Diagnosis of Open Circuit Faults by Monitoring Phase Currents of Voltage Source Inverter

Öz

With the widespread use of voltage source inverters in industrial applications, the identification of faults has become an important research topic. In this study, 24 different single and multiple open switch circuit faults in a three-phase two-level inverter were examined, and the branch where the fault was located and the faulty switch were identified. The load dependency problem was eliminated by using the average, rms (effective) values of the output phase currents of the inverter simulated in the Matlab/Simulink environment as well as the average/rms ratios. In the study, four different classification models such as Support Vector Machines (SVM), K-nearest Neighbors (KNN), Artificial Neural Network (ANN) and Long Short Term Memory (LSTM) were used and the performance of each model was evaluated separately. From the simulation results, the prediction success of the proposed fault diagnosis and classification techniques in single, double and triple switch fault cases was achieved with high accuracy.

Anahtar Kelimeler

• Open circuit fault
• machine learning
• deep learning
• phase currents

Gerilim Kaynaklı Eviricinin Faz Akımlarının İzlenmesiyle Açık Devre Arızalarının Teşhisi

Öz

Gerilim kaynaklı eviricilerin endüstriyel uygulamalarda yaygın olarak kullanılmasıyla, meydana gelen arızaların tanımlanması önemli bir araştırma konusu haline gelmiştir. Bu çalışmada, üç-fazlı iki-seviyeli eviricideki 24 farklı tekli ve çoklu açık anahtar devre arızaları incelenmiş, arızanın bulunduğu kol ve arızalı anahtarın tespiti yapılmıştır. Matlab/Simulink ortamında benzetimi yapılan eviricinin çıkış faz akımlarının ortalama, rms (etkin) değerlerinin yanı sıra ortalama/rms oranları da kullanılarak yük bağımlılığı problemi giderilmiştir. Çalışmada, destek vektör makineleri (SVM: Support Vector Machines), K-en yakın komşular (KNN: K-Nearest Neighbours), yapay sinir ağı (ANN: Artificial Neural Network) ve uzun kısa süreli bellek (LSTM: Long Short Term Memory) gibi dört farklı sınıflandırma modeli kullanılmış olup her bir modelin başarınımı ayrı ayrı değerlendirilmiştir. Benzetim sonuçlarından, önerilen arıza teşhis ve sınıflandırma tekniklerinin tekli, çiftli ve üçlü anahtar arıza durumlarındaki tahmin başarısı yüksek doğrulukla sağlanılmıştır.

Anahtar Kelimeler

• Açık devre arızası
• makine öğrenmesi
• derin öğrenme
• faz akımları

Kaynakça

1. Kharjule S. Voltage source inverter. In: 2015 International Conference on Energy Systems and Applications; 2015; Pune, India. pp. 537-542.
2. Xia Y, Xu Y. A transferrable data-driven method for IGBT open-circuit fault diagnosis in three-phase inverters. IEEE Trans Power Electron 2021; 36(12): 13478-13488.
3. Hang C, Ying L, Shu N. Transistor open-circuit fault diagnosis in two-level three-phase inverter based on similarity measurement. Microelectron Reliab 2018; 91: 291-297.
4. Ibem CN, Farrag ME, Aboushady AA, Dabour SM. Multiple open switch fault diagnosis of three phase voltage source inverter using ensemble bagged tree machine learning technique. IEEE Access 2023; 11: 85865-85877.
5. Deng X, Wan C, Jiang L, Gao G, Huang Y. Open-switch fault diagnosis of three-phase PWM converter systems for magnet power supply on EAST. IEEE Trans Power Electron 2023; 38(1): 1064-1078.
6. Prejbeanu RG. A sensor-based system for fault detection and prediction for EV multi-level converters. Sensors 2023; 23(9): 4205.
7. Achintya P, Kumar Sahu L. Open circuit switch fault detection in multilevel inverter topology using machine learning techniques. In: 2020 IEEE 9th Power India International Conference (PIICON); 2020; Sonepat, India. pp. 1-6.
8. Dabour SM, Masoud MI. Open-circuit fault detection of five-phase voltage source inverters. In: 2015 IEEE 8th GCC Conference & Exhibition; 2015; Muscat, Oman. pp. 1-6.

