Abstract
Optimal control of power converters to avoid voltage instability in cases such as system loading or faults is one of the most studied nonlinear problems that affect energy quality in power systems. The optimization problem related to converter control becomes more difficult with the inclusion of renewable energy systems while trying to fulfill power system constraints and providing an adequate amount of energy. In this paper, a simple approach based on artificial neural networks (ANNs) has been proposed and applied to photovoltaic-fed high-voltage DC and high-voltage AC systems interconnection consisting of PI-controlled power converters. By using the proposed method, converter control parameters are optimized for different cases to improve steady-state and dynamic voltage stability while also avoiding any kind of system faults. In order to implement hybrid control methodology by using ANN and PI control, the network should be well trained with samples including not only global best values but also the whole possible system characteristic. For this reason, a novel optimization algorithm, differential search algorithm, is used to sample solution space and train ANN by using random and localized samples. Obtained and presented results of the proposed approach show that due to robust and fast response, ANNs can be successfully used to overcome nonlinear security and optimization problems concerning power system stability.
Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks