Improved Grid Stability Optimisation and Fault Detection in PV +EV Integrated Systems with Partial Shade Using NCNN-EGSA Optimization Techniques

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : S. Venkata Ramudu Naik, Pulivarthi Nageswara Rao

10.14445/23488549/IJECE-V12I6P130

How to Cite?

S. Venkata Ramudu Naik, Pulivarthi Nageswara Rao, "Improved Grid Stability Optimisation and Fault Detection in PV +EV Integrated Systems with Partial Shade Using NCNN-EGSA Optimization Techniques," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 383-401, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P130

Abstract:

The co-utilization of Electric Vehicles (EVs) in photovoltaic (PV)-based distribution networks bridges the opportunities and challenges associated with grid stability and operational reliability, particularly under partial shading conditions. In this study, an improved grid management scheme based on a Novel Convolutional Neural Network (NCNN) was developed and trained using an Enhanced Golden Search Algorithm (EGSA). The proposed system solves two important problems: (1) high-precision fault detection and (2) real-time stability improvement in PV–EV integrated grids. NCNN is designed to learn spatial and temporal information in system parameters such as voltage, current, and power flow. Meanwhile, EGSA can adjust hyperparameters effectively, which promotes model performance and accelerates the convergence rate. Common failures such as line-to-ground and partial shading-induced faults are identified with high sensitivity, and a diagnosis accuracy of 99.51% and a fast response time of 0.5 s are obtained. The simulation results indicate a 25% enhancement in the grid stability and a 12% decrease in energy consumption owing to EV integration. In addition to improving energy efficiency and operational robustness, the framework improves the robustness of the smart grid . These findings confirm that the NCNN-EGSA is an effective and intelligent strategy for future PV–EV distribution systems.

Keywords:

Electric Vehicles, Photovoltaic systems, Grid Stability, Fault Detection, Novel Convolutional Neural Network (NCNN), Enhanced Golden Search Algorithm (EGSA), Partial Shading, Fault Classification.

References:

[1] Montaser Abdelsattar, Ahmed AbdelMoety, and Ahmed Emad-Eldeen, “Advanced Machine Learning Techniques for Predicting Power Generation and Fault Detection in Solar Photovoltaic Systems,” Neural Computing and Applications, vol. 37, pp. 8825-8844, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Mahbub Ul Islam Khan et al., “Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification,” IEEE Access, vol. 12, pp. 71566-71584, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S. Aslam et al., “Hamiltonian Deep Neural Network Technique Optimized with Lyrebird Optimization Algorithm for Detecting and Classifying Power Quality Disturbances in PV Combined DC Microgrids System,” Environment, Development and Sustainability, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Mohammed H. Ibrahim, Ebrahim A. Badran, and Mansour H. Abdel-Rahman, “Detect, Classify, and Locate Faults in DC Microgrids Based on Support Vector Machines and Bagged Trees in the Machine Learning Approach,” IEEE Access, vol. 12, pp. 139199-139224, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] K. Mohana Sundaram et al., “Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications the State-of-the-Art Approaches,” IEEE Access, vol. 9, pp. 41246-41260, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Nien-Che Yang, and Mohd Faizan, “Long Short-Term Memory-Based Feedforward Neural Network Algorithm for Photovoltaic Fault Detection under Irradiance Conditions,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mauricio Lavador-Osorio et al., “An Enhanced Frequency Analysis and Machine Learning Based Approach for Open Circuit Failures in PV Systems,” IEEE Access, vol. 12, pp. 96342-96357, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Ehtisham Lodhi et al., “A Novel Deep Stack-Based Ensemble Learning Approach for Fault Detection and Classification in Photovoltaic Arrays,” Remote Sensing, vol. 15, no. 5, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Yue Zhang et al., “Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model,” IEEE Transactions on Industry Applications, vol. 56, no. 6, pp. 7185-7192, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Montaser Abdelsattar et al., “Assessing Machine Learning Approaches for Photovoltaic Energy Prediction in Sustainable Energy Systems,” IEEE Access, vol. 12, pp. 107599-107615, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] R. Sundaramoorthi, and S. Chitraselvi, “Integration of Renewable Resources in Electric Vehicle Charging Management Systems Using Deep Learning for Monitoring and Optimization,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 49, pp. 313-335, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Debabrata Mazumdar et al., “An Enhanced MPPT Approach Based on CUSA for Grid-Integrated Hybrid Electric Vehicle Charging Station,” International Journal of Energy Research, vol. 2024, no. 1, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ali Teta et al., “Fault Detection and Diagnosis of Grid-Connected Photovoltaic Systems Using Energy Valley Optimizer Based Lightweight CNN and Wavelet Transform,” Scientific Reports, vol. 14, no. 1, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Chavan Vinaya Chandrakant, and Suresh Mikkili, “A Typical Review on Static Reconfiguration Strategies in Photovoltaic Array under Non-Uniform Shading Conditions,” CSEE Journal of Power and Energy Systems, vol. 9, no. 6, pp. 2018-2039, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Dhritiman Adhya, Soumesh Chatterjee, and Ajoy Kumar Chakraborty, “Performance Assessment of Selective Machine Learning Techniques for Improved PV Array Fault Diagnosis,” Sustainable Energy, Grids and Networks, vol. 29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ahmed Faris Amiri et al., “Faults Detection and Diagnosis of PV Systems Based on Machine Learning Approach Using Random Forest Classifier,” Energy Conversion and Management, vol. 301, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Xiaotian Zhang et al., “Artificial Intelligence Technique-Based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review,” IEEE Sensors Journal, vol. 23, no. 15, pp. 16481-16500, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hicham El Hadraoui et al., “Toward an Intelligent Diagnosis and Prognostic Health Management System for Autonomous Electric Vehicle Powertrains: A Novel Distributed Intelligent Digital Twin-Based Architecture,” IEEE Access, vol. 12, pp. 110729-110761, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Ahmed Althobaiti et al., “Intelligent Data Science Enabled Reactive Power Optimization of a Distribution System,” Sustainable Computing: Informatics and Systems, vol. 35, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] P. Balakumar, Senthil Kumar Ramu, and T. Vinopraba, “Optimizing Electric Vehicle Charging in Distribution Networks: A Dynamic Pricing Approach Using Internet of Things and Bi-Directional LSTM Model,” Energy, vol. 294, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Abdullah M. Shaheen et al., “A Forensic-Based Investigation Algorithm for Parameter Extraction of Solar Cell Models,” IEEE Access, vol. 9, pp. 1-20, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] M. Aishwarya, and R.M. Brisilla, “Design and Fault Diagnosis of Induction Motor Using ML-Based Algorithms for EV Application,” IEEE Access, vol. 11, pp. 34186-34197, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Anil Kumar et al., “Novel Convolutional Neural Network (NCNN) for the Diagnosis of Bearing Defects in Rotary Machinery,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mohammad Noroozi et al., “Golden Search Optimization Algorithm,” IEEE Access, vol. 10, pp. 37515-37532, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] GPVS-Faults-Detection, Github. [Online]. Available: https://github.com/anila14-del/GPVS-Faults-Detection