Delay Dependent Wide Area Damping Controller Using Deep Learning Technique

Assurance of power system stability is gaining more and more importance due to integration of power systems with renewable energy sources. Although wide area measurement system (WAMS) based wide area damping controller (WADC) can provide sufficient damping and has the capability to strengthen the rotor angle stability limit of the system, the performance of the WADC reduces considerably due to lag in communication channel. In this work, a delay aware reinforcement learning based WADC is developed to reduce the inter-area swinging and to strengthen the rotor angle stability of an interconnected power system. The rotor angle stability can be enhanced by providing sufficient damping with the help of a supplementary damping controller (SDC) along with a thyristor controlled series capacitor (TCSC). The efficacy of the reinforcement learning based WADC is tested with the help of a 10 machine test system along with a solar PV and wind power plant.