Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization

Robles, G.; Fresno, José Manuel; Martínez-Tarifa, Juan Manuel; Ardila-Rey, Jorge Alfredo; Parrado-Hernández, Emilio

doi:10.3390/s18030746

Cited by 24 publications

(29 citation statements)

References 30 publications

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“…At every iteration, the position of each particle is changed according to Eqs. (19) and (20) [80,81].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard stateof-the-art methods in finding meaningful clustering solutions to the problem at hand.

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“…At every iteration, the position of each particle is changed according to Eqs. (19) and (20) [80,81].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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“…In summary, the process would start leaving antenna 1 fixed and rotating antenna 2 acquiring signals with both antennas every angle step. Then, classifying the signals in as many classes as emitting sources are using the information in the power spectral density with the power ratio maps classification method described in [ 23 , 28 ] and summarized in the next section. Finally, signals are extracted from the same source to determine their origin using multilateration techniques.…”

Section: Separation and Localization Strategy For Two Sourcesmentioning

confidence: 99%

“…The power ratios map is applied to separate signals through their spectral power finding those bands of frequency where their spectra is different [ 28 ]. Let

and

be the limits for the low and high frequency intervals, respectively.…”

Section: Power Ratio Maps Enhanced With Particle Swarm Optimizatiomentioning

confidence: 99%

Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers

Robles

Fresno

Martínez-Tarifa

2018

Sensors

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Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions.

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“…The frequency of electromagnetic waves extended well into the UHF range (300 MHz~3 GHz) [ 8 , 9 , 10 , 11 , 12 ]. Thus, the UHF monitoring technique is adopted into the PD detection [ 12 , 13 , 14 , 15 , 16 , 17 ]. It has the advantages of high frequency of detection signal, less interference from outside, and easy to install.…”

Section: Introductionmentioning

confidence: 99%

“…It is suitable for live tests in actual operation. Typical UHF methods are mainly based on the UHF signals such as time of arrival, time difference of arrival and angle of arrival, among them, the time difference of arrival is widely used in PD positioning [ 13 , 14 ]. However, as the distance from the PD source increases, the signals will be greatly attenuated, which makes it difficult to determine the starting pulse time of the signals, and results in a large error [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Field Applications of Ultra High Frequency Techniques for Defect Detection in GIS

Jun¹,

Wang

Guo

et al. 2018

Sensors

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The reliable and stable operation of power apparatus is important for the development of GIS. It is important to utilize condition monitoring technologies and anticipate possible failures in advance. Many papers have been published about the partial discharge detection with UHF or X-ray in laboratory, but seldom in field application. Thus, many engineers at project sites are not familiar with the current professional diagnosis techniques. Recently, during the GIS routine data analysis obtained by partial discharge online monitoring system, it was found that the UHF monitoring signals’ developing trend of the 220 kV GIS No. 2 high-voltage side of transformer in phase C at an actual station was abnormal and needed further detection. In order to precisely investigate the problem and then guide the operation and maintenance activities, a series of professional diagnoses were conducted. Three new types of partial discharge detection and positioning methods were applied for accuracy, including UHF partial discharge detection based on multi-stage amplified signal demodulation and multiple weighted averages processing; the partial discharge detection based on the signal radiation hole of insulation disk at the ground connection; and the positioning method based on UHF-SHF. After a series of troubleshooting works, the partial discharge defects have been diagnosed, and the case can be referred in the field monitoring of GIS.

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Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization

Cited by 24 publications

References 30 publications

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers

Field Applications of Ultra High Frequency Techniques for Defect Detection in GIS

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