Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Senthil Raja, M.; Sowmya Sree, V.; Jayakumar, V.; Karthick, B. Title A name given to the resource Power quality enhancement of renewable energy systems using a hybrid orangutan optimization algorithm and continuous spiking graph neural network with series active power filter Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Measurement: Journal of the International Measurement Confederation;Volume;276;Issue;;Article No.;121340; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.measurement.2026.121340" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.measurement.2026.121340</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035092471?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035092471?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Senthil Raja M., Department of Electronics &amp; Communication Engineering, J.N.N Institute of Engineering (Autonomous), Tiruvallur, Tamil Nadu-601102, India; Sowmya Sree V., Department of Electrical &amp; Electronics Engineering, Ashoka Women's Engineering College (Autonomous), Kurnool, Andhra Pradesh-518218, India; Jayakumar V., Department of Electrical &amp; 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 &amp; Science (MITS), Deemed to be University, Madanapalle, Andhra Pradesh 517325, India Description An account of the resource 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 Subject The topic of the resource and Wind Turbine; Continuous Spiking Graph Neural Network; Grid; Orangutan Optimization Algorithm; Photovoltaic; Renewable Energy Systems; Total Harmonic Distortion Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 2632241; CODEN: MSRMD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online