Automatic lightning stroke location on transmission lines using data mining and synchronized initial travelling

Morales, John; Anane, Zahira; Cabral, Roberto J.

doi:10.1016/j.epsr.2018.01.025

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…The DTs are also updated hourly by using newly predicted conditions for robustness improvement. Morales et al [30] present a data mining methodology to perform signal detection, calculate traveling wave times and determine the lightning stroke location along the transmission line after an outage. Since the methodology is immune to flash currents, it is swift and effective in locating the impact point, especially in situations that the factors, such as flash current values, inception angles, distances from the impact point to protection relays and direct and indirect lightning, are considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

et al. 2019

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With the further liberalization of the electricity market of China, customers' requirements, characteristics, and distribution, as well as the quality, security, and reliability of power supplies without interruption, have received considerable attention from power companies, policymakers, and researchers. How to deeply explore the distribution characteristics of electricity customers and analyze their sensitivities to electricity blackouts has become an especially important problem. This paper takes over 0.1 billion data, collected by various smart devices of the Internet of Things in the power system of China, such as smart meters, intelligent power consumption interactive terminals, data concentrators, and other cross-platform data, for example, 95 598 telephone records, complaint information, user bills, user information, and maintenance records, as study objects, to analyze the consumption characteristics of power users. It has been found that there is a wide range of power users who pay different electricity bills; a long-tail distribution following a power law lies in the number of users versus their paid electricity bills. Meanwhile, there are two Pareto effects (2-8 rule): the number of residents and non-residents versus their electricity bills, and the number of large industrial users and general industry (business users) versus in their electricity consumption and bills. Then, a decision tree algorithm is proposed to capture the characteristics of electricity consumers and to recognize the crowd who is power blackout sensitive. The evaluation indexes and parameters of the decision tree are discussed in detail, and a comparison with other intelligent algorithms shows that the decision tree has a good recognition performance over that of others, and the characteristics used to identify the blackoutsensitive crowd is various. All the results state that except for economic factors, positive social effects should also be considered. Various marketing strategies to satisfy different requirements of power users should be provided to promote long-term relationships between the power companies and power customers. INDEX TERMS Blackout sensitivity, big data, decision tree, electricity market, Internet of Things, longtailed, Pareto effect. NOMENCLATURE Notations Description Y The dependent variable, or target variable. It can be ordinal categorical, nominal categorical or continuous. If Y is categorical with J classes, its class takes values in C = {1,. .. , }.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

et al. 2019

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“…Power transmission process needs to be based on high-voltage transmission lines to complete, as the core composition of the smart grid, high-voltage transmission lines, and the safe and stable operation of the entire grid will have a direct impact on the scale and scope of the power system is constantly expanding the high-voltage transmission line monitoring put forward higher requirements, the design and improvement of high-voltage transmission line online monitoring system is still the focus of the current research areas [2][3]. The continuous development and improvement of smart grid and dynamic capacity increase technology for transmission line real-time efficient monitoring process provides strong support, but at present, due to technical and cost constraints, there are single-point monitoring is the main, has not yet formed a network monitoring system and other issues, the transmission line online monitoring products and monitoring process of the level of intelligence, automation, interactivity is still to be improved, and need to be further improved to reduce the failure rate in the actual use of the process and to improve the system.…”

Section: Introductionmentioning

confidence: 99%

Automated monitoring of high-voltage transmission lines based on machine vision

Wang

2024

Applied Mathematics and Nonlinear Sciences

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This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the equipment in the inspection image, using image processing methods for the collected high-voltage transmission line image color image grayscaling and grayscale image stretching, and then grayscale image smoothing and segmentation, so as to achieve the identification of the location of high-voltage transmission line fault location. Simulation test for image processing and transmission equipment identification methods, and then carry out automated monitoring experiments with high-voltage transmission line conductor stranding as an example to verify the feasibility and reliability of the model in this paper. The results show that for the automatic detection of individual conductor strand breakage, the method of this paper detects the number of strand breakage is only 1-2 less or more than the actual number of strand breakage; there is a certain difference, but basically in line with the actual situation, and the method of this paper, on average, detects the efficiency of the method is faster than the traditional method by 5.69~7.18 s/m. The research to enhance the quality of automated monitoring of high-voltage power transmission lines has a certain application value.

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“…For the direct lightning strike on conductor, many techniques are used to identify abnormal positions, and they are not only used for locating lightning, but are also able to compute fault locations. These techniques include phasor measurement [26][27][28][29][30][31][32], travelling waves [33][34][35][36][37][38][39], a discrete wavelet transform [40][41][42][43][44][45][46][47], and artificial intelligence [48][49][50][51], among others.…”

Section: Introductionmentioning

confidence: 99%

“…The travelling wave technique is based on the natural aspects of an electromagnetic wave which results from the disturbance in a conductor during the transient state. Its properties can be widely applied to locate a disturbance in a power system [33][34][35][36][37][38][39]. The research proposed a novel algorithm to detect and identify the stepped leader stage and lightning channel height of cloud-to-ground lightning using the electric field [33].…”

Section: Introductionmentioning

confidence: 99%

“…Traveling-waved-based fault-location methods have the advantage of less interference from the saturation characteristics of the current transform, as well as the fault resistance, fault type, and operating mode of system; thus, these methods have been successfully applied to locating faults in transmission networks. A developed a method based on using synchronized travelling waves to identify lightning strike locations on transmission lines or the ground has been proposed [35]. Protection relays based on an ellipsoidal pattern were used for signal processing and transient signal detection.…”

Section: Introductionmentioning

confidence: 99%

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Identify Direct Lightning Strike Location Based on Discrete Wavelet Transform for 115-kV Transmission System

Chiradeja

Ngaopitakkul

2022

IEEE Access

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This paper proposes three techniques for locating lightning strikes in a 115 kV transmission system over 88.5 km, based on the arrival time of a transient wave obtained from a discrete wavelet transform (DWT). The first technique calculates the location based on the 'type D' travelling wave method. The second technique analyses the first arrival time and same reflected-back wave arrival time between two substations. In the third technique, two sensors are installed to record arrival times at each sensor. Case studies of direct lightning strikes to phase conductors (shielding failure) are simulated using the 'Alternative Transients Program/Electromagnetic Transients Program' (ATP/EMTP). The influences of three parameters are considered: inception angles, the phase of the conductor, and the position at which the lightning struck the transmission system. By performing an operation scheme for all techniques, a positive sequence current is calculated, using a three-phase current from the ATP/EMTP program which is converted using Clark's transform. A positive sequence current is extracted to several scales using the DWT, and the orders of arrival times for both substations are determined. After the arrival times are obtained, each technique uses a different order of arrival time for calculating the lightning location. By comparing the average error among the three techniques, it is shown that the arrival time of the two sensors' technique is less than that of the other techniques.

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Automatic lightning stroke location on transmission lines using data mining and synchronized initial travelling

Cited by 8 publications

References 39 publications

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

Analysis and Identification of Power Blackout-Sensitive Users by Using Big Data in the Energy System

Automated monitoring of high-voltage transmission lines based on machine vision

Identify Direct Lightning Strike Location Based on Discrete Wavelet Transform for 115-kV Transmission System

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