A Coyote Optimization-Based Residual Attention Echo State Reactive Controller for Improving Power Quality in Grid-PV Systems

Abstract

Improving the power quality and reactive injection capability of grid-PV systems represent the most demanding and crucial tasks in power systems. In the conventional works, many types of converters and regulating approaches have been designed for this goal. The multi-level inverter (MLI) is the best solution for grid-PV systems since it helps to improve power quality while reducing losses. However, the existing works face the key problems of the complex system model, increased components utilization, computational burden, presence of harmonics, and high switching frequency. Therefore, the proposed work aims to develop novel and advanced controlling techniques for improving the reactive power compensation ability and power quality of grid-PV systems. The original contribution of this paper is to implement an advanced soft-computing methodologies for developing the controlling mechanisms. At first, an ATOM search optimization (AOS) based MPPT controlling technique is used to extract the maximum electrical energy from the PV panels under changing climatic situations. Then, the output voltage of PV is effectively regulated with the help of a non-isolated high voltage gain DC-DC converter, which also supports the reduction of the switching loss and frequencies. In order to generate the switching pulses for operating the converter, a novel coyote optimized converter control (COCC) mechanism is developed in this work. Moreover, a residual attention echo state reactive controller (RaERC) is implemented for generating the controlling signals to actuate the switching components of the nine-level inverter. This kind of controlling mechanism could highly improve the power quality of grid system with less processing time. For assessment, the simulation and comparison results of the proposed controlling mechanisms are validated and tested using various parameters.