پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 587 |
| تعداد مقالات | 8,742 |
| تعداد مشاهده مقاله | 66,611,131 |
| تعداد دریافت فایل اصل مقاله | 7,174,130 |
Stator Turn-to-Turn Fault Estimation of Induction Motor by Using Probabilistic Neural Network | ||
| Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 1، شماره 3 - شماره پیاپی 5، بهمن 2021، صفحه 35-40 اصل مقاله (916.64 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2022.24809.1073 | ||
| نویسندگان | ||
| Hamed Babanezhad* 1؛ Hamid Yaghobi2؛ Mostafa Hamidi1 | ||
| 1Faculty of Electrical Engineering, Islamic Azad University, Sari Branch, Iran | ||
| 2Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 26 مهر 1400، تاریخ بازنگری: 19 آذر 1400، تاریخ پذیرش: 16 اسفند 1400 | ||
| چکیده | ||
| Induction machines are extensively used in industry due to the wide demand and diverse applications. Managing dealing with various faults, accurately detecting the fault and its severity as one of the biggest challenges will have a significant impact on the induction machine health and the quality of system operation. Ignoring the faults will cause irreparable damage to the electrical machine and then to the industrial complex. Knowing about exact fault conditions is the most basic issue in dealing with fault management. In this paper, turn to turn fault as one of the major problems of induction machines is discussed. For this purpose first, the fault is evaluated by negative sequences current, and second, a mechanism is used to distinguish between the source imbalance fault and the turn-to-turn fault. With the help of the information obtained from the faulty machine and two layers of the probabilistic neural network, the number of the turn-to-turn fault will be estimated. The simulation was performed under normal conditions as well as under fault conditions for a specified number of turn-to-turn faults. This method is tested for non-training data with different common ranges and a number of turn-to-turn faults. Neural network output results are compared with the simulation in Matlab, which shows the correct training and high accuracy of the proposed method to detect the number of stator faults. | ||
| کلیدواژه‌ها | ||
| Short circuit fault؛ negative sequence current؛ probabilistic neural network؛ turn to turn estimation؛ inter-turn fault | ||
| مراجع | ||
|
[1] L. Hou and N. W. Bergmann, “Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 61, no. 10, pp. 2787-2798, 2012.
[2] M. Seera, C. P. Lim, D. Ishak, and H. Singh, “Fault Detection and Diagnosis of Induction Motors Using Motor Current Signature Analysis and a Hybrid FMM–CART Model,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 1, pp. 97-108, 2012.
[3] G. H. Bazan, P. R. Scalassara, W. Endo, A. Goedtel, R. H. C. Palácios, and W. F. Godoy, “Stator Short-Circuit Diagnosis in Induction Motors Using Mutual Information and Intelligent Systems,” IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3237-3246, 2019.
[4] “Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I,” IEEE Transactions on Industry Applications, vol. IA-21, no. 4, pp. 853-864, 1985.
[5] P. F. Albrecht, J. C. Appiarius, R. M. McCoy, E. L. Owen, and D. K. Sharma, “Assessment of the Reliability of Motors in Utility Applications - Updated,” IEEE Transactions on Energy Conversion, vol. EC-1, no. 1, pp. 39-46, 1986.
[6] A. H. Bonnett and G. C. Soukup, “Analysis of rotor failures in squirrel-cage induction motors,” IEEE Transactions on Industry Applications, vol. 24, no. 6, pp. 1124-1130, 1988.
[7] O. V. Thorsen and M. Dalva, “Failure identification and analysis for high voltage induction motors in petrochemical industry,” in Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242), vol. 1, pp. 291-298 , 1998.
[8] M. A. Awadallah and M. M. Morcos, “Application of AI tools in fault diagnosis of electrical machines and drives-an overview,” IEEE Transactions on Energy Conversion, vol. 18, no. 2, pp. 245-251, 2003.
[9] M. Negrea, P. Jover, and A. Arkkio, “Electromagnetic flux-based condition monitoring for electrical machines,” in 2005 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 1-6,2005.
[10] R. M. Tallam et al., “A Survey of Methods for Detection of Stator-Related Faults in Induction Machines,” IEEE Transactions on Industry Applications, vol. 43, no. 4, pp. 920-933, 2007.
[11] H. Yaghobi, K.Ansari and H.Rajabi mashhadi, “Analysis of Magnetic Flux Linkage Distribution in Salient-Pole Synchronous Generator with Different Kinds of Inter-Turn Winding Faults,” Iranian Journal of Electrical & Electronic Engineering, Vol., No. 4, pp.260-272, Dec. 2011.
[12] H. Yaghobi, H.R.Mashhadi and K.Ansari,“Artificial neural network approach for locating internal faults in salient-pole synchronous generato “ELSEVIER, Expert Systems with Applications, Vol. 38, pp.13328–13341, 2011.
[13] Y. ChiaChou, R. J. Povinelli, B. Mirafzal, and N. A. O. Demerdash, “Diagnosis of stator winding inter-turn shorts in induction motors fed by PWM-inverter drive systems using a time-series data mining technique,” in 2004 International Conference on Power System Technology, PowerCon, Vol 1, pp. 891-896,2004.
[14] W. Long, L. Bin, H. Xianghui, T. G. Habetler, and R. G. Harley, “Improved online condition monitoring using static eccentricity-induced negative sequence current information in induction machines,” in 31st Annual Conference of IEEE Industrial Electronics Society, vol IECON, p. 6 pp,2005.
[15] L. Tong and C. Huahua, “Detection of stator turn fault in induction motors using the extension of multiple reference frames theory,” in 31st Annual Conference of IEEE Industrial Electronics Society,vol IECON , p. 4 pp,2005.