9. Kumar MD, Kodad SF, Sarvesh B. Simplified fault detection algorithm for voltage source fed induction motor. Mater Today Proc 2018; 5(1): 1401-1410.
10. Ibem CN, Farrag ME, Aboushady AA. Open circuit fault diagnosis technique for inverter switches and gate drive malfunction. In: 2023 58th International Universities Power Engineering Conference (UPEC); 2023; Dublin, Ireland. pp. 1-6.
11. Zdiri MA, Bouzidi B, Abdallah HH. Improved diagnosis method for VSI fed IM drives under open IGBT faults. In: 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD); 2018; Yasmine Hammamet, Tunisia. pp. 905-910.
12. Gao Z, Cecati C, Ding SX. A survey of fault diagnosis and fault-tolerant techniques-Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 2015; 62(6): 3757-3767.
13. Malik A, Haque A, Kurukuru VSB, Khan MA, Blaabjerg F. Overview of fault detection approaches for grid connected photovoltaic inverters. e-Prime-Advances in Electrical Engineering, Electronics and Energy 2022; 2:100035.
14. Zhuo S, Gaillard A, Xu L, Liu C, Paire D, Gao F. An observer-based switch open-circuit fault diagnosis of DC-DC converter for fuel cell application. IEEE Trans Ind Appl 2020; 56(3): 3159-3167.
15. Wassinger N, Penovi E, Retegui RG, Maestri S. Open-circuit fault identification method for interleaved converters based on time-domain analysis of the state observer residual. IEEE Trans Power Electron 2019; 34(4): 3740-3749.
16. Berriri H, Naouar MW, Slama-Belkhodja I. Easy and fast sensor fault detection and isolation algorithm for electrical drives. IEEE Trans Power Electron 2012; 27(2): 490-499.
17. Zhou D, Yang S, Tang Y. A voltage-based open-circuit fault detection and isolation approach for modular multilevel converters with model-predictive control. IEEE Trans Power Electron 2018; 33(11): 9866-9874.
18. Xie D, Ge X. A state estimator-based approach for open-circuit fault diagnosis in single-phase cascaded H-bridge rectifiers. IEEE Trans Ind Appl 2019; 55(2): pp. 1608-1618.
19. Poon J, Jain P, Konstantakopoulos IC, Spanos C, Panda SK, Sanders SR. Model-based fault detection and identification for switching power converters. IEEE Trans Power Electron 2017; 32(2): 1419-1430.
20. Poon J, Jain P, Spanos C, Panda SK, Sanders SR. Fault prognosis for power electronics systems using adaptive parameter identification. IEEE Trans Ind Appl 2017; 53(3): 2862-2870.
21. Yan H, Peng Y, Shang W, Kong D. Open-circuit fault diagnosis in voltage source inverter for motor drive by using deep neural network. Eng Appl Artif Intell 2023; 120; 105866.
22. Shahbazi M, Poure P, Saadate S, Zolghadri MR. FPGA-based fast detection with reduced sensor count for a fault-tolerant three-phase converter. IEEE Trans Industr Inform 2013; 9(3): 1343-1350.
23. Freire NMA, Estima JO, Cardoso AJM. A voltage-based approach without extra hardware for open-circuit fault diagnosis in closed-loop PWM AC regenerative drives. IEEE Trans Ind Electron 2014; 61(9): 4960-4970.
24. Mendes AMS, Cardoso AJM. Voltage source inverter fault diagnosis in variable speed AC drives, by the average current Park's vector approach. IEEE International Electric Machines and Drives Conference; 1999; Seattle, WA, USA. pp.704-706.
