Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.
Circuit breakers (CBs) play an important role in modern society because they make the power transmission and distribution systems reliable and resilient. Therefore, it is important to maintain their reliability and to monitor their operation. A key to ensure a reliable operation of CBs is to monitor their condition. In this work, we performed an accelerated life testing for mechanical failures of a vacuum circuit breaker (VCB) by performing close-open operations continuously until failure. We recorded data for each operation and made the collected run-to-failure dataset publicly available. In our experiments, the VCB operated more than 26000 closeopen operations without current load with the time span of five months. The run-to-failure long-term monitoring enables us to monitor the evolution of the VCB condition and the degradation over time. To monitor CB condition, closing time is one of the indicators, which is usually measured when the CB is taken out of operation and is completely disconnected from the network. We propose an algorithm that enables to infer the same information on the closing time from a nonintrusive sensor. By utilizing the short-time energy (STE) of the vibration signal, it is possible to identify the key moments when specific events happen including the time when the latch starts to move, and the closing time. The effectiveness of the proposed algorithm is evaluated on the VCB dataset and is also compared to the binary segmentation (BS) change point detection algorithm. This research highlights the potential for continuous online condition monitoring, which is the basis for applying future predictive maintenance strategies.
Recent developments in low-cost and low-power data acquisition technology and machine learningbased algorithms, combined with rapidly increasing decentralized embedded computing power, offer the opportunity to develop improved and intelligent maintenance strategies based on continuous monitoring and real-time health evaluation of the equipment. This paradigm changes the prospects in equipment maintenance, offering significantly reduced costs in asset management. The lifetime of properly dimensioned equipment is expected to be several decades, sometimes as much as 40 to 60 years. Most maintenance checks therefore only confirm the excellent condition of the component and would have not been necessary at this point in time. Thus, not only delaying the replacement, but also delaying a maintenance interval based on the health condition would be very welcome. Hence, the most commonly used maintenance concepts are changed from time-based checks to a new and more intelligent approach, oriented towards a just-in-time service. This article aims to introduce the concepts of intelligent maintenance strategies, trends, and challenges of T&D equipment condition monitoring, as well as the automatic estimation of equipment health. Biographies
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