Identification of faulted line section in microgrids using data mining method based on feature discretisation

Jamali, S.; Ranjbar, Siavash; Bahmanyar, Alireza

doi:10.1002/2050-7038.12353

Cited by 15 publications

(17 citation statements)

References 33 publications

(83 reference statements)

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“…The traditional approaches for fault protection are prone to network disturbances caused by noise. Recent researches have focused on data mining for microgrid protection because data mining techniques, as opposed to the hard threshold, use a soft criterion for fault detection and have an outstanding capability of handling data with noise [38], [39], [40]. Hence, the proposed scheme employs the multiresolution analysis (MRA) of a DWT and RF.…”

Section: Proposed Schemementioning

confidence: 99%

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

2021

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The protection problems in microgrid effect the reliability of the power system caused due to high distributed generator penetrations. Therefore, fault protection in microgrid is extremely important and needs to be resolved to enhance the robustness of the power system. This manuscript proposes a combined signal processing and data mining-based approach for microgrid fault protection. In this study, first the multiresolution decomposition of wavelet transform is employed to preprocess the voltage and current signals to compute the total harmonic distortion of the voltage and current. Then, the statistical indices of standard deviation, mean, and median of the total harmonic distortion and the negative sequence components of active and reactive power are used to collect the input data. After that, all the available data is provided to the random forest-based classifier to evaluate the efficiency of the proposed scheme in terms of the detection, identification, and classification of faults. This study used different aspects for data collection by simulating various fault and no-fault cases for both looped and radial configurations under grid-connected and islanded modes of operation. The simulations were performed on a standard medium voltage microgrid using MATLAB/SIMULINK, whereas the analysis for testing and training of the random forest were conducted in Python. It is recognized that the proposed method performed better than support vector machines and decision tree that are reported in the literature. The results further demonstrate that the proposed method can also detect simultaneous faults, and it is also effective against measurement noise.INDEX TERMS Data-mining, discrete wavelet transform, feature extraction, fault protection, multiresolution analysis, random forest.

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Section: Proposed Schemementioning

confidence: 99%

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

2021

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“…In the normal operating condition, an active distribution system is useful for the utilities, resulting in increased use of these units [4, 21, 28, 34]. The main problem of integration of DG units into the load side appears in the abnormal operating condition [17, 35–39]. The generated power of DG units are variable and dependent on the environmental conditions; also, they are distributively installed in the distribution system from small rating up to large farms [40].…”

Section: Introductionmentioning

confidence: 99%

Fuse saving coordination scheme for active distribution systems: State‐of‐the‐art and a novel quasi‐voltage current based scheme

Bisheh,

Fani,

Shahgholian

et al. 2024

IET Generation Trans & Dist

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Increasing the penetration level of distributed generation (DG) units requires overcoming the technical challenges associated with their integration into the distribution systems, especially protection problems. Change in the current profile of the distribution system due to the presence of DG units disrupts the operation of the conventional fuse saving coordination (FSC) scheme. The first objective of this paper is to provide an overview of the state‐of‐the‐art of FSC schemes in distribution systems with distributed generators that has not been systematically presented yet. In addition to comparing the features of reliability, cost, speed, implementation, calculation burden, and requirements, the impact of presence of distributed generations on the performance of the conventional FSC scheme is investigated in details. The second objective of this paper is to propose an FSC restoration scheme for minimizing the challenges of previous works. Using a quasi‐voltage current term, the proposed scheme modifies the adjustable time coefficient of the recloser in two ways of pro and plus. The former scheme provides an approximate FSC with a simple setting while the latter scheme provides complete coordination at the expense of a more complex setting. No need for voltage measurement makes its implementation practical in available distribution systems. The effective performance of the proposed FSC scheme is verified through extensive simulation studies in the ETAP environment.

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“…Another fault detection scheme based on deep neural networks and wavelet transform for microgrid was proposed in [9]. e authors in [10] employed an approach focusing on identifying and evaluating the faulted line section by implementing data mining and wavelet packet transform. At present, the scale of microgrid data has increased exponentially, and the traditional data processing methods cannot afford to process large-scale data.…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid Model of Big Data and BP Network on Fault Diagnosis Strategy for Microgrid

Jiang

Xia

2022

Computational Intelligence and Neuroscience

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Aiming at the characteristics of timely transmission, rapid update, and large magnitude of microgrid data, based on the large data samples generated by microgrid operation, a fault diagnosis and analysis method of microgrid systems supported by big data is proposed in this paper. The multisource joint feature vectors of microgrid are extracted using Wavelet transform, Rayleigh entropy, and big data technology, which combine short-circuit current and voltage. The extracted feature dataset is clustered and segmented to realize deep data mining. Combining BP neural network and big data, the fault diagnosis of microgrid is realized. The simulation results show that the BP neural network algorithm based on big data support can accurately identify the type and phase of internal faults in microgrid, which is more suitable for extracting the temporal characteristics of information and spatiotemporal correlation of data to realize the prediction of big data and solve the core problems in the analysis of big data of microgrid faults, and the accuracy is as high as 96.8%.

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Identification of faulted line section in microgrids using data mining method based on feature discretisation

Cited by 15 publications

References 33 publications

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

Fault Protection in Microgrid Using Wavelet Multiresolution Analysis and Data Mining

Fuse saving coordination scheme for active distribution systems: State‐of‐the‐art and a novel quasi‐voltage current based scheme

Application of a Hybrid Model of Big Data and BP Network on Fault Diagnosis Strategy for Microgrid

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