A MISO Nonlinear Model of Photovoltaic Panel Based on System Identification

Rodrigues, Marcelo P.; Oroski, Elder; Stadzisz, Paulo Cézar; Costa, Clayton H. da; Lazzaretti, André E.; Linhares, Robson R.; Góes, Rafael Eleodoro de

doi:10.1109/isgt-la.2019.8895325

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Finally, it is worth mentioning that this work is an extension and combination of previous works [8,17,21] of the authors of this paper, which presented initial and individual results of monitoring system, fault detection, and classification, respectively.…”

Section: Contributions Of This Workmentioning

confidence: 78%

“…Based on the Models NRMSE values, Table 4 , and on Figure 12 , it is possible to conclude that ARX model overcame the Diode models, reaching a NRMSE of

, (for more details about the DDM, refer to [ 56 ]). However, the Hammerstein–Wiener (for more information, see [ 17 ]) has reached a NRMSE of

. It is worth mentioning that, in this part of the analysis, only data representing the system without fault was employed on the parameter estimation of the models.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to mention that the proposed model is not the only one. In a previous work, the authors have already investigated Diode Models and Hammerstein-Wiener models, applied to the same problem [17]. The comparisons will be presented in Section 7.…”

Section: Proposed Fault Detection Methodsmentioning

confidence: 99%

“…Recently, several methods based on the modeling of PV systems have been proposed [ 14 , 15 , 16 ], achieving state-of-the-art results in real PV plants. However, such recent models are mostly based on static models, discarding relevant dynamic modeling, and making it difficult to detect events that occur in short intervals of time [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

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Section: Contributions Of This Workmentioning

confidence: 78%

“…Based on the Models NRMSE values, Table 4 , and on Figure 12 , it is possible to conclude that ARX model overcame the Diode models, reaching a NRMSE of

, (for more details about the DDM, refer to [ 56 ]). However, the Hammerstein–Wiener (for more information, see [ 17 ]) has reached a NRMSE of

. It is worth mentioning that, in this part of the analysis, only data representing the system without fault was employed on the parameter estimation of the models.…”

Section: Resultsmentioning

confidence: 99%

Section: Proposed Fault Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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“…The mathematical models of real systems can be divided into static and dynamic models 13 , the dynamical model for the PV system is preferred due to the loads always are not constant, and the PWM which is used for switching converters or inverters, the static model for PV module is introduced in many pieces of literature such as in [14][15][16][17][18] , a single diode model is considered as a static PV model and is the most one which is used, the static model can be derived from parameter identi cation, parameter identi cation method tray to make approximation and solve analytically the equation of the PV as a polynomial of the rst order [15], this means the static model is simpli ed and has low accuracy.…”

Section: Introductionmentioning

confidence: 99%

System identification of photovoltaic system based on fractional-order model

Ebead

Abou-Zalam

Nabil

2022

Preprint

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A fractional-order model of a photovoltaic (PV) system is proposed in this paper. The system identification method is used to construct an accurate dynamical model for a PV system, a real PV module and boost converter are used to collect experimentally input-output data for the identification process. A black box modeling is considered for system identification to obtain a transfer function, without needing to make mathematical analysis. the input-output data which is measured is fitted by the least-square curve fitting method, and the parameters of the curve fitting are optimized by the Levenberg-Marquardt algorithm. the proposed fractional-order model is employed with the MATLAB and Simulink software to obtain a fractional order PID controller, which is used to extract maximum power from the PV system.

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System identification of photovoltaic system based on fractional-order model

2022

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A fractional-order model of a photovoltaic (PV) system is proposed in this paper. The system identification approach is used to develop an effective dynamical model for a PV system. A real PV module and a boost converter are used to gather the experimental input–output data for the identification process. The black box modeling is applied to the system identification to obtain the transfer function, without the requirement to perform any mathematical analysis. The identification process is based on the least squares criteria for minimizing model output error, and the Levenberg–Marquardt algorithm is used for optimizing the model parameters. The proposed fractional-order model (FOM) is investigated using MATLAB to study the frequency response and the model's stability. Simulation results verify the effectiveness and advantages of the FOM in comparison to identified integer-order model.

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A MISO Nonlinear Model of Photovoltaic Panel Based on System Identification

Cited by 6 publications

References 20 publications

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

System identification of photovoltaic system based on fractional-order model

System identification of photovoltaic system based on fractional-order model

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