Qualification: 
Ph.D
r_anand@blr.amrita.edu

Dr. Anand. R currently serves as an Assistant Professor (Senior Grade) at the Department of Electrical and Electronics Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru. His area of interest in research includes Power Electronics, Electrical Drives, Renewable Energy Systems, Soft Computing and Machine Learning. He completed his PhD in Electrical Engineering from Anna University, Chennai. India. He has filed and published four Indian patents. He has 11 years of teaching and research experience. He has published his research works in 5 National Conferences, 10 International Conferences and 10 International Journals out of which majority of the papers are indexed in SCOPUS, SCI and Web of Science. He is the Guest editor in Electronics Journal, Bosnia. He is the reviewer for many journals like IEEE, AECS, IGI global publications and JEET. He completed Industrial Consultancy work for Smart Water Management System. He developed low cost product for local Municipal water indication system. He has Senior membership in professional body like IEEE  & IAENG. He is an active member in various societies like an IEEE PELS, PES, IES, IEEE Young Professionals, Smart Grid Community, EDS and IoT Community.

Publications

Publication Type: Journal Article

Year of Publication Title

2018

Anand Rajendran, “Guest Editorial Special Issue on Recent Advances in Power Electronics and Internet of Things”, Electronics Journal , vol. 22, no. 1, 2018.[Abstract]


Power management and the Internet of Things (IoT) are essential parts of the modern cyber physical system. This field is in rapid change in terms of technology, devices and future trends. Nowadays, the IoT and power management are prevalent in daily life, from cell phone to household applications. An individual’s quality of life mainly depends on the amount of electrical power consumption. The energy resource usage has been considered as the most important and ubiquitous issue of the present era

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2016

Anand Rajendran and Saravanan, D. S., “A Correlative Study of Perturb and Observe Technique and GA-RBF-NN Method Supplying a Brushless DC Motor”, Circuits and Systems, vol. 7, no. 8, pp. 1653-1664, 2016.[Abstract]


A comparative study is done in regards to the performance of the popular Perturb and Observe algorithm and the Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) algorithm, both incorporating the Interleaved Boost converter. The Perturb and Observe method (P&O) is inarguably the most commonly used algorithm as its advantages pertaining to its ease in implementation and simplicity enable to track the Maximum Power Point (MPP). However, it is absolutely unreliable when subjected to rapidly fluctuating irradiation and temperature levels. More importantly, the system has the tendency to swing back and forth about the Maximum Power Point without reaching stability. At this juncture, the implementation of the Genetic-Assisted Radial Basis Function (GA-RBF) algorithm helps the system achieve MPP at a shorter time when compared to the Perturb and Observe technique. The ever reliable and robust Levenberg-Marquardt algorithm is included along with the MPPT controller that minimizes the Mean Square Error (MSE) and aids in faster training of the neural network. This PV system drives a brushless DC motor (BLDC), employing rotor position sensors.

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2016

D. S. Saravanan and Anand Rajendran, “Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive”, Circuits and Systems, vol. 7, no. 8, pp. 1635-1652, 2016.[Abstract]


This paper proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC (BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time. More »»

2015

D. .S.Saravanan and Anand Rajendran, “Analysis of Genetic – Assisted Neural Network Based MPPT Controller of a Standalone Solar Photovoltaic System”, International Journal of Applied Engineering Research, vol. 10, no. 6, pp. 172-183, 2015.

2015

R. Swaminathan and Anand Rajendran, “A Three Phase Seven Level Inverter for Grid Connected Photovoltaic System by Employing PID Controller”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 6, pp. 1085-1091, 2015.[Abstract]


This paper presents a three phase seven level photovoltaic (PV) inverter topology for grid connected PV systems with a novel Pulse Width Modulated(PWM) control scheme. Two reference signals identical to each other with an offset equivalent to the amplitude of the triangular carrier signal were used to generate PWM signals for the switches. A digital Proportional-Integral-Derivative (PID) current control algorithm is implemented in DSPTMS320F2812 to keep the current injected into the grid sinusoidal and to have high dynamic performance with rapidly changing atmospheric conditions. The inverter offers much less total harmonic distortion and can operate at near-unity power factor. The proposed system is verified through simulation and is implemented in a prototype.Experimental results are compared with the conventional three phase three level grid connected PWM inverter.

