Optimized operational control for an electric power system on more electric aircraft
On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). It is essential to design a system-level operational control to coordinate the power and energy among different subsystems,...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2023
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| Online Access: | https://eprints.nottingham.ac.uk/72484/ |
| _version_ | 1848801198819246080 |
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| author | Wang, Xin |
| author_facet | Wang, Xin |
| author_sort | Wang, Xin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). It is essential to design a system-level operational control to coordinate the power and energy among different subsystems, ensuring the flight mission and reducing the operation costs while guaranteeing the safety requirements. This thesis presents an operational control design which adopts different techniques for a novel modular converter-based aircraft EPS architecture, providing an efficient, reliable, and robust operating strategy in both normal and faulty conditions.
Four main issues are integrated into the operational control for the modular converter-based EPS architecture: intelligent load shedding; power allocation and scheduling (PAS); EPS (re)configuration; and energy storage management (ESM). Firstly, this thesis studies the mathematical formulation and optimization methods for the studied EPS and the control targets. The optimal PAS and EPS (re)configuration are studied for a modular converter power distribution system, demonstrating mixed-integer linear programming (MILP) and linearization techniques for mathematical modeling with operation constraints, including the power balancing and limitations, contactor switching logic, the transmission losses of cables, and the nonlinear characteristics of converter efficiencies. The results are compared when two different objective functions are applied for power loss and generator sizing reduction.
Secondly, the aforementioned MILP formulation techniques are applied for decisions on load shedding and ESM for the system in study. With the aim of avoiding worst-case scenarios during the flight, for both cost reduction and safety improvement, a MILP-based model predictive control (MPC) operational control is proposed based on future predictions. The proposed MILP-MPC method aims to achieve multiple control targets, including maintaining energy storage (ES) and prolonging the battery life cycle, minimizing load shedding, and reducing switching activities to improve a device’s lifetime and avoid unnecessary transients. To achieve these control targets, three different objective functions are proposed. The importance of the different objective functions and prediction horizons are compared by the proposed evaluation framework.
Thirdly, this thesis proposes an intelligent two-level hierarchical supervisory control method for the entire EPS, achieving all of the multiple control targets in various faulty scenarios. The control targets here include the reduction of total power losses and generator sizing, intelligent ESM and load shedding, and reducing switching activities. The MILP-MPC-based high level (HL) controller improves the long-term (i.e., the entire flight stages) EPS performance of these control targets with future predictions utilizing a slow clock, to reduce the computation burden. In addition, a rule-based lower level (LL) controller is designed with a faster clock to provide quick responses to load changes and faults which occur over a short timeframe within one of the HL sample intervals. Hence, the proposed hierarchical control provides optimized performances while maintaining quick responses to EPS changes. In addition, the LL controller is designed with the consideration of handling the situation when the HL is in a faulty condition, which improves the reliability of the hierarchical control.
Finally, uncertainties of fluctuating load demands are considered for the aforementioned MILP-MPC operational control of the entire EPS, aiming to avoid poor performance due to imprecise load predictions. To improve the robustness of the control strategy, a chance-constrained stochastic MPC (CC-SMPC) method is proposed to improve both the system operation in terms of the system’s objectives as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted to compare the performance of the proposed CC-SMPC and deterministic MPC (DMPC) techniques.
