Optimal Design and Performance Investigation of Artificial Neural Network Controller for Solar- and Battery-Connected Unified Power Quality Conditioner

Abstract

Nowadays, integration of renewable sources into the local distribution system and the nonlinear behavior of advanced power electronic equipment have made a large impact on the power quality (PQ). The unified power quality conditioner (UPQC) is a multifunctional FACTS device, which is a combination of both shunt active filter and series active filters via a common DC link. Presently, the artificial intelligence is playing a vital role in the development of the intelligent control methods. Traditional training methods of artificial neural network (ANN) like back propagation and Levenberg-Marquardt may get stuck in local optimal solution which leads to the invention of ANN trained optimally by metaheuristic algorithms. This paper develops a firefly algorithm-trained ANN (FF-ANNC) controller for the shunt active filter and proportional integral controller (PI-C) for the series active filter of the UPQC integrated with the solar energy system and battery energy storage via boost converter (B-C) and buck boost converters (B-B-C). The main aim of the proposed FF-ANNC is to reduce the mean square error (MSE) thereby achieving the constant DC link capacitor voltage (DLCV) during load and irradiation variations, reduction of imperfections in current waveforms, improvement in power factor (PF), and mitigation of sag, swell, disturbances, and unbalances in the grid voltage. The working of developed FF-ANNC was tested on five test studies with different types of loads and source voltage balancing/unbalancing conditions. However, to demonstrate supremacy of the suggested FF-ANNC, a comparative study with the training methods like genetic algorithm (GA) and ant colony optimization (AC-O) and also with other methods that exist in literature like PI-C, fuzzy logic controller (FL-C), and artificial neuro fuzzy interface system (ANFI-S) was conducted. The proposed method reduces the total harmonic distortion to 2.39%, 2.32%, 2.27%, 2.45%, and 2.66% which are lower than the existing methods that are available in literature. The FF-ANNC shows an excellent performance in reducing voltage fluctuations and total harmonic distortion (THD) successfully and thereby improving PF.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

“…The ANN-based method was suggested for five-level UPQC to control the PQ problems (Sudheer and Kota, 2017). The firefly-based optimization algorithm was used to train the ANNC, which was developed for the shunt VSC for the PV/battery UPQC to reduce the mean-square error (MSE) and minimize the THD (Ramadevi et al, 2023). The self-tuning filter-based method was developed for UPQC integrated with renewable sources to address PQ issues (Mansoor et al, 2023).…”

Performance evaluation of solar PV mini grid system in Nepal: a case study Thabang and Sugarkhal

“…Furthermore, the integration of renewable sources into local distribution systems and the non-linear behavior of advanced power electronics impact power quality. Approaches like the firefly algorithm-trained ANN controller for the shunt active filter and proportional-integral controller for the series active filter of UPQC, integrated with solar energy systems and battery storage via boost and buck boost converters, show excellent performance in reducing voltage fluctuations and total harmonic distortion, thereby improving power factor (Ramadevi et al, 2023). These approaches highlight the importance of advanced control methods for enhancing the performance of solar grid systems.…”

Optimal design of solar/wind/battery and EV fed UPQC for power quality and power flow management using enhanced most valuable player algorithm

“…(Srilakshmia et al, 2020). On the other hand, optimized AI based techniques were adapted to renewable energy sources associated UPQC address PQ issues (Srilakshmi et al, 2022a;Srilakshmi et al, 2022b;Srilakshmi et al, 2023a;Srilakshmi et al, 2023b;Ramadevi et al, 2023).…”