Huanan Yu¹, Xiang Zhang^1,*, Jian Wang²

Energy Engineering, Vol.122, No.12, pp. 5055-5071, 2025, DOI:10.32604/ee.2025.067152 - 27 November 2025

Abstract Recent advances in AC/DC hybrid power distribution systems have enhanced convenience in daily life. However, DC distribution introduces significant power quality challenges. To address the identification and classification of DC power quality disturbances, this paper proposes a novel methodology integrating Compressed Sensing (CS) with an enhanced Stacked Denoising Autoencoder (SDAE). The proposed approach first employs MATLAB/SIMULINK to model the DC distribution network and generate DC power quality disturbance signals. The measured original signals are then reconstructed using the compressive sensing-based generalized orthogonal matching pursuit (GOMP) algorithm to obtain sparse vectors as the final dataset. Subsequently, More >

He Wang, Shiqiang Li^*, Yiqi Liu, Jing Bian

Energy Engineering, Vol.122, No.11, pp. 4497-4521, 2025, DOI:10.32604/ee.2025.065244 - 27 October 2025

Abstract With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures, the number of measurement points supporting synchronous communication remains relatively limited. This poses challenges for conventional fault distance estimation methods, which are often tailored to simple topologies and are thus difficult to apply to large-scale, multi-node DC networks. To address this, a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper. First, a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range… More >

Zeyi Wang¹, Mingxi Jiao¹, Daliang Wang¹, Minxu Liu¹, Minglei Jiang², He Wang³, Shiqiang Li^3,*

Energy Engineering, Vol.121, No.8, pp. 2275-2291, 2024, DOI:10.32604/ee.2024.028223 - 19 July 2024

Abstract This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method. Three different electrical quantities are selected as observations in the compressed sensing algorithm. The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels. Subsequently, by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,… More >

Huanan Yu, Hangyu Li, He Wang, Shiqiang Li^*

Energy Engineering, Vol.121, No.6, pp. 1535-1555, 2024, DOI:10.32604/ee.2024.046936 - 21 May 2024

Abstract The escalating deployment of distributed power sources and random loads in DC distribution networks has amplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimal configuration of measurement points, this paper presents an optimal configuration scheme for fault location measurement points in DC distribution networks based on an improved particle swarm optimization algorithm. Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing. The model aims to achieve the minimum number of measurement points while attaining the best compressive sensing reconstruction effect. It incorporates constraints from… More >

Zhiqing Dong¹, Zhao Zhang^1,*, Hongyan Zhou², Xuebo Chen²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1977-1993, 2024, DOI:10.32604/cmc.2024.047233 - 27 February 2024

Abstract With the advent of the information security era, it is necessary to guarantee the privacy, accuracy, and dependable transfer of pictures. This study presents a new approach to the encryption and compression of color images. It is predicated on 2D compressed sensing (CS) and the hyperchaotic system. First, an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security. Then, the processed images are concurrently encrypted and compressed using 2D CS. Among them, chaotic sequences replace traditional random measurement matrices to increase the system’s security. Third, the processed images are More >

Guangfei Jia^*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang

Structural Durability & Health Monitoring, Vol.17, No.5, pp. 383-405, 2023, DOI:10.32604/sdhm.2023.026885 - 07 September 2023

Abstract With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the… More > Graphic Abstract

Huanan Yu¹, Gang Han^1,*, Hansong Luo², He Wang¹

Energy Engineering, Vol.120, No.9, pp. 2029-2057, 2023, DOI:10.32604/ee.2023.028365 - 03 August 2023

Abstract Aiming at the problem that most of the cables in the power collection system of offshore wind farms are buried deep in the seabed, which makes it difficult to detect faults, this paper proposes a two-step fault location method based on compressed sensing and ranging equation. The first step is to determine the fault zone through compressed sensing, and improve the data measurement, dictionary design and algorithm reconstruction: Firstly, the phase-locked loop trigonometric function method is used to suppress the spike phenomenon when extracting the fault voltage, so that the extracted voltage value will not… More >

Ingo Paetsch^1,*, Roman Gebauer², Christian Paech², Frank-Thomas Riede², Sabrina Oebel¹, Andreas Bollmann¹, Christian Stehning³, Jouke Smink⁴, Ingo Daehnert², Cosima Jahnke¹

Congenital Heart Disease, Vol.18, No.3, pp. 279-294, 2023, DOI:10.32604/chd.2023.029634 - 09 June 2023

Abstract Background: In congenital heart disease (CHD) patients, detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making. Hence, the present study investigated the applicability of an advanced cardiovascular magnetic resonance (CMR) whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography. Methods: 86 consecutive pediatric patients and adults with congenital heart disease (age, 1 to 74 years; mean, 35 years) underwent CMR imaging including a free-breathing, ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed… More >

Yanjun Zhang¹, Yongqiang He², Jingbo Zhang¹, Yaru Zhao³, Zhihua Cui^1,*, Wensheng Zhang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 363-383, 2023, DOI:10.32604/cmes.2023.025832 - 23 April 2023

Abstract The video compression sensing method based on multi hypothesis has attracted extensive attention in the research of video codec with limited resources. However, the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task. To resolve this problem, this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimization method. It mainly includes the optimization of prediction blocks (OPBS), the selection of search windows and the use of neighborhood information. Specifically, the OPBS consists of two parts: the selection of blocks and the optimization of prediction blocks. We combine… More >

Aravindan Madhavan^1,*, Yamuna Govindarajan¹, Neelakandan Rajamohan²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2049-2058, 2023, DOI:10.32604/csse.2023.031566 - 03 November 2022

Abstract In an underdetermined system, compressive sensing can be used to recover the support vector. Greedy algorithms will recover the support vector indices in an iterative manner. Generalized Orthogonal Matching Pursuit (GOMP) is the generalized form of the Orthogonal Matching Pursuit (OMP) algorithm where a number of indices selected per iteration will be greater than or equal to 1. To recover the support vector of unknown signal ‘x’ from the compressed measurements, the restricted isometric property should be satisfied as a sufficient condition. Finding the restricted isometric constant is a non-deterministic polynomial-time hardness problem due to that More >