monitoring intelligence: pv system health scan (karel de

PV System Health Scan using Operation Data

This work has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 308991.

[email protected] | www.3E.eu

1st European Performance Plus Workshop

Author: Karel De Brabandere

Date: November 18, 2014

How to optimize O&M?

Early and accurate anomaly detection and alerting

Accurate model of expected system behaviour in the field


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Pro-active maintenance

Optimized O&M

detection and alerting


� How to assess the expected behavior of a well-funct ioning PV system?



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� Use datasheets / lab tests / bottom-up modelling ?

PV System Performance Evaluation throughBottom -up Modelling (1)

� Many different losses affect the performance ratio (PR)

� Different losses are estimated


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� Different losses are estimated beforehand

� Measuring different losses on a PV plant is extremely difficult

PV System Performance Evaluation throughBottom -up Modelling (2)

� Production differs from system to system, both forecasted and real

� Not always good correlation


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� Not always good correlation between forecasted and real production

� Root cause of deviation often not identified

� Theoretic performance ≠ Operational performance Source: Leloux et al., “Review of the performance of residential PV systems

in Belgium,” Renewable and Sustainable Energy Reviews, 2012.


� How to assess the expected behavior of a well-funct ioning PV system?



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� Use datasheets / lab tests / bottom-up modelling ?� OFTEN NOT REPRESENTATIVE OF THE FIELD REALITY

� Use on site tests ?� TOO COSTLY, CUMBERSOME, NOT EFFECTIVE

� Use operational data ?� OUR METHODOLOGY

Automated PV System Health Characterisation from operational data

� Overall Objective� Development and implementation of methods to identify root cause of

sudden or gradual changes of PV system performance

� Approach� Accurate model of field behaviour of well-performing system


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� Accurate model of field behaviour of well-performing systemo Model structure reflects known physical dependencieso ‘Fitting’ model parameters (electrical, optical, thermal)

� Continuously monitor the evolution of these parameters� Compare parameters with expected range� Relate parameter deviations with possible root causes

� Sneak preview of work in progress

Use cases

� Understand difference with the forecasted PR:� Identify where the losses / gains come from

o PV modules? Inverters? String connections? Grid? …o Design? Dimensioning? O&M? …o Inter-array shading? Soiling? Sensor issues? ...

� In order to o Initiate pro-active maintenance actions


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o Initiate pro-active maintenance actionso Learn and make better future design choiceso Identify components which systematically under- or over-perform

� Alternative for IV sweeping of PV arrays� No additional equipment, no interruption of production� Evolution over time, not just a one-off snapshot� Cost-effective� Fitting operation points that matter (MPP)

� Commissioning, due diligence� …

PV System Health Scan Process

Configuration, Datasheet,

Laboratory Tests

Theoretic System

Parameters

Theoretic Performance

Irradiation


Analyse

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Monitoring DataMeasured

Performance

Irradiation

Analyse Performance ratio of many inverters on one site?

� Box plot chart: the principle

75th percentilePR


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median

25th percentile

outliers

Analyse Performance ratio of MANY Inverters on one Site?


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Grass growth

Benefits

String issues

ONE chart to give a precise and accurate view about ALL the inverters Concerns specific time AND historical : all stakeholders have interest


Theoretic System

Parameters


Laboratory Tests


Irradiation

Analyse


Analyse

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Performance

Irradiation

Modelled System

Parameters

Bottom -up versus Top -Down

� Bottom-up� Detailed ex-ante modelling of system components and its parameters

o Estimation of yield before production startso e.g. PVsyst

� Lots of ways to tune parameters


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� Sometimes large deviations between model and reality

� Top-Down� Ex-post fitting of monitoring data to system equations in order to derive

system characteristics� Parameters are result from fitting, no tuning� Small deviations or ‘residuals’ between model and reality

Fitting of System Parameters


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MPP voltage fitting: fitted versus measuredE

stim

ated


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Measured

Est

imat

ed

Fitting of System Parameters

� Similar approach for other monitored data, e.g.:� MPP current and voltage� AC values� Irradiation sensors� Temperature sensors� …


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� …

� Estimation of other system parameters, e.g.:� Offset and slope of sensors� Reflection� Shading� Equivalent thermal resistance� Time constants (thermal, MPP tracking)� Inverter derating limits� …

Fitting of System ParametersMPP Current PV module temperature


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� How visualize estimations for all these parameters and compare with expectations in a user-friendly way?

Parameters PV Temp Model-2.01

1.00

0.0388

0.12

8.4

RMSE [°C] 1.02

Think of a blood analysis…

Expected range

Measured Value

Measured component


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Fitted compared to expected parametersEstimated component

Expected range

EstimatedValue

Benefits

If one coefficient is out of range Plant issue is detected


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If one coefficient is out of range Plant issue is detected

The coefficients are the “expression” of the field reality of the plant


Theoretic System

Parameters


Laboratory Tests


Analyse

Irradiation

Analyse


Analyse

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Performance

Modelled Performance

Irradiation

Modelled System

Parameters


Theoretic System

Parameters


Laboratory Tests


Analyse

Irradiation

Analyse


Analyse

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Performance

Analyse

Modelled Performance

Irradiation

ModelledSystem

Parameters

Analysis of residuals


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You foresee these losses

These losses trigger further analysis

Sankey diagram


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Analysis of MPP operating range


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partial shading



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Missing string in June

Challenges

� Data quality / availability in commercial plants� Data gaps, sensor availability, communication issues� Sensor accuracy� Correctness of configuration data

� Parameter uncertainties (e.g. depending on data length, system configuration)


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� Parameter uncertainties (e.g. depending on data length, system configuration)� Confidence margins of estimated parameters

� Build up a knowledge base of typical health scan results� Determination of typical distribution, expected range� Dependency on components, design choices, …� Link between parameter deviations and probable root causes

� Visualisation of results understandable to system owners, O&M� Health scan, Sankey diagram, Scatter plots, …

Conclusions

� Pro-active maintenance requires accurate model of expected behaviour of well-performing PV plant for early and reliable detection of performance issues.

� Such an accurate MPP model is ideally created and updated using operational data.


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operational data.

� The 3E Health Scan methodology for characterising the physical parameters of a PV array shows how it provides insight in root causes of performance losses

� We analyse field reality

� All the stakeholders gain benefits with this approach

Thank you

� Brussels� Toulouse� London� Milano� Beijing� Istanbul


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� Istanbul� Cape Town

[email protected]

www.perfplus.eu

www.3E.eu

monitoring intelligence: pv system health scan (karel de

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