Feasibility study -...

Appendix G Feasibility Report

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Feasibility study

Electronic signature identification

by

Marίa Fernanda Garcés

ID: 8538278

MSc. Electrical Energy Conversion Systems

10th May, 2013

Dr. Rebecca Todd


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Abstract

Confidentiality of power converter manufacturers limits the modelling of systems within aircrafts; given this problem engineers are using mathematical approaches (black and grey boxes) to obtain their behaviour. Fortunately, this solution provides accurate results that can be used to predict stability to obtain acceptable power quality in power systems.

This feasibility study provides the planning to obtain the black and grey boxes of an induction machine fed by an inverter mainly applied in the aircraft industry. It presents a brief introduction of more electric aircraft concepts to justify the necessities of modelling the proposed system. In section two, it describes some techniques used to obtain black and grey boxes usually applied in previous publications. Section three presents the methodology, where it is proposed how to build models (black and grey boxes) by means PLECS-Simulink platforms. Finally, in the last two sections the project planning along with milestone chart and a list of possible risks along with their mitigation techniques are shown.


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CONTENTS

1. Introduction _____________________________________________________ 69

1.2 Aim _________________________________________________________________ 70

1.3 Objectives ____________________________________________________________ 70

2. Literature review _________________________________________________ 70

2.1 More Electric Aircraft (MEA) _____________________________________________ 70

2.2 Electrical power systems in MEA __________________________________________ 71

2.3 Existing signal identification techniques ____________________________________ 72

2.3.1 Non parametric estimation methods ___________________________________________ 72

2.3.2 Parametric estimation methods _______________________________________________ 72

2.4 Modelling of power converter using black and grey boxes _____________________ 72

2.4.1 White Box. ________________________________________________________________ 73

2.4.2 Black Box _________________________________________________________________ 73

2.4.3 Grey box __________________________________________________________________ 73

2.5 Modelling of an Induction motor fed by an Inverter using identification methods __ 74

2.6 Stability of the power system _____________________________________________ 74

2.7 Influence of inverter loads in stability of the system __________________________ 75

3. Methodology ____________________________________________________ 76

3.1 Theoretical framework (research and reading) _______________________________ 76

3.2 PLECS model creation ___________________________________________________ 77

3.3 Black and grey boxes obtaining ___________________________________________ 78

3.3.1 Input/output data experiment. ________________________________________________ 78

3.3.2 Choice of model structure ____________________________________________________ 78

3.3.3 Validation of the model ______________________________________________________ 79

3.4 Application of black and grey boxes in a power system ________________________ 80

4. Conclusion _______________________________________________________ 80

5. Project planning & milestone chart ___________________________________ 81

6. Project Risks _____________________________________________________ 83

7. References ______________________________________________________ 84

8. APPENDIX _______________________________________________________ 86

Risk assessment PRO-FORMA _______________________________________________ 86


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1. Introduction

Nowadays, the more electric concept is embracing the design of systems in the industry; one of which is the design of aircrafts. In this particular application, design engineers are facing challenges such as the certifications needed for more-electric aircraft (MEA) and the designing of devices capable of being exposed to extreme conditions. The certifications involve to meet high standards which require failure rate of critical loads to be 10�� for military aircraft and 10��for civil platforms [1]. As a result, the target of designing efficient and well equipped system is a tough task which must be intelligently modelled, verified and tested in order to meet the MEA requirements.

Figure 1. Aircraft industry [2].

The benefits obtained with the more electric concept involve less weight and less volume for the aircraft, more efficient systems since mechanical, hydraulic and pneumatic systems are replaced, less fuel consumption, less costs and less pollution produced. All of these reasons are encouraging to apply electrical systems so; it is therefore needed to focus on the creation of new systems that can satisfy the requirements demanded. Then, in order to fulfil the target it is necessary to understand that the electrical power technology involves several consequences such as adding a substantial amount of high power dynamic motor loads plus the fact that most of these loads have constant power characteristic. These factors can impact the power quality and the stability [3]. Taking into account that the power quality and the stability are vulnerable, it is important to obtain excellent performance and integrity of the loads and the sources in the power system.

