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Numerical models play important roles in power system operation. They are widely used for planning studies to identify and mitigate issues, determine transfer capability, and develop transmission reinforcement plans. These models need to be accurate and updated regularly to serve these purposes faithfully over time. In this paper, we formulate the problem of parameter calibration for machine models in a power system into the framework of reinforcement learning and demonstrate the feasibility of applying Deep Deterministic Policy Gradient (DDPG) for a two-parameter generator model calibration on a 4-bus system. To improve the efficiency and accuracy of DDPG, we introduce memory forgetting mechanism and dynamic range adjustment (DRA) into the original DDPG, i.e., DRA-DDPG. To reduce the parameter estimation errors due to partially observable disturbance states in the power system, we introduce the concept of maximal K-Nearest-Neighbor (KNN) reward to enable our reinforcement learning algorithm to accommodate a finite set (K) of unknown disturbance states in the system. Our experimental results show that the proposed DRA-DDPG outperforms the baseline DDPG in terms of accuracy and efficiency and the proposed maximal KNN reward is well-suited for resolving the uncertainties from partially observable system states.

In power systems, having accurate device models is crucial for grid reliability, availability, and resiliency. Existing model calibration methods based on mathematical approaches often lead to multiple solutions due to the ill-posed nature of the problem, which would require further interventions from the field engineers in order to select the optimal solution. In this paper, we present a novel deep-learning-based approach for model parameter calibration in power systems. Our study focused on the generator model as an example. We studied several deep-learning-based approaches including 1-D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), which were trained to estimate model parameters using simulated Phasor Measurement Unit (PMU) data. Quantitative evaluations showed that our proposed methods can achieve high accuracy in estimating the model parameters, i.e., achieved a 0.0079 MSE on the testing dataset. We consider these promising results to be the basis for further exploration and development of advanced tools for model validation and calibration.