An improved approximate dynamic programming and its application in SVC control

The main idea of approximate dynamic programming (ADP) is approximately computing cost function to avoid the curse of dimension. However, it needs many times learning to converge due to the randomly choosing initial weights. So it is greatly limited in the application. This paper presents a direct heuristic dynamic programming (DHDP) based on an improved proportion integration differentiation PID neural network (IPIDNN). This method constructs an equivalent between the initial action network and PID controller. Therefore, well-designed PID controller can guide the initial weights choosing, so that the convergence of this algorithm will be remarkably improved. Moreover, compared with the traditional PID neural network, the configuration of IPIDNN is flexible and easy to expand, as well as a better robust performance. The simulation results show the validity of this algorithm and initial weights choosing method by the static var compensator (SVC) supplementary control in four-machine two-area system. It also has a good performance in the circumstance of partial state feedback and state delay.