 15, 2025

Rui Wang

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With the advancement of globalization, the rapid development of circulating energy has attracted the attention of all walks of life. Timely monitoring of the accuracy assessment in the Chinese and English transfer environment has an extremely important impact on understanding and promoting the development of circulating energy storage equipment. In the process of Chinese and English transfer, the circulation of energy storage equipment has become more convenient and quickly accepted by users because of its wide application. Based on this background, this study proposed an ICE-LDA model based on the LDA model for developing accuracy assessment in Chinese and English transfer. Through the method of transfer assessment in Chinese and English, the detection of transfer information in Chinese and English and the similarity of accuracy distribution are measured. This study will model the transfer data collected in Chinese and English. In this process, this study will use the TF-IDF algorithm to extract the transfer information in circulating energy storage devices to improve the accuracy of Chinese and English.
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Optimized multivariable control structures for voltage regulation in active distribution networks

May 6, 2025

Pablo Rullo, Patricio Luppi, Lautaro Braccia, Diego Feroldi, David Zumoffen

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The increasing penetration of distributed energy resources (DERs) into Active Distribution Networks (ADNs) demands innovative control solutions to address critical challenges such as voltage stability and power quality. Existing decentralized control strategies often fail to handle the intricate interactions between control loops in highly coupled systems, leading to suboptimal performance, excessive reactive power requirements, and operational limitations. This paper introduces a novel multivariable control framework designed for voltage regulation in ADNs. The proposed framework incorporates advanced Control Allocation and Measurement Combination modules, developed through a systematic methodology that merges Process Systems Engineering and Power Systems principles. By formulating the control design as a bilevel mixed-integer nonlinear programming (BMINLP) optimization problem, the framework minimizes voltage deviations, reactive power usage, and the reliance on actuators and sensors, ensuring system stability even under extreme conditions such as significant active power generation drops. Dynamic simulations performed on the IEEE 33-bus system demonstrate that the proposed approach outperforms traditional decentralized controllers by effectively preventing reactive power saturation and delivering superior voltage regulation. The results indicate that the voltage at all nodes stays within the permissible limits, with a maximum deviation of 0.7 %. Moreover, it achieves these results with reduced hardware and fewer control loops, highlighting its resource efficiency. This work represents a significant step forward in the control of ADNs with high DER penetration. By addressing the limitations of conventional strategies, it offers a scalable and robust solution that optimizes control resources, enhances system stability, and supports the transition to decentralized, renewable-powered distribution networks.
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Review of sub-synchronous oscillation (SSO): from historical challenges to modern inverter-based renewable energy sources

June 2, 2025

Behnam Zamanzad Ghavidel, Yuan Liao

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Sub-synchronous oscillations (SSO) pose significant challenges to the stability and reliability of modern power systems, especially in grids with high penetration of renewable energy sources such as wind and solar power. This paper provides a comprehensive review of SSO phenomena, including their evolution, mechanisms, and impacts on power system stability. A detailed classification of SSO is presented, covering grid-level, device-level, and control strategy-dependent oscillations, along with renewable-energy-specific interactions. Notably, it is found that GFL inverter-based sources are highly prone to SSO due to their PLL-based synchronization, whereas GFM inverters are generally more stable, offering improved damping and inertia-like responses. This study highlights recent SSO incidents, such as the 2009 Texas PV case and the 2019 Hornsea wind event, to underscore practical implications. Additionally, a taxonomy of SSO types and inverter technologies is provided. For instance, GFL systems in weak grids with SCR < 3 showed higher oscillation risks. The insights gained from this review suggest that robust control strategies and coordination between GFL and GFM units are essential. These findings pave the way for enhanced grid resilience and provide numerical indicators to guide future renewable deployment in weak grid conditions. While GFM inverters are generally more stable than GFL inverters, it is important to note that they are not entirely immune to SSO. Under certain conditions – such as improper tuning of droop or virtual inertia parameters, lack of coordination with GFL inverters, or interaction with mechanical systems, GFM inverters themselves can become a source of SSOs. Therefore, robust and well-coordinated control design is essential even for GFM-based systems to prevent unintended dynamic instabilities.
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Design and implementation of an adaptive protection strategy for a 0.4 kV safe anti-electric-shock maintenance power supply box in smart grids

