A Malfunction Detection Method For PV Systems

Date

2019-01-01

Author

Yildiz, Tuna
Göl, Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

47
views

0
downloads

Worldwide solar energy production increased in the recent years with the rapid population of the rooftop PV systems. This growth caused operational and planning problems due to the uncertainty of the solar power-based generation. Therefore, proper monitoring of solar generations is critical for distribution system operators, as the operators use solar generation data collected from the field, to make accurate forecasts. In case of a malfunction, the system operator should be aware, and make the required modifications in the forecast process. On the other hand, PV system owners also require monitoring of their systems to take action in case of a malfunction. Considering that number of operational PV systems is very high, and occurrence of a malfunction is rare, use of automatic malfunction detection methods necessitates. This paper develops a method that detects any malfunction of a PV system. The malfunction may refer to failure of communication network, sensor malfunctions or data logging unit errors as well as problems of inverters and PV panels. The proposed method employs Deep Feed Forward Neural Network to predict solar generation data based on current weather conditions and solar radiation measurements, and compares the prediction with the measurements using the normalized residuals test. The proposed method is tested using real field data recorded at the Ayasli Research Center of the Middle East Technical University.

Subject Keywords

Solar generation prediction, Median filter, Normalized residual test, Fault detection

URI

https://hdl.handle.net/11511/36179

DOI

https://doi.org/10.1109/ptc.2019.8810946

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Solar Power Prediction with an Hour-based Ensemble Machine Learning Method Ertekin Bolelli, Şeyda (2020-03-01) In recent years, the share of solar power in total energy production has gained a rapid increase. Therefore, prediction of solar power production has become increasingly important for energy regulations. In this study we proposed an ensemble method which gives competitive prediction performance for solar power production. This method firstly decomposes the nonlinear power production data into components with a multi-scale decomposition technique such as Empirical Mode Decomposition (EMD). These components a...
Design and realization of a new concentrating photovoltaic solar energy module based on lossless horizontally staggered light guide Selimoğlu, Özgür; Turan, Raşit; Department of Physics (2013) Concentrating Photovoltaic systems are good candidates for low cost and clean electricity generation from solar energy. CPV means replacing much of the expensive semiconductor photovoltaic cells with the cheaper optics. Although the idea is simple, CPV systems have several problems inherent to their system design, such as module thickness, expensive PV cells and overheating. Light guide systems are good alternatives to classical CPV systems that can clear off most of the problems of those systems. In this t...
Highly efficient Multi-Junction Solar Cells Performance Improvement for AC Induction Motor Control Using the dsPIC30F Microcontroller Dida, Abdelkader Hadj; Bourahla, Mohamed; Ertan, Hulusi Bülent (2019-01-01) Solar energy is an abundant renewable source which is expected to play an increasing role in terrestrial and space systems future infrastructure for distributed power generation. Improving of solar performance and efficiency is a key goal of research and the prominent factor in photovoltaic systems which make PV technologies cost-competitive with conventional sources of energy. Multi-junction solar cells are the most efficient technology for generation of electricity from solar irradiation. It's estimated t...
Seasonal variation of the monthly efficiencies of thin film PV modules Özden, Talat; Turan, Raşit; Akınoğlu, Bülent Gültekin (null; 2017-12-04) Outdoor testing of solar photovoltaic (PV) modules must be carried out at different climates as PV power plants nowadays are installed and continues to be installed in many countries. Present study is outdoor testing results and analyses of 5 thin film modules in Ankara, Turkey. The analyses are carried out using monthly efficiencies of the modules that are derived using the energy yield and input. The average efficiencies of the CIS, CIGS and μc-Si/a-Si thin film modules are. We present the monthly and ave...
Dual-band perfect metamaterial absorber for solar cell applications Rufangura, Patrick; Sabah, Cumali (2015-10-01) The efficiency of solar photovoltaic (PV) cells has been one of the major problems impeding its global adoption as one of the sustainable substitutes to fossil fuel based technologies. Metamaterial (MTM) based solar cells offer an opportunity towards increasing the system efficiency by enhancing the total absorbed solar radiation incident on this device. In this study, a nanostructure-based MTM perfect absorber has been designed and simulated. By adjusting geometrical parameters and MTM structure properties...

Citation Formats

T. Yildiz and M. Göl, “A Malfunction Detection Method For PV Systems,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36179.