93 research outputs found
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Graph signal processing (GSP) has emerged as a powerful tool for practical
network applications, including power system monitoring. By representing power
system voltages as smooth graph signals, recent research has focused on
developing GSP-based methods for state estimation, attack detection, and
topology identification. Included, efficient methods have been developed for
detecting false data injection (FDI) attacks, which until now were perceived as
non-smooth with respect to the graph Laplacian matrix. Consequently, these
methods may not be effective against smooth FDI attacks. In this paper, we
propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph
total variation (TV) under practical constraints. In addition, we develop a
low-complexity algorithm that solves the non-convex GDFI attack optimization
problem using ell_1-norm relaxation, the projected gradient descent (PGD)
algorithm, and the alternating direction method of multipliers (ADMM). We then
propose a protection scheme that identifies the minimal set of measurements
necessary to constrain the GFDI output to high graph TV, thereby enabling its
detection by existing GSP-based detectors. Our numerical simulations on the
IEEE-57 bus test case reveal the potential threat posed by well-designed
GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed
protection design with GSP-based detection can lead to significant hardware
cost savings compared to previous designs of protection methods against FDI
attacks.Comment: This work has been submitted to the IEEE for possible publication.
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Power System State Estimation and Renewable Energy Optimization in Smart Grids
The future smart grid will benefit from real-time monitoring, automated outage management, increased renewable energy penetration, and enhanced consumer involvement. Among the many research areas related to smart grids, this dissertation will focus on two important topics: power system state estimation using phasor measurement units (PMUs), and optimization for renewable energy integration.
In the first topic, we consider power system state estimation using PMUs, when phase angle mismatch exists in the measurements. In particular, we build a measurement model that takes into account the measurement phase angle mismatch. We then propose algorithms to increase state estimation accuracy by taking into account the phase angle mismatch. Based on the proposed measurement model, we derive the posterior Cramér-Rao bound on the estimation error, and propose a method for PMU placement in the grid. Using numerical examples, we show that by considering the phase angle mismatch in the measurements, the estimation accuracy can be significantly improved compared with the traditional weighted least-squares estimator or Kalman filtering. We also show that using the proposed PMU placement strategy can increase the estimation accuracy by placing a limited number of PMUs in proper locations.
In the second topic, we consider optimization for renewable energy integration in smart grids. We first consider a scenario where individual energy users own on-site renewable generators, and can both purchase and sell electricity to the main grid. Under this setup, we develop a method for parallel load scheduling of different energy users, with the goal of reducing the overall cost to energy users as well as to energy providers. The goal is achieved by finding the optimal load schedule of each individual energy user in a parallel distributed manner, to flatten the overall load of all the energy users. We then consider the case of a micro-grid, or an isolated grid, with a large penetration of renewable energy. In this case, we jointly optimize the energy storage and renewable generator capacity, in order to ensure an uninterrupted power supply with minimum costs. To handle the large dimensionality of the problem due to large historical datasets used, we reformulate the original optimization problem as a consensus problem, and use the alternating direction method of multipliers to solve for the optimal solution in a distributed manner
Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping
This paper proposes a fully decentralized adaptive re-weighted state
estimation (DARSE) scheme for power systems via network gossiping. The enabling
technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which
allows to harness the computation capability of each area (i.e. a database
server that accrues data from local sensors) to collaboratively solve for an
accurate global state. The DARSE scheme mitigates the influence of bad data by
updating their error variances online and re-weighting their contributions
adaptively for state estimation. Thus, the global state can be estimated and
tracked robustly using near-neighbor communications in each area. Compared to
other distributed state estimation techniques, our communication model is
flexible with respect to reconfigurations and resilient to random failures as
long as the communication network is connected. Furthermore, we prove that the
Jacobian of the power flow equations satisfies the Lipschitz condition that is
essential for the GGN algorithm to converge to the desired solution.
Simulations of the IEEE-118 system show that the DARSE scheme can estimate and
track online the global power system state accurately, and degrades gracefully
when there are random failures and bad data.Comment: to appear in IEEE JSA
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