Quantum deep reinforcement learning control method of doubly-fed wind generator

Abstract

The invention provides a quantum deep reinforcement learning control method of a doubly-fed wind generator. The method can solve the control problem of stator flux linkage change of the doubly-fed wind generator after a power grid fault is removed and when the power grid voltage is asymmetrically and suddenly increased. The method is a control method combining Bayesian quantum feedback control, deep learning and reinforcement learning. The Bayesian quantum feedback control process is divided into two steps of state estimation and feedback control, and feedback input is historical measurement and current measurement records. Bayesian quantum feedback can effectively control decoherence in solid quantum bits. The deep learning part adopts a convolutional neural network model and a back propagation method. In the reinforcement learning part, Q learning based on a Markov decision process is used as a control framework of the whole method. According to the method, the control stability of the doubly-fed wind generator can be effectively improved, and the wind energy utilization efficiency is improved.

Description

technical field [0001] The invention belongs to the field of scheduling and control of new energy wind power generation in electric power systems, relates to a novel control method combining quantum feedback method and artificial intelligence algorithm, and is suitable for the control of double-fed wind power generators in electric power systems. Background technique [0002] With the continuous application of new energy generation in the power system, the application of wind power in the power system has also become widespread. As the main working device of wind power generation, doubly-fed wind turbine has played a powerful role in the power generation system of the power system. However, there are still some problems in actual production and application. If these problems are not solved in time, it will seriously affect the normal operation of the generator. Most of the existing research focuses on the electromagnetic transient analysis of the DFIG from the beginning of ...

AI Technical Summary

Problems solved by technology

At the same time, due to the complexity of the graph model in deep learning, the time complexity of the algorithm has increased sharply. In order to ensure the real-time performance of the algorithm, higher parallel programming skills and more and better hardware support are required.

[0004] In order to control the change of the stator flux linkage and the overall operating state of the doubly-fed wind power generator after the grid failure, and prevent the generator from losing synchronization after the fault occurs, the present invention proposes a quantum deep reinforcement learning control method for the doubly-fed wind power generator

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