Design and Simulation of Photovoltaic Array

Photovoltaic power supplied to the utility grid is gaining more and more visibility while the world’s powers demand is increases. Growing demand, advancements in semiconductor technology and magnetic materials such as high frequency inductor cores, has a significant impact on PV inverter topologies and their efficiencies, on the improvement of the control circuits on the potential of costs reduction. The user naturally wants to operate the Photovoltaic (PV) array at its highest energy conversion output by continuously utilizing the maximum available solar power of the array. The electrical system PV modules are powered by solar arrays requires special design considerations due to varying nature of the solar power generated resulting from unpredictable and sudden changes in weather conditions which change the solar irradiation level as well as the cell operating temperature. This paper, a mathematical model of a Photovoltaic (PV) cell used matlab-simulink environment, is developed and presented. The model is developed using basic circuit equations of the photovoltaic solar cells including the effects of solar irradiation and temperature changes. The main objective is to find the parameters of the nonlinear I–V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. the method finds the best I–V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances – Key words : Photovoltaic system (PV), maximum power, PV array,PV cell DOI: http://dx.doi.org/10.11591/ijece.v2i1.117

It is urgent to develop the photovoltaic (PV) array simulator for designing and research the photovoltaic power generation system to save cost and space. Therefore, two kinds of photovoltaic array simulators based on voltage source PWM rectifier and current source PWM rectifier are presented. They can be adopted for different applications. By the way of PV cells mathematical model, the output characteristics of PV array simulators are corresponded to the output characteristics of PV array. By establishing the simulation model of entire system, the output characteristics of PV array simulators and the stability of the entire system combined with PV power generator are analyzed and compared. The results of simulation show their characteristics of these two kinds of PV array simulators, and prove that they can match the output characteristic of PV array and test the performance of PV power generators well. So it will benefit the photovoltaic manufactures significantly.

A method for modeling photovoltaic (PV) arrays - based on artificial intelligence techniques, namely genetic algorithm (GA) and cuckoo optimization algorithm (COA) - is presented. COA and GA are used to obtain the parameters of the PV array model using the PV cell’s datasheet information. The adopted models – using GA and COA - are implemented on a simulation platforms using MATLAB 2020a environment for two-diode and single-diode models. The proposed optimization method fits the mathematical current-voltage (I-V) characteristic to the three (V, I) remarkable points without the need to guess or to estimate any other parameter. The obtained models are tested and validated with experimental data taken from the Mutah university’s PV power plant. The results show that for both of the employed optimization algorithms, the two-diode model is more accurate than the single-diode model. The results also disclose that, at different values of temperature and solar irradiance, the COA – compared to the GA – better handles the optimization problem with low iterations and better fitness value.

This paper presents a MATLAB-based modeling and simulation scheme suitable for studying the I-V and P-V characteristics of a PV under a nonuniform insolation due to partial shading. In the first, part a solar cell block is available in MATLAB®/Similink, which was used with many other blocks to plot I-V and P-V characteristics under variations of parameters considering one parameter variation at a time. The effect of variation of parameters such as series resistance, Rs, shunt resistance Rsh, diode parameter: diode saturation current, I0, could be seen on the characteristics of a solar cell. Effect of two environmental parameters of temperature and irradiance variations could also be observed from simulated characteristics. In the second part, the performance of a photovoltaic (PV) array is affected by temperature, solar insolation, shading. Often, the PV arrays get shadowed, completely or partially, by the passing clouds neighboring buildings, towers or by trees, etc. The situation is of a particular interest in a case of the large PV power plants. In the case of the shading the characteristics of the PV module are more complex with the several peak values. Under such conditions, it is very difficult to determine the maximum power point (MPP). Finally, MATLAB-programmed modeling and simulation of photovoltaic systems is presented here, by focusing on the effects of partial shading on the output of the photovoltaic (PV) systems. The proposed model facilitates simulating the dynamic performances of PV-based power systems and also has been validated by means of simulation study.

