Automated Analysis of Monitoring Data with Regression Methods
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1 Automated Analysis of Monitoring Data with Regression Methods Achim Woyte 3E sa, Brussels, Belgium
2 Outline PV plant monitoring: why, what and how? Systematic approach for interpretation => Examples for periodic linear regression Application Conclusions
3 PV Monitoring Why? I like to see how much power my installation produces. For our investors, I want to compare the performance of their portfolio to the initial business plan. If anything goes wrong, I want to be alerted by SMS within 5 minutes. I want to systematically evaluate faults and losses for a period of operation.
4 Monitoring Data: What to Measure? G I V DC I DC P DC V AC I AC P AC PV array Inverter Grid T amb, T mod, S W y r Reference yield y A Array yield y f System yield I C Array capture losses I S System losses PR
5 Data Plots A Visual Tool Courtesy of Christian Reise, Fraunhofer ISE
6 Systematic Approach Use data for identifying the system and component parameters Identify simplified physical relationships Derive linear model parameters from regression Update periodically and compare the results Deviations indicate irregular operation
7 Energy Flow in a PV System G I V DC I DC P DC V AC I AC P AC PV array Inverter Grid T amb, T mod, S W y r Reference yield y A Array yield y f System yield I C Array capture losses I S System losses PR
8 Physical Relationships of Quantities System Level Performance System performance y f vs y r Influence of module temperature PR vs T Mod Module Temperature Module temp. T Mod T Amb vs y r Array Level Performance Array performance y A vs y r Influence of module temperature PR A vs T Mod Grid Connection Resilience of grid voltage on active power v AC vs y f Temp. Secondary Effects Influence of wind speed on module heat transfer k th vs S W Array Level Specific Effects Array voltage V A vs T Mod
9 Physical Relationships of Quantities System Level Performance System performance y f vs y r Influence of module temperature PR vs T Mod
10 Final vs. Reference Yield 8 weeks in summer 2012 No shadow, June Growing vegetation, May Installation in Belgium, monitored by 3E
11 Irradiance, PR and Module Temp. May 2012: inverter failure (1 out of 3) Final vs reference yield Temperature influence Installation in Sweden, monitored by ABB
12 Physical Relationships of Quantities System Level Performance System performance y f vs y r Influence of module temperature PR vs T Mod Module Temperature Module temp. T Mod T Amb vs y r Array Level Performance Array performance y A vs y r Influence of module temperature PR A vs T Mod Grid Connection Resilience of grid voltage on active power V AC vs y f Temp. Secondary Effects Influence of wind speed on module heat transfer k th vs S W Array Level Specific Effects Array voltage V A vs T Mod
13 Thermal Behavior Irradiance, equivalent thermal resistance and wind speed DT vs reference yield (y r ) k th vs wind speed (S W ) Installation in Northern Italy, monitored by EURAC
14 Physical Relationships of Quantities System Level Performance System performance y f vs y r Influence of module temperature PR vs J Mod Module Temperature Module temp. J Mod J Amb vs y r Array Level Performance Array performance y A vs y r Influence of module temperature PR A vs J Mod Grid Connection Resilience of grid voltage on active power V AC vs y f Temp. Secondary Effects Influence of wind speed on module heat transfer k th vs S W Array Level Specific Effects Array voltage V A vs J Mod
15 Array Voltage vs Module Temp. Array voltage as main indicator for the operating point (April/May 2010) Near shadow, from end of April Near shadow continued (May) Installation in Southern Spain, data from GL Garrad Hassan
16 Periodic Linear Regression Simple standard method for data analysis Automate and apply online or offline Interpret visually or analytically Reconstruct PV system models The method can confirm smooth operations and reveal distortions.
17 Regression Applied in Practice PV Health Scan by 3E Fast parameter check Foreseen versus unexpected losses over time Further split of unexpected losses Results from Performance Plus receives funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement No
18 Think of a Blood Analysis Measured component Measured Value Expected range Acknowledgement for P+
19 Use Parameters Estimated from Regression Estimated component Expected Value range
20 Analysis of Residuals You foresee these losses These losses trigger further analysis
21 Sankey Diagrams From PVSyst From operational data Data for 2013
22 Conclusions Different monitoring users have different expectations Detailed analytical monitoring allows to identify disturbances quickly and draw conclusions on the cause Periodic linear regression modeling is a powerful yet simple tool for interpreting your masses of data For practical application, the devil is in the detail (e.g., PV Health Scan)
23 Acknowledgements Contributors from 9 countries: M. Richter (3E, Belgium), B. Stridh (ABB, Sweden), D. Moser (EURAC, Italy), N. Reich (FhG-ISE, Germany), S. Mau (GHGL, Spain), H. Collin (INES, France), M. Van Iseghem (EdF R&D, France) A. Driesse (PV Performance Labs, Canada), A. Imenes (Teknova, Norway), S. bin Shaari & A. Maliki bin Omar (Universiti Teknologi MARA, Malaysia) Regions of Belgium for funding of our IEA PVPS work Brussels Capital Region Région Wallonne Vlaams Gewest Performance Plus has received funding from the European Union s Seventh Framework Programme for research, technological development and demonstration under grant agreement No
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TURBO 2 ULTRAFAST HIGH VOLTAGE RECTIFIER
STTH5R6 TURBO 2 ULTRST HIGH VOLTGE RECTIIER Table 1: Main Product Characteristics I (V) 5 V RRM 6 V T j 175 C V (typ) 1.4 V t rr (max) 4 ns ETURES ND BENEITS Ultrafast switching Low reverse recovery current
by S. Solve +, R. Chayramy +, M. Stock +, D. Vlad*
Bilateral Comparison of 1 V and 10 V Standards between the SMD (Belgium) and the BIPM, October to November 2014 (part of the ongoing BIPM key comparison BIPM.EM-K11.a and b) by S. Solve +, R. Chayramy
Order codes Part numbers DPAK (tape and reel) PPAK (tape and reel)
800 ma fixed and adjustable output very low drop voltage regulator Features ery low dropout voltage (typ. 0.4 at 800 ma) Guaranteed output current up to 800 ma Fixed and adjustable output voltage (± 1