Fault Location and Fault Type Recognition of Power System Based on Wavelet Transform

Tang, Xiaotian; Zhang, Zhenyuan; Huang, Qi; Gong, Yanzhang

doi:10.1109/isgt-asia.2019.8881101

Cited by 12 publications

(3 citation statements)

References 1 publication

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“…The HPF is derived from the mother wavelet function and is measured in detail within a given input. The LPF smooths the input signal and it is derived from the scale function corresponding to the mother wavelet [23].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

Novizon

Syahri

Wulandari

et al. 2021

AJEEET

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To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

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Section: Discrete Wavelet Transformmentioning

confidence: 99%

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

Novizon

Syahri

Wulandari

et al. 2021

AJEEET

View full text Add to dashboard Cite

show abstract

“…However, the vibration signals from CPs are largely nonstationary, and Fourier transforms (FTs) are mostly suited for stationary signals [13,14]. Time-frequency methods like the wavelet transform (WT) have been applied in recent years to address this nonstationary behavior [15,16], but finding the optimal wavelet function remains challenging. Empirical mode decomposition (EMD) has been introduced to address the limitations of the wavelet transform [17], and while effective, it also has its limitations [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network

Zaman,

Ahmad,

Kim

2024

Sensors

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This paper proposes a new fault diagnosis method for centrifugal pumps by combining signal processing with deep learning techniques. Centrifugal pumps facilitate fluid transport through the energy generated by the impeller. Throughout the operation, variations in the fluid pressure at the pump’s inlet may impact the generalization of traditional machine learning models trained on raw statistical features. To address this concern, first, vibration signals are collected from centrifugal pumps, followed by the application of a lowpass filter to isolate frequencies indicative of faults. These signals are then subjected to a continuous wavelet transform and Stockwell transform, generating two distinct time–frequency scalograms. The Sobel filter is employed to further highlight essential features within these scalograms. For feature extraction, this approach employs two parallel convolutional autoencoders, each tailored for a specific scalogram type. Subsequently, extracted features are merged into a unified feature pool, which forms the basis for training a two-layer artificial neural network, with the aim of achieving accurate fault classification. The proposed method is validated using three distinct datasets obtained from the centrifugal pump under varying inlet fluid pressures. The results demonstrate classification accuracies of 100%, 99.2%, and 98.8% for each dataset, surpassing the accuracies achieved by the reference comparison methods.

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“…The comparison uses different signal processing with a filtered frequency range close to 50 Hz. These filters were chosen as signal processing tools because this method is primarily used among researchers and is suitable for a wide range of frequencies [15]- [17]. Comparative harmonic filters and their influence on estimating the location of faults have been further addressed in sections 3 and 5.…”

Section: Introductionmentioning

confidence: 99%

Differential equation fault location algorithm with harmonic effects in power system

et al. 2023

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About 80% of faults in the power system distribution are earth faults. Studies to find effective methods to identify and locate faults in distribution networks are still relevant, in addition to the presence of harmonic signals that distort waves and create deviations in the power system that can cause many problems to the protection relay. This study focuses on a single line-to-ground (SLG) fault location algorithm in a power system distribution network based on fundamental frequency measured using the differential equation method. The developed algorithm considers the presence of harmonics components in the simulation network. In this study, several filters were tested to obtain the lowest fault location error to reduce the effect of harmonic components on the developed fault location algorithm. The network model is simulated using the alternate transients program (ATP)Draw simulation program. Several fault scenarios have been implemented during the simulation, such as fault resistance, fault distance, and fault inception angle. The final results show that the proposed algorithm can estimate the fault distance successfully with an acceptable fault location error. Based on the simulation results, the differential equation continuous wavelet technique (CWT) filter-based algorithm produced an accurate fault location result with a mean average error (MAE) of less than 5%.

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Fault Location and Fault Type Recognition of Power System Based on Wavelet Transform

Cited by 12 publications

References 1 publication

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

Fault Diagnosis in Centrifugal Pumps: A Dual-Scalogram Approach with Convolution Autoencoder and Artificial Neural Network

Differential equation fault location algorithm with harmonic effects in power system

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