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Study of Problem and Effect Mitigation Technique of Unbalance Capacitance in 500 kV Single Phase Transformers
(2024-01-01)
Suksagoolpanya, Suthat
;
Pattanadech, Norasage
This article presents the study of the effect of unbalanced stray capacitance in single-phase 500 kV power transformers and establishes the mitigation technique. The power transformers were operated in Wang Noi Substation for transmitting the electrical power. There are 3 units of single-phase power transformer. Previously, a transformer of phase B failed after being serviced for several years. Thus, the new power transformer was ordered instead of the failed one. However, once the new system had been operated, the hotspots at the surge arrester of tertiary winding (22 kV) were found and the unbalance of phase voltages was observed, resulting in overvoltage as a root cause of the problem. Once a thorough investigation was done, it was found that there was an effect of unbalanced stray capacitance between tertiary winding and ground core since the new power transformer has some different designs compared with the remaining. Hence, the mitigation technique was performed by adding the capacitor parallel to the tertiary winding until the voltage balance was achieved. Simulation with the PSCAD program was performed to determine the optimum value for additional capacitors, i.e., voltage balance, using an iterative method. In this article, the capacitors used for additional were capacitor voltage transformers, which have been installed. As a result of the troubleshooting, the voltage at the 22 kV bus was more balanced as well as not causing overvoltage, and the hotspots at the surge arrester of tertiary winding were not detected resulting in gaining the stability and reliability of using these transformers.
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Application of probabilistic neural networks using high-frequency components’ differential current for transformer protection schemes to discriminate between external faults and internal winding faults in power transformers
(2021-11-01)
Chiradeja, Pathomthat
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Pothisarn, Chaichan
;
Phannil, Nattanon
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Ananwattananporn, Santipont
;
Leelajindakrairerk, Monthon
Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a 1/4 cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the 1/4 cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.
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Application of discrete wavelet transform and back-propagation neural network for internal and external fault classification in transformer
(2019-01-01)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
;
Asfani, Dimas Anton
;
Negara, Yulistya
This paper proposes an algorithm for internal and external fault discrimination in the three-phase two-winding power transformer based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN). The maximum ratio obtained from division algorithm between DWT coefficient value of differential current and zero sequence component in post-fault condition differential current signals is employed as an input for the training pattern for BPNN in order to discriminate between internal fault and external short circuit. The proposed algorithm performance has been test using various cases studies based on Thailand electricity transmission and distribution systems data. Results show that the proposed technique can achieved satisfy accuracy for internal and external fault detection and discrimination in the considered system. This methodology and result can be used to further improve protection system of power transformer in the future.
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The new developed Health Index for transformer condition assessment
(2016-11-28)
Wattakapaiboon, W.
;
Pattanadech, N.
Health Index (HI) technique is useful for maintenance strategy planning of transformers utilized in industrial sectors and electrical power systems. The conventional HI method requires many testing parameters to evaluate the transformer conditions. Therefore, it may lead to be an impractical task in many reality cases. Besides, some required testing parameters are not clearly defined. The aim of this paper is to represent the new method to assess the conditions of transformers by applying a new developed HI table which was improved from the conventional HI table. The new developed HI table needs only some simple testing parameters for evaluating the transformer conditions. Thirteen transformer case studies were used to evaluate the performance of the new develop HI table by comparing the score value obtained from the conventional HI table and the new developed HI table. The test results show that only 7% different score obtained from the developed HI table compared with conventional HI table. Therefore, the new developed HI technique should be useful for condition analysis of the transformers and also for transformer strategy planning. Applying the new HI technique could reduce the operation cost and adapt well for practical work.
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Application of support vector machines algorithm for discriminating between external fault and internal winding fault in power transformer
(2014-02-04)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Leelajindakrairerk, Monthon
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Pothisarn, Chaichan
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Suechoey, Boonlert
The differential relaying principle is used for protection of medium and large power transformers. In the past decade, several decision algorithms have different solutions and techniques. This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify and discriminate between external fault and internal fault in power transformer. The DWT is used to detect the high frequency components from fault signals. The variations of first scale high frequency component that detects fault are used as input for the SVM. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems. © 2014 ICIC International.
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A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only
(2014-01-01)
Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low-frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer-rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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Selection of proper activation functions in back-propagation neural network algorithm for single-circuit transmission line
(2014-01-01)
Suttisinthong, N.
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Seewirote, B.
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Ngaopitakkul, A.
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Pothisarn, C.
This paper proposes an appropriate activation function for the fault classification decision algorithm. The decision algorithm based on the hybrid of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) has been proposed to classify the fault type. The DWT is employed to decompose high frequency component of current signals. The maximum coefficient from the first scale at 1/4 cycle of phase A, B, and C of post-fault current signals and zero sequence current obtained by the DWT have been used as an input variable in a decision algorithm. The activation functions in each hidden layer and output layer have been varied, and the results obtained from the decision algorithm have been investigated with the variation of fault inception angles, fault types, and fault locations. The results have illustrated that the use of Hyperbolic tangent sigmoid function in the first and the second layers with Linear function in the output layer is the most appropriate scheme for the transmission system.
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Analysis of electrical losses in transformers using artificial neural networks
(2014-01-01)
Suttisinthong, N.
;
Pothisarn, C.
This paper proposes a technique to analysis electrical losses in distribution transformers 1-phase 30 kVA using of back-propagation neural networks (BPNN). Experimental data at various temperature of transformers obtained from manufacturer, are employed as an input pattern for BPNN while output pattern which corresponding to total losses in transformers. The total number of test set are 150 sets in order to verify the validity of the proposes technique. The results show that average accuracy obtained from the proposes technique gives satisfactory accuracy.
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Behaviour of internal fault and external fault in power transformer using discrete wavelet transform
(2012-06-12)
Ngaopitakkul, Atthapol
;
Pothisarn, Chaichan
The differential protection is aimed at detecting internal faults in transformer windings. Therefore, it is necessary to understand fault behaviour before doing performing decision algorithm. This paper presents behaviour of internal winding fault and external short circuit in a three-phase two-winding transformer. The advantage of the discrete wavelet transform (DWT) is considered herein. The fault is simulated using ATP/EMTP and the behaviour analysis of signals is performed using DWT. The variation of high frequency components of differential current signals is presented in this paper. The results obtained from the analysis will be useful in the development of a fault detecting scheme for power transformer in the future.
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Discrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network
(2012-01-01)
Jettanasen, C.
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Klomjit, J.
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Bunjongjit, S.
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Ngaopitakkul, A.
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Suechoey, B.
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for detecting and identifying internal winding fault of three-phase two-winding transformer. The maximum ratio obtained from division algorithm between coefficient from DWT of differential current and zero sequence for post-fault differential current waveforms is employed as an input for the training pattern in order to discriminate between internal fault and external short circuit. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system. © 2012 IEEE.

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