• Journal of the Korean Institute of Gas
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• v.20 no.5
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• pp.64-71
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• 2016

According to the urban gas business law, electrical corrosion prevention measures shall be installed to the buried gas pipelines and the pipe-to-soil potentials should be measured at the test box at least once a year. Most of the test boxes installed in urban area are usually located on the road where the vehicle travels, therefore, it is difficult to measure the CP potentials at the test boxes. That is, we need traffic control when carrying out the measurement of the CP potentials on daytime when the traffic is heavy, or we have to measure the potentials in the late night when the traffic is light. To solve these difficulties, we have studied remotely CP potential measuring method by using the patrol car. We have installed solid reference electrodes and data loggers under the test boxes on the site and received the CP potentials from the data loggers when the vehicle moves. It was difficult to send and receive the data because the data logger was located under the ground. We have applied 3 different method including 2 antenna systems to achieve best effective way in receiving the data. We have found the remote CP measuring method by using a car can save more 20 times of measuring time than conventional measuring methods.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.49-56
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• 2023

As AI (Artificial Intelligence)-related technologies are highly developed due to the 4th industrial revolution, cases of applying AI in various fields are increasing. The main reason is that there are practical limits to direct processing and analysis of exponentially increasing data as information and communication technology develops, and the risk of human error can be reduced by applying new technologies. In this study, after collecting the data received from the 'remote potential measurement terminal (T/B, Test Box)' and the output of the 'remote rectifier' at that time, AI was trained. AI learning data was obtained through data augmentation through regression analysis of the initially collected data, and the learning model applied the value-based Q-Learning model among deep reinforcement learning (DRL) algorithms. did The AI that has completed data learning is put into the actual city gas supply area, and based on the received remote T/B data, it is verified that the AI responds appropriately, and through this, AI can be used as a suitable means for electricity management in the future. want to verify.