Output Power Of Photovoltaic System Research Articles

Influence of degradation in units of PV modules on electric power output of PV system

The time variations and daily integrated amounts of electric power output of a photovoltaic (PV) system for domestic use were calculated for various degradation cases and diurnal insolation pattern...

Forecasting Power Output of Photovoltaic System Using A BP Network Method

The characteristics of intermittent and stochastic of solar energy has brought great challenges to power grid system in terms of operation and regulation. Power forecasting is an important factor for optimal schedule of power grid system and assessing the working performance of PV systems. In order to forecast the power output of a PV system located in Ashland at 24-hour-ahead for higher efficiency, a back propagation (BP) neural network model is proposed. Before designing the model, correlation analysis is done to investigate the relationship between power output and solar irradiance and ambient temperature, which are key parameters affecting the power output of PV systems. Based on a correlation analysis, the model admitted the following input parameters: hourly solar radiation intensity, the highest, the lowest daily and the average daily temperature, and hourly power output of the PV system. The output of the model is the forecasted PV power output 24 hours ahead. Based on the datasets, the neural network is trained to improve its accuracy. The best performance is obtained with the BP neural network structure of 28-20-11. The analysis of the error indicator MAPE shows that the proposed model has great accuracy and efficiency for forecasting the power output of photovoltaic systems.

Review of Maximum Power Point Tracking Control of Photovoltaic Systems in Case of Uniform & Non-uniform Irradiance Conditions

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. One of the approaches to increase the efficiency of PV power generation system is to operate the PV systems optimally at the maximum power point. However, the PV system can be optimally operated only at a specific output voltage; otherwise the output power fluctuates under intermittent weather conditions. In addition, it is very difficult to test the performance of PV systems controller under the same weather condition during the development process where the field testing is costly and time consuming. For these reasons, the presentation is about the state of the art techniques to track the maximum available output power of photovoltaic systems called maximum power point tracking (MPPT) control systems. This topic could be also one of the most challenges in photovoltaic systems application that has been receiving much more attention worldwide. The talks will cover the application of intelligent techniques by means the artificial neural network (ANN) and fuzzy logic controller scheme using polar information to develop a novel real-time simulation technique for MPPT control by using dSPACE real-time interface system. In this case, the three-layer feed-forward ANN is trained once for different scenarios to determine the global MPP voltage and power and the fuzzy logic with polar information controller takes the global maximum power point (MPP) voltage as a reference voltage to generate the required control signal for the power converter. This type of fuzzy logic rules is implemented for the first time in MPPT control application. The proposed method has been tested using different solar cell technologies such as 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. In other cases, one of the main causes of reducing energy yield of photovoltaic systems is the partially shaded condition. Although the conventional MPPT control algorithms operate well in a uniform solar irradiance, they do not operate well in non-uniform solar irradiance conditions. 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 power point may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognize the global operating point under partially shaded conditions. From these reasons, the presentation will address the effectiveness of the proposed MPPT method to solve the partially shaded conditions under the experimental real-time simulation technique based dSPACE real-time interface system for different size of PV arrays, such as 3x3(0.5kW) and 20x3(3.3kW) and different interconnected PV arrays, for instance series-parallel (SP), bridge link (BL) and total cross tied (TCT) configurations.

Accurate photovoltaic power forecasting models using deep LSTM-RNN

Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.

Topology and control strategy of power optimisation for photovoltaic arrays and inverters during partial shading

In the outside environment, the partial shading on the photovoltaic (PV) arrays occurs frequently and the generated power of PV arrays is far lower than the rated power of inverters, thereby it will cause the total output power of PV system decline sharply. In this study, a flexible topology structure of PV power generation network, connecting PV arrays and inverters by the switching matrix is presented. Then, it introduces the working principle of topology structure and puts forward the corresponding multi-population genetic algorithm to realise the group management of the multi-objective control, so as to achieve the power matching optimisation between PV arrays and inverters during partial shading. Finally, the simulation and experiment show that the conversion efficiency of PV system used by the control strategy is much better than that of conventional distributed structure of PV power for the shade of PV arrays, and provide a new way for the effective use of solar energy.

Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

The power output (PO) of a photovoltaic (PV) system is highly variable because of its dependence on solar irradiance and other meteorological factors. Hence, accurate PO forecasting of a grid-connected PV system is essential for grid stability, optimal unit commitment, economic dispatch, market participation and regulations. In this paper, a day ahead and 1 h ahead mean PV output power forecasting model has been developed based on extreme learning machine (ELM) approach. For this purpose, the proposed forecasting model is trained and tested using PO of PV system and other meteorological parameters recorded in three grid-connected PV system installed on a roof-top of PEARL laboratory in University of Malaya, Malaysia. The results obtained from the proposed model are compared with other popular models such as support vector regression (SVR) and artificial neural network (ANN). The performance in terms of accuracy and precision of the prediction models is conducted with standard statistical error indicators including: relative root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE) and coefficient of determination (R2). The comparison of results obtained from the proposed ELM model to other models showed that ELM model enjoys higher accuracy and less computational time in forecasting the daily and hourly PV output power.

Experimental investigation on power output of different photovoltaic array configurations under uniform and partial shading scenarios

The power output of photovoltaic systems is considerably affected by complete or partial shading of the modules in an array and it depends on the type of configuration, module position, and shading scenario. In this paper, the effect of uniform and non-uniform shading scenarios on the output of solar photovoltaic modules of different interconnection schemes has been investigated. In order to determine the characteristics curves of a photovoltaic system, experiments have been conducted on a given array size under different shaded conditions. Further, random shading patterns have been artificially generated using meshes of different transmissivity. The impact of bypass diode on the power output of a photovoltaic system has also been observed. Finally, the performance of different module configurations is analyzed and the most efficient configuration under uniform insolation and partial shading conditions have been identified. The results found under the present experimental study have a guiding significance for photovoltaic system design and operation under the influence of partial shading.

Development of Probabilistic Reliability Models of Photovoltaic System Topologies for System Adequacy Evaluation

The contribution of solar power in electric power systems has been increasing rapidly due to its environmentally friendly nature. Photovoltaic (PV) systems contain solar cell panels, power electronic converters, high power switching and often transformers. These components collectively play an important role in shaping the reliability of PV systems. Moreover, the power output of PV systems is variable, so it cannot be controlled as easily as conventional generation due to the unpredictable nature of weather conditions. Therefore, solar power has a different influence on generating system reliability compared to conventional power sources. Recently, different PV system designs have been constructed to maximize the output power of PV systems. These different designs are commonly adopted based on the scale of a PV system. Large-scale grid-connected PV systems are generally connected in a centralized or a string structure. Central and string PV schemes are different in terms of connecting the inverter to PV arrays. Micro-inverter systems are recognized as a third PV system topology. It is therefore important to evaluate the reliability contribution of PV systems under these topologies. This work utilizes a probabilistic technique to develop a power output model for a PV generation system. A reliability model is then developed for a PV integrated power system in order to assess the reliability and energy contribution of the solar system to meet overall system demand. The developed model is applied to a small isolated power unit to evaluate system adequacy and capacity level of a PV system considering the three topologies.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

Both linear and nonlinear models have been proposed for forecasting the power output of photovoltaic systems. Linear models are simple to implement but less flexible. Due to the stochastic nature of the power output of PV systems, nonlinear models tend to provide better forecast than linear models. Motivated by this, this paper suggests a fairly simple nonlinear regression model known as multivariate adaptive regression splines (MARS), as an alternative to forecasting of solar power output. The MARS model is a data-driven modeling approach without any assumption about the relationship between the power output and predictors. It maintains simplicity of the classical multiple linear regression (MLR) model while possessing the capability of handling nonlinearity. It is simpler in format than other nonlinear models such as ANN, k-nearest neighbors (KNN), classification and regression tree (CART), and support vector machine (SVM). The MARS model was applied on the daily output of a grid-connected 2.1kW PV system to provide the 1-day-ahead mean daily forecast of the power output. The comparisons with a wide variety of forecast models show that the MARS model is able to provide reliable forecast performance.

