In order to further improve the efficiency of operation and maintenance of transmission line tower grounding, this paper proposes a method of status assessment and risk classification of tower grounding devices. This ...
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ISBN:
(纸本)9798350303896
In order to further improve the efficiency of operation and maintenance of transmission line tower grounding, this paper proposes a method of status assessment and risk classification of tower grounding devices. This method designs the deviation rate of the required value according to the measured value of the grounding resistance of the transmission line tower sigma Change rate of measured value of grounding resistance gamma, based on the statistical method of quartile method, the status of tower grounding device is evaluated, and the whole transmission line tower is divided into five levels of ABCDE according to the risk degree. On the premise of accumulating a large amount of data on the transmission line tower grounding parameters, the maximum expectation algorithm (EM algorithm) is used to predict the distribution law of tower grounding parameters over time, so as to correct the risk classification of tower grounding devices through the predicted parameters before the measurement of tower grounding parameters.
To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises-Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, th...
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To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises-Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, the synchronous phasor measuring unit is employed to gather the post-fault steady-state voltage phase quantities, and then, the voltage phase angle values are combined to form a three-dimensional feature quantity. Subsequently, the mov-MF distribution model is initialized through the spherical K-means algorithm and the minimum message length algorithm. This model is further refined via the expectation-maximization algorithm to iteratively optimize distribution parameters. The test set data are input into the mov-MF distribution model, which has been constructed using typical fault data, to discern fault types. Finally, the efficacy of the proposed method is validated through simulation verification conducted on the IEEE 33-node distribution system. The analysis of the examples demonstrates the accuracy of the mov-MF distribution model-based fault identification method in identifying single-phase ground, two-phase ground, two-phase interphase, and three-phase short-circuit faults.
Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture tumid and Gaussian...
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ISBN:
(纸本)9781728143286
Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture tumid and Gaussian mixture model is proposed to preserve the weak feature information of the sonar image. Under strong noise interference, the traditional enhancement method has certain difficulties in measuring the similarity of the details of the sonar image. For this reason, wavelet multi-scale analysis is performed on the sonar image to extract the weak feature information of each resolution. Secondly, a directed probability map model between adjacent scales in the wavelet domain is constructed to realize the similar weak feature information association. According to the parent node state probability and the transition probability matrix, the state values of the corresponding child nodes are determined, and the relationship between the state information of parent and child node is constructed. Thirdly, the Gaussian mixture model is used to fit the wavelet coefficient state distribution, and the neighborhood coefficient correlation is used to describe the relationship between the weak feature information in the scale. Finally, the wavelet coefficient estimation of the restored image signal is calculated by the state probability obtained in the expectation maximization (EM) algorithm, and the sonar image is reconstructed. The results of the contrast experiment are verified by visual effects and objective evaluation. the proposed algorithm can preserve the weak edge and contour information while suppressing the noise of the sonar image. It has better peak signal-to-noise ratio, signal-to-noise ratio and Structural similarity.
Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can ...
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Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications.
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