Energy Central News

Curated power industry news from thousands of top sources.

News

Study Results from Zheng Wang and Colleagues Broaden Understanding of Machine Learning (The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning)

Source: 
Information Technology Daily

2020 OCT 15 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Investigators publish new report on Machine Learning. According to news reporting originating in Yinchuan, People’s Republic of China, by NewsRx journalists, research stated, “To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set.”

The news reporters obtained a quote from the research, “Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms.”

According to the news reporters, the research concluded: “In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.”

For more information on this research see: The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning. Plos One, 2020;15(10):e0237994. (Public Library of Science - www.plos.org; Plos One - www.plosone.org)

Our news correspondents report that additional information may be obtained by contacting Zheng Wang, State Grid Ningxia Electric Power, Eco-Tech Research Institute, Yinchuan, People’s Republic of China. Additional authors for this research include Bo Yu, Shangke Liu, Xiaomin Liu and Ruixin Gou.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1371/journal.pone.0237994. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

Discussions

No discussions yet. Start a discussion below.

Get Published - Build a Following

The Energy Central Power Industry Network is based on one core idea - power industry professionals helping each other and advancing the industry by sharing and learning from each other.

If you have an experience or insight to share or have learned something from a conference or seminar, your peers and colleagues on Energy Central want to hear about it. It's also easy to share a link to an article you've liked or an industry resource that you think would be helpful.

                 Learn more about posting on Energy Central »