Electrical Equipment Management Research Articles

Operation and Management of Electrical Equipment in Iron and Steel Enterprises

How to ensure the reliable operation of the complex and huge electrical system composed of a large number of electrical equipment in iron and steel enterprises? Combined with working experience, the author introduces four main factors affecting the normal operation of equipment, analyzes five main problems existing in the operation and management of electrical equipment, and puts forward corresponding im⁃ provement measures, so as to improve the management level of electrical equipment in iron and steel enterprises.

Research on Strengthening the Mechanical and Electrical Equipment Management and Maintenance

Research on Strengthening the Mechanical and Electrical Equipment Management and Maintenance

Discussion on Operation and Management of Electrical Equipment in Iron and Steel Enterprises

Discussion on Operation and Management of Electrical Equipment in Iron and Steel Enterprises

State Monitoring and Information Management of Electrical Equipment in Hydropower Plant

State Monitoring and Information Management of Electrical Equipment in Hydropower Plant

Analysis on Maintenance and Management of Electrical Equipment in Power Plant

Analysis on Maintenance and Management of Electrical Equipment in Power Plant

Brief Analysis of Key Points of Hospital Building Electrical Equipment Management

Brief Analysis of Key Points of Hospital Building Electrical Equipment Management

The Development of a State-Aware Equipment Maintenance Application Using Sensor Data Ranking Techniques.

Billions of electric equipment are connected to Internet of Things (IoT)-based sensor networks, where they continuously generate a large volume of status information of assets. So, there is a need for state-aware information retrieval technology that can automatically recognize the status of each electric asset and provide the user with timely information suitable for the asset management of electric equipment. In this paper, we investigate state-aware information modeling that specializes in the asset management of electric equipment. With this state-aware information model, we invent a new asset state-aware ranking technique for effective information retrieval for electric power and energy systems. We also derive an information retrieval scenario for IoT in power and energy systems and develop a mobile application prototype. A comparative performance evaluation proves that the proposed technique outperforms the existing information retrieval technique.

Research of University Building’s Electrical Energy Consumption Management Based on IoT & AI

Energy is an important factor in socio-economic development. The level of energy reserves is closely related to the national development, and has a great impact on the national economic development. However, many buildings in university/college, the management of electrical equipment, e.g., lighting and air conditioning is still under the control of students in manual way. According to the published data combined with the often seen phenomenon, the wastage of electrical energy in university/college is serious; and it has become a hot issue. This paper provides a solution to the above challenges in an automatic way through a combined application of technologies like Internet of things, AI, sensors, intelligent control devices and wireless communication technologies. It organized in four sections; the second is a comprehensive literature review after a brief introduction; the third section provides a case of Building Electrical Energy Consumption (BEEC) management and then presents a recommended solution; the future development trend and future work is given in the final part

Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy

Management of electric equipment has a direct impact on companies’ performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. However, a majority of these electric power materials exhibit an intermittent demand pattern characterized by random arrivals interspersed with time periods with no demand at all. These characteristics cause additional difficulty for companies in managing these electric power material inventories. In response to the above problem, this paper, based on the electric power material demand data of Shanghai Electric Power Company, develops a new method to determine the optimal order quantity Q⁎ in a cost-oriented periodic review (T,Q) system, whereby unsatisfied demands are backordered and demand follows a compound Erlang distribution. Q⁎corresponds to the value of Q that gives the minimum expected total inventory holding and backordering cost. Subsequently, an empirical investigation is conducted to compare this method with the Newsvendor model. Results verify its superiority in cost savings. Ultimately, considering the complicated calculation and low efficiency of that algorithm, this paper proposes an approximation and a heuristic algorithm which have a higher level of utility in a real industrial context. The approximation algorithm simplifies the calculation process by reducing iterative times while the heuristic algorithm achieves it by generalizing the relationship between the optimal order quantity Q⁎ and mean demand interarrival rate λ.

Intelligent Control System Design for Electrical Equipment Management

The article design a kind of intelligent control system with the function that can synthesize data, signal transmission and power control at the same time that it includes six components such as terminal equipment, network, controller, circuit, intelligent socket, electrical equipment. This article introduces the three detailed system implementation case with electrical equipment management mode of supply, working mode and working state of intelligent control is realized through the design of the system and improve the applicability of the system.

The Design of the Power Energy Management System Based on Wireless Sensor Network

With the development of science and technology, people's demand for energy is more and bigger, in addition to consider renewable energy. Electricity management system to study the effects of energy saving effect is also very necessary measures. Development of a complete set of monitoring, energy saving, control of intelligent power management system has very important practical significance. Finally, the paper puts forward the energy saving effect of validation, the energy saving analysis and evaluation. Based on the research of the content of this article, it finally realizes the intelligent management and scientific control of electric equipment, to solve the management of electric equipment of large power place management control is limited by geographical, effectively eliminate the phenomenon of energy waste. This system has good scalability can be slightly modified to meet more big power.

Mechanical & Electrical Equipment Management Mode Control Computer-Based Network (Wireless)

The right management managing direction is an important prerequisite for generating qualified parts, an important step before the management forming simulation is to determine the reasonable direction of the managing. Manually adjust parts in order to overcome rely on experience, the drawbacks to the managing direction, the direction of the managing tool and forming the contact area of the sheet as the goal of automatic determination mode. Objective function of the direction of the managing for the variable contact area in the managing direction of the feasible region, the use of heritage modes to optimize the objective function of the contact area and, ultimately feasible within the contact area corresponding to the managing direction, that is the best managing direction. The measured results show that the direction of the managing based on genetic mode, the automatic mode can fast and accurate to obtain the optimal direction of managing.