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2015

T. Nivethitha and Anand Rajendran, “PV Connected BLDC Motor Drive by Employing P and O MPPT Controller”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 6, 2015.[Abstract]


In this paper Brushless DC motor drive is utilized as the load of a Photovoltaic system with Maximum Power Point Tracking (MPPT) controller. Perturbation and Observation (P&O) based approach for MPPT controller is presented. A brushless DC motor that unites a motor controller with Proportional Integral (PI) speed control loop is effectively instigated. MATLAB/Simulink is used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses MPPT controller where the output signal is used to govern the DC-DC boost converters to accomplish the MPPT. This proposed MPPT controller produces an average power increase and this unit provides rapid achievement of the MPPT and current control of the BLDC motor drive.

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2015

S. .Selvakani, Sindhu, D., and Anand Rajendran, “PV System Based MPPT Controller Supplying BLDC Motor Drive”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 4, no. 6, 2015.[Abstract]


This paper presents photovoltaic system with a maximum power point tracking (MPPT) controller is connected to brushless dc motor drive for heating, ventilating and air conditioning fans. The MPPT controller is based on a genetic assisted, multi-layer perceptron neural network (GA-MLP-NN) structure and includes a DC–DC boost converter. Genetic assistance in the neural network is used to optimize the size of the hidden layer. Also, for training the network, a genetic assisted, Levenberg–Marquardt (GA-LM) algorithm is utilized. The off line GA-MLP-NN, trained by this hybrid algorithm, is utilized for online estimation of the volt-age and current values in the maximum power point. A brushless dc (BLDC) motor drive system that incorporates a motor controller with proportional integral (PI) speed control loop is successfully implemented to operate the fans. The digital signal processor (DSP) based unit provides rapid achievement of the MPPT and current control of the BLDC motor drive.

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2012

Anand Rajendran and Kumar, G. A., “A Multilevel Inverter for Grid Connected Photovoltaic System by employing PID Controller”, International Journal of Engineering and Advanced Technology (IJEAT), vol. 2, no. 1, 2012.[Abstract]


This paper presents a single phase five level photovoltaic (PV) inverter topology for grid connected PV systems with a novel Pulse Width Modulated (PWM) control scheme. Two reference signals identical to each other with an offset equivalent to the amplitude of the triangular carrier signal were used to generate PWM signals for the switches. A digital Proportional-Integral- Derivative (PID) current control algorithm is implemented in DSP TMS320F2812 to keep the current injected into the grid sinusoidal and to have high dynamic performance with rapidly changing atmospheric conditions. The inverter offers much less total harmonic distortion and can operate at near-unity power factor. The proposed system is verified through simulation and is implemented in a prototype, and the experimental results are compared with that with the conventional single phase three level grid connected PWM inverter.

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2011

Anand Rajendran and A. Ali, N., “A Single phase Five level Inverter for Grid Connected Photovoltaic System by employing PID Controller”, African Journal of Scientific Research, vol. 6, no. 1, pp. 306-315, 2011.[Abstract]


This paper proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC (BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time.

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Publication Type: Book Chapter

Year of Publication Title

2018

Anand Rajendran, “Solar PV System Energy Conservation Incorporating an MPPT based BLDC”, in LAP LAMBERT Academic Publishing, 2018.[Abstract]


This book proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC(BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted–Radial Basis Function– Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time.

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Publication Type: Conference Paper

Year of Publication Title

2017

Anand Rajendran, Kanagachidambaresan, G. R., Balaji, M., and Mauryan, K. S. Chandragup, “Design of Smart Meter for Smart Grid application through True Time – MATLAB”, in IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, India, 2017.[Abstract]


Smart environment and Pervasive Computing has deeply influenced the present power system. The present smart grid is capable of determining and handling the load anywhere, anytime due to the Wireless communication. The Smart Meter (SM) is enabled with communication module, potential transformer and current transformer to measure the amount of power being used. A Zigbee communication enabled SM is designed for a smart grid and its performance is evaluated in this paper. The data error rate is also evaluated.

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2017

G. R. Kanagachidambaresan, Anand Rajendran, and Kalam, A., “Perturb and Observe (P and O) Based MPPT Controller for PV Connected Brushless DC Motor Drive”, in IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, India, 2017.[Abstract]


Maximum Power Point Tracking (MPPT) algorithm for different drives serving different application is the current interest of many researchers. In this paper a P & O (P&O) based MPPT controller is designed to investigate the performance of BLDC motor. Here Perturb and Observe (P&0) based MPPT controller with and without interleaved converter is compared. The proposed model and controller methodology provides reduced current and voltage ripple and promotes efficiency. The speed control of BLDC is also tested for its novel working under different load condition. The model is designed in Matlab Simulink to ensure its novel working and also compared with traditional Boost Converter (BC). The proposed model outperforms the traditional BC as per the claim made.

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