scholarly journals Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nandkumar Wagh ◽  
D. M. Deshpande
Keyword(s):  
Distribution System ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Correct Diagnosis ◽  
Well Being ◽  
Gas Analysis ◽  
Inference System ◽  
Training Time ◽  
Multiple Faults ◽  
Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

scholarly journals FAULT DIAGNOSIS OF POWER TRANSFORMER BASED ON DISSOLVED GAS ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

2013 ◽  
pp. 298-300
Author(s):  
PALLAVI PATIL ◽  
VIKAL INGLE
Keyword(s):  
Power System ◽  
Power Transformer ◽  
Fuzzy Rule ◽  
Well Being ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Inference System ◽  
Multiple Faults ◽  
Dissolved Gas Analysis ◽  
Neuro Fuzzy

Power Transformers are a vital link in a power system. Well-being of power transformer is very much important to the reliable operation of the power system. Dissolved Gas Analysis (DGA) is one for the effective tool for monitoring the condition of the transformer. To interpret the DGA result multiple techniques are available.IEC codes are developed to diagnose transformer faults. But there are cases of errors and misleading judgment due to borderline and multiple faults. Methods were developed to solve this problem by using fuzzy membership functions to map the IEC codes and heuristic experience to adjust the fuzzy rule. This paper proposes a neuro-fuzzy method to perform self learning and auto rule adjustment for producing best rules.

scholarly journals Fuzzy Algorithm for Power Transformer Diagnostics

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Nitin K. Dhote ◽  
Jagdish B. Helonde
Keyword(s):  
Gas Chromatography ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Inference System ◽  
Incipient Faults ◽  
The World

Dissolved gas analysis (DGA) of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS) is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.

Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Computational Intelligence ◽  
Support Vector ◽  
Machine Condition ◽  
Data Set ◽  
Inference System ◽  
Training Time ◽  
Vibration Data ◽  
Monitoring Index ◽  
Improved Performance ◽  
Adaptive Neurofuzzy Inference System

A study is presented on applications of computational intelligence (CI) techniques for monitoring and prognostics of machinery conditions. The machine condition is assessed through an energy-based feature, termed as “energy index,” extracted from the vibration signals. The progression of the “monitoring index” is predicted using the CI techniques, namely, recursive neural network (RNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). The proposed procedures have been evaluated through benchmark data sets for one-step-ahead prediction. The prognostic effectiveness of the techniques has been illustrated through vibration data set of a helicopter drivetrain system gearbox. The prediction performance of SVR was better than RNN and ANFIS. The improved performance of SVR can be attributed to its inherently better generalization capability. The training time of SVR was substantially higher than RNN and ANFIS. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage or degradation, and their progression.

Innovated Fault Diagnosis for Power Transformer Using Hybrid Fuzzy Dissolved Gas Analysis

2013 ◽  
Vol 284-287 ◽  
pp. 1082-1086
Author(s):  
Chih Hsuan Liu ◽  
Leehter Yao ◽  
Tung Bin Lin ◽  
Shun Yuan Wang
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Inference ◽  
Power Transformer ◽  
Gas Analysis ◽  
Web Browser ◽  
Inference System ◽  
Power Company ◽  
Fault Type ◽  
Two Factors ◽  
Maintenance Personnel

The objective of this paper is to integrate five traditional criteria of the Dissolved Gases Analysis published in different standards into a more reliable approach of the fault diagnosis of power transformer for maintenance personnel of Taiwan Power Company(TPC). This paper employs Fuzzy Inference System(FIS) to develop two factors as a integrated fault diagnosis for power transformer. One is the identifiable factor which interprets the fault type, the other is the fault factor which asseses the operating condition of transformer. The result of diagnosis can be observed by web browser on TPC intranet. The designed synthetic method has been verified by TPC historical transformers gas records and shows its effectiveness in transformers diagnosis.

Power Transformers Fault Diagnosis Based on Fuzzy-RBF Neural Network

2012 ◽  
Vol 614-615 ◽  
pp. 1303-1306 ◽  
Author(s):  
Hui Da Duan ◽  
Xin Yao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Power Transformers ◽  
The Neural Network ◽  
Gradient Descent Algorithm ◽  
Neural Network Input

Dissolved Gas Analysis (DGA) is a popular method to detect and diagnose different types of faults occurring in power transformers. Improved three-ratio is an effective method for transformer fault diagnosis used in recent years. This paper applies appropriate Artificial Neural Networks (ANN) to resolve the online fault diagnosis problems for oil-filled power transformer based on improved three-ratio. Because of the characteristic of improved three-ratio boundary is too absolute, a method using fuzzy math theory to deal with the data of the neural network input is also proposed. A major kind of neural network, i.e. radial basis function neural network (RBFNN), is used to model the fault diagnosis structure. In addition, to improve the convergence speed, an improved gradient descent algorithm is used in training RBFNN. Through on-line monitoring the concentrations of the dissolved gases, the proposed diagnostic system can offer a way to interpret the incipient faults. The simulation diagnosis demonstrates the effectiveness and veracity of the proposed method.

scholarly journals A Methodology for the Calculation of Typical Gas Concentration Values and Sampling Intervals in the Power Transformers of a Distribution System Operator

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5891
Author(s):  
Sergio Bustamante ◽  
Mario Manana ◽  
Alberto Arroyo ◽  
Raquel Martinez ◽  
Alberto Laso
Keyword(s):  
Distribution System ◽  
Power Transformer ◽  
Gas Analysis ◽  
Predictive Maintenance ◽  
Power Transformers ◽  
Health Index ◽  
Simple Method ◽  
Calculated Concentration ◽  
Gas Concentration ◽  
Sampling Intervals

