Imitation and Transfer Q-Learning-Based Parameter Identification for Composite Load Modeling

Fast and accurate load parameter identification has a large impact on power systems operation and stability analysis. This article proposes a novel Imitation and Transfer Q-learning (ITQ)-based method to identify parameters of composite constant impedance-current-power (ZIP) and induction motor (IM) load models. Firstly, an imitation learning process is introduced to improve the exploitation and exploration processes. Then, a transfer learning method is employed to overcome the challenge of time-consuming optimization when dealing with new identification tasks. An associative memory is designed to realize dimension reduction, knowledge learning and transfer between different identification tasks. Agents can exploit the optimal knowledge from source tasks to accelerate the search rate in new tasks and improve solution accuracy. A greedy action selection rule is adopted for agents to balance the global and local search. The performance of the proposed ITQ approach has been validated on a 68-bus test system. Simulation results in multi-test cases verify that the proposed method is robust and can estimate load parameters accurately. Comparisons with other methods show that the proposed method has superior convergence rate and stability.