Artificial Neural Network based Fast and Accurate Static Security Assessment of 380 kV Saudi Power Grid

Abstract

This paper proposes an artificial neural network (ANN) framework utilizing a multilayer feed-forward structure to evaluate the static security of a representative 380 kV Saudi transmission grid. The proposed model estimates a composite Static Security Index (SSI) that accounts for both line loading violations and bus voltage deviations. The ANN, designed with one hidden layer of ten neurons, receives normalized active and reactive power demands from load buses as inputs, while producing the SSI corresponding to each contingency as the output. Training is conducted using a back-propagation learning algorithm, where datasets are derived from Newton-Raphson load-flow simulations at various loading levels. Comparative analysis confirms that the proposed ANN approach matches the accuracy of the conventional NRLF-based evaluation while achieving substantially faster computation. The obtained performance suggests that the model can serve as an effective real-time decision-support tool for online contingency ranking and system security assessment in control centers.