Prognostic Reasoner based Adaptive Power Management System for a More Electric Aircraft

ROBIN KUTTIKKADAN SEBASTIAN, Suresh Perinpinayagam, Ian.K.Jennions, and Alireza Alghassi
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phmc_16_046.pdf1.1 MBSeptember 5, 2016 - 12:04am

Abstract: This work presents an approach that addresses the concept of adaptive power management system framed in the PHM perspective of an Electrical Power System. The prognostic reasoner will implement automated health state detection and prediction algorithm that can accurately anticipate failures, predict the remaining useful life and in many cases accommodate (or reconfigure) for the identified faults. By introducing these approach on Electrical Power System, we are gaining a few minutes lead time to failures on critical systems and subsystem components that may introduce catastrophic secondary damages. The warning time on critical components must permits safe return to landing as the minimum criteria and would enhance safety.
Integrated Drive Generators (IDGs) and Transformer Rectifier Units (TRUs) are the main source of electrical power for a number of critical systems in an aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable operation of the IDG and the aircraft electrical components. IDGs are complex systems, and a majority of the existing fault detection and isolation techniques are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes, but building sufficiently accurate models of complex IDGs is a difficult task. Dq0 models have been developed for design and control of generators, but these models are not suitable for fault situations, where the generator may become unbalanced.
In this paper, we present a mathematical model for the aircraft generator, Contactor/Relay and convertor that accurately represents both nominal and parametric faulty behaviors. We present the details of the hybrid modeling approach and simulation results of nominal operation and fault behaviors associated with parametric faults in the aircraft generator and convertor modules. The simulation results show that the developed model is capable of accurately capturing the generator dynamics under a variety of normal and faulty configurations. Building sufficiently accurate models of the IDG electrical subsystem is a difficult task because of the complex nonlinearities and the time varying spatial relations involved in defining the dynamics of the electromagnetic behavior. In addition, the rectifier subsystem that converts the exciter AC voltage output into DC voltage that excites the main generator field includes switching components that introduce discrete dynamics into the overall system behavior.
In this paper we present a classification of faults that are typically encountered during operation of AC generators and Convertors. A subset of this list will be used to inject faults into the developed model to study the resultant faulty behaviors. Generator faults can be classified into one of two main categories: parametric faults and structural faults. Parametric faults are characterized by a change in the magnitude of one (or more) system model parameters. These faults do not affect the structure of the system, and, therefore, the system model is still a valid representation for the actual system. Structural faults change the system configuration and cannot be represented by a magnitude change in the system parameters. The system model under these faults is no longer valid, and a new model representing the new configuration is necessary to generate the system behavior. Structural faults can be classified into internal faults and external faults. External faults are the ones that happen outside the machine terminals, and although they do not change the structure of the machine, the overall system model changes. Sometimes it is possible to represent external faults by a parameter change by introducing auxiliary elements in the original model, but this may not always be possible. Phase to ground and phase to phase short circuits are the most common external faults.
Internal faults are intrinsic to the machine itself (within the machine boundary). In some situations, parametric and structural faults are not independent. For example, an incipient parametric fault representing a small number of shorted turns may cause overheating in the magnetic core of the generator, which after a period of time causes structural winding to ground fault. Therefore, detecting incipient faults at early stages is very important for machine protection. An example of an internal fault that changes the structure of the system is one phase to ground fault. The short circuit from winding to the ground creates a new loop with additional current component and with loop voltage equal to Zero. The situation becomes more complex when there is a dual fault from two phases to the ground.
This paper also describes the design and development of an Intelligent Controller based Prognostic Power Management System for a more electric aircraft. The design of electrical power management system will be validated in an Electrical test bench. The Prognostics & Health Monitoring (PHM) features will be added into the electrical power management system such that the system can re-configure the electrical system effectively using adaptive control algorithm.

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CBM and informed logistics

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