Solar photovoltaic (PV) energy production is important in reducing global energy crises since it is transportable, scalable, and highly customizable dependent on the needs of the industry or end-user. In addition, compared to other renewable resources, photovoltaic systems can produce electricity without moving parts and have a long lifespan. Nevertheless, solar photovoltaic (PV) systems provide intermittent output electricity with a nonlinear output voltage. Due to this intermittent availability, PV installations are facing significant challenges. As a result, in PV power systems, a Maximum Power Point Tracker (MPPT), a power extraction mechanism, is required to assure maximum power delivery at any given moment. The main objective of this work is to study the MPPT method of extracting the maximum power from photovoltaic modules under different solar irradiation and temperatures. Several MPPT methods have been developed for photovoltaic systems to achieve MPP, depending on weather conditions and applications, ranging from simple to more complex methods. Among these methods, five techniques have been presented and compared that are P&O perturbation and observation method, INC incremental conductance method, the ANN neural network method, the open circuit voltage based neural network method FVCO, and the neural network method at the base of FCC (short circuit current).

The integration of photovoltaic (PV) arrays to the power grid has continued to increase globally. Large PV array installations in the order of 100s of megawatt have already been installed. One of such large PV array installations is the Topaz PV array with an installed capacity of 500MW. With the increasing penetration of PV arrays, it is important to develop an accurate model for PV arrays. Such model will aid in performing state estimation, condition monitoring, load flow analysis, and other analytical studies. Several models for the PV module have been proposed in various publications. However, these models are written in a form that is effective for analyzing the PV module as an individual device. To analyze the PV array in an integrated power system, the form of the PV array model needs to be modified. This modification enables the establishment of connectivity between power system devices, and the definition of the admittance between the buses of the power system network. Consequently, computations such as load flow analysis and state estimation can be performed seamlessly. In this paper, a novel method for modeling a two-diode PV module is proposed. This model applies a scaling factor to the Taylor series expansion of the PV array model exponential terms. Next, the higher order terms of series expansion is reduced to at most second order terms using quadratization technique. This modeling method can also be applied to other PV array models, such as the one-diode and three-diode models. The form of the proposed PV array model enables seamless simulation of the PV array system in an integrated power system network.

All-dielectric metasurface absorbers have great potential in many scientific and technical applications. The emerging metasurfaces show strong and versatile capabilities in controlling absorptance, reflectance, and transmittance of electromagnetic waves. In this work, we propose and investigate all-dielectric metasurface absorbers with an equivalent circuit model. In the proposed circuit model, we satisfy the first Kerker condition. To verify the accuracy of the proposed model, the obtained results for an all-dielectric cubic metasurface absorber are compared with the existing experimental data. Moreover, using the proposed circuit model, we propose a hemisphere structure and compare the results of the proposed model with those of full-wave simulations. With this novel structure, we achieve higher absorptance and quality factor in comparison to a cubic one. Additionally, our proposed model reduces the calculation time and needs less memory compared to full-wave simulations. The results of the circuit model have an acceptable agreement with the experimental data and those of full-wave simulations. The proposed circuit model is simple yet general. It provides physical insight into the design and operation of various sub-wavelength structures in the broad frequency range, including THz and visible regions.

Due to the accessibility and advances of PV technology, photovoltaic (PV) systems have gained considerable attention in the past few decades. The primary obstacles to the realization of cost effective and eco-friendly PV systems, however, are the protection of PV systems against the faults including String to String (SS), String to Ground (SG), Open circuit (OC), and partial shading. Such abnormal conditions decrease the maximum available PV power. For improved system efficiency and reliability, partial shading and failures must therefore be detected promptly in a PV array. The significant fault current in PV systems can be detected using available protection devices in PV systems such as fuses and residual current detectors. However, if the solar and/or fault mismatch is small and the fault resistance is high, the flowing fault current is insufficient to be recognised by current protection devices. As a result, conventional protection systems fail to identify faults in hazy and low-irradiance circumstances, which leads to photovoltaic fire hazards and reliability issues. In this context, a fault diagnosis scheme is proposed for PV systems which comprise the feature extraction, using the appropriate feature extraction technique, of obtaining the discriminatory attributes of voltage and current information and the classification and various faults on the PV system using a suitable machine learning technique.