Towards Optimal Solar Tracking: A Dynamic Programming Approach

The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.

Performance Evaluation of Photovoltaic String with Compound Parabolic Concentrator

 Abstract—Photovoltaic (PV) system is used to directly converting the solar energy into the electrical energy. Compound Parabolic Concentrator (CPC) is a non-imaging concentrator which is considered in this study for reducing the cost of electrical energy. Two configurations are numerically studied namely one with simple typical flat PV string and the other PV string with CPC. The truncated CPC with concentration ratio of 2.3 and an acceptance angle of 41.75° is considered in the analysis of PV string with CPC (PV-CPC). Transient System Simulation Software (TRNSYS) is used for the evaluation of PV cell performance with and without CPC. The mathematical model for PV and PV-CPC is developed for the performance estimation of thermal and electrical characteristic of the system. Engineering Equation Solver (EES) code is written to solve the mathematical model and is linked with TRNSYS for simulation. The simulation is carried out for the average day of the months of June and December for Riyadh city. Results indicated that the use of the CPC increases the absorbed energy and electrical power output of PV system. The electrical power of PV string increases almost 35% when CPC is used with PV compared to simple typical flat PV string.

The Impact of Haze on Performance Ratio and Short-Circuit Current of PV Systems in Singapore

The spectral content of sunlight directly affects the power output of solar photovoltaic (PV) devices. The extent of the effect of seasonal and weather-related spectral variations on the power output will depend largely on the semiconductor bandgap. In this study, haze, which is a common weather condi- tion in many parts of the world, is found to affect the power output of PV systems. An analysis of a recent haze event in Singapore in mid-June 2013 reveals that haze has an impact on the performance ratios and short-circuit currents of PV systems. The performance ratio of amorphous silicon thin-film PV systems dropped during the haze event, while that of crystalline silicon wafer-based systems ex- hibited a slight increase. A detailed analysis showed that the main cause of the observed performance ratio variations was changes in the generated short-circuit currents, which were due to a red shift of the solar spectrum arriving in the module plane during the haze period. Therefore, PV systems with different semiconductor bandgaps were affected differently. Index Terms—Average photon energy (APE), haze, performance ratio (PR), photovoltaic (PV) module, spectral analysis.

Architecture and Control Algorithms for Combating Partial Shading in Photovoltaic Systems

Partial shading is a serious obstacle to the effective utilization of photovoltaic (PV) systems since it can result in a significant degradation in the PV system output power. A PV system is organized as a series connection of PV modules, each module comprising a number of series-parallel connected PV cells. Backup PV cell employment and PV module reconfiguration techniques have been proposed to improve the performance of the PV system under the partial shading effects. However, these approaches are not very effective since they are costly in terms of their PV cell count and/or cell connectivity requirements. In contrast, this paper presents a cost-effective, reconfigurable PV module architecture with integrated switches in each PV cell. This paper also presents a dynamic programming algorithm to adaptively produce near-optimal reconfigurations of each PV module so as to maximize the PV system output power under any partial shading pattern. We implement a working prototype of reconfigurable PV module with 16 PV cells and confirm 45.2% output power level improvement. Using accurate PV cell models extracted from prototype measurement, we have demonstrated up to a factor of 2.36X output power improvement of a large-scale PV system comprised of three PV modules with 60 PV cells per module.

Solar photovoltaic system modeling and performance prediction

Abstract A simulation model for modeling photovoltaic (PV) system power generation and performance prediction is described in this paper. First, a comprehensive literature review of simulation models for PV devices and determination methods was conducted. The well-known five-parameter model was selected for the present study, and solved using a novel combination technique which integrated an algebraic simultaneous calculation of the parameters at standard test conditions (STC) with an analytical determination of the parameters under real operating conditions. In addition, the simulation performance of the model was compared with other models, and further validated by outdoor tests, which indicate that the proposed model fits well the entire set of experimental field test I–V curves of the PV module, especially at the characteristic points. After validation, this model was employed to predict the PV system power output under real conditions. The results show that the predictions agree very well with the PV plant field collected data. Thus, the operating performance of a standalone PV system located on a remote island in Hong Kong has been further evaluated with the aid of this model. It is found that the PV array power output is restricted by the status of the battery bank. This research demonstrates that the PV simulation model developed during the study is simple, but very helpful to PV system engineers in understanding the I–V curves and for accurately predicting PV system power production under outdoor conditions.

