Static and Dynamic Photovoltaic Cell/Module Parameters Identification

The accurate parameters extraction is an important step to obtain a robust PV outputs forecasting for static or dynamic modes. For these aims, several approaches have been proposed for photovoltaic (PV) cell modeling including electrical circuit-based model, empirical models, and non-parametrical models. Moreover, numerous parameter extraction methods have been introduced in the literature depending on the proposed model and the operating mode. These methods can be classified into two main approaches including automatic numerical and analytical approaches. These approaches are commonly applied in the static mode, whereas they can be employed for dynamic parameters extraction. In this chapter, as a first stage, the static parameters extraction for both single and double diodes models is exposed wherein Genetic Algorithm and outdoor measurements are considered for fixed irradiation and temperature. In the second stage, a dynamic parameters extraction is carried out using Levenberg-Marquardt algorithm, where 1 day profile outdoor measurement is considered. After that, the robustness of the proposed approaches is evaluated and the parameters obtained by the static method and that given by the dynamic technique are compared. The test is carried out using 3 days with different weather conditions profiles. The obtained results show that the parameters extraction by dynamic techniques gives satisfactory performances in terms of agreement with the real data

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

Solar Photovoltaic (PV) systems represent key and transformative technology at the forefront of the global shift towards sustainable energy solutions [...

Monitoring, modelling and simulation of PV systems using LabVIEW

WOS: 000318135700033This paper presents a detailed characterization of the performance and dynamic behaviour of photovoltaic systems by using LabVIEW real-time interface system. The developed software tool integrates several types of instruments into a single system which is able to offer online measurements all data sources and comparison simulation results with monitored data in real-time. Comprehensive monitoring and analyzing of PV systems play a very important role. The proposed method is a low-cost solution to provide fast, secure and reliable system by making the system database-ready for performance analysis of PV systems. The proposed method is also applied to a grid connected PV system in the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The results show that there is a good agreement between the measured and simulation results values. The integration methodology of robust simulation and monitored data in real-time can be extended to study the fault diagnosis of a PV system. (C) 2012 Elsevier Ltd. All rights reserved

Monitoring, modelling and simulation of PV systems using LabVIEW

This paper presents a detailed characterization of the performance and dynamic behaviour of photovoltaic systems by using LabVIEW real-time interface system. The developed software tool integrates several types of instruments into a single system which is able to offer online measurements all data sources and comparison simulation results with monitored data in real-time. Comprehensive monitoring and analyzing of PV systems play a very important role. The proposed method is a low-cost solution to provide fast, secure and reliable system by making the system database-ready for performance analysis of PV systems. The proposed method is also applied to a grid connected PV system in the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The results show that there is a good agreement between the measured and simulation results values. The integration methodology of robust simulation and monitored data in real-time can be extended to study the fault diagnosis of a PV system.Peer Reviewe

Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System

Fault detection is a necessary component to perform ongoing monitoring of photovoltaic plants and helps in their safety, maintainability, and productivity with the desired performance. In this study, an innovative technique is introduced by amalgamating Latent Variable Regression (LVR) methods, namely Principal Component Regression (PCR) and Partial Least Square (PLS), and the Triple Exponentially Weighted Moving Average (TEWMA) statistical monitoring scheme. The TEWMA scheme is known for its sensitivity to uncovering changes of small magnitude. Nevertheless, TEWMA can only be utilized for monitoring single variables and ignoring the correlation among monitored variables. To alleviate this difficulty, the LVR methods (i.e., PCR and PLS) are used as residual generators. Then, the TEWMA is applied to the obtained residuals for fault detection purposes, where the detection threshold is computed via kernel density estimation to improve its performance and widen its applicability in practice. Real data with different fault scenarios from a 9.54 kW photovoltaic plant has been used to verify the efficiency of the proposed schemes. Results revealed the superior performance of the PLS-TEWMA chart compared to the PLS-TEWMA chart, particularly in detecting anomalies with small changes. Moreover, they have almost comparable performance for large anomalies

Ensemble Learning Techniques-Based Monitoring Charts for Fault Detection in Photovoltaic Systems

Over the past few years, there has been a significant increase in the interest in and adoption of solar energy all over the world. However, despite ongoing efforts to protect photovoltaic (PV) plants, they are continuously exposed to numerous anomalies. If not detected accurately and in a timely manner, anomalies in PV plants may degrade the desired performance and result in severe consequences. Hence, developing effective and flexible methods capable of early detection of anomalies in PV plants is essential for enhancing their management. This paper proposes flexible data-driven techniques to accurately detect anomalies in the DC side of the PV plants. Essentially, this approach amalgamates the desirable characteristics of ensemble learning approaches (i.e., the boosting (BS) and bagging (BG)) and the sensitivity of the Double Exponentially Weighted Moving Average (DEWMA) chart. Here, we employ ensemble learning techniques to exploit their capability to enhance the modeling accuracy and the sensitivity of the DEWMA monitoring chart to uncover potential anomalies. In the ensemble models, the values of parameters are selected with the assistance of the Bayesian optimization algorithm. Here, BS and BG are adopted to obtain residuals, which are then monitored by the DEWMA chart. Kernel density estimation is utilized to define the decision thresholds of the proposed ensemble learning-based charts. The proposed monitoring schemes are illustrated via actual measurements from a 9.54 kW PV plant. Results showed the superior detection performance of the BS and BG-based DEWMA charts with non-parametric threshold in uncovering different types of anomalies, including circuit breaker faults, inverter disconnections, and short-circuit faults. In addition, the performance of the proposed schemes is compared to that of BG and BS-based DEWMA and EWMA charts with parametric thresholds

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved

Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study

The capacity of photovoltaic solar power installations has been boosted last years by reaching a new record with 175 GWdc of newly installed solar power in 2021. To guarantee reliable performances of photovoltaic (PV) plants and maintain target requirements, faults have to be reliably detected and diagnosed. A method for an effective and reliable fault diagnosis of PV plants based on the behavioral model and performance analysis under the LabVIEW environment is presented in this paper. Specifically, the first phase of this study consists of the behavioral modeling of the PV array and the inverter in order to estimate the electricity production and analyze the performance of the 9.54 kWp Grid Connected PV System (GCPVS). Here, the results obtained from the empirical models were validated and calibrated by experimental data. Furthermore, a user interface for modeling and analyzing the performance of a PV system under LabVIEW has been designed. The second phase of this work is dedicated to the design of a simple and efficient diagnostic tool in order to detect and recognize faults occurring in the PV systems. Essentially, the residuals obtained using the parametric models are analyzed via the performance loss rates (PLR) of four electrical indicators (i.e., DC voltage, DC current, DC power, and AC power). To evaluate the proposed method, numerous environmental anomalies and electrical faults affecting the GCPVS were taken into account. Results demonstrated the satisfactory prediction performance of the considered empirical models to predict the considered variables, including DC current, DC power, and AC power with an R2 of 0.99. Moreover, the obtained results show that the detection and recognition of faults were successfully achieved