December 4, 2025

Yong Wang, Bowen Zhang, Songnan Liu, Chun Liu, Weidong Wang

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Ensuring personnel safety and operational reliability in 0.4 kV (kilovolt) distribution networks during maintenance remains a critical challenge, especially under diverse environmental and load conditions. Traditional fixed-threshold protection schemes often fail to adapt to dynamic fault signals caused by varying earth impedance, moisture, and nonlinear loads, resulting in false alarms or missed detections. To address these limitations, this paper proposes an adaptive protection strategy for 0.4 kV maintenance environments, combining harmonic distortion rate and transient waveform slope as detection criteria. An experimental platform was constructed, featuring a 10/0.4 kV transformer, dual-feeder topology, configurable loads, adjustable fault resistances, and arc generators to emulate realistic fault scenarios. Multi-point measurements of voltages, currents, and residual currents were conducted under varying conductor cross-sectional areas, fault locations, contact areas, and humidity levels. Results show that the proposed method accurately detects earth faults and electric shock events with higher sensitivity and specificity than conventional fixed-threshold methods, even under adverse conditions. The strategy effectively mitigates the impact of load switching and environmental noise, improving protection reliability.
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Data mining based transient stability prediction for power system integrated with varying levels of renewable penetration

October 17, 2025

Gayathri K, Manas Kumar Jena

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The power system’s transient stability is affected by the increase in generation from renewable energy sources (RES). Applications such as the transient stability predictor assist the operator in initiating emergency control actions. In this work, predictors such as relative center of active power speed reference (RCOAPSR), relative center of reactive power voltage reference (RCORPVR), and inertia distribution index (IDI) are derived from the phasor measurement unit (PMU) measurements and fed to data mining models such as decision tree (DT) and random forest (RF). The aforementioned parameter’s area-wise calculations provide insights into the regional dynamics, which are important for RES-dominated systems. The simulations are carried out on the IEEE-39 bus system and northern regional power grid (NRPG) using DSA Tools software. The DT and RF models show a prediction accuracy of more than 97 % and 99 % respectively. The suggested scheme has an average early prediction time of more than 18 s. This can help prevent large-scale disturbance propagation.
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A new step up nine level switched capacitor multilevel inverter

June 2, 2025

Sharvendra Kumar Omre, Ritula Thakur, Kaibalya Prasad Panda

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In response to rising expectations for more efficient and better power generation, the switched-capacitor multilevel inverter (SCMLI) was developed. Most recently, SCMLIs were created to allow for step-up ac output via inversion and single-stage voltage boosting. This study presents a boost type single source nine-level (9-level) SCMLI that uses two capacitors and one diode to reduce the number of components. The capacitor’s voltage boost from a single source is achieved through the series-parallel connecting procedure. No more than twice the input voltage can be applied to the semiconductor devices under any given condition. To raise the voltage levels without adding more dc input, the suggested SCMLI can be expanded with a minimal number of components. After a thorough evaluation of the power losses and circuit performance, the design is confirmed to be superior by comparing it to newly designed single-phase 9-level MLIs. To confirm the key characteristics of the 9-level SCMLI in dynamic operational settings, we offer comprehensive simulation and experimental findings.
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Distributed secondary control of DC microgrids with event-triggered quantization communication under DoS attacks

