Abstract
Focusing on the shortage of mechanical defect detection and diagnosis technology for disconnectors, a wireless monitoring method for the mechanical state of disconnectors is proposed. The split-core current sensors and improved voltage sensors are used to measure the motor currents and voltages of the disconnector under typical mechanical states at different working voltages. The wireless communication network is used to upload the acquisition data to the cloud server quickly, and the received data are processed by the software system. By comparing and analyzing the curves of current, input power, and output power under different states, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector. Twenty-three time-domain features of the output power time curve are extracted to form the original feature vector. Kernel principal component analysis (KPCA) method is used to reduce the dimension of the nonlinear features, and the Fisher's criterion function is constructed to determine the width parameter of the kernel function in the feature optimization. Grid search algorithm is used to optimize the kernel parameters of the support vector machine (SVM), and the trained SVM model is used to classify the mechanical state data whose working voltage part is known, and part is unknown, with a classification accuracy of 100%. The results show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector and has a good generalization ability.
Highlights
Due to various factors, such as complex outdoor environment, improper maintenance and debugging, etc., the faults and defects of disconnector frequently occur [1]
Through the established wireless monitoring system, the current, input power and output power of the motor with different operating voltages are measured when the disconnector is under different mechanical states
By comparing and analyzing these data, it is concluded that the motor output power can adequately reflect the mechanical state of the disconnector
Summary
Due to various factors, such as complex outdoor environment, improper maintenance and debugging, etc., the faults and defects of disconnector frequently occur [1]. T. Zhou et al.: Defect Diagnosis of Disconnector Based on Wireless Communication and SVM proposed to judge the mechanical state of the disconnector by measuring the strain of the operating shaft or the rotating insulator. Zhou et al.: Defect Diagnosis of Disconnector Based on Wireless Communication and SVM proposed to judge the mechanical state of the disconnector by measuring the strain of the operating shaft or the rotating insulator This method is theoretically feasible, but the measurement results are affected by the paste position of the strain gauge and the working environment. According to the trained SVM model, data in different mechanical states with known working voltage and unknown working voltage are classified accurately These show that the proposed wireless monitoring method can effectively diagnose the mechanical state of the disconnector, FIGURE 1. Make up the lack of mechanical defect detection and diagnosis technology for disconnectors
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