Application of Artificial Neural Networks as a State Selector in Direct Power Control of DSTATCOM for Voltage Flicker Mitigation

摘要

Neural networks are receiving attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of a plant or a process. Issues such as teaming speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. This paper discusses the application of neural networks to control DSTATCOM using direct power control (DPC). A neural network is used to emulate the state selector of the DPC. The training algorithms used in this paper are the backpropagation and the parallel recursive prediction error. Computer simulations of the DPC with neural network system using the abovementioned algorithms are presented and compared. Discussions about the backpropagation and the parallel recursive prediction error algorithms as the most promising training techniques are presented, giving their advantages and disadvantages.