Feature extraction for load identification by means of CPC in smart grid

MedPower 2014 ◽  
2014 ◽  
Author(s):  
Y. Beck ◽  
N. Calamero ◽  
G. Golan ◽  
A. Braunstein
Keyword(s):  
Feature Extraction ◽  
Smart Grid ◽  
Load Identification

Load Identification for Smart Grid Management

2019 ◽  
Author(s):  
Jose Restrepo ◽  
Francisco Naranjo ◽  
Jhonny Barzola ◽  
Claudio Otero ◽  
Pedro Garcia
Keyword(s):  
Smart Grid ◽  
Load Identification ◽  
Grid Management

scholarly journals A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4443 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Electrical Energy ◽  
Demand Side Management ◽  
Current Sensor ◽  
The Internet ◽  
Load Identification ◽  
Demand Side ◽  
Power Meter ◽  
Iot Devices

Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today’s IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.

Feature Extraction-Based Hellinger Distance Algorithm for Nonintrusive Aging Load Identification in Residential Buildings

2016 ◽  
Vol 52 (3) ◽  
pp. 2031-2039 ◽  
Author(s):  
Hsueh-Hsien Chang ◽  
Meng-Chien Lee ◽  
Wei-Jen Lee ◽  
Chao-Lin Chien ◽  
Nanming Chen
Keyword(s):  
Feature Extraction ◽  
Residential Buildings ◽  
Hellinger Distance ◽  
Load Identification

scholarly journals Feature Extraction for Load Identification Using Long-Term Operating Waveforms

2015 ◽  
Vol 6 (2) ◽  
pp. 819-826 ◽  
Author(s):  
Liang Du ◽  
Yi Yang ◽  
Dawei He ◽  
Ronald G. Harley ◽  
Thomas G. Habetler
Keyword(s):  
Feature Extraction ◽  
Load Identification

A deep learning based non-intrusive household load identification for smart grid in China

2021 ◽  
Author(s):  
Chen Chen ◽  
Pinghang Gao ◽  
Jiange Jiang ◽  
Hao Wang ◽  
Pu Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Smart Grid ◽  
Load Identification

scholarly journals Optimal Strategy to Select Load Identification Features by Using a Particle Resampling Algorithm

2019 ◽  
Vol 9 (13) ◽  
pp. 2622 ◽  
Author(s):  
He ◽  
Lin ◽  
Xiao ◽  
Qian ◽  
Zhou
Keyword(s):  
Feature Extraction ◽  
Fuzzy Theory ◽  
Sliding Window ◽  
Monitoring Device ◽  
Load Identification ◽  
The Real ◽  
Serial Data ◽  
Resampling Method ◽  
Cumulative Sum Control ◽  
Cusum Method

This paper proposes a robust strategy to select the load identification features, which is based on particle resampling to promote the performance for the successive load identification. Firstly, the sliding window incorporated with the bilateral cumulative sum control chart (CUSUM) method is utilized to obtain the load event. Then, the minimum inner-class variance, using the time-serial data, is introduced to judge the happened time as precise as possible, thus marking the changing point of the state of load for the following feature extraction. Due to the fluctuating data of current and voltage sampled by the monitoring device, the particle resampling method, containing the importance principle, is applied to find the steady and effectiveness point, ensuring that the obtained features have the desired fit with its actual features. The fitness measurement is then carried out by using the 2-D fuzzy theory. Finally, the proposed method was tested on the real household measurements in the labs. The result demonstrates an improvement in obtaining the desired load features when applied to the real household for the following load identification.

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