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2024 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electrification Conference, ESARS-ITEC 2024

作者： Wang, Xin Atkin, Jason Yeoh, Shen Bozhko, Serhiy University of Nottingham The Department of Electrical and Electronic Engineering Nottingham United Kingdom Computational Optimisation and Learning Lab University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9798350373905

With the development of More Electric Aircraft (MEA), adopting higher bus voltage levels is becoming a tendency for onboard Electrical Power Systems (EPSs) to efficiently deliver the increased power ratings and reduce EPS weight. However, this tendency potentially raises the risk of electrical insulation failures. Consequently, selecting optimal bus voltage levels to consider the trade-offs is the key to the design of light, efficient, and safe MEA EPSs. However, the mathematical relationships between the EPS weight/efficiency and bus voltage levels are complex and implicit, making it infeasible to solve this continuous optimisation problem. Facing these challenges, this study proposes an optimisation model to select bus voltage levels by selecting a combination of components, initially without considering cables, from an available component set, to meet voltage and power requirements, while achieving the objectives. Furthermore, the Integrated Converter Component (ICC) concept is proposed to improve feasibility when cables are also considered. Each ICC combines one converter with its lightest input/output cables as a new optional component for the proposed optimisation model. Case studies are provided indicating that the selected optimal bus voltage levels save EPS weight while reducing power losses. © 2024 IEEE.

关键词： Aircraft power systems

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Choice of Optimal Voltage for DC Power Distribution Systems on More Electric Aircraft

Choice of Optimal Voltage for DC Power Distribution Systems ...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Wang, Xin Atkin, Jason Yeoh, Seang Shen Bozhko, Serhiy University of Nottingham Faculty of Engineering The Department of Electrical and Electronic Engineering Nottingham United Kingdom University of Nottingham Computational Optimisation and Learning Lab School of Computer Science Nottingham United Kingdom

ISBN: (纸本)9798350364446

The new generation of aircraft will experience increasing electrical power demands because of the more electric trends and advances in recent years. Adopting higher voltage levels is widely discussed for More Electric Aircraft (MEA) Power Distribution Systems (PDSs) to efficiently deliver the increased power ratings and reduce the system weight. However, higher voltage levels also indicate the risk of electrical insulation failures. Hence, it is essential to select optimised voltage levels for onboard PDSs considering the trade-offs. In addition, the relationship between the PDS weight/efficiency and voltage levels is complex rather than linear, resulting in infeasibility issues when optimising voltage levels. This study proposes a voltage optimisation method based on selecting among a set of components, i.e., converters and cables, for onboard DC PDSs. Moreover, to improve feasibility, the Integrated Converter Component (ICC) concept is proposed to combine each converter with its lightest input/output cables as an integrated block. Hence, the optimal bus voltages can be decided by selecting the lightest and most efficient ICCs while meeting the power delivery and voltage conversion requirements. © 2024 IEEE.

关键词： Bus voltage DC power distribution integrated converter component more electric aircraft optimisation

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Optimal Voltage Selection for Electrical Power Systems on More Electric Aircraft

Optimal Voltage Selection for Electrical Power Systems on Mo...

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International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)

作者： Xin Wang Jason Atkin Shen Yeoh Serhiy Bozhko The Department of Electrical and Electronic Engineering University of Nottingham Nottingham UK University of Nottingham Computational Optimisation and Learning Lab Nottingham UK

ISBN: (数字)9798350373905

ISBN: (纸本)9798350373912

关键词： Insulation Optimization models Atmospheric modeling Transportation Voltage Electrification Power systems Aircraft Optimization Cables

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Choice of Optimal Voltage for DC Power Distribution Systems on More Electric Aircraft

Choice of Optimal Voltage for DC Power Distribution Systems ...

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European Conference on Power Electronics and Applications

作者： Xin Wang Jason Atkin Seang Shen Yeoh Serhiy Bozhko The Department of Electrical and Electronic Engineering Faculty of Engineering University of Nottingham Nottingham UK Computational Optimisation and Learning Lab School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350364446

ISBN: (纸本)9798350364453

关键词： Insulation Power demand Power distribution Europe Optimization methods Voltage Market research Energy efficiency Aircraft Cables

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Optimised architecture design for an MEA power distribution system considering load profile and fault-tolerance

Optimised architecture design for an MEA power distribution ...

