Hierarchical Information Fault Diagnosis Method for Power System Based on Fireworks Algorithm
DOI:
https://doi.org/10.13052/dgaej2156-3306.3634Keywords:
Fireworks algorithm, power system, hierarchical fault diagnosisAbstract
Power system fault diagnosis is an important means to ensure the safe and
stable operation of power system. According to the specific situation of
China’s current power grid automation level, a hierarchical fault diagnosis
method based on switch trip signal, protection information and fault record-
ing information is proposed. This method can not only diagnose simple fault
and complex fault, but also judge fault type and phase, and complete fault
location, which provides reliable guarantee for operators to quickly remove
fault and resume operation. The diagnosis method based on this principle has
good application effect in simulation test.
Downloads
References
Zhou X, Feng LU, Huang J. Fault diagnosis based on measurement
reconstruction of HPT exit pressure for turbofan engine[J]. Chinese
Journal of Aeronautics, 2019, 32(05):103–117.
Liu S, Gao X, He H, et al. Soft sensor modelling of acrolein conversion
based on hidden Markov model of principle component analysis and
F. Haixun et al.
fireworks algorithm[J]. The Canadian Journal of Chemical Engineering,
, 97(12):3052–3062.
Ji J, Xiao H, Yang C. HFADE-FMD: a hybrid approach of fireworks
algorithm and differential evolution strategies for functional module
detection in protein-protein interaction networks[J]. Applied Intelli-
gence, 2021, 51(6788):1–15.
Kong X, Xu Y, Jiao Z, et al. Fault Location Technology for Power
System Based on Information About the Power Internet of Things[J].
IEEE Transactions on Industrial Informatics, 2020, 16(10):6682–6692.
Wu X, Wang D, Cao W, et al. A Genetic-Algorithm Support Vector
Machine and D-S Evidence Theory Based Fault Diagnostic Model
for Transmission Line[J]. IEEE Transactions on Power Systems, 2019,
(99):4186–4194.
Xu X, Liu Z, Wu J, et al. Misfire Fault Diagnosis of Range Extender
Based on Harmonic Analysis[J]. International Journal of Automotive
Technology, 2019, 20(1):99–108.
X Wang, Fu Z, Wang Y, et al. A Non-Destructive Testing Method
for Fault Detection of Substation Grounding Grids[J]. Sensors, 2019,
(9):2046.
Mallikarjuna PB, Sreenatha M, Manjunath S, et al. Aircraft Gear-
box Fault Diagnosis System: An Approach based on Deep Learning
Techniques[J]. Journal of Intelligent Systems, 2020, 30(1):258–272.
Weijia, Liu, Junpeng, et al. Availability Assessment Based Case-
Sensitive Power System Restoration Strategy[J]. IEEE Transactions on
Power Systems, 2019, 35(2):1432–1445.
P Sobanski, M Kaminski. Application of artificial neural networks for
transistor open-circuit fault diagnosis in three-phase rectifiers[J]. IET
Power Electronics, 2019, 12(9):2189–2200.
Givi H, Farjah E, Ghanbari T. A Comprehensive Monitoring System for
Online Fault Diagnosis and Aging Detection of Non-Isolated DC–DC
Converters’ Components[J]. IEEE Transactions on Power Electronics,
, 34(7):6858–6875.
Kumar GK, Elangovan D. Review on fault-diagnosis and fault-tolerance
for DC–DC converters[J]. IET Power Electronics, 2020, 13(1):1–13.
Costamagna P, Giorgi A D, Moser G, et al. Data-driven fault diagnosis
in SOFC-based power plants under off-design operating conditions[J].
International Journal of Hydrogen Energy, 2019, 44(54): 29002–29006.
Han X, Yue L, Dong Y, et al. Efficient hybrid algorithm based on moth
search and fireworks algorithm for solving numerical and constrained
Hierarchical Information Fault Diagnosis Method 283
engineering optimization problems[J]. The Journal of Supercomputing,
, 76(12):9404–9429.
Cao MN, Qiu YN, Feng YH, et al. Fault Diagnosis of a Wind Generator
Based on Equivalent Thermal Network Method[J]. Kung Cheng Je Wu
Li Hsueh Pao/Journal of Engineering Thermophysics, 2019, 40(2):306–
Wu S, Zeng F, Tang J, et al. Triangle Fault Diagnosis Method for SF[J].
IEEE Transactions on Power Delivery, 2019, 34(4):1470–1477.
D Yang, Wang Y, Chen Z. Robust fault diagnosis and fault tolerant
control for PEMFC system based on an augmented LPV observer[J].
International Journal of Hydrogen Energy, 2020, 45(24):13508–13522.
Zhuo S, Xu L, Gaillard A, et al. Robust Open-Circuit Fault Diagnosis
of Multi-Phase Floating Interleaved DC–DC Boost Converter Based
on Sliding Mode Observer[J]. IEEE Transactions on Transportation
Electrification, 2019, 5(3):638–649.
H Esponda, Vazquez E, Andrade MA, et al. Extended second central
moment approach to detect turn-to-turn faults in power transformers[J].
IET Electric Power Applications, 2019, 13(6):773–782.
Anand A, Akhil VB, Raj N, et al. A Generalized Switch Fault Diag-
nosis for Cascaded H-Bridge Multilevel Inverters Using Mean Volt-
age Prediction[J]. IEEE Transactions on Industry Applications, 2020,
(2):1563–1574
