Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems

As a novel and promising learning technology, extreme learning machine (ELM) is featured by its much faster training speed and better generalization performance over traditional learning techniques. ELM has found applications in solving many real-world engineering problems, including those in electric power systems. Maintaining frequency stability is one of the essential requirements for secure and reliable operations of a power system. Conventionally, its assessment involves solving a large set of nonlinear differential-algebraic equations, which is very time-consuming and can be only carried out off-line. This paper firstly reviews the ELM's applications in power engineering and then develops an ELM-based predictor for real-time frequency stability assessment (FSA) of power systems. The inputs of the predictor are power system operational parameters, and the output is the frequency stability margin that measures the stability degree of the power system subject to a contingency. By off-line training with a frequency stability database, the predictor can be online applied for real-time FSA. Benefiting from the very fast speed of ELM, the predictor can be online updated for enhanced robustness and reliability. The developed predictor is examined on the New England 10-generator 39-bus test system, and the simulation results show that it can exactly (within acceptable errors) and rapidly (within very small computing time) predict the frequency stability.