This paper presents a comprehensive review on solar tracking systems and their potentials in solar energy applications. The paper overviews the design parameters, construction, types and drive system techniques covering different usage application. There are two main solar tracking systems types that depending on their movement degrees of freedoms are single axis solar tracking system and dual axis solar tracking system, which are addressed in the recent studies. The solar tracker drive systems encompassed five categories based on the tracking technologies, namely, active tracking, passive tracking, semi-passive tracking, manual tracking, and chronological tracking. The paper described the various designs and components of the tracking systems. There are 42.57% of the studies discussed and presented single axis tracking systems while 41.58% of these studies to the dual axes tracking systems. In the recent research studies, the most common solar tracker drive type was active tracker by 76.42% usage in applications while in the second most impact type is the chronological solar tracker by 7.55%. Furthermore, in the solar tracking techniques, Azimuth and altitude tracking achieved 16.67% in usage, Horizontal tracking by 16.67%, Azimuth tracking by 10%, and polar tracking by 4.44%.

Solar tracking systems: Technologies and trackers drive types – A review

In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.

A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System

This paper presents a new design of a standalone photovoltaic system which is supplying the required power to a direct current water pump that have difficulty to supply by the utility electricity. The system is controlled by an artificial neural networks (ANN) algorithm with function softening by PI controller that to guarantee the maximum power point tracking (MPPT) working conditions. A parallel connected PV array is designed to supply the required power to the water pump. The proposed design considers Permanent Magnet DC motor (PMDC) of 48 Volts, and 500 Watts as a water pump’s motor, the direct current (DC) pump is adopted to avoid the complexity of the alternating current AC pumping system which includes inverter, power filter, and insulated step up transformer, so the presented design avoids the mentioned AC system components. A feed forward ANN algorithm is adopted in this study to produce the reference voltage for the MPPT functioning of the PV system, Proportional Integral (PI) controller is inserted to soften the MPPT controller performance. System design, MATLAB simulation with results and the results’ analysis all are presented in this paper. The study conclusion confirms the effectiveness of the proposal as a successful system for practical applications.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/17307/12856

High performance PV system based on artificial neural network MPPT with PI controller for direct current water pump applications

This paper presents the design and execution of an active two axes solar tracker with  fuzzy controller based on PSO. The proposed system  uses the light sensors to detect the position of the sun. The output of the sensors are used as the control inputs in moving the PV panel according to the sun’s position. Two PSO based fuzzy controllers were designed and implemented to the solar tracker in which PSO was used to tune the parameters of fuzzy logic controller to obtain better performance. The conventional fuzzy controllers were also applied to the system to measure the improvement that might be achieved by the proposed method. Before the system is implemented into the real plant, it must be first modeled using MATLAB/ simulink for simulation purpose. Therefore, the performance of the controllers and power gain of PV panel are then observed, investigated and analized in this work. The results show that there are some agreements of both experiment and simulation.

Design and Implementation of Active Two Axes Solar Tracking System Using Particle Swarm Optimization Based Fuzzy Logic Controller

It is stated in the present study that extreme learning machines (ELM) will display a greater performance in solar radiation estimation compared to artificial neural networks (ANN). The data acquired from Karaman province during 2010–2018 were used for evaluating the performance of the suggested approach. It was put forth when results were compared that ELM has displayed a greater estimation performance. Moreover, ANN and ELM were tested with different activation functions in order to obtain the best estimation response. While the best estimation result for ANN was obtained with the tansig function as 0.9828, mean square error (MSE) was obtained as 0.000129. The best estimation result for ELM was obtained with the sin function as 0.991 and MSE was calculated as 0.000881. Additionally ELM, training time 0.295 s, test time 0.266 s, MSE time 0.558 s was obtained. ELM displayed a high estimation performance in a very short amount of time. The ELM achieved a root mean square error (RMSE) value of 0.0297. This algorithm has achieved high accuracy with minimal error. Confidence interval estimations were carried out for the acquired correlation coefficients and the results were compared. ELM estimation performance is better than ANN with 95% confidence interval.

