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Data Treatment and Analysis of Power Outages in Solar-Powered Systems

This study analyzes outage data from a solar power system over a 257-day period, examining 475 instances of outages categorized into three cases: timer setting issues, insufficient radiation, and system/human errors. The research employs an advanced data treatment algorithm to determine whether to interpolate outage data based on specific criteria, such as voltage levels and radiation measurements. The findings reveal insights into the system's performance, informing modeling and capacity planning for solar-powered networks.

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Data Treatment and Analysis of Power Outages in Solar-Powered Systems

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  1. Data treatment • Collect 257 days, (2010/12~2011/1, 2011/3/15~2011/9) • Outage raw data 475 times • Case I: timer setting => 253 times • Case II: insufficient radiation => 73 times • Case III: system/ human error => 149 times • Determine whether interpolation or not • Data treatment algorithm

  2. Data treatment algorithm Tstart: start time of outage, Tend: end time of outage, Tdur=Tstart - Tend T={12:00, 13:00, 5:00, 7:00}, τ1= 10min, τ2=5min Voff: the last voltage before outage Von: the first voltage after outage Rd1<-- 1 day total radiation, Rd2 <-- 2 day total radiation S(AB) <-- △VoltageAB/ △tAB, S(BC) <-- △VoltageBC/ △tBC ======================================== Case1 IfTstart = T{i} ± τ 1 ANDTdur < τ2 then interpolate Included; Case2 if {Voff <11.6 AND Von>12.0 AND date < 2010/3/14} OR {Voff <11.2 AND Von>11.5 AND date > 2010/3/14} if 4.53<Rd1<14 AND 12.61<Rd2<23.71, then discard else Included ; Case 3 if |S(AB)-S(BC)|>0.015, then discard elseifTstart ∩ {t| 7pm<t<6pm} ≠ ΦOR Tend ∩ {t| 7pm<t<6pm} ≠ Φ, then discard else interpolate Included; outage V B C A D t

  3. Data treatment summary

  4. Modeling • B(t) = B(t-1) + λ(t) – μ(t) ,if λ(t) – μ(t) ＜24 Bmax, drop(t) = λ(t) – μ(t)-24 ≧ 0 • B(t): energy in battery • λ(t): energy from radiation • μ(t): energy consumption • Bmax: energy in fully charged battery B(t) λ(t) μ(t)

  5. Derive [ λ(t) – μ(t)] • Collect continued 2 days CASEII outage: 35 • Lifetime vs Radiation w/o battery effect • Lifetime(hour) = 1.8516x(Radiation) + 0.7029

  6. Derive Bmax • Collect outage day due to insufficient radiation & work over last night : 18 • Expected lifetime(hr) by radiation • B(t-1) = actual lifetime – {λ(t) – μ(t)} < 15.78

  7. example

  8. Testing Bmax =15.78 • Collect outage day after continuous days which past nights • 6 data sets (4/4, 4/17, 5/13, 5/24, 5/28, 9/21, )

  9. Lifetime(hour) = 1.8516x(Radiation) + 0.7029 • Sufficient for past one day : radiation > 12.58 • Outage data 18 + past w/ insufficient radiation 31 = 49 day

  10. Used Slide (backup)

  11. Derive B(t) • Collect CaseII outage and occur in night: • Record Lifetime vs Battery voltage(V) • Lifetime = 30.896V2 -727.51V + 4284

  12. Modified Modeling • B(t) = B(t-1) + λ(t) – μ(t) ; if B(t-1) + λ(t) – μ(t)<Bmax Bmax, drop(t) = λ(t) – μ(t); if B(t-1) + λ(t) – μ(t) ≧ Bmax • Bmax = 25hr

  13. One day radiation vsλ(t) – μ(t)

  14. Radiation vs life time

  15. Outage case

  16. Preliminary analysis • Case I (253)=> interpolate 108 +81days • Case II (73)=> discard 18 days • Data ambiguous • High amount radiation but short life time • Case III (149)=> discard 8+9 days • Empty:2 +3 (12/29 30, 8/5 8 9 ) • manual:3 (6/7, 7/16, 8/18) • unknown:1 (5/11) • no log:4 (12/10 14, 1/7, 3/14) • Keep 224 out of 257 days for modeling

