Abstract
In this paper, an unsupervised automatic method based on a current signature neural network (NN) is presented to on-line diagnose stator fault without the inspection of any supervisor or technician. To extract the fault regime, the knowledge of current signature will not be enough; therefore, mathematical model, numerical analysis, as well as artificial intelligence (AI) are taken into account to extract the exact unbalanced voltage stator fault. Analytical expressions are derived for a stator conductor segment in order to find out the conductors that are responsible for the generation of magnetomotive force (MMF). A test rig is designed using three-phase induction motor, two-axis PASPORT sensor, PC, and PASCO interface to compute the effect of MMF at the stator side through a new series of harmonics which are helpful to tackle the scrupulous effect of an unbalanced voltage at the incipient stage. Further, an unsupervised NN has been introduced that endeavors the principal components of the new series of harmonics. The statistical parameters of a new series of harmonics are contemplated as input features for NN that not only diagnose unbalanced voltage but also identify the degree of unbalanced voltage through feed-forward multilayer perceptron (MLP) trained by backpropagation. The validation and performance of proposed methods have been theoretically and experimentally analyzed on a custom-designed test rig under various levels of unbalanced voltage. Moreover, the NN classification method shows higher accuracy with enough robustness to various levels of unbalanced voltage, which states that the proposed method is suitable for the real-world applications.
Similar content being viewed by others
References
Bollen MH (2000) Understanding power quality problems, vol 3. IEEE, New York
Von Jouanne A, Banerjee B (2001) Assessment of voltage unbalance. IEEE Transactions on Power Delivery 16:782–790
de Oliveira J and Neto L (2000) Induction motors thermal behaviour and life expectancy under non-ideal supply conditions. In: Harmonics and quality of power, 2000. Proceedings. Ninth International Conference on, pp. 899–904.
Donner G, Oakes BK, Evon ST (2003) Motor primer. III. IEEE Trans Ind Appl 39:1467–1474
Cusido J, Romeral L, Espinosa AG, Ortega JA, Riba Ruiz JR (2011) On-line fault detection method for induction machines based on signal convolution. European Transactions on Electrical Power 21:475–488
Xie Y, Gu C, Cao W (2013) Study of broken bars in three-phase squirrel-cage induction motors at standstill. International Transactions on Electrical Energy Systems 23:1124–1138
Eltabach M, Antoni J, Najjar M (2007) Quantitative analysis of noninvasive diagnostic procedures for induction motor drives. Mech Syst Signal Process 21:2838–2856
Chow TW, Hai S (2004) Induction machine fault diagnostic analysis with wavelet technique. IEEE Trans Ind Electron 51:558–565
Acosta G, Verucchi C, Gelso E (2006) A current monitoring system for diagnosing electrical failures in induction motors. Mech Syst Signal Process 20:953–965
Jee-Hoon J, Lee J-J, Bong-Hwan K (2006) Online diagnosis of induction motors using MCSA. Industrial Electronics, IEEE Transactions on 53:1842–1852
Isermann R (2005) Model-based fault-detection and diagnosis—status and applications. Annu Rev Control 29:71–85
Bachir S, Tnani S, Trigeassou JC, Champenois G (2006) Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. Industrial Electronics, IEEE Transactions on 53:963–973
Uraikul V, Chan CW, Tontiwachwuthikul P (2007) Artificial intelligence for monitoring and supervisory control of process systems. Eng Appl Artif Intell 20:115–131
Fernández XML, Coimbra A, Pinto J, Antunes C, and Donsion MP (1998) Thermal analysis of an induction motor fed by unbalanced power supply using a combined finite element-symmetrical components formulation. In: Power system technology, 1998. Proceedings. POWERCON’98. 1998 International Conference on, pp. 620–624.
Kersting W, Phillips W (1997) Phase frame analysis of the effects of voltage unbalance on induction machines. IEEE Trans Ind Appl 33:415–420
Wang Y-J (2001) Analysis of effects of three-phase voltage unbalance on induction motors with emphasis on the angle of the complex voltage unbalance factor. IEEE Transactions on Energy Conversion 16:270–275
Eftekhari M, Moallem M, Sadri S, and Shojaei A (2013) Review of induction motor testing and monitoring methods for inter-turn stator winding faults. In: 2013 21st Iranian Conference on Electrical Engineering (ICEE), pp. 1–6.
Joksimović GM, Penman J (2000) The detection of inter-turn short circuits in the stator windings of operating motors. Industrial Electronics, IEEE Transactions on 47:1078–1084
Nandi S (2006) Detection of stator faults in induction machines using residual saturation harmonics. IEEE Trans Ind Appl 42:1201–1208
Quispe EC and Lopez ID (2015) Effects of unbalanced voltages on the energy performance of three-phase induction motors. In: Power electronics and power quality applications (PEPQA), 2015 I.E. Workshop on, pp. 1–6. doi:10.1109/PEPQA.2015.7168237
Thomson WT, Fenger M (2001) Current signature analysis to detect induction motor faults. Industry Applications Magazine, IEEE 7:26–34
Lamim P, Pederiva R, and Brito J (2007) Detection of stator winding faults in induction machines using an internal flux sensor. In: Diagnostics for electric machines, power electronics and drives, 2007. SDEMPED 2007. IEEE International Symposium on, pp. 432–437.
Krishnamoorthy C, Rajeev S (1996) Artificial intelligence and expert systems for engineers, vol 11. CRC, Boca Raton
Mellit A, Kalogirou SA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34:574–632
Hwang H (1965) Unbalanced operations of AC machines. IEEE Transactions on Power Apparatus and Systems 84:1054–1066
Krause PC, Thomas C (1965) Simulation of symmetrical induction machinery. IEEE Transactions on Power Apparatus and Systems 84:1038–1053
Menghal P and Laxmi AJ (2014) Scalar control of an induction motor using artificial intelligent controller. In: Power, automation and communication (INPAC), 2014 International Conference on, pp. 60–65.
Eldin EMT, Emara HR, Aboul-Zahab EM, and Refaat SS (2007) Monitoring and diagnosis of external faults in three phase induction motors using artificial neural network. In: Power Engineering Society General Meeting, 2007. IEEE, pp. 1–7.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Sheikh, M.A., Nor, N.M., Ibrahim, T. et al. Unsupervised on-line method to diagnose unbalanced voltage in three-phase induction motor. Neural Comput & Applic 30, 3877–3892 (2018). https://doi.org/10.1007/s00521-017-2973-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-017-2973-0