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从应用角度出发,尝试将模糊神经网络控制原理应用在中压无功补偿控制中,通过网络的学习和记忆功能来调节模糊控制的自适应性和鲁棒性,从而来克服SVG系统中存在的电容器反复投切所引起的电网信号的波动,减少投切谐波,提高控制精度。同时也控制电压分接头的投切次数,来保护分接头开关,延长开关的使用寿命,减少系统的维修成本。针对电力系统无功补偿的特点,确定了模糊控制的输入输出隶属度函数和控制规则,并引入BP网络来调节隶属度函数和控制规则,对电压和无功的双重控制,达到对电压和无功补偿的目的,从而提供更高质量的电能。并进行实验仿真,将实验数据与理论数据进行比较,证明模糊控制理论在无功补偿控制中的应用是可行可靠的。
From the application point of view, try to apply the fuzzy neural network control principle to the MV reactive power compensation control, and adjust the adaptability and robustness of the fuzzy control through the learning and memory functions of the network, so as to overcome the existing problems of SVG system Capacitor switching caused by repeated fluctuations in the power grid signal to reduce the switching harmonic, improve control accuracy. At the same time also control the number of switching voltage tap to protect the tap switch to extend the life of the switch and reduce system maintenance costs. According to the characteristics of reactive power compensation in power system, the input and output membership functions and control rules of fuzzy control are determined. The BP network is introduced to adjust the membership function and control rules. The dual control of voltage and reactive power, The purpose of power compensation, thus providing higher quality power. And the experimental simulation is carried out. The experimental data and theoretical data are compared to prove that the application of fuzzy control theory in reactive power compensation control is feasible and reliable.