Hierarchical Information Fault Diagnosis Method for Power System Based on Fireworks Algorithm
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 recording 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.
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 fireworks algorithm[J]. The Canadian Journal of Chemical Engineering, 2019, 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 Intelligence, 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, 34(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, 19(9):2046.
Mallikarjuna PB, Sreenatha M, Manjunath S, et al. Aircraft Gearbox 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, 2019, 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 engineering optimization problems[J]. The Journal of Supercomputing, 2020, 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):306–313.
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 Diagnosis for Cascaded H-Bridge Multilevel Inverters Using Mean Voltage Prediction[J]. IEEE Transactions on Industry Applications, 2020, 56(2):1563–1574.