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Development of overcurrent relay based on wavelet transform for fault detection in transmission line
(2024-12-01)
Ananwattanaporn, Santipont
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Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.
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Comparative Study for Discrete Wavelet Transform Between Single and Double Detection Approach to Fault Classification on Transmission Line
(2024-01-01)
Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
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Ananwattanaporn, Santipont
This study presented a novelty method for a protection relay using a microcontroller. The method able to detect and classify faults in high voltage transmission lines based on the Discrete Wavelet transform (DWT). In addition, novelty of proposed method when compared to traditional methods was a signal analysis process applied Clark's Transformation and Double Detection Technique. A performance of proposed method was verified by created experimental model in our KMITL laboratory. All fault types (SL, LL, DLG and 3P) were observed. This result of this study found that not only applied DWT method can improved the accuracy of signal analysis, applied Clark's Transformation able to filter noise of signal, without loss of information. Meanwhile, Double Detection Technique able to re-confirm the accuracy of fault detection and classify. Therefore, it results in the performance of a protection relay increase which is beneficial to the electrical system. It can increase the accuracy and reliability of the system.
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A phase diagram approach to the detection and location of faulty capacitor units in A 115-kV capacitor bank based on unbalanced current arguments
(2023-03-01)
Pothisarn, Chaichan
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Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
The protection of traditional high voltage capacitor banks relies on an unbalance relay which operates when an internal fuse is blown. However, the unbalance relay cannot indicate cause or position of the fault. Thus, an operator wastes time and human resources investigating the fault issues. To address this issue, a method to locate the fault position in a capacitor bank is developed in this study. The study was simulated by using PSCAD software and modeled on the 115-kV system of the Electricity Generating Authority of Thailand (EGAT). Case studies involving faults with varied phases, side and branch connections, row connections, and inception angles were considered. Moreover, the magnitude and argument of the current phase and unbalanced current were analyzed to identify the fault location in a capacitor bank. The performance of the proposed method was verified via comparison with that of traditional methods and the results of laboratory experiments. In addition, various voltage systems were observed to verify the flexibility and accuracy of the proposed method. The results demonstrate that the proposed method was more efficient than traditional methods at locating the fault position in a capacitor bank.
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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
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Phannil, Nattanon
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Ananwattananporn, Santipont
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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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Reliability enhancement of LNG fuel gas supply system in combined-cycle power plant
(2020-12-01)
Thepmanee, Teerawat
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Nachasingha, Chakri
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Kummool, Sart
This article aims to present a technique to enhance reliability of liquefied natural gas (LNG) fuel gas supply system (FGSS) of gas turbine unit in a combined-cycle power plant. In order to prevent unplanned plant shutdowns, the proposed technique is based on the use of failure modes, effects, and diagnostic analysis (FMEDA) to identify and evaluate the effects of possible failure modes, to determine what could minimize the chance of failures, and to design a new safety interlock. The studied FGSS consists of gas analytical system (GAS) in 2-out-of-2 voting scheme, shutdown valve system (SDV) in 1-out-of-2 voting scheme, fuel gas controller (FGC), and engineering/operator workstations. Two automatic GAS subsystems with different measurement methods are installed in the GAS. One of two GAS subsystems uses three gas chromatography analyzers in 2-out-of-3 voting scheme. The gas chromatography results and diagnostic alarm historian are utilized to consider the failure behavior. The reliability models for the GAS subsystem using gas chromatography and the overall FGSS are also included to confirm the effectiveness of utilizing diagnostic information from the gas analyzers. Based on the FMEDA results, the new safety interlock with no additional software and hardware costs can be achieved. The workability of the proposed safety interlock is demonstrated by test results.
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Early fault detection based on ball bearing vibration analysis using multinomial logistic regression
(2017-03-25)
Kerdsiri, Thanadat
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Gullayanon, Rutchanee
Rotating machines play an important role in several applications such as transportation, industry and military. Maintaining continuous operations of these machines is vital to these applications. The reliability of these rotating machines depends on ball bearing health. Bearing can fail from many factors, e.g., impurity of lubricant, improper installation, etc. All of these defects can be detected by vibration analysis. Therefore, vibration signals are utilized to develop early fault detection of ball bearing to warn operators of any anomalies in the system. This paper presents a fault detection algorithm using a logistic regression method to assess the health state of the system. This regression utilizes the node energy of wavelet packet decomposition, kurtosis and crest factor as key features. The resulting signals are processed using the Savitzky-Golay smoothing algorithm before determining the time location where the defects occur with a confidence interval of 99%. The proposed method was tested with actual vibration data of bearing which were provided by the FEMTO-ST institute and showed that it is capable of accurately predicting defect time only 10 seconds after the actual defect occurred. The worst-case prediction of 970 seconds after defect occurs also observed during experiment. Although the system did not set off alarm for fault detection after 970 seconds, one can observe an increase in the fault probability trends in early state. Therefore, the proposed algorithm can be utilized for early fault detection in ball bearing applications.
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Stator faults detection based on the dq0 voltage components
(2008-10-30)
Manop, Chalermchat
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Kinnares, Vijit
This paper presents the technique for detection of shorted turns in stator winding of induction motors. The strategy is based on the dq0 voltage components. The proposed technique determines dq components of time domain of harmonics obtained from Park transformation instead of the stator phase voltage. Zero component (0) is calculated from the fundamental voltage excitation only. In the testing, the supply is sinusoidal waveform and the motor must be star connected with floating neutral. From experimental results, pattern of dq0 voltage components in order to use for the stator faults detection is possible. ©2008 IEEE.

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