In summary, this thesis provides the design engineer with different aspects and techniques for designing an optimized operational controller for an aircraft EPS. The practical techniques, performances, and benefits of the proposed methods are presented and discussed, inspiring further studies in the target area with different EPS architectures, requirements, and control targets. |
| first_indexed | 2025-11-14T20:56:26Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-72484 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:03:39Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-724842025-07-31T04:30:11Z https://eprints.nottingham.ac.uk/72484/ Optimized operational control for an electric power system on more electric aircraft Wang, Xin On more electric aircraft (MEA), reducing fuel consumption and guaranteeing flight safety are pursued by efficient operational management of the electrical power system (EPS). It is essential to design a system-level operational control to coordinate the power and energy among different subsystems, ensuring the flight mission and reducing the operation costs while guaranteeing the safety requirements. This thesis presents an operational control design which adopts different techniques for a novel modular converter-based aircraft EPS architecture, providing an efficient, reliable, and robust operating strategy in both normal and faulty conditions. Four main issues are integrated into the operational control for the modular converter-based EPS architecture: intelligent load shedding; power allocation and scheduling (PAS); EPS (re)configuration; and energy storage management (ESM). Firstly, this thesis studies the mathematical formulation and optimization methods for the studied EPS and the control targets. The optimal PAS and EPS (re)configuration are studied for a modular converter power distribution system, demonstrating mixed-integer linear programming (MILP) and linearization techniques for mathematical modeling with operation constraints, including the power balancing and limitations, contactor switching logic, the transmission losses of cables, and the nonlinear characteristics of converter efficiencies. The results are compared when two different objective functions are applied for power loss and generator sizing reduction. Secondly, the aforementioned MILP formulation techniques are applied for decisions on load shedding and ESM for the system in study. With the aim of avoiding worst-case scenarios during the flight, for both cost reduction and safety improvement, a MILP-based model predictive control (MPC) operational control is proposed based on future predictions. The proposed MILP-MPC method aims to achieve multiple control targets, including maintaining energy storage (ES) and prolonging the battery life cycle, minimizing load shedding, and reducing switching activities to improve a device’s lifetime and avoid unnecessary transients. To achieve these control targets, three different objective functions are proposed. The importance of the different objective functions and prediction horizons are compared by the proposed evaluation framework. Thirdly, this thesis proposes an intelligent two-level hierarchical supervisory control method for the entire EPS, achieving all of the multiple control targets in various faulty scenarios. The control targets here include the reduction of total power losses and generator sizing, intelligent ESM and load shedding, and reducing switching activities. The MILP-MPC-based high level (HL) controller improves the long-term (i.e., the entire flight stages) EPS performance of these control targets with future predictions utilizing a slow clock, to reduce the computation burden. In addition, a rule-based lower level (LL) controller is designed with a faster clock to provide quick responses to load changes and faults which occur over a short timeframe within one of the HL sample intervals. Hence, the proposed hierarchical control provides optimized performances while maintaining quick responses to EPS changes. In addition, the LL controller is designed with the consideration of handling the situation when the HL is in a faulty condition, which improves the reliability of the hierarchical control. Finally, uncertainties of fluctuating load demands are considered for the aforementioned MILP-MPC operational control of the entire EPS, aiming to avoid poor performance due to imprecise load predictions. To improve the robustness of the control strategy, a chance-constrained stochastic MPC (CC-SMPC) method is proposed to improve both the system operation in terms of the system’s objectives as well as the ability to cope with uncertainties due to fluctuating load demands. Both normal and faulty operating conditions are investigated with multi-failure cases, resulting in different uncertainty propagation paths. To verify the effectiveness of the proposed strategy, a comprehensive comparison study is conducted to compare the performance of the proposed CC-SMPC and deterministic MPC (DMPC) techniques. In summary, this thesis provides the design engineer with different aspects and techniques for designing an optimized operational controller for an aircraft EPS. The practical techniques, performances, and benefits of the proposed methods are presented and discussed, inspiring further studies in the target area with different EPS architectures, requirements, and control targets. 2023-07-21 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by_nc https://eprints.nottingham.ac.uk/72484/1/PhD%20Thesis_Xin_submit_final_revision_submit.pdf Wang, Xin (2023) Optimized operational control for an electric power system on more electric aircraft. PhD thesis, University of Nottingham. Operational control Optimization Electric Power System More Electric Aircraft Power and Energy Management Model Predictive Control Hierarchical Control Stochastic Control |
| spellingShingle | Operational control Optimization Electric Power System More Electric Aircraft Power and Energy Management Model Predictive Control Hierarchical Control Stochastic Control Wang, Xin Optimized operational control for an electric power system on more electric aircraft |
| title | Optimized operational control for an electric power system on more electric aircraft |
| title_full | Optimized operational control for an electric power system on more electric aircraft |
| title_fullStr | Optimized operational control for an electric power system on more electric aircraft |
| title_full_unstemmed | Optimized operational control for an electric power system on more electric aircraft |
| title_short | Optimized operational control for an electric power system on more electric aircraft |
| title_sort | optimized operational control for an electric power system on more electric aircraft |
| topic | Operational control Optimization Electric Power System More Electric Aircraft Power and Energy Management Model Predictive Control Hierarchical Control Stochastic Control |
| url | https://eprints.nottingham.ac.uk/72484/ |