As a result of the MEA initiative the demand in using semiconductors, static power conversion, and electrical machine technologies has been increased significantly over the past twenty years [4]. Therefore a power system in an aircraft is composed by different power converters depending on the end load. Power converters need to be modelled before they can be implemented in the future systems so that the necessity of behavioural modelling is crucial but most of these devices are commercial and there are restrictions to access in the internal architecture. Previous solutions have been proposed such as creation of white, black and greys boxes obtained by means of identification


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techniques. The application of these models has been successful since the results are an approach close enough to the physical system.

Induction machines commonly applied within aircraft system, present advantages such as the simplicity of construction, the lack of commutators and brushes and the reliability needed to work in these kinds of systems. However, the lack of speed control and the high ratio of starting and running torque are limitations which engineers are faced with [5]. Furthermore, it is crucial to use links like converters in order to have induction machines handling the loads in the aircraft. All of these limitations are encouraging engineers to predict the behaviour of the power system by means of the model creation using mathematical approaches. Fortunately, in most of the cases the results have been satisfactory.

1.2 Aim

The aim of this project is to model an induction machine fed by an inverter by means of a black and a grey box obtained through identification techniques in order to obtain the behaviour of the system itself and its behaviour within a power system to obtain its effects in the stability.

1.3 Objectives

- To cover and to understand the literature about identification techniques.

- To familiarise with the software PLECS used within Simulink platform.

- To create a model of an induction machine fed by an inverter using PLECS and Simulink to obtain the data required and to create the black and grey boxes.

- To obtain the black and grey boxes of the system by means of the identification techniques.

- To validate the models obtained by comparing the results of the PLECS model and the results of the black and grey boxes.

- To analyse the behaviour of the black and grey boxes within a power system by determining the effects in the stability of the power system in the aircraft.

2. Literature review

2.1 More Electric Aircraft (MEA)

Aircrafts systems use a combination of powers such as hydraulic, pneumatic, electrical and mechanical sources which are extracted from the aircraft main engine [6]. Whilst mechanical power is obtained from the engine by a driven shaft and distributed by a gearbox, pneumatic power is extracted by a bleeding compressor and electrical power is obtained from generation. As a result each power source has its own role within the aircraft driving different subsystems.


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Due to the lack of power electronic systems, fault-tolerant electric machines, electro-hydrostatic actuators and electromechanical actuators in the past few years, the tendency of applying conventional powers has been maintained. However, since the recent breakthroughs, the panorama has changed and the MEA concept has started to lead the aircraft industry [6].

Some of the benefits of using the MEA concept include an increase in reliability, the reduction of weight and volume in the aircraft, the reduction of complexity, installation and running costs, moreover the significant improvements that have been achieved. All of these factors have led to an increase of MEA demand.

In conclusion, the concept of MEA is to use the electrical power to extract and to distribute the non-propulsive powers within the aircraft by the implementation of systems such as power electronic circuits (AC/DC, DC/DC, DC/AC) and electrical machines [6].

2.2 Electrical power systems in MEA

The complexity of a power system within MEA is greater compared with a conventional aircraft as shown in Figure 2 below. As a result, the distribution network has had considerable growth in terms of the number of electric loads whose behaviour when they interact between each other in the same network could conduce to instability and power quality problems [7].

In this new network, most of the functions that used to be operated by hydraulic, pneumatic and mechanical power are being replaced by electric power. This involves an increase of power electronics, electric drives, control electronics and microprocessors which has improved the performance and reliability of the aircraft. Therefore, novel connectivity topologies involving AC, DC, hybrid and frequency wild have emerged. For example, the architecture of the Boeing 787 is based on AC distribution whilst the Airbus A380 employs hybrid architecture with a frequency-wild ac subsystem [8].

Figure 2. Comparison between conventional aircraft systems and MEA systems [9].