25. Im WS, Kim JS, Kim JM, Lee DC, Lee KB. Diagnosis methods for IGBT open switch fault applied to 3-phase AC/DC PWM converter. Journal of Power Electronics 2012; 12(1):120-127.
26. Im WS, Kim JM, Lee DC, Lee KB. Diagnosis and fault-tolerant control of three-phase AC-DC PWM converter systems. IEEE Trans Ind Appl 2013; 49(4): 1539-1547.
27. Freire NMA, Estima JO. Cardoso AJM. Open-circuit fault diagnosis in PMSG drives for wind turbine applications. IEEE Trans Ind Electron 2013; 60(9): 3957-3967.
28. Peuget R, Courtine S, Rognon JP. Fault detection and isolation on a PWM inverter by knowledge-based model. IEEE Trans Ind Appl 1998; 34(6): 1318-1326.
29. Trabelsi M, Boussak M, Gossa M. Multiple IGBTs open circuit faults diagnosis in voltage source inverter fed induction motor using modified slope method. The XIX International Conference on Electrical Machines - ICEM 2010; 2010; Rome, Italy. pp. 1-6.
30. Shi T, He Y, Wang T, Tong J, Li B, Deng F. An improved open-switch fault diagnosis technique of a PWM voltage source rectifier based on current distortion. IEEE Trans Power Electron 2019; 3(12): 12212-12225.
31. Cai B, Zhao Y, Liu H, Xie M. A data-driven fault diagnosis methodology in three-phase inverters for PMSM drive systems. IEEE Trans Power Electron 2017; 32(7): 5590-5600.
32. Li Z, Gao Y, Zhang X, Wang B, Ma H. A model-data-hybrid-driven diagnosis method for open-switch faults in power converters. IEEE Trans Power Electron 2020; 36(5): 4965-4970.
33. Xia Y, Xu Y, Gou B. A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification. IEEE Trans Industr Inform 2020;16(8): 5223-5233.
34. Moosavi SS, Djerdir A, Ait‐Amirat Y, Khaburi DA, N'Diaye A. Artificial neural network‐based fault diagnosis in the AC-DC converter of the power supply of series hybrid electric vehicle. IET Electrical Systems in Transportation 2016; 6(2): 96-106.
35. Gou B, Xu Y, Xia Y, Deng Q, Ge X. An online data-driven method for simultaneous diagnosis of IGBT and current sensor fault of three-phase PWM inverter in induction motor drives. IEEE Trans on Power Electron 2020; 35(12): 13281-13294.
36. Wang B, Cai J, Du X, Zhou L. Review of power semiconductor device reliability for power converters. CPSS Transactions on Power Electronics and Applications 2017; 2(2): 101-117.
37. Liang W, Wang J, Luk PCK, Fang W, Fei W. Analytical modeling of current harmonic components in PMSM drive with voltage-source inverter by SVPWM technique. IEEE Transactions on Energy Conversion 201; 29(3): 673-680.
38. Rocabert J, Luna A, Blaabjerg F, Rodríguez P. Control of power converters in AC microgrids. IEEE Trans Power Electron 2012; 27(11): 4734-4749.
39. Khelif AM, Bendiabdellah A, Cherif BDE. A combined RMS-mean value approach for an inverter open-circuit fault detection. Periodica Polytechnica Electrical Engineering and Computer Science 2019; 63(3): 169-177.
40. Caseiro JAA, Mendes AMS, Marques Cardoso AJ. Fault diagnosis on a PWM rectifier AC drive system with fault tolerance using the average current Park's vector approach. In: 2009 IEEE International Electric Machines and Drives Conference; 2009; Maimi, FL, USA. pp. 695-701.
41. Harman G. Destek vektör makineleri ve naive bayes sınıflandırma algoritmalarını kullanarak diabetes mellitus tahmini. Avrupa Bilim ve Teknoloji Dergisi 2021; 32: 7-13.