April 24, 2025

Baoyan Wang, Xin Cai, Xinyuan Nan, MingZhe Dai

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A resilient distributed secondary control strategy is proposed for DC microgrids (MGs) with denial-of-service (DoS) attacks on communication channels to achieve accurate current sharing and voltage regulation. According to the designed resilient control strategy, when there is no DoS attacks, the quantization information is sent to neighboring distributed generators (DGs) only when the triggering condition is satisfied. When there is a DoS attack, the communication can not be carried out normally. Then, each DG would attempt to send messages to its neighbors at a preset period until the attack disappears. The maximum allowable communication attempt period is determined by theoretical analysis, which is one of sufficient conditions to ensure current sharing and voltage regulation. As a result, the control signals are updated by discrete-time quantized information, which greatly reduces the communication burden, and effectively defends against DoS attacks. The asymptotic stability of the system is analyzed to show the results that voltage regulation and accurate current sharing are achieved. The proposed control strategy is verified through simulations.
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Multi-objective predictive maintenance optimization of electric energy meters based on LSTM-XGBoost and modified firefly algorithm

August 29, 2025

Hanxiang Jing, Huafeng Cao, Shangbin Sun

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Due to the rapid development of modern smart grid and Internet of Things technology, the operational and maintenance demands on smart meters, the core metering equipment of the power system, are becoming increasingly significant. Traditional maintenance methods waste resources and are inefficient. Therefore, a new maintenance paradigm combining stable, efficient prediction technology and intelligent, decision-making optimization is urgently needed. This paper proposes a multi-objective predictive maintenance optimization framework based on a long short-term memory (LSTM)-extreme gradient boosting (XGBoost) hybrid model and an improved firefly algorithm (IFA). The framework addresses three core challenges in smart meter operation and maintenance: complex failure modes, limited resources, and conflicting objectives. The main innovations of this study are: 1. A spatiotemporal hybrid prediction model for meter failures is proposed for the first time. This model combines bidirectional LSTMs to capture long-term parameter dependencies, such as voltage and current, and introduces an enhanced XGBoost with a dynamic threshold Huber loss function (DTHL). This reduces error by 25 % in voltage mutation scenarios. 2. A maintenance decision optimization model is designed based on an improved firefly algorithm. The model suggests a maintenance-specific brightness index and a cosine annealing step strategy. The convergence speed is 3.2 times faster than NSGA-II. 3. Support edge-cloud collaborative computing with a prediction response time of less than 200 ms. Using real data from the Gansu Electric Power Company from 2020 to 2024 covering 9,823 devices, the LSTM-XGBoost model performs well in predicting faults. Under budget and manpower constraints, IFA’s cost savings and risk reduction rates are 8.2 % and 9.5 % higher than NSGA-II, respectively, and resource utilization is 89.7 %. SHAP analysis shows that temperature, voltage fluctuations and communication packet loss rate are key fault characteristics.
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Power data center distributed storage system based on RDMA with high concurrent data and low delay transmission

July 28, 2025

Boling Chen, Xiaoying Mo, Dengbin Liao, Junbing Pan, Anni Huang

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New data centers today demand more scalable and efficient solutions with the rapid advances in deep learning, distributed systems, and high-concurrency data processing. Despite the significant improvements in these areas, existing systems continue to suffer from latency, congestion control, and resource allocation, especially during high workloads and network failures. Two new frameworks for optimizing RDMA-based data flow in remote settings – ConVer and DynoFlow – are introduced in this research. ConVer employs user-space path management and multipath transfer to achieve low latency and high throughput, whereas DynoFlow provides a software-defined, modular approach that efficiently handles network failures and varying traffic loads. Per experimental results, ConVer significantly improves throughput and achieves a latency of 1.3 m s (99th percentile). DynoFlow fares better in coping with network failures and optimizing route utilization. We solve significant problems in modern data centers by demonstrating through extensive experimental evaluation that the frameworks significantly enhance throughput and resilience in distributed systems.
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Investigation of performance characteristics of blended mineral oil and ester oil for transformer application

