Performance Of Photovoltaic Power Plants Research Articles

Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review

The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.

Benchmarking performance of photovoltaic power plants in multiple periods

Benchmarking performance of photovoltaic power plants in multiple periods

Photovoltaic Power Prediction Model Using Pre-train and Fine-tune Paradigm Based on LightGBM and XGBoost

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-XGBoost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid.

Research on Joint Control Strategy of Optical Energy Storage System

The energy storage system in photovoltaic power plants has become the main way to solve the current problems, including the volatility and uncontrollability of photovoltaic power generation, as well as improving the performance of photovoltaic power plants and grids. The energy storage system with reasonable charging/discharging strategies can prolong the service life of energy storage system. This article proposes a method based on the combination of optical power prediction technology and low-pass filtering to decompose the output curve of photovoltaic power plants. High-frequency fluctuations and low-frequency fluctuations are respectively stabilized by the BMS control technology of the energy storage system to avoid frequent overall storage batteries. While charging and discharging affect its life, it can also reduce the impact on the grid and voltage fluctuations. It has a significant practical guiding for planning and constructing optical power-storage stations in the future.

Energy analysis of utility-scale PV plant in the rain-dominated tropical monsoon climates

To limit the adverse impact of fossil fuel-generated power, energy generation from solar photovoltaic (PV) power is gaining importance. A lot of utility-scale PV power plants are being installed in tropical regions owing to the increased sunshine hours especially during the summer season. The influence of rain on the performance of PV power plants during monsoon seasons in a tropical climate is not studied in detail. This paper analyses the operational performance of a 2 MWp photovoltaic plant commissioned at the Kuzhalmannam site, Palakkad district, Kerala State, South India. The methodology includes the analysis of standard performance metrics of the plant utilizing two-year measured data and comparison with other climates. The PV plant's average performance ratio (PR) is 73.39, with an average of 15.41% capacity utilization factor (CUF) over the study period. The most deficit generation for the PV plant is observed in the months from June to August, which are part of the Southwest monsoon season. It is observed that the monsoon seasons prevailing in the region have a more substantial influence leading to a 35% reduction in energy generation from the annual average generation. Further, a comparison with other climates revealed that the specific yield obtained in rain-dominated monsoon tropical climates is lesser than dry and temperate climates.

A deep learning based hybrid method for hourly solar radiation forecasting

Solar radiation forecasting is a key technology to improve the control and scheduling performance of photovoltaic power plants. In this paper, a deep learning based hybrid method for 1-hour ahead Global Horizontal Irradiance (GHI) forecasting is proposed. Specifically, a deep learning based clustering method, deep time-series clustering, is adopted to group the GHI time series data into multiple clusters to better identify its irregular patterns and thus providing a better clustering performance. Then, the Feature Attention Deep Forecasting (FADF) deep neural network is built for each cluster to generate the GHI forecasts. The developed FADF dynamically allocates different importance to different features and utilizes the weighted features to forecast the next hour GHI. The solar forecasting performance of the proposed method is evaluated with the National Solar Radiation Database. Simulation results show that the proposed method yields the most accurate solar forecasting among the smart persistence and state-of-the-art models. The proposed method reduces the root mean square error as compared to the smart persistence by 11.88% and 12.65% for the Itupiranga and Ocala dataset, respectively.

100 kwp solar photovoltaic power plant performance analysis using PVSYST

100 kwp solar photovoltaic power plant performance analysis using PVSYST

A Long-Term Condition Monitoring and Performance Assessment of Grid Connected PV Power Plant with High Power Sizing Factor under Partial Shading Conditions

Partial shading conditions of photovoltaic (PV) modules often occurs in urban areas leading to losses in electricity power generation of the PV power plant. The purpose of this study is to present how the PV power plant with high value of inverter power sizing factor (Kinv) can achieve high performance and power production under partial shading conditions with high shading losses. In this paper the results of long-term monitoring, performance analysis and experimental results are presented, while the results are compared to the estimated values calculated using PVsyst software. The study focused on the PV power plant at the Faculty of Technical Sciences (FTS) in Novi Sad, Republic of Serbia, for the period between the years 2012 and 2019. It has been shown that the values of PV power plant performance parameters are better than expected (very high), and resemble the power plants operating without shading. The high value of the inverter power sizing factor may lead to occasional saturation of the inverter when certain conditions are met, but most of the times it allows the inverter to operate at a more optimal power level. PV module soiling and power degradation is within the limits mentioned in the literature. The increase in Kinv in the partial shading conditions favorably affects the performance, does not degrade the efficiency of the inverter at saturation, reduces the effect of soiling and aging of PV modules, leading to higher power production.

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions

An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method.

