A Comparison of Machine Learning-Based Methods for Fault Classification in Photovoltaic Systems

Costa, Clayton H. da; Moritz, Guilherme Luiz; Lazzaretti, André E.; Mulinari, Bruna Machado; Ancelmo, Hellen Cristina; Rodrigues, Marcelo P.; Oroski, Elder; Góes, Rafael Eleodoro de

doi:10.1109/isgt-la.2019.8895279

Cited by 19 publications

(15 citation statements)

References 7 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: 79%

“…Additionally, some auxiliary elements were introduced to simulate the considered system faults: switches that model open circuit faults, resistors that model string degradation, a variable that models partial shadowing, and switches that model short-circuit faults. Details of these elements can also be observed in [21].…”

Section: System Simulationmentioning

confidence: 99%

“…The implemented simulation blocks can be observed in Figure 7 , where

models PV strings configured with eight PV modules with 330 W each, which are individually simulated using the single-diode model [ 33 ], which takes the cell’s irradiance and temperature as inputs. The parameters of the model were chosen to match the PV panels specification from Table 1 , as detailed in [ 21 ]. Next,

represents the simulated system irradiance, while

represents the simulated module temperature.…”

Section: Datasetsmentioning

confidence: 99%

“…In the fault classification context, there are different approaches, such as visual methods [ 18 ], thermographic image analysis methods [ 13 ], and mathematical methods using theoretical and simulated models of PV plants [ 19 ]. More recently, some machine learning-based techniques have been proposed, improving the classification performance in different cases, mainly including the shadowing and degradation of PV modules [ 9 , 20 , 21 ]. Notwithstanding, most of the methods are focused on simulated data and the authors do not present an extensive analysis of methods for online fault classification.…”

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: 79%

Section: System Simulationmentioning

confidence: 99%

“…The implemented simulation blocks can be observed in Figure 7 , where

represents the simulated system irradiance, while

represents the simulated module temperature.…”

Section: Datasetsmentioning

confidence: 99%

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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show abstract

“…• Model structure: Relatively simple structures are most often considered, 63.6% (21 out of 33 models) with 1 hidden layer and the remaining with 2 hidden layers. The number of neurons in the hidden layer is generally less or around 10, while in some models more neurons are selected [36,41,42]. As for the number of parameters, its value is generally less than 500 in the reviewed cases.…”

Section: ) Integration Of Snn In Pv Fddmentioning

confidence: 99%