Power Module Degradation Monitoring with Artificial Neural Network

2020 Virtual Wind II ORE Catapult & Fraunhofer IWES08/07/2020 Chunjiang Jia

GLASGOWORE Catapult

Agenda

• Background• Why power module?• Failure mechanism

• Heat flux detection• Artificial neural network

• ANN introduction• ANN-based detection

• Test and verification• Test rig design• Experimental verification

• Summary and next work

Background

• ReliaWind (European FP7 research project) report concluded that the power converters cause 13% of the failure rate

• 18.4% of the downtime of wind turbines is due to power converter

• Converter reliability could be even more critical to offshore wind turbines

*Reliability Study for Wind Turbines (RELIAWIND project, 2008-2011)

Power Converter Failure Breakdown

*P. Tavner et al., “Reliability of the Electrical Parts of Wind Energy Systems - a statistical evaluation of practical experiences,” in EPE Wind Energy Chapter Symposium 2010, Stafford, UK, 2010.

• The failures in power semiconductors (i.e. IGBT, Chopper, Rectifier, Inverter, and Diodes) account for almost half (48%) of the converter failure

• Power semiconductor reliability is going to be more serious in 10MW+ offshore turbine converter

• Effectively monitoring the degradation of power module would be a prosperous way to control O&M cost for offshore wind turbines

• Data source stands the test of time• Data source from this and previous slide

remains relevant• Still widely referenced/used by our sector

(despite the analysis being 10 years old)

Power Module Failure Mechanism

Bond wire lift-off Solder delamination

Detection methods: • TSEP (thermal sensitive electrical parameter)• Heat flux

Electro-thermal modelling

The measured Rth of the heatsink is increasing with the solder ageing. Therefore, lessheat is dissipated through the heatsink during the degradation.

Heatsink Thermal Resistance:

Rth= (Tc-Th)/power loss

Heat Flux Detection Method

• Measurement of temperature and electrical parameters could be combined to detect the degradation level of the power module

• However, degradation is non-linear and a multiphysics progression. The module performance is heavily coupled with the electrothermal condition

• Therefore, it is difficult to apply conventional analytical methods

Heatsink Thermal Resistance:

Rth= (Tc-Th)/power loss

Introduction of Artificial Neural Network

• Data-driven machine learning technology has achieved extraordinary results in numerous domains in the past decade

• Artificial Neural Network (ANN) based solutions have shown outstanding potential in the classification and regression of data analysis

• Introduction of ANN into heat flux detection could provide a feasible method to detect solder degradation

ANN-based Heat Flux Method

• First stage: Neural Network (NN) regression model to correlate thermal and electrical measurement

• Second stage: NN classification model to differentiate the degradation level

• DL 0, DL 1, …, DL n, represent different degrading levels

Test rig design

Basic ANN Verification Result

DNN Verification Results

Shallow NN Deep NN

Summary and Next Work

• The DNN-based heat flux detection method is proposed to monitor the module degradation. It can achieve online monitoring with minimal intrusion

• The proposed method can successfully detect the degradation level even under complex operating conditions

• Next work proposed is to verify the method with a more complicated multi-chip power module, i.e. PrimePack IGBT module

B. Hu et al., "Deep Learning Neural Networks for Heat-Flux Health Condition Monitoring Method of Multi-Device Power Electronics System," 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA, 2019, pp. 3769-3774, doi: 10.1109/ECCE.2019.8912666.

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