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Optimal control of a hybrid power compensator using an artificial neural network controller

机译:使用人工神经网络控制器的混合动力补偿器的最优控制

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A hybrid power compensator (HPC) consisting of a static VAr compensator (SVC) and a dynamic compensator (DC) needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimising its total cost. The use of an artificial neural network (ANN) to control the HPC amidst a very dynamic environment to achieve the above, is investigated. A state space model of the power distribution network together with the HPC forms the basis of evaluation of the mentioned controller. The model was calibrated against actual in-network measurements. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.
机译:在非线性负载的补偿期间,需要在非线性载荷补偿期间最佳地控制由静态VAR补偿器(SVC)和动态补偿器(DC)组成的混合动力补偿器(HPC)。必须控制HPC,以满足功率因数和谐波失真方面的最低要求,同时最小化其总成本。研究了使用人工神经网络(ANN)在非常动态的环境中控制HPC以实现上述。配电网络的状态空间模型与HPC一起形成所提到的控制器的评估的基础。该模型针对实际网络测量校准。得到的结果表明,在适当地选择ANN参数的情况下,ANN在控制HPC时可行是可行的。

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