Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Research on Model to Diagnose Efficiency Reduction of Inverters using Multilayer Perceptron

다층 퍼셉트론을 이용한 인버터의 효율 감소 진단 모델에 관한 연구

  • Received : 2022.09.20
  • Accepted : 2022.09.29
  • Published : 2022.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper studies a model to diagnose efficiency reduction of inverter using Multilayer Perceptron(MLP). In this study, two inverter data which started operation at different day was used. A Multilayer Perceptron model was made to predict photovoltaic power data of the latest inverter. As a result of the model's performance test, the Mean Absolute Percentage Error(MAPE) was 4.1034. The verified model was applied to one-year-old and two-year-old data after old inverter starting operation. The predictive power of one-year-old inverter was larger than the observed power by 724.9243 on average. And two-year-old inverter's predictive value was larger than the observed power by 836.4616 on average. The prediction error of two-year-old inverter rose 111.5572 on a year. This error is 0.4% of the total capacity. It was proved that the error is meaningful difference by t-test. The error is predicted value minus actual value. Which means that PV system actually generated less than prediction. Therefore, increasing error is decreasing conversion efficiency of inverter. Finally, conversion efficiency of the inverter decreased by 0.4% over a year using this model.

Keywords

Acknowledgement

This work was supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP) grant funded by the Korea government(MOTIE)(2020 3040010420, ICT safety management technology development and business model demonstration through performance enhancement of small-capacity obsolescene photovoltaic(heating) system facilities).

