Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model
Abstract
:1. Introduction
- (1)
- Existing diagnostic methods (traditional/AI or traditional + DIP) have poor applicability, they are difficult to form uniform rules. They need to design diagnostic rules and methods according to the equipment to be diagnosed [34]. Latest researches focused on the optimization of diagnostic rules through intelligent algorithms [35]. But researchers have rarely combined intelligent methods with DIP yet. Besides, even the intelligent methods are not adaptable.
- (2)
- The background interferences are not taken into considerations. For offline FRA, after obtaining the response curves, they need to be transmitted to an equipment with fault diagnostic algorithms installed whether it is based on traditional methods, intelligent algorithms or the proposed deep learning DIP processes. In addition, with the advancement of UPIoT, there have been studies on online FRA in recent years [14]. In that case, the obtained data needs to be sent directly to the cloud computation platforms for fault diagnosis. Whether in the case of offline or online, the data set needs to be transmitted wirelessly or by wires before the diagnosis process. Therefore, Researches on fault diagnosis of power equipment need to take background noises into account [36,37]. Harsh interference would impose a significant impact on the quality and reliability of data [38]. In recent years, Research on improving anti-interference performance has made some progress [39]: for example, the noise reduction method [40], calculation of interference intensities [41,42] and noise reduction algorithms, such as the adaptive stochastic resonance filter [43] and Hilbert time-time (IHTT) transformations [44]. But they are unavailable for transformer FRA procedures because there exist only slight differences between the FR curves. Fault characteristics would be overwhelmed during the de-noising processes. Besides, power equipment such as transformers are in a high voltage, strong magnetic field environment, which tend to generate relatively large environmental white noises. For these reasons, traditional FRA diagnostic methods are difficult to put into applications: it is easy for the traditional diagnostic methods to be submerged under noise and go out of order.
- (3)
- Although there are lots of guidelines and articles to implement FRA, only in recent years a few of them studied its localization methods [45,46]. It is necessary to provide positioning information of the faults for further intelligent diagnosing systems. An effective fault localization method is by comparing the tested FRA signatures of different winding sections with the fingerprint curves: larger variation (longer distance) means that there is lower possibility for fault winding in this area [46,47]. This idea was also used in the localization studies of partial discharge [48]. Works in [49] deduced the relationships between ladder network components and FR function, obtaining the features at different nodes, so that fault location can be acquired. And there’s another type of FRA localization method: by deliberately setting internal faults and then investigating the influence of various fault locations to the FR curves [50]. The studies are belonging to the traditional + localization methods, however, which require specific rules or inferences for different transformers [51]. Fault localization via graphical method could standardize and visualize diagnostic process, and reduce the interferences [52], which aroused attentions in recent two years. The concept ‘fault identification’ includes both fault type classification and fault localization. The current localization researches are based on statistical indicators, which are not intelligent enough and have poor adaptability [53].
2. Deep Visual Identification Method
2.1. Acquisition of Transformer FRA Graphical Dataset
2.2. Fault Localization Based on CNN or Space Relationship
2.2.1. Basic Theories of MobileNet-V2 and the CNN-DIP Method
2.2.2. Spatial-probabilistic Mapping Relationship Based on Traditional Method
2.3. Data Visualization Theory
3. Model Test and Results
3.1. Fault Identification Results without Considering Noises
3.1.1. Traditional Spatial-Probabilistic Model
3.1.2. Deep Visual Identification via MobileNet-V2
3.2. Anti-Interference Analysis
3.2.1. Traditional Spatial-Probabilistic Model
3.2.2. Proposed CNN-DIP Method
3.3. Deviation Distances of Diagnosis Error
3.3.1. Traditional Method
3.3.2. Proposed CNN-DIP Method
4. Conclusions
- The proposed fault identification method based on a lightweight Convolutional Neural Network (MobileNet-V2) and DIP could improve the diagnostic adaptabilities and accuracies.
- Through noise analysis, the anti-interference ability of the proposed CNN-DIP method was compared with that of the traditional method, which indicates that the proposed method has good stabilities under strong interference.
Author Contributions
Funding
Conflicts of Interest
Appendix A
Structure | Size/mm | Structure | Size/mm | Material | Relative Dielectric Constant |
---|---|---|---|---|---|
Axial height | 432 | Pad thickness | 10 | Insulation paper | 2.6 |
The height of a winding disc | 6 | The width of a winding disc | 36.5 | pad | 4.5 |
Inter diameter of iron | 196 | Inter diameter of disc | 221 | Strut | 4.4 |
Outer diameter of phenolic paper tube | 215 | Thickness of bond terminal | 9 | Bond terminal | 4.4 |
Iron thickness* | 0.1 | Strut thickness* | 4 | Phenolic paper tube | 3.8 |
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Ground Capacitance Cg | Disc-to-disc Capacitance Cs | Self-Inductance Ls | Mutual Inductance | Resistance R | |
---|---|---|---|---|---|
Mi(i + 1) | Mi(i + 2) | ||||
30.05 pF | 582.98 pF | 0.101 mH | 0.079 mH | 0.053 mH | 261 mΩ |
Type of Data | Example of Data Visualization Process | Samples |
---|---|---|
Monitoring image | No conversion required. | |
Waveform image | Can be trained directly. | |
Waveform data | Decompose the data into characteristic spectrums; or the data can be directly drawn into a waveform diagram. | |
Parameter value or text expression | Draw a suitable image according to the features of the values; or convert through text visualization technology. |
Labels | Dataset | Color Adjustment | Rotate | Crop | Noises | Sum |
---|---|---|---|---|---|---|
00 | 10 | 60 | 80 | 50 | 50 | 250 |
11/21/31 | 30 | 180 | 240 | 150 | 150 | 750 |
12/22/32 | 30 | 180 | 240 | 150 | 150 | 750 |
13/23/33 | 30 | 180 | 240 | 150 | 150 | 750 |
14/24/34 | 30 | 180 | 240 | 150 | 150 | 750 |
15/25/35 | 30 | 180 | 240 | 150 | 150 | 750 |
16/26/36 | 30 | 180 | 240 | 150 | 150 | 750 |
17/27/37 | 30 | 180 | 240 | 150 | 150 | 750 |
Total | 220 | 1320 | 1760 | 1100 | 1100 | 5500 |
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Share and Cite
Duan, J.; He, Y.; Wu, X.; Zhang, H.; Wu, W. Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model. Sensors 2019, 19, 4153. https://doi.org/10.3390/s19194153
Duan J, He Y, Wu X, Zhang H, Wu W. Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model. Sensors. 2019; 19(19):4153. https://doi.org/10.3390/s19194153
Chicago/Turabian StyleDuan, Jiajun, Yigang He, Xiaoxin Wu, Hui Zhang, and Wenjie Wu. 2019. "Anti-Interference Deep Visual Identification Method for Fault Localization of Transformer Using a Winding Model" Sensors 19, no. 19: 4153. https://doi.org/10.3390/s19194153