Optimised DC microgrid for future aircraft platforms
The aircraft industry's continued push towards the concept of a More Electric Aircraft (MEA), and All Electric Aircraft (AEA) to optimize its performance, decrease operating and maintenance costs, increase dispatch reliability, and reduce gas emissions. Particularly, the MEA concept aims to use...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/68577/ |
| _version_ | 1848800498573901824 |
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| author | Mohamed, Mohamed |
| author_facet | Mohamed, Mohamed |
| author_sort | Mohamed, Mohamed |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The aircraft industry's continued push towards the concept of a More Electric Aircraft (MEA), and All Electric Aircraft (AEA) to optimize its performance, decrease operating and maintenance costs, increase dispatch reliability, and reduce gas emissions. Particularly, the MEA concept aims to use electric power for all non-propulsive systems. Currently, the electrification trend is accelerating as aircraft Original Equipment Manufacturers (OEMs) engage with their suppliers to build new systems and install new power-dense electrically intensive architectures. This will lead to a significant challenge in the design of aircraft Electric Power systems (EPSs). The engineering community is currently researching various power system topologies. Among these topologies, single DC bus EPS architecture is considered one of the promising candidates for future MEA/AEA due to its simplicity. In this topology, the electrical power is provided from engine-driven generators to the single DC bus through Active Rectifiers (ARs).
In this thesis, the potential solutions at the device and system levels for optimization of a single DC bus-based, multiple-source multiple-load power system with battery integration are investigated in terms of Energy Management (EM) and system losses minimization. The optimization of the single DC bus is achieved in this study by considering the following proposed control approaches:
1. An innovative multi-function battery controller is presented and the Back Tracing (BT) algorithm for a seamless transition between controllers is used. The battery controller performs different functions i.e. providing DC power, maintaining DC bus voltage, controlling battery voltage and battery current for charging and discharging process purposes.
2. A control scheme for Permanent Magnet Machine (PMM) based aircraft Starter/Generator (S/G) operated in Flux Weakening (FW) mode is presented. The proposed scheme helps the previous approach adopted for the Variable Voltage Bus (VVB) concept for an aircraft EPS to cover a wide speed range in motoring and generation modes. The control plants are derived specifically to design the controllers for the S/G control scheme. Moreover, a detailed small-signal analysis is performed on the derived plant while considering the aircraft operating speed and load range.
3. Droop control has been widely used as a load-sharing method among paralleled power sources due to its inherent modularity, reliability, and ease of implementation. In this thesis, a streamlined design approach for optimal droop gains is presented, to achieve minimum power losses for an MEA DC microgrid while relying only on knowledge of the local converter power losses model. Additionally, a simplified, but sufficiently accurate, converter losses model is proposed to help in the optimal droop gains design.
4. A new approach is presented here for optimal droop gain design in a DC microgrid, using an Artificial Neural Network (ANN) to achieve multiple objectives, such as minimum system losses, improved voltage regulation, and current sharing of power converters. The inputs of the ANN are the normalized power system losses, DC bus voltage regulation, and current sharing while the outputs are optimal droop gains.
5. Due to the increase of electrical loads onboard an MEA, the EPS is becoming more and more complex. Therefore, there is a need to develop a control strategy to manage the overall energy ow and ensure the operation of safety-critical loads under different operating scenarios considering system losses minimization (maximum efficiency), exploiting the thermal capability of generators, using variable load priorities, and battery charging and discharging schedules. This thesis presents an EM strategy based on an optimal droop gain approach to minimize the total system losses for MEA applications taking into account the aforementioned optimized objectives. The Finite State Machine (FSM) structure has been chosen to implement the control strategy to realize the EPS reconfiguration operation.
