Study of Partial Discharge Localization Using Ultrasonics in Power Transformer Based on Particle Swarm Optimization

Tang, Lijun; Luo, Rong; Deng, Min; Su, Jun

doi:10.1109/tdei.2008.4483469

Cited by 51 publications

(4 citation statements)

References 8 publications

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“…In (10), w k t+1 is the new adaptive weight, t is the iteration count, w t k is the k th particle weight, ∆w t k is the change in weight value. In (11), v k t+1 is the new velocity vector, v k t is the k th particle velocity, t is the iteration count, w k t+1 is the adaptive inertia weight, c 1 and c 2 are learning parameters, rand 1 and rand 2 are random numbers, p best and g best are local best solution and global best solution, x k t is the current particle solution. In (12), x k t+1 is the new position vector and other terms are the same as in (11).…”

Section: Fuzzy Adaptive Pso Formulationmentioning

confidence: 99%

“…Veloso et al [9], [10] compared the least square (LS) method and genetic algorithm for localization of PD source and it is located accurately with large population size and iterations by genetic method whereas iterative LS method located with inaccurate manner. Tang et al [11] compared iterative LS method and PSO method for localization of PD source, when compared to LS method, PSO method gives better location results. Kundu et al [12] illustrated a non-iterative method for localization of PD source and its demerit is, it also yields two different solutions similar to GPS algorithm and in that only one solution is true.…”

Section: Introductionmentioning

confidence: 99%

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PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

Meka¹,

Giridhar²,

Sarma³

2018

RADIOENGINEERING

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Partial discharge (PD) source location using acoustic emission (AE) is widely utilized by many transformer manufacturers and power utility engineers in routine and critical situation for optimal operation of the electrical power system as well as further risk management and repair planning. The PD detection is not enough to take solution, so identification of PD source is essential to restore apparatus condition. This work aim is to localize the defect geometrically by means of TDOA (time difference of arrival) signals from the sensors fixed on the power transformer. The solution for PD source location is acquired by making these nonlinear equations as optimization problem. In this technique, the inertia weight is effectively regulated by using 49 and 9 simple IF-THEN fuzzy rules to improve the global optimal solution and impairs the local convergence problem and improves the accuracy in estimating the PD source location. The simulation results reveal that PD location accuracy with minimum of maximum deviation error, absolute error and relative error is better when compared to other constant parameter intelligent methods which were reported in the literature.

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Section: Fuzzy Adaptive Pso Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

Meka¹,

Giridhar²,

Sarma³

2018

RADIOENGINEERING

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“…There are many applications in which efficiently (accurately and promptly) solving the localization problem is crucial, such as navigation [21], underwater networks [22], surveillance [23,24], or power systems [25,26]. When considering the fourth industrial transformation and the fundamental advanced digital changes-known as Industry 4.0robust and precise localization can be seen as a key feature in pervasive systems in future industry and factory applications.…”

Section: Introductionmentioning

confidence: 99%

“…In its early stages, the PSO algorithm [9,10] was also used for solving some acoustic localization problems, namely, those related to the localization of partial discharge sources in power transformers [31,32]. Both non-linear and binary forms of the optimization algorithm were successfully applied [25,33]. The PSO algorithm contributed to the beginning of a new approach to the nonlinear optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

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In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

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Phase determination of partial discharge source in a three-phase transmission line based on high-frequency equivalent circuit model and signal detection using HFCT

Chen

Lin

2011

Int. Trans. Electr. Energ. Syst.

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SUMMARY This paper proposes a method for determining the phase where partial discharges (PD) occur based on a high‐frequency equivalent circuit model of the cable. The high‐frequency current transformer (HFCT) is employed for signal measurement. Since the PD singles are propagated as a travelling wave along the power cable, the HFCT can be used to detect electricity signal and determine where PD sources are taken place from the transmission lines. The conventional observations of PD signals via oscilloscopes are affected by capacitive effect (i.e., cross‐talk). Measuring the polarity of the waveform can improve the accuracy of PD source determination which can be identified by an equivalent circuit model. From simulation and measurement results, the polarity of the waveform in the phase where PD occurs is just the inverse of the signal waveforms in other two phases. Furthermore, the crest value of the waveform, estimation of the power spectral density and average power are used to verify this new approach. Finally, the proposed approach can accurately determine the phase location of PD sources in the field under high noise environment. Copyright © 2011 John Wiley & Sons, Ltd.

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Study of Partial Discharge Localization Using Ultrasonics in Power Transformer Based on Particle Swarm Optimization

Cited by 51 publications

References 8 publications

PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

PD Source Location Utilizing Acoustic TDOA Signals in Power Transformer by Fuzzy Adaptive Particle Swarm Optimization

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Phase determination of partial discharge source in a three-phase transmission line based on high-frequency equivalent circuit model and signal detection using HFCT

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