SVC supplementary damping control using direct neural dynamic programming

The great scales nonlinearities and uncertainties in modern power systems mean that they are among the most intractable problems in dynamic control. In the present paper, direct neural dynamic programming (direct NDP) is introduced for a real time supplementary control application. Direct NDP is an on-line learning control paradigm that learns to improve system performance by following the computed gradient toward meeting the overall learning objective. As such the method makes use of on-line measurements to generate proper control actions. This feature is of critical significance when dealing with dynamic systems that are difficult to model or model precisely. In This work, a static VAr compensator (SVC) supplementary damping control in a 4-generator 2-area system is implemented using direct NDP. The self-learning and adaptive abilities of direct NDP are analyzed in the MATLAB environment. Simulation results demonstrate the advantages of direct NDP over conventional control.