Novel Hybrid Optimization Maximum Power Point Tracking and Normalized Intelligent Control Techniques for Smart Grid Linked Solar Photovoltaic System

Abstract

Introduction of photovoltaic (PV) systems in grid causes some complex power quality problems such as harmonics, voltage fluctuation, and load imbalance that can affect the grid coordinated PV system performance. Therefore, herein, a novel normalized reasoning‐based fuzzy neural adaptive (NRFNA) control technique is proposed for the double‐stage grid coupled solar PV system. In addition, the utmost power in a double‐stage PV system is tracked at dissimilar environmental states by the proposed novel hybrid krill herd spider monkey (HKHSM)‐based maximum power point tracking (MPPT) algorithm. The current computation control method preserves the grid current in unity power factor as sinusoidal for the system at point common coupling (PCC). The simulation of this proposed work is done on MATLAB/Simulink. Subsequently, the performance of the developed replica results is validated under dissimilar conditions of power quality. Furthermore, the proposed HKHSM‐based MPPT along with a normalized reasoning herd spider monkey (NRHSM) control technique in a grid coordinated solar PV system has achieved 1.70% load current harmonics and 3.7% grid current harmonics. Consequently, the simulation outcomes are compared with the various conventional methods for proving the significance of the proposed system.

Long Short Term Memory Tracker‐Based Modified DC‐DC Converter for Power Quality Improvement in Grid‐PV Systems under Uniform and Partial Shading Environments

“…This section compares proposed approach to various other published papers, including. [ 21–24 ] Table 3 compares the proposed and existing methodologies, demonstrating that the proposed methodology offers well‐performed result in contrast with previous techniques. Proposed LSTM provide a low THD level of 1.57% for voltage and 1.15% for current.…”

“…Abdul Baseer et al [ 22 ] represented a two‐stage grid‐coupled solar PV system, a normalized reasoning‐based fuzzy neural adaptive control technique was developed. Under diverse climatic conditions, the highest power in a double‐stage PV system was tracked using newly proposed hybrid krill herd spider monkey‐based MPPT algorithms.…”

“…From the above‐mentioned related work, for MPPT to improve power efficiency in a PV system under all circumstances. Even these existing methods have some drawbacks like curve under MPP conditions, [ 19 ] voltage fluctuation is uncontrollable, [ 22 ] the algorithms fall short of obtaining the global maximum under partial shade conditions, [ 25 ] and parameter values are difficult to measure in practise. [ 28 ] In order to overwhelm such drawbacks, an advanced intelligent MPPT method is developed to improve power efficiency in a PV with an integrated grid system.…”

Low Carbon Operation Control Technology for Smart Parks Combined with Intelligent Optimization Algorithms

“…Artificial intelligence, as an emerging technology, is being widely used in various fields, including industry, agriculture, transportation, and other industries. A zero-carbon park is a park with zero or near-zero net carbon emissions through the use of renewable energy and energy-efficient technologies [1][2][3]. AI can improve energy efficiency, reduce waste, and lower carbon emissions.…”

Dual‐Objective Optimization for Maximizing Photovoltaic Output Power in a Two‐Stage Single‐Phase Grid‐Tied System

“…[ 1 ] Research on energy conversion has significantly increased, especially in integrating solar photovoltaic (SPV) systems into the grid network. [ 2,3 ] The SPV array is connected to the grid through an inverter and boost converter. The maximum power generated by the PV modules is reduced as a result of the varying environmental conditions.…”