Automatic failure detection in photovoltaic systems - IEEE Xplore

3rd World Confireme on Phorovolioic Energy Conversion

May 11.I8.2003

O m h , Jopon

7P-83-22

AUTOMATIC FAILURE DETECTION IN PHOTOVOLTAIC SYSTEMS D.Guasch, S.Silvestrc and R.Calatayud Electronics Engineering Department. Universidad Politemica de Catalunya. M6dul C4, Campus Nord UPC, Gran Capitan s/n, 08034 Barcelona. Spain

ABSTRACT The present work introduces a ncw method for the automatic detection of misbehaviours in photovoltaic systems, minimizing the amount of data to be sensed. Different anomalous situations, including frame ( ground ) derivations, highly resistive connections, battery or panel short circuits, etc. are parameterised based on a model of the PV system under study. The same characteristic parameters are extracted from a reduccd set of measures and, through a statistical analysis, a correspondence can be established which indicates the state o f the physical system. In an experiment, several failures were introduced in a real system, including series resistances in connections, c u m n t lcak to ground, short circuit of a battery vessel, disconnection of a branch of panels, and they were accurately detected by the algorithm.

2. EVALUATED CONFIGURATION

We have considered a typical stand-alone PV system which comprises solar panels, batteries and loads connected in parallel, as can be seen in figure I .

1.P.

1. INTRODUCTION Pr. I,

A new approach to the automatic dctection of a

wrong behaviour in photovoltaic systems and to the determination afthe causer of failure or excessive losses is presented at this work. The method is based on the establishment of statistical correspondence between possible causes of system failure, results of simulation and model parameter extraction of the whole PV system done in Matlab and Simulink, and results obtained from monitoring ofthe PV system. The detection of a wrong behaviour in photovoltaic systems or, more specifically, the determination of the causes of failure or excessive losses, is traditionally performed by sensing a large amount of electric variables of the system. such as currents flowing through solar panels and voltages on batteries together with environmental data such as irradiance and temperature. As the systems becomes more complex, the number of scnsors needed and the amount o f data gathered may turn out of to be overwhelming, specially when these data are to be transmitted for remote diagnosis. The method that we have developed allows the diagnosis of photovoltaic systems from a reduced set of measures. Different anomalous situation are parameterised based on a model of the photovoltaic system. The same characteristic parameters are extracted from a reduced set of measures and, through a statistical analysis [I], a correspondence can be established which indicates the state of the physical system. The method uses Matlab and its simulation environment Simulink. The use of these tools allows for the modelling of the system [2-4], the extraction of parameters and the establishment of statistical correspondence.

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Fig.] Basic Stand-alone PV system configuration. In absence of losses and leakage currents, equations (I)to ( 5 ) describe the relations between the different parameters involved in figure 1, voltages, currents, temperamres, irradiance and battery level of energy (LOE) [3].

Thc PV generator is formed by a matrix of np parallel connected branches of n, series connected panels. The battery set consists of a matrix of b, x b, 2V lead-acid vessels. Losses owed to bad connections are modelled as series resistances between panels and battery, R,,,, and between banery and loads, RpbV A derivation to ground is modelled as a resistance %,,.

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3rd World Conference on Phoiovoltoic Energv Conversion

May 11-18.2003

The parameters considered are the following : N,, : number of series connected solar panels ( panels per branch ), each panel is formed by 36 solar cells in series. N,, : number of parallel connected solar panel branches Nbi : number of serial connected ZV battery vessels ( vessels per branch ) Nbp: number of parallel connected N,, x 2V battery vessels N,, : number of parallel connected loads Rpgb(R): loss resistance in the connectioil between panels and battery Rph(C2) : loss resistance in the connection between battery and loads R,, (a):resistance to ground for leak current loss C,o( Ah) : battery capacity Attending to the parameters described above, and taking into account power losses, equations (3-5) can be rewritten as shown helow :

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in Matlab environment, has been successfully applied before to study internal characteristics of photovoltaic devices [5-61, as well as on-line photovoltaic system performance [24].

3. EXPERIMENTAL RESULTS The test embodiment was formed by an N,;2, N,,=4 solar panels array; Nbr=12, Nb,=l battery array; having a C,O of 550Ah and several loads. A charge controller Atersa Leo 1 12/24 has been also included between solar panels, battery and loads; and an inverter Atersa Taurus-1024 (1KVA) is also present, because loads are AC loads. Figure 3 shows the environmental conditions dunng the experiment, irradiance and temperature profiles.

