This study deals with an approach for photovoltaic (PV) panel monitoring based on extended Kalman filter (EKF). For detection of anomalous condition, I–V and P–V characteristics derived by the PV model in normal condition (named reference model) are compared with the corresponding ones extracted from the PV model in the test condition (named test model). Parameters of the reference model are obtained from the standard test condition. Generally, maximum power point (MPP) tracking algorithms deviate operating point of PV systems to find the global MPP. In the proposed approach, the measured operating points of the PV system are passed through the EKF. In fact, the EKF is an adaptive low-pass filter for estimation of the operating MPP dictated by the MPP tracker. During a predefined inspection time interval, parameters of the test model are derived from the estimated MPP. The performance of the proposed method is evaluated using some simulations and experiments in different normal and anomalous conditions. The results confirm desirable accuracy and reliability of the proposed method for detection of the PV panels’ anomalous condition.

References

1. 1)
  - 17. Platon, R., Martel, J., Woodruff, N., et al: ‘Online fault detection in PV systems’, IEEE Trans. Sustain. Energy, 2015, 6, (4), pp. 1200–1207 (doi: 10.1109/TSTE.2015.2421447).
2. 2)
  - 30. Bruckman, L.S., Wheeler, N.R., Ma, J., et al: ‘Statistical and domain analytics applied to PV module lifetime and degradation science’, IEEE Access, 2013, 1, pp. 384–403 (doi: 10.1109/ACCESS.2013.2267611).
3. 3)
  - 10. Lee, B., Byun, J., Choi, M.-i., et al: ‘Degradation diagnosis system of photovoltaic panels with mobile application’, IEEE Trans. Consum. Electron., 2014, 60, (3), pp. 338–346 (doi: 10.1109/TCE.2014.6937316).
4. 4)
  - 21. Kim, K.A., Xu, C., Lei, J., et al: ‘Dynamic photovoltaic model incorporating capacitive and reverse-bias characteristics’, IEEE J. Photovolt., 2013, 3, (14), pp. 1334–1341 (doi: 10.1109/JPHOTOV.2013.2276483).
5. 5)
  - 7. Thevenard, D., Pelland, S.: ‘Estimating the uncertainty in long-term photovoltaic yield predictions’, Sol. Energy, 2013, 91, pp. 432–445 (doi: 10.1016/j.solener.2011.05.006).
6. 6)
  - 2. IEA: ‘Survey report of selected IEA countries between 1992 and 2012’. Technical Report,International Energy Agency, Paris, France, 2013.
7. 7)
  - 12. Ando, B., Baglio, S., Pistorio, A., et al: ‘Sentinella: smart monitoring of photovoltaic systems at panel level’, IEEE Trans. Instrum. Meas., 2015, 64, (8), pp. 2188–2199 (doi: 10.1109/TIM.2014.2386931).
8. 8)
  - 4. Carullo, A., Vallan, A.: ‘Outdoor experimental laboratory for long-term estimation of photovoltaic-plant performance’, IEEE Trans. Instrum. Meas., 2012, 61, (5), pp. 1307–1314 (doi: 10.1109/TIM.2011.2180972).
9. 9)
  - 1. Gaëtan, M., Sinead, O., Manoël, R.: ‘Global market outlook for photovoltaics 2014–2018’. Proc. European Photovoltaic Industry Association (EPIA), April 2014, pp. 1–60.
10. 10)
  - 9. David Bastidas-Rodríguez, J., Franco, E., Petrone, G., et al: ‘Model based degradation analysis of photovoltaic modules through series resistance estimation’, IEEE Trans. Ind. Electron., 2015, 62, (11), pp. 7256–7265 (doi: 10.1109/TIE.2015.2459380).
11. 11)
  - 7. Sakis Meliopoulos, A.P., Cokkinides, G., Huang, R., et al: ‘Smart grid technologies for autonomous operation and control’, IEEE Trans. Smart Grid, 2011, 2, (1), pp. 1–10 (doi: 10.1109/TSG.2010.2091656).
12. 12)
  - 25. Batzelis, E.I., Kampitsis, G.E., Papathanassiou, S.A., et al: ‘Direct MPP calculation in terms of the single-diode PV model parameters’, IEEE Trans. Energy Convers., 2015, 30, (1), pp. 226–236 (doi: 10.1109/TEC.2014.2356017).
13. 13)
  - 19. Mellit, A., Kalogirou, S.A.: ‘ANFIS-based modelling for photovoltaic power supply system: a case study’, Renew. Energy, 2011, 36, pp. 250–258 (doi: 10.1016/j.renene.2010.06.028).
14. 14)
