Robust Bad Data Detection Method for Microgrid Using Improved ELM and DBSCAN Algorithm

2018 ◽  
Vol 144 (3) ◽  
pp. 04018026 ◽  
Author(s):  
Heming Huang ◽  
Fei Liu ◽  
Xiaoming Zha ◽  
Xiaoqi Xiong ◽  
Tinghui Ouyang ◽  
...  
Keyword(s):  
Detection Method ◽  
Data Detection ◽  
Bad Data Detection ◽  
Dbscan Algorithm ◽  
Bad Data

Malicious data deception attacks against power systems: A new case and its detection method

2018 ◽  
Vol 41 (6) ◽  
pp. 1590-1599 ◽  
Author(s):  
Dajun Du ◽  
Rui Chen ◽  
Xue Li ◽  
Lei Wu ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Power Systems ◽  
High Probability ◽  
Detection Method ◽  
Optimization Method ◽  
Limited Resources ◽  
Security Evaluation ◽  
Data Detection ◽  
Bad Data Detection ◽  
Operational Costs ◽  
Bad Data

Power systems usually employ bad data detection (BDD) to avoid faulty measurements caused by their anomalies, and hence can ensure the security of the state estimation of power systems. However, recently BDD has been found vulnerable to malicious data deception attacks submerged in big data. Such attacks can purposely craft sparse measurement values (i.e. attack vectors) to mislead power estimates, while not posing any anomalies to the BDD. Some related work has been proposed to emphasize this attack. In this paper, a new malicious data deception attack by considering a practical attacking situation is investigated, where the attacker has limited resources for corrupting measurements. In this case, attackers generate attack vectors with less sparsity to evade conventional BDD, while using a convex optimization method to balance the sparsity and magnitude of attack vectors. Accordingly, the effects of such an attack on operational costs and the risks of power systems are analysed in detail. Moreover, according to security evaluation for individual measurements, such attacks can be detected with high probability by just securing one critical measurement. Numerical simulations illustrate the effectiveness of the proposed new attack case and its detection method.

Rapid Double-Layer Identification Method of Multi-Relevant Bad Data in Large-Scale Power Grids

2014 ◽  
Vol 672-674 ◽  
pp. 1294-1300
Author(s):  
Zhao Kun ◽  
Xing Ying ◽  
Jie Xu ◽  
Yin Zhang ◽  
Yan Lei ◽  
...  
Keyword(s):  
Double Layer ◽  
Large Scale ◽  
Detection Method ◽  
Power Grid ◽  
Power Grids ◽  
Data Detection ◽  
Bad Data Detection ◽  
Identification Method ◽  
Detection And Identification ◽  
Bad Data

Bad data detection and identification is an important part of state estimation. When the relevant bad data appears, however, there is residual pollution and residual submerged condition in currently available methods of bad data detection and identification. In view of the above problem, this article presents a double-layer bad data detection and identification technique. At first, it is based on regularization residual detection method (Rn detection method) to identify the suspect measurement sets. And then, it presents a fast search technique of interrelated suspect measurements to search interrelated measurements in all the suspect measurements of the entire power grid and produce interrelated suspect measurement sets. Furthermore, use double-layer identification method to fast identify the bad data in interrelated suspect measurement sets, in other words, identify all the bad data in entire power grid. At last, taking IEEE39 node power grid for example, this detection method of bad data is analyzed, the accuracy and effectiveness of this method is to be verified.

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
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