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2.3 Existing signal identification techniques

Identification techniques are used to obtain an experimental approach to determine the dynamics of a system which does not have access to the inner parameters. This approach is obtained by means of four stages: input/output data experiment, choice of model structure, estimation of the model parameter and validation of the identified model [10]. In order to obtain the model, it is necessary to review the existing techniques used for this purpose. According to [11] and [12] identification methods are classified as nonparametric and parametric.

2.3.1 Non parametric estimation methods

These methods use direct techniques to determine the transfer functions which describe a linear time-invariant model. The methods are non-parametric because the resulting models are curves or functions and they are not parameterised by a parameter vector. The following methods are presented within this category.

Transient analysis: A step response or an impulse response is used as the input to the system and the output obtained is the model.

Frequency response: a sinusoidal signal is used as the input and the variations in the output will be considered in steady state. The variations of amplitude and phase will give the frequency response according to the frequency used in the input.

Correlation analysis: this method is used to obtain a linear system of finite dimension which is simplified using white noise as an input. The weighting function is obtained by means of a normalised cross variance function between output and input.

Spectral analysis: the output obtained is developed from statistical methods, so the frequency response is obtained by dividing the cross-spectrum between output and input to the input spectrum.

2.3.2 Parametric estimation methods

The models obtained by means of parametric methods are the result of recorded data which are used to get the estimated parameter vector. Generally, data is determined by statistical methods.

2.4 Modelling of power converter using black and grey boxes

Previous works have been published about black and grey box modelling of power converters; one example was proposed in [13], it consists of a procedure to obtain a black box of a three phase voltage source inverter based on transient response analysis. In this proposal, simple experiments are used which consist of measuring the transient waveforms produced by step tests that are carried out in power electronics laboratories. After the tests, the results are used to accurately fit the parameters of the dynamic system in the black box. The Model is defined by means of a transfer function which is based on the least-squared method; this method is defined by a set of parameters


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obtained by tests and it is applied because the goal of this study is to focus on parametric methods of identification. Finally, the evaluation of the performance in the proposed black box modelling method is achieved by comparing one model simulated in a different platform and the black box model.

Referring to [14], it is used is a simple circuit to obtain the data and its identification is achieved by analysing the transient response when load steps are applied. The main purpose of the survey is to obtain a grey box which models voltage mode controlled buck derived converters. These kind of converters have a nonlinear dynamic response because they depend on the input voltage value so the proposed model is a simplified large-signal averaged model. The identification procedure is obtained by means of load step tests. The validation is carried out by the comparison of the PSIM simulation and the behavioural identified model. As a result of the comparison it is shown that both responses are close enough. Therefore, the non-linear dependence between the input voltage and the dynamic response of the converter is successfully modelled.

In order to understand the purpose of each box modelling, brief explanations are presented in the following section

2.4.1 White Box.

In this approach the systems have access to their internal architecture. As a result, the behaviour of power converters becomes very easy to model. The limitation with this approach is the difficulty to obtain internal architecture since most of the converters available are commercial and the restriction with the manufacturer data is crucial. In fact these models are not used due to the existing restrictions [14].

2.4.2 Black Box

As it was previously explained, the information inside the power converters is restricted by the confidentiality of manufacturers. Therefore the creation of a black box is an option to obtain an approach of a system without having any knowledge of their internal algorithm [15]. Black boxes are obtained using the identification techniques previously presented. One of the benefits is the optimisation in the simulation time because the converter is expressed with a mathematical equivalent instead of elements like switches, etc.

2.4.3 Grey box

In grey box models the parameters have a physical interpretation; this is the main difference between grey and black boxes. Some of the knowledge about the internal architecture is known so that the access to the source code is limited [14]. In fact, a grey box is the composition of a white and a black box.


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2.5 Modelling of an Induction motor fed by an Inverter using identification methods

Some publications suggest that the system identification and parameter estimation technology has reached a certain level of reliability so that this routine can be used to model induction machines. For example, the survey presented in [16] shows an identification procedure based on input and output records, obtaining a first order discrete time model which passes the validation test whilst demonstrating that the black box obtained for the induction machine can be used for multiple purposes like modelling and simulation. Since the identification is an experimental approach, it requires carrying out experiments that can produce the data to create the black box.

