Bernoulli polynomial‐based control technique for PV‐integrated grid system under distorted supply

Abstract

Summary This paper discusses the performance of the Bernoulli Polynomial‐based artificial neural network (BePANN) for the control of a single‐phase grid‐integrated solar photovoltaic (PV) system. A single‐phase, single‐stage topology of a grid‐integrated PV system is utilized to feed nonlinear loads at the point of common coupling. The fundamental load component is extracted from distorted load current using a single‐layer neural network. There are several control techniques mentioned in the literature for load compensation. However, many techniques show slower convergence response, higher oscillations, and a large computational burden. The designed BePANN controller attenuates harmonic components from nonlinear current and improves the power quality (PQ) under normal and distorted grid conditions. The single‐layer BePANN control has a simplified structure based on several polynomial terms; this reduces the computational burden and complexity of the controller. The objective of the designed controller is to fulfill the load's active power demand from the generated solar PV power and feed the excess power back to the grid when surplus. When solar PV is not integrated with the grid, the voltage source converter acts as a distribution static compensator, improving the system's utilization factor. The proposed control technique is simulated in MATLAB Simulink, and results are experimentally verified through laboratory prototype under normal and abnormal grid conditions. The application of higher order polynomials for PV‐integrated systems with multifunctional PQ capabilities has been implemented for the first time in this article. The main outcomes of the proposed control technique are solving current‐related PQ issues and reactive power compensation and providing improved power factor under both normal and distorted polluted grids.

Design and development of leaky least mean fourth control algorithm for single‐phase grid‐connected multilevel inverter

“…The DSTATCOM can be configured to solve PQ problems such as harmonics filtering, reactive power compensation, voltage support in low‐voltage distribution networks, load balancing, and lowering SPV power fluctuations using several control algorithms. These algorithms include synchronous reference frame theory (SRFT), 10 P‐Q theory, 11 salp swarm optimization algorithm (SSOA), 12 cascaded enhanced second‐order general integrator with a prefilter (CESOGI‐WPF) control technique, 13 LCL‐filter‐based DSTATCOM, 14 ANOVA Kernel Kalman filter (AKKF), 15 Hermite function‐based ANN, 16 optimized multi‐layer perceptron (MLP) neural network, 17 Bernoulli polynomial‐based control technique, 18 proportionate affine projection algorithm (PAPA), 19 Laguerre polynomial (LP)‐based algorithm, 20 instantaneous symmetrical components theory (ISCT), 21 normalized least mean absolute third (NLMAT) algorithm, 22 and immune feedback control algorithm 23 . The DC link of the DSTATCOM can also be used to interface solar photovoltaic (SPV) arrays to extract DC power to three‐phase system 24 .…”