Fuzzy Inference-Based Safety Monitoring Framework for Substation Power Operation Sites Using IoT Sensor Networks
Abstract
An important aspect of the country's power supply is the infrastructure development of the power grid. There are a lot of people and pieces of machinery involved in power building, and it often involves teams from several industries working together. As a result, SPOS (substation power operation safety) safety management is notoriously difficult to attain. An enhanced security administration and oversight system based on the IoT with smart sensors is suggested and implemented to enhance the safety management with control of SPOS. An examination of the safety management system's building process is conducted according to the SPOS's characteristics. Intelligent sensors collect data on operational parameters, which are then sent by means of industrial gateways and Internet of Things connectivity devices. The BIM map would display the outcomes of the analysis carried out through artificial intelligence algorithms on the monitoring data. At long last, a control and management system that includes monitoring, early warning, and alarms has been built. In SPOS's safety control and administration section, the topic of smart construction site solutions is also covered. When used in conjunction with the real field system, it confirms that the safety control and management system is reasonable. Operators can make better, more proactive choices about maintenance and resource allocation because to the continual nature of the output. An example would be giving more priority to a substation with a risk index of 0.85 than one with an index of 0.65, even though both would be deemed "high risk" according to the conventional approach. Compared to previous models, this one has a greater prediction accuracy (96 percent), can appropriately produce signals of varying levels, and may serve as a reference for the development of substation safety warning, preventive, and control systems in the future.
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PDFDOI: https://doi.org/10.31449/inf.v49i25.9587
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