New hypothesis testing-based rapid change detection for power grid system monitoring

Abstract

The vulnerability of power grid systems to malicious attacks is one of the most pressing problems faced concerning power grid systems. Based on the dynamics of the generators, we show that the time evolution of the power grid system can be modelled by a discrete-time linear state-space model. We employ approaches based on hypothesis testing for failure and intrusion detection of the monitored power grid system. We develop a new locally optimum unknown direction (LOUD) test to detect changes in matrices or vectors and apply this approach to power grid failure and intrusion detection problems. We provide numerical results which show that, unlike the standard generalised likelihood ratio-based approach, the LOUD test is able to produce decisions right after the change has occurred without waiting to collect additional data while it performs nearly as good, within a few percent in the case considered, as the optimum but unachievable likelihood ratio test for the known change. We employ realistic simulations of the IEEE 14 bus system to more fully evaluate the LOUD test.

Keywords::

• signal detection
• hypothesis testing
• power grid

Acknowledgement

The authors would like to thank Mr Liang Zhao for his help in obtaining simulation results in the paper.

Notes

^1. Email: [email protected]

^2. We only consider , that are full rank (invertible) so that we will not have a singular detection problem. Thus, . To avoid similar problems, we also assume that is full rank.

^3. The regularity conditions can be satisfied in many cases of interest. For example, a pdf that has absolute continuity, such that exists for almost all , and satisfies will make the case. The situation is similar to [8, p. 30].

^4. We estimate the state space model with Canonical Parameterisation, representing a state-space system in its minimal form, which uses the minimum number of free parameters to capture the dynamics.

Additional information

Funding

This material is based on the research supported by the National Nature Science Foundation of China [grant no. 61102142]; the International Science and Technology Cooperation and Exchange Research Plan of Sichuan Province [grant no. 2013HH0006]; the National Science Foundation [grant no. CCR-0829958]; and by a Lehigh University Energy and Environment Research and Commercialization Grant, funded by the Commonwealth of Pennsylvania through the Ben Franklin Technology Development Authority.