Stability Assessment in Power Network Using Artificial Neural Network

Abstract

This paper presents the application of different Neural Network (NN) models for classifying the power system states as secure/insecure. Traditional method of security evaluation involves performing load flow and transient stability analysis for each contingency, making it infeasible for real time application. Pattern Recognition (PR) approach is recognized as an alternative tool. The NN models adopted for classification includes Multilayer Perception (MLP), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN) and Adaptive Resonance Theory Mapping (ARTMAP). A two-step modeling procedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the distribution level. Then, the test bed data is used to develop a composite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controlling an induction motor load is presented.
Keywords- Newton Rap son (NR) load flow; Voltage stability assessment; feed forward neural network; fast voltage stability indicator (FVSI)