One-Class Classifier Based Fault Detection in Distribution Systems With Distributed Energy Resources

Lin, Zhidi; Duan, Dongliang; Yang, Qi; Cheng, Xiang; Yang, Liuqing; Cui, Shuguang

doi:10.1109/globalsip.2018.8646526

Cited by 5 publications

(15 citation statements)

References 14 publications

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“…However, even though in our previous work [3], it has been shown that the proposed method can effectively detect faults in the system, it still cannot accurately localize the fault detected. Intuitively, one may think that fault localization can be achieved by applying fault detectors in all different regions of the system and checking which region reports fault.…”

Section: Introductionmentioning

confidence: 79%

“…As a result, it is difficult to calculate the normal and faulty current values and coordinate the operation of multiple relays in a system. This first leads to inaccurate fault detection in the system as reported in our previous work [3]. Furthermore, even when faults are correctly detected, the overcurrent relays might still work in an undesirable manner if the faults are not well located, leading to trippings of more lines (i.e., mal-operations) than necessary to clear a fault, hence more customers will be affected by the fault.…”

Section: Problem Formulationmentioning

confidence: 94%

“…Hence, fault detection and localization are crucial for safe operation of distribution systems. However, as more and more Distributed Energy Resources (DERs) are introduced into the grid, the conventional model-based protection schemes suffer from high mal-operation and miss-trip rates [3] due to the more complicated power flow patterns that are highly volatile and cannot be captured by existing models [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Considering that the data available for power systems are those collected under its normal operations, it would be more suitable to apply one-class classifier to detect the fault instead of using other classifiers involved with multi-class datasets. In our recent work [3], we have proposed a data-driven method using the support vector data descriptor (SVDD) to detect faults in power systems that only requires training data collected under normal operations and hence overcomes the limitations of most existing data-driven approaches. As a matter of fact, there has been a substantial body of work successfully applying SVDD for fault detection and isolation in different kinds of systems [20].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

et al. 2020

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The integration of Distributed Energy Resources (DERs) introduces a non-conventional two-way power flow which cannot be captured well by traditional model-based techniques. This brings an unprecedented challenge in terms of the accurate localization of faults and proper actions of the protection system. In this paper, we propose a data-driven fault localization strategy based on multi-level system regionalization and the quantification of fault detection results in all subsystems/subregions. This strategy relies on the tree segmentation criterion to divide the entire system under study into several subregions, and then combines Support Vector Data Description (SVDD) and Kernel Density Estimation (KDE) to find the confidence level of fault detection in each subregion in terms of their corresponding p-values. By comparing the p-values, one can accurately localize the faults. Experiments demonstrate that the proposed data-driven fault localization can greatly improve the accuracy of fault localization for distribution systems with high DER penetration.

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Section: Introductionmentioning

confidence: 79%

Section: Problem Formulationmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

et al. 2020

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“…This subsection shows the performance of the proposed method for fault localization in the IEEE-123 node test feeder with 50% DER penetrations. Firstly, based upon the criterion given in Section 3.1, we use the three-phase line as the trunk of the distribution system and roughly divide the IEEE 123-node test feeder into 5 subregions, i.e., subregion 1 to subregion 4 and a trunk subregion (the trunk is the region that contains all nodes that are not included by subregions [1][2][3][4], as shown in Figure 9. Next, we apply the proposed data-driven fault localization strategy to localize the fault in the system.…”

Section: Fault Localization Via the Proposed Data-driven Strategymentioning

confidence: 99%

Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

Lin

Duan

Yang

et al. 2019

2019 IEEE Sustainable Power and Energy Conference (iSPEC)

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One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources

et al. 2020

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The integration of Distributed Energy Resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flow. Conventional protection schemes are based upon local measurements and simple linear system models, and are thus not capable of handling the new complexity and power flow patterns in systems with high DER penetration. In this paper, we propose a data-driven protection framework to address the challenges introduced by DERs. Firstly, considering the limited available data under fault conditions, we adopt the Support Vector Data Description (SVDD) method, a commonly used one-class classifier, for distribution system fault detection, which only requires the normal data for its training process. Secondly, incremental learning is incorporated into the proposed SVDD-based protection framework to accommodate variations of the integration level of DERs in distribution systems over time. In particular, the artificial uniform-hyperspherical data generation model is incorporated into the incremental SVDD to boost the training speed. Finally, we validate the proposed method under the IEEE 123-node test feeder. Simulation results demonstrate that our proposed SVDD-based fault detection framework significantly improves the robustness and resilience against DERs in comparison with conventional protection systems. Meanwhile, the proposed online updating model outperforms the existing incremental SVDD models in terms of successful training speed.

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One-Class Classifier Based Fault Detection in Distribution Systems With Distributed Energy Resources

Cited by 5 publications

References 14 publications

Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources

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