Throughout the thesis, the technical results and time-domain simulations are supported by Matlab/Simulink based models and validated by experimental work on small-scaled rigs. |
| first_indexed | 2025-11-14T20:52:31Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-68577 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:52:31Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-685772025-02-28T15:14:53Z https://eprints.nottingham.ac.uk/68577/ Optimised DC microgrid for future aircraft platforms Mohamed, Mohamed The aircraft industry's continued push towards the concept of a More Electric Aircraft (MEA), and All Electric Aircraft (AEA) to optimize its performance, decrease operating and maintenance costs, increase dispatch reliability, and reduce gas emissions. Particularly, the MEA concept aims to use electric power for all non-propulsive systems. Currently, the electrification trend is accelerating as aircraft Original Equipment Manufacturers (OEMs) engage with their suppliers to build new systems and install new power-dense electrically intensive architectures. This will lead to a significant challenge in the design of aircraft Electric Power systems (EPSs). The engineering community is currently researching various power system topologies. Among these topologies, single DC bus EPS architecture is considered one of the promising candidates for future MEA/AEA due to its simplicity. In this topology, the electrical power is provided from engine-driven generators to the single DC bus through Active Rectifiers (ARs). In this thesis, the potential solutions at the device and system levels for optimization of a single DC bus-based, multiple-source multiple-load power system with battery integration are investigated in terms of Energy Management (EM) and system losses minimization. The optimization of the single DC bus is achieved in this study by considering the following proposed control approaches: 1. An innovative multi-function battery controller is presented and the Back Tracing (BT) algorithm for a seamless transition between controllers is used. The battery controller performs different functions i.e. providing DC power, maintaining DC bus voltage, controlling battery voltage and battery current for charging and discharging process purposes. 2. A control scheme for Permanent Magnet Machine (PMM) based aircraft Starter/Generator (S/G) operated in Flux Weakening (FW) mode is presented. The proposed scheme helps the previous approach adopted for the Variable Voltage Bus (VVB) concept for an aircraft EPS to cover a wide speed range in motoring and generation modes. The control plants are derived specifically to design the controllers for the S/G control scheme. Moreover, a detailed small-signal analysis is performed on the derived plant while considering the aircraft operating speed and load range. 3. Droop control has been widely used as a load-sharing method among paralleled power sources due to its inherent modularity, reliability, and ease of implementation. In this thesis, a streamlined design approach for optimal droop gains is presented, to achieve minimum power losses for an MEA DC microgrid while relying only on knowledge of the local converter power losses model. Additionally, a simplified, but sufficiently accurate, converter losses model is proposed to help in the optimal droop gains design. 4. A new approach is presented here for optimal droop gain design in a DC microgrid, using an Artificial Neural Network (ANN) to achieve multiple objectives, such as minimum system losses, improved voltage regulation, and current sharing of power converters. The inputs of the ANN are the normalized power system losses, DC bus voltage regulation, and current sharing while the outputs are optimal droop gains. 5. Due to the increase of electrical loads onboard an MEA, the EPS is becoming more and more complex. Therefore, there is a need to develop a control strategy to manage the overall energy ow and ensure the operation of safety-critical loads under different operating scenarios considering system losses minimization (maximum efficiency), exploiting the thermal capability of generators, using variable load priorities, and battery charging and discharging schedules. This thesis presents an EM strategy based on an optimal droop gain approach to minimize the total system losses for MEA applications taking into account the aforementioned optimized objectives. The Finite State Machine (FSM) structure has been chosen to implement the control strategy to realize the EPS reconfiguration operation. Throughout the thesis, the technical results and time-domain simulations are supported by Matlab/Simulink based models and validated by experimental work on small-scaled rigs. 2022-07-31 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/68577/1/Optimised%20DC%20Microgrid%20for%20Future%20Aircraft%20Platforms.pdf Mohamed, Mohamed (2022) Optimised DC microgrid for future aircraft platforms. PhD thesis, University of Nottingham. DC Microgrid More Electric Aircraft (MEA) Droop Control Battery Management Energy Management of Aircraft Starter/Generator Control Artificial Neural Network |
| spellingShingle | DC Microgrid More Electric Aircraft (MEA) Droop Control Battery Management Energy Management of Aircraft Starter/Generator Control Artificial Neural Network Mohamed, Mohamed Optimised DC microgrid for future aircraft platforms |
| title | Optimised DC microgrid for future aircraft platforms |
| title_full | Optimised DC microgrid for future aircraft platforms |
| title_fullStr | Optimised DC microgrid for future aircraft platforms |
| title_full_unstemmed | Optimised DC microgrid for future aircraft platforms |
| title_short | Optimised DC microgrid for future aircraft platforms |
| title_sort | optimised dc microgrid for future aircraft platforms |
| topic | DC Microgrid More Electric Aircraft (MEA) Droop Control Battery Management Energy Management of Aircraft Starter/Generator Control Artificial Neural Network |
| url | https://eprints.nottingham.ac.uk/68577/ |