Extreme solar overirradiance events: Occurrence and impacts on utility-scale photovoltaic power plants in Brazil

In contrast to the decades-long accumulated experience of photovoltaic (PV) installations in more temperate climates, the effects of some extreme operating temperatures and extreme solar irradiance levels start to become more noticeable with the uptake of large-scale PV in the sunbelt regions of the world. One such effect is caused by extreme solar overirradiance events, which up to now have been reported in the literature much more for their scientific interest than as a potential problem affecting the operational performance of PV power plants. Solar overirradiance events with a time span of tens of seconds or minutes are associated with the high operating temperatures prevailing in the field at many sites where utility-scale PV power plants start to become widespread, deleterious effects can be observed, which will negatively impact the performance of these generators. Using irradiance data from seven solar measuring stations deployed over distinct regions in Brazil, many of which are hosting megawatt-scale PV power plants, this paper reports on several extreme solar overirradiance events, with measured values above 1367 W/m2 up to 1845 W/m2, lasting from many seconds to a few minutes. Analysis of impacts and consequences from these events on the operational performance of PV generators are addressed, mainly focused on combiner box fuses, inverter overload losses, and inverter maximum power point tracker.

PV Module Monitoring System Based on Low-Cost Solutions: Wireless Raspberry Application and Assessment

The integration of renewables into power systems has led to multiple studies and analysis in terms of grid-power quality, reliability, and/or feasibility. Among different resources to be considered as alternative energy systems, wind and solar emerge as the most mature technologies. With regard to photovoltaic (PV) installations, monitoring problems requires detailed analysis, since solar-radiation fluctuations, soiling on solar panels, or deficiency of PV-panel performance can involve unexpected power-output oscillations and, subsequently, undesirable power-generation oscillations. Under this framework, this paper describes and assesses a wireless low-cost PV-module monitoring system based on open-source solutions. Our proposal allows us to monitor installations at the PV-module level, giving detailed information regarding PV power-plant performance. The proposed monitoring system is based on the IEC-61724 standard requirements, as a flexible and ad hoc solution with relevant connectivity options. Meteorological and electrical data are collected from the developed nodes and available for subsequent analysis. Detailed information of the solution, as well as extensive results collected in Spanish PV power plants connected to the grid, are also included in the paper.

A novel climate classification criterion based on the performance of solar photovoltaic technologies

The response of solar photovoltaic (PV) technologies with respect to environmental parameters is governed by the physical and chemical properties of the PV material. The PV modules are being tested under standard test conditions (STC), however, these conditions are hardly available in real operating conditions, of the locations where the PV modules are supposed to be operational. Therefore, the performance of PV power plant varies with the environmental conditions of the location and technology of the PV module. In this paper, seven climatic zones have been proposed for India, based on the annual daytime irradiance and ambient temperature specifically for the deployment of PV system. A novel methodology has been proposed in order to identify the most efficient PV technology for a particular climatic zone. The parametric study of efficiency for four different technologies i.e. multi- crystalline silicon (m-Si), amorphous silicon (a-Si), cadmium telluride (CdTe), and hetero junction intrinsic thin film (HIT), has also been evaluated with respect to the irradiance and ambient temperature based on the indoor measurements. The m-Si PV technology is found to be most efficient in all the irradiance range, while CdTe technology has the lowest impact of temperature with high efficiency at lower irradiance range.

Degradation Analysis of a Rooftop Solar Photovoltaic System—A Case Study

The solar photovoltaic (PV) market has grown very rapidly throughout the past 15 years and is quickly becoming an international, non-subsidized market with increased demand for performance certainty. An increasing number of studies analyze long-term monitoring data to determine degradation rates, or “rates of change” (RoCs). Analyses of long-term monitoring data give insight in the performance of PV power plants over time, but are found to be sensitive to uncertainties, especially those related to irradiance measurements using silicon reference cells. The case study analysis shows, however, that the RoCs are much lower if reference cells are adjusted for soiling, drifting and sensor replacements or if satellite irradiance data is used. This indicates that the impact of biased irradiance data should not be underestimated. It was also found, that the distribution of RoCs is strongly influenced by the age and size of the selected systems, underlining the importance of system selection. The case study analysis indicates that crystalline silicon PV systems operating for 8+ years are expected to show a “rate of change” of -0.5% per year or fewer. In this paper the 15-minute data has been collected from a solar photovoltaic generating station installed on the roof of engineering college building and efficiency/degradation of solar panels have been analyzed and reported.

