21,611 research outputs found
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection
A significant challenge in energy system cyber security is the current
inability to detect cyber-physical attacks targeting and originating from
distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible
loads, and electric vehicles. We address this concern by designing and
developing a distributed, multi-modal anomaly detection approach that can sense
the health of the device and the electric power grid from the edge. This is
realized by exploiting unsupervised machine learning algorithms on multiple
sources of time-series data, fusing these multiple local observations and
flagging anomalies when a deviation from the normal behavior is observed.
We particularly focus on the cyber-physical threats to the distributed PVs
that has the potential to cause local disturbances or grid instabilities by
creating supply-demand mismatch, reverse power flow conditions etc. We use an
open source power system simulation tool called GridLAB-D, loaded with real
smart home and solar datasets to simulate the smart grid scenarios and to
illustrate the impact of PV attacks on the power system. Various attacks
targeting PV panels that create voltage fluctuations, reverse power flow etc
were designed and performed. We observe that while individual unsupervised
learning algorithms such as OCSVMs, Corrupt RF and PCA surpasses in identifying
particular attack type, PCA with Convex Hull outperforms all algorithms in
identifying all designed attacks with a true positive rate of 83.64% and an
accuracy of 95.78%. Our key insight is that due to the heterogeneous nature of
the distribution grid and the uncertainty in the type of the attack being
launched, relying on single mode of information for defense can lead to
increased false alarms and missed detection rates as one can design attacks to
hide within those uncertainties and remain stealthy
A Framework for Robust Steady-State Voltage Stability of Distribution Systems
Power injection uncertainties in distribution power grids, which are mostly
induced by aggressive introduction of intermittent renewable sources, may drive
the system away from normal operating regimes and potentially lead to the loss
of long-term voltage stability (LTVS). Naturally, there is an ever increasing
need for a tool for assessing the LTVS of a distribution system. This paper
presents a fast and reliable tool for constructing \emph{inner approximations}
of LTVS regions in multidimensional injection space such that every point in
our constructed region is guaranteed to be solvable. Numerical simulations
demonstrate that our approach outperforms all existing inner approximation
methods in most cases. Furthermore, the constructed regions are shown to cover
substantial fractions of the true voltage stability region. The paper will
later discuss a number of important applications of the proposed technique,
including fast screening for viable injection changes, constructing an
effective solvability index and rigorously certified loadability limits.Comment: 11 pages, 9 figs, published on Transactions on Smart Gri
Impact of Data Quality on Real-Time Locational Marginal Price
The problem of characterizing impacts of data quality on real-time locational
marginal price (LMP) is considered. Because the real-time LMP is computed from
the estimated network topology and system state, bad data that cause errors in
topology processing and state estimation affect real-time LMP. It is shown that
the power system state space is partitioned into price regions of convex
polytopes. Under different bad data models, the worst case impacts of bad data
on real-time LMP are analyzed. Numerical simulations are used to illustrate
worst case performance for IEEE-14 and IEEE-118 networks.Comment: Compared to the first version, the authors added in Section IV-E
details about the computational issue of the proposed analysi
Enabling Distributed Optimization in Large-Scale Power Systems
Distributed optimization for solving non-convex Optimal Power Flow (OPF)
problems in power systems has attracted tremendous attention in the last
decade. Most studies are based on the geographical decomposition of IEEE test
systems for verifying the feasibility of the proposed approaches. However, it
is not clear if one can extrapolate from these studies that those approaches
can be applied to very large-scale real-world systems. In this paper, we show,
for the first time, that distributed optimization can be effectively applied to
a large-scale real transmission network, namely, the Polish 2383-bus system for
which no pre-defined partitions exist, by using a recently developed
partitioning technique. More specifically, the problem solved is the AC OPF
problem with geographical decomposition of the network using the Alternating
Direction Method of Multipliers (ADMM) method in conjunction with the
partitioning technique. Through extensive experimental results and analytical
studies, we show that with the presented partitioning technique the convergence
performance of ADMM can be improved substantially, which enables the
application of distributed approaches on very large-scale systems
Grid-side Flexibility of Power Systems in Integrating Large-scale Renewable Generations: A Critical Review on Concepts, Formulations and Solution Approaches
Though considerable effort has been devoted to exploiting generation-side and
demand-side operational flexibility in order to cope with uncertain renewable
generations, grid-side operational flexibility has not been fully investigated.
In this review, we define grid-side flexibility as the ability of a power
network to deploy its flexibility resources to cope with the changes of power
system state, particularly due to variation of renewable generation. Starting
with a survey on the metrics of operational flexibility, we explain the
definition from both physical and mathematical point of views. Then conceptual
examples are presented to demonstrate the impacts of grid-side flexibility
graphically, providing a geometric interpretation for a better understanding of
the concepts. Afterwards the formulations and solution approaches in terms of
grid-side flexibility in power system operation and planning are reviewed,
based on which future research directions and challenges are outlined
A Polynomial-Time Method for Testing Admissibility of Uncertain Power Injections in Microgrids
We study the admissibility of power injections in single-phase microgrids,
where the electrical state is represented by complex nodal voltages and
controlled by nodal power injections. Assume that (i) there is an initial
electrical state that satisfies security constraints and the non-singularity of
load-flow Jacobian, and (ii) power injections reside in some uncertainty set.
