Three-phase power quality disturbance detection method based on convolutional neural network

Abstract

The invention relates to the field of electrical automation, and discloses a three-phase power quality disturbance detection method based on a convolutional neural network. The three-phase power quality disturbance detection method includes the following steps of (A) collecting three-phase power disturbance signal data, (B) performing data pre-processing on three-phase power disturbance signals toobtain three-phase power RGB pictures, (C) make training sets and test sets, (D) building a convolutional neural network model and (E) obtaining three-phase power quality disturbance detection results. The three-phase power quality disturbance detection method is high in efficiency, the influence of human subjective factors is reduced, the collected three-phase electrical energy signals are converted into the RGB pictures, signal characteristics are more compact, calculation amount is less, the characteristics of the disturbance signals are extracted intelligently through the convolutional neural network model to achieve the accurate classification of the disturbance signals, multiple disturbance signals can be detected at the same time, and the situation of a fault occurred between two-phase or three-phase can be detected.

Description

Technical field [0001] The invention relates to the technical field of electrical automation, in particular to a three-phase power quality disturbance detection method based on a convolutional neural network. Background technique [0002] In recent years, with the dual development of national economy and science and technology, people's daily demand for electricity has been rising, and the requirements for power quality have become more stringent. However, as various new types of electrical equipment and loads with different performance are connected to the power system, the voltage in the grid is more likely to be disturbed, causing various problems related to power quality, which will shorten the life of electrical equipment and increase the line. Loss rate, and even cause abnormal operation or damage to electrical facilities, cause large-scale power outages, and bring huge economic losses and consequences. Nowadays, the detection of power quality disturbances has become an im...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of large amount of calculation and low accuracy of detection results in the traditional power quality disturbance detection method, the present invention provides a three-phase power quality disturbance detection method based on convolutional neural network. The present invention converts the collected three-phase power signal into It is an RGB picture, and after being converted to an RGB picture, the signal features are more compact, the amount of calculation is less, and the three-phase power quality disturbance data can be processed quickly and effectively. The disturbance signal features are intelligently extracted by establishing a convolutional neural network model to achieve accurate classification of the disturbance signal. Simultaneous detection of multiple disturbance signals, and detection of faults between two or three phases

Images