May 5, 2025

Sujit Khandai, Jasper D., Nirmal Kumar Roy

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A transformer is one of the most important high voltage (HV) power apparatus in any electrical system. In transformers, liquid insulation, primarily mineral oil, is used in bulk. It acts as an insulation and cooling medium in the transformer. Therefore, it plays an important role in the safe and reliable operation of the transformer. It is understood from several studies that because of the shortage of petroleum oil and its non-environment-friendly nature, scientists, engineers, and researchers are concentrating their studies on synthetic oil, natural ester oil, so that they can replace mineral oil successfully. Studies related to the characterization of different types of natural ester oil are made for their superiority over mineral oil. However, very few studies have been conducted on different combinations of mineral oil (MO) with natural ester oil, such as coconut oil (CO) and rice bran oil (RO), as an alternative insulation. In this paper, a study has been made on different combinations of MO with CO and RO for their performance characteristic studies like breakdown voltage (BDV), flash point, DC resistivity, and tan delta on the lines of International Electrotechnical Commission (IEC) and American Society for Testing Materials (ASTM) standards. It is observed from the studies that different combinations of MO with CO and RO enhanced its BDV, which is the prime electrical property for studying the dielectric strength of any insulation. However, blended RO with MO has balanced properties in terms of dielectric strength, flash point, and DC resistivity compared to all other virgin oil and blended oils. It is also less toxic compared to the mineral oil. Therefore, blended rice bran oil with mineral oil may be considered an alternative to traditional mineral oil as it is cost-effective and less toxic. Therefore, it may be an efficient alternative to insulating oil for the transformer industries.
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A study of wireless power transfer using series-series magnetic resonant coupling topology

July 18, 2025

Uday Kumar Mudhigollam, Mohana Rao Mandava, Nagesh Kumar Kamma Veera Venkata, Anil Kumar Siluveru

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Wireless Power Transfer (WPT) technology has got wide range of applications in which Electric Vehicle (EV) wireless charging is gaining more popularity as there is a provision of dynamic charging in addition to static charging. The main objective of this paper is to design and analyze 5 kW rated Magnetic Resonant Coupling (MRC) wireless power transfer charging circuit for EV. The Series-Series MRC WPT circuit’s design formulae are derived in this paper. The circuit parameters like inductances and capacitances could be determined for any power rating and mutual coupling coefficient between transmitter and receiver coils by using the design formulae derived in this paper. The performance of SS topology based EV charging circuit has been studied analytically at different coupling coefficients by estimating the power transfer efficiency. The analytical design of Series-Series MRC WPT circuit has been verified by comparing the output power and load current with the results obtained from MATLAB and TINA (Toolkit for Interactive Network Analysis) software simulation. An experimental set-up is established for SS topology-based charging circuit rated for output power of 5 W with circuit elements designed by considering coupling coefficient of 0.2 between the coils. The measurements are conducted to verify the constant current characteristics under various load conditions. Finally, the effect on output power and load current of SS topology based EV charging circuit at different coupling coefficients between transmitter and receiver coils is discussed in the paper.
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Integration of embedded systems and artificial intelligence in electrical automation for smart grid management

July 7, 2025

Bo Fu, BinHua Yuan

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With the continuous progress of social civilization, electricity plays an indispensable role in human life. China is a vast country with wide grid coverage and frequent natural disasters, which cause certain losses to the national economy. Therefore, this paper fully considers the role of meteorological information in the emergency decision-making of smart grid disaster prevention, uses the Dijkstra algorithm to clarify the best path, and integrates many factors such as traffic coefficient, road level, road congestion and road quality to establish the optimal path model, which can effectively improve the management level, reduce the smart grid loss by more than 48 %, protect the residents and industrial electricity consumption, and provide safe operation of the smart grid. Powerful guarantee.
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Choice of supervised machine learning based approaches to predict PV panel faults

