Optimal Tuning of PID Controller for Boost Converter using Meta-Heuristic Algorithm for Renewable Energy Applications

Abstract

The Dynamic Levy Flight Chimp optimisation (DLFC) method is used in this study to optimise the Proportional- Integral-Derivative (PID) Controller for the Boost converter. As a possible application, the tuned PID controller is utilised to adjust voltages in the use of renewable power sources. The maximum power point tracking control approach based on machine learning (ML) is used to anticipate the reference voltages for the solar system based on the irradiance and the ambient temperature. The tuned PID controller uses this reference signal to regulate the maximum power point (MPP) voltages. To finetune the PID controller, comparisons are done with grey wolf optimiser (GWO), Harris hawk optimisation algorithms (HHO), and particle swarm optimisation (PSO) algorithms. The tuned PID controller has fewer oscillations and requires little tracking time to adapt to changing load and environment conditions. Additionally, statistical analysis, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) between the reference voltage and the output voltage, is presented. Since the DLFC tuned PID controller performs better than HHO, GWO, and PSO in terms of RMSE and MAE, it may be a promising way for optimising PID controller tuning for boost converters in photovoltaic (PV) system applications.