Another approach of an induction machine was obtained in [10] where the black box modelling of an induction machines was performed by means of an artificial neural method. The data was sampled using a program (C++) and the model was obtained by using the identification toolbox from Matlab with half of the data previously obtained and the remaining half used to validate the black box. The model passed the residual test and the correlation test to probe that there is no over-fitting or under-fitting characteristics. The goal of the paper was to provide a simple technique to obtain a black box which can produce an accurate modelling of the induction machine.

In conclusion, modelling of induction machines and power converters has been successfully achieved. In addition to this, the requirements have been covered with reasonable mathematical approaches which encourage these studies since inverters are commonly used in new aircraft power distribution systems.

2.6 Stability of the power system

The increasing of more electric aircraft involves having distribution systems that interconnect a large variety of components such as AC/DC rectifiers, DC/AC inverters, and DC/DC choppers (Figure 3) which are required to handle existing loads within an aircraft. Generally, each subsystem is designed according to its own operation so when the subsystems are interconnected it can lead to system instability [17]. Hence, previous publications have developed effective models capable of representing the behaviour of an electrical power system. It is possible to obtain approaches that can be used to analyse the performance of power converters within a power system. When the system involves many power converters in dc distribution systems it is optimal to use the generalised state space averaging technique. Its performance has been optimal but, unfortunately not adequate to model three phase systems. The limitation is due to high order models obtained from three phase systems and they are not easy to analyse. Another method used is the average-value modelling which is useful for 6 and 12 pulse diode rectifiers modelling and also for generators with line-commutated rectifiers. However, it is not applicable to analyse the stability of the whole power system. Finally, the d-q transformation theory has been applied to model AC distribution systems obtaining good results even when it is comprising of a vector-controlled converter where the previous methods cannot be applied [18].


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Figure 3. Boeing 767 electric power distribution system.

Due to the importance of analysing stability in the systems, plenty methods have been developed one of them is the eigenvalue theorem. Where, eigenvalues are calculated from a Jacobian matrix. Another example is the impedance-admittance method on the middlebrooks criterion more used for analysing stability in DC power systems but not suitable for complex ac system having parallel power converter connections According to the methods described in this section the power system stability can be predicted and analysed to have high standards of power quality.

2.7 Influence of inverter loads in stability of the system

Inverter loads produce instability in power systems and the problem has been addressed with varying degrees of success in the few past years. For example, one method applied was by using arbitrary changes in time response of the elements. However, unfortunately this solution has no scientific meaning and it provides no guidance to the system designer. One technical solution is proposed in publication [19] which suggests that the instability in the system produced by inverter loads must be analysed by pointing out measurable properties of the system in order to predict stability. The results obtained from the analysis in this publication indicates that the main factors which affect the stability due to inverter loads involve the separation of natural frequencies plus the effects from the power transfer properties of the system, and those related to the signal properties of the system. In conclusion, what is suggested to obtain the influence of inverters within the system is to obtain models by means of mathematical methods that can be derived from terms of measurable properties; where the outcomes can be


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validated with laboratory tests and so they can be verified in order to obtain suggested requirements within the power system.

3. Methodology

In order to achieve the proposed aim of the project, the methodology will be divided into three sections. Firstly, research will be carried out detailing the mathematical methods to be applied and to handle the new software to be used. Secondly, experiments will be carried out to obtain sets of data and then to get a mathematical approach of the system. Finally, the validation of the system will be carried out in order to verify the accuracy of the obtained models and to apply the models in a power system to evaluate its stability. Each section is presented according to the proposed objectives (Section 3.3).

3.1 Theoretical framework (research and reading)

This section of the methodology aims to cover all sections presented in the literature review whilst taking into account: in-depth research, detailing methods, mathematical techniques and to address the new platforms to be used.

