Deep Reinforcement Learning Framework for Short-Term Voltage Stability Improvement
- Resource Type
- Conference
- Authors
- Sarwar, Muhammad; Matavalam, Amarsagar R. R.; Ajjarapu, Venkataramana
- Source
- 2023 IEEE Texas Power and Energy Conference (TPEC) Power and Energy Conference (TPEC), 2023 IEEE Texas. :1-6 Feb, 2023
- Subject
- Power, Energy and Industry Applications
Deep learning
Reactive power
Power system dynamics
Stability criteria
Reinforcement learning
Power system stability
Numerical simulation
Hybrid PV plants
Short term voltage stability
Deep Reinforcement Learning (DRL)
- Language
This paper investigates the mitigation of fault-induced delayed voltage recovery (FIDVR) using dynamic voltage support from hybrid PV plants and optimal load control using deep reinforcement learning (DRL). We characterize and quantify the delayed voltage recovery phenomenon through probability density-based metrics. We propose a DRL-based load control by optimally tripping stalled induction motor loads to recover the voltage quickly. The amount of load tripping depends on system operating conditions, so the data-driven framework gives optimal load control adaptable to the system conditions. The numerical simulations show that the dynamic reactive power injection and DRL-based load control improve the voltage recovery and decrease the amount of load tripped significantly.