Parameters extraction of photovoltaic module for long-term prediction using artifical bee colony optimization

Garoudja, Elyes; Kara, Kamel; Chouder, Aissa; Silvestre, S.

doi:10.1109/ceit.2015.7232993

Cited by 26 publications

(20 citation statements)

References 14 publications

(18 reference statements)

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“…This is mainly due to its capacity to converge, regardless initial conditions, to accurate global solutions, high convergence speed and its simplicity in terms of implementation [25]. Moreover, in our previous works [2,[29][30][31], the canonical ABC algorithm [32] was successfully applied to extract the ODM parameters.…”

Section: Pv Module Parameters Extractionmentioning

confidence: 99%

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Garoudja

Chouder

Kara

et al. 2017

Energy Conversion and Management

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158

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In this paper, a novel procedure for fault detection and diagnosis in the direct current (DC) side of PV system, based on probabilistic neural network (PNN) classifier, is proposed. The suggested procedure consists of four main stages: (i) PV module parameters extraction, (ii) PV array simulation and experimental validation (iii) elaboration of a relevant database of both healthy and faulty operations, and (iv) network construction, training and testing. In the first stage, the unknown electrical parameters of the one diode model (ODM) are accurately identified using the best-so-far ABC algorithm. Then, based on these parameters the PV array is simulated and experimentally validated by using a PSIM™/Matlab™ co-simulation. Finally, efficient fault detection and diagnosis procedure based on PNN classifier is implemented. Four operating cases were tested in a grid connected PV system of 9.54 kWp: Healthy system, three modules short-circuited in one string, ten modules short-circuited in one string, and a string disconnected from the array. Moreover, the PNN method was compared, under real operating conditions, with the feed forward back-propagation Artificial Neural Network (ANN) classifiers method, for noiseless and noisy data to evaluate the suggested method’s accuracy and test its aptitude to support noisy data. The obtained results have demonstrated the high efficiency of the proposed method to detect and diagnose DC side anomalies for both noiseless and noisy data cases.Peer ReviewedPostprint (author's final draft

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Section: Pv Module Parameters Extractionmentioning

confidence: 99%

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

Garoudja

Chouder

Kara

et al. 2017

Energy Conversion and Management

Self Cite

158

View full text Add to dashboard Cite

show abstract

“…There are some mathematical modeling approaches for fault detection. For instance, references [16][17][18] use the one-diode model approaches to detect faults for a PV array. Kang et al [19] propose a novel method to diagnose output power lowering in a PV array based on using the Kalman-filter algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation

Pei

Hao

2019

Energies

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Photovoltaic (PV) power generation systems work chronically in various climatic outdoor conditions, therefore, faults may occur within the PV arrays in PV systems. Online fault detection for the PV arrays are important to improve the system’s reliability, safety and efficiency. In view of this, a fault-detection method based on voltage and current observation and evaluation is presented in this paper to detect common PV array faults, such as open-circuit, short-circuit, degradation and shading faults. In order to develop this detection method, fault characteristic quantities (e.g., the open-circuit voltage, short-circuit current, voltage and current at the maximum power point (MPP) of the PV array) are identified first to define the voltage and current indicators; then, the fault-detection thresholds are defined by utilizing voltage and current indicators according to the characteristic information of various faults; finally, voltage and current indicators evaluated at real-time voltage and current data are compared with the corresponding thresholds to detect potential faults and fault types. The performances of the proposed method are simulated verifying by setting eight different fault patterns in the PV array. Simulation experimental results show the effectiveness of the proposed method, especially the capacities of distinguishing the degradation faults, partial shading faults and variable shading faults.

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“…Moreover, the accuracy of the PV module models is strongly affected by the way of extracting their unknown parameters. Several research works discussed the topic of PV model parameters estimation, by applying different methods based on analytical [11], numerical [12,13] and bio-inspired optimization solution [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

et al. 2016

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Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/µc-Si:H).\ud \ud The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.Peer ReviewedPostprint (author's final draft

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Parameters extraction of photovoltaic module for long-term prediction using artifical bee colony optimization

Cited by 26 publications

References 14 publications

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

An enhanced machine learning based approach for failures detection and diagnosis of PV systems

A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

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