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The incorporation of clean sources into the power grid, along with the introduction of power electronic gadgets to regulate loads that are nonlinear, had a significant effect on power quality (PQ). Nevertheless, PQ emerges as the primary issue for the integration of these intermittent renewable sources. This study combines the advantageous characteristics of the Sliding mode controller (SM-C) and fractional-order proportional integral derivative controller (FPIDC) to manage both the shunt and series active filters of the Unified Power Quality Conditioner (UPQC) integrated with the wind power system (WPS) and battery systems (BS). The primary objective of the proposed system is to maintain a consistent voltage across the DC Link capacitor, even when there are fluctuations in load and wind velocity. Additionally, the system aims to reduce the presence of harmonics in waveform of current signal, improve power factor (PF), and mitigate any disruptions or imbalances in the source voltage. The effectiveness of the created model has been assessed by four case studies involving diverse load types and grid voltage situations, including fluctuations. Besides, to validate the developed method the comparative analysis is conducted with respect to other methods like PI and fuzzy logic controllers, as well as with other ways documented in the literature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]