Performance enhancement of photovoltaic array through string and central based MPPT system under non-uniform irradiance conditions

Energy Conversion and Management, Vol. 62, No. 10, pp. 131???140, 2012Mismatching losses reduction of photovoltaic (PV) array has been intensively discussed through the increasing penetration of residential and commercial PV systems. Many causes of mismatching losses have been identified and plenty of proposed methods to solve this problem have been recently proposed. This paper deals with reducing method of mismatching losses due to the non-uniform irradiance conditions. It is well-known that a certain number of multiple peaks occur on the power???voltage curve as the number of PV modules in one-string increases under non-uniform operating conditions. Since the conventional control method only drives the operating points of PV system to the local maxima close to open circuit voltage, only small portion of power can be extracted from the PV system. In this study, a radial basis function neural network (RBF-ANN) based intelligent control method is utilized to map the global operating voltage and non-irradiance operating condition in string and central based MPPT systems. The proposed method has been tested on 10 x 3 (2.2 kW), 15 x 3 (2.5 kW) and 20 x 3 (3.3 kW) of series???parallel PV array configuration under random-shaded and continuous-shaded patterns. The proposed method is compared with the ideal case and conventional method through a simple power-voltage curve of PV arrays. The simulation results show that there are significant increases of about 30-60% of the extracted power in one operating condition when the proposed method is able to shift the operating voltage of modules to their optimum voltages

Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells

The emerging non-crystalline silicon (c-Si) solar cell technologies are starting to make significant inroads into solar cell markets. Most of the researchers have focused on c-Si solar cell in maximum power points tracking applications of photovoltaic (PV) systems. However, the characteristics of non-c-Si solar cell technologies at maximum power point (MPP) have different trends in current???voltage characteristics. For this reason, determining the optimum operating point is very important for different solar cell technologies to increase the efficiency of PV systems. In this paper, it has been shown that the use of fuzzy system coupled with a discrete wavelet network in Takagi???Sugeno type model structure is capable of identifying the MPP voltage of different non-c-Si solar cells with very high accuracy. The performance of the fuzzy-wavelet network (FWN) method has been compared with other ANN structures, such as radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN). The simulation results show that the single FWN architecture has superior approximation accuracy over the other methods and a very good generalization capability for different operating conditions and different technologie

Development of real-time simulator based on intelligent techniques for maximum power point controller of photovoltaic system

The power conversion efficiency of solar cell depends on material science. On the other hand. it is a very important issue to reduce the power losses in photovoltaic systems. Many available commercial PV modules have been used. However, since their characteristics are not unique and on-site testing of PV system is costly. time-consumed and highly dependent on the prevailing weather conditions. a real-time simulator becomes an important tool to support the research and development in PV system. The impact of operating conditions on different solar cells performance should be well understood at optimal operating points to increase the efficiency of photo voltaic systems. This paper firstly explores the relationships between solar intensity and operating temperature variations and key solar cell paramrters for commercial available photovoltaic modules. The results show that the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in current-voltage characteristic. In this reason a robust real-time simulator is very important for different solar cell technologies. Then this paper presents intelligent real-time simulator for simulating and testing the effect of the fluctuation of irradiance level and cell temperature on the MPP performance of PV modules. Intilligent techniques on becoming useful for non-linrar problems because of their symbolic reasoning flexibility and generalization capabilities. There is a trade-off between the complexity of system and efficiency in optimally operating photo voltaic modules. This mrthod is highly drpendent on ANN training process for each cell technology and simply generates control signal required in fuzzy logic controller. The developed realtime simulator has been successfully demonstrated for different commercially available photovoltaic modules

Simple and high-efficiency photovoltaic system under non-uniform operating conditions

WOS: 000285460300006The interest in improving the efficiency of photovoltaic (PV) system has emerged because of increasing the number of home-based or small-scale PV power system. However, the home-based PV system is vulnerable to the non-uniform operating conditions. Under such circumstances, multiple-local maximum power points (MPPs) occur on the power-voltage characteristics and an advanced control algorithm is required to track the global MPP. It is very difficult to provide a sophisticated control algorithm because of the non-linear characteristics of PV system. This study describes the potential to improve the efficiency of PV arrays under non-uniform operating conditions by using the conventional hill-climbing MPP tracking method in total cross tied (TCT) connected PV arrays, in which each group of series connected solar cells that belong to single bypass diode is interconnected. The various scenarios were tested and the results indicate that the efficiency of the proposed system is much higher than that of the same size of series-parallel (SP) PV array configuration

