In this paper, we propose an AC power flow based cascading failure model that explicitly considers external weather conditions, extreme temperatures in particular, and evaluates the impact of extreme temperature on the initiation and propagation of cascading blackouts. Based on this model, resilience analysis of the power system is performed with extreme temperatures. Specifically, the changes of load and dynamic line rating are modeled due to temperature disturbance. The probabilities for transmission line and generator outages are evaluated, and the timing for each type of events is calculated to decide the actual event sequence. It should be emphasized that the correlated events, in the advent of external temperature changes, could contribute to voltage instability. Besides, we model undervoltage load shedding and operator re-dispatch as control strategies for preventing the propagation of cascading failures. The effectiveness of the proposed model is verified by simulation results on the RTS-96 3-area system. It is found that temperature disturbances can lead to correlated load change and line/generator tripping, which will greatly increase the risk of cascading and voltage instability. Critical temperature change, critical area with temperature disturbance, the identification of most vulnerable buses, and the comparison of different control strategies are also investigated.
In this paper, a novel Q-learning based approach is proposed for estimating the parameters of synchronous generators using PMU measurements. Event playback is used to generate model outputs under different parameters for training the agent in Q-learning. We assume that the exact values of some parameters in the model are not known by the agent in Q-learning. Then, an optimal history-dependent policy for the exploration-exploitation trade-off is planned. With given prior knowledge, the parameter vector can be viewed as states with a specific reward, which is a function of the fitting error compared with the measurements. The agent takes an action (either increasing or decreasing the parameter) and the estimated parameter will move to a new state. Based on the reward function, the optimal action policy will move the parameter set to a state with the highest reward. If multiple events are available, they will be used sequentially so that the updated Q-value can be utilized to improve the computational efficiency. The effectiveness of the proposed approach is validated through estimating the parameters of the dynamic model of a synchronous generator.
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