Detection of Transient Signals in Smart Grid Using Artificial Neural Network Modeling and JAYA Optimization

In order to solve the power quality issues facing by Smart Grid with the rapidly growing of renewable energy sources and types, a data-driven approach to detect transient evens using Artificial Neural Network modeling and optimization is proposed in this paper. This approach is realized by building a detection model based on the historic dataset, using statistical processing strategy and modeling methods. To be specific, a fault reconstruction stage is first designed to pre-process the group of dataset to check the synchronization of different signals and roughly detect the transients or faults that most likely happened. Then, the unsynchronized signal is further processed using a detection model. In order to improve the model accuracy without adding more inputs or neural network nodes, a recently proposed optimization method named JAYA is applied to tune the parameters of the network, with a simple one phase of evolutionary process. At last, the linear residuals are generated and modeled using partial Principal Component Analysis, and only $T^{2}$ statistic is calculated and monitored instead of the traditional two statistics, thus the new detection scheme is easier and faster to apply. The proposed method is demonstrated to be effective using real-system experiment, and it is a reliable monitoring scheme to improve the level of safety for Smart Grid.