Improving the Performance of Photovoltaic Power Plants with Determinative Module for the Cooling System

The objective of this work is to analyze and evaluate the impact of cooling systems on photovoltaic modules (for electricity generation), applied at a pilot Testing Facility. The results obtained during this step are used as input in order to determine the best model to be applied at a real-scale Photovoltaic Power Plant (PVPP). This methodology is based on the monitoring and supervision of the operating temperature of commercial photovoltaic modules (PV), both with and without cooling systems, as well as on the study of the water supply design of the cooling system applied on a micro photovoltaic power plant which is connected to the commercial network. Through the analysis of the data, we observed that photovoltaic modules with cooling systems always operate at lower temperatures than the ones without cooling systems. During the testing period, the operating temperatures of the photovoltaic modules without cooling systems were above 60oC (with a maximum temperature equaling 68.06oC), whereas the maximum temperatures registered on the sensors of the model “A” were 43.55oC and 44.75oC, and the ones registered on the sensors of the model “B” were 46.76 and 48.33oC. Therefore, we conclude that the photovoltaic module with the cooling system model “A” is the most suitable for large-scale application, since it was the only model to present temperatures lower than the nominal operating condition temperature (NOCT) of the cell (47oC ± 2oC).

MUĞLA SITKI KOÇMAN ÜNIVERSITESI'NDEKI GÜNEŞ ENERJISI UYGULAMALARI

In this paper we presented the electricity yield of photovoltaic power plant applications starting from 1996 is described and obtained results are summarized in the main campus of Mugla Sitki Kocman University, Turkey. There are four small scale photovoltaic power plants installed in the campus. They are installed on the top of Mentese Library of Mugla Sitki Kocman University, on the roof of Turkevi Student Cafeteria, on the facade and on the east and west towers of Staff's Block of the Building of Education Faculty and two double axis tracking systems. The systems are installed in 2001, 2003, 2008 and 2009 with rated powers of 10 kW p to 40 kW p . There are also several lamp-posts at the campus in various locations. All power plants are monitored from the start up and the collected data are analysed in several conference proceedings and papers individually. This paper summarizes the electricity yield and the performance of photovoltaic power plants by the way that indicator of system quality which is called performance ratio (PR) and efficiency for each photovoltaic power plants, after their installation and also their presentation itemized

PV outdoor tests and 12 years of Muğla experiences

AbstractIn this paper, the electricity yield of photovoltaic power plant applications in the main campus of Muğla Sıtkı Koçman University, Turkey is presented. There are four small scale grid connected photovoltaic power plants installed in the main campus. They are installed on the top of Menteşe Library of Muğla Sıtkı Koçman University, on the roof of Turkevi Student Cafeteria, on the façade and on the east and west towers of Staff's Block of the Building of Education Faculty and two double axis tracking systems. The systems are installed in 2001, 2003, 2008 and 2009 with rated powers between 10 kWp and 40 kWp. There are also several lamp‐posts at the campus in various locations. All power plants are monitored from the start up and the collected data are analysed in several conference proceedings and papers individually. This paper summarizes the electricity yield and the performance of photovoltaic power plants by the way that indicator of system quality which is called performance ratio (PR) and efficiency for each photovoltaic power plants, after their installation and also their presentation is itemized. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Optimal capacity design for hybrid energy storage system supporting dispatch of large-scale photovoltaic power plant

This paper presents a methodology to evaluate the optimal capacity and economic viability of a hybrid energy storage system (HESS) supporting the dispatch of a 30MW photovoltaic (PV) power plant. The optimal capacity design is achieved through a comprehensive analysis of the PV power plant performance under numerous HESS capacity scenarios. The analysis has been conducted using a high performance computing cluster which generated a large amount of simulation data based on a PV power profile of one month. The HESS, consisting of a vanadium redox battery and a supercapacitor bank with a power rating ratio between the two energy storage technologies of 5:1, is connected at the point of common coupling to support the PV power plant to comply with the dispatch rules in the Australian national electricity market. A quantitative relation is developed based on surface fitting, which relates the HESS capacity with the dispatch performance of the PV power plant in terms of energy yield and required ancillary services. The paper concludes the optimal capacity of HESS which will provide the maximum profit improvement under the actual market conditions.

A Study on the Effects of Solar Tracking Systems on the Performance of Photovoltaic Power Plants

The use of renewable energy resources to produce the electricity is a rising trend in various countries worldwide. This is especially because these energies do not produce the greenhouse gases; therefore, don’t become a destructive factor on the ozone layer and the environment. Besides, the fossil energies are always exposed to price changes and will eventually end, while the renewable energies don’t pose such problems and are freely acquired from the nature. Some of the most important of these reproducible energy resources are the energies of the wind and the solar energy. The generation capability of a photovoltaic power plant is largely dependent on the intensity of the sun radiation. On the other hand, the changing of position causes the sun to have a variable shining intensity in different seasons and different times of the year; therefore, some of the solar power plants possess solar tacking systems. This paper studies the different types of photovoltaic systems including fixed panel, photovoltaic farms equipped to the single axis and double axis tracking systems and their effects on the performance of the solar power plants. In order to determine the position of the sun in the sky, the zenith and azimuth angles of it are used to extract the relations between the different parameters of the sun radiation. The resulted parameters are used in the modeling of the single axis and double axis systems. In the paper it is utilized from the sun parameters in Qeshm Island of Iran in the summer of 2011.