Picture of model of urban architecture

Open Access research that is exploring the innovative potential of sustainable design solutions in architecture and urban planning...

Strathprints makes available scholarly Open Access content by researchers in the Department of Architecture based within the Faculty of Engineering.

Research activity at Architecture explores a wide variety of significant research areas within architecture and the built environment. Among these is the better exploitation of innovative construction technologies and ICT to optimise 'total building performance', as well as reduce waste and environmental impact. Sustainable architectural and urban design is an important component of this. To this end, the Cluster for Research in Design and Sustainability (CRiDS) focuses its research energies towards developing resilient responses to the social, environmental and economic challenges associated with urbanism and cities, in both the developed and developing world.

Explore all the Open Access research of the Department of Architecture. Or explore all of Strathclyde's Open Access research...

Partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy

Shang, Haikun and Lo, Kwok Lun and Li, Feng (2017) Partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy. Entropy, 19 (9). ISSN 1099-4300

[img]
Preview
Text (Shang-etal-Entropy-2017-Partial-discharge-feature-extraction-based-on-ensemble-empirical-mode)
Shang_etal_Entropy_2017_Partial_discharge_feature_extraction_based_on_ensemble_empirical_mode.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (4MB) | Preview

Abstract

Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.