Estimation of solar radiation using modern methods

This paper concerns the design and realization of a solar tracking system oriented to the PV conversion panels. In general, the electricity generated by the PV panels is influenced by the intensity of solar radiation and ambient temperature. They will generate maximum electrical power when the intensity of solar radiation received is also maximum, therefore the PV must be controlled so that its position is always perpendicular to the sun. The proposed single axis solar tracking system offers optimal energy conversion process of solar energy into electricity through appropriately orienting the PV panel in accordance with the real position of the sun. The mechanism of the experiment is based on a DC motor which is intelligently controlled by fuzzy logic controller that moves prototype according to the inputs received from LDR sensors. The performance of the solar tracking system is experimentally investigated. The designed system has power gain of 47% compared to the fixed system.

Design of single axis solar tracking system at photovoltaic panel using fuzzy logic controller

Characteristic I-V of photovoltaic is depended on solar irradiation and operating temperature. Solar irradiation particularly affects the output current where the increasing solar irradiation will tend to increase the output current. Meanwhile, the operating temperature of photovoltaic module affects the output voltage where increasing temperature will reduce the output voltage. There is a point on the I-V curve where photovoltaic modules produce maximum possible output power that is called Maximum Power Point (MPP). A technique to track MPP on the I-V curve is known as Maximum Power Point Tracking (MPPT). In this study, the MPPT has been successfully designed based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and integrated with solar tracking system to improve the conversion efficiency of photovoltaic modules. The designed ANFIS MPPT system consists of current and voltage sensors, buck-boost converter, and Arduino MEGA 2560 microcontroller as a controller. Varying amounts of lamp with 12V 10W rating arranged in series is used as load. Solar tracking system that is equipped with MPPT ANFIS able to increase the output power of photovoltaic modules by 46.198% relative to the fixed system when 3 lamps is used as load.

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Implementation of Maximum Power Point Tracking (MPPT) Technique on Solar Tracking System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

A solar tracking system is made to energy eficiency improvement of PV. The significant increase of solar energy is recieved by PV in the end of day when PV is connected to a solar tracker. Solar tracker can be classified into two models based on tracking mechanism: active (electrooptics) and passive (solar path). Electrooptical based a solar tracker system  has weakness in locating the sun position when cloudy or it can be easily influenced wheather condition. In this research, a passive two axis solar tracking system, where the proposed tracking method is based on solar trajectory calculation, is designed, tested and installed on PV to overcome weather problems. A type-2 Fuzzy Logic Controller (FLC) was designed and implemented for controlling this system. Meanwhile, type-1 FLC was also realized on the system and the performance of the control method was investigated and compared with type-2 FLC. The experiment results obtained showed overall type-2 FLC has a better performance than type-1. The efficiency of the proposed solar tracker over the period of the experiment was around 48 % of fixed system.

Type-2 Fuzzy Logic Controller Based PV Passive Two-Axis Solar Tracking System

Solar radiation is a source of alternative energy that is very influential on the photovoltaic performance in generating energy. The need for solar radiation estimation has become a significant feature in the design of photovoltaic (PV) systems. Recently, the most popular method used to estimate solar radiation is artificial neural network (ANN). However, a new approach, called the extreme learning machine (ELM) algorithm is a new learning method of feed forward neural network with one hidden layer or known as Single Hidden Layer Feed Forward Neural Network (SLFN). In this research, ELM and a multilayer feed-forward network with back propagation are implemented to estimate hourly solar radiation on horizontal surface in Surabaya. In contrast to previous researches, this study has emphasized the use of meteorological data such as temperature, humidity, wind speed, and direction of speed as inputs for ANN and ELM model in estimating solar radiation. The MSE and learning rate has been used to measure the performance of two methods. The simulation results showed that the ELM model built had best performance for 400 nodes in which MSE and learning rate achieved were 5,88e-14 and 0,0156 second, respectively. The values were much smaller compared with the results of ANN. Overall, the ELM provided a better performance.

Imam Abadi

Papers

Design of single axis solar tracking system at photovoltaic panel using fuzzy logic controller

Design and Implementation of Active Two Axes Solar Tracking System Using Particle Swarm Optimization Based Fuzzy Logic Controller

Implementation of Maximum Power Point Tracking (MPPT) Technique on Solar Tracking System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Type-2 Fuzzy Logic Controller Based PV Passive Two-Axis Solar Tracking System

Extreme learning machine approach to estimate hourly solar radiation on horizontal surface (PV) in Surabaya -East java