Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning

Abstract

In power energy distribution networks, switchgear is considered critical equipment. This is because the act of monitoring the operation and condition of switchgear, as well as performing the required corrective maintenance on any potentially problematic equipment, is critical. A single event may harm thousands of customers over time and pose a significant risk to operational staff. Many considerations must be put in place before using outages to switch down the system since they may raise maintenance costs and disrupt the power supply to users. As a result, proper interpretation of switchgear status evaluations is critical for the early identification of possible faults. Existing ultrasound condition-based monitoring (CBM) diagnostic testing techniques require the tester to manually interpret test data. This study aims to review the status of the recent development of CBM techniques with faults in switchgear. The current trend in electrification will be toward the safety and sustainability of power grid assets, which involves an evaluation of the electrical systems’ and components’ health and grids for medium-voltage distribution. This work provides a current state-of-the-art evaluation of deep learning (DL)-based smart diagnostics that were used to identify partial discharges and localize them. DL techniques are discussed and categorized, with special attention given to those sophisticated in the last five years. The key features of each method, such as fundamental approach and accuracy, are outlined and compared in depth. The benefits and drawbacks of various DL algorithms are also examined. The technological constraints that hinder sophisticated PD diagnostics from being implemented in companies are also recognized. Lastly, various remedies are suggested, as well as future research prospects.

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

“…A corona is formed when the electric field strength around a conductor is strong enough to create a conductive zone but not strong enough to cause electrical breakdown or arcing. Nevertheless, corona discharge can have detrimental effects on high-voltage equipment and cause a loss of power [ 2 ]. Figure 1 a–c illustrates the corona discharge phenomenon and how the conductance and avalanche processes in Medium-Voltage (MV) switchgear are influenced by the electrode material.…”

“…While deep CNNs were initially used for image classification tasks, they have since been used for video analysis and recognition. However, the use of 1D sequence data for classification and prediction is relatively new [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The corona and non-corona categories can be seen as a sequential modeling effort, making 1D CNNs a suitable choice.…”

“…The term “switching gear” refers to a variety of switching devices that perform functions such as controlling, protecting, and isolating power systems. This definition also encompasses devices used to regulate and meter power systems, as well as circuit breakers and other similar technologies [ 2 ]. Switchgear plays a critical role in ensuring the safety of downstream maintenance, including fault clearing, upkeep, equipment substitutions, and a range of other related concerns, especially in power-generating systems.…”

Underwater propeller fault diagnosis based on deep learning

“…LSTM networks utilize gating mechanisms to effectively control the flow and storage of information, thereby better capturing long-term dependencies and achieving significant performance improvements in many sequence modeling tasks [9] . When 1D CNN and LSTM are combined, 1D CNN is responsible for extracting local features of the input sequence, while LSTM is responsible for capturing the long-term dependencies of the sequence [10] . 1D CNN processes sequence data by using convolution operations.…”

Influence of buffer volume on the effect of pressure relief for the metal‐enclosed switchgear due to arc fault

“…During the arcing, the first serious effect is the rapid pressure rise in a short time. In order to reduce the influence of overpressure on the enclosure, many passive measures have been proposed, such as pressure relief device [8,9], buffer volume (BV) [10], arc energy absorber [11][12][13][14], metal melting [15], and so on. For arc-resistant switchgear, the pressure relief devices are important technique to mitigate the effects of overpressure, which mainly contain bursting disc, exhaust duct, and flaps.…”