Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

Detection of false data injection attacks on power systems using graph edge-conditioned convolutional networks

State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.