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Big Data Approach for Fire Prevention and Warning for Power Systems

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Abstract

In recent years, video monitoring equipment has covered almost all production aspects of the power system, realizing real-time recording of power system operation information. However, for the abnormal operation information appearing in the monitoring video, it can only be observed and identified by security personnel uninterruptedly at present, which consumes a lot of labor cost and has low reliability. To tackle the above problems, in this paper, based on the state-of-the-art object detection algorithm, a real-time analysis system for fire detection and early warning of power systems is built to monitor and warn of sudden fires by detecting whether flames or smoke appear in the surveillance video. Meanwhile, a deployable Web version and an IoT (Internet of Things) version suitable for industrialized scenarios are provided. Specifically, to address the problem of lack of data for fire warning in power systems, we construct a fire image dataset for power systems, containing 7634 fire images, to train an object detection model; To improve the convergence speed of the general target detection algorithm, we use genetic algorithm to optimize the super parameters in the model. Finally, 200 video clips containing flames, smoke, and confusing targets mixed with fire clouds, dark clouds, and red headlights are selected as the test set, and the experiments show that the accuracy and recall are 0.985, and the recognition speed is about 207 fps on an Nvidia RTX 3080 10GB GPU.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to the authors do not have the permission, but are available from the corresponding author on reasonable request.

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Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. State Grid WenZhou Power Supply Company, Wenzhou, 325000, China

    Li Zheng

  2. State Grid ShaoXing Power Supply Company, Shaoxing, 312000, China

    Xinxin Zhang

  3. State Grid Zhoushan Power Supply Company, Zhoushan, 316000, China

    Haolei Wang

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  1. Li Zheng
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  2. Xinxin Zhang
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  3. Haolei Wang
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Contributions

Li Zheng: Writing, Methodology, Supervision, Software. Xinxin Zhang: Writing, reviewing, Idea. Haolei Wan: Software, Draw figures, Data set.

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Correspondence to Li Zheng.

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Zheng, L., Zhang, X. & Wang, H. Big Data Approach for Fire Prevention and Warning for Power Systems. J Sign Process Syst 95, 1391–1403 (2023). https://doi.org/10.1007/s11265-023-01857-9

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