This paper provides a comprehensive review of the maximum power point tracking (MPPT) techniques applied to photovoltaic (PV) power system available until January, 2012. A good number of publications report on different MPPT techniques for a PV system together with implementation. But, confusion lies while selecting a MPPT as every technique has its own merits and demerits. Hence, a proper review of these techniques is essential. Unfortunately, very few attempts have been made in this regard, excepting two latest reviews on MPPT [Salas, 2006], [Esram and Chapman, 2007]. Since, MPPT is an essential part of a PV system, extensive research has been revealed in recent years in this field and many new techniques have been reported to the list since then. In this paper, a detailed description and then classification of the MPPT techniques have made based on features, such as number of control variables involved, types of control strategies employed, types of circuitry used suitably for PV system and practical/commercial applications. This paper is intended to serve as a convenient reference for future MPPT users in PV systems.

A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.

Artificial neural networks in renewable energy systems applications: a review

Artificial intelligence techniques for photovoltaic applications: A review

This paper presents a review on the state-of-the-art maximum power point tracking (MPPT) techniques for PV power system applications. The main techniques that will be deliberated are the Perturb and Observe, Incremental Conductance and Hill Climbing. The coverage will also encompass their variations and adaptive forms. In addition, the more recent MPPT approaches using soft computing methods such as Fuzzy Logic Control, Artificial Neural Network and Evolutionary Algorithms are included. Whilst the paper provides as thorough treatment of MPPT at normal (uniform) insolation, its focus will be on the applications of the abovementioned techniques during partial shading conditions. It is envisaged that this review work will be a source of valuable information for PV professionals to keep abreast with the latest progress in this area, as well as for new researchers to get started on MPPT.

A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition

This paper presents design and digital implementation of a fuzzy controller for achieving improved performance of Brushless dc (BLDC) servomotor drive. The performance of fuzzy and PID controller-based BLDC servomotor drives is investigated under different operating conditions such as change in reference speed, parameter variations, load disturbance, etc. BLDC servomotors are used in aerospace, instrumentation systems, space vehicles, electric vehicles, robotics, and industrial control applications. In such applications, conventional controllers like P, PI, and PID are being used with the BLDC servomotor drive control systems to achieve satisfactory transient and steady-state responses. However, the major problem associated with the conventional PID controller is that the tuned gain parameters obtained for such BLDC servomotor drive control systems do not yield better transient and steady-state responses under different operating conditions such as parameter variations, load disturbances, etc. In this paper, design and implementation of fuzzy controller is presented and its performance is compared with PID controller to show its capability to track the error and usefulness of fuzzy controller in control applications.

Implementation and Performance Analysis of Digital Controllers for Brushless DC Motor Drives

ANN based peak power tracking for PV supplied DC motors

Fuzzy based coordinated controller for power system stability and voltage regulation

This paper presents the design and development of dynamic partially reconfigurable pulsewidth modulation (DPRPWM) controller for three-phase voltage-source inverters (VSI) in a single Xilinx Spartan 3 XCS400PQ208 field programmable gate array (FPGA). The DPRPWM controller is designed such that it switches between the popular PWM techniques like sinusoidal PWM (SPWM) and space vector PWM (SVPWM). The FPGA platform supports the run-time reconfiguration of control functions and algorithms directly in hardware and meets hard real-time performance criteria in terms of timings for PWM generation as well as reconfiguration. The DPRPWM control is simulated and experimentally verified using a low-cost Xilinx Spartan FPGA. The results of SPWM and SVPWM controller are presented and the results prove that the DPRPWM controller for three-phase inverter is highly possible and can be extended to any level/phase PWM controllers.

Narri Yadaiah

Papers

Implementation and Performance Analysis of Digital Controllers for Brushless DC Motor Drives

ANN based peak power tracking for PV supplied DC motors

Fuzzy based coordinated controller for power system stability and voltage regulation

Dynamically Reconfigurable PWM Controller for Three-Phase Voltage-Source Inverters

Linearisation of multi-machine power system: Modeling and control – A survey