Photovoltaic (PV) systems have recieved a lot of attention in recent decades due to their accessibility and advancements in PV technology. The protection of PV systems from faults such as String to String (SS), String to Ground (SG), Open circuit (OC), and partial shading are the key challenges to the realization of cost-effective and environmentally friendly PV systems. Such unusual circumstances reduce the maximum available PV power. Partial shading and breakdowns in a PV array must therefore be noticed quickly for enhanced system efficiency and reliability. The significant fault current in PV systems can be detected using the existing safety devices in PV systems, such as fuses and residual current detectors. The flowing fault current being of low order is not significant enough for current protection devices to detect if the solar and/or fault mismatch is modest and the fault resistance is high. As a result, under cloudy and low irradiance conditions, the traditional protection devices fail to identify problems, resulting in reliability concerns and photovoltaic fire threats. In this context, a fault diagnosis scheme for PV systems is presented in this paper, which includes feature extraction using the Discrete wavelet transform, and classification of various defects on the PV system using Decision tree.

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study.

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays.

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.

One of the most promising forms of renewable energy is solar photovoltaic (PV) energy, which directly generates electric power from sun irradiation. Since the solar photovoltaic system has non-linear nature, therefore despite the benefits, partial shading presents a significant barrier to photovoltaic (PV) systems’ efficient utilization. Partial shading may be due to nearby buildings, trees, electric poles, etc. Partial shading reduces the maximum power output of photovoltaic arrays, and arrays have a few peaks in their PV characteristics. The output power reduction depends on the shading pattern, array structure, and physical placement of the array’s modules rather than being exactly proportional to the shading area. There are numerous techniques that can be used to mitigate partial shading problems in a photovoltaic (PV) array. One of the most productive methods to avoid this barrier is by adopting a reconfiguration strategy. In this article, to reduce the consequences of partial shadowing, a strategy of PV array reconfiguration is implemented. There are several other topologies that can be implemented to reduce the impact of partial shading, among them the five topologies described here: Total Cross Tied (TCT), Honey-Comb (HC), Series Parallel (SP), Bridge-Link (BL), and Su-Do-Ku. In this article, numerous performance parameters such as short circuit current, global maximum output power, and open circuit voltage are taken to examine the effectiveness of each topology. In furthermore, the fill effect is taken into account in order to conduct a fair comparative analysis under various shading patterns. The responses of each pattern are carried out in Matlab/Simulink.

Improving the efficiency of photovoltaic (PV) systems has gained priority in current research due to the large volumes of PV panels installed. Moreover, the remarkable efforts made to investigate different methods of diagnosing PV failures have multiplied, giving additional impetus to research on the efficiency of PV systems. However, most of these methods are limited in the number of faults that can be identified; some are expensive and complex, and others require huge amounts of data to train. In this paper, a simple and robust multivariate statistical analysis method is proposed for the diagnosis and identification of faults in a PV system. From the multitude of data that can be collected on a PV system in operation or available on most new inverters on the market, the method used here is based on kernel principal component analysis, which looks at and analyses the variance between these data. Combined with the Hotelling statistic ( T 2 ) and Squared Prediction Error (SPE) index associated with Rolle’s theorem, this analysis identifies six operating states of the PV system: normal operation, short-circuited panels, open-circuit panels, partially shaded panels, serial resistance degradation, and MPPT error. 200 samples of different faults at different irradiance have been generated between an irradiance from 50 to 500 w/m2. The accuracy rate was 64% for the degradation of series resistance, 91.11% for partial shading, 100% for open circuit, 100% for short circuit, and 100% for MPPT error. The various results obtained first from a Matlab-Simulink model, and then from a real system of 18 modules, demonstrate the efficiency and performance of the proposed algorithms.

Chapter 6 - Flexible Power Control of Photovoltaic Systems