Multi-frequency point non-integer period disturbance signal generation and control method and system

By using a multi-frequency non-integer period disturbance signal generation and control method, the problems of high device cost, insufficient resolution, and poor power quality in power system impedance measurement are solved, achieving high-precision impedance measurement and improved power quality.

CN120870670APending Publication Date: 2025-10-31WUXI POWER SUPPLY BRANCH OF STATE GRID JIANGSU ELECTRIC POWER CO LTD +1
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Patent Information

Application Number
CN202510972481.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-15
Publication Date
2025-10-31

AI Technical Summary

Technical Problem

In existing power system impedance measurement technologies, traditional disturbance signal injection methods suffer from high equipment costs, insufficient resolution, low accuracy, and poor power quality. In particular, in new energy interface converter equipment, integer period disturbance signals are prone to interact with system harmonics, affecting detection accuracy.

Method used

A multi-frequency non-integer periodic disturbance signal generation and control method is adopted. The disturbance frequency is generated by multi-layer neural network and perceptron. Combined with reinforcement learning and discrete controller, the frequency and amplitude of the disturbance signal are optimized to achieve uniform distribution and precise injection of the disturbance signal on the logarithmic scale, thereby reducing the device capacity requirements.

Benefits of technology

It improves the resolution and accuracy of impedance measurement, reduces the capacity requirement for disturbance signal injection, enhances power quality, and ensures system stability and measurement accuracy.

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Abstract

According to the multi-frequency-point non-integer periodic disturbance signal generation and control method and system, reference frequencies are generated based on a multi-layer neural network, and a candidate frequency set is formed by the reference frequencies and integer multiples of the reference frequencies; the perceptron distributes the candidate frequency of which the state coefficient is 1 as the disturbance frequency; determining an amplitude coefficient in an amplitude range by adopting a single-layer neural network; grouping the disturbance frequencies according to the reference frequency; taking uniform distribution of the disturbance frequency on the logarithmic scale, minimum capacity required for injecting the signal at the disturbance frequency and uniform distribution of the disturbance frequency in each group as optimization objectives to obtain multiple groups of disturbance signals; determining dq-axis reference values of each group of disturbance signals injected into the power grid by adopting a reinforcement learning method by taking maximization of the direct-current voltage utilization rate and minimization of the capacity of disturbance injection equipment as targets; a discrete controller of disturbance signals is formed; the discrete controller is integrated in a control system of the disturbance injection device. Therefore, the capacity required by the injection disturbance is smaller, the DC voltage utilization rate is higher, and the disturbance signal is accurately controlled.
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