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Ames Research Center

Prognostics for Microgrid Components Batteries, Capacitors, and Power Semiconductor Devices

Abhinav Saxena Stinger Ghaffarian Technologies, Inc.

NASAAmes Research Center, Moffett Field CA 94035

Presented at

Advanced Microgrid Concepts and Technologies Workshop Beltsville, MD

June 7 - 8,2012

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PROGNOSTICS CENTER OF EXCELLENCE

STINGER

GHAFFARIAN

, ___ , TECHNOLOGIES

https://ntrs.nasa.gov/search.jsp?R=20120015367 2018-04-21T02:02:13+00:00Z


Prognostics and Health Management

In Perspective


Goals for Prognostics What does prognostics aim to achieve?

Improved mission planning

Ability to reassess mission feasibility

Avoid cascading effects onto healthy

subsystems

Maintain consumer confidence,

product reputation

More efficient maintenance

planning

Reduced spares


Service only specific aircraft

which need servicing

Service on Iy when it is needed

Contingency Management View Maintenance Management View

• Prognostics goals should be defined from users' perspectives

• Different solutions and approaches apply for different users

PROGNOSTICS CENTER OF EXCELLENCE 3

Integrated Logistics information

Health Management Ames Research Center

Contingency Management View

Condition Based Mission Planning

~m 1 Reconfigurntion

•

Control Reconfiguration

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Condition Monitoring Safety and Risk

Analyses

Prognostic Control

Data Comm - Sensors

- Reporting - Scheduled Inspections

Troubleshooting and Repair

Maintenance Management View

Tech Support

T03;1;09 / ~_ • V // '. ~ r:O~l ~!-- ;;;;:------a::::;;;::;::~ ~ ..... \ ~ondition Based

Knowledg~~~se \ Maintenance

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Maintenance Aids

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Control

Predictive Mairitenance

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Condition Monitoring Reliability Analysis

Schematic adapted from: A. Saxena, Knowledge-Based Architecture for Integrated Condition Based Maintenance of Engineering Systems, PhD Thesis, Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta May 2007. Liang Tang, Gregory J. Kacprzynski, Kai Goebel, Johan Reimann, Marcos E. Orchard, Abhinav Saxena, and Bhaskar Saha, Prognostics in the Control Loop, Proceedings of the 2007 AAAI Fall Symposium on Artificial Intelligence for Prognostics, November 9-11,2007, Arlington, VA.


Prognostics for Microgrids

• Key components Power storage

• Batteries • Capacitors and SuperCapacitors

Power components and devices

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• Power switches (semiconductor switches and packagl" ""-

• Passive components (inductors, capacitors, high frequency transformers)

• Controllers and Gate drivers

• Microgrids PHM - Potential benefits* Advanced inverter controls for microgrids Robust operation during fault conditions More informed decision support

* Key R&D Areas for micro grid reliability as identified in 2011 DoE Microgrid Workshop, San Diego CA



5

Grid PHM Framework

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Classification Algorithms

• • Take Corrective Action

• Prognostic Framework

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• Grid Map

High load (peak hours)



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Fundamentals of Predicting Remaining Useful Life

Understanding the Prognostic Process


Prognostics Categories Ames Research Center

• Type I: Reliability Data-based - Use population based statistical model - These methods consider historical time to failure data which are used to model

the failure distribution. They estimate the life of a typical component under nominal usage conditions

- Example: Weibull Analysis

• Type II: Stress-based - Use population based fault growth model - learnt from accumulated knowledge - These methods also consider the environmental stresses (temperature, load,

vibration, etc.) on the component. They estimate the life of an average component under specific usage conditions

- Example: Proportional Hazards Model

• Type III: Condition-based - Individual component based data-driven model - These methods also consider the measured or inferred component degradation.