The references shown through this feasibility study present different mathematical methods used to obtain the required models. Therefore, further reading and investigation of each method will be carried out. The topics to be covered are as follows:

- Identification techniques and their mathematical procedures (Selection of adequate techniques to model the system proposed).

- Model structures and its benefits and limitations, to define which model will be applied.

- Methods to process the data and its accuracy.

- PLECS user’s manual to become familiar with this new platform.

Once the topics described in the research have been addressed, the methods and the mathematical procedures will be applied in order to formulate the strategy to obtain the black box shown in Figure 4 and the grey box shown in Figure 5.

Figure 4. Black box model

Inputs Outputs Mathematical approach

Control + System


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Figure 5. Grey box model

3.2 PLECS model creation

As it is necessary to model the electrical and control circuits and given that` Simulink cannot model them in a straightforward way, a toolbox called PLECS will be used within the Simulink environment. This tool offers circuit simulation which can be built with linear elements (RLC) based in state space formulation [20]. In fact, PLECS blocks behave as a regular Simulink subsystem and the measurements obtained inside PLECS blocks can be displayed on a scope and processed in Matlab to show the results. However, it is not possible to place Simulink objects in a PLECS schematic and vice versa since both programs do not share the same Graphical User Interface. Some PLECS components are shown in Figure 6 and will be used to assemble the induction machine and the inverter circuit.

Figure 6. PLECS components used for simulation [21].

To become familiar with this tool, the intention is to model an RC low pass filter because it is a simple circuit that can help to understand the software and its tools. Following this, the real system will be assembled. The model will be composed by a three phase induction machine fed by an inverter modelled within a subsystem in PLECS and the control will be modelled with a subsystem in Simulink (applying a typical DQ control). The basic scheme it is shown in Figure 7 below:

Control Mathematical approach

System only Inputs Outputs


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Figure 7. Schematic Diagram of the system.

The objective of the model in PLECS is to produce the data required to create the black and grey box approaches furthermore, the model will be used to validate the mathematical approaches.

3.3 Black and grey boxes obtaining

Since the system identification is an experimental method for determining the dynamic of the system, it is necessary to take into account the following stages.

3.3.1 Input/output data experiment.

In this stage the model assembled in Figure 7 is used to acquire the data. Two types of experiments will be carried out. Firstly, the model will be simulated during normal operation of the system and secondly perturbations will be applied. In order to obtain reasonable results perturbations applied must be small. During this stage two important factors will be selected:

- Selection of the sample time: This factor can affect the structure selection and the parameter estimation. As a result, it needs to be selected appropriately. Usually shorter sample times are favoured to obtain good models [22] but the selection of the sample time in these projects will be done according to the required parameters.

- Selection of the input signals. This factor is a critical aspect because it directly affects the model quality [23].

The experiments that are going to be performed mainly consist of applying certain values to the input and measuring results in the output in order to obtain the sets of data required. This method is called transient analysis and according to the literature review is one of the most used methods due to its simplicity. Another methods previously described will be attempted within this project taking into account that the more suitable model will have a transfer function with a reasonable order that can be manageable in power systems simulation.

3.3.2 Choice of model structure

In this stage it is required to select a model structure. The options available are: state-space, transfer function or polynomial forms such as ARX, ARMAX, OE, BJ. It is also required to establish the order of the model. The choice will be influenced by the analysis of the data obtained.

Control Inverter Induction Machine

Simulink Subsystem PLECS Subsystem PLECS Subsystem


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One test will be carried out by using second order transfer function and if the accuracy obtained is close enough the will not be changed. If the results do not fit the requirements the transfer function order will be increased. Another possible method to be used is the Matlab system identification toolbox which allows obtaining a model straightforwardly. The obtained sets of data will be used to do the estimation of the model parameters with the corresponding accuracy.

3.3.3 Validation of the model

The black or greys boxes obtained need to be checked by comparing its response with the real system (PLECS model) response. Their behaviour must be similar and the curves must be close enough. The main idea of comparing results is to illustrate what it is shown in Figure 8. The curves will be shown in the same graphs to appreciate the accuracy the results.

