Abstract
To realize real-time fault detection in power devices and enhance reliability of inverter circuits, this paper proposes a detection method based on Park’s transform algorithm and neural network. Park’s transform is applied to obtain the three-phase current base wave amplitude as the characteristic variable for fault detection. Faulty switch devices can be located using a backpropagation neural network in combination with simple logic analyses. The simulation results verify the effectiveness and the feasibility of the proposed method.
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Cui B, Ren Z (2006) Fault detection and isolation of inverter based on FFT and neural network. Trans China Electrotech Soc 21(7):37–43 (in Chinese)
An Q, Sun L, Sun L et al (2011) Recent developments of fault diagnosis methods for switches in three-phase inverters. Trans China Electrotech Soc 26(4):135–144 (in Chinese)
Mohsenzade S, Zarghany M, Kaboli S (2018) A series stacked IGBT switch with robustness against short-circuit fault for pulsed power applications. IEEE Trans Power Electron 33(5):3779–3790
Mai-Khanh NN, Nakajima S, Iizuka T et al (2017) Experimental demonstration of non-destructive detection of IGBT fault positions by magnetic sensor. IEEE Sensors Appl Symp 1:1–4
Hoevenaars AH, Evans IC, Desai B (2013) Preventing AC drive failures due to commutation notches on a drilling rig. IEEE Trans Ind Appl 49(3):1215–1220
Spee R, Wallace AK (1990) Remedial strategies for brushless DC drive failures. IEEE Trans Ind Appl 26(2):259–266
Rothenhagen K, Fuchs FW (2004) Performance of diagnosis methods for IGBT open circuit faults in voltage source active rectifiers. In: Proceedings of the 35th annual IEEE power electronics specialists conference, vol 1, pp 4348–4354
Hu R, Wang J, Sen B et al (2017) PWM ripple currents based turn fault detection for multiphase permanent magnet machines. IEEE Trans Ind Appl 53(3):2740–2751
Park JH, Kim DH, Kim SS et al (2004) C-ANFIS based fault diagnosis for voltage-fed PWM motor drive systems. Proc IEEE Ann Meet Fuzzy Inf 1:379–383
Zhao H, Cheng L (2018) Open-switch fault-diagnostic method for back-to-back converters of a doubly fed wind power generation system. IEEE Trans Power Electron 33(4):3452–3461
Karimi S, Gaillard A, Poure P et al (2008) FPGA-based real-time power converter failure diagnosis for wind energy conversion systems. IEEE Trans Ind Electron 55(12):4299–4308
An Q, Sun L, Zhao K et al (2010) Diagnosis method for inverter open-circuit fault based on switching function model. Proc CSEE 30(6):1–6
Zhao GL, Liu BZ, Xiao XN et al (2004) Application of improved d-q transform without time delay in dynamic voltage disturbance identification. Power Syst Technol 28(7):53–57
Lopes FV, Fernandes D, Neves WL (2013) A traveling-wave detection method based on Park’s transformation for fault locators. IEEE Trans Power Deliv 28(3):1626–1634
Arashloo RS, Romeral M, Salehifar M (2013) A novel broken rotor bar fault detection method using park’s transform and wavelet decomposition. In: 9th IEEE international symposium on diagnostics for electric machines, vol 1, pp 412–419
Önel IY, Benbouzid ME (2008) Induction motor bearing failure detection and diagnosis: park and concordia transform approaches comparative study. IEEE/ASME Trans Mechatron 13(2):257–262
Houlian W, Gongbo Z (2018) State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network. IET Electr Power Appl 12(4):588–594
Shi D, Gao Y (2013) A new method for identifying electromagnetic radiation sources using backpropagation neural network. IEEE Trans Electromagn Compat 55(5):842–848
Sun Q, Wang Y, Jiang Y (2018) A novel fault diagnostic approach for DC–DC converters based on CSA-DBN. IEEE Access 6:6273–6285
Xiao P, Venayagamoorthy GK, Corzine KA et al (2010) Recurrent neural networks based impedance measurement technique for power electronic systems. IEEE Trans Power Electron 25(2):382–390
Dong JR, Zheng CY, Kan GY et al (2015) Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting. Neural Comput Appl 26(3):603–611
Das P, Banerjee I (2011) An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector. Neural Comput Appl 20(2):287–296
Lin YC, Chen DD, Chen MS et al (2018) A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine. Neural Comput Appl 29(9):585–596
Ren T, Liu S, Yan G, Mu H (2016) Temperature prediction of the molten salt collector tube using BP neural network. IET Renew Power Gener 10(2):212–220
Liu S, Hou Z, Yin C (2016) Data-driven modeling for UGI gasification processes via an enhanced genetic bp neural network with link switches. IEEE Trans Neural Netw Learn Syst 27(12):2718–2729
Chen M, Xu D, Zhang T et al (2018) A novel DC current injection suppression method for three-phase grid-connected inverter without the isolation transformer. IEEE Trans Ind Electron 65(11):8656–8666
Huang J, Liu Q, Wang X et al (2018) A carrier-based modulation scheme to reduce the third harmonic component of common-mode voltage in a three-phase inverter under high DC voltage utilization. IEEE Trans Ind Electron 65(3):1931–1940
Arora TG, Renge MM, Aware MV(2017) Effects of switching frequency and motor speed on common mode voltage, common mode current and shaft voltage in PWM inverter-fed induction motors. In: 12th IEEE conference on industrial electronics and applications, vol 1, pp 583–588
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The work is supported by Natural Science Foundation of Jiangsu Province (No. BK20170841).
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Ji, Z., Liu, W. Open-circuit fault detection for three-phase inverter based on backpropagation neural network. Neural Comput & Applic 31, 4665–4674 (2019). https://doi.org/10.1007/s00521-018-3663-2
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DOI: https://doi.org/10.1007/s00521-018-3663-2