Data Driven Solar Panel Anomaly Detection

1. Data Driven Solar Panel Anomaly Detection Presented by Gao, Xiang 2013-06-20

2. Background • The annual installed capacity was about 29.6 Gigawatts (GW) in 2011 and 31 GW in 2012 • Solar energy efficiency is vulnerable to external factors(e.g., dust on panels can result in up to a 40% degradation )

3. Related Work • Bo Hu, S. Keshav. Solar panel anomaly detection and classification. 2012 • A. Luque and S. Hegedus. Chapter 20. Handbook of Photovoltaic Science and Engineering. • E. Skoplaki, J.A. Palyvos. On the temperature dependence of photovoltaic module electrical performance

4. Our goals • Predict the type of particular anomaly • Report the energy loss due to each type of anomaly, including weather factors • Objective anomalies • Shadows: correlation of covering area and decreasing effect • Dust • Snow • wind/ leaves

5. Basic Idea • Derive theoretical power output from irradiance data and compare with real power output No anomaly With anomalies

6. Challenge 1 • theoretical power output = in-plane irradiance * efficiency • in-plane irradiance: measured by pyranometers • Efficiency: changing with temperature, irradiance,.. • What if we do not have in-plane irradiance data but only horizontal? • How to derive current efficiency from standard efficiency in manual?

7. An irradiance model Theoretical Measurement

8. Horizontal Irradiance 1367W/m2 Θzs not a strictly circular Orbit!

9. Horizontal Irradiance(Non-tracking) δ :Solar declination φ: latitude Fitting constants Meinel A, Mainel M, Applied Solar Energy, An Introduction, Addison- Wesley, Reading, MA(1976)

10. Fit constants to local data(TRCA) • Sample selection • Top 10% irradiation from each month • Visual inspection • 11 perfect sunny days evenly distributed from spring to fall • Optimal(MMSE) • (0.8,0.41) • (0.7,0.678) obtains 344.69% MSE compared with this • Ensure theoretical one is the maximum • (0.831,0.549) • 5.7% worser than optimal

11. In-plane Irradiance

12. Evaluation with data from TRCA(2012-3-11)

13. Efficiency • Influence factors: temperature, irradiance, materials, etc. E. Skoplaki, J.A. Palyvos. On the temperature dependence of photovoltaic module electrical performance. Solar Energy 83, 2009.

14. Efficiency • Is it proper to ignore the anomalies with irradiance less than 200 ?

15. Anomaly detection • ratio = real power output / theoretical power output • December 2011 : • examining period: 8-18 (daytime) • Single time point • ratio < 0.7: 4658 anomalies (59.7%) • ratio < 0.5: 3529 anomalies (45.2%) • Anomaly sets(ratio < 0.7) • 56. group all continuous time point anomalies • 13. examine the anomalies with irradiance>200

16. Anomaly detection • 2012 (TRCA data) • Single time point • Irra. > 200 && ratio < 0.7: 9311 anomalies (4.7%) • Anomaly sets : 896 • Distributions:

17. Anomalies time distribution • 0.7>Ratio>=0.6 • 0.1>Ratio>=0

18. Are anomalies at sunset real ? • The anomalies in 17:00 – 18:00 • Dynamic examining period

19. An example of snow melting

20. Anomaly classification • Some training sets • More training sets and test sets are to be collected

21. Tentative Characteristics • Value • Season • Range • Duration • Speed • Arrays • Correlations: • panels position • whether be affected • Descriptions: • Slope # • Slope order • Slope value

22. Challenge 2 • Ground truth • Keep changing • Difficult to derive the truth naturally(e.g. most snow happens at night) • Data quality • Granularity • Alignment • Small values (set a threshold) • Measurement error(e.g. temperature = 850, etc.)

23. Typical measurement error