Filter Design and Optimization of Electromechanical Actuation Systems Using Search and Surrogate Algorithms for More-Electric Aircraft Applications

Gao, Yuan; Yang, Tao; Bozhko, Serhiy; Wheeler, Patrick; Dragičević, Tomislav

doi:10.1109/tte.2020.3019729

Cited by 22 publications

(13 citation statements)

References 39 publications

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“…When the voltage drop on the cable (in Fig. 4) is put into consideration and the voltage control dynamics are ignored, the steady-state dc bus voltage can be expressed as in (6).…”

Section: A Analysis Of the Conventional Droop Control Methodsmentioning

confidence: 99%

“…Other applicable AI methods include expert systems, fuzzy logic, and metaheuristic methods [3]. To distinguish ML from the metaheuristic search algorithms, [6] proposed a simple algorithm categorization that comprises the search algorithm and the surrogate algorithm (2SA). This paper will only focus on the AI applications using ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Inverse Application of Artificial Intelligence for the Control of Power Converters

Gao

Wang

Hussaini

et al. 2023

IEEE Trans. Power Electron.

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This paper proposes a novel application method, Inverse Application of Artificial Intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of Artificial Neural Network (ANN) is discussed, an optimization-based design. Then, a two-sourceconverter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multi-level converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility and practicality of IAAI.

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“…When the voltage drop on the cable (in Fig. 4) is put into consideration and the voltage control dynamics are ignored, the steady-state dc bus voltage can be expressed as in (6).…”

Section: A Analysis Of the Conventional Droop Control Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse Application of Artificial Intelligence for the Control of Power Converters

Gao

Wang

Hussaini

et al. 2023

IEEE Trans. Power Electron.

View full text Add to dashboard Cite

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“…This paper proposes a ML based correction model to give the correction factors ( ) for motor loss estimation. This model is implemented by establishing a forward Artificial Neural Network (ANN) which is a common and powerful learning method in ML, deep learning and artificial intelligence areas [11][12][13][14]. ANN is based on a nonparametric regression model which is a technique for supervised learning.…”

Section: B Proposed ML Based Correction Modelmentioning

confidence: 99%

Machine Learning Based Correction Model in PMSM Power Loss Estimation for More-Electric Aircraft Applications

Gao

Yang

Wang

et al. 2020

2020 23rd International Conference on Electrical Machines and Systems (ICEMS)

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This study utilizes the machine learning (ML) technique to estimate the power loss of surface-mounted Permanent Magnet Synchronous Motor (PMSM) for More-Electric Aircraft (MEA). Existing approaches do not consider ML methods in power loss calculation and only depend on empirical correction factors. The proposed ML aided model is proved to be more precise. Matching the analytical loss estimation with finite-element analysis (FEA) is the main research goal which includes two aspects: iron loss and permanent magnet (PM) loss. They are both based on conventional formulae but this study analyzes the limitation of these equations and the ML correction model can provide dedicated factors for the analytical motor model to make sure that the loss estimation is accurate in the whole motor design space. Average correction factor (ACF) approach is regarded as the comparison method to verify the excellent performance of the proposed ML model.

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“…It has been proved that applying filtered data to ANNs can help achieve better prediction results [22]. Reference [23] used SVM-ANN integration to predict the mass and loss of aircraft drive system filters, demonstrating good predictive performance. Reference [24] optimized the membership function of fuzzy controllers in the same way, which greatly improved the efficiency of energy management systems At present, the integration of SVM and ANN in the design of circuit parameters is still relatively rare.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

Liu,

Kui,

Gao

et al. 2023

Energies

Self Cite

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For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method for passive components and the switching frequency of the converter to improve efficiency and power density. A machine learning-integrated optimization method is proposed to minimize the converter mass and power loss of the converter. In this method, first, a component-based automatic design model is built with embedded SLIC-QBC simulation. Then, a series of design schemes is generated within a large parameter range, and training data for machine learning are collected. Support vector machine (SVM) and artificial neural network (ANN) are used to validate the converter design scheme, where ANN establishes the mapping relationship from design parameters to optimization objectives. After the optimization, an experimental prototype is built for experimental verification. The simulation and experimental results verify the practicability of the proposed method.

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Filter Design and Optimization of Electromechanical Actuation Systems Using Search and Surrogate Algorithms for More-Electric Aircraft Applications

Cited by 22 publications

References 39 publications

Inverse Application of Artificial Intelligence for the Control of Power Converters

Inverse Application of Artificial Intelligence for the Control of Power Converters

Machine Learning Based Correction Model in PMSM Power Loss Estimation for More-Electric Aircraft Applications

Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

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