Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Robust Fault Detection of Multilevel Inverter using Optimized Radial Basis Function based Artificial Neural Network in Renewable Energy Power Generation Application

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
T. G. Manjunath, Ashok Kusagur

T G Manjunath and Ashok Kusagur. Robust Fault Detection of Multilevel Inverter using Optimized Radial Basis Function based Artificial Neural Network in Renewable Energy Power Generation Application. International Journal of Computer Applications 180(48):8-15, June 2018. BibTeX

	author = {T. G. Manjunath and Ashok Kusagur},
	title = {Robust Fault Detection of Multilevel Inverter using Optimized Radial Basis Function based Artificial Neural Network in Renewable Energy Power Generation Application},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2018},
	volume = {180},
	number = {48},
	month = {Jun},
	year = {2018},
	issn = {0975-8887},
	pages = {8-15},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2018917231},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The Optimized Artificial Neural Network based fault detection of the multilevel inverters are of research interest since the HVDC system and Electrical Drives system has started using the multilevel inverters as the power processing units. While the power supply to the multilevel inverters is constant the results of the THD obtained would be stable. But the multilevel inverters that are used with Renewable Energy Resources as the power supply, the variation in the supply would largely affect the THD values thus a robust model for the fault detection of multilevel inverters which not only considers the THD but the positive sequence voltage, negative sequence voltage, zero sequence voltage and angle of the inverter output is considered. This paper is an attempt to develop a robust fault detection method using the optimized Artificial Neural Network (ANN) using the Radial Basis function. The objective function of this optimization algorithm is the minimization of the Mean Square Error while the ANN is getting trained. The optimization is carried out by the use of the weight and the bias value search in the search space, which would enhance the training of the ANN. Particle Swarm Optimization (PSO) and the Cuckoo Search Algorithm (CSA) are considered for the optimization algorithms. Matlab based implementation is carried out and the results are measured and tabulated. It is observed that the CSA algorithm is performing better while training the ANN for the fault detection.


  1. T.G.Manjunath, Dr.Ashok Kusagur “Fault Diagnosis and Reconfiguration of Multi level Inverter switch failure- A performance perspective ” , International Journal of Electrical and computer Engineering.,vol.6,no.6, Dec 2016,pp.2610-2620
  2. T.G.Manjunath, Dr.Ashok Kusagur, “ Multilevel Inverter Fault Diagnosis using optimized Radial Basis Neural Network –A Novel performance Enhancement ”, IEEE International Conference of Electrical, Electronics , communication, computer and optimization Techniques Systems (ICEECCOT), Jan. 2017.
  3. T.G.Manjunath, Dr.Ashok Kusagur, “Performance Evaluation of Modified Genetic Algorithm over Genetic Algorithm Implementation on Fault Diagnosis of Cascaded Multilevel Inverter”, IEEE 2015 International Conference on Conditioning Assessment Techniques in Electrical Systems (CATCON), CPRI, Bangalore, Dec. 2015.
  4. Wei Jiang et al,” Fault Detection and Remedy of Multilevel Inverter Based on BP Neural Network”, IEEE 2012,Power and Energy Engineering Conference (APPEEC), Shanghai, China, Mar. 2012.
  5. D. Kastha and B. K. Bose, “Investigation of fault modes of voltage-fedinverter system for induction motor drive,” IEEE Trans. Ind. Appl., vol.30, no. 4, Jul. 1994, pp. 1028–1038.
  6. Wenchao Song, Alex Q. Huang, “Fault Tolerant Design and Control Strategy for Cascaded H-bridge Multilevel Converter based STATCOM” IEEE Trans. Ind. Electron., vol.56,no.6,June 2009, pp.2275-2283.
  7. Pablo Lezana, Ricardo Aguilera, and José Rodríguez, “Fault Detection on Multicell Converter Based on Output Voltage Frequency Analysis”, IEEE Trans. Ind. Electron., vol.57,no.8,June 2009, pp.2700-2708.
  8. Frédéric Richardeau, Philippe Baudesson, Thierry A. Meynard,“Failures-Tolerance and Remedial Strategies of a PWM Multicell Inverter” IEEE Trans. Of Power Electronics., Vol.17 ,no.6,June 2000, pp.905-912.
  9. Ho-In Son , Tae-Jin Kim, Dae-Wook Kang and Dong-Seok Hyun “Fault Diagnosis and Neutral Point Voltage Control When The 3-Level Inverter Faults Occur”,2004 IEEE 35th Annual Power Electronics Specialists Conference (PESC),Nov 2004.
  10. Shengming Li, Longya Xu, “Strategies of Fault Tolerant Operation for Three-Level PWM Inverters” IEEE Trans. Power Electronics., Vol.21, no.4, Oct, 2005, pp.933-940.
  11. Kalpani Thantirige et al “An open-switch fault detection method for cascaded H-bridge multilevel inverter fed industrial drives”, 2016 IEEE 42nd Annual Conference of the Industrial Electronics Society (IECON), Florence, Italy ,2016.
  12. A. Anand, N. Raj, S. George and Jagadanand G, "Open switch fault detection in Cascaded H-Bridge Multilevel Inverter using normalized mean voltages”, 2016 IEEE 6th International Conference on Power Systems (ICPS), New Delhi, India, 2016, pp. 1-6.
  13. Hyun-Woo Simet al,”Detecting Open-Switch Faults: Using Asymmetric Zero-Voltage Switching States”, IEEE Industry Applications Magazine .,Vol. 22, no. 2, March-April 2016 , pp.27-37
  14. Tianzhen Wang et al, ”Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach” IEEE Trans. on Power Electronics., Vol. 30, no. 12, Dec. 2015, pp.7006 – 7018.
  15. M Shahbazi, M. R. Zolghadri, P. Poure, and S. Saadate, “Fast short circuit power switch fault detection in cascaded H-bridge multilevel converter,” in Proc. IEEE Power and Energy Society General Meeting (PES), July 2013, pp.1,5, 21-25.
  16. Houshang Salimian et al ”Fault-Tolerant Operation of Three-Phase Cascaded H-Bridge Converters Using an Auxiliary Module”, IEEE Trans. Ind. Electron., Vol. 64, no.2, Feb. 2017 
  17. B. Lu, S. K. Sharma, “A Literature Review of IGBT Fault Diagnostic and Protection Methods for Power Inverters,” IEEE Trans. Ind. Appl. ,vol. 45, no. 5, Sep./Oct. 2009, pp. 1770 – 1777.
  18. J. Druant, T. Vyncke, F. D. Belie, P. Sergeant, J. Melkebeek, “Adding Inverter Fault Detection to Model-Based Predictive Control for Flying-Capacitor Inverters,” IEEE Trans. Ind. Electron., vol. 60, no. 4, Aprl. 2015, pp.2054-2063.
  19. Rajabioun, Ramin. (2011). “Cuckoo Optimization Algorithm”. Applied Soft Computing. 11. 5508-5518. 10.1016/j.asoc.2011.05.008
  20. A Kusagur, SF Kodad, BVS Ram,” Modeling, design & simulation of an adaptive neuro-fuzzy inference system (ANFIS) for speed control of induction motor”, International Journal of Computer Applications 6 (12), 29-44,2010
  21. Ashok Kusagur, ”Design and implementation of Neuro fuzzy based speed control of induction motor drive by space vector pulse width modulation for voltage source inverters”, 2011.


Parameter Estimation, Artificial Neural Network, Particle Swarm Optimization, Cuckoo Search Algorithm and Radial Basis Function.