This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results

Fault detection and classification in transmission lines based on wavelet transform and ANN

A comprehensive review on the methods used for fault detection, classification and location in transmission lines and distribution systems is presented in this study. Though the three topics are highly correlated, the authors try to discuss them separately, so that one may have a more logical and comprehensive understanding of the concepts without getting confused. Great significance is also attached to the feature extraction process, without which the majority of the methods may not be implemented properly. Fault detection techniques are discussed on the basis of feature extraction. After the overall concepts and general ideas are presented, representative works as well as new progress in the techniques are covered and discussed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance.

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Fault detection, classification and location for transmission lines and distribution systems: a review on the methods

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

Accurate and timely diagnosis of transmission line faults is key for reliable operations of power systems. Existing fault-diagnosis methods rely on expert knowledge or extensive feature extraction, which is also highly dependent on expert knowledge. Additionally, most methods for fault diagnosis of transmission lines require multiple separate subalgorithms for fault classification and location performing each function independently and sequentially. In this research, an integrated framework combining fault classification and location is proposed by applying an innovative machine-learning algorithm: the summation-wavelet extreme learning machine (SW-ELM) that integrates feature extraction in the learning process. As a further contribution, an extension of the SW-ELM, i.e., the summation-Gaussian extreme learning machine (SG-ELM), is proposed and successfully applied to transmission line fault diagnosis. SG-ELM is fully self-learning and does not require ad-hoc feature extraction, making it deployable with minimum expert subjectivity. The developed framework is applied to three transmission-line topologies without any prior parameter tuning or ad-hoc feature extraction. Evaluations on a simulated dataset show that the proposed method can diagnose faults within a single cycle, remain immune to fault resistance and inception angle variation, and deliver high accuracy for both tasks of fault diagnosis: fault type classification and fault location estimation.

Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction

A novel technique for fault detection and classification in the extremely high-voltage transmission line using the fault transients is proposed in this paper. The novel technique, called wavelet singular entropy (WSE), incorporates the advantages of the wavelet transform, singular value decomposition, and Shannon entropy. WSE is capable of being immune to the noise in the fault transient and not being affected by the transient magnitude so it can be used to extract features automatically from fault transients and express the fault features intuitively and quantitatively even in the case of high-noise and low-magnitude fault transients. The WSE-based fault detection is performed in this paper, which proves the availability and superiority of WSE technique in fault detection. A novel algorithm based on WSE is put forward for fault classification and it is verified to be effective and reliable under various fault conditions, such as fault type, fault inception time, fault resistance, and fault location. Therefore, the proposed WSE-based fault detection and classification is feasible and has great potential in practical applications.

Fault Detection and Classification in EHV Transmission Line Based on Wavelet Singular Entropy

This paper presents a new approach to real-time fault classification in power transmission systems using fuzzy-logic-based multicriteria approach Only the three line currents are utilized to detect fault types such as LG, LL, and LLG, and then to define the faulty line An online wavelet-based preprocessor stage is used with data window of ten samples (based on 45-kHz sampling rate and 50-Hz power frequency) The multicriteria algorithm is developed based on fuzzy sets for the decision-making part of the scheme Computer simulation has been conducted using EMTP programs Results are shown and they indicate that this approach can be used as an effective tool for high-speed digital relaying, as the correct detection is achieved in less than half a cycle and that computational burden is much simpler than the recently postulated fault classification techniques

O.A.S. Youssef

Papers

Combined fuzzy-logic wavelet-based fault classification technique for power system relaying