Determining Impact of Lightning Strike Location on Failures in Transmission Network Elements Using Fuzzy Decision-Making

Petrović, Ivica; Nikolovski, Srete; Baghaee, Hamid Reza; Glavaš, Hrvoje

doi:10.1109/jsyst.2019.2923690

Cited by 12 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of protection systems against atmospheric discharges and their different typologies are vital in interconnected or isolated electrical systems and buildings of all socio-economic sectors worldwide [19], [20]. Overvoltage is increasing in voltage that can appear on electrical distribution lines, and that can cause, among other things, damage to equipment connected to the network.…”

Section: Methodsmentioning

confidence: 99%

Lightning rod system: mathematical analysis using the rolling sphere method

Martínez¹,

Angarita

Álvarez

et al. 2022

IJPEDS

View full text Add to dashboard Cite

In electrical protection, there is a method of electrical protection of buildings against atmospheric discharges called the electro-geometric method or the rolling sphere method. So far, it is possible to achieve the implementation of this method graphically, that is, representing through plans and technical drawings, the protection conditions of the analyzed structure and obtaining from these graphic representations the protection parameters with the consequent errors caused by the scales and dimensions of the work plane. In the present work, a mathematical model is obtained that allows, using specific calculations, to analyze the dynamic behavior of a protection system against atmospheric discharges without worrying about the limitations given by the scales and planes. The set of equations obtained in the model allows us to determine the different parameters that define the protection system against atmospheric discharges (lightning) without depending on the graphical representation of the system's topology.

show abstract

Section: Methodsmentioning

confidence: 99%

Lightning rod system: mathematical analysis using the rolling sphere method

Martínez¹,

Angarita

Álvarez

et al. 2022

IJPEDS

View full text Add to dashboard Cite

show abstract

“…L IGHTNING strikes impacting transmission lines are known to provoke disturbances within the power system [1].Such discharges can hit the equipment directly or hit transmission lines originating overvoltage surges sufficiently high to cause severe fails on equipment insulation and consequently to unexpected outages [2].In extreme scenarios, these incidents can inflict substantial financial losses on power utilities and businesses reliant on electricity [3]. The development and implementation of a robust lightning protection system, which includes line lightning rods, grounding systems, surge protectors, and overhead ground wires, have undergone continuous refinement and gained widespread acceptance in the global electric power industry [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Considering the Methods of Lightning Protection and Early Warning for Power Transmission Lines Based on Lightning Data Analysis

Huo,

Wang,

Chen

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The lightning discharges generated by lightning strikes can reach several hundred kiloamperes, causing significant electromagnetic, mechanical, and thermal effects. These effects induce extremely high overvoltages in power transmission systems, leading to the tripping of transmission lines. Therefore, the study of lightning protection and early warning methods is crucial for safeguarding transmission lines. However, previous research in the realm of active lightning protection has been limited, focusing either solely on assessing the risk of lightning-induced tripping on the power grid side or merely predicting the patterns of lightning activity on the disaster side. This paper proposes a novel method for lightning protection and early warning of power transmission lines based on the analysis of lightning data. It encompasses both the risk of lightning-induced tripping of transmission lines on the grid side and the hazards of real-time lightning activities on the disaster side. In the warning phase, the method extensively mines historical lightning location data and, based on predictions of thunderstorm cloud movement trajectories, determines if thunderstorm clouds are nearing tightly packed transmission corridors. It then assesses whether these clouds present a high-risk storm or are approaching transmission lines with low lightning protection capabilities, subsequently integrating these findings to issue a continuous lightning-induced trip warning. In a case study conducted in a certain part of northern China, the proposed continuous lightning strike trip warning method achieved an accuracy rate of 80% for transmission corridors of particular interest to power grid companies' dispatch departments, enhancing the efficiency of warnings and reducing false alarms and missed reports.INDEX TERMS Data-driven; Lightning movement trajectory prediction; High-risk thunderstorm assessment; Lightning-induced trip alarm warning.

show abstract

“…With fault detection, the variations of electrical signals are commonly used as the startup criteria, and the if-then conditions can achieve the fault detection task. For the fault-location methods, Bayesian networks [8], expert systems [9], Petri Nets [10], fuzzy logic [11], and analytical models [12] are commonly cited in recent decades. However, these techniques still have certain drawbacks [13].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Transmission Line Fault Diagnosis Based on Signal Segmentation Approach

Abdul Malek Saidina Omar,

Ahmad Asri Abd Samat,

Fasihah Faisal

et al. 2023

ARASET

View full text Add to dashboard Cite

Convolutional Neural Network (CNN) is a Deep Learning algorithm that can take in an input signal, assign importance (learnable weights and biases) to various aspects of the signals and distinguish between them. The CNN algorithm trains a sample and obtains a CNN model capable of identifying different fault types to analyse transmission line faults. Fault analysis methods typically require feature extraction to synthesise the relevant and non-redundant information from the raw signals. However, these traditional methods are time-consuming and inconsistent, and they can produce biased results due to the reliance on human expertise and experience. Hence, this research focused on developing an intelligent system for fault detection in a three-phase transmission line using CNN. This research aims to develop a CNN model for automatic fault detection in a three-phase transmission line and evaluate the performance of the CNN model for analysing transmission line faults. The three-phase transmission line model was developed using MATLAB-Simulink. CNN was implemented to detect transmission line faults. The performance of CNN based on the signal segmentation approach was evaluated through three different types of data: ideal and noise-added signal data. The simulation result shows good performance accuracy of 99.11% for the ideal case, 99.36%, and 99.39% for 20 dB and 30 dB noise-added cases, respectively. The result shows that a higher noise value in transmission line fault current could increase the performance of CNN. In conclusion, the utilisation of CNN based on a signal segmentation approach for transmission line fault analysis has showed promising performance.

show abstract

Determining Impact of Lightning Strike Location on Failures in Transmission Network Elements Using Fuzzy Decision-Making

Cited by 12 publications

References 34 publications

Lightning rod system: mathematical analysis using the rolling sphere method

Lightning rod system: mathematical analysis using the rolling sphere method

Considering the Methods of Lightning Protection and Early Warning for Power Transmission Lines Based on Lightning Data Analysis

Convolutional Neural Network for Transmission Line Fault Diagnosis Based on Signal Segmentation Approach

Contact Info

Product

Resources

About