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18 pages, 2748 KiB

Open AccessArticle

A Satellite Incipient Fault Detection Method Based on Decomposed Kullback–Leibler Divergence

by Ge Zhang, Qiong Yang, Guotong Li, Jiaxing Leng and Mubiao Yan

Entropy 2021, 23(9), 1194; https://doi.org/10.3390/e23091194 - 9 Sep 2021

Viewed by 2162

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions

F_{1} (w)

and

F_{3} (w)

to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults. Full article

(This article belongs to the Special Issue Fault Diagnosis Method Based on Information Theoretic: From Theory to Applications)

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19 pages, 3679 KiB

Open AccessArticle

A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence

by Ge Zhang, Qiong Yang, Guotong Li, Jiaxing Leng and Long Wang

Appl. Sci. 2021, 11(2), 797; https://doi.org/10.3390/app11020797 - 15 Jan 2021

Cited by 5 | Viewed by 2344

Abstract

Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively. Full article

(This article belongs to the Special Issue Machine Fault Diagnostics and Prognostics II)

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14 pages, 5261 KiB

Open AccessArticle

Maximum Power Point Tracking of PV System Based on Machine Learning

by Maen Takruri, Maissa Farhat, Oscar Barambones, José Antonio Ramos-Hernanz, Mohammed Jawdat Turkieh, Mohammed Badawi, Hanin AlZoubi and Maswood Abdus Sakur

Energies 2020, 13(3), 692; https://doi.org/10.3390/en13030692 - 5 Feb 2020

Cited by 28 | Viewed by 4714

Abstract

This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks. Full article

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