Intelligent Fault-Tolerant Control for AC/DC Hybrid Power System of More Electric Aircraft

Xiao, Lingfei; Sattarov, Robert R.; Liu, Peisong; Chen, Lin

doi:10.3390/aerospace9010004

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…Common monitoring techniques include vibration monitoring [3], voltage monitoring [4], current monitoring [5], thermal parameters estimation [6], acoustic analysis [7], * Author to whom any correspondence should be addressed. etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model

Wang¹,

Liu²,

Luo³

et al. 2022

Meas. Sci. Technol.

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In view of the complexity of the engine mechanical structure and the diversity of faults, this paper presents a 1DCNN-ViT ensemble model for identifying engine faults based on acoustic emission (AE) signals. The 1DCNN-ViT ensemble model combines one-dimensional Convolutional Neural Network (1DCNN) and Vision Transformer (ViT). Firstly, AE signals of various faults are collected on the engine fault test rig. The dataset is constructed from its High-Mel Filterbank (H-Fbank) feature, which applies to AE signals. The proposed model has advantageous performance on this dataset. Secondly, the proposed model has a higher test accuracy than other new models. Finally, the fault data with different Signal-to-Noise Ratios (SNR) are input into the trained models, and the proposed model has better anti-noise performance. Overall, the proposed method can more accurately identify the AE signals of engine faults. It can be used as an effective method to diagnose engine faults.

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Section: Introductionmentioning

confidence: 99%

Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model

Wang¹,

Liu²,

Luo³

et al. 2022

Meas. Sci. Technol.

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“…The process of power grid load identification can usually be completed by intelligent classification method [8]. The key technologies of intelligent identification mainly focus on load signature and classification algorithms [7,9]. Usually, the characteristic parameters of electric signals such as power, current waveform, current harmonics, and transient power spectrum are selected as the load signature [10,11].…”

Section: Introductionmentioning

confidence: 99%

Load Identification for the More Electric Aircraft Distribution System Based on Intelligent Algorithm

2022

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Accurate identification of electrical load working status can provide information support to the remote electrical distribution system (EDS) of more electric aircraft (MEA), which could use it to realize redundant switching and protection. This paper presents a method to automatically identify the load status on the remote power distribution unit (RPDU) of MEA by using an intelligent algorithm. The experimental platform is built in an aircraft Electrical Power System (EPS) distribution large-scale test cabin. Four pieces of typical aviation equipment are installed in the test cabin and powered from RPDU. Voltage and current values under 15 working combinations on the RPDU are measured to extract the steady-state V-I trajectory. In total, 750 group samples were collected in the feature parameter database. A generalized regression neural network (GRNN) identification model was established, and the smoothing factor was calculated by using a conventional cross-validation method to train and reach an optimal value. However, the identification results are not ideal. In order to improve the accuracy, the parameter of GRNN was optimized by genetic algorithms. The proposed model shows great performance as accuracy of all 15 classifications reached 100%. The proposed model has advantages of flexible network structure, high fault tolerance, and robustness. It can realize global approximation optimization, avoid local optimization, effectively improve GRNN fitting accuracy, improve model generalization ability, and reduce model training calculation.

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Predictive direct power control with phase‐locked loop technique of three‐level neutral point clamped inverter based shunt active power filter for power quality improvement

Debdouche,

Benbouhenni,

Deffaf

et al. 2024

Circuit Theory & Apps

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SummaryIn this study, an improved anti‐windup proportional‐integral predictive direct power control strategy with a phase‐locked loop (PLL) is proposed and applied to a three‐level shunt active power filter (SAPF) neutral point clamped (NPC) inverter using space vector modulation (SVM) technique. The SAPF has to ensure sinusoidal waveforms and unity power factor in the grid side, by eliminating harmonic currents and compensating reactive power of the nonlinear loads. Active and reactive powers as well as the DC bus voltage controls are performed using anti‐windup proportional and integral actions with a predictive algorithm, without a need for an exhaustive mathematical model. The control law is modulated using the SVM technique allowing fixed switching frequency and low power ripples. The performances of the suggested control have been verified through an electronic STM32F407 card under diverse and severe conditions. The obtained results confirmed the expected objectives in terms of current and voltage quality, robustness, and dynamic response.

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Intelligent Fault-Tolerant Control for AC/DC Hybrid Power System of More Electric Aircraft

Cited by 7 publications

References 32 publications

Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model

Identification of engine faults based on acoustic emission signals using a 1DCNN-ViT ensemble model

Load Identification for the More Electric Aircraft Distribution System Based on Intelligent Algorithm

Predictive direct power control with phase‐locked loop technique of three‐level neutral point clamped inverter based shunt active power filter for power quality improvement

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