Optimal Design of an Artificial Intelligence Controller for Solar-Battery Integrated UPQC in Three Phase Distribution Networks

In order to minimize losses in the distribution network, integrating non-conventional energy sources such as wind, tidal, solar, and so on, into the grid has been proposed in many papers as a viable solution. Using electronic power equipment to control nonlinear loads impacts the quality of power. The unified power quality conditioner (UPQC) is a FACTS device with back-to-back converters that are coupled together with a DC-link capacitor. Conventional training algorithms used by ANNs, such as the Back Propagation and Levenberg–Marquardt algorithms, can become trapped in local optima, which motivates the use of ANNs trained by evolutionary algorithms. This work presents a hybrid controller, based on the soccer league algorithm, and trained by an artificial neural network controller (S-ANNC), for use in the shunt active power filter. This work also presents a fuzzy logic controller for use in the series active power filter of the UPQC that is associated with the solar photovoltaic system and battery storage system. The synchronization of phases is created using a self-tuning filter (STF), in association with the unit vector generation method (UVGM), for the superior performance of UPQC during unbalanced/distorted supply voltage conditions; therefore, the necessity of the phase-locked-loop, low-pass filters, and high-pass filters are totally eliminated. The STF is used for separating harmonic and fundamental components, in addition to generating the synchronization phases of series and shunt filters. The prime objective of the suggested S-ANNC is to minimize mean square error in order to achieve a fast action that will retain the DC-link voltage’s constant value during load/irradiation variations, suppress current harmonics and power–factor enhancement, mitigate sagging/swelling/disturbances in the supply voltage, and provide appropriate compensation for unbalanced supply voltages. The performance analysis of S-ANNC, using five test cases for several combinations of loads/supply voltages, demonstrates the supremacy of the suggested S-ANNC. Comparative analysis was carried out using the GA, PSO, and GWO training methods, in addition to other methods that exist in the literature. The S-ANNC showed an extra-ordinary performance in terms of diminishing total harmonic distortion (THD); thus PF was improved and voltage distortions were reduced.