Figure 8. Comparisons of the results [13].

To process the data and to obtain the graphs it will be used Matlab tools. Some of the curves required will be:

- PLECS result vs. Black box results (second order transfer function )

- PLECS results vs. Black box results (higher order transfer function )

- PLECS result vs. grey box results (second order transfer function )

- PLECS results vs. grey box results (higher order transfer function )


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3.4 Application of black and grey boxes in a power system

The main application of the black and grey boxes in this project is to obtain its behavior within the power systems. Thus, in order to appreciate the behavior of these loads within an aircraft it will be assembled a power system as it is shown in Figure 9. Certain limits must be taken into account to have sensible power quality in the network. The limits to be considered are shown in Table 1.

Figure 9. Power system for stability analysis

Table 1. DC normal operation characteristics [24].

Simulink tools will be used to measure the main bus voltage values. These values might change according to the variation of the parameters and according to the number of black/grey boxes installed. The black boxes shown in Figure 9 have a different name in order to illustrate that during the simulation each model will have different values as input parameters.

The same simulation will be carried out for the greys boxes. The results acquired will define whether the induction machine fed by an inverter influences the stability of the system and what types of changes in the loads are more critical for the bus parameters.

The methodology presented in this section might have some variations according to the results obtained in the experiments; it may be required to change the mathematical methods or to change the software to process the data. These variations will be done according to the accuracy obtained in each model.

4. Conclusion

Every section of this feasibility project aims to contribute to the realization of the proposed black and grey boxes. Firstly, the introduction presented gives a summary of the main reasons that support the modelling of the induction machine fed by an inverter.

28 Volts DC system 270 Volt DC system

Steady state voltage 22.0 to 29.0 Volts 250.0 to 280.0 Volts

Distortion factor 0.035 maximum 0.015 maximum

Ripple Amplitude 1.5 Volts maximum 6.0 Volts maximum

Limits Steady state characteristics


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Secondly, it was covered some of the strategies that will be used to carry out the project. Some examples from previous publications are shown in the literature review to illustrate the scope of this project. Finally, it was presented the planning along with the activities to follow within twelve weeks to develop the project and it is evaluated possible risks to face during the development of the dissertation.

It is concluded that it is possible to develop this project under the required parameters and within the time shown in the planning chart. In case of finding any withdrawn it was taking account mitigation strategies and different methods to apply.

5. Project planning & milestone chart

The planning of this project is divided into seven activities that will be carried out in twelve weeks. The first activity will be started on June 1st, during one week all the investigation and reading material will be analysed. In the second week the PLECS model will be assembled and it will take one week more to interconnect the subsystems. It is proposed to design and to model the black and grey boxes in three weeks. All the stability analysis will be carried out in 2 weeks. Each activity has sub activities according to the projection shown in Figure 10 and as a contingency strategy it is considered two weeks as float time if any drawback is found. The finalization of this project is expected for the 3rd of September.


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Figure 10. Milestone chart

Activity ID Activity Title

Week

No: 1

Week

No: 2

Week

No: 3

Week

No: 4

Week

No: 5

Week

No: 6

Week

No: 7

Week

No: 8

Week

No: 9

Week

No: 10

Week

No: 11

Week

No: 12

A1 1: Initial model creation

1.1: Investigate Identification Techniques

1.2: Investigate Model Structures

1.3: Revision of PLECS introduction manual

A2

2: Develop the initial model in PLECS-Simulink

platform

2.1: Develop of a simple model

2.2: Develop of the induction machine (IM) in

PLECS

2.3: Develop of the IM control In Simulink

2.4: Creation of an inverter subsystem in Simulink

2.5: Interconnection of subsystems

2.6: Initial texts of the PLECS-Simulink model

A3 3: Develop of black box of the system

3.1: Obtain data by means of experiments

3.2: Choice of model structure according to the

literature

3.3: Process the data from experiments

3.3: Estimation of the model parameters

3.4: Perform tests of the black box Model

Milestone 1: Complete black box Δ

Contingency

A4 4: Develop of grey box of the system

4.1: Identify data to be used for the grey box

4.2: Develop of the grey box

4.4: Perform of test of the grey box Model

Milestone 2: Complete grey box Δ

A5 5: Validation of the models

5.1: Process data obtained from PLECS model

5.2: Process data Obtained from Black box

5.3: Process data Obtained from Grey box

5.4: Comparison of the results from the 3 sources:

PLECS, black and grey box model

Milestone 3: Complete validation Δ

A6 6: Analysis of the model in a Power system

6.1: Application of the model in a power system

6.2: Stability of power system tests

6.3: Analysis of the Stability and power quality

Contingency

A7 7: Generating Dissertation Δ

7.1: Process models obtained in the report

7.2: Process data from experiments in the report

7.3: Register the results of the Tests

7.4: Writing

7.5: Revise report

7.6: Report Binding

Milestone 4: Complete dissertation Δ


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6. Project Risks

In this section a list of issues that may provoke drawbacks during the project are listed along with their respective mitigation strategies.

Possible risk Mitigation strategy

1. – Given the software (PLECS) is a new tool, it is possible to lose the software licence.

To mitigate this risk the simulation will be carried out using Simulink. All circuits will be expressed with mathematical expression, if it is not possible to use PLECS.

2. - Some drawbacks can be found using the new tool (PLECS). Due to this, it may cause delays in the developing of the project

To mitigate this problem it is planned to create a model of a simple application and to understand the advantages and disadvantages of PLECS.

3.- Transient analysis is one method to be used in order to obtain the model by means of identification techniques, it is possible to test this method and find limitations during the experiments

To mitigate this problem other methods are described in the literature review and can be used instead of Transient analysis

4.- Since all models are going to be assembled within platforms that are installed in a personal laptop there is a risk of possible software or hardware damage

All models and files related to the dissertation project will have a backup in Dropbox tool (online storage), in order to have a copy and not to lose information.

5. - There is a risk respecting to the student health which can be unpredictable.

To mitigate this risk, it is planned to have float time in the milestone chart and to take account the health and safety recommendation provided during the feasibly module.

Table 2. Project risks and mitigation techniques


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7. References

[1] R. D. Telford, S. J. Galloway, and G. M. Burt, "Evaluating the reliability & availability of more-electric aircraft power systems," in Universities Power Engineering Conference (UPEC), 2012 47th International, 2012, pp. 1-6.

[2] J. A. Ferreira, I. Kane, P. Klinkert, and T. Hage, "An inverter for generating harmonic rich current waveforms for aircraft mounted electromagnetic surveying systems," in Industry Applications Conference, 2002. 37th IAS Annual Meeting. Conference Record of the, 2002, pp. 1599-1604 vol.3.

[3] J. A. Weimer, "Electrical power technology for the more electric aircraft," in Digital Avionics Systems Conference, 1993. 12th DASC., AIAA/IEEE, 1993, pp. 445-450.

[4] R. Burgos, C. Gang, F. Wang, D. Boroyevich, W. G. Odendaal, and J. D. Van Wyk, "Reliability-Oriented Design of Three-Phase Power Converters for Aircraft Applications," Aerospace and Electronic Systems, IEEE Transactions on, vol. 48, pp. 1249-1263, 2012.

[5] "Some aspects of the application of induction motors to aircraft," Electrical Engineering, vol. 63, pp. 1476-1477, 1944.

[6] A. A. AbdElhafez and A. J. Forsyth, "A review of more-electric aircraft," pp. 26-28.

[7] D. Izquierdo, R. Azcona, F. del Cerro, Ferna, x, C. ndez, et al., "Electrical power distribution system (HV270DC), for application in more electric aircraft," in Applied Power Electronics Conference and Exposition (APEC), 2010 Twenty-Fifth Annual IEEE, 2010, pp. 1300-1305.