These parameters are related with the currents and voltages through a model of the system implemented using Simulink that considers the relationships shown by equations (6-8). The different components of the system are modelled as Simulink modules - namely solar panels, battery, regulator and load modules - and then connected togethcr to form a system according to a parallel configuration. Input data are solar irradiance, temperahlre of each module and load. The results obtained are the voltages and currents in the circuit and the state of charge of the battery. The system is organized in three levels: system settings, system model and device models, as shown in figure 2.

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F j( i < i z - l > q Z / . , ,i

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)F' fN___II_j ' Extracted Parameter W l U e

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Fig.2. Simulator levels

Parameter

The top level sets system configuration and input data, through Matlab's Workspace, from a storage device, command line or online communication. The second level interconnects the different modules to build the whole system. At this level, relationships between devices can be easily evaluated. Also, although the devices are compacted into standard Simulink blocks, all the embedded internal variables are available for designers. The third level describes the individual devices, which are implemented using basic Sirnulink blocks in order to make use of Sirnulink's optimised numerical algorithms and the graphical environment provided. Then, simulation results are adjusted to the measured data by the LevenbergMarquardt regression method [Z]. The software, developed

N,,

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4

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n

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550

After that, several "losses" and " failures" were inserted into the system simultaneously : R,,b=O.IR, RPk=0.2R, RP,=500n; NQQ=3(open circuit in one branch

3rd World Conference on Phorovoltoic Energy Conversion

May 11-18,2003 Osoku, Jopon

of the panels ); Nb,=l1 (short circuit in a battely vessel ). The system parameters have been extracted again, considering the two days of operation. Table 11 shows the new values extracted for the system parameters. As can be seen the introduced failures: short circuit in a battery vessel and open circuit in one branch of the PV modules are clearly identified, as well as the losses and leakage current.

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Table 11. Extracted parameters of the PV system after inserting losses and failures after 36 hours of normal operation PV system parameter

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Extracted parameter Yahe 2

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operation, as can be the battery state of charge evolution [2-31. The combination of the data obtained in the monitoring process of the PV system operation with the parameter extraction functions developed in Matlab environment, allow the diagnostic of failures in the photovoltaic system in real time.

REFERENCES N. Amparis and S.J. Perantonis, 'ZevenbergMarquardr Algorithm wirh Adaprive Momentum for the Eficienr Training of Feedforward Merhuds", IEEE 0-7695-0619-4/00,, pp. 126.191, (zona). SSilvestre, D.Guasch, U.Goethe, and L.Castarier, Tmproved PV battery modelling using Matlab", Proc. Of the Seventeenth European Photovoltaic Solar Energy Conference and Exhibition, (Munich, Germany, 2001), pp. 507-509. D. Guasch and S. Silvestre, "Dynamic battery model for Photovoltaic applicuriow ", Progress in Photovoltaics: Research and applications,Vol 11, pp.193-206, (2003). S.Silvestrc, D.Guasch, A.Moreno, J.lulve and L.Castaiier "A comparison on modelling and simulation of PV systems using Marlab and Spice ". Tech. digest of 1Ith International Photovoltaic Science and Engineering Conference, (Hokaido, Japan 1999). pp. 901-902. SSilvestre, D.Guasch, A.Morcno, J.Julve and L.Castarier, "Characteristics ofsolar cells simulated using Maflab", Proc. o f the CDE'99, (Madrid, Spain, 1999), pp. 275-278. SSilvestre and A.Bravo, "Estimation merhodolow of Bulk Minwiv carrier lifetirnefiom IQE datu bosTd Marlab", Proc. Of the 17th European Photovoltaic Solar Energy Conference and Exhibition, (Munich, Germany, 2001), pp.lI4-I 16. Acknowledgment :Work supported by a ClCYT project Ref: MAT2001-3541-C03-03 (APSIDE)

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Fig.4. Working point evolution of the PV array, battery and load Figure 4 shows the evolution of the working point, where the thick line represents the normal operation and the thin line its evolution with failures. As can be seen, the working point ofthe different elements of the PV system is the same during the first 36 hours of normal system operation, after that, a different evolution of the working points, consequence of losses and failures introduced can be identified in figure 4. The algorithm could correctly detect all of the failures using five working point samples during the abnormal operation period.

4. CONCLUSIONS A new methodology, based in parameter extraction techniques using Matlab/Simulink, for automatic failure detection in photovoltaic systems has been presented. This methodology of work can be also used to obtain relevant information of the system

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