Metaheuristic Optimization of Fractional Order Incremental Conductance (FO-INC) Maximum Power Point Tracking (MPPT)

Hossam Hassan Ammar,Ahmad Taher Azar,M I Mahmoud,Raafat Shalaby

doi:10.1155/2019/7687891

Abstract

This paper seeks to improve the photovoltaic (PV) system efficiency using metaheuristic, optimized fractional order incremental conductance (FO-INC) control. The proposed FO-INC controls the output voltage of the PV arrays to obtain maximum power point tracking (MPPT). Due to its simplicity and efficiency, the incremental conductance MPPT (INC-MPPT) is one of the most popular algorithms used in the PV scheme. However, owing to the nonlinearity and fractional order (FO) nature of both PV and DC-DC converters, the conventional INC algorithm provides a trade-off between monitoring velocity and tracking precision. Fractional calculus is used to provide an enhanced dynamical model of the PV system to describe nonlinear characteristics. Moreover, three metaheuristic optimization techniques are applied; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and AntLion Optimizer (ALO) are used for tuning the FO parameters of the proposed INC-MPPT. A MATLAB-Simulink-based model of the PV and optimization have been developed and simulated for different INC-MPPT techniques. Different techniques aim to control the boost DC-DC converter towards the MPP. The proposed optimization algorithms are, also, developed and implemented in MATLAB to tune the target parameters. Four performance indices are also introduced in this research to show the reliability of the comparative analysis of the proposed FO-INC with metaheuristic optimization and the conventional INC-MPPT algorithms when applied to a dynamical PV system under rapidly changing weather conditions. The simulation results show the effective performance of the proposed metaheuristic optimized FO-INC as a MPPT control for different climatic conditions with disturbance rejection and robustness analysis.

Highlights

Green energy sources are the primary research goal nowadays as they are viable, ecological, and cost-e ective energy sources
Fixed-step INC Method. e INC algorithm is used to detect the condition of MPP via the conductance behavior of the PV system. e INC-maximum power point tracking (MPPT) can be executed through the following sequence [20]: (1) e voltage and current of the PV module are sensed by the MPPT controller
A closed loop of PV with MPPT and Buck–Boost converter is running in Simscape environment for two seconds to measure the PV output power. e mean squared-error (MSE) between the MPP (PMPP) and output power of the PV system (PPV) is the cost function of the metaheuristic optimizer calculated

Summary

Introduction

Green energy sources are the primary research goal nowadays as they are viable, ecological, and cost-e ective energy sources. MPPT algorithms are usually used as electronic power conversion devices and the control signal is a duty cycle for peak load energy [7]. FO-INC based on the nonlinear and fractional order changes of the PV voltage and current has been proposed to track the maximum output power [13]. Metaheuristic abilities are powerful techniques of resolving optimization problems for nonlinear and fractional order systems [15]. E main function of the MPPT algorithm is to automatically track the voltage/current change of the PV panel and feed the Buck–Boost converter with the appropriate duty cycle to get the MPP under specific climatic conditions.

Ω 1 mH 4700 μF 47 μF 25000 Hz

Metaheuristic Optimization Algorithms

Result

Conclusion and Future