42. Chandra MA, Bedi SS. Survey on SVM and their application in image classification. Int J Inf Technol 2021; 13(59): 1-11.
43. Metlek S, Kayaalp K. Derin öğrenme ve destek vektör makineleri ile görüntüden cinsiyet tahmini. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 2020; 8(3): 2208-2228.
44. Gong W, Chen H, Zhang Z, Zhang M, Gao H. A data-driven-based fault diagnosis approach for electrical power DC-DC inverter by using modified convolutional neural network with global average pooling and 2-D feature image. IEEE Access 2020; 8: 73677-73697.
45. Zhang S, Li J. KNN classification with one-step computation. IEEE Trans Knowl Data Eng 2021; 35(3): 2711-2723.
46. Zhang S. Challenges in KNN classification. IEEE Trans Knowl Data Eng 2022; 34(10): 4663-4675.
47. Zhang S. Cost-sensitive KNN classification. Neurocomputing 2020; 391: 234-242.
48. Coşar M, Deniz E. Makine öğrenimi algoritmaları kullanarak kalp hastalıklarının tespit edilmesi. Avrupa Bilim ve Teknoloji Dergisi 2021; 28: 1112-1116.
49. Pham BT, Nguyen MD, Nguyen-Thoi T, Ho LS, Koopialipoor M, Quoc NK, Armaghani DJ, Van Le H. A novel approach for classification of soils based on laboratory tests using adaboost, tree and ANN modeling. Transportation Geotechnics 2021; 27: 100508.
50. Bhagya Raj GVS, Dash KK. Comprehensive study on applications of artificial neural network in food process modeling. Crit Rev Food Sci Nutr 2022; 62(10): 2756-2783.
51. Konakoğlu B. Çok katmanlı algılayıcı yapay sinir ağı ile jeodezik elipsoidal koordinatların (φ, λ, h) 3 boyutlu global kartezyen koordinatlara (x, y, z) dönüşümü. Gümüşhane Üniversitesi Fen Bilimleri Dergisi 2020; 10(3): 702-710.
52. Kuşkapan E, Çodur MK, Çodur MY. Türkiye’deki demiryolu enerji tüketiminin yapay sinir ağları ile tahmin edilmesi. Konya Mühendislik Bilimleri Dergisi 2022; 10(1): 72-84.
53. Guillod T, Papamanolis P, Kolar JW. Artificial neural network (ANN) based fast and accurate ınductor modeling and design. IEEE Open J Power Electron 2020; 1: 284-299.
54. Rajendran GB, Kumarasamy UM, Zarro C, Divakarachari PB, Ullo SL. Land-use and land-cover classification using a human group-based particle swarm optimization algorithm with an LSTM classifier on hybrid pre-processing remote-sensing images. Remote Sens 2020; 12(24): 4135.
55. Kłosowski G, Rymarczyk T, Niderla K, Rzemieniak M, Dmowski A, Maj M. Comparison of machine learning methods for image reconstruction using the LSTM classifier in industrial electrical tomography. Energies 2021; 14(21): 7269.
56. Gür YE. Stock price forecasting using machine learning and deep learning algorithms: A case study for the aviation industry. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2024; 36(1): 25-34.
57. Aslan E. LSTM-ESA hibrit modeli ile MR görüntülerinden beyin tümörünün sınıflandırılması. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 2024; 11(22): 63-81.
58. Bouchiba N, Kaddouri A. Application of machine learning algorithms for power systems fault detection. In:2021 9th International Conference on Systems and Control (ICSC); 2021; Caen, France. pp. 127-132.
59. Sağıroğlu S, Arslan Tuncer S, Karahan B, Özercan İH. Evrişimsel sinir ağları kullanarak ÇKA sınıflandırıcısı ile mide displazisinin tespiti. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2024; 36(1): 291–300.