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.
Highlights
As a fundamental piece of electrical equipment with current breaking capacity under normal and fault conditions, the high-voltage circuit breaker (HVCB) is of great significance for power system secure operation
After receiving a test sample with lost measurements we find the closest K samples in the historical database, and synthesize the information of the K
In the HVCB fault identification framework integrated with the lost data repair technique, the k-NN algorithm is adopted to gather a set of similar samples, and the extreme learning machine (ELM) is utilized to establish a map from the sample data to the measurement in terms of the dimension or feature with lost data
Summary
As a fundamental piece of electrical equipment with current breaking capacity under normal and fault conditions, the high-voltage circuit breaker (HVCB) is of great significance for power system secure operation. Due to the limitations of network topology, geographical environment, and economic factors, most of the electrical noninvasive monitoring systems still use the distribution carrier, Zigbee, Bluetooth, or other wireless ICT with relatively poor quality. These low-cost channels are often interrupted by strong electromagnetic interference, such as over-voltage or large current surge, resulting in monitoring anomalies, which severely affect the precision of fault identification, and even invalidate the system [11,12]. The quality of the measurements has become a realistic concern for HVCB monitoring, and exploiting the missing data to realize the accurate online diagnosis of HVCB has become a hotspot. The iterative scanning procedure is capable of being implemented online, owing to an efficient k-dimensional (K-D) tree data structure
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