They estimate the life of a specific component under specific usage and degradation conditions

- Example: Cumulative Damage Model, Filtering and State Estimation • For more details please refer to last year's PHM09 tutorial on Prognostics by Dr. J. W. Hines: [http://WININ.phmsociety.org/events/conference/phm/09/tutorialsj


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probability of failure at the given damage size

Decision Point

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Risk is now a compound function of chosen failure threshold and the decision point


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Failure Threshold (aFT)

Probability of damage size being greater than

the critical value at time t3


00

7[ts = J Pa (ait = t3)da OFT

OFT

= 1 - J Pa(ait = t3)da o

= 1 - Pa (ap1' it = t3 )

Damage size pdf at a given time can be useful when planning a mission (usage) profile Answers how risky it is to go on a mission of known duration?

,--------------------- ----~

to t1 t2 13 Eol • Ti me (t)

We can figure out if the system would withstand by the time mission is completed


Particle Filters • Allows model adaptation • State estimation, tracking and prediction • Nice tradeoff between Me and KF • Useful in both diagnostics and prognostics • Represents uncertainty • Manages uncertainty

• actual state value

x measured state value

• stateparticlevalue

• statepdf(belief)

P(x)

x

actual state trajectory

estimated state trajectory

particle propagation

particle weight

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tk tk+l

Measurement

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Initialize PF Parameters

Propose Initial Population, <xo,wo>

Propagate Particles using State Model, Xk_l -7Xk

Update Weights, W k-l -7Wk

No

11

Particle Filter-Based Prognostics

System Sensors

1 Raw Data

Feature Extraction/ Direct Measurement

Model Parameters are part of the State Vector X == rxk,Okl

•••••••••••••••••••• ~

• ••••••••••••••••••• ~

Failure Threshold

• Parameter Identification •

Measurement Zk

System Model

Prior p(xklxk_1)

Particle Filter

Posterior p(xklxk_lZk)

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System Model

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State Tracking Loop Prediction Loop



Prediction

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Model Validation in Realistic Environments

• Battery discharge algorithm was used in e-UAV BHM • More than 3 dozen successful flights

Prediction update rates at 1 Hz Limited onboard computational power

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EOD Threshold

5 10 15 mins

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r==:J Scaled EOD pdf -~Fwd Motor Battery 1 -~Fwd Motor Battery 2 -~Aft Motor Battery 1 -~Aft Motor Battery 2

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25 900 1000 1100 1200 1300 1400

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Power Electronics Failure - MOSFETs Ames Research Center

• Objective: predict abnormal functioning of power electronics devices Prediction approach validated in data from 1 OOV power MOSFETs

The failure mechanism for the stress conditions is determined to be die-attachment degradation

Change in ON-state resistance is used as a precursor of failure due to its dependence on junction tem perature

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Increase in RD5(ON) due 10 [)e,,.;ce Degradation

-- Rre{a-J) De_ceB

- Rre{a-J) De_ceg

- Rre{a-J) De·.ce1 1 ' - - - - - - - - - -

- Rre{a-J) C".ce12 ' - - - - -

- Rre{a-J) [".ce14 ,

obe:~~-----------100 200 300 400 500

aging time (m mites I 600 700 800

RoS(on) Prediction

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50 100 nl { u } n ! J lEOL~ J _______ " ~J,ft - ~_ -1--

Aging Time (minutes)

Celaya, J., Saxena, A., Wysocki, P., Saha, S., Goebel, K., "Towards Prognostics of Power MOSFETs: Accelerated Aging and Precursors of Failure" Annual conference of the PHM Society, Portland OR, October 2010.


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Prognostic Performance Metrics Ames Research Center

• Metrics Hierarchy


21

Challenges in Prognostics Ames Research Center

-• Requirements Specification

- How can a requirement be framed for prognostics considering uncertainty?

- How to define and achieve desired prognostics fidelity

• Uncertainty in prognostics - Quantification, representation, propagation and management

- To what extent the probability distribution of a prediction represent reality

• Validation and Verification - How can a system be tested to determine if it satisfies specified

requirements?

- If a prediction is acted upon and an operational component is removed from service, how can its failure prediction be validated since the failure didn't happen?

- Prognostics performance evaluation - offline and online?

- Verifiability of prognostics algorithms


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