August 4, 2025

Somnath Hazra, Debashis Chatterjee, Krishna Roy

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The increasing deployment of photovoltaic (PV) systems necessitates efficient fault detection methods to ensure optimal performance and reliability. The performance of the PV panels can be significantly affected by faults and anomalies. Various machine learning techniques can be used for this purpose, each with its strengths and weaknesses. Developing a proper fault diagnosis strategy to identify PV faults for the smooth and uninterrupted operation of PV panels plays a vital role. This paper presents a comparative assessment of different machine learning techniques for photovoltaic (PV) panel fault diagnosis. This study aims to evaluate and compare the performance of various machine learning algorithms including support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), discriminant analysis (DA) and ensemble classifier (EC) in identifying and diagnosing faults in PV panels. Through extensive experimentation and analysis, the effectiveness of these machine learning techniques in fault detection and classification is assessed in terms of accuracy, precision and efficiency. Additionally, the robustness and scalability of the models are examined under different operating conditions and fault scenarios. This study used simulated data to train and test machine learning models and evaluated their performance based on accuracy and precision. The findings of this study provide valuable insights into the applicability and performance of data-driven machine learning techniques for fault identification in PV panels, thereby contributing to the development of more efficient and reliable PV systems for the renewable energy industry.
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Enhancing legacy OLTC performance through Cost-effective retrofitting: a field-tested approach

December 1, 2025

Sadheesh Kumar S. Jayaraman, Ram Jethmalani C. Hemparuva, Navin Sam Kamaraj

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Legacy On-Load Tap Changer (OLTC) systems in distribution substations are often vulnerable to phase reversal errors, voltage fluctuations, and mechanical degradation, particularly in resource-constrained environments. These challenges degrade voltage regulation accuracy and accelerate wear in OLTC components due to excessive or incorrect tap operations. This paper presents a cost-effective retrofit control strategy designed to enhance the reliability and performance of mechanical OLTC systems. The proposed solution integrates real-time phase sequence detection, adaptive tap control logic using hysteresis thresholds, and automatic motor direction correction to prevent erroneous tap movements. A comprehensive MATLAB-based simulation validates the methodology under various operational scenarios, including reverse power flow, phase reversal, and voltage sags/swells. The strategy is also experimentally deployed in a live 11 kV/433 V university distribution substation. It covers diverse loads such as administrative cum academic blocks, laboratories, guest house cum hostels, and HVAC systems. In this university campus scenario, it demonstrates a 66 % reduction in tap changes, enhanced Mean Time Between Failures (MTBF), and 80 % fewer manual interventions. The outcomes confirm that the proposed retrofit enhances voltage stability, operational efficiency, and system longevity, making it a scalable and economically viable solution for ageing power distribution infrastructure.
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A novel triple boost inverter with high efficiency for grid connected applications

August 1, 2025

Naga Venkata Vamshi Kumar Vanapalli, Nakka Jayaram, Jami Rajesh, Pulavarthi Satya Venkata Kishore

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This article introduces a novel single-phase triple boost inverter based on switched capacitor (SC) technology, designed for grid integration applications. The proposed topology utilizes a single DC source, 13 switches (including 5 bidirectional switches), and 3 capacitors to achieve a voltage gain of three. Notably, this topology can generate 13 output levels, resulting in reduced total harmonic distortion (THD) and improved efficiency compared to existing triple boost topologies. Additionally, the capacitors are self-balanced through the control scheme, eliminating the need for external balancing circuits. The proposed topology is compared with other existing topologies in the literature, and its performance is validated through MATLAB/Simulink simulations using level-shifted pulse width modulation (LSPWM) and experimental results.
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Modeling the cell temperature of rooftop PV panels under dynamic environmental conditions: implications for power output and temperature in shaded areas

September 24, 2025

Pramod Kumar, Sanjay Kumar Maurya, Indresh Yadav, Santanu Chowdhury

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Photovoltaic (PV) module performance is heavily influenced by temperature, requiring precise modelling to optimise rooftop systems that support the UN Sustainable Development Goals. This paper presents a simplified mathematical model for estimating the temperature of solar cells in rooftop installations under dynamic environmental conditions. The model considers primary convective and conductive heat transfer, with the heat transfer coefficient being adaptively adjusted to reflect the interaction between the rear surface of the PV panel and the ambient temperature at different wind speeds. Additionally, the thermal model is used to predict power generation. Validation through real-time experiments on a 100-W PV panel showed a deviation of only 1–2% in temperature predictions and around 4 % and 3 % in power output in unshaded and shaded conditions, respectively. These results confirm the model’s reliability and its practical usefulness for accurate performance evaluation of rooftop PV systems in real-world environments. Furthermore, power loss caused by leakage current in the bypass string under shading conditions was experimentally measured, opening a new path for future research.
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A novel approach for enhancing substation grounding grid design using diverse shapes

