Search Results (7)

Given the increasing threat of lightning to modern electronic systems and human activities, the accurate identification and classification of lightning electromagnetic pulses has become a critical research focus, prompting the present study. A dataset was established by collecting lightning electromagnetic signals generated by various types of lightning under diverse environmental conditions via the lightning location system of the Institute of Electrical Engineering, Chinese Academy of Sciences. Subsequently, A deep learning model integrating a convolutional neural network was developed for feature extraction and pattern recognition using the multi-station data. Experimental results demonstrate that the proposed model significantly improves LEMP identification accuracy (exceeding 97%) compared to existing single-station methods. Moreover, it effectively uncovers complex hidden features within the data, outperforming conventional approaches in both accuracy and robustness. In conclusion, the proposed deep learning model offers a reliable technical foundation for lightning monitoring and localization based on LEMP signals. Full article

This paper deals with an optimal operation method for surge protective devices (SPDs) to calculate the maximum continuous operating voltage (U_C) and the voltage protection level (U_P) by considering the sum of the voltage protection level and the dielectric continuous voltage limit of surge protective devices in order to effectively protect energy storage system (ESS) from switching and lightning surges. This paper also implements a test device for SPDs in ESSs based on the concept of a lightning electromagnetic surge protection measurement system (LPMS) by combining an SPD coordinated with spatial shielding with an ESS configuration. Here, the test device for the SPD in the ESS is composed of a power distribution unit (PDU), uninterruptible power supply (UPS), and a lightning electromagnetic pulse (LEMP) protection device, which combines two units of SPDs and disconnection switches (DSs) connected in parallel with two units of main circuit breakers (MCBs) and noise cut transformers (NCTs) connected in series. From the test results based on the proposed optimal operation method and test device, it is clear that the residual voltage with a third-class combination waveform can be kept within 1.5 kV of the surge voltage limit in all test scenarios, and it is confirmed that the proposed test device for SPDs can protect ESSs from switching and lightning surges. Therefore, it is confirmed that the SPD tested using the proposed method can effectively reduce switching and lightning surges, while the existing SPDs installed in ESS sites cannot protect ESSs from such surges. Full article

Time of arrival (TOA) is a widely utilized method for positioning lightning return strokes, with its accuracy contingent upon the arrival times of signals from different detection sites. Typically, the peak value method is employed to directly extract the peak times of lightning electromagnetic pulse (LEMP) waveforms. By correlating these peak times with the coordinates of the sites, the spatiotemporal parameters of the LEMP can be determined. However, due to the dispersion phenomenon of broadband LEMP signals during propagation, the positioning accuracy of the peak method is relatively low. This paper introduces a novel lightning positioning technique that leverages continuous wavelet transform (CWT) for narrowband feature extraction. Specifically, narrowband signal characteristics were derived through CWT applied to simulation and measured data obtained from six detection sites. Subsequently, positional analysis was performed on both datasets. The results demonstrate that compared to traditional peak value methods, the proposed approach significantly enhances horizontal positioning accuracy for lightning; specifically, positioning error for simulation data decreased from 94.7 m to 5.6 m, while it reduced from 121 m to 9.2 m for practical measured data. Full article

The impact of a lightning electromagnetic pulse (LEMP) on a power line or power station produces an effect similar to that of switching between a significant power source and a power line circuit. This switch closure causes a sudden change in routing conditions, creating a transient state. This situation has been studied in terms of electrostatic and electromagnetic induction, as well as overvoltage changes. Appropriate mathematical models were used to analyze these changes. While vertical electric field analysis has been carried out in a few studies, magnetic field and horizontal electric field vectors have not been studied. In this study, the Rusck formulation and the Heidler current formulation are combined at the current level, developed and analyzed. This is because the Rusck expression can sometimes give incorrect results at the current level. Also, in the analysis, electromagnetic field formulations based on accelerating charges are used instead of the dipole approximation to eliminate the need for interpolation in the graphical results. In contrast to other studies in the literature, this study proposes the use of moving and accelerating load techniques to better understand the effects of LEMPs on power transmission lines. Also, in this study, the double exponential problem of the current form in Rusck’s formulation is addressed in order to obtain a close approximation of the physical form of the LEMP. Additionally, the field–line (coupling) relationship is studied according to a unique closed formulation, leading to important determinations about the overvoltages generated on a line depending on the propagation speed of the LEMP sprout and the electrical changes in the area where the LEMP first occurs. Full article

Lightning Electromagnetic Pulses, or LEMPs, propagate in the Earth–ionosphere waveguide and can be detected remotely by ground-based lightning electric field sensors. LEMPs produced by different types of lightning processes have different signatures. A single thunderstorm can produce thousands of LEMPs, which makes their classification virtually impossible to carry out manually. The lightning classification is important to distinguish the types of thunderstorms and to know their severity. Lightning type is also related to aerosol concentration and can reveal wildfires. Artificial Intelligence (AI) is a good approach to recognizing patterns and dealing with huge datasets. AI is the general denomination for different Machine Learning Algorithms (MLAs) including deep learning and others. The constant improvements in the AI field show us that most of the Lightning Location Systems (LLS) will soon incorporate those techniques to improve their performance in the lightning-type classification task. In this study, we assess the performance of different MLAs, including a SVM (Support Vector Machine), MLP (Multi-Layer Perceptron), FCN (Fully Convolutional Network), and Residual Neural Network (ResNet) in the task of LEMP classification. We also address different aspects of the dataset that can interfere with the classification problem, including data balance, noise level, and LEMP recorded length. Full article

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination $R^2$ of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission. Full article

When the nacelle of a wind turbine is struck by lightning, lightning electromagnetic pulse (LEMP) is generated inside the nacelle and consequently impacts inside electronic devices or even seriously destroys them. In order to study the LEMP inside the nacelle, this paper firstly built a full-scale model of a non-metallic nacelle. The lightning electromagnetic environment in the nacelle was simulated and analyzed by the transmission-line matrix method. Then the protective measures of applying metallic shielding mesh on the nacelle were studied, including the mesh size and material of the shielding mesh on the protective effect. The results show that LEMP in the nacelle can be effectively attenuated by metallic shielding meshes. The shielding effect is highly dependent on the conductivity of the shielding mesh material and the mesh size. Full article