Predictive maintenance strategies in power transformers aim to assess the risk through the calculation and monitoring of the health index of the power transformers. The parameter most used in predictive maintenance and to calculate the health index of power transformers is the dissolved gas analysis (DGA). The current tendency is the use of online DGA monitoring equipment while continuing to perform analyses in the laboratory. Although the DGA is well known, there is a lack of published experimental data beyond that in the guides. This study used the nearest-rank method for obtaining the typical gas concentration values and the typical rates of gas increase from a transformer population to establish the optimal sampling interval and alarm thresholds of the continuous monitoring devices for each power transformer. The percentiles calculated by the nearest-rank method were within the ranges of the percentiles obtained using the R software, so this simple method was validated for this study. The results obtained show that the calculated concentration limits are within the range of or very close to those proposed in IEEE C57.104-2019 and IEC 60599:2015. The sampling intervals calculated for each transformer were not correct in all cases since the trend of the historical DGA samples modified the severity of the calculated intervals.

scholarly journals The Application of RBF Neural Network Optimized by K-means and Genetic-backpropagation in Fault Diagnosis of Power Transformer

2021 ◽  
Vol 242 ◽  
pp. 03002
Author(s):  
Xinxin Mi ◽  
Gopinath Subramani ◽  
Mieowkee Chan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Gas Analysis ◽  
Transformer Oil ◽  
Training Data ◽  
Transformer Fault

Through the dissolved gas analysis (DGA) in transformer oil, the fault of the power transformer can be diagnosed. However, the DGA method has the disadvantage of low accuracy because it couldn’t exactly reflect the nonlinear relationship between the characteristic gases and fault types. Radial basis function neural network (RBFNN) has the advantage of dealing with complex nonlinear problems, so it can be applied to transformer fault diagnosis based on DGA. The centers, widths and weights has important effects on the performance of the RBFNN. However, it is difficult to find the global optimal solution of these parameters when RBFNN training. This paper creatively designs a method to improve these parameters of RBFNN, firstly using the K-means algorithm to optimize the centers and widths of RBFNN, then using the genetic algorithm-backpropagation (GA-BP) algorithm optimize the weights. Finally, establish the K-means RBF-genetic backpropagation (KRBF-GBP) algorithm model through a large amount of training data. The test results show that the fault diagnosis accuracy of the KRBF-GBP algorithm is 96.4%, higher than the unoptimized RBFNN with 71.43%.

scholarly journals Power Transformer Fault Diagnosis using DGA based on Three Gas Ratio and Fuzzy Logic

2019 ◽  
Vol 8 (2) ◽  
pp. 95-100
Keyword(s):  
Fuzzy Logic ◽  
Power Transformer ◽  
Electrical Engineering ◽  
Gas Analysis ◽  
Ratio Method ◽  
Sharp Change ◽  
Multiple Faults ◽  
Engineering And Technology ◽  
College Of Engineering ◽  
Gas Ratio

For power system equipment with oil as insulating medium such as power transformer, Dissolved Gas Analysis (DGA) of oil is very helpful method in order to detect faults below oil level. Early detection of fault conditions in a transformer is possible if analysis of gases is done which gets evolved in it. Analysis of the specific value of every gas helps in diagnosing faults. Faults which can be identified by this method include disturbances like presence of corona discharge, partial discharges, arcing and increase in temperature. If correct preventive actions are initiated early for the diagnosis of gases produced, failure to equipment will get prevented. Even though many methods are researched for fault identification and analysis in power transformers, DGA is much superior in comparison with other techniques as it gives more helpful data about the condition of the transformers in running condition. Different techniques, like key gases and their ratio, and their analyzing them graphically are mainly used to understand DGA samples. For a transformer having multiple faults, above methods fail to diagnose. IEC standards are in use for DGA from many years and valuable experience gained over theses years around the world is in use to diagnose internal faults on transformers. IEC three gas ratio technique suggested by IEC is mainly preferred, but in some conditions it can’t correctly identify conditions like no suitable codes for diagnosis and multiple faults. The limitations of the traditional three gas ratio method are: with gas ratio is on the verge of crossing the coding boundary, there is a sharp change in the codes, but actually fuzzied boundary should be used. In this paper, codes "zero", "one", "two" are represented by fuzzy membership functions, then "AND" and "OR" conditions of three gas ratio method are coded into fuzzy logic based statements. MATLAB based scripts prove that the presented technique surely overcome the limitations of the traditional three gas ratio method, hence, it largely reduces the errors in diagnosis. In this paper, a method on the basis of fuzzy logic is explained which is able to identify many faults in oil insulated equipment. The presented diagnosis technique uses values of the ratios C2H4/C2H6, C2H2/C2H4 and CH4/H2 and the concentration of specific gases namely methane (CH4), hydrogen (H2), acetylene (C2H2), carbon monoxide (CO), ethylene (C2H4), carbon dioxide (CO2) and ethane (C2H6). Values of these three ratios reflect various patterns of faults inside the transformer. Fuzzy three ratio technique can also quantitatively indicate the likelihood of identified fault with more accuracy as compared to conventional three ratio method. Revised Manuscript Received on July 02, 2019. Nagesh Kalidas Bhosale, M.E. Student, Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. Prof. Chandra O. Reddy, H.O.D., Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. Prof. Pankaj Bhakre, Assistant Professor, Department of Electrical Engineering, M.S.S.’s College of Engineering and Technology, Jalna, MS, India. This tool will prove to be very useful to the engineers in DGA result interpretation.

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 Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 172
Author(s):  
Sunny Katyara ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Shaikh ◽  
Zahid Hussain Khand ◽  
Lukasz Staszewski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Management Strategies ◽  
Test System ◽  
Optimal Placement ◽  
Inference System ◽  
Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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