Grid-connected energy storage has a wide application prospect in building a new power system in the future because of its ability to solve new energy consumption and improve system frequency support. In this paper, th...
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Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller and the buyer. We cannot check our invoices at any time for em...
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Although it has many design restrictions, the discipline of power electronics is essential to robotics and automation applications. Key design restrictions are examined in this study, including those related to dynami...
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In order to solve the problem of chaotic program formation in portable bionic robotic arms, which leads to excessive output angle of automation control methods and severe shaking during operation. A portable bionic ro...
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With the widespread application of non-stop power distribution network operations, traditional manual inspection and manual monitoring methods can no longer meet the needs of efficient and accurate safety assurance. T...
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ISBN:
(纸本)9798350377040;9798350377033
With the widespread application of non-stop power distribution network operations, traditional manual inspection and manual monitoring methods can no longer meet the needs of efficient and accurate safety assurance. To address this problem, this study proposes an automatic risk identification and early warning technology for non-stop power distribution network operations based on machine vision. Specifically, the YOLOv5 (You Only Look Once version 5) algorithm is used to detect the targets of workers and safety protection equipment, and to identify the wearing status of workers' safety protection equipment in real time. The U-Net image segmentation algorithm is then used to accurately identify the insulating shields and safety areas in the working environment. Next, multi-task learning and data enhancement methods are used to improve the robustness and recognition accuracy of the model under different lighting and scenes. Finally, through the integration of video surveillance and risk warning modules, combined with virtual electronic fence technology, the system can automatically identify the illegal behavior of operators and trigger immediate alarms. Experimental results show that the accuracy of the YOLOv5 model can reach up to 93% under strong light conditions;the U-Net model can effectively detect and identify non-compliant occlusion operations when detecting obstructions;and the accuracy of out-of-bounds behavior detection in simple daytime scenarios can reach up to 95%. From the above data conclusions, the combination of YOLOv5 algorithm and U-Net algorithm can not only automatically identify security risks, but also provide real-time and accurate early warning support in complex environments, which has broad application prospects and promotion value.
Nowadays, machines playing a major role everywhere in our lives, which makes the work smooth and simple. This machine helps to shorten times and works based on automation. One of the most challenging tasks is cutting ...
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In order minimize power variations caused by erratic wind energy output, this research proposes a hybrid energy storage system that combines supercapacitors and all-vanadium liquid batteries. The grid-connected and va...
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The key to solving the power quality problem of the traditional traction power supply system was the through-connected traction power supply system. This study analyses the impact of the traditional through-connected ...
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In this study, the smoothing reactor and DC filter of a DC transmission line are found to significantly attenuate the high-frequency components of the transient signal through simulation and analysis of internal and e...
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ISBN:
(纸本)9798350377040;9798350377033
In this study, the smoothing reactor and DC filter of a DC transmission line are found to significantly attenuate the high-frequency components of the transient signal through simulation and analysis of internal and external faults of high-voltage direct current (HVDC) transmission lines. Consequently, the time-domain waveforms of the voltage fault component at the measurement end of transmission lines differ significantly among different fault types. When an external fault occurs, the high frequency component is low and the waveform is smooth, while when an internal fault occurs, the high frequency component is high and the waveform is steep. A multifractal spectrum is calculated based on the curvature characteristic value K-max of the multifractal spectrum that measures the irregularity of the voltage fault time-domain waveform. If the external fault occurs, K-max is greater than 10, and if the internal fault occurs, K-max is less than 10. Simulation tests with PSCAD demonstrate that this criterion is highly reliable, does not depend on fault location, and is resistant to transition resistance.
Voltage and reactive power control via inverter-based distributed generators is necessary for distribution power network to mitigate voltage violations. Voltage control is vital to keeping distribution power system vo...
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