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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 130023-130035
Author(s):  
Zhidi Lin ◽  
Dongliang Duan ◽  
Qi Yang ◽  
Xuemin Hong ◽  
Xiang Cheng ◽  
...  
Keyword(s):  
Fault Detection ◽  
Distribution Systems ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy ◽  
One Class Classifier

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

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 275 ◽  
Author(s):  
Zhidi Lin ◽  
Dongliang Duan ◽  
Qi Yang ◽  
Xuemin Hong ◽  
Xiang Cheng ◽  
...  
Keyword(s):  
Fault Detection ◽  
Distribution Systems ◽  
Fault Localization ◽  
Energy Resources ◽  
Data Driven ◽  
Distributed Energy Resources ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Distributed Energy ◽  
P Values

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.

Optimal Planning and Operation of Distribution Systems Considering Distributed Energy Resources and Automatic Reclosers

2018 ◽  
Vol 16 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Luis Fernando Grisales ◽  
Oscar Danilo Montoya ◽  
Alejandro Grajales ◽  
Ricardo Alberto Hincapie ◽  
Mauricio Granada
Keyword(s):  
Distribution Systems ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Optimal Planning ◽  
Distributed Energy ◽  
Planning And Operation

scholarly journals Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks

2018 ◽  
Vol 8 (9) ◽  
pp. 1563 ◽  
Author(s):  
Cesar Orozco-Henao ◽  
Arturo Suman Bretas ◽  
Juan Marín-Quintero ◽  
Andres Herrera-Orozco ◽  
Juan Pulgarín-Rivera ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Fault Location ◽  
Real Life ◽  
Distribution Networks ◽  
Maximum Error ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Distributed Energy ◽  
Power Sources ◽  
Location Algorithm

Modern fault location methods are robust; however, they depend strongly on the availability of the measurements given by Distributed Energy Resources (DER). If the communication or synchronism of this information is lost, the fault location is not possible. This paper proposes an adaptive impedance-based fault location algorithm for active distribution systems. The proposal combines information provided by Intelligent Electronic Devices (IEDs) located at the substation, the knowledge of the network topology and parameters, as well as the distributed power sources, to estimate the fault location. Its adaptive feature is given by the use of a Distributed Energy Resources (DER) electrical model. This model is used to estimate the DER current contribution to the fault, in case the information provided by a local IED is not available. The method takes two types of DER technologies into account: Inverter non-interfaced DER (INIDER) and Inverter-interfaced DER (IIDER). The proposed method is validated on a modified IEEE 34-node test feeder, which was simulated with ATP/EMTP. The results obtained using the IEDs information, presented a maximum error of 0.8%. When this information is not available, the method’s performance decreases slightly, obtaining a maximum error of 1.1%. The proposed method showed better performance when compared with two state of the art methods, indicating potential use for real-life applications.

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