Data Mining-based Fault Detection Method for Distributed Power Generation Systems
DOI:
https://doi.org/10.13052/dgaej2156-3306.3646Keywords:
Data mining, distributed generation systems, grid faults, artifi- cial intelligence, fault detection.Abstract
Traditional fault detection methods for power generation systems use cen-
tralized fault processing analysis, which leads to long accuracy and response
time of fault detection. To address these problems, a data mining-based
distributed power generation system fault artificial intelligence detection
method is studied. The depth-first search tree algorithm is used to divide the
grid of distributed generation system. The network structure is modified to
locate fault zones by processing anomaly mining of the system data after
grid division. The combination of fuzzy logic and wavelet singular entropy
is used to complete the detection and identification of system faults. Through
simulation experiments, it is verified that the response time of the detection
method is only 0.016 s, and its detection error rate and false negative rate are
1.23% and 1.25%, which are far lower than other methods.
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References
AbdLkader AG, Saleh SM. Zero noncetection zone assessment for anti-
islanding protection in rotating machines based distributed generation
system[J]. International Journal of Energy Research, 2020, 45(10):521–
Murthy G, Sivanagaraju S, Rao BH. Reliability improvement of radial
distribution system with distributed generation[J]. International Journal
of Engineering Science and Technology, 2020, 4(9):4003.
F. Dewi Cahya, Daniar Fahmi, I Made Yulistya Negara, et al. Load-
ing Effect with Distributed Generation (DG) Injection on Distribution
System on Temperature and Breakdown Voltage of Oil Transformer
in Distributed System[J]. Journal of Physics: Conference Series, 2019,
(1):012011. [4] Maged N.F. Nashed, Mona N. Eskander. Novel circuit to compensate
the effect of source open circuit fault in distributed generation system[J].
Energy Reports, 2020, 6(Supl.2):76–80.
Hannoon, Naeem M.S., Vijayakumar, V., Vengatesan, K., et al. Small
Signal Fault Analysis for Renewable Energy (Wind) Power System Dis-
tributed Generation by Using MATLAB Software (Simulink)[J]. Jour-
nal of Computational and Theoretical Nanoscience, 2019, 16(2):537–
(7).
Donghan Feng, Fan Wu, Yun Zhou, et al. Multi-agent-based Rolling
Optimization Method for Restoration Scheduling of Distribution Sys-
tems with Distributed Generation[J]. Journal of Modern Power Systems
and Clean Energy, 2020, 8(04):737–749.
Galiveeti Hemakumar Reddy, Arup Kumar Goswami, Nalin B. Dev
Choudhury. Impact of Distributed Generation Integration on Distribu-
tion System Reliability[J]. Indian Journal of Science and Technology,
, 11(34):1–13.
Huadong Mo, Giovanni Sansavini. Impact of aging and performance
degradation on the operational costs of distributed generation sys-
tems[J]. Renewable Energy, 2019, 143:426–439.
Faiz MT, Khan MM, Jiang H, et al. H ∞ robust control with improved
harmonics suppression for inverter-based distributed generation sys-
tems[J]. IET Power Electronics, 2020, 13(13):2742–2755.
Emilio Barocio, Petr Korba, Walter Sattinger, et al. Online coherency
identification and stability condition for large interconnected power sys-
tems using an unsupervised data mining technique[J]. IET Generation,
Transmission & Distribution, 2019, 13(15):3323–3333.
Yingzhe Kan, Dongye Sun, Yong Luo, et al. Optimal design of the gear
ratio of a power reflux hydraulic transmission system based on data
mining[J]. Mechanism and Machine Theory, 2019, 142:103600.
Shan Liu, Li Sun, Senlin Zhu, et al. Operation strategy optimization of
desulfurization system based on data mining[J]. Applied Mathematical
Modelling, 2020, 81:144–158.
Oujdi Sihem, Belbachir Hafida, Boufares Faouzi. C4.5 Decision Tree
Algorithm for Spatial Data, Alternatives and Performances[J]. Journal
of computing and information technology, 2019, 27(3):29–43.
Sandoval Betsy, Barocio Emilio, Korba Petr, et al. Three-way unsu-
pervised data mining for power system applications based on tensor
decomposition[J]. Electric Power Systems Research, 2020, 187:106431.
H. Wu
Michał Jasi ́nski, Tomasz Sikorski, Zbigniew Leonowicz, et al. The
Application of Hierarchical Clustering to Power Quality Measure-
ments in an Electrical Power Network with Distributed Generation[J].
Energies, 2020, 13(9):1–19.
Beatrice Marchi, Simone Zanoni, Marco Pasetti. Multi-Period Newsven-
dor Problem for the Management of Battery Energy Storage Systems in
Support of Distributed Generation[J]. Energies, 2019, 12(23):1–13.
Lucas F, Costa P, Batalha R, et al. Fault detection in smart grids with
time-varying distributed generation using wavelet energy and evolving
neural networks[J]. Evolving Systems, 2020, 11(2):165–180.
Sharma Nikhil Kumar, Samantaray Subhransu Ranjan. Assessment of
PMU-based wide-area angle criterion for fault detection in microgrid[J].
IET Generation, Transmission & Distribution, 2019, 13(19):4301–4310.
Roy Subhamita, Debnath Sudipta. PSD based high impedance fault
detection and classification in distribution system[J]. Measurement,
, 169(1):108366.
Du Wenhua, Guo Xiaoming, Wang Zhijian, et al. A New Fuzzy Logic
Classifier Based on Multiscale Permutation Entropy and Its Application
in Bearing Fault Diagnosis.[J]. Entropy (Basel, Switzerland), 2019,
(1):27.
Ghahderijani MM, Camacho A, Moreira C, et al. Imbalance-Voltage
Mitigation in an Inverter-Based Distributed Generation System Using
a Minimum Current-Based Control Strategy[J]. IEEE Transactions on
Power Delivery, 2019, PP(99):1–1
