Abstract—This paper presents a model predictive control of electric power systems based on the multiple Gaussian process predictors. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance. The multistep ahead predictors for the phase angle in transient state of the electric power system are formed by training multiple Gaussian process models in accordance with the direct approach. Based on these predictors, model predictive control is accomplished, where the input signal is optimized so that the error between the predicted future output and the reference signal becomes small taking the uncertainty of the predicted future output into account. Simulation results for a simplified electric power system are shown to illustrate the effectiveness of the proposed model predictive control.

Index Terms—model predictive control, electric power system, multistep ahead prediction, Gaussian process model, direct method

Cite: Tomohiro Hachino, Hitoshi Takata, Seiji Fukushima, and Yasutaka Igarashi, "Model Predictive Control of Electric Power Systems Based on Gaussian Process Predictors," Jounal of Automation and Control Engineering, Vol. 3, No. 5, pp. 418-424, October, 2015. doi: 10.12720/joace.3.5.418-424