Fuzzy and droop controller based hybrid PV and Battery

Fuzzy and droop controller-based standalone and parallel operation hybrid PV and Battery

Abstract

Microgrid systems have been recognized as a promising means for renewable energy integration, grid resilience, and power supply to remote areas. The optimal performance of these systems entails numerous challenges in terms of power sharing, stability, and energy harvesting from photovoltaic (PV) systems. The current study aims to improve microgrid performance using advanced control strategies, such as droop control and fuzzy logic-based maximum power point tracking (MPPT), for hybrid PV and battery energy systems.

The study commenced with an introduction that portrays the significance of microgrid systems and the reasons for enhancing the operational efficiency of microgrids. The principal challenges associated with microgrid performance, including power quality, frequency regulation, and energy management, were addressed. The research goals were set by concentrating on control strategy development and comparative analysis. The thesis structure is also discussed to navigate the reader through the research methodology and findings.

A literature review is provided below, discussing the development of droop control methods for microgrids, MPPT control methods for PV systems, and fuzzy logic applications in power systems. The hybrid battery and PV system integration is discussed, and the problems and solutions given in the literature are highlighted. The review allows the identification of research gaps, especially in optimizing the performance of hybrid microgrids under different load conditions, and leads to the development of the problem statement.

The research then investigated the droop control mechanisms and their application to MPPT in PV systems. Droop control, which is a decentralized method, has been studied for its application in voltage and frequency control. A detailed explanation of the concepts, simulation setup, and experimental verification are provided. The performance of droop-controlled MPPT was tested under different environmental and load conditions, demonstrating its versatility and limitations.

Fuzzy logic-based MPPT has been studied as an alternative method to improve energy extraction from PV systems. Fuzzy logic controllers (FLCs) provide flexibility and insensitivity to parameter changes, making them suitable for dynamic solar conditions. The development of a fuzzy logic-based MPPT controller is presented, and a comparison of traditional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), is provided. The simulation results confirm the improved tracking efficiency and stability of the fuzzy logic-based MPPT method.

A large part of the research has focused on the parallel operation of hybrid PV and battery systems in a standalone microgrid model. This study focuses on the system design, performance evaluation under linear and nonlinear load conditions, and a comparative analysis of various control strategies. The controllers analyzed included proportional-integral (PI), Particle Swarm Optimization (PSO), Droop Control, and Fuzzy Logic Control. Thus, these controllers were considered with regard to system stability, load sharing, and dynamic response.

THD analysis of the voltage and current waveforms was performed to evaluate the power quality improvements achieved using different control strategies. The results provide insights into the trade-offs between response time, accuracy, and computational complexity associated with each control method. The discussion highlights the key observations and interpretations derived from the simulation studies.

This thesis summarizes the major findings, outlines the contributions of this research, and identifies the limitations and challenges encountered. This study shows that MPPT with fuzzy logic and optimized droop control enhances the stability and energy efficiency of a microgrid. Nevertheless, practical implementation complexity and real-time adaptability are areas that require further exploration. Future research directions are proposed, emphasizing the integration of artificial intelligence (AI)-driven optimization techniques, real-time hardware implementation, and the development of adaptive hybrid control strategies.

This work contributes to the advancement of microgrid control methodologies and provides a basis for the future development of resilient and intelligent renewable energy management systems.