Total views : 223

A New Modified Artificial Neural Network Based MPPT Controller for the Improved Performance of an Asynchronous Motor Drive


  • Department of Electrical and Electronics Engineering, Vignan’s Foundation for Science Technology and Research University, Guntur, A.P, India


Objectives: To improve the performance of asynchronous motor drive, the proposed Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) controller has been used to fed the asynchronous motor drive with the obtained output voltage and currents of PV MPPT. Methods and Analysis: DC-DC boost converter and space vector modulation technique inverter are used to provide the required supply to the load. The proposed ANN based MPPT improves the system efficiency even at abnormal weather conditions. Findings: Solar energy is an important alternative out of the various renewable energy sources. On an average the sunshine hour in India is about 6hrs per day also the sun shines in India is about 9 months in a year. To generate electricity from the sun, the Solar Photo Voltaic (SPV) modules are used. The SPV comes in various power outputs to meet the load requirements. Maximization of power from a solar photo voltaic module is a special case to increase the efficiency of the system. The proposed artificial neural network (ANN) based MPPT controller is used to track the maximum power. DC-DC boost converter and space vector modulation technique inverter are used to provide the required supply to the load. The proposed ANN based MPPT improves the system efficiency even at abnormal weather conditions. Torque and current ripple contents have been reduced to a large extent with the help of proposed ANN based MPPT for an asynchronous motor drive. Also the better performance of an asynchronous motor drive is analyzed by the comparison of existed conventional and proposed MPPT controller using Matlab-simulation results. Improvement: Improvements in torque and current ripple and better speed performances are clearly analyzed with the help of practical validations. And also few of the observations are carried out and tabulated.


Artificial Neural Network (ANN), Asynchronous Motor (ASM) drive, DC-DC Boost Converter, Maximum Power Point Tracking (MPPT) Controller, Ripple, Solar Photovoltaic (SPV) System, Space Vector Modulation (SVM), Torque.

Full Text:

 |  (PDF views: 270)


  • Faranda R, Leva S. Energy Comparison of MPPT techniques for PV systems. WSEAS Transactions on Power Systems. 2008; 3(6):446–55.
  • Mellit A, Kalogirou SA. Artificial Intelligence Techniques for Photovoltaic Applications. A Review Progress in Energy and Combustion Science. 2008; 34(5):574–632.
  • Saadi A, Moussi A. Neural Network use in the MPPT of photovoltaic pumping system. Renewable and Sustainable Energy Reviews. 2003;39–45.
  • Kanth KM, Kishore RD. Implementation of MPPT techniques for a high step-up converter with voltage multiplier module based photovoltaic system. Indian Journal of Science and Technology. 2015; 8(23):1–6.
  • Kumar MY, Varma PS. A Comparative Study for Alleviation of Current Harmonics using PI/Fuzzy Controller based PV-APF System. Indian Journal of Science and Technology.2016; 9(23):1–7.
  • Pakkiraiah B, Sukumar GD. Research Survey on Various MPPT Performance Issues to Improve the Solar PV System Efficiency. Journal of Solar Energy. 2016; 1–20.
  • Jiang L, Nayanasri DR, Maskell DL, Vilathgamuwa DMA Simple and Efficient Hybrid Maximum Power Point tracking Method for PV Systems Under Partially Shaded Conditions. IEEE Industrial Electronics Society.2013;1513–8.
  • Alireza R, Maziar I, Majid G, Saeed V. Investigation of ANN-GA and Modified Perturb and Observe MPPT Techniques for Photovoltaic System in the Grid Connected Mode. Indian Journal of Science and Technology. 2015; 8(1):pp.87–95.
  • Lee HH, Phuong LM, Dzung PQ, Vu NTD, Khoa LD. The New Maximum Power point Tracking Algorithm using ANN-based Solar PV Systems. IEEE TENCON Conference on Korea. 2010.
  • Sheraz M, Abido MA. An Efficient MPPT Controller using Differential Evolution and Neural Network. IEEE Power and Energy (PECon) International Conference on Saudi Arabia. 2012.
  • Xu J, Shen A, Yang C, Rao W, Yang X. ANN based on Incremental Conductance Algorithm for MPP Tracker.IEEE 6th International Conference on Bio-Inspired Computing: Theories and Applications, China. 2011.p.129–34.
  • Jie L, Ziran C. Research on the MPPT Algorithms for Photovoltaic System Based on PV Neural Network.IEEE Control and Decision Conference, China.2011.p.1851–4.
  • Zhang N, Behera PK, Williams C. Solar Radiation Prediction Based on Particle Swarm Optimization and Evolutionary Algorithm using Recurrent Neural Networks. IEEE International Systems Conference, USA. 2013 p.280–6.
  • Ramaprabha R, Mathur BL, Sharanya M. Solar Array Modeling and Simulation of MPPT using Neural Network. IEEE Transactions on Control, Automation, Communication and Energy and Conservation. 2009.p.1–5.
  • Adly M, Ibrahim M, Sherif H E. Comparative study of improved energy generation maximization techniques for photovoltaic systems. IEEE Asia-Pacific Power and Energy Engineering Conference,Egypt. 2012. p.1–5.
  • Mei Q, Shan M, Liu L, Guerrero MJ. A novel improved variable step-size incremental-resistance MPPT method for PV systems. IEEE Transactions on Industrial Electronics. 2011; 58(6):2427–34.
  • Pakkiraiah B, Sukumar GD. A New Modified MPPT Controller for Solar Photovoltaic System. IEEE International Conference on Research in Computational Intelligence and Communication Networks. India, 2015.
  • Mohammed SS, Devaraj D, Ahamed TPI. Modeling, Simulation and Analysis of Photovoltaic Modules under Partially Shaded Conditions. Indian Journal of Science and Technology.2016; 9(16):1–8.
  • Joshi J J, Karthick P, Kumar R S. A solar panel connected multilevel inverter with SVM using fuzzy logic controller.IEEE International Conference on Energy Efficient Technologies for Sustainability, India. 2013. p.1201–6.
  • Aleenejad M, Eini HI, Farhangi S. A minimum loss switching method using space vector modulation for cascaded H-bridge multilevel inverter. IEEE 20th Iranian Conference on Electrical Engineering, Iran.2012.p.546–51.
  • Sreeja C, Arun S. A novel control algorithm for three phase multilevel inverter using SVM. IEEE PES Innovative Smart Grid Technologies-India (ISGT India). 2011;262–67.
  • Pakkiraiah B, Sukumar GD.A New Modified MPPT Controller for Improved Performance of an Asynchronous Motor Drive under Variable Irradiance and Variable Temperature. International Journal of Computers and Applications-Taylor and Francis. 2016;1–14.
  • Mbarushimana A, Ai X. Real time digital simulation of PWM converter control for grid integration of renewable energy with enhanced power quality. IEEE International Conference on Electric Utility Dereglation and Restructing and Power Technologies. 2011.p.712–8.
  • Kim DH. GA-PSO based vector control of indirect three phase induction motor. Elsevier Science Direct Applied Soft Computing.2007;7(2):601–11.
  • Sukumar D, Jitendranath J, Saranu S. Three-level Inverterfed Induction Motor Drive Performance Improvement with Neuro-fuzzy Space Vector Modulation. Electrical Power Components and Systems. 2014;42(15):1633–46.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.