  - 15. Hu, Y., Cao, W., Ma, J., et al: ‘Identifying PV module mismatch faults by a thermography-based temperature distribution analysis’, IEEE Trans. Device Mater. Reliab., 2014, 14, (4), pp. 951–960 (doi: 10.1109/TDMR.2014.2348195).
15. 15)
  - 22. Bai, J., Liu, S., Hao, Y., et al: ‘Development of a new compound method to extract the ﬁve parameters of PV modules’, Energy Convers. Manage., 2014, 79, pp. 294–303 (doi: 10.1016/j.enconman.2013.12.041).
16. 16)
  - 24. Cristaldi, L., Faifer, M., Rossi, M., et al: ‘A simple photovoltaic panel model: characterization procedure and evaluation of the role of environmental measurements’, IEEE Trans. Instrum. Meas., 2012, 61, (10), pp. 2632–2641 (doi: 10.1109/TIM.2012.2199196).
17. 17)
  - 18. Bolognani, S., Tubiana, L., Zigliotto, M.: ‘Extended Kalman filter tuning in sensorless PMSM drives’, IEEE Trans. Ind. Appl., 2003, 39, (6), pp. 1741–1747 (doi: 10.1109/TIA.2003.818991).
18. 18)
  - 8. Kaplanis, S., Kaplani, E.: ‘Energy performance and degradation over 20 years performance of BP c-Si PV modules’, Simul. Model. Pract. Theory, 2011, 19, (4), pp. 1201–1211 (doi: 10.1016/j.simpat.2010.07.009).
19. 19)
  - 16. Cristaldi, L., Faifer, M., Lazzaroni, M., et al: ‘Diagnostic architecture: a procedure based on the analysis of the failure causes applied to photovoltaic plants’, Measurement, 2015, 67, pp. 99–107 (doi: 10.1016/j.measurement.2015.02.023).
20. 20)
  - 18. Bonsignore, L., Davarifar, M., Rabhi, A., et al: ‘Neuro-fuzzy fault detection method for photovoltaic systems’, Energy Procedia, 2014, 62, pp. 431–441 (doi: 10.1016/j.egypro.2014.12.405).
21. 21)
  - 11. Kahoul, N., Houabes, M., Sadok, M.: ‘Assessing the early degradation of photovoltaic modules performance in the Saharan region’, Energy Convers. Manage., 2014, 82, pp. 320–326 (doi: 10.1016/j.enconman.2014.03.034).
22. 22)
  - 26. Mahmoud, Y., El-Saadany, E.: ‘Accuracy improvement of the ideal PV model’, IEEE Trans. Sustain. Energy, 2015, 6, (3), pp. 909–911 (doi: 10.1109/TSTE.2015.2412694).
23. 23)
  - 23. Accarino, J., Petrone, G., Ramos-Paja, C.A., et al: ‘Symbolic algebra for the calculation of the series and parallel resistances in PV module model’. 2013 Int. Conf. on Clean Electrical Power (ICCEP), June 2013.
24. 24)
  - 6. Khorshed Alam, M., Khan, F., Johnson, J., et al: ‘A comprehensive review of catastrophic faults in PV arrays: types, detection, and mitigation techniques’, IEEE J. Photovolt., 2015, 5, (3), pp. 982–997 (doi: 10.1109/JPHOTOV.2015.2397599).
25. 25)
  - 29. Ye, J.Y., Reindl, T., Aberle, A.G., et al: ‘Performance degradation of various PV module technologies in tropical Singapore’, IEEE J. Photovolt., 2014, 4, (5), pp. 1288–1294 (doi: 10.1109/JPHOTOV.2014.2338051).
26. 26)
  - 13. Davarifar, M., Rabhi, A., El Hajjaji, A.: ‘Comprehensive modulation and classiﬁcation of faults and analysis their effect in DC side of photovoltaic system’, Energy Power Eng., 2013, 5, (4B), pp. 230–236 (doi: 10.4236/epe.2013.54B045).
27. 27)
  - 20. Hejria, M., Mokhtarib, H., Aziziana, M.R., et al: ‘An analytical numerical approach for parameter determination of a ﬁve-parameter single-diode model of photovoltaic cells and modules’, Int. J. Sustain. Energy, 2013, 35, (4), pp. 396–410 (doi: 10.1080/14786451.2013.863886).
28. 28)
  - 5. Kim, K.A., Seo, G.-S., Cho, B.-H., et al: ‘Photovoltaic hot spot detection for solar panel substrings using AC parameter characterization’, IEEE Trans. Power Electron., 2016, 31, (2), pp. 1121–1130 (doi: 10.1109/TPEL.2015.2417548).
29. 29)
  - 14. Chine, W., Mellit, A., Pavan, A.M., et al: ‘Fault detection method for grid-connected photovoltaic plants’, Renew. Energy, 2014, 66, pp. 99–110 (doi: 10.1016/j.renene.2013.11.073).
30. 30)
  - 28. Meyer, E.L., Ernest van Dyk, E.: ‘The effect of reduced shunt resistance and shading on photovoltaic module performance’. Conf. Record of the 31st IEEE Photovoltaic Specialists Conf., 2005.

KF-based technique for detection of anomalous condition of the PV panels

References

Related content