ItemArticle

Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter

Title
Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter
Creator
Senthil Raja, M.; Sowmya Sree, V.; Jayakumar, V.; Karthick, B.
Description
Interconnected renewable energy systems (RES) often experience power quality (PQ) issues, such as harmonics and voltage disturbances. Nevertheless, conventional Series Active Power Filter (SAPF) control schemes have disadvantages, such as slow adaptation and reduced accuracy in a fluctuating renewable environment. To overcome these limitations, this work proposes a hybrid adaptive SAPF-based PQ optimization technique. The proposed method combines the Orangutan Optimization Algorithm (OOA) and Continuous Spiking Graph Neural Network (CSGNN), referred to as the OOA-CSGNN method. Reduction of total harmonic distortion (THD), increase of PQ, and stabilize of voltage profiles in interconnected RES are the goals of the proposed technique. The OOA offers the best SAPF control parameters to maximize convergence and dynamic tracking, and the CSGNN is effective to predict the compensation signals using graph-based spiking computations. The suggested technique is implemented in MATLAB and evaluated against existing approaches, such as the Gorilla Troops Algorithm (GTA), Genetic Algorithm (GA), Adaptive Bald Eagle Optimization Algorithm (ABE-OA), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). The proposed OOA-CSGNN approach achieves a load voltage THD of 0.11% under steady-state operating conditions after SAPF compensation, while maintaining voltage THD well within IEEE-519 limits during transient disturbances such as voltage sag, swell, and dip. These results demonstrate the efficiency and robustness of the proposed hybrid architecture for PQ optimization in renewable-integrated systems. 2026 Elsevier Ltd
Source
Measurement: Journal of the International Measurement Confederation;Volume;276;Issue;;Article No.;121340;
Date
01-01-2026
Publisher
Elsevier B.V.
Subject
and Wind Turbine; Continuous Spiking Graph Neural Network; Grid; Orangutan Optimization Algorithm; Photovoltaic; Renewable Energy Systems; Total Harmonic Distortion
Coverage
Senthil Raja M., Department of Electronics & Communication Engineering, J.N.N Institute of Engineering (Autonomous), Tiruvallur, Tamil Nadu-601102, India; Sowmya Sree V., Department of Electrical & Electronics Engineering, Ashoka Women's Engineering College (Autonomous), Kurnool, Andhra Pradesh-518218, India; Jayakumar V., Department of Electrical & Electronics Engineering, SRM Institute of Science and Technology, Ramapuram, Tamil Nadu, Chennai, India; Karthick B., Department of Electrical and Electronics Engineering, Madanapalle Institute of Technology & Science (MITS), Deemed to be University, Madanapalle, Andhra Pradesh 517325, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 2632241; CODEN: MSRMD
Format
online
Language
English
Type
Article

Citation

Senthil Raja, M.; Sowmya Sree, V.; Jayakumar, V.; Karthick, B., “Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22388.

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