  Fast and accurate load parameters identification has great impact on the
power systems operation and stability analysis. This paper proposes a novel
transfer reinforcement learning based method to identify composite ZIP and
induction motor (IM) load models. An imitation learning process is firstly
introduced to improve the exploitation and exploration process. The transfer
learning process is then employed to overcome the challenge of time consuming
optimization when dealing with new tasks. An Associative memory is designed to
realize demension reduction and knowledge learning and transfer between
different optimization tasks. Agents can exploit the optimal knowledge from
source tasks to accelerate search rate and improve solution accuracy. The
greedy rule is adopted to balance global search and local search. Convergency
analysis shows that the proposed method can converge to the global optimal
solution with probability 1. The performance of the proposed ITQ appraoch have
been validated on 68-bus system. Simulation results in multi-test cases verify
that the proposed method has superior convergence rate and stability.