June 2, 2025

Avani Mistry, Ravindrakumar Yadav, Kaustubh Vyas

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Substations are one of the key elements in electrical power system network. Efficient grounding in substation design forms the base for ensuring the safety of the person and equipments. Depending on the geometrical aspect of the substation, five different grid shapes are applicable. In this paper all 5-grid shape analysis is carried out to have comparison on performance analysis of the grid designing process. Also, in grid design calculations a concept of three different lengths is introduced which makes the grid designing simple and error free. Also, all the calculations are carried out owing to five different grid designing methods. Various softwares are used to make grid designing effective and lesser time consuming. Mathematically calculated results incorporating the length updation with grid shape applications are verified by ESGSD software. The methodology developed in this paper is applicable to any shape of the grid and ensures the safety and reliability of the grounding grid.
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Application of integrated learning in power grid fake data intrusion detection

June 30, 2025

Yan Zeng, Siwei Wang, Wenli Chen, Yingying Cheng, Guangcheng Xie, Wenbo Yao, Yu Su

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Cybersecurity is a critical component influencing the safe and stable operation of the smart grid, as the smart grid (SG) is an information-physical fusion system (also known as a Cyber-Physical System [CPS]) integrating sensing, communication, computation, decision-making, and control built on the foundation of the traditional grid. The most common cyberattack that compromises data integrity in the smart grid is the False Date Injection Attack (FDIA). If this type of attack is not discovered in time, it can take control of physical equipment or target a network transmission line to obstruct control decisions, which can result in power network failure or even a cascade failure in the grid. An integrated learning-based detection method is currently proposed to biclassify power grid data in order to address the issues of low accuracy, high false detection rate, and poor model differentiation ability when applying a single classifier in machine learning to detect false data. The integrated learning detection method is based on GBDT (Gradient Boosting Decision Tree), XGBoost, and Light GBM, with RF- Light GBM and Bagging classifier as the base classifiers, which are integrated by voting strategy after Bayesian tuning. Following simulation experiments, the algorithm is able to significantly outperform the checking rate and accuracy of traditional detection algorithms in detecting false data on the power grid by effectively addressing the issues of low checking rate and accuracy of single classifier detection as well as instability of single classifier detection.
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Optimal capacity of renewable energy and methanation system in a remote island model considering frequency fluctuation

July 31, 2025

Masahiro Furukakoi, Akito Nakadomari, Akie Uehara, Ahmed Rashwan, Ashraf M. Hemeida, Mitsunaga Kinjo, Tomonobu Senjyu

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This paper presents a novel optimization framework for determining optimal renewable energy and methanation system capacities in remote island power grids while ensuring frequency stability constraints. The proposed method combines hierarchical clustering with mixed-integer linear programming to solve large-scale optimization problems efficiently while maintaining grid frequency within ±0.3 Hz. When applied to Japan’s Tsushima Island power system (peak demand: 36 MW), our approach achieves reductions of over 10 % in operating costs and 15 % in CO 2 emissions compared to conventional diesel-only systems. The key innovation lies in simultaneously addressing frequency stability constraints, renewable energy integration, and methanation system sizing through a comprehensive optimization framework that effectively manages both the variability of renewable sources and decarbonization goals. The optimal facility sizing is determined through the ɛ -constraint method and Pareto optimization to minimize both costs and emissions, thereby establishing a systematic methodology for implementing sustainable energy systems in isolated power grids.
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Applying the concept of load modeling with multiple impact factors in suburban field

