Photovoltaic Module Simultaneous Open-and Short-Circuit Faults Modeling and Detection using the I-V Characteristic

International audienceThis paper created a new research space in the faults modeling and detection area of the industrial systems, especially photovoltaic generators. It reserved for modeling and detection the hybrid defects, like the presence of cells open- and short-circuit within the same photovoltaic cells group.For a small investment, the new algorithm created a new platform. It exposed a display screen of the database, which presented the power of the PV module production in each period. The display screen allows real-time monitoring of the PV module production throughout the year, and detecting its anomalies

A Smart Algorithm for the Diagnosis of Short-Circuit Faults in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing short-circuit faults detection and diagnosis of PV generators. The proposed algorithm is based on the hybridization of a support vector machines (SVM) technique optimized by a k-NN tool for the classification of observations on the classifier itself or located in its margin. To test the proposed algorithm performance, a PV generator database containing observations distributed over classes is used for simulation purposes

A Regression Algorithm for the Smart Prognosis of a Reversed Polarity Fault in a Photovoltaic Generator

International audienceThis paper deals with a smart algorithm allowing reversed polarity fault diagnosis and prognosis in PV generators. The proposed prognosis (prediction) approach is based on the hybridization of a support vector regression (SVR) technique optimized by a k-NN regression tool (K-NNR) for undetermined outputs. To test the proposed algorithm performance, a PV generator database containing sample data is used for simulation purposes

Electrical Faults Modeling of the Photovoltaic Generator

International audienceIn this paper, we presented a new methodology for the mathematical modeling of the photovoltaic generator's characteristics based on known electrical laws. This proposed new methodology in this work consists of a three new algorithms, each one presents the characteristic of the cell, group of cells, module, string and generator, when one or more of its components : cells, bypass diodes and blocking diodes subjected to these types of defaults: reversed polarity, open circuit, short circuit or impedance. The three new algorithms obtained can facilitate the prediction for the prognosis or the detection for the diagnosis of these photovoltaic generator's defaults

Optimization of SVM Classifier by k-NN for the Smart Diagnosis of the Short-Circuit and Impedance Faults in a PV Generator

International audienceThis paper deals with a new algorithm allowing short-circuit and impedance faults smart diagnosis of PV generators. It is based on the use of the SVM technique for the classification of observations not located in its margin, otherwise the proposed algorithm is used a k-NN method. A PV generator database containing observations distributed over classes is used for testing the new algorithm performance, which shows therefore its contribution and its effectiveness in the diagnosis area. * Parameter j of new observation x *. I' Identity matrix. J Tuning parameter for error accepted. I Current. V Voltage. P Power. PH Photocurrent. I/V Cell Current / Voltage of PV cell. I/V Group Current / Voltage of PV group. I/V Module Current / Voltage of PV module. I/V String Current / Voltage of PV string. I Bypass_Diode Bypass diode current. R s series resistance. t Temperature

Smart Algorithm Based on the Optimization of SVR Technique by k-NNR Method for the Prognosis of the Open-Circuit and the Reversed Polarity Faults in a PV Generator

International audience– This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors. Nomenclature PV Photovoltaic SVM Support Vector Machines SVR Support Vector Regression k-NNR k-Nearest Neighbor Regression X SVR input vector Y SVR output vector f Linear function Ф Nonlinear mapping function w Weight vector e Squared loss function x Problem variable x * New problem variable α Lagrange multipliers N Number of classes m Number of index of minimum distances I / V Current / Voltage IPH Photocurren

Modeling the PV generator behavior submit to the open-circuit and the short-circuit faults

International audience— In this paper, we proposed a new mathematical model of a faulty photovoltaic generator operation. It presents its behavior, when it's subjected to the open-circuit and the short-circuit faults at its basic components as: cells, bypass diodes and blocking diodes. Such kind of modeling will allow developing fault detection and diagnosis methods. Indeed, the proposed model will be used to set normal and fault operation conditions database, which will facilitate learning and classifications phases. NOMENCLATURE PV = Photovoltaic Generator. phi = Photocurrent. V Cell_Open-circuit = Open-Circuit Voltage of Cell. I Cell_Short-circuit = Short-Circuit Current of Cell. I 0 = Reverse Saturation Current of the Diode. R S = Cell Series Resistance. R SH = Cell Shunt Resistance. nc: ncg / ncp = Cell Number: Good / Defective. ng: ngg / ngp = Group Number: Good / Defective. nm: nmg / nmp = Module Number: Good / Defective. ns: nsg / nsp = String Number: Good / Defective. nfg / nfp = Good / Defective Generator. N Cells = Cells Number in each Group. N Groups = Groups Number in each Module. N Modules = Modules Number in each String. N Strings = Strings Number in the Generator. V / I = Voltage / Current. P = Power. V Cell_imposed = Voltage Imposed. DTV = Diode Thermal Voltage. a = Diode Ideality Factor