[16] I.F. El-Arabawy, M.I. Masoud, A.E.Mokhtari, “Stator Inter-turn Faults Detection and Localization using stator currents and concordia pattern,”Neural Network Applications," 5th International Conference-Workshop Compatibility in Power Electronics, CPE 2007.
[17] H.Yaghobi,”Stator Turn-to-Turn Fault Detection of Induction Motor by Non-Invasive Method Using Generalized Regression Neural Network,” Iranian Journal of Electrical & Electronic Engineering, Vol. 13, No. 1, March 2017.
[18] G. Bucci, F. Ciancetta, and E. Fiorucci, “Apparatus for Online Continuous Diagnosis of Induction Motors Based on the SFRA Technique,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4134-4144, 2020.
[19] R. Sadeghi, H. Samet, and T. Ghanbari, “Detection of Stator Short-Circuit Faults in Induction Motors Using the Concept of Instantaneous Frequency,” IEEE Transactions on Industrial Informatics, vol. 15, no. 8, pp. 4506-4515, 2019.
[20] L. S. Maraaba, S. Twaha, A. Memon, and Z. Al-Hamouz, “Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals,” Symmetry, vol. 12, no. 8, 2020.
[21] E. Solodkiy, D. Dadenkov, and S. Salnikov, “Detection Of Stator Inter-turn Short Circuit In Three-Phase Induction Motor Using Current Coordinate Transformation,” in 2019 26th International Workshop on Electric Drives: Improvement in Efficiency of Electric Drives (IWED), , pp. 1-4,2019.
[22] A. V. B, M. F. Alam, G. Jagadanand, and A. A. K, “Stator Inter Turn fault diagnosis by High-Frequency Modeling of Inverter Fed Induction Motor,”in 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), pp. 1-5,2020.
[23] H. Chen, G. Han, W. Yan, S. Lu, and Z. Chen, “Modeling of a Switched Reluctance Motor Under Stator Winding Fault Condition,” IEEE Transactions on Applied Superconductivity, vol. 26, no. 4, pp. 1-6, 2016.
[24] N. R. Devi, D. V. S. S. S. Sarma, and P. V. R. Rao, “Diagnosis and classification of stator winding insulation faults on a three-phase induction motor using wavelet and MNN,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 5, pp. 2543-2555, 2016.
[25] P. F. Albrecht, J. C. Appiarius, R. M. McCoy, E. L. Owen, and D. K. Sharma, “Assessment of the reliability of motors in utility applications - updated,” IEEE Power Engineering Review, vol. PER-6, no. 3, pp. 31-32, 1986.
[26] V. N. G. a. S. V, “Fault Diagnosis of Three Phase Induction Motor Using Neural Network Techniques,” IEEE, Second International Conference on Emerging Trends in Engineering & Technology, pp. 922-928 ,2009.
[27] L. Souad, B. Azzedine, C. B. D. Eddine, B. Boualem, M. Samir, and M. Youcef, “Induction machine rotor and stator faults detection by applying the DTW and N-F network,” in 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 431-436,2018
[28] R. R. Kumar et al, “Induction Machine Stator Fault Tracking Using the Growing Curvilinear Component Analysis,” IEEE Access, vol. 9, pp. 2201-2212, 2021.
[29] L. S. Maraaba, Z. M. Al-Hamouz, and M. A. Abido, “An Accurate Tool for Detecting Stator Inter-Turn Fault in LSPMSM,” IEEE Access, vol. 7, pp. 88622-88634, 2019.
[30] M. B. K. Bouzid and G. Champenois, “Neural network based method for the automatic detection of the stator faults of the induction motor,” in 2013 International Conference on Electrical Engineering and Software Applications, pp. 1-7,2013.
[31] G. Sheng-wei, W. You-Hua, C. Yan, and Z. Chuang, “Design and Simulation of Artificial-Neural-Network-Based Rotor Resistance Observer of Induction Motors,” in Second International Conference on Intelligent Networks and Intelligent Systems, pp. 593-596,2009.
[32] L. S. Maraaba, A. S. Milhem, I. A. Nemer, H. Al-Duwaish, and M. A. Abido, “Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs,” IEEE Access, vol. 8, pp. 81960-81970, 2020.
[33] M. B. K. Bouzid, G. Champenois, N. M. Bellaaj, L. Signac, and K. Jelassi, “An Effective Neural Approach for the Automatic Location of Stator Interturn Faults in Induction Motor,” IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4277-4289, 2008.
[34] V. N. Ghate and S. V. Dudul, “Fault Diagnosis of Three Phase Induction Motor Using Neural Network Techniques,” in Second International Conference on Emerging Trends in Engineering & Technology, pp. 922-928,2009.
[35] R. N. Dash, B. Subudhi, and S. Das, “A comparison between MLP NN and RBF NN techniques for the detection of stator inter-turn fault of an induction motor,”in International Conference on Industrial Electronics, Control and Robotics, pp. 251-256,2010.
[36] E. M. T. Eldin, H. R. Emara, E. M. Aboul-Zahab, and S. S. Refaat, “Monitoring and Diagnosis of External Faults in Three Phase Induction Motors Using Artificial Neural Network,” in IEEE Power Engineering Society General Meeting, pp. 1-7,2007.
[37] M. Arkan, D. Kostic-Perovic, and P. J. Unsworth, “Modelling and simulation of induction motors with inter-turn faults for diagnostics,” Electric Power Systems Research, vol. 75, no. 1, pp. 57-66, 2005/07/01/ 2005. | ||
|
آمار تعداد مشاهده مقاله: 463 تعداد دریافت فایل اصل مقاله: 250 |
||