Andreas LiveraResearch Associate University of Cyprus, FOSS Research Centre for Sustainable Energy - PV Technology Laboratory

Advanced Diagnostic Approach of Failures for Grid-connected PV Systems

Outline

• Introduction

• Methodology

• Results

• Conclusions

• Future Work

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Introduction

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• Key factor for future PV uptake (PV value chain) is to reduce Levelized Cost ofElectricity (LCoE).

• Increasing performance and reducing operating costs (advanced monitoring).

Robust condition monitoring

Failure detection and classification

Data quality and sanity

System health state

Added Values Services: Performance loss quantification

Degradation rate estimation

Quality Control

Cost-effective O&M

Background & Objective

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Example:100 MWp plantLCoE of 0.08 €/kWh160,000 MWh/yr (M€ 12.8/yr)

~0.64 M€/year

5 % Performance improvement (availability and quality control)

Main challenge in the quest for ensuring quality of operation and reduced LCoE is to safeguard reliability and cost-effective O&M (advanced monitoring).

Background & Objective

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Specific Objective: Development of an innovative condition monitoring platform for proactive and reactive O&M with enhanced data analytic functionalities.

Advanced baseline condition monitoring solution to ensure operational quality and optimise energy production.

Partners: GI and UCYProject: Innovative Performance Monitoring System for Improved Reliability and Optimized Levelized Cost of Electricity IPERMON [Solar-ERA.net project]Budget: €400,000Duration: 36 MonthsWeblink: http://www.pvtechnology.ucy.ac.cy/projects/ipermon/

Approach – Advanced condition monitoring

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Experimental setup – Data acquisition system (DAQ)

• Test-bench PV system in Cyprus• OTF GI in Arizona

Data quality routines (DQRs)• Identify missing/erroneous data• Correction of data

PV system model prediction• Predict electrical characteristics of

the system

Degradation rate Health state detector Failure diagnosis routines (FDRs)

Methodology – Data quality routines (DQRs)

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• Identify missing (or erroneous) data, outliers and outages.• Estimate system availability and sensor deviations.• Correct data through data imputation techniques (k-NN and Kalman filtering).

Methodology – PV system model prediction

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• Parametric and machine learning simulation models.

Highest prediction accuracy - FFNN

Bypass diode pattern

Methodology – Failure signatures

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• Different failure types (open- and short-circuit PV module, inverter shutdown,shorted bypass diode and partial shading) emulated to test-bench PV system.

• Characterise the effect of failure on the main DC electrical parameters.• Create failure signature profiles and patterns - Fault Introduction.

Inverter shutdownPartial shading

• Comparative assessment between measured and predicted measurementsagainst set threshold levels (TL).

• Statistical outlier detection rules between and predicted measurements.• Health state detector (real time).

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Methodology – Failure detection stage

Methodology – Failure classification stage

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• Unsupervised procedure based on Fuzzy Logic Rules.• Supervised learning models (k-NN, DT, SVM and FIS) on data-set partition.• Performance assessment: confusion matrix and specificity

Train set (70 % - 2083 data points)

Test set (30 % - 893 data points)

Monthly data-set (2976 data points)

2062 normal points

21 fault points

884 normal points

9 fault pointsGpoa Tm ……… PDC Label

1 % Fault signatures

Results – Open-circuit failure (Inverter/Fuse/Interconnect)

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All FDR models diagnosed the open-circuit PV module faults – Specificity 100 %

Parameters required for failure classification:DC power, voltage and current.

Affected parameters:↓ DC power↑ DC voltage ↓ DC current

Results – Short-circuit failure

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FDR k-NN model diagnosed the short-circuit PV module faults – Specificity 90 %

Parameters required for failure classification:DC power, voltage and current.

Affected parameters:↓ DC power↓ DC voltage

Results – Partial shading

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FDR k-NN model diagnosed partial shading – Specificity 100 %

Parameters required for failure classification:DC power, voltage, current, AIS and AzS.

Affected parameters:↓ DC power ↓ DC voltage ↓ DC current

Results – Bypass diode failure

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FDR k-NN model diagnosed bypass diode failures – Specificity 70 %

Parameters required for failure classification:Time, DC power, voltage and current.

Affected parameters:↓ DC power↓ DC voltage

Conclusions

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• A methodology that will assist in maintaining optimal level of operation of PV

plants was proposed through the development of FDRs.

• The developed FDRs were capable of detecting accurately the faults (100 %).

• The classification models showed good accuracy of classifying each failure

occurrence within the test set used for benchmarking.

• The developed k-NN model exhibited the best classification performance

(average specificity of 92 %).

• However, a combination of models is recommended for achieving high

classification accuracies.

Future work

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• Use of historic data for further assisting the classification stage.

• Operational verification of the algorithms on experimental test-setup at the

Outdoor Test Facility (OTF) of Gantner Instruments (GI) in Arizona and other 3rd

party datasets.

Thank you for your attention

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Andreas LiveraResearch Associate

FOSS - PV Technology LaboratoryUniversity of Cyprus

Email: livera.andreas@ucy.ac.cy

More information…Website: www.pvtechnology.ucy.ac.cy

Acknowledgments