Defect Diagnosis of Disconnector Based on Wireless Communication and Support Vector Machine

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 splitcore 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.

Estimating the failure probability of an instrument transformer cell in the high voltage distributing device using an expert-statistical method

“…When applying the expert-statistical model for determining the probability of a cell failure, the error was ∆ f(D-02) =1-Q apost =0.034, or 3.4 %. The advantage of the developed model over those proposed in [1][2][3][4][5][6][10][11][12][13][14][15] is predetermined by taking into consideration the influence of a TC of each piece of equipment of the cell on a failure probability, and by the integrated assessment of controlled diagnostic parameters that influence the TC. This leads to the higher accuracy of the forecast, in compar-ison with existing methods of current control over the TC of electrical equipment.…”

“…Somewhat less attention is paid to the TC of disconnectors but there are studies into determining the degree of wear of separate nodes in the machine. For example, papers [10,11] proposed monitoring the state of a disconnector drive by controlling the motor currents during the mechanical operations involving the device. In addition, work [10] suggested controlling the thermal condition of contact connections.…”

“…For example, papers [10,11] proposed monitoring the state of a disconnector drive by controlling the motor currents during the mechanical operations involving the device. In addition, work [10] suggested controlling the thermal condition of contact connections. However, the hardware base for the application of this method is quite expensive.…”

Research and Application of Mechanical State Diagnosis Method of Horizontal Telescopic Type Disconnector

“…GW23 type disconnector is a typical open disconnector, with the advantages of obvious fracture and small phase spacing [1][2][3], and has been widely used in power grids in recent years [4][5][6][7]. For the detection of the mechanical state of the isolating switch [8], different scholars have proposed to use different signals such as the driving motor running signal [9][10][11][12], the torque and rotation angle signal IOP Publishing doi:10.1088/1742-6596/2452/1/012024 2 [13][14][15], and the vibration signal [16][17][18][19][20] as the method to judge the mechanical state. After using different detection techniques to obtain samples representing the mechanical state of the isolating switch, classifying the samples according to their characteristics of the samples is called state diagnosis.…”

Research on mechanical state identification method of multi-type disconnectors based on difference curve and SVM

“…To sum up, most scholars choose the GW4 isolating switch with high usage as the research object. However, there are many types of isolating switches [17][18][19][20][21][22][23], and the mechanical systems and motion principles of different isolating switches are quite different. The detection method of the mechanical state of the isolating switch is difficult to meet the engineering requirements [24][25][26][27][28][29], and the intelligent identification method of the mechanical state that can meet the general purpose of different isolating switches remains to be studied.…”