September 24, 2025

Ernad Jabandžić, Tatjana Konjić, Sabina Baraković

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To contribute to quality understanding of the multiple factors’ impact on load management, we provided the analysis of the concurrent impact of available multiple impact factors on load, examined correlations between the factors and load, and offered and validated the multifactor load model for the suburban field. The results show the concurrent impact of the factors under consideration on load in suburban field. This study continues to confirm that the application of load modeling using multiple impact factors is justified and reveals the importance of concurrent context and user factors effect already analyzed in urban region.
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Wind power generation prediction based on WRT and neural network

August 12, 2025

Renqiang Wen, Hao Zhang, Guohan Zhao, Songxiong Wu, Zhiyong Shen, Yicheng Fan

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The objective of this study is to meticulously forecast wind power generation utilizing Wavelet Transform and neural network techniques. Leveraging extensive wind power generation data, we delve into the intricate characteristics of the dataset to formulate a prediction model that seamlessly integrates WRT and neural network principles. The aim is to attain a precise prediction of wind power generation, thereby furnishing invaluable decision-making insights for optimal dispatching and energy management in the realm of wind power generation. This endeavour holds significant implications for enhancing the efficiency and sustainability of wind energy utilization. Firstly, WRT is used to preprocess the wind power generation data. Utilizing wavelet decomposition and reconstruction techniques, we meticulously extract the pertinent information embedded within the data, thereby mitigating the detrimental effects of noise and outliers on prediction outcomes. Subsequently, a neural network is employed as the core prediction model, facilitating the realization of wind power generation prediction through the meticulous training of network parameters. To rigorously assess the predictive prowess of our model, we leverage actual wind power generation data for rigorous testing and benchmark it against other prediction methodologies. This comprehensive evaluation ensures the reliability and accuracy of our approach in forecasting wind power generation. The results show that the wind power generation prediction model based on WRT and neural network has high prediction accuracy and stability. Specifically, the prediction accuracy of the model on the test set is over 90 %, and the prediction error is reduced by over 20 % compared with the traditional prediction methods. This study also further analysed the source of forecasting error, found that the main reason is due to the uncertainty and volatility of wind power data. Therefore, the research proposed targeted improvement scheme, including optimizing WRT parameters, improving neural network structure and so on. The wind power generation forecasting model based on WRT and neural network proposed in this paper has high forecasting performance and practical value, and can provide strong support for optimal scheduling and energy management of wind power generation.
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Detection of cyber attacks in smart grids using deep learning

December 12, 2025

Mukilan Karuppasamy, Vikash Sunkara, Maddikara Jaya Bharata Reddy

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This paper explores the detection of False Data Injection (FDI) cyberattacks on smart grids using deep learning techniques. Cyberattacks can lead to undesirable outcomes, such as damage to electrical equipment, unnecessary relay tripping, or system-wide blackouts. The utilization of a Feature Tokenizer Transformer (FT-Transformer) model is proposed for robust detection accuracy compared to traditional tree-based classifiers. An FDI attack is mimicked on an IEEE 14 bus system to detect it using various machine learning algorithms. The proposed solution has also been validated against cyberattacks on a three-bus system. The results obtained demonstrate the superior performance of the FT-Transformer model in identifying FDI cyberattacks in smart grids. This research contributes to the advancement of modern cybersecurity power systems, ensuring their reliability against malicious cyber threats.

Issue 0

Issue 6

Special Issue: Control Strategies for Distributed Renewable Energy Resources using AI

Special Issue on Smart Energy Infrastructures for Smart Cities

Issue 3

Issue 2

Issue 1

Special Issue: Sustainable Energy Management using Green Computing; Guest Editors: Fadi Al-Turjman, Walaa Hamouda, and Shahid Mumtaz

Issue 6

Special Issue: Sustainable Energy Management using Green Computing; Guest Editors: Fadi Al-Turjman, Walaa Hamouda, and Shahid Mumtaz

Special Issue: Emerging Sustainable Green Energy Technologies for Smart Grid; Guest Editors: Surender Reddy Salkuti, Papia Ray and Sandeep Vuddanti

Issue 5

Special Issue Title: Emerging Sustainable Green Energy Technologies for Smart Grid; Guest Editors: Surender Reddy Salkuti, Papia Ray and Sandeep Vuddanti

