6 Maximum power point tracking control under partial shading conditions using particle swarm optimization algorithm
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Jia Shun Koh
Abstract
This chapter presents maximum power point tracking (MPPT) control using particle swarm optimization (PSO) algorithm for photovoltaic (PV) panel affected by partial shading due to shadow casting. Partial shading casted on a PV panel will produce multiples peaks power characteristic curve; thus, tracking the global peak becomes a challenge especially under dynamic changing partial shading condition. The MPPT PSO used DC-DC buck converter as energy conversion medium to deliver the tracked power to the load. PSO algorithm tracks the MPP by reading the PV panel output power and changing the PWM duty cycle of the buck converter for the PV panel to operate at the maximum power point. The MPPT PSO is implemented in MATLAB/Simulink platform, the model includes a PV Panel with three bypass diodes, power electronics DC-DC buck converter circuitry connected to a load resistor and the PSO algorithm is coded in an embedded function block. The criteria of convergent and reinitialization of PSO algorithm are discussed. The maximum power tracking performance is evaluated on different set of PSO parameters including the population size, acceleration coefficients and inertia weight. The results from each sets of parameters are discussed, and it is reported that the populations size of 4, inertia weight of 0.1, as well as acceleration coefficients of c1 = 0.8 and c2 = 1.2 are the optimal parameter settings of PSO used in this study. Last, the MPPT PSO is also evaluated with dynamic changing partial shading conditions to evaluate its tracking robustness. The best average tracking time and tracking error recorded by the proposed method are 0.06 s and 0.15%, respectively, implying its feasibility for real time application. This chapter provides a comprehensive insight on how PSO parameters affect the performance of MPPT in terms of tracking time, accuracy and stability for practical tracking application.
Abstract
This chapter presents maximum power point tracking (MPPT) control using particle swarm optimization (PSO) algorithm for photovoltaic (PV) panel affected by partial shading due to shadow casting. Partial shading casted on a PV panel will produce multiples peaks power characteristic curve; thus, tracking the global peak becomes a challenge especially under dynamic changing partial shading condition. The MPPT PSO used DC-DC buck converter as energy conversion medium to deliver the tracked power to the load. PSO algorithm tracks the MPP by reading the PV panel output power and changing the PWM duty cycle of the buck converter for the PV panel to operate at the maximum power point. The MPPT PSO is implemented in MATLAB/Simulink platform, the model includes a PV Panel with three bypass diodes, power electronics DC-DC buck converter circuitry connected to a load resistor and the PSO algorithm is coded in an embedded function block. The criteria of convergent and reinitialization of PSO algorithm are discussed. The maximum power tracking performance is evaluated on different set of PSO parameters including the population size, acceleration coefficients and inertia weight. The results from each sets of parameters are discussed, and it is reported that the populations size of 4, inertia weight of 0.1, as well as acceleration coefficients of c1 = 0.8 and c2 = 1.2 are the optimal parameter settings of PSO used in this study. Last, the MPPT PSO is also evaluated with dynamic changing partial shading conditions to evaluate its tracking robustness. The best average tracking time and tracking error recorded by the proposed method are 0.06 s and 0.15%, respectively, implying its feasibility for real time application. This chapter provides a comprehensive insight on how PSO parameters affect the performance of MPPT in terms of tracking time, accuracy and stability for practical tracking application.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- 1 Artificial intelligence and Internet of things for renewable energy systems 1
- 2 Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller 41
- 3 An IoT-based approach for efficient home automation 91
- 4 Design and implementation of IoT-enabled smart single-phase energy meter monitoring system 123
- 5 Internet of things (IoT)-based smart grids 165
- 6 Maximum power point tracking control under partial shading conditions using particle swarm optimization algorithm 185
- 7 Wireless monitoring of substation using IoT 215
- 8 Smart grid–based big data analytics using machine learning and artificial intelligence: a survey 241
- 9 IoT-based intelligent solar energyharvesting technique with improved efficiency 279
- Editor’s Brief Biographies 303
- Index 307
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- 1 Artificial intelligence and Internet of things for renewable energy systems 1
- 2 Power control of modified type III DFIG-based wind turbine system using four-mode type I fuzzy logic controller 41
- 3 An IoT-based approach for efficient home automation 91
- 4 Design and implementation of IoT-enabled smart single-phase energy meter monitoring system 123
- 5 Internet of things (IoT)-based smart grids 165
- 6 Maximum power point tracking control under partial shading conditions using particle swarm optimization algorithm 185
- 7 Wireless monitoring of substation using IoT 215
- 8 Smart grid–based big data analytics using machine learning and artificial intelligence: a survey 241
- 9 IoT-based intelligent solar energyharvesting technique with improved efficiency 279
- Editor’s Brief Biographies 303
- Index 307
