Application Analysis of Power System Control and Fault Diagnosis Based on Ant Colony Algorithm

Abstract

Due to the frequent occurrence of power system faults and the complexity of fault types, the speed of traditional control methods is slow, so a power system control method based on ant colony dispersion walk algorithm is proposed. Artificial neural network is a method and technology in machine learning, which has been widely used in engineering and technology. The problem of power system fault diagnosis is complex and changeable, so it is difficult to achieve accurate diagnosis by conventional methods. At present, the commonly used neural networks include perceptron, single-layer network, BP network and radial basis network, each of which has different characteristics and uses. BP network and radial basis network are commonly used in power system fault diagnosis. The network can be created, trained and simulated in MATLAB, and the power system fault model can also be established and simulated. According to the characteristics of the system nodes, a nonlinear control model composed of a set of differential-algebraic equations is established to establish the steady-state model of the power system structure, and the ant walk algorithm is used to solve the model and analyze the node information of the power system. The control output principle of power system power node is set to judge whether the operation state of power system is normal or not, so as to complete the design of power system control method based on ant colony dispersion walk algorithm. The experimental results show that the proposed method has fast control speed for power system faults. The synchronous frequency signal and voltage signal provided by the power system are extracted, the characteristics of the power system signal are classified, and the hidden fault location is determined by the time-frequency characteristic of the power system voltage signal. The experimental results verify the robustness of the above methods for fast hidden fault location.