References

  1. F. Dincer, "The Analysis on Photovoltaic Electricity Generation Status, Potential and Policies of the Leading Countries in Solar Energy," Renewable and Sustainable Energy Reviews, Vol. 1, No. 15, pp. 713-720, 2011. https://doi.org/10.1016/j.rser.2010.09.026
  2. Y. Wu, Y. Ke, C. Xu, and L. Li, "An Integrated Decision-Making Model for Sustainable Photovoltaic Module Supplier Selection based on Combined Weight and Cumulative Prospect Theory," Energy, Vol. 181, pp. 1235-1251, 2019. https://doi.org/10.1016/j.energy.2019.06.027
  3. Y. Wang, J. He, and W. Chen, "Distributed Solar Photovoltaic Development Potential and a Roadmap at the City Level in China," Renewable and Sustainable Energy Reviews, Vol. 141, 110772, 2021. https://doi.org/10.1016/j.rser.2021.110772
  4. D. Won, B. Lee, and H. Ju, "Analysis on Status and Trend of Supporting Policies for Renewable Energies," Journal of Korea Society of Innovation, Vol. 12, No. 3, pp. 83-115, 2017.
  5. C. Park, "Study on the Obsolescence Forecasting Judgment of PV Systems Adapted Micro-Inverters," Journal of Korea Multimedia Society, Vol. 18, No. 7, pp. 864-872, 2015. https://doi.org/10.9717/KMMS.2015.18.7.864
  6. S. Kaplanis and E. Kaplani, "Energy Performance and Degradation over 20years Performance of BP c-Si PV Modules," Simulation Modelling Practice and Theory, Vol. 19, No. 4, pp. 1201-1211, 2011. https://doi.org/10.1016/j.simpat.2010.07.009
  7. S. Lim, S. Hong, C. Park, H. Cho, B. Song, and J. Kim, "Heterogeneous Equipment Support Monitoring System for Operation and Maintenance of Solar Power Plan," Journal of Korea Multimedia Society, Vol. 23, No. 9, pp. 1171-1180, 2020. https://doi.org/10.9717/KMMS.2020.23.9.1171
  8. A. Livera, M. Theristis, A. Charalambous, J.S. Stein, and G.E. Georghiou, "Decision Support System for Corrective Maintenance in LargeScale Photovoltaic Systems," 48th IEEE Photovoltaic Specialists Conference (PVSC 48), pp. 1-6, 2021.
  9. F. Vignola, F. Mavromatakis, and J. Krumsick "Performance of PV Inverters," Proceedings of the 37th ASES Annual Conference, 2008.
  10. A.A. Ajmerwala, R. Gupta, and V. Agarwal, "Model-Based Maximum Power Point Tracking of an Ageing Solar PV Module," 2018 International Conference on Power, Instrumentation, Control and Computing (PICC), pp. 1-6, 2018.
  11. J. Kim, J. Huh, and J. Ko, "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, Vol. 11, No. 21, pp. 5891-5917, 2019. https://doi.org/10.3390/su11215891
  12. Q. Wang, N. Phung, D.D. Girolamo, P. Vivo, and A. Abate, "Enhancement in Lifespan of Halide Perovskite Solar Cells," Energy & Environmental Science, Vol. 12, No. 3, pp. 865-886, 2019. https://doi.org/10.1039/C8EE02852D
  13. M.D. Benedetti, F. Leonardi, F. Messina, C. Santoro, and A. Vasilakos, "Anomaly Detection and Predictive Maintenance for Photovoltaic Systems," Neurocomputing, Vol. 310, pp. 59-68, 2018. https://doi.org/10.1016/j.neucom.2018.05.017
  14. C. Park, S. Hong, S. Lim, B. Song, S. Park, J. Huh, et al., "Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation," Processes, Vol. 8, No. 10, pp. 1225-1243, 2020. https://doi.org/10.3390/pr8101225
  15. A. Asrari, T.X. Wu, and B. Ramos, "A Hybrid Algorithm for Short-Term Solar Power Prediction-Sunshine State Case Study," IEEE Transactions on Sustainable Energy, Vol. 8, No. 2, pp. 582-591, 2017. https://doi.org/10.1109/TSTE.2016.2613962
  16. S. Lim, S. Hong, C. Park, B. Song, and J. Kim, "Development of General-Purpose Remote Maintenance Controller Gateway for MultiModal Photovoltaic Equipment," Journal of Korea Multimedia Society, Vol. 23, No. 10, pp. 1307-1317, 2020. https://doi.org/10.9717/KMMS.2020.23.10.1307
  17. S. Lee and W. Lee, "Development of a System for Predicting Photovoltaic Power Generation and Detecting Defects Using Machine Learning," KIPS Transactions on Computer and Communication Systems, Vol. 5, No. 10, pp. 353-360, 2016. https://doi.org/10.3745/KTCCS.2016.5.10.353
  18. M. Noh, J. Bang, J. Rhee, P. Cho, M. Kwon J. Lim, et al., "Fault Diagnosis for Photovoltaic Inverters using a Multi-layer Neural Network," The Transactions of the Korean Institute of Electrical Engineers, Vol. 70, No. 7, pp. 1056-1063, 2021. https://doi.org/10.5370/KIEE.2021.70.7.1056
  19. J. Lim and P. Ji, "Development of Fault Diagnosis Algorithm Using Correlation Analysis and ELM," The Transactions of the Korean Institute of Electrical Engineers P, Vol. 65, No. 3, pp. 204-209, 2016. https://doi.org/10.5370/KIEEP.2016.65.3.204
  20. A. Gulli, P. Sujit, and K. Amita, Deep Learning with TensorFlow 2 and Keras, Packt, Birmingham, UK, 2019.
  21. M.T. Camacho Olmedo, M. Paegelow, J.F. Mas, and F. Escobar, Geomatic Approaches for Modeling Land Change Scenarios, Springer, Berlin, Germany, 2018.
  22. A. Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, Sebastopol, CA., 2019.
  23. H. Ramchoun, M. Amine, J. Idrissi, Y. Ghanou, and M. Ettaouil, "Multilayer Perceptron: Architecture Optimization and Training," International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 4, No. 1, pp. 26-30, 2016. https://doi.org/10.9781/ijimai.2016.415
  24. A.P.S. Lins and T.B. Ludermir, "Hybrid Optimization Algorithm for the Definition of MLP Neural Network Architectures and Weights," Fifth International Conference on Hybrid Intelligent Systems (HIS'05), pp. 6-11, 2005.
  25. S. Ruder, "An Overview of Gradient Descent Optimization Algorithms," arXiv P reprint, arXiv:1609.04747, 2016.