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Öğe ANN based real-time estimation of power generation of different PV module types(2009) Syafaruddin; Karatepe E.; Hiyama T.Distributed generation is expected to become more important in the future generation system. Utilities need to find solutions that help manage resources more efficiently. Effective smart grid solutions have been experienced by using real-time data to help refine and pinpoint inefficiencies for maintaining secure and reliable operating conditions. This paper proposes the application of Artificial Neural Network (ANN) for the real-time estimation of the maximum power generation of PV modules of different technologies. An intelligent technique is necessary required in this case due to the relationship between the maximum power of PV modules and the open circuit voltage and temperature is nonlinear and can't be easily expressed by an analytical expression for each technology. The proposed ANN method is using input signals of open circuit voltage and cell temperature instead of irradiance and ambient temperature to determine the estimated maximum power generation of PV modules. It is important for the utility to have the capability to perform this estimation for optimal operating points and diagnostic purposes that may be an early indicator of a need for maintenance and optimal energy management. The proposed method is accurately verified through a developed real-time simulator on the daily basis of irradiance and cell temperature changes. © 2009 The Institute of Electrical Engineers of Japan.Öğe Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions(Inst Engineering Technology-Iet, 2009) Syafaruddin; Karatepe, E.; Hiyama, T.The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power-voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.Öğe Comparison of ANN models for estimating optimal points of crystalline silicon photovoltaic modules(2010) Syafaruddin; Karatepe E.; Hiyama T.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.Öğe Controlling of artificial neural network for fault diagnosis of photovoltaic array(2011) Syafaruddin; Karatepe E.; Hiyama T.High penetration of photovoltaic (PV) systems is expected to play important roles as power generation source in the near future. One of the typical deployments of PV systems is without supervisory mechanisms to monitor the physical conditions of cells or modules. In the longer term operation, the cells or modules may undergo fault conditions since they are exposure to the environment. Manually module checking is not recommended in this case because of time-consuming, less accuracy and potentially danger to the operator. Therefore, provision of early automatic diagnosis technique with quick and efficient responses is highly necessary. Since high accuracy is the important issue in the diagnosis problems, the paper present fault diagnosis method using three-layered artificial neural network. A single artificial neural network (ANN) is not suitable to provide precise solution for this fault identification. Therefore, several ANNs are developed, then automatic control based module voltage terminal is established. The proposed method is simple and accurate to detect the exact location of short-circuit condition of PV modules in array. © 2011 IEEE.Öğe DEVELOPMENT OF REAL-TIME SIMULATOR BASED ON INTELLIGENT TECHNIQUES FOR MAXIMUM POWER POINT CONTROLLER OF PHOTOVOLTAIC SYSTEM(Icic Int, 2010) Syafaruddin; Karatepe, Engin; Hiyama, TakashiThe 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.Öğe Development of real-time simulator based on intelligent techniques for maximum power point controller of photovoltaic system(2010) Syafaruddin; Karatepe E.; Hiyama T.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.Öğe Electric double layer capacitor (EDLC) based mismatching losses reduction under fast-shaded conditions of PV modules(2011) Syafaruddin; Tanaka Y.; Karatepe E.; Hiyama T.Fast-moving irradiance condition is one of problems that need to be solved in the non-stationary conventional maximum power point (MPP) trackers of PV system. Under sudden irradiance changes, the output power is changed drastically that leads to the shifting in MPP voltage. Conventional MPP algorithms may start continuously to search for finding the optimum point. However, suddenly another shadow can occur prior to complete removing of previous shadow. Continuing the tracking process under this condition will cause to lose energy. This paper presents the electric double layer capacitor (EDLC) as the power compensation method for improving the maximum power transfer of PV system under short-term period of shading. Several scenarios are tested in this work by measurement the percentage of power compensation, for instance the effect of capacitor size to the period of shading, the effects of shading period to the level shading intensity and cell temperature. This paper is directly purposed to reduce the power losses for moving objects powered by solar energy, such as solar car and solar boat systems. © 2011 The Institute of Electrical Engineers of Japan.Öğe Feasibility of artificial neural network for maximum power point estimation of non crystalline-Si photovoltaic modules(2009) Syafaruddin; Hiyama T.; Karatepe E.Solar cell markets are growing favorably. The emerging non crystalline silicon (c-Si) technologies are starting to make significant in-roads into solar cell markets. The most of the artificial neural network (ANN) have been used in maximum power points tracking applications for c-Si solar cell technology. However, the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in currentvoltage characteristic. In this reason, the investigation of feasibility using neural networks is very important for different solar cell technologies to increase the efficiency of photovoltaic (PV) systems. The paper investigates three different ANN structures, such as radial basis function (RBF), adaptive neurofuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN) for identification the optimum operating voltage of non c-Si PV modules. These ANN models have been trained and verified for double junction amorphous Si (2j a-Si), triple junction amorphous Si (3j a-Si), Cadmium Indium Diselenide (CIS) and thin film Cadmium Telluride (CdTe) solar cell technologies. The results show that the flexibility of training process, the simplicity of network structure and the accuracy of validation error are important factors to select a suitable ANN model. © 2009 IEEE.Öğe Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells(Pergamon-Elsevier Science Ltd, 2012) Syafaruddin; Karatepe, Engin; Hiyama, TakashiThe 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 technologies. (C) 2011 Elsevier Ltd. All rights reserved.Öğe Investigation of ANN performance for tracking the optimum points of PV module under partially shaded conditions(2010) Syafaruddin; Hiyama T.; Karatepe E.Solving partially shaded condition still remains an important task in the PV system practice. Under such condition, the global maximum point shifts continuously in wide voltage range from the local maxima, which make it difficult for conventional controllers. Intelligent techniques based on artificial neural network are well-known as the promising methods to identify the global maxima. However, there are many variants of neural networks and they have strong and weak points during the implementation. This paper investigates the performance of radial basis function (RBF) neural network and three layered feed-forward neural network (TFFN) under partial shadow operation of PV module. These two ANN structures are well-recognized for optimization, forecasting and control in PV system application due to the simplicity of structure and high performance accuracy. The investigation is focused on the network structure, training and validation process of these methods. To determine to which method is preferable to handle this task, the adaptive neuro-fuzzy inference system (ANFIS) is used as the comparator. The proposed method is verified and tested using developed real-time simulator. ©2010 IEEE.Öğe Performance enhancement of photovoltaic array through string and central based MPPT system under non-uniform irradiance conditions(Pergamon-Elsevier Science Ltd, 2012) Syafaruddin; Karatepe, Engin; Hiyama, TakashiMismatching 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. (C) 2012 Elsevier Ltd. All rights reserved.Öğe Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system(Pergamon-Elsevier Science Ltd, 2009) Syafaruddin; Karatepe, Engin; Hiyama, TakashiIt is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system(Pergamon-Elsevier Science Ltd, 2009) Syafaruddin; Karatepe, Engin; Hiyama, TakashiIt is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Simple and high-efficiency photovoltaic system under non-uniform operating conditions(Inst Engineering Technology-Iet, 2010) Karatepe, E.; Syafaruddin; Hiyama, T.The 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.
