Direct heuristic dynamic programming method for power system stability enhancement

In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP is a learning and approximation based approach to address nonlinear system control under uncertainty. In the present paper, real-time system responses provided by wide area measurement system (WAMS) are used to construct such controllers which are uniquely tailored for the problems under consideration. In addition, the controller learning objective is formulated as a reward function that reflects global characteristics of the power system low frequency oscillation under the consideration of coupling effect among system components. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework, as well as case study to illustrate the proposed learning control algorithm. The case study aims at providing a new solution to a difficult large scale system coordination problem where the China Southern Power Grid is used for.