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GRID CONNECTED WIND POWER SYSTEM AND SENSORLESS MAXIMUM POWER POINT TRACKING CONTROL METHOD THEREOF USING NEURAL NETWORK
GRID CONNECTED WIND POWER SYSTEM AND SENSORLESS MAXIMUM POWER POINT TRACKING CONTROL METHOD THEREOF USING NEURAL NETWORK
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机译:并网风电系统及其神经网络的无传感器最大功率点跟踪控制方法
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PURPOSE: A grid connected wind power generation system using a neural network and a sensorless maximum power point tracking control method thereof are provided to control a duty ratio of an input voltage of a boost convert in order to follow a maximum power point of a generator by optimizing the width of a fuzzy membership function by learning the neural network using an output power and an output voltage of the generator. CONSTITUTION: A grid connected wind power generation system comprises a blade(1) which generates a mechanical energy rotated by a wind speed; a generator(2) converting generated mechanical energy into electrical energy; a rectifier(3) rectifying the electrical energy; a boost convert(4) which boosts and outputs an output voltage of a current transformer; and an inverter(5) supplied to a grid by converting the output voltage of the boost convert into an alternating current. An output power fluctuation and an output voltage fluctuation of a generator varied according to the wind speed are inputted. A fuzzy membership function width of the output power fluctuation and a fuzzy membership function width of the voltage fluctuation are determined learning through the neural network. A fuzzy control for the output voltage fluctuation and the output power fluctuation of the generator is performed. A neuro fuzzy controller(6), which outputs an input voltage of a boost convert in which a duty ratio is controlled in order to reach a generator power output electricity to a maximum power point, is included. [Reference numerals] (3) Rectifier; (4) Boost convert; (5) Inverter; (6) Neuro fuzzy controller; (AA) Wind; (BB) Direct current terminal; (CC) Grid
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