scholarly journals An Intelligent Fault Diagnosis Method for Transformer Based on IPSO-gcForest

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kezhen Liu ◽  
Shizhe Wu ◽  
Zhao Luo ◽  
Zeweiyi Gongze ◽  
Xianlong Ma ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Characteristic Parameter ◽  
Economic Losses ◽  
Stable Operation ◽  
Diagnosis Method ◽  
Transformer Fault ◽  
Ipso Algorithm ◽  
Better Than ◽  
Fault Characteristic

Transformers are the main equipment for power system operation. Undiagnosed faults in the internal components of the transformer will increase the downtime during operation and cause significant economic losses. Efficient and accurate transformer fault diagnosis is an important part of power grid research, which plays a key role in the safe and stable operation of the power system. Existing traditional transformer fault diagnosis methods have the problems of low accuracy, difficulty in effectively processing fault characteristic information, and superparameters that adversely affect transformer fault diagnosis. In this paper, we propose a transformer fault diagnosis method based on improved particle swarm optimization (IPSO) and multigrained cascade forest (gcForest). Considering the correlation between the characteristic gas dissolved in oil and the type of fault, firstly, the noncode ratios of the characteristic gas dissolved in the oil are determined as the characteristic parameter of the model. Then, the IPSO algorithm is used to iteratively optimize the parameters of the gcForest model and obtain the optimal parameters with the highest diagnostic accuracy. Finally, the diagnosis effect of IPSO-gcForest model under different characteristic parameters and size samples is analyzed by identification experiments and compared with that of various methods. The results show that the diagnostic effect of the model with noncode ratios as the characteristic parameter is better than DGA data, IEC ratios, and Rogers ratios. And the IPSO-gcForest model can effectively improve the accuracy of transformer fault diagnosis, thus verifying the feasibility and effectiveness of the method.

Hierarchical Information Fault Diagnosis Method for Power System Based on Fireworks Algorithm

2021 ◽  
Author(s):  
Feng Haixun ◽  
Yi Kenan ◽  
Jia Zihang ◽  
Bi Huijing
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Fault Location ◽  
Stable Operation ◽  
Specific Situation ◽  
Fireworks Algorithm ◽  
Protection Information ◽  
Diagnosis Method ◽  
Important Means ◽  
Fault Recording

Power system fault diagnosis is an important means to ensure the safe and stable operation of power system. According to the specific situation of China’s current power grid automation level, a hierarchical fault diagnosis method based on switch trip signal, protection information and fault recording information is proposed. This method can not only diagnose simple fault and complex fault, but also judge fault type and phase, and complete fault location, which provides reliable guarantee for operators to quickly remove fault and resume operation. The diagnosis method based on this principle has good application effect in simulation test.

Transformer Fault Diagnosis Method Based on Gross Error Examination

2012 ◽  
Vol 482-484 ◽  
pp. 2350-2354
Author(s):  
Jie Su ◽  
Xu Guang Wang
Keyword(s):  
Fault Diagnosis ◽  
Ratio Method ◽  
Fault Probability ◽  
Gross Error ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Transformer Fault ◽  
Gas Ratio ◽  
Fault Characteristic

This paper proposes a gross error judgment criterion and diagnoses the transformer fault by integrating the gross error judgment criterion and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measured values of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

A Novel Transformer Fault Diagnosis Method Based on Gross Error Examination

2011 ◽  
Vol 291-294 ◽  
pp. 2779-2786
Author(s):  
Jie Su ◽  
Xu Guang Wang
Keyword(s):  
Fault Diagnosis ◽  
Ratio Method ◽  
Fault Probability ◽  
Gross Error ◽  
Measurement Series ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Transformer Fault ◽  
Gas Ratio ◽  
Fault Characteristic

The study applied the gross error theory to the transformer fault diagnosis, proposing a method for transformer fault diagnosis by integrating the gross error examination and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measurement series of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

Augmented-Reality-Based Mechanical Fault Diagnosis Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 720-722
Author(s):  
Hong Lei Jing ◽  
Jing Nie ◽  
Nian Zhang
Keyword(s):  
Fault Diagnosis ◽  
Augmented Reality ◽  
Rapid Development ◽  
Modern Society ◽  
Economic Losses ◽  
Stable Operation ◽  
Wear And Tear ◽  
Diagnosis Method ◽  
High Degree

With the rapid development of modern society, the industrial mechanized production reached unprecedented climax in this era. Science and technology advance increasingly, modern equipment from structure to function tends to be complex and improved, and gradually achieve a high degree of automation. However, due to the inevitable factors such as wear and tear, abrasion and chemicals infection, machinery equipment will inevitably appear unforeseen fault, causing the machine to detract from the performance, or even causing serious economic losses. Therefore, mechanical fault diagnosis can reduce equipment accident rate and ensure the long-term stable operation of the device. And applying the augmented reality to machinery fault diagnosis method research can maximize the efficiency of mechanical fault diagnosis and equipment efficiency. This article explores the prospects for the development of mechanical fault diagnosis methods based on the theoretical basis and application value of augmented reality.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

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