Study on Power Equipment Condition Based Maintenance (CBM) Technology in Smart Grid

Yang, Zhichun; Zhang, Zhiqin; Wu-de, Xue; Chen, Yuning.; Xueyun, Mou; Qiping, Yang

doi:10.1109/spies52282.2021.9633892

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…With the continuous development of artificial intelligence (AI) technology, AI-based monitoring methods have garnered attention [11]. These methods utilize techniques such as machine learning and deep learning to analyze the characteristic parameters of capacitive equipment and diagnose them based on historical data and experience.…”

Section: Related Workmentioning

confidence: 99%

Defect Prediction for Capacitive Equipment in Power System

Peng,

Zheng,

2024

Applied Sciences

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As a core component of the smart grid, capacitive equipment plays a critical role in modern power systems. When defects occur, they pose a significant threat to the safety of both other equipment and personnel. Hence, it is of great significance to predict whether defects occur in capacitive equipment in advance. To achieve this goal, we propose a novel method that integrates the weight of evidence (WOE) feature encoding with machine learning (ML). Five models, including support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and linear classification, are employed with WOE features for defect prediction. Furthermore, based on the prediction of equipment with defects, an additional prediction is conducted to determine the potential defect level of the equipment. Experimental results demonstrate that the performance of each algorithm significantly improves with WOE encoding features. Particularly, the RF model with WOE encoding features exhibits optimal performance. In conclusion, the proposed method offers a promising solution for predicting the occurrence of defects and the corresponding defect levels of capacitive equipment. It enables relevant personnel to focus on and inspect equipment predicted to be at risk of defects, thereby preventing major malfunctions.

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Section: Related Workmentioning

confidence: 99%

Defect Prediction for Capacitive Equipment in Power System

Peng,

Zheng,

2024

Applied Sciences

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Choosing a Policy of Power Equipment Repairs Management. Methodology of Assessment and Decision-Making

Levin,

Guzhov,

Boyarova

2023

2023 IEEE XVI International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering (APEIE)

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

et al. 2022

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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.

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Study on Power Equipment Condition Based Maintenance (CBM) Technology in Smart Grid

Cited by 3 publications

References 4 publications

Defect Prediction for Capacitive Equipment in Power System

Defect Prediction for Capacitive Equipment in Power System

Choosing a Policy of Power Equipment Repairs Management. Methodology of Assessment and Decision-Making

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

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