  17. Data treatment algorithm Tstart: start time of outage, Tend: end time of outage, Tdur=Tstart - Tend T={12:00, 13:00, 5:00, 7:00}, τ1= 10min, τ2=5min Voff: the last voltage before outage Von: the first voltage after outage Rd1<-- 1 day total radiation, Rd2 <-- 2 day total radiation S(AB) <-- △VoltageAB/ △tAB, S(BC) <-- △VoltageBC/ △tBC ======================================== IfTstart = T{i} ± τ 1 ANDTdur < τ2 then interpolate else if {Voff <11.6 AND Von>12.0 AND date < 2010/3/14} OR {Voff <11.2 AND Von>11.5 AND date > 2010/3/14} if 4.53<Rd1<14 AND 12.61<Rd2<23.71, then discard else if |S(AB)-S(BC)|>0.015, then discard elseifTstart ∩ {t| 7pm<t<6pm} ≠ ΦOR Tend ∩ {t| 7pm<t<6pm} ≠ Φ, then discard else interpolate outage V B C A D t

  18. 2 continuous day outage(35 days)

  19. III outage • Occur 42 days in 165 days • Reboot: 6 • Error: 3 • Empty: 13 • Manual: 3 • Unknown: 4(5/7, 9, 10, 11) • No log: 12

  20. 8 days filtered in 42 days • Empty:2(12/29 30), • manual:1(6/7), • unknown:1(5/11), • no log:4(12/10 14, 1/7, 3/14) • 10,14,29,30 Dec., 7 Jan., 14 Mar., 11 May, 7 Jun.

  21. 開始 III類斷電 斷電前後電壓是否符合控制器設定 抓出每天斷電資料 No 斷電前後斜率差是否<0.015 No Yes 斷電時間是否符合timer 設定 No Yes II類斷電 斷電時間是否跨越晚上(7:00pm~6:00am) Yes Yes No 前一天日照累積是否>4.53 I類斷電 No Yes 不能採用的資料discard 前二天日照累積是否<12.61 No interpolation interpolation Yes 不能採用的資料discard 可以採用的資料

  22. 0< 有電壓值斜率差 <0.015 0.000315<III類斷電斜率差 <1.4365

  23. smooth最大斜率差0.015 =>只要斜率差>0.015,挑出來,不能用

  24. Related works • Design, modeling and capacity planning for micro-solar power sensor networks, IPSN08 • Cloudy computing: leverage weather forecasts in energy harvesting sensor systems, secon10 • Solarstore: enhancing data reliability in solar-powered storage-centric sensor network, mobisys09 • Steady and Fair rate allocation for rechargeable sensors in perpetual sensor networks, Sensys08 • Networking low-power energy harvesting devices: measurements and algorithm, infocom11

  25. Mobisys09 • 計算是否有多餘電量來讓系統備份data • If 目前電池電量>(平均消耗功率/平均充電功率)x電池容量, 就可以保持供電 • Evaluation data loss (無備份,always備份,電池滿才備份)

  26. Sensys 08 • 用太陽能收到多少來決定data collection rate • 讓node都不會因為傳太快而沒電 • Centralized/distributed algorithm

  27. Infocom11 • 室內光產生電源,實際測量8個環境 • Model 分三類:可預測,半預測,隨意型 • Ep(i) = a x E(i-1) +(1-a)Ep(i-1), 0<a<1 • 但誤差還是很大 • 所以在某些環境採用SECON10 天氣model,誤差比較小

  28. IPSN08 • Solar Model • Network & HW design • 19nodes for collect/receive 溫濕度 • Sizing and selection • 實際去量測每個component profile • 比較用不同電池壽命多久 • Evaluation for two test deployments • 市區佈建 VS 森林佈建

  29. Secon10 • 分析一整年的資料,搭配氣象局資料 • 日照輻射(W/m2)與充電功率(W)=>線性關係 • Solar power = 0.0444*radiation -2.65 • 每日充電能量(W) vs時間(hr)=>2次方程式 • Power = a*(Time+b)^2 + c

  30. Solarstore: enhancing data reliability in solar-powered storage-centric sensor network, mobisys09 • Networking low-power energy harvesting devices: measurements and algorithm, infocom11

  31. BSpower consumption in one hr

  32. BS 電壓與溫度相關性

  33. Outage III algorithm • if (abs(mAB-mBC)>Threshold) || (abs(mBC-mCD)>Threshold) filter out Threshold = 0.019 • else if(13V<斷電前電壓) &&(duration > 19600 ) filter out • else if(12V<斷電前電壓<12.5V) &&(duration > 10800 ) filter out • else if (斷電前電壓<12V) &&(duration > 3600) filterout

  34. III類挑出不能用 • 12/14 • 12/29

  35. How to filter for III類 • 一階微分用斜率來判斷 • |mAB-mBC|< δ • |mBC-mCD|< δ • 二階微分 • |F’’ABC- F’’BCD| < δ D C B A C B D A

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