An important issue for the growth and management of grid-connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods ...based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid-connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.
It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as ...the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its ...clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.
•Degradation rate of large-scale crystalline (c-Si) PV array.•Four years performance ratio of c-Si PV array.•Energy derate of c-Si PV array under semi-arid climates in India.
We report the ...preliminary results of the degradation study of large-scale photovoltaic (PV) system installed using 1006.74 kWp crystalline silicon (c-Si) PV array. A linear least square (LLS) fitting method is adopted, and the degradation rate (DR) is calculated for the four years based on monitored operational data in semi-arid climates of India.
Harnessing energy from the sunlight using solar photovoltaic trees (SPVTs) has become popular at present as they reduce land footprint and offer numerous complimentary services that offset ...infrastructure. The SPVT’s complimentary services are noticeable in many ways, e.g., electric vehicle charging stations, landscaping, passenger shelters, onsite energy generated security poles, etc. Although the SPVT offers numerous benefits and services, its deployment is relatively slower due to the challenges it suffers. The most difficult challenges include the structure design, the photovoltaic (PV) cell technology selection for a leaf, and uncertainty in performance due to weather parameter variations. This paper aims to provide the most practical solution supported by the performance prioritization approach (PPA) framework for a typical multilayered SPVT. The proposed PPA framework considers the energy and sustainability indicators and helps in reporting the performance of a multilayered SPVT, with the aim of selecting an efficient PV leaf design. A three-layered SPVT (3-L SPVT) is simulated; moreover, the degradation-influenced lifetime energy performance and carbon dioxide (CO2) emissions were evaluated for three different PV-cell technologies, namely crystalline silicon (c-Si), copper indium gallium selenide (CIGS), and cadmium telluride (CdTe). While evaluating the performance of the 3-L SPVT, the power conversion efficiency, thermal regulation, degradation rate, and lifecycle carbon emissions were considered. The results of the 3-L SPVT were analyzed thoroughly, and it was found that in the early years, the c-Si PV leaves give better energy yields. However, when degradation and other influencing weather parameters were considered over its lifetime, the SPVT with c-Si leaves showed a lowered energy yield. Overall, the lifetime energy and CO2 emission results indicate that the CdTe PV leaf outperforms due to its lower degradation rate compared to c-Si and CIGS. On the other side, the benefits associated with CdTe cells, such as flexible and ultrathin glass structure as well as low-cost manufacturing, make them the best acceptable PV leaf for SPVT design. Through this investigation, we present the selection of suitable solar cell technology for a PV leaf.
The alleged reliability has led the longest warranty period for Photovoltaic (PV) modules up to 20–25 years; it becomes possible after understanding the failure mode and degradation analysis of PV ...module. Failure mode decreases the performance of the PV module throughout the long-term outdoor exposure. The main objective of the present study is to identify the failure mechanism and failure mode of solar PV modules and their impact on degradation in operating conditions. Assessment of previous studies on rate indicates the highest performance losses at initial stage of outdoor exposure and a degradation drop-off of 0.014% per year. In this context, risk priority number (RPN) analysis is carried out to identify the severity of the failure mode, which affect the system performance for c-Si technologies. However, hot spot and de-lamination are degradation modes related to safety issue with lower value of RPN <50.
•Failure mechanism and failure mode of solar PV modules in real outdoor conditions.•Studies on the mono-crystalline PV modules over a period of 25 years are considered to identify defects and failure modes.•Impact of PV module defects and failure modes on its degradation is presented.•Risk priority number (RPN) analysis for identifying the severity of the PV failure mode.
The presented data are related to the article “Solar Irradiance and Temperature Influence on the Photovoltaic Cell Equivalent-Circuit Models” (Chaibi et al., 2019). Data include the open-circuit ...voltage, the short-circuit current and the output power of the Shell SM55 mono-crystalline Photovoltaic (PV) Solar Module obtained from a PV panel modelling based on the single-diode and the double-diode circuit models, coupled with Chaibi and Ishaque parameter extraction techniques (Chaibi et al., 2018, Ishaque et al., 2011). The I–V curves as simulation results are provided at various levels of solar irradiance and temperature.
Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation ...(PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.
This article presents performance data concerning a 1MW crystalline photovoltaic (PV) plant installed in the semi-arid climate of India. Data includes the daily average samples from January 2012 to ...February 2016, related to solar irradiance on the plane of the array, electrical energy injected into the grid, reference yield, final yield, and the performance ratio. Furthermore, the decomposition time series for the performance ratio by applying the classical seasonal decomposition (CSD), Holt-Winters seasonal model (HW), and Seasonal and Trend decomposition using Loess (STL) is also provided for quantifying of the degradation rate of the PV system. The data are provided in the supplementary file included in this article. The dataset is related to the paper entitled “Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India.” 1.
•Performance characteristics of a 1 MW grid-connected PV in tropical semi-arid climate.•Performance ratio and final yield comparison with other large-scale PV system in various climates.•Degradation ...rate estimation of PV exposed to outdoors for 4 years.•Statistical methods to quantify PV degradation rate.•Comparison analysis for degradation of mono-Si PV systems in different sites.
The performance and degradation of a 1 MWp utility-scale photovoltaic (PV) system located in the tropical semi-arid climate of India is investigated based on four years of monitored data. The reference yield, final yield, system efficiency, capacity factor, and performance ratio are 4.64 h/day 6.23 h/day, 11%, 19.33%, and 74.73%, respectively, according to the standard IEC 61724. The performance is compared to other large-scale PV systems in different climate conditions. The degradation of the PV plant is quantified by using various statistical methods. These methods include the linear least-squares regression (LLS), the classical seasonal decomposition (CSD), the Holt-Winters seasonal model (HW), and the seasonal and trend decomposition using loess (STL). The degradation rate is estimated at 0.27%/year, 0.32%/year, 0.50%/year, and 0.27%/year, respectively, after 50 months operating period. The degradation accuracy analysis classifies the LLS and HW as lower accuracy methods (0.22%) than CSD (0.11%) and STL (0.15%). A comparison of the degradation of mono-Si PV systems for various locations is performed using different statistical methods. This study contributes to the improvements in the knowledge of PV degradation in the Indian climate.
PDF