Reliability assessment for components of large scale photovoltaic systems

Photovoltaic (PV) systems have significantly shifted from independent power generation systems to a large-scale grid-connected generation systems in recent years. The power output of PV systems is affected by the reliability of various components in the system. This study proposes an analytical approach to evaluate the reliability of large-scale, grid-connected PV systems.The fault tree method with an exponential probability distribution function is used to analyze the components of large-scale PV systems. The system is considered in the various sequential and parallel fault combinations in order to find all realistic ways in which the top or undesired events can occur. Additionally, it can identify areas that the planned maintenance should focus on. By monitoring the critical components of a PV system, it is possible not only to improve the reliability of the system, but also to optimize the maintenance costs. The latter is achieved by informing the operators about the system component's status. This approach can be used to ensure secure operation of the system by its flexibility in monitoring system applications. The implementation demonstrates that the proposed method is effective and efficient and can conveniently incorporate more system maintenance plans and diagnostic strategies.

Estimation of Power Output of Photovoltaic Systems based on Solar Irradiation, Statistical Models and Power Flow in Distribution Line

Estimation of Power Output of Photovoltaic Systems based on Solar Irradiation, Statistical Models and Power Flow in Distribution Line

Control Strategy of the Hybrid Energy Storage System for Leveling Off Fluctuating Power Output of Stand-Alone Photovoltaic System

Hybrid energy storage system (HESS) including battery and super-capacitor can take advantages of both high energy density and high power density. In the stand-alone PV micro-grid, in which two buck/boost bidirectional converters are connected to the DC bus directly, a novel energy management scheme is proposed. After filtering the fluctuating power of the HESS, charge and discharge currents of the battery are controlled by hysteresis control method, the super-capacitor supplies the difference of the power. To leveling off the fluctuating power output of photovoltaic system and control the voltage of the DC bus, a new control strategy applied to the bidirectional converter of the super capacitor is put forward. The feed-forward loops of input voltage, load current and output voltage are introduced to improve the response speed and stability of the system. Results of the simulation show the effectiveness of the proposed energy management and control strategy.

New results in forecasting of photovoltaic systems output based on solar radiation forecasting

Accurate short term forecasting of photovoltaic (PV) systems output has a great significance for fast development of PV parks in South-East Europe, as well as in the case of Romania. Our approach on solar radiation forecast is based on two methods: autoregressive integrated moving average and artificial neural network. We have analyzed the daily solar irradiation variability and defined four synoptic situations to include the influence of cloudiness changes. Decadal variations of global solar radiation were also considered for long term forecast. The results were obtained using a database from Bucharest/Afumati Meteorological Station. We have developed an accurate forecasting model for a PV system's power output based on solar radiation forecasting results. By using complete datasets and including meteorological parameters such as cloudiness, relative humidity, air temperature, atmospheric pressure, and sunshine duration, as input for our model, we have managed to minimize forecasting errors and to obtain a more accurate forecast of the power output for the analyzed demo PV system.

Decision Technique of Solar Radiation Prediction Applying Recurrent Neural Network for Short-Term Ahead Power Output of Photovoltaic System

In recent years, introduction of a renewable energy source such as solar energy is expected. However, solar radiation is not constant and power output of photovoltaic (PV) system is influenced by weather conditions. It is difficult for getting to know accurate power output of PV system. In order to forecast the power output of PV system as accurate as possible, this paper proposes a decision technique of forecasting model for short-term-ahead power output of PV system based on solar radiation prediction. Application of Recurrent Neural Network (RNN) is shown for solar radiation prediction in this paper. The proposed method in this paper does not require complicated calculation, but mathematical model with only useful weather data. The validity of the proposed RNN is confirmed by comparing simulation results of solar radiation forecasting with that obtained from other method