Partial Discharge Detection and Location in Transformers Using UHF Techniques

2013 ◽  
pp. 487-520
Author(s):  
Martin D. Judd
Keyword(s):  
High Frequency ◽  
Partial Discharge ◽  
Gas Analysis ◽  
Power Transformers ◽  
Dissolved Gas ◽  
Insulation System ◽  
Time Difference Of Arrival ◽  
Corrective Maintenance ◽  
System A ◽  
Complete Failure

Power transformers can exhibit partial discharge (PD) activity due to incipient weaknesses in the insulation system. A certain level of PD may be tolerated because corrective maintenance requires the transformer to be removed from service. However, PD cannot simply be ignored because it can provide advance warning of potentially serious faults, which in the worst cases might lead to complete failure of the transformer. Conventional monitoring based on dissolved gas analysis does not provide information on the defect location that is necessary for a complete assessment of severity. This chapter describes the use of ultra-high frequency (UHF) sensors to detect and locate sources of PD in transformers. The UHF technique was developed for gas-insulated substations in the 1990s and its application has been extended to power transformers, where time difference of arrival methods can be used to locate PD sources. This chapter outlines the basis for UHF detection of PD, describes various UHF sensors and their installation, and provides examples of successful PD location in power transformers.

Study of Remote Monitoring and Diagnostic System for Transformers Based on Multi-Agent Techniques

2013 ◽  
Vol 805-806 ◽  
pp. 896-901
Author(s):  
Ming Jun Liu ◽  
Zhi Hua Huang ◽  
Ben Xing Yang
Keyword(s):  
Remote Monitoring ◽  
High Frequency ◽  
Partial Discharge ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Hierarchical Decomposition ◽  
Dissolved Gas ◽  
Diagnostic Confidence ◽  
Development Direction ◽  
Multi Agent

The development direction of transformer monitoring and diagnosis is remote and intelligent. But the existing systems have the following problems: lack of diagnostic knowledge and methods, poor flexibility etc. To solve these problems, the architecture of a remote monitoring and diagnostic system for transformers based on multi-agent techniques is presented. On the basis of ultra high frequency (UHF) partial discharge (PD), core earth current and dissolved gas analysis (DGA), the hierarchical decomposition model is established. The tasks, functions and structure involved in the collaboration agents are defined. Then, the fusion strategy from multi-experts conclusions is proposed based on diagnostic confidence and weights. In final, the system implementation is described. Some functions of the system have been put into service and worked well.

scholarly journals Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2223 ◽  
Author(s):  
Sayed A. Ward ◽  
Adel El-Faraskoury ◽  
Mohamed Badawi ◽  
Shimaa A. Ibrahim ◽  
Karar Mahmoud ◽  
...  
Keyword(s):  
Power Transformer ◽  
Partial Discharge ◽  
Electrical Power ◽  
Diagnostic Techniques ◽  
Gas Analysis ◽  
Economic Losses ◽  
Dissolved Gas ◽  
Electricity Transmission ◽  
Dissolved Gas Analysis ◽  
Precise Interpretation

Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

scholarly journals A Fuzzy Logic Model for Power Transformer Faults’ Severity Determination Based on Gas Level, Gas Rate, and Dissolved Gas Analysis Interpretation

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1009 ◽  
Author(s):  
Rahman Azis Prasojo ◽  
Harry Gumilang ◽  
Suwarno ◽  
Nur Ulfa Maulidevi ◽  
Bambang Anggoro Soedjarno
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Local Population ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
New Approach ◽  
Dissolved Gas Analysis ◽  
Novel Approach

In determining the severity of power transformer faults, several approaches have been previously proposed; however, most published studies do not accommodate gas level, gas rate, and Dissolved Gas Analysis (DGA) interpretation in a single approach. To increase the reliability of the faults’ severity assessment of power transformers, a novel approach in the form of fuzzy logic has been proposed as a new solution to determine faults’ severity using the combination of gas level, gas rate, and DGA interpretation from the Duval Pentagon Method (DPM). A four-level typical concentration and rate were established based on the local population. To simplify the assessment of hundreds of power transformer data, a Support Vector Machine (SVM)-based DPM with high agreements to the graphical DPM has been developed. The proposed approach has been implemented to 448 power transformers and further implementation was done to evaluate faults’ severity of power transformers from historical DGA data. This new approach yields in high agreement with the previous methods, but with better sensitivity due to the incorporation of gas level, gas rate, and DGA interpretation results in one approach.

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