Issue 4

Special Issue Title: Emerging Sustainable Green Energy Technologies for Smart Grid; Guest Editors: Surender Reddy Salkuti, Papia Ray and Sandeep Vuddanti

Special Issue on Power Electronics Applications in Smart Distribution Grids with Renewable Energy Sources; Guest Editors: B.Chitti Babu, Sabha Raj Arya, Ahmed F. Zobaa

Special Issue: Renewable Energy Systems, Guest Editors: Mohamed Becherif, Paul Lefley, Olivier Bethoux, Haitham Saad Mohamed Ramadan

International Journal of Emerging Electric Power Systems publishes special and thematic issues focussed on important and emerging topics in the field of study. The journal has established a rigorous process to ensure that any special issue manuscripts follow the same high-quality standards and peer review processes as regular manuscripts. For further information on the journal’s peer review policy please see the "Instructions for Authors".

Recent Special Issues

Volume 26 | Issue 6 Special Issue: Control Strategies for Distributed Renewable Energy Resources using AI

Journal Impact Factor	0.7	2024, Journal Citation Reports (Clarivate, 2025)
5-year Journal Impact Factor	0.8	2024, Journal Citation Reports (Clarivate, 2025)
Journal Citation Indicator	0.18	2024, Journal Citation Reports (Clarivate, 2025)
CiteScore	3.3	2024, Scopus (Elsevier B.V., 2025)
SCImago Journal Rank	0.291	2024, SJR (Scimago Lab, 2025; Data Source: Scopus)
Source Normalized Impact per Paper	0.694	2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus)

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Editor-In-Chief
Prof. Tarlochan Sidhu

Professor
Faculty of Engineering and Applied Science
Ontario Tech University
2000 Simcoe Street North
Oshawa, Ontario
CANADA L1H7K4

Associate Editors
Bhaveshkumar R. Bhalja, Indian Institute of Technology Roorkee, INDIA
Gyu-Jung Cho, Pai Chai University, REPUBLIC OF KOREA
Fei Gao, University of Technology of Belfort-Montbéliard, FRANCE
Y. Liao, University of Kentucky, USA
M. Jaya Bharata Reddy, National Institute of Technology Tiruchirappalli, INDIA
Salkuti Surender Reddy, Woosong University, REPUBLIC OF KOREA
Ke Xu, Nanjing Institute of Technology, CHINA

Editorial Board
O.H. Abdalla, University of Helwan, EGYPT
M. El Hachemi Benbouzid,University of Western Brittany, FRANCE
W. Fan, Commonwealth Edison Company (ComEd), USA
Andrea Fioravanti, University of L'Aquila, ITALY
G. Harrison, The University of Edinburgh, UK
Y-L. Ke, Kun Shan University of Technology, TAIWAN
P. Lopes, INESC Porto, PORTUGAL
H. Nouri, Universi ty of the West of England, UK
R. Mota Palomino, National Polytechnic Institute, MEXICO
A. Pahwa, Kansas State University, USA
A.G. Phadke, Virginia Tech, USA
S. R. Salkuti, Woosong University, REPUBLIC OF KOREA
M. Sanaye-Pasand, University of Tehran, IRAN
M. Shahidehpour, Illinois Institute of Technology, USA
C. Singh, Texas A&M University, USA
E. Tag-Eldin, Cairo University, EGYPT
D. Thukaram, Indian Institute of Science, INDIA
D. Tziouvaras, Schweitzer Engineering Labs, USA
F. Wang, Shanghai University, Shanghai, CHINA
F. Wen, Zhejiang University, CHINA
I. Zamora, ETSI de Bilbao - University of the Basque Country, SPAIN

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Additional information

eISSN:

1553-779X

ISSN:

2194-5756

Language:

English

Publisher:

De Gruyter

Additional information

First published:

January 1, 2004

eISSN: 1553-779X

ISSN: 2194-5756

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researchers and professionals in the field of power systems

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International Journal of Emerging Electric Power Systems

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