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D Upton

Bio: D Upton is an academic researcher from University of Huddersfield. The author has contributed to research in topics: Partial discharge & Wireless sensor network. The author has an hindex of 9, co-authored 24 publications receiving 212 citations.

Papers
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Journal ArticleDOI
TL;DR: It is concluded that an RSS-based PD WSN is a plausible solution for the monitoring of insulation integrity in electricity substations.
Abstract: A wireless sensor network (WSN) with the potential to monitor and locate partial discharge (PD) in high-voltage electricity substations using only received signal strength (RSS) is proposed. The advantages of an RSS-based operating principle over more traditional methods (e.g., time-of-arrival and time-difference-of-arrival) are described. Laboratory measurements of PD that emulate the operation of a PD WSN are presented. The hardware architecture of a prototype PD WSN is described and the particular challenges of an RSS-based location approach in an environment with an unknown, and spatially varying, path-loss index are discussed. It is concluded that an RSS-based PD WSN is a plausible solution for the monitoring of insulation integrity in electricity substations.

43 citations

Journal ArticleDOI
TL;DR: A low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision that uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs.
Abstract: Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise.

37 citations

Journal ArticleDOI
TL;DR: In this paper, a wireless sensor network is proposed that utilizes only received signal strength to locate areas of PD activity within a highvoltage electricity substation, which consists of low power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of PD.
Abstract: Monitoring of partial discharge (PD) activity within high-voltage electrical environments is increasingly used for the assessment of insulation condition. Traditional measurement techniques employ technologies that either require off-line installation or have high power consumption and are hence costly. A wireless sensor network is proposed that utilizes only received signal strength to locate areas of PD activity within a high-voltage electricity substation. The network comprises low-power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of PD. Results are reported from several empirical tests performed within a large indoor environment and a substation environment using a network of nine sensor nodes. A portable PD source emulator was placed at multiple locations within the network. Signal strength measured by the nodes is reported via WirelessHART to a data collection hub where it is processed using a location algorithm. The results obtained place the measured location within 2 m of the actual source location.

17 citations

Proceedings ArticleDOI
19 Aug 2017
TL;DR: A wireless sensor network is proposed that utilizes only received signal strength to locate partial discharge within a high-voltage electricity substation using low-power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of partial discharge.
Abstract: Monitoring of partial discharge (PD) activity within high voltage electrical environments is frequently used for the assessment of insulation condition. Traditional measurement techniques employ technologies that require either offline installation or high power consumption and cost. A wireless sensor network is proposed that utilizes only received signal strength to locate partial discharge within a high-voltage electricity substation. The network comprises low-power and low-cost radiometric sensor nodes which receive the radiation propagated from a source of partial discharge. Results are reported from a test performed within a large indoor environment with a network of nine sensor nodes. An emulated PD source was placed at multiple locations within the network. Signal strength measured by the nodes is reported via WirelessHART to a data collection hub where it is processed using a location algorithm. The results obtained place the measured location within 2 m of the actual source location.

14 citations

Proceedings ArticleDOI
13 Mar 2016
TL;DR: An RTL-SDR (Software Defined Radio) based spectrum analyser has been proposed to provide a potentially low cost solution for PD detection and monitoring and exhibits promising results for spectral detection within the VHF and UHF band.
Abstract: Partial discharge (PD) is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment, especially in the future smart grid. The emergence of wireless technology has provided numerous opportunities to optimise remote monitoring and control facilities that can play a significant role in ensuring swift control and restoration of HV plant equipment. In order to monitor PD, several approaches have been employed, however, the existing schemes do not provide an optimal approach for PD signal analysis, and are very costly. In this paper an RTL-SDR (Software Defined Radio) based spectrum analyser has been proposed in order to provide a potentially low cost solution for PD detection and monitoring. Initially, a portable spectrum analyser has been used for PD detection that was later replaced by an RTL-SDR device. The proposed schemes exhibit promising results for spectral detection within the VHF and UHF band.

13 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Reference EntryDOI
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

3,792 citations

Journal ArticleDOI
TL;DR: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition.
Abstract: This paper presents a state-of-the-art review on machine learning (ML) based intelligent diagnostics that have been applied for partial discharge (PD) detection, localization, and pattern recognition. ML techniques, particularly those developed in the last five years, are examined and classified as conventional ML or deep learning (DL). Important features of each method, such as types of input signal, sampling rate, core methodology, and accuracy, are summarized and compared in detail. Advantages and disadvantages of different ML algorithms are discussed. Moreover, technical roadblocks preventing intelligent PD diagnostics from being applied to industry are identified, such as insufficient/imbalanced dataset, data inconsistency, and difficulties in cost-effective real-time deployment. Finally, potential solutions are proposed, and future research directions are suggested.

95 citations

Journal ArticleDOI
28 Feb 2019-Sensors
TL;DR: The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.
Abstract: Condition monitoring of an operating apparatus is essential for lifespan assessment and maintenance planning in a power system. Electrical insulation is a critical aspect to be monitored, since it is susceptible to failure under high electrical stress. To avoid unexpected breakdowns, the level of partial discharge (PD) activity should be continuously monitored because PD occurrence can accelerate the aging process of insulation in high voltage equipment and result in catastrophic failure if the associated defects are not treated at an early stage. For on-site PD detection, the ultra-high frequency (UHF) method was employed in the field and showed its effectiveness as a detection technique. The main advantage of the UHF method is its immunity to external electromagnetic interference with a high signal-to-noise ratio, which is necessary for on-site monitoring. Considering the detection process, sensors play a critical role in capturing signals from PD sources and transmitting them onto the measurement system. In this paper, UHF sensors applied in PD detection were comprehensively reviewed. In particular, for power transformers, the effects of the physical structure on UHF signals and practical applications of UHF sensors including PD localization techniques were discussed. The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.

73 citations

Journal ArticleDOI
TL;DR: From the results, the proposed WPART method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane.
Abstract: This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farming systems. The crop productivity and drought predictions is very important to the farmers and agriculture’s executives, which greatly help agriculture-affected countries around the world. Drought prediction plays a significant role in drought early warning to mitigate its impacts on crop productivity, drought prediction research aims to enhance our understanding of the physical mechanism of drought and improve predictability skill by taking full advantage of sources of predictability. In this work, an intelligent method based on the blend of a wrapper feature selection approach, and PART classification technique is proposed for crop productivity and drought predicting. Five datasets are used for estimating the proposed method. The results indicated that the projected method is robust, accurate, and precise to classify and predict crop productivity and drought in comparison with the existing techniques. From the results, the proposed method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane. The WPART method attains the maximum accuracy compared to the existing supreme standard algorithms, it is obtained up to 92.51%, 96.77%, 98.04%, 96.12%, and 98.15% for the five datasets for drought classification, and crop productivity respectively. Likewise, the proposed method outperforms existing algorithms with precision, sensitivity, and F Score metrics.

58 citations