scholarly journals Real-time Monitoring System and Method of PFC Capability of Power Grid Based on Data Analysis

2021 ◽  
Vol 245 ◽  
pp. 01012
Author(s):  
Jun Li ◽  
WeiWei Miao ◽  
WenDong Zhang
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Frequency Control ◽  
Power Grid ◽  
Frequency Regulation ◽  
Stable Operation ◽  
Real Time Monitoring ◽  
Monitoring Method ◽  
Primary Frequency ◽  
Grid Based

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.

scholarly journals A WeChat-Based System of Real-Time Monitoring and Alarming for Power Grid Operation Status under Virtual Private Cloud Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chunjie Lian ◽  
Hua Wei ◽  
Xiaoqing Bai ◽  
Zhongliang Lyu
Keyword(s):  
Real Time ◽  
Failure Time ◽  
Power Grid ◽  
Stable Operation ◽  
Cloud Environment ◽  
Real Time Monitoring ◽  
Private Cloud ◽  
Set Up ◽  
Grid Operation ◽  
Operation Status

The existing power grid alarm system using SMS (SMSAS) is complex and suffers some problems such as high latency in data transmission, low reliability, and poor economy. For solving these problems, this paper proposes a WeChat-based system under the virtual private cloud environment to achieve real-time monitoring and alarming for the power grid operation status (WMAS). For WMAS, the WeChat mini program (WMP) is adopted, and it has the dedicated data channel using the Https protocol, which is set up in the WMP and the web API to encrypt the data content to ensure the integrity of the data. Combined with virtual private cloud technology, the hardware resources are virtualized, and the proposed system has strong disaster recovery capability, which significantly improves the flexibility and reliability of the system. Compared with SMSAS, our simulation shows that the time from sending to receiving the information in the proposed system is reduced from 4.9 seconds to 172 milliseconds, with the latency reduced by 28 times. On the contrary, the reliability of the proposed system is as high as 99.9971%, and the annual failure time is 15.24 minutes, which is 380 times lower than 96.51 hours of the SMSAS. The proposed system has been implemented in the Lipu power system in Guangxi, China. More than one year of stable operation indicates that the proposed system is safe, reliable, flexible, and convenient with a bright prospect for future applications.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Real-Time Monitoring Applications for the Power Grid under Geomagnetic Disturbances

2020 ◽  
Author(s):  
Cecilia Klauber ◽  
Komal S. Shetye ◽  
Zeyu Mao ◽  
Thomas J. Overbye ◽  
Jennifer Gannon ◽  
...  
Keyword(s):  
Real Time ◽  
Power Grid ◽  
Real Time Monitoring ◽  
Geomagnetic Disturbances ◽  
Monitoring Applications

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

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.

Research on the Primary Frequency Regulation of the Large Capacity Thermal Power Units

2014 ◽  
Vol 608-609 ◽  
pp. 915-919 ◽  
Author(s):  
Hong Xia Wu
Keyword(s):  
Power Plant ◽  
Control Method ◽  
Power Grid ◽  
Thermal Power ◽  
Frequency Regulation ◽  
Load Curve ◽  
Large Capacity ◽  
Continuous Increase ◽  
Important Index ◽  
Primary Frequency

Frequency is an important index of power quality, primary frequency regulation is of great significance for maintaining the grid frequency. In recent years, with the expansion of the power grid capacity and the continuous increase of the generator capacity, the large capacity units play a role is becoming more and more important in the primary frequency regulation of power grid. This paper takes ultra supercritical coal-fired units (1000MW) of a power plant of Hubei for example, primary frequency regulation control method, requency offset load curve and so on were studied through relevant test.

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