PNKLMF-Based Neural Network Control and Learning-Based HC MPPT Technique for Multiobjective Grid Integrated Solar PV Based Distributed Generating System

In this paper, a novel power normalized kernel least mean fourth algorithm based neural network (NN) control (PNKLMF-NN) technique and learning-based hill climbing (L-HC) maximum power point tracking (MPPT) algorithm are proposed for grid-integrated solar photovoltaic (PV) system. Here three-phase single-stage topology of a grid-integrated PV system is used for feeding the nonlinear/linear load at the point of common coupling. A single layer neuron structure is used for active load component (ALC) extraction from distorted load current. During ALC extraction, PNKLMF-NN control very precisely attenuates harmonics components, noise, dc offset, bias, notches, and distortions from the nonlinear current, which improves the power quality under normal as well as under abnormal conditions. This single layer PNKLMF-NN control has a very simple architecture, which reduces the computational burden and complexity. Therefore, it is easy in implementation. Moreover, proposed L-HC is the improved form of hill climbing (HC) algorithm, where inherent problems of traditional HC algorithm, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed PNKLMF-NN control is to meet the active power requirement of the loads from generated solar PV power and excess power fed into the grid. However, when generated PV power is less than the required load power, then PNKLMF-NN control meets the load by taking extra required power from the grid. During these processes, power quality is maintained at the grid. Moreover, when solar irradiation is zero, voltage source converter (VSC) acts as distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed prototype in adverse conditions, which test results have satisfied the objectives of the proposed system and the IEEE-519 standard.