Comparison of ANN models for estimating optimal points of crystalline silicon photovoltaic modules

Various artificial neural network (ANN) structures have been utilized to determine the maximum power points of PV system. The most common methods are radial basis function neural network (RBF), adaptive neuro-fuzzy inference system neural network (ANFIS) and three layered feed-forward neural network (TFFN). These ANN methods are recognized with simple computational techniques and high pattern recognition capabilities to deal with non-linear characteristic and intermittent output of PV system. However, there still might be strong and weak points for these methods during the optimization process. Since the characteristic of crystalline Silicon PV modules technology is almost similar, it is possible to select a single prominent ANN structure for identification the optimum points of this type solar cell technology. The paper discusses the most suitable ANN structure for estimation the MPP crystalline Silicon PV modules through their optimum operating voltages. To reach this objective, the ANN models have been trained and verified for multi-crystalline Silicon based edge defined film-fed growth (EFG) and wafer solar cell technologies, mono-crystalline Silicon and thin-film Silicon solar cell technologies. Then, the performance of ANN models is compared with hill-climbing (HC) based MPPT technique in terms of tracking the MPP voltage and the energy index. © 2010 The Institute of Electrical Engineers of Japan

Controlling of Artificial Neural Network for Fault Diagnosis of Photovoltaic Array

The 16th International Conference on Intelligent System Application to Power Systems (ISAP), 2011High penetration of photovoltaic (PV) systems is\ud expected to play important roles as power generation source in\ud the near future. One of the typical deployments of PV systems is\ud without supervisory mechanisms to monitor the physical\ud conditions of cells or modules. In the longer term operation, the\ud cells or modules may undergo fault conditions since they are\ud exposure to the environment. Manually module checking is not\ud recommended in this case because of time-consuming, less\ud accuracy and potentially danger to the operator. Therefore,\ud provision of early automatic diagnosis technique with quick and\ud efficient responses is highly necessary. Since high accuracy is\ud the important issue in the diagnosis problems, the paper present\ud fault diagnosis method using three-layered artificial neural\ud network. A single artificial neural network (ANN) is not suitable\ud to provide precise solution for this fault identification.\ud Therefore, several ANNs are developed, then automatic control\ud based module voltage terminal is established. The proposed\ud method is simple and accurate to detect the exact location of\ud short-circuit condition of PV modules in array

DEVELOPMENT OF REAL-TIME SIMULATOR BASED ON INTELLIGENT TECHNIQUES FOR MAXIMUM POWER POINT CONTROLLER OF PHOTOVOLTAIC SYSTEM

WOS: 000276578000007The power conversion efficiency of solar cell depends on material science. On the other hand, it is a very important issue to reduce the power losses in photovoltaic systems. Many available commercial P V modules have been used. However, since their characteristics are not unique and on-site testing of PV system is costly, time-consumed and highly dependent on the prevailing weather conditions, a real-time simulator becomes an important tool to support the research and development in P V system. The impact of operating conditions on different solar cells performance should be well understood at optimal operating points to increase the efficiency of photovoltaic systems. This paper firstly explores the relationships between solar intensity and operating temperature variations and key solar cell parameters for commercial available photovoltaic modules. The results show that the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in current-voltage characteristic. In this reason, a robust real-time simulator is very important for different solar cell technologies. Then, this paper presents intelligent real-time simulator for simulating and testing the effect of the fluctuation of irradiance level and cell temperature on the MPP performance of PV modules. Intelligent techniques are becoming useful for non-linear problems because of their symbolic reasoning, flexibility and generalization capabilities. There is a trade-off between the complexity of system and efficiency in optimally operating photovoltaic modules. This method is highly dependent on ANN training process for each cell technology and simply generates control signal required in fuzzy logic controller. The developed real-time simulator has been successfully demonstrated for different commercially available photovoltaic modules.Graduate School Action Scheme for internationalization of University Students (GRASIUS) at Kumamoto University, JapanThis work is a part of project report that was supported by Graduate School Action Scheme for internationalization of University Students (GRASIUS) Project in 2008 at Kumamoto University, Japan