A Bayesian Approach for Fault Location in Medium Voltage Grids With Underground Cables

Xiang, Yu; Cobben, J.F.G.

doi:10.1109/jpets.2015.2477598

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…Therefore, these differences can be utilized to discriminate the faulty pole, and the summation of u fp0 is adopted to enlarge the disparity, as shown in (15).…”

Section: B Fault Pole Discriminationmentioning

confidence: 99%

“…Artificial intelligence (AI) algorithms are powerful tools in solving non-linear problems and have been widely used in pattern recognition. Various AI algorithms including fuzzy systems [12], expert systems [13], rough set theory [14], Bayes classifier [15], neural networks (NNs) [16], and support vector machines (SVMs) [17] have already been applied in fault detection in power systems, among which NN-based algorithms are the most widely investigated. Artificial neural networks (ANNs) have shown to be effective for fault detection in AC power grids in [16], and their performance in high-voltage direct current (HVDC) system is also verified in [18] with the ability to enable both DC bus and DC line protection.…”

Section: Introductionmentioning

confidence: 99%

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Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter

Zhou

Zhang

Han

et al. 2023

Journal of Modern Power Systems and Clean Energy

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This paper proposes a single-ended fault detection scheme for long transmission lines using support vector machine (SVM) for multi-terminal direct current systems based on modular multilevel converter (MMC-MTDC). The scheme overcomes existing detection difficulties in the protection of long transmission lines resulting from high grounding resistance and attenuation, and also avoids the sophisticated process of threshold value selection. The high-frequency components in the measured voltage extracted by a wavelet transform and the amplitude of the zero-mode set of the positive-sequence voltage are the inputs to a trained SVM. The output of the SVM determines the fault type. A model of a four-terminal DC power grid with overhead transmission lines is built in PSCAD/EMTDC. Simulation results of EMTDC confirm that the proposed scheme achieves 100% accuracy in detecting short-circuit faults with high resistance on long transmission lines. The proposed scheme eliminates mal-operation of DC circuit breakers when faced with power order changes or AC-side faults. Its robustness and time delay are also assessed and shown to have no perceptible effect on the speed and accuracy of the detection scheme, thus ensuring its reliability and stability.

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“…Therefore, these differences can be utilized to discriminate the faulty pole, and the summation of u fp0 is adopted to enlarge the disparity, as shown in (15).…”

Section: B Fault Pole Discriminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter

Zhou

Zhang

Han

et al. 2023

Journal of Modern Power Systems and Clean Energy

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show abstract

“…In order to solve this problem, we introduced a new model-variational autoencoder [25,26]. Although the model is also trained to train encoders and decoders, the essence of the encoder in this model is used to calculate the mean and variance in the normal distribution, so two encoders are generated for the mean and variance, which is essentially based on the conventional autoencoder.…”

Section: Variational Auto-encodermentioning

confidence: 99%

Deep Anomaly Detection Based on Variational Deviation Network

Wang

Wei

et al. 2022

Future Internet

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There is relatively little research on deep learning for anomaly detection within the field of deep learning. Existing deep anomaly detection methods focus on the learning of feature reconstruction, but such methods mainly learn new feature representations, and the new features do not fully reflect the original features, leading to inaccurate anomaly scores; in addition, there is an end-to-end deep anomaly detection algorithm, but the method cannot accurately obtain a reference score that matches the data themselves. In addition, in most practical scenarios, the data are unlabeled, and there exist some datasets with labels, but the confidence and accuracy of the labels are very low, resulting in inaccurate results when put into the model, which makes them often designed for unsupervised learning, and thus in such algorithms, the prior knowledge of known anomalous data is often not used to optimize the anomaly scores. To address the two problems raised above, this paper proposes a new anomaly detection model that learns anomaly scores mainly through a variational deviation network (i.e., not by learning the reconstruction error of new features, distance metrics, or random generation, but by learning the normal distribution of normal data). In this model, we force the anomaly scores to deviate significantly from the normal data by a small amount of anomalous data and a reference score generated by variational self-encoding. The experimental results in multiple classes of data show that the new variational deviation network proposed in this paper has higher accuracy among the mainstream anomaly detection algorithms.

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“…Applying deterministic approach to calculate this high-dimensional integral is almost impossible. Thus, Monte Carlo integration should be applied with independent importance sampling [36], [37]. Since the parameters in G c are not dependent on each other, it is viable to sample each component independently and merge them together.…”

Section: Monte Carlo Integration With Independent Importance Samplingmentioning

confidence: 99%

Harmonic disturbance location by applying Bayesian inference

Xiang

Ćuk

et al. 2016

Electric Power Systems Research

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A Bayesian Approach for Fault Location in Medium Voltage Grids With Underground Cables

Cited by 13 publications

References 22 publications

Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter

Single-ended Fault Detection Scheme Using Support Vector Machine for Multi-terminal Direct Current Systems Based on Modular Multilevel Converter

Deep Anomaly Detection Based on Variational Deviation Network

Harmonic disturbance location by applying Bayesian inference

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