Transient stability assessment using artificial neural networks

This paper presents a study of the feasibility of using artificial neural networks (ANNs) in transient stability assessment for power systems. In the study ANNs have been developed to synthesize the complex mapping that carries the power system operating variables and fault locations into the Critical Clearing Times. The training of the ANNs was achieved through the method of backpropagation. The critical fault clearing time values were obtained by the Extended Equal Area Criterion method and used for training. In this work, an attempt was made to avoid the restrictions on load and topology variations. The parameters of the ANNs consist of the generation and loading levels. None of these inputs require any computation. This feature is desirable for on-line transient stability assessment purposes. Training of the ANNs was achieved using a combined production learning phase. The training patterns were not limited to a given collection of samples. This scheme eliminates the problem that an ANN may be influenced by the regions of attraction of a specific category.