9,820 research outputs found
Line Outage Detection and Localization via Synchrophasor Measurement
Since transmission lines are crucial links in the power system, one line
outage event may bring about interruption or even cascading failure of the
power system. If a quick and accurate line outage detection and localization
can be achieved, the system operator can take necessary actions in time to
mitigate the negative impact. Therefore, the objective of this paper is to
study a method for line outage detection and localization via synchrophasor
measurements. The density of deployed phasor measurement units (PMUs) is
increasing recently, which greatly improves the visibility of the power grid.
Taking advantage of the high-resolution synchrophasor data, the proposed method
utilizes frequency measurement for line outage detection and power change for
localization. The procedure of the proposed method is given. Compared with
conventional methods, it does not require the pre-knowledge on the system.
Simulation study validates the effectiveness of the proposed method
Micro-Synchrophasors for Power Distribution Monitoring, a Technology Review
The smart grid revolution is creating a paradigm shift in distribution
networks that is marked by new, significant intermittencies and uncertainties
in power supply and demand. These developments include the dramatic increase in
the adoption of distributed energy resources (DER), electric vehicles, energy
storage, and controllable loads. This transformation imposes new challenges on
existing distribution infrastructure and system operations for stockholders,
engineers, operators and customers. Unfortunately, distribution networks
historically lag behind transmission networks in terms of observability,
measurement accuracy, and data granularity. The changes in the operation of the
electric grid dramatically increase the need for tools to monitor and manage
distribution networks in a fast, reliable and accurate fashion. This paper
describes the development process of a network of high-precision micro phasor
measurement units or uPMUs, beginning with an overview of the uPMU technology
that provides synchronous measurements of voltage phase angles, or
synchrophasors. Next, the uPMU network and communications infrastructure are
discussed, followed by an analysis of potential diagnostic and control
applications of uPMU data in the electric grid at the distribution level.Comment: 18 page
Fast Distribution Grid Line Outage Identification with PMU
The growing integration of distributed energy resources (DERs) in urban
distribution grids raises various reliability issues due to DER's uncertain and
complex behaviors. With a large-scale DER penetration, traditional outage
detection methods, which rely on customers making phone calls and smart meters'
"last gasp" signals, will have limited performance, because the renewable
generators can supply powers after line outages and many urban grids are mesh
so line outages do not affect power supply. To address these drawbacks, we
propose a data-driven outage monitoring approach based on the stochastic time
series analysis from micro phasor measurement unit (PMU). Specifically, we
prove via power flow analysis that the dependency of time-series voltage
measurements exhibits significant statistical changes after line outