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      LOAD FREQUENCY CONTROL STRATEGY USING HYBRID ADAPTIVE PARTICLE SWARM-SPIRAL DYNAMIC OPTIMIZATION ALGORITHM FOR STAND-ALONE RENEWABLE RURAL NETWORK

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            Abstract

            This paper proposes an advanced Load Frequency Contol (LFC) strategy, using a hybrid adaptive particle swarm-spiral dynamic optimization algorithm (HAPSSDOA). The optimization algorithms iteratively adjust the PID gains based on the evaluation of the cost function and eventually find the optimal values that minimize the cost function and result in a satisfactory frequency value of 50Hz. The P-I-D parameters for the APSO, SDA, and HAPSSDOA algorithms were 3.5112, 2.9691 and 1.1972; 2.3712, 2.8479 and 0.9827; 2.3519, 1.7989 and 0.8864 respectively. The system’s performance showed the rise time, settling time, percentage overshoot and ITAE of 0.28s, 2.33s, 42.10%, and 0.1076 for APSO; 0.324s, 3.67s, 38.4%, and 0.3590 for SDA; 0.36S, 2.2S, 33.5%, and 0.2430 for HAPSSDOA respectively. The LFC strategy using the hybrid HAPSSDOA achieved better performance, improved robustness, and the best response. The optimized PID controllers’ values brought more stability to the designed microgrid network, by ensuring that the frequency remained at 50Hz.

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            Author and article information

            Journal
            Nigerian Journal of Tropical Engineering
            Abubakar Tafawa Balewa University
            1595-5397
            24 April 2024
            : 18
            : 1
            : 21-29
            Affiliations
            [1 ] Department of Electrical Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi – Nigeria. ( https://ror.org/019vfke14)
            Author notes
            Article
            10.59081/njte.18.1.003
            f0a4dee8-919b-4801-a707-5731b2d9d21b
            The authors

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

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            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Data structures & Algorithms,Applied computer science,Performance, Systems & Control,Computer science,General engineering,Electrical engineering
            Adaptive particle swarm optimization,Hydropower,Micro-grid,Load frequency control,Rural electrification,Spiral dynamic optimization

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