Artificial Intelligence based Multi-Objective Hybrid Controller for PV-Battery Unified Power Quality Conditioner

Koganti Srilakshmi, Sravanthy Gaddameedhi, Sumanth Yamparala, Srinivas Nakka, YSR Kamal, Surendra Babu B, Guduguntla Anil

Abstract


This work presents an artificial intelligence-based hybrid control technique for the unified power quality conditioner integrated with solar PV and battery storage systems. The UPQC comprises a series and a shunt voltage source converter, connected with a dc link. The hybrid controller adapts both the Fuzzy Logic controller and artificial neural network. The Neuro-Fuzzy Hybrid Controller (NFHC) is adapted to control DC-Link voltage. The prime objectives of the proposed work are minimization of harmonics in current waveforms and power factor improvement, rapid action for dc-link voltage balancing, elimination of sag/swell in the source voltage, superior performance for large disturbances, and suitable compensation for unbalanced networks. The performance analysis of the proposed controller was carried out with three different test cases for various combinations of loads, solar irradiation and compared with those of existing methods.


Keywords


Renewable Energy, solar, Electrical engineering

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i1.12806.g8425

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