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摘要
Based on the low adaptability and interaction of the conventional artificial intelligence (AI) in a complex stochastic environment, this paper proposes a novel fast dynamic optimization algorithm with memory and self-learning (MSL) by combining the classical Q(λ) learning with the collaboration mechanism of multiagent. The proposed algorithm can learn new knowledge through the self-learning by interaction with the external environment, and can storage the memory of state-action pairs by updating the value function matrices. The association memory is introdued in the action space to reduce the size of the memory matrices, thus the matrices can be updated by multiagent simultaneously because of the combination of multiagent collaboration mechanism, and the convergence speed is obviously increased. After obtaining the good memory matrices in the pre-learning process, the MSL can be used for fast dynamic optimization. The performance of MSL has been fully tested for reactive power optimization on the benchmarked IEEE 118-bus and IEEE 300-bus systems. Comparative studies have not only demonstrated the high convergence stability of the proposed algorithm but also confirmed its fast convergence speed which can be approximate 5 to 40 times faster than that of conventional AI algorithms such as genetic algorithm, ant colony system, particle swarm optimization, and so on, and it provides a powerful tool for reactive power optimization of large-scale power systems.
- 出版日期2016-3-1
- 单位华南理工大学