[8] S. V. Bozhko, T. Wu, Y. Tao, and G. M. Asher, "More-electric aircraft electrical power system accelerated functional modeling," in Power Electronics and Motion Control Conference (EPE/PEMC), 2010 14th International, 2010, pp. T9-7-T9-14.

[9] M. J. Provost, "The More Electric Aero-engine: a general overview from an engine manufacturer," in Power Electronics, Machines and Drives, 2002. International Conference on (Conf. Publ. No. 487), 2002, pp. 246-251.

[10] F. A. Mohamed and H. Koivo, "Modelling of induction motor using non-linear neural network system identification," in SICE 2004 Annual Conference, 2004, pp. 977-982 vol. 2.

[11] L. Lennart and L. Ljung, System identification : theory for the user / Lennart Ljung. Englewood Cliffs ; London: Englewood Cliffs ; London : Prentice-Hall, 1987.

[12] S. d. m. Torsten and T. So�derstro�m, System identification / Torsten So derstro m and Petre Stoica. New York ; London: New York ; London : Prentice Hall, 1989.

[13] V. Valdivia, A. M. Roldan, A. Barrado, P. Zumel, Ferna, x, et al., "Black-box modeling of three phase voltage source inverters based on transient response analysis," in Applied Power Electronics Conference and Exposition (APEC), 2010 Twenty-Fifth Annual IEEE, 2010, pp. 1279-1286.

[14] V. Valdivia, A. Barrado, A. M. Roldan, P. Zumel, and C. Raga, "New Nonlinear Dynamic "Grey Box" Behavioral Modeling and Identification of Voltage Mode Controlled Buck Derived DC-DC Converters," in Applied Power Electronics


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Conference and Exposition, 2009. APEC 2009. Twenty-Fourth Annual IEEE, 2009, pp. 312-317.

[15] K. Mohd. Ehmer and K. Farmeena, "A Comparative Study of White Box, Black Box and Grey Box Testing Techniques," International Journal of Advanced Computer Sciences and Applications, vol. 3, p. 12.

[16] S. Yaacob and F. A. Mohamed, "Black-box modelling of the induction motor," in SICE '98. Proceedings of the 37th SICE Annual Conference. International Session Papers, 1998, pp. 883-886.

[17] H. Zhang, C. Saudemont, B. Robyns, N. Huttin, and R. Meuret, "Stability analysis on the DC Power Distribution System of More Electric Aircraft," in Power Electronics and Motion Control Conference, 2008. EPE-PEMC 2008. 13th, 2008, pp. 1523-1528.

[18] K. N. Areerak, T. Wu, S. V. Bozhko, G. M. Asher, and D. W. P. Thomas, "Aircraft Power System Stability Study Including Effect of Voltage Control and Actuators Dynamic," Aerospace and Electronic Systems, IEEE Transactions on, vol. 47, pp. 2574-2589, 2011.

[19] R. E. Thomas, "The stability of aircraft D-C power systems with inverter loads," American Institute of Electrical Engineers, Part II: Applications and Industry, Transactions of the, vol. 76, pp. 183-188, 1957.

[20] J. H. Alimeling and W. P. Hammer, "PLECS-piece-wise linear electrical circuit simulation for Simulink," in Power Electronics and Drive Systems, 1999. PEDS '99. Proceedings of the IEEE 1999 International Conference on, 1999, pp. 355-360 vol.1.

[21] M. Ciobotaru, T. Kerekes, R. Teodorescu, and A. Bouscayrol, "PV inverter simulation using MATLAB/Simulink graphical environment and PLECS blockset," in IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on, 2006, pp. 5313-5318.

[22] S. A. Billings and L. A. Aguirre, "Effects of the sampling time on the dynamics and identification of nonlinear models," International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, vol. 5, pp. 1541-1556, 1995.

[23] A. H. Siddiqi, I. S. Duff, and O. Christensen, "Optimal Input Signal Design in Data-Centric System Identification," 2006.

[24] M. Standard, "Aircraft Electrical Power Characteristics," MIL-STD-704F2004.


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8. APPENDIX

Risk assessment PRO-FORMA


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