Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

https://www.mdpi.com/1424-8220/19/12/2750/pdf

Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform.

This paper presents a novel intelligent fault classification scheme for fixed series capacitor compensated transmission line. The singular value decomposition principle is applied along with the fast discrete orthonormal S-transform (FDOST) and bagged tree ensemble classifier for classification of faults under different scenarios. Classification input features are extracted from 1/2 cycle post-fault current data through the FDOST. The feasibility of the proposed relaying scheme is tested on a modified WSCC 3-machine 9-bus system under different fault conditions using PSCAD/EMTDC, and the results indicate the proposed scheme to be fast and accurate. The robustness of the proposed scheme to noise and current transformer saturation is also established. The obtained results under different fault scenarios confirm the efficacy of the proposed scheme.

A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier

Non-technical losses (NTLs) have been a major concern for power distribution companies (PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal consumer activities. Various studies have explored different methodologies for efficiently identifying fraudster consumers. This study proposes a new approach for NTL detection in PDCs by using the ensemble bagged tree (EBT) algorithm. The bagged tree is an ensemble of many decision trees which considerably improves the classification performance of many individual decision trees by combining their predictions to reach a final decision. This approach relies on consumer energy usage data to identify any abnormality in consumption which could be associated with NTL behavior. The key motive of the current study is to provide assistance to the Multan Electric Power Company (MEPCO) in Punjab, Pakistan for its campaign against energy stealers. The model developed in this study generates the list of suspicious consumers with irregularities in consumption data to be further examined on-site. The accuracy of the EBT algorithm for NTL detection is found to be 93.1%, which is considerably higher compared to conventional techniques such as support vector machine (SVM), k-th nearest neighbor (KNN), decision trees (DT), and random forest (RF) algorithm.

Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan

Transmission lines are one of the most widely distributed engineering systems meant for transmitting bulk amount of power from one corner of a country to the farthest most in the other directions. The expansion of the lines over different terrains and geographic locations makes these most vulnerable to different kinds of atmospheric calamities which more often develops faults in line. It is imperative to remove the faulty line at the earliest to restrict undue outflow of bulk power through the faulted point as well as restore system stability earliest to resume normal power flow operation. Here lays the importance of having a robust fault identification, classification and localization algorithm which would be successfully able to drive as well as actuate the digital relaying system. Researchers have worked out several methodologies in developing improved power system protection algorithms which would be able to serve to eliminate faults immediately on occurrence of the same. A brief yet exhaustive review has been presented in this article including the several methodologies adopted by numerous researchers for developing effective fault diagnosis schemes, mentioning about the highlights as well as the shortcoming of each of the methods. This compact and effective survey of literature works would help researchers to take up appropriate techniques for different purposes of transmission line fault analysis.

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

A Novel Protection Scheme for Multi-Terminal Transmission Lines Based on Wavelet Transform

A non-unit protection scheme for double circuit series capacitor compensated transmission lines

The conventional distance protection scheme malfunctions sometimes in case of a fixed series capacitor compensated transmission line due to the change in relaying impedance of the protected line during faulty conditions. In order to mitigate this problem, a combined discrete Fourier transform and fuzzy (CDFTF) based algorithm has been proposed in this paper. This method has been tested on a 400 km, 735 kV series compensated transmission line network and WSCC 3-machine 9-bus system for all fault types using MATLAB/Simulink and PSCAD platforms, respectively. A fixed series capacitor is located at the middle of the protected line. The fundamental components of phase currents, phase voltages, and zero-sequence current are fed as inputs to the proposed scheme. The fault detection, faulty phase selection, and fault classification are achieved within 1/2–1 cycle of power frequency. The proposed CDFTF-based scheme is less complex and is better than other data mining techniques which require huge training and testing time. Test results corroborate the proposed scheme reliability with wide variations in fault location, fault resistance, fault inception angle, evolving faults, compensation level, and heavy load interconnection. The results discussed in this work indicate that the proposed technique is resilient to wide variations in fault and system conditions.

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Combined DFT and Fuzzy Based Faulty Phase Selection and Classification in a Series Compensated Transmission Line

This paper proposes a new method for diagnosis of fault type and faulty phase of a series compensated transmission line. The standard deviation (SD) principle together with the fast discrete orthonormal s-transform (FDOST) and the decision tree (DT) is applied for the purpose of fault classification. The FDOST, as an efficient signal processing tool, is used for extracting the features from a half cycle window of voltage and current signals sampled from one end of the power system network. Finally, the SD of a half cycle post-fault samples of the FDOST coefficients is calculated to form the input feature vector for the DT-based classifier. The features are processed by the DT to classify faults. The practicability of the proposed method is validated by modified Western System Coordinating Council 3-machine 9-bus system simulated in the PSCAD/EMTDC software and field fault data captured from a real transmission network of Chhattisgarh state, India. The results confirm that the proposed method reliably classifies all types of faults with high efficacy.

Praveen Kumar Mishra

Papers

A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier

A non-unit protection scheme for double circuit series capacitor compensated transmission lines

Combined DFT and Fuzzy Based Faulty Phase Selection and Classification in a Series Compensated Transmission Line

FDOST-Based Fault Classification Scheme for Fixed Series Compensated Transmission System

Resilience-oriented protection scheme for TCSC-compensated line