Development of a New Methodology for Modeling the PV Generator Behavior in the Presence of Open-Circuit and Short-Circuit Faults

International audience— In this paper, we proposed a new methodology for the faults photovoltaic generator modeling, especially when it subjected to the open-circuit and short-circuit faults at its components: cells, bypass diodes and blocking diodes. The highlight of the proposed algorithm focused on the mathematical modeling that based on known electrical laws, of the IV characteristic of the faulty PV generator. This model is able to develop a rich database , containing six electrical faults types, which can uses in the diagnosis area of the photovoltaic generators. NOMENCLATURE I_SRC = String Reversed Current. I_SSC = String Supplied Current. I_Cell = Cell Current. V_Cell = Cell Voltage. I_Cell_Short_Circuit = Cell Short Circuit Current. V_Cell_Open_Circuit = Cell Open Circuit Voltage. nc = Cells Number. I_Group = Group Current. V_Group = Group Voltage. I_Bypass_Diode = Bypass Diode Current. ng = Groups Number. I_Module = Module Current. V_Module = Module Voltage. nm = Modules Number. I_String = String Current. V_String = String Voltage. ns = Strings Number. I_PV = Generator Current. V_PV = Generator Voltage. PHI = Photo-Current. I 0 = Reverse Saturation Current. DTV = Diode Thermal Voltage. a = Diode Ideality Factor. R S = Cell Series Resistance. R SH = Cell Shunt Resistance. V / I = Voltage / Current. V imposed = Voltage Imposed

Faults modeling of the impedance and reversed polarity types within the PV generator operation

International audience— In this paper, we proposed a new mathematical model of the I-V characteristic of a faulty photovoltaic generator. It presents its behavior in normal and faulty operations. In particular, when its basic components such as cells, bypass and blocking diodes are subjected to the impedance or reversed polarity faults. The developed model of the faulty PV generator will allow studying of the I-V characteristic, measures the tolerances of the technical functions, avoids numerous experiments, and ensure better assessment of fault consequences. NOMENCLATURE I ph = Photocurrent standard condition. I 0 = Reverse saturation current of the diode. Z = Electrical impedance R S = Cell series resistance. nc: ncg / ncp = Cell number: good / defective. ng: ngg / ngp = Group number: good / defective. nm: nmg / nmp = Module number: good / defective. ns: nsg / nsp = String number: good / defective. nfg / nfp = Good / defective generator. N Cells = Number of cells in each group. N Groups = Number of groups in each module. N Modules = Number of modules in each string. N Strings = Number of strings in each generator. V / I = Voltage / current. P = Power. I Bypass_Diode = Bypass diode current. V Cell_Imposed = Voltage imposed. a = Diode ideality factor. V t = Diode Thermal voltage

New Algorithm for the IV Characteristic Modeling of the Photovoltaic Generator Malfunction within Impedance and Reversed Polarity Faults

International audience— In this paper, we proposed a new methodology that can improved and developed the faults detection and diagnosis methods of the photovoltaic generator, especially when it subjected to the impedance and reversed polarity defects. This proposed algorithm is based on the mathematical modeling of the IV characteristic, of the faulty photovoltaic generator hierarchies as: cell, cells group, module, string and the entire generator, when they submitted to one or more of: cells, bypass and blocking diodes in impedance and reversed polarity faults. This new methodology can facilitated the study of the faulty generator characteristics, and obtained a database for the learning phase and the classification of the new observations collected on the system during its operation. NOMENCLATURE I_phi = Photo-Current. N_Cells = Cells Number in Each Group. N_Groups = Groups Number in Each Module. N_Modules = Modules Number in Each String. N_Strings = Strings Number in the Generator. V_Cell_Imp = Cell Voltage Imposed. I_Cells = Cell Current. V_Cells = Cell Voltage. I_PV = Generator Current. V_PV = Generator Voltage. R_S = Cell Series Resistance. R_SH = Cell Shunt Resistance. I_S1 = Reverse Saturation Current of 1 st Diode. I_S2 = Reverse Saturation Current of 2 nd Diode. m1 = Ideality Factors of 1 st Diode. m2 = Ideality Factors of 2 nd Diode