Particle Swarm Optimization Control of a Grid Connected Solar / Wind Energy System

Hybrid renewable energy system has been introduced as a green and safe power system. The hybrid unit is widely used and therefore, optimization problem solving for this system is necessary. Research and development efforts in renewable sources are required to improve the hybrid system performances. In this study, we propose the control of an hybrid system comprising a wind turbine and a shaded photovoltaic generator using an evolutionary method based on particle swarm optimization. Keywords—hybrid; renewable; wind turbine; photovoltaic; shading; particle swarm optimization; grid I. INTRODUCTION During the last decade, energy consumption has increased considerably due to massive industrialization. The use of alternative resources such as solar and wind energy is a solution to meet the needs of large-scale power generation. Despite the above-mentioned renewable energy systems are considered as promising power-generating sources, the major drawback of these resources is their variable nature and dependence on the weather and climatic conditions. For this reason, a combination of some sources to form a single renewable energy system called as hybrid renewable energy system (HRES) [1], allows maximum optimization of power systems, both technically and economically. Indeed, integrating the mentioned resources in a suitable hybrid combination provides the potential to improve the system efficiency and the energy supply reliability. In this framework, several researchers have studied the design, optimization, operation and control of HRES. Celik [2] presented a technique to evaluate the performance of hybrid solar/wind energy system using synthetically generated weather data. Nehrir et al. [3] proposed a new computer-modeling approach for evaluating the general performance of hybrid photovoltaic/wind energy system. Rajkumar et al [4] used adaptative neuro-fuzzy inference system to model the hybrid systems. In [5], Yang et al studied an optimal sizing method for stand-alone hybrid solar/wind system with the loss of power supply probability technology by using a genetic algorithm. In fact, due to the high complexity and high nonlinearity of the renewable systems, this work suggests metaheuristics called particle swarm optimization (PSO) [6] to control a grid connected photovoltaic/wind system. This method is used to control the pitch angle of the wind turbine system and to assure the maximum power point tracking (MPPT) of a shaded photovoltaic (PV) system. In our study, we started with the development of the studied HRES system, and then the concept of using PSO method in controlling the wind turbine pitch angle and global maximum power point (GMPP) tracking is detailed. Finally, before concluding, the performance of the developed control strategies is discussed using some simulation results. II. THE STUDIED HYBRID RENEWABLE ENERGY SYSTEM A. Wind Turbine Model The wind conversion system (WECS) is generally constituted of the rotor, the transmission system, and the power generator unit [7]. Fig.1 gives an overview of a horizontal-axis wind turbine. The rotor includes the blades that assure the aerodynamic conversion, the hub that connects the blades to the transmission and the pitch servos which are in charge to rotate the blades around their longitudinal axes. The transmission system assures the transfer of the captured mechanical power to the electric machine. It comprises the lowand high-speed shafts, the gearbox and the brakes. The gearbox increases the rotor speed to values more suitable for driving the generator. For the studied variable speed WECS, the wind turbine is directly coupled to the PMSG without a gearbox to convert the wind power into electrical one, the electric generator converts mechanical power into electricity. Fig.1. overview of a horizontal-axis wind turbine The general model for the whole WECS is structured by several interconnected subsystem models as it is given in fig.2. The aerodynamic subsystem outlines the transformation of the three-dimensional wind speed field into forces on the blades which initiate the rotational movement. The mechanical subsystem includes the drive-train and the support structure. In fact, the drive-train transmits the aerodynamic torque on the blades to the generator shaft and the structure, composed of the tower and foundations, sustains the thrust force. The electrical subsystem permits the conversion of mechanical power at the generator shaft into electricity. Concerning the actuator subsystem, it models the pitch servo behaviour [8]. Fig.2. Subsystem-level block diagram of a variable-speed variable-pitch WECS The output power of the wind turbine is expressed by: 2 3 w w p 1 P ρπR V C ( λ,β ) 2  (1) Where: ρ is the air density A is the turbine swept area W V is the wind speed The power coefficient p C is a non-linear function depending on the tip speed ratio λ and β blade pitch angle. It is given by: 2.14 p i i 151 18.4 C ( , ) 0.53 0.58 0.002 13.2 exp                                 (2)