Distributed Kalman-like Filtering and Bad Data Detection in Large-scale Power System

2021 ◽  
pp. 1-1
Author(s):  
Jun Yang ◽  
Wen-An Zhang ◽  
Fanghong Guo
Keyword(s):  
Power System ◽  
Large Scale ◽  
Data Detection ◽  
Bad Data Detection ◽  
Bad Data

Bad data detection in power system state estimation based on generalized likelihood ratio test

2016 ◽  
Vol 04 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Zahid Khan ◽  
Radzuan B. Razali ◽  
Hanita Daud ◽  
Nursyarizal Mohd Nor ◽  
Mahmud Fotuhi-Firuzabad ◽  
...  
Keyword(s):  
Power System ◽  
State Estimation ◽  
Likelihood Ratio Test ◽  
Generalized Likelihood Ratio Test ◽  
Data Detection ◽  
Ratio Test ◽  
System State ◽  
Power System State Estimation ◽  
Bad Data Detection ◽  
Bad Data

scholarly journals PSO Supported Ensemble Algorithm for Bad Data Detection Against Intelligent Hacking Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Levent Yavuz ◽  
Ahmet Soran ◽  
Ahmet Onen ◽  
SM Muyeen
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Detection Algorithm ◽  
Cyber Attacks ◽  
Data Detection ◽  
Particle Swarm Optimizer ◽  
Bad Data Detection ◽  
Detection Mechanisms ◽  
Bad Data ◽  
Advanced Computer

Power system cybersecurity has recently become important due to cyber-attacks. Due to advanced computer science and machine learning (ML) applications being used by malicious attackers, cybersecurity is becoming crucial to creating sustainable, reliable, efficient, and well-protected cyber-systems. Power system operators are needed to develop sophisticated detection mechanisms. In this study, a novel machine-learning-based detection algorithm that combines the five most popular ML algorithms with Particle Swarm Optimizer (PSO) is developed and tested by using an intelligent hacking algorithm that is specially developed to measure the effectiveness of this study. The hacking algorithm provides three different types of injections: random, continuous random, and slow injections by adaptive manner. This would make detection harder. Results shows that recall values with the proposed algorithm for each different type of attack have been increased.

Based on the Fractal Technology of Bad Data Detection Method in Power System

2012 ◽  
Vol 490-495 ◽  
pp. 1358-1361
Author(s):  
Yan Hong Li
Keyword(s):  
Power System ◽  
Search Space ◽  
Detection Algorithm ◽  
Fractal Model ◽  
Detection Methods ◽  
Data Detection ◽  
Data Set ◽  
Bad Data Detection ◽  
Time Space ◽  
Bad Data

detection and identification of bad data is an important part of state estimation in power system. To solute the problem generates a variety of detection methods and means in academic and industrial circles, commonly used methods include objective function detection, weighted residual detection, measurement suddenly-change detection and the comprehensive application of above methods. In order to detection the bad data from large amounts of data over the multiple sliding windows, bad data detection algorithm is proposed based on fractal technology building monotonic search space. Firstly, it gives the data set on the piecewise fractal model, and then based on this model to design a detection algorithm. The algorithm can reduce detection processing time greatly. The subsection fractal model can accurately model on the data self similarity and compress data. Theoretical analysis and experimental results show that, the algorithm has higher precision and lower time / space complexity, more suitable for bad data detection.

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