Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

Abstract

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.

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

“…The summation function sums up the product of the inputs and the weight, with bias as shown in equation (6), where w pk is the weight connecting I p to neuron k, β k is a bias, and m is the number of input neurons. In general, a (7). Hence, the k th neuron output can be determined as in equation (8).…”

“…A neurofuzzy controller was designed for battery-and solar-integrated UPQC with a goal of DLCV balancing with maximum elimination of current waveform imperfections and voltage distortions [6]. The hybrid fuzzy-based sliding mode (F-SMC) controller was suggested for the wind battery-associated SHAF to reduce currentrelated distortions and improve power factor (PF) [7].…”

Parameter Design of Half-Bridge Converter Series Y-Connection Microgrid Grid-Connected Filter Based on Improved PSO-LSSVM

“…In microgrids, active and passive power flters are generally used to reduce the amplitude of harmonics in the system. In the literature [8][9][10], corresponding active flter optimization design methods are proposed for diferent types of microgrids, which efectively improve the power quality of the AC busbar. Compared to the active flter, the passive flter is more suitable for scenarios with a higher power.…”

Improvement of power quality parameters using modulated-unified power quality conditioner and switched-inductor boost converter by the optimization techniques for a hybrid AC/DC microgrid

“…• The suggested approach is on time to execute, in comparison with time limitation concerns in enforcing metaheuristic methods [13][14][15][16] . • Contrary to the literature presented in Refs.…”

“…• Contrary to the literature presented in Refs. 8,9,11,13 , which has a relatively complex control structure, the proposed approach in this paper is not complicated. • The techniques suggested in the papers 3,12 are more effective for systems with generators and similar machines.…”