Performance of the European Lightning Detection Network EUCLID in Case of Various Types of Current Pulses From Upward Lightning Measured at the Peissenberg Tower

Paul, Christian; Heidler, F.; Schulz, Wolfgang

doi:10.1109/temc.2019.2891898

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…The peak value of the lightning, the line phase which lightning strikes, and the impulse of lightning are determined as unchanged, as shown in Table 3, whereas the inception angle, lightning position, and lightning phase are varied, as also shown in Table 3. Moreover, the surge source is a Heidler model which uses a lightning impulse with 3.1/77.5 µs [29]. The peak value of the lightning is 20 kA, and the lightning occurs at 0.002 s.…”

Section: Simulationmentioning

confidence: 99%

“…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%

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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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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Chiradeja

Ngaopitakkul

2022

IEEE Access

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“…Paul et al. (2020) reported that out of 40 RSs detected at the Peissenberg Tower, 12 (30%) were falsely classified as IC discharges.…”

Section: Introductionmentioning

confidence: 99%

“…Leal et al (2019) found that compact intracloud discharges with estimated peak currents larger than 50 kA were all falsely classified as RSs by both NLDN and ENTLN. Paul et al (2020) reported that out of 40 RSs detected at the Peissenberg Tower, 12 (30%) were falsely classified as IC discharges.…”

mentioning

confidence: 99%

High‐Accuracy Classification of Radiation Waveforms of Lightning Return Strokes

Wang

Takagi

2023

JGR Atmospheres

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Ground-based lightning location systems (LLSs) are widely used to monitor lightning activities. A prominent feature of ground-based LLSs is that lightning activities in a wide area can be monitored in real time with only a limited number of sensors. Some famous national and continental LLSs include the National Lightning Detection Network (NLDN) covering the continental United States (e.g., Cummins & Murphy, 2009), the European Cooperation for Lightning Detection network (EUCLID) covering the European continent (e.g., Schulz et al., 2016), and the Earth Networks Total Lightning Network (ENTLN) (e.g., Zhu et al., 2022) with the aim of a global coverage.It is a basic requirement for LLSs to automatically and efficiently classify cloud-to-ground (CG) lightning flashes from intracloud (IC) flashes as the former consist of discharges with direct connections to the ground and thus pose a much larger threat to the human society. The fundamental difference between a CG flash and an IC flash is that a CG flash contains one or more return strokes (RSs), so the classification of CG flashes is basically realized by classifying RSs. Further, it is well known that RSs produce characteristic electric field radiation waveforms that are largely different from those of IC discharges (e.g., Lin et al., 1979), so most LLSs classify RSs based on their waveform characteristics.

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“…In recent validation studies for the abovementioned networks, estimated classification accuracy for return strokes ranges from 70% to 92% (Kohlmann et al., 2017; Paul et al., 2020; Zhu et al., 2016, 2017). The estimated classification accuracy for IC discharges was estimated only in a few studies on the flash/pulse sequence level (as opposed to individual pulse).…”

Section: Introductionmentioning

confidence: 99%

A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning

Zhu

Bitzer

Rakov

et al. 2021

Geophysical Research Letters

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Lightning can be classified into two types according to whether it reaches the ground or not. About three-quarters of lightning discharges do not involve ground, most of which belong to the intracloud (IC) lightning category (Rakov, 2016). Lightning discharges that reach the ground are referred to as cloud-toground (CG) lightning. Whether a lightning discharge strikes the ground or remains inside the cloud can be determined from their low-frequency (LF) electromagnetic field waveforms. Pulses generated by lightning processes inside the cloud differ from those generated when lightning strikes the ground (the process referred to as return stroke). Typical return-stroke field waveforms measured at a distance beyond several tens of kilometers or so are characterized by a short rise time of several microseconds and a relatively slow decay time of several tens of microseconds (Haddad et al., 2012; Lin et al., 1979). Compared to return strokes, field pulses of most intracloud lightning processes are generally narrower. Also, due to the greater diversity of lightning processes inside the cloud, pulse waveshapes tend to have larger variation from one event to another. The differences in field waveforms produced by in-cloud lightning processes and return strokes are used in the multiparameter waveform classification methods implemented in many regional and continental scale ground-based lightning detection networks (e.g., U.S. National Detection Network (NLDN), Earth Networks Total Lightning Network (ENTLN), and European Cooperation for Lightning Detection (EUCLID) network) to classify the detected lightning field pulses into two categories: cloud pulses (referred to as ICs) and return strokes (referred to as CGs). In recent validation studies for the abovementioned networks, estimated classification accuracy for return strokes ranges from 70% to 92% (

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Performance of the European Lightning Detection Network EUCLID in Case of Various Types of Current Pulses From Upward Lightning Measured at the Peissenberg Tower

Cited by 9 publications

References 24 publications

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

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

High‐Accuracy Classification of Radiation Waveforms of Lightning Return Strokes

A Machine‐Learning Approach to Classify Cloud‐to‐Ground and Intracloud Lightning

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