Iterative Online Fault Identification Scheme for High-Voltage Circuit Breaker Utilizing a Lost Data Repair Technique

Abstract

Most of the prior-art electrical noninvasive monitoring systems adopt Zigbee, Bluetooth, or other wireless communication infrastructure. These low-cost channels are often interrupted by strong electromagnetic interference and result in monitoring anomalies, particularly packet loss, which severely affects the precision of equipment fault identification. In this paper, an iterative online fault identification framework for a high-voltage circuit breaker utilizing a novel lost data repair technique is developed to adapt to low-data quality conditions. Specifically, the improved efficient k-nearest neighbor (kNN) algorithm enabled by a k-dimensional (K-D) tree is utilized to select the reference templates for the unintegrated samples. An extreme learning machine (ELM) is utilized to estimate the missing data based on the selected nearest neighbors. The Softmax classifier is exploited to calculate the probability of the repaired sample being classified to each of the preset status classes. Loop iterations are implemented where the nearest neighbors are updated until their labels are consistent with the estimated labels of the repaired sample based on them. Numerical results obtained from a realistic high-voltage circuit breaker (HVCB) condition monitoring dataset illustrate that the proposed scheme can efficiently identify the operation status of HVCBs by considering measurement anomalies.

Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

“…The problem of energy consumption data quality cannot be ignored. In fact, in other related fields, some scholars have also conducted relevant research, for example, Gang Zhou et al developed an iterative online fault identification framework utilizing a novel lost data repair technique [13].…”

“…From the literature [1][2][3][4][5][6][7], we can discover the necessity of building energy efficiency, especially for public buildings. Therefore, how to use energy consumption data in monitoring platform is important, such as studying human behavior for an energy retrofit shown in the literature [8][9][10][11][12][13]. This means that accurate itemization measurement is important in the research of public building energy efficiency.…”