Real Time Monitoring Method of Power Grid User Side Energy Consumption Data Based on Internet of Things

Author(s):  
Lixia Wang ◽  
Xin Zhang ◽  
Lingli Meng ◽  
Wei Li ◽  
Dawei Wang
2021 ◽  
Vol 245 ◽  
pp. 01012
Author(s):  
Jun Li ◽  
WeiWei Miao ◽  
WenDong Zhang

The development of wind power and other new energy sources has a great impact on the stability of power system frequency. By analyzing the characteristics of the primary frequency control(PFC) assessment standard of the power grid, one real-time monitoring method of the unit’s PFC capability of the power grid based on data analysis is proposed. The Power network dispatching department can fully grasps the overall frequency regulation capability of the operating units. It can improve the grid’s response methods to deal with high power gaps and ensure the safe and stable operation of the grid.


Author(s):  
Cecilia Klauber ◽  
Komal S. Shetye ◽  
Zeyu Mao ◽  
Thomas J. Overbye ◽  
Jennifer Gannon ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1627
Author(s):  
Giovanni Battista Gaggero ◽  
Mario Marchese ◽  
Aya Moheddine ◽  
Fabio Patrone

The way of generating and distributing energy throughout the electrical grid to all users is evolving. The concept of Smart Grid (SG) took place to enhance the management of the electrical grid infrastructure and its functionalities from the traditional system to an improved one. To measure the energy consumption of the users is one of these functionalities that, in some countries, has already evolved from a periodical manual consumption reading to a more frequent and automatic one, leading to the concept of Smart Metering (SM). Technology improvement could be applied to the SM systems to allow, on one hand, a more efficient way to collect the energy consumption data of each user, and, on the other hand, a better distribution of the available energy through the infrastructure. Widespread communication solutions based on existing telecommunication infrastructures instead of using ad-hoc ones can be exploited for this purpose. In this paper, we recall the basic elements and the evolution of the SM network architecture focusing on how it could further improve in the near future. We report the main technologies and protocols which can be exploited for the data exchange throughout the infrastructure and the pros and cons of each solution. Finally, we propose an innovative solution as a possible evolution of the SM system. This solution is based on a set of Internet of Things (IoT) communication technologies called Low Power Wide Area Network (LPWAN) which could be employed to improve the performance of the currently used technologies and provide additional functionalities. We also propose the employment of Unmanned Aerial Vehicles (UAVs) to periodically collect energy consumption data, with evident advantages especially if employed in rural and remote areas. We show some preliminary performance results which allow assessing the feasibility of the proposed approach.


2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.


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