No abstract

The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, which provide benefits in terms of the speed and accuracy of the process. Measurement devices in utilities and buses are vulnerable to communication interruptions between phasor measurement units and operators, who can be easily manipulated by false data. While Kalman filters are incapable of detecting the majority of such cyber-attacks, this article proves that the proposed unsupervised machine learning method is able to detect more than 90 percent of the mentioned attacks. The simulation results on the IEEE 9-bus with 3-machines and IEEE 14-bus with 5-machines systems verify the efficiency of the proposed approach.

This paper presents the challenges of optimal measurement devices placement (MDP) in the distribution system by considering the improvement of accuracy and speed for state estimation (SE) in the presence of distributed generations (DGs). We assumed that active and reactive power measurements (both injection and flow) with voltage magnitude measurements were used to estimate the power system’s state. The paper employed phase measurement unit (PMU) and smart meters, which are the two commonly used measuring devices. For numerical evaluation of the system, the power system states are based on the angle and magnitude of voltages at every bus. The issues normally experienced in the optimal measurement devices placement in distribution networks were investigated using the binary dragonfly algorithm (BDA), in this study. As a way forward to proffer solutions to these issues, a fair compromise between accuracy, speed, and the number of measurements (NoMs) was reached, and the proposed solution was tested in two different scenarios applied in the IEEE 33-bus distribution test system. The results illustrate that by increasing the accuracy, NoMs and the cost are going to rise as well. On the other hand, by escalating the speed, NoMs decrease and the accuracy falls dramatically.