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Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

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Abstract

A 25 kVA generator is one of the most important components in the power system of an indigenous defensive fighter (IDF). However, the generators have failed frequently since being equipped on IDFs. The poor reliability and long repair time of a 25 kVA generator have seriously impacted the availability of IDFs. This study aims to build a failure-processing mechanism for the 25 kVA generators based on the state-estimation function of the Kalman filter. By way of this mechanism, the left- or right-side load of the fighter can be switched from the supplied generator that is going to fail to the other generator before the abnormal generator actually fails. Therefore, this mechanism not only mitigates the impact resulting from the irregular power on systems of the fighter, but also obtains reaction time to shutdown the generator that is going to fail so as to perform a preventive maintenance or other effective actions on the generator such that the further failures can be avoided. In the simulations of this study, the failure times predicted by the Kalman filter are compared with the results obtained by Monte Carlo simulation for judging the prediction accuracy. In order to simulate more accurately, an exponential attenuator is placed at each end of the outputs of both the generator and the Kalman filter so as to conform to the actual aging model. The prediction errors are acceptable. Besides, reliabilities of the generators in four different configurations are compared to know the effect of redundancy design.

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Abbreviations

A c, B c, C c, D c :

Coefficient matrix of the state equation for a continuous system

A D, B D, C D, D D :

Coefficient matrix of the state equation for a discrete system

A T :

Transpose matrix of A

A −1 :

Inverse matrix of A

B :

Damping coefficient

B k :

Coefficient matrix for the input term of a discrete state equation

H k :

Measurement sensitivity matrix

K k :

Kalman gain

P k/k−1 :

Estimation error covariance matrix

Q k :

Plant noise covariance matrices

R k :

Measurement noise covariance matrices

U k :

Control input of a discrete state equation at state k

V k :

Noise, measurement error vector

W k :

Disturbance, system stochastic input vector

\( X_{{D_{0} }} \) :

Initial states resulting from deterministic input

X k :

System state vector at state k

\( X_{{S_{0} }} \) :

Initial states resulting from stochastic input

Y k :

System output vector at state k

Z k :

Output measurement vector

Φ k :

State transition matrix

ωr :

Angular speed of the rotor, electrical rad/s

ω0 :

Rated speed of the rotor, electrical rad/s

δ:

Rotation angle of the rotor, electrical radians

T m :

Mechanical torque, N·m

T e :

Electromagnetic torque, N·m

K S :

Synchronizing torque coefficient, pu torque/rad

K D :

Damping torque coefficient, pu torque/pu speed deviation

H :

Inertia constant, MW·s/MVA

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Chin Yi University of Technology, Taichung 411, Taiwan, R.O.C

    S. K. Yang

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  1. S. K. Yang
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Correspondence to S. K. Yang.

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Yang, S.K. Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators. J Fail. Anal. and Preven. 11, 417–431 (2011). https://doi.org/10.1007/s11668-011-9445-0

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  • DOI: https://doi.org/10.1007/s11668-011-9445-0

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