We say that the uncertainty set is admissible for the initial electrical state
if any continuous trajectory of the electrical state is ensured to be secured
and non-singular as long as power injections remain in the uncertainty set. We
use the recently proposed V-control and show two new results. First, if a
complex nodal voltage set V is convex and every element in V is nonsingular,
then V is a domain of uniqueness. Second, we give sufficient conditions to
guarantee that every element in some power injection set S has a load-flow
solution in V, based on impossibility of obtaining load-flow solutions at the
boundary of V. By these results, we develop a framework for the
admissibility-test method; this framework is extensible to multi-phase grids.
Within the framework, we establish a polynomial-time method, using the
infeasibility check of convex optimizations. The method is evaluated
numerically.Comment: 12 pages, 6 figure
Cyber-Physical Systems Security: a Systematic Mapping Study
Cyber-physical systems are integrations of computation, networking, and
physical processes. Due to the tight cyber-physical coupling and to the
potentially disrupting consequences of failures, security here is one of the
primary concerns. Our systematic mapping study sheds some light on how security
is actually addressed when dealing with cyber-physical systems. The provided
systematic map of 118 selected studies is based on, for instance, application
fields, various system components, related algorithms and models, attacks
characteristics and defense strategies. It presents a powerful comparison
framework for existing and future research on this hot topic, important for
both industry and academia.Comment: arXiv admin note: text overlap with arXiv:1205.5073 by other author
Cellular-Base-Station Assisted Device-to-Device Communications in TV White Space
This paper presents a systematic approach to exploit TV white space (TVWS)
for device-to-device (D2D) communications with the aid of the existing cellular
infrastructure. The goal is to build a location-specific TVWS database, which
provides a look-up table service for any D2D link to determine its maximum
permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To
achieve this goal, the idea of mobile crowd sensing is firstly introduced to
collect active spectrum measurements from massive personal mobile devices.
Considering the incompleteness of crowd measurements, we formulate the problem
of unknown measurements recovery as a matrix completion problem and apply a
powerful fixed point continuation algorithm to reconstruct the unknown elements
from the known elements. By joint exploitation of the big spectrum data in its
vicinity, each cellular base station further implements a nonlinear support
vector machine algorithm to perform irregular coverage boundary detection of a
licensed DTV transmitter. With the knowledge of the detected coverage boundary,
an opportunistic spatial reuse algorithm is developed for each D2D link to
determine its MPEP. Simulation results show that the proposed approach can
successfully enable D2D communications in TVWS while satisfying the
interference constraint from the licensed DTV services. In addition, to our
best knowledge, this is the first try to explore and exploit TVWS inside the
DTV protection region resulted from the shadowing effect. Potential application
scenarios include communications between internet of vehicles in the
underground parking, D2D communications in hotspots such as subway, game
stadiums, and airports, etc.Comment: Accepted by IEEE Journal on Selected Areas in Communications, to
appear, 201
A Stochastic Sizing Approach for Sharing-based Energy Storage Applications
In order to foster renewable energy integration, improve power quality and
reliability, and reduce hydrocarbon emissions, there is a strong need to deploy
energy storage systems (ESSs), which can provide a control medium for peak hour
utility operations. ESSs are especially desired at the residential level, as
this sector has the most untapped demand response potential. However,
considering their high acquisition, operation, and maintenance costs,
individual ESS deployment is not economically viable. Hence, in this paper, we
propose a \emph{sharing-based} ESS architecture, in which the demand of each
customer is modeled stochastically and the aggregate demand is accommodated by
a combination of power drawn from the grid and the storage unit when the demand
exceeds grid capacity. Stochastic framework for analyzing the optimal size of
energy storage systems is provided. An analytical method is developed for a
group customers with \emph{single} type of appliances. Then, this framework is
extended to any network size with arbitrary number of customers and appliance
types. The analytical method provides a tractable solution to the ESS sizing
problem. Finally, a detailed cost-benefit analysis is provided, and the results
indicate that sharing-based ESSs are practical and significant savings in terms
of ESS size can be achieved.Comment: Accepted by IEEE Transactions on Smart Gri
Identification of Smart Jammers: Learning based Approaches Using Wavelet Representation
Smart jammer nodes can disrupt communication between a transmitter and a
receiver in a wireless network, and they leave traces that are undetectable to
classical jammer identification techniques, hidden in the time-frequency plane.
These traces cannot be effectively identified through the use of the classical
Fourier transform based time-frequency transformation (TFT) techniques with a
fixed resolution. Inspired by the adaptive resolution property provided by the
wavelet transforms, in this paper, we propose a jammer identification
methodology that includes a pre-processing step to obtain a multi-resolution
image, followed by the use of a classifier. Support vector machine (SVM) and
deep convolutional neural network (DCNN) architectures are investigated as
classifiers to automatically extract the features of the transformed signals
and to classify them. Three different jamming attacks are considered, the
barrage jamming that targets the complete transmission bandwidth, the
synchronization signal jamming attack that targets synchronization signals and
the reference signal jamming attack that targets the reference signals in an
LTE downlink transmission scenario. The performance of the proposed approach is
compared with the classical Fourier transform based TFT techniques,
demonstrating the efficacy of the proposed approach in the presence of smart
jammers
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