M. D. Udayakumar∗ and A. Shunmugalatha∗∗
Electric spring (ES), artificial neural networks, smart grid, controller, renewable energy sources, critical and non-critical loads, proportional integral
Integrating intermittent renewables into power grids poses stability and power quality (PQ) challenges, leading to voltage fluctuations. Conventional methods, like genetic algorithms, face local optimal entrapment. To address this, nature-inspired algorithms are emerging for improved grid optimisation; this paper proposes a novel artificial intelligence (AI)-based controller for electric springs (ES) in smart grids. The artificial neural network (ANN) is comprehensively learnt the intricate relationship between ES-control inputs and the desired grid voltage and frequency. The study chooses ANN due to their exceptional ability to learn and adapt to the dynamic nature of smart grids. ES provide rapid energy buffering, and power electronics play a crucial role in managing power-flow. The ANN acts as an intelligent- controller, capable of adapting to the complex dynamics of the grid. The proposed-controller is evaluated on a MATLAB-platform, comparing its performance against existing controllers like PID, PD, and FOPID. Metrics like total harmonic distortion (THD) of grid voltage and voltage/frequency regulation are assessed. The ANN achieves a notably lower THD of 3.4%, outperforming other neural network methods like RNN at 4.5%, RBFNN at 5.6%, and CNN at 3.4%. The AI-based ES-controller excels in regulating voltage and frequency, enhancing grid stability and reliability during intermittent renewable energy sources (RES) integration. Its adaptive learning ensures effective handling of changing conditions, making it a valuable tool for grid operators to improve PQ and overall grid resilience. Overall, this paper contributes to the field of smart-grid- management by presenting a novel and effective AI-based solution for integrating-RES while ensuring grid stability and power-quality. ∗ Department of Electrical and Electronics Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, Tamilnadu 621112, India; e-mail: [email protected] ∗∗ Department of Electrical and Electronics Engineering, Velammal College of Engineering and Technology, Madurai, Tamilnadu 625009, India; e-mail: [email protected] Corresponding author: M. D. Udayakumar
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