Multi-time-scale reliability evaluation method of wind power IGBT considering fatigue damage and system thereof

Patent metadata

Field	Value
Publication number	US-11543446-B2
Application number	US-202117168127-A
Country	US
Kind code	B2
Filing date	Feb 4, 2021
Priority date	Sep 7, 2020
Publication date	Jan 3, 2023
Grant date	Jan 3, 2023

Abstract

The disclosure discloses a multi-time-scale reliability evaluation method of a wind power IGBT considering fatigue damage and a system thereof. Lifetime information of a power device is comprehensively extracted by using multiple time scales. An electro-thermal coupling model of an IGBT module is established to obtain a junction temperature data. A steady-state junction temperature database of the IGBT in different aging states is established. Based on a SCADA monitoring data, the junction temperature data is outputted in real-time through the electro-thermal coupling model and a real-time thermal stress cycle number is calculated in a short-term time-scale profile, and a wind speed probability distribution curve is obtained in a long-term time-scale profile. A maximum thermal stress cycle number that the IGBT can withstand in different aging stages is obtained in advance and a cumulative damage degree and an estimated lifetime of the IGBT of the wind power converter are calculated.

First claim

What is claimed is:

1. A multi-scale reliability evaluation method of a wind power Insulated Gate Bipolar Transistor (IGBT) considering fatigue damage, comprising:
(1) dividing a time scale of reliability evaluation, and comprehensively extracting lifetime information of a power device by using multiple time scales;
(2) establishing an electro-thermal coupling model of an IGBT module for a topology of a wind power converter and an IGBT model to obtain a junction temperature data, and combined with an IGBT fatigue damage theory, establishing a steady-state junction temperature database of the IGBT in different aging states;
(3) in a short-term time scale profile, fully considering effects of an ambient temperature and a wind speed based on a Supervisory Control and Data Acquisition (SCADA) monitoring data, outputting the junction temperature data in real-time through the electro-thermal coupling model, and calculating a real-time thermal stress cycle number;
(4) in a long-term time-scale profile, establishing a Weibull probability distribution model of the wind speed based on the SCADA monitoring data of a wind turbine to obtain a wind speed probability distribution curve, and after normalization, conducting a thermal stress shock inspection number probability distribution, and combined with a Bayerer lifetime prediction model and the steady-state junction temperature database, obtaining in advance a maximum thermal stress cycle number that the IGBT can withstand in different aging stages;
and
(5) calculating a cumulative damage degree and an estimated lifetime of the IGBT of the wind power converter by taking the maximum thermal stress cycle number as a connection between evaluation results of the different time scales.

2. The method according to claim 1 , wherein the step of dividing the time scale of reliability evaluation comprises:
in the long-term time-scale profile, primarily considering power device aging characteristics, and ignoring transient details of junction temperature fluctuations to only consider a steady-state junction temperature and thereby maintain a high calculation efficiency; and
in the short-term time-scale profile, primarily considering the effects of the wind speed and the ambient temperature, and fully considering an observed junction temperature fluctuation data.

3. The method according to claim 2 , wherein the step of establishing the steady-state junction temperature database of the IGBT in different aging states comprises:
using a cumulative damage degree D to reflect a damage degree of the IGBT module based on a fatigue damage theory, wherein when D≤a thermal network parameter of the electro-thermal coupling model is not corrected; when the cumulative damage degree a<D≤b, the thermal network parameter of the electro-thermal coupling model is increased according to a first predetermined value; when the cumulative damage degree b<D≤c, the thermal network parameter of the electro-thermal coupling model is increased according to a second predetermined value; when the cumulative damage degree c <D≤d, the thermal network parameter of the electro-thermal coupling model is increased according to a third predetermined value; and when the cumulative damage degree d<D≤e, the thermal model parameter of the electro-thermal coupling model is increased according to a fourth predetermined value, wherein the first predetermined value, the second predetermined value, the third predetermined value, and the fourth predetermined value sequentially increase, and values of a, b, c, d, and e sequentially increase; and
performing sampling between a cut-in wind speed and a cut-out wind speed of the wind turbine to obtain characteristic operating conditions of the wind power IGBT, and calculating steady-state junction temperature values outputted by the electro-thermal coupling model in different aging states of each characteristic operating condition to establish the steady-state junction temperature database of the IGBT.

4. The method according to claim 3 , wherein the cumulative damage degree is determined according to

D
=

N

N
f

,

wherein N f is a fail cycle number of the IGBT module under a cyclic effect of a stress with an amplitude unchanged, and N represents a number of cycles of being subjected to the stress.

5. The method according to claim 1 , wherein Step (4) comprises:
establishing the Weibull probability distribution model of the wind speed based on the SCADA monitoring data of the wind turbine to obtain the wind speed probability distribution curve;
after normalization, conducting the thermal stress shock inspection number probability distribution, and determining an aging process of the IGBT to select a steady-state junction temperature database corresponding to an aging stage; and
combined with the Bayerer lifetime prediction model and the selected steady-state junction temperature database, obtaining in advance the maximum thermal stress cycle number that the IGBT can withstand in different aging stages.

6. The method according to claim 2 , wherein Step (4) comprises:
establishing the Weibull probability distribution model of the wind speed based on the SCADA monitoring data of the wind turbine to obtain the wind speed probability distribution curve;
after normalization, conducting the thermal stress shock inspection number probability distribution, and determining an aging process of the IGBT to select a steady-state junction temperature database corresponding to an aging stage; and
combined with the Bayerer lifetime prediction model and the selected steady-state junction temperature database, obtaining in advance the maximum thermal stress cycle number that the IGBT can withstand in different aging stages.

7. The method according to claim 3 , wherein Step (4) comprises:
establishing the Weibull probability distribution model of the wind speed based on the SCADA monitoring data of the wind turbine to obtain the wind speed probability distribution curve;
after normalization, conducting the thermal stress shock inspection number probability distribution, and determining an aging process of the IGBT to select a steady-state junction temperature database corresponding to an aging stage; and
combined with the Bayerer lifetime prediction model and the selected steady-state junction temperature database, obtaining in advance the maximum thermal stress cycle number that the IGBT can withstand in different aging stages.

8. The method according to claim 4 , wherein Step (4) comprises:
establishing the Weibull probability distribution model of the wind speed based on the SCADA monitoring data of the wind turbine to obtain the wind speed probability distribution curve;
after normalization, conducting the thermal stress shock inspection number probability distribution, and determining an aging process of the IGBT to select a steady-state junction temperature database corresponding to an aging stage; and
combined with the Bayerer lifetime prediction model and the selected steady-state junction temperature database, obtaining in advance the maximum thermal stress cycle number that the IGBT can withstand in different aging stages.

9. The method according to claim 5 , wherein Step (5) comprises:
in the short-term time-scale profile, obtaining the real-time thermal stress cycle number of the IGBT module in a current operating state;
in the long-term time-scale profile, obtaining a maximum withstan

Assignees

• Univ Wuhan

Inventors

• He Yigang
• Wang Chuankun
• Wang Chenyuan
• Li Lie

Classifications

• F05B2270/3032

  excessive temperatures, e.g. caused by overheating · CPC title

• G06F2119/02

  Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA] · CPC title

• G06F2119/08

  Thermal analysis or thermal optimisation · CPC title

• G05B2219/2619

  Wind turbines · CPC title

• F05B2270/332

  Maximum loads or fatigue criteria · CPC title