Intelligent control of SSSC via an online self-tuning PID to damp the subsynchronous oscillations

This paper proposes an intelligent control system which is an online self-tuning PID for controlling a static synchronous series compensator (SSSC) to suppress subsynchronous resonance (SSR). By considering the PID controller similar to a single layer neural network, its parameters can be updated in online mode. To train the PID controller, the gradient descent method is employed where the learning rate is adapted in every iteration in order to accelerate the speed of convergence. In the proposed controller design, the parameters of PID are intelligently adjusted according to the design objectives. A wavelet neural network (WNN) is also used to identify the controlled system dynamic. To update the parameters of WNN, the gradient descent (GD) along with the adaptive learning rates derived by the Lyapunov method is used. To show the performance of proposed controller, the computer simulations using MATLAB are carried out on the IEEE second benchmark model.