Superfast autoconfiguring artificial neural networks and their application to power systems

This paper presents the new generation of artificial neural netoworks (ANNS) for solving the task of power system operation planning. Today the error back-propagation ANNs are used most because of their simplicity and the possibility of parallel implementation on neuro-computers for high-speed execution. In spite of their popularity they have two major drawbacks: the learning process is time consuming and there is no exact rule for setting the number of neurons to avoid overfitting or underfitting and to achieve, hopefully, a converging learning phase. To avoid these difficulties, a new generation of ANNs has been developed based on the theory of radial basis functions for approximations. A comparison test on an actual problem in power system operation was performed. The results show that this new algorithm is superior to back-propagation ANNs and optimal configured back-propagation ANNs achieved with genetic algorithms.