Power distribution network overcurrent protection method based on deep reinforcement learning

Abstract

The invention discloses a power distribution network overcurrent protection method based on deep reinforcement learning. The method comprises the steps: starting protection, and judging whether current quick-break protection in a cycle acts or not: determining that constant value optimization does not need to be performed if the current quick-break protection does not act; if the current quick-break protection action is performed, carrying out constant value optimization; determining an optimal constant value according to an MA-DDPG algorithm after training is completed; judging a size relationship between a current effective value and the optimal constant value; if the current effective value is greater than the optimal constant value, making a protection outlet act; if the current effective value is less than or equal to the optimal constant value, judging the size relationship between the current and a starting value; if the current is less than the starting value, returning protection; otherwise, returning to a step of judging whether the current quick-break protection in one cycle acts or not and carrying out circulation. In the invention, a content of deep reinforcement learning is applied to the field of relay protection for the first time, and an artificial intelligence technology and a traditional relay protection technology are combined to improve protection efficiency.

Description

technical field [0001] The invention relates to the technical field of electric power system relay protection, in particular to a distribution network overcurrent protection method based on deep reinforcement learning. Background technique [0002] With the increasing energy and environmental problems, distributed generation (DG), which is characterized by high energy efficiency, compatibility with the environment, and adaptation to renewable energy, has increasingly become a research hotspot. The joint operation of DG and the large power grid has social benefits such as power supply flexibility, reliability and safety, and also has economic benefits such as peak shaving and valley filling, reducing network loss, and improving the utilization rate of existing equipment. But on the other hand, the access of DG has changed the radial structure of the single power source of the distribution network, and also changed the operating status and fault level of the power system, ther...

AI Technical Summary

Problems solved by technology

Every time the Actor-Critic main network interacts with the environment, it will generate a set of samples and put them into the memory bank, and randomly take them out from the memory bank when needed, which reduces the correlation between samples

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