Condition monitoring and diagnosis of power equipment: review and prospective

Li, Shengtao; Li, Jianying

doi:10.1049/hve.2017.0026

Cited by 201 publications

(104 citation statements)

References 83 publications

(90 reference statements)

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“…In general, it is enough to make a general analysis and understanding of the electric field distribution law of the rest parts, and it is not necessary to accurately calculate the board. It is not necessary to accurately calculate the electric field intensity of each point in the edge area of the plate so that the simplified calculation method can be carried out and the calculation time can be saved [10].…”

Section: Structural Advantagementioning

confidence: 99%

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Zhao

Zhang

Ren

2020

Mathematical Problems in Engineering

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Nowadays, power electronic technology is widely affecting people’s daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development.

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Section: Structural Advantagementioning

confidence: 99%

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Zhao

Zhang

Ren

2020

Mathematical Problems in Engineering

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“…In recent years, the rapid development of information technologies, computer technologies, and other new technologies bring the new progresses in condition monitoring and fault diagnosis of equipment [4][5]. The power plant uses big data technology to deeply mine data value [6][7], which also makes itself more optimized, safer, and more economical.…”

Section: Introductionmentioning

confidence: 99%

Novel Intelligent Method for Fault Diagnosis of Steam Turbine Based on T-SNE and XGBoost

Wang

Zhang

2020

Preprint

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For fault failures of a steam turbine occur frequently and cause huge losses, it is important to identify the fault category. A steam turbine clustering fault diagnosis method based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed. Firstly, the t-SNE algorithm is used to map high-dimensional data to low-dimensional space, and data clustering is performed in low-dimensional space. Combined with the fault records of the power plant, the fault data and health data of the clustering result are distinguished. Then, the imbalance problem in the data is processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, we used the XGBoost to solve this multiclassification problem. In the experiment, the method achieved the best performance with an overall accuracy of 97% and early warning at least two hours in advance. The experimental results show that this method can effectively evaluate the state and make fault warning for power plant equipment.

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“…PD activity-if ignored-could damage the insulating system until a total breakdown, i.e., a complete short circuit between two conductors. Detecting PDs and recognizing the possible sources of defects is therefore crucial for (1) estimating the status of the insulation system and (2) scheduling a maintenance program that can prevent sudden breakdowns [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

Gianoglio

Bruzzone

Ragusa

et al. 2020

Lecture Notes in Electrical Engineering

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The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and-in the long term-can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.

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Condition monitoring and diagnosis of power equipment: review and prospective

Cited by 201 publications

References 83 publications

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Fault Diagnosis and Identification of Power Capacitor Based on Edge Cloud Computing and Deep Learning

Novel Intelligent Method for Fault Diagnosis of Steam Turbine Based on T-SNE and XGBoost

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

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