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International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)

作者： Xin Wang Jason Atkin Seang Yeoh Serhiy Bozhko Department of Electrical and Electronic Engineering Faculty of Engineering University of Nottingham Nottingham UK Computational Optimisation and Learning Lab School of Computer Science University of Nottingham Nottingham UK

With the development of More Electric Aircraft (MEA), novel power distribution systems (PDSs) are required to meet significantly increased electrical load demands onboard. Nevertheless, newer designs must comply with the strict flight operational and safety requirements, while achieving optimisation aims, such as reducing aircraft weight and energy consumption. This paper proposes a two-stage optimisation design method for a novel PDS investigated for MEA. Two optimal power scheduling-based models for minimising power losses and PDS weight are formulated in Mixed-Integer Linear Programming (MILP). The first model considers varying load demands and is solved for all flight stages simultaneously in the first design stage for weight minimisation. The obtained architecture is further optimised for all faulty scenarios and selected typical fight stages simultaneously in the second design stage to improve the PDS reliability. Consequently, a low power loss, light weighted, and fault-tolerant PDS architecture is obtained. A case study is provided to exemplify the improvement in the PDS by adopting the proposed method.

关键词： Design methodology Atmospheric modeling Fault tolerant systems Redundancy Power distribution Transportation Reliability engineering

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Operation Management of More-Electric Aircraft Using Two-stage Stochastic Model Predictive Control

Operation Management of More-Electric Aircraft Using Two-sta...

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European Conference on Power Electronics and Applications

作者： Xin Wang Najmeh Bazmohammadi Jason Atkin Serhiy Bozhko Juan C. Vasquez Josep M. Guerrero Department of Electrical and Electronic Engineering Faculty of Engineering University of Nottingham Nottingham UK Center for Research on Microgrids (CROM) AAU Energy Aalborg University Aalborg Denmark Computational Optimisation and Learning Lab School of Computer Science University of Nottingham Nottingham UK

This paper proposes a two-stage stochastic model predictive control (SMPC) for the operation management of more electric aircraft (MEA). The goal is to minimize load shedding and switching activities in the system, considering the uncertainty of load profile, while optimally charging/discharging the battery-based energy storage system (BESS). In addition, several performance evaluation criteria are introduced to evaluate the effectiveness of the proposed approach. According to the results obtained in 50 random scenarios, SMPC leads to less load-shedding time, higher stored energy levels, and lower uncertainty compensation costs compared to its deterministic counterpart.

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Exploring Automated Feature Engineering for Energy Consumption Forecasting with AutoML

Exploring Automated Feature Engineering for Energy Consumpti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nasser Alkhulaifi Alexander L. Bowler Direnc Pekaslan Isaac Triguero Nicholas J. Watson Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham UK Faculty of Environment School of Food Science and Nutrition University of Leeds Leeds UK Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham UK Department of Computer Science and Artificial Intelligence University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Machine learning methods are widely used to predict energy consumption, aiming to enhance efficiency and support environmental goals. However, developing these models is traditionally time-consuming and expert-dependent. While Automated Machine learning (AutoML) has emerged as a valuable approach to streamlining machine learning pipelines, including appropriate preprocessing and learning algorithms, it may still require human experts. These experts might be needed to generate new, interpretable features that could significantly improve model performance. This is particularly relevant in complex settings such as the energy domain, where deep learning's automatic feature extraction lacks interpretability. To address this challenge, this exploratory work introduces an automated feature engineering method tailored for energy forecasting problems. It involves generating a comprehensive set of features that can be fed into AutoML, thereby reducing the need for domain knowledge in feature engineering. The proposed method has been validated using eleven datasets from various energy domains, including residential buildings, renewable energy, and regional energy consumption, with state-of-the-art AutoML methods, namely H20, TPOT, AutoGluon, and FLAML. The results demonstrate a noticeable reduction in prediction errors across all the examined datasets.

关键词： Knowledge engineering Deep learning Energy consumption Renewable energy sources Machine learning algorithms Pipelines Automated machine learning Prediction algorithms Feature extraction Forecasting

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An Initial Step Towards Stable Explanations for Multivariate Time Series Classifiers with LIME

An Initial Step Towards Stable Explanations for Multivariate...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Han Meng Christian Wagner Isaac Triguero Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham United Kingdom DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

LIME, or ‘Local Interpretability Model-agnostic Explanations’ is a well-known post-hoc explanation technique for the interpretation of black-box models. While very useful, recent studies show that LIME suffers from stability problems: explanations provided for the same process can be different, making it difficult to trust their reliability. This paper investigates the stability of LIME when explaining multivariate time series classification problems. We demonstrate that due to the temporal dependency in time series data, the traditional artificial neighbour generation methods used in LIME have a higher risk of creating out-of-distribution inputs. We disucss how this behavior is one of the reasons resulting in unstable explanations. In addition, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune, and we show how unsuitable hyperparameters can contribute to the generation of unstable explanations. As a preliminary step towards addressing these issues, we propose to employ a generative approach with an adaptive weighting method in the LIME framework. Specifically, we adopt a generative model based on variational autoencoder to create within-distribution neighbours, reducing the out-of-distribution problem, while the adaptive weight method eliminates the need for user-defined hyperparameters. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework.

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Machine learning-based algorithm selection for irregular three-dimensional packing in additive manufacturing

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Expert Systems with Applications 2025年 287卷

作者： de Araújo, Luiz Jonatã Pires Özcan, Ender Atkin, Jason A.D. Baumers, Martin Drake, John H. The Machine Learning Group School of Computer Science University of Lincoln United Kingdom Computational Optimisation and Learning Lab School of Computer Science University of Nottingham United Kingdom Centre for Additive Manufacturing Faculty of Engineering University of Nottingham United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom

In additive manufacturing (AM), a common problem is the efficient arrangement of arbitrary three-dimensional objects, subject to geometric constraints. This can be mapped to three-dimensional irregular packing (3DIP) problems, which have been systematically addressed by many academics and practitioners. This study demonstrates the utilisation of machine learning for algorithm selection to find efficient layout configurations during the design stage of the AM process. The choice of the most suitable approach to use, typically a packing algorithm, is not trivial, and depends on the non-obvious relationship between the characteristics of the instance in hand and the portfolio of algorithms available. The matching between problem features and algorithm performance forms the basis of the well-known algorithm selection problem. This study introduces the first empirical investigation of algorithm selection for 3DIP problems, conducting extensive experiments with hundreds of combinations of well-known supervised machine learning classifiers and different parameter settings to identify an initial state-of-the-art for this problem. We generate a comprehensive dataset, labelled with the performance of two of the most popular 3DIP algorithms, and analyse the features which can be used to support decision making when selecting a method to solve a 3DIP instance. Our results show that machine learning-based algorithm selection methods are able to outperform the results obtained by the individual constituent packing algorithms applied independently, with the best algorithm selection method obtaining a 1.48% higher average build volume utilisation over the 2000 problem instances tested. © 2025

关键词： Semi-supervised learning

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Neural Network based Weighting Factor Selection of MPC for Optimal Battery and Load Management in MEA

Neural Network based Weighting Factor Selection of MPC for O...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Xin Wang Yuan Gao Jason Atkin Serhiy Bozhko Faculty of Engineering University of Nottingham Nottingham UK Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9784886864192

ISBN: (纸本)9781728189307

This paper presents a Neural Network (NN)-based weighting factor (WF) selection method for the multi-objective cost function in Model Predictive Control (MPC). MPC is adopted for scheduling the loads and charging/discharging the battery intelligently on More-Electric Aircraft (MEA) in a preferred manner. The decisions which are made while the MPC is running utilize a cost function which weights together different objectives (using WFs). The final overall evaluation is performed by considering various objectives with full knowledge of what happened throughout the whole operation, which are weighted together by utilising weights appropriate to the user. The WFs utilized by the MPC to get the best overall result will usually differ from the weights used in the final evaluation. A NN is trained to predict the effects of different combinations of WF values, facilitating optimisation to find the minimum evaluation index, i.e. the most suitable weighting factors for the applied MPC.

关键词： Batteries State of charge Indexes Load modeling Cost function Artificial neural networks Switches

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