© 2011 ACEEE DOI: 01.IJEPE.02.02. ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 02, August 2011 2 37 Intelligent Gradient Detection on MPPT Control for VariableSpeed Wind Energy Conversion System Ahmad Nadhir 1,2 , Agus Naba 1 , and Takashi Hiyama 2 1 Department of Physics Brawijaya University, Malang, Indonesia Email: anadhir@ub.ac.id 2 Electric Power Systems Laboratory Kumamoto University, Kumamoto, Japan Abstract—The problem of control associated wind energy conversion systems using horizontal-axis fixed-pitch variable speed low-power, working in the partial load region, consisting in the energy conversion maximization, is approached here under the assumption that the wind turbine model and its parameters are poorly known. Intelligent gradient detection method by using Maximum Power Point Tracking (MPPT) fuzzy control approach is proposed control solution aims at driving the average position of the operating point near to optimality. The reference of turbine rotor speed is adjusted such that the turbine operates around maximum power for the current wind speed value. In order to establish whether this reference must be either increased or decreased, it is necessary to estimate the current position of the operating point in relation to the maximum power-rotor speed curve characteristic by many fuzzy rules. Numerical simulations are used for preliminary checking performance of the MPPT control law based on this intelligent gradient detection. Index Terms—MPPT, wind energy, optimal control, WECS I. INTRODUCTION The worldwide concern about the environmental pollution and the possible energy shortage has led to increasing interest in technologies for generation of renewable electrical energy. Among various renewable energy sources, wind generation has been the leading source in the power industry. In order to meet power needs, taking into account economical and environmental factors, wind energy conversion is gradually gaining interest as a suitable source of renewable energy [1]. The wind energy conversion system (WECS) control field vary in accordance with some assumptions concerning the known models or parameters, the measurable variables, the control method employed, and the version of WECS model used. The power that developed by a wind turbine depends not only on the air velocity but also on the speed of the turbine. The speed at which maximum power is developed a function of wind velocity. In order to extract maximum power, the speed of the turbine has to be controlled as a function of wind velocity. Control of WECS in the partial load regime generally aims at regulating the power harvested from wind by modifying the electrical generator speed; in particular, the control goal can be to capture the maximum power available from the wind. For each wind speed, there is a certain rotational speed at which the power curve of a given wind turbine has a maximum (reaches its maximum value) [2]. Many researchers have proposed different control schemes in WECS. Some controller designs employ anemometers to measure wind velocity [3]. These mechanical sensors increase the cost and reduce the reliability of the overall system. The measurements can be seriously perturbed by turbulence. Due to the difficulties in wind speed measurement, a control strat- egy based on the tip-speed ratio is practically difficult to imple- ment. Consequently methods of wind speed estimation have been suggested [4-6], the approach employs the hill-climbing method for dynamically driving the operating point, by using some searching signal in order to obtain gradient estimations of some measurable variables. Based on the operating point position on the power characteristic, the rotational speed is controlled in the sense of approaching the maximum power available. In this paper the improvement optimal control of variable-speed fixed-pitch WECS based upon maximum power point tracking (MPPT) will be discussed, when the tips speed and power coefficient parameters are not known. Intelligent gradient detection on MPPT uses the generator speed and active power output measurements to search for the optimum speed at which the turbine should operate for producing maxi- mum power. MPPT controller will generate a rotor speed refer- ence based on the result of intelligent gradient detection sys- tem. Performances of classical MPPT control and MPPT fuzzy control based on intelligent gradient detection will be com- pared. Effectiveness of the proposed control scheme will be validated through computer simulations under varying wind speeds. II. WIND ENERGY CONVERSION SYSTEMS Figure 1. Wind energy conversion systems. Fig. 1 presents wind power conversion systems, which uses squirrel-cage induction generator (SCIG). From the system viewpoint, the conversion chain can be divided into four interacting main components which will be separately modeled: the aerodynamic subsystem S1 and the electromagnetic subsystem S2 interact by means of the drive train mechanical transmission S3, whereas S4 denotes the grid interface. A. Wind Turbine Characteristics Fig. 2 shows a variable speed wind turbines have three