An oil depot, which is used for accepting, storing and distributing oil, is accident-prone because of the flammability and explosibility characteristics of oil. As research shown most accidents in oil depots are caused by unsafe acts of human. But it is difficult to monitor and trace the operations of employees with traditional management methods. An internet of things based safety management information system for oil depot is introduced in this paper. It has the following unique characteristics: (1) It adopts the IoT technology to monitor the process of the routine works and prevent the violation acts; (2) RFID tags and PDAs are used in permit to work management to assure that the worksite audition is conducted at the site; (3) IoT technology is introduced in the system to ensure the quality of the inspection. The use in two oil depots shows that the management information system can provide firm support for oil depot safety management.

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HSE (Health, Safety, and Environment) management is one of the most concerned matters of every business, especially in petroleum Industry. Currently, analyzing the origin of accident and tracing the responsibility of accident commonly happened after the accident due to the lack of analytical theories, methods and models. This paper presents a HSE big data analysis framework which is capable of analyzing historical data of HSE management to promote the practicality and scientificity of HSE management. This paper has done much research of HSE data analytics. Based on the features of HSE management in petroleum Industry, it elaborate the open source projects and its applicable scenes in data analytics. Then, it gives suggestions of choosing open source projects to establish data analytics platform under given conditions. Last, by using data warehouse, data mining, machine learning and pattern identification technology, a HSE big data analytics framework was presented in this paper. This framework includes the level of data acquisition, data storage, data processing, data analysis, and data application. The efficient use of this model can help to untangle doubts of HSE big data analytics, discover the regularity and characteristics of accidents, and enhance supervision and warning of safety production.

In recent years, with the continuous promotion of clean and low-carbon energy strategy, power system’s operation mode has been transformed gradually. Consequently, the extended time of fault location when it occurs will jeopardize the fault restoration of distribution system and reduce the grid resilience. Hence, this paper proposes a novel fault location algorithm based on the combination of power-on signal and power-off signal, which can improve the efficiency of the fault location. First, the principle of numbering node is presented, making it convenient for computer to identify the relationship between device in the distribution network. Then, some assumptions and principles of the algorithm are shown, together with the termination condition of the proposed algorithm. Finally, case study involving an actual distribution network is carried out. Results show that the proposed algorithm is able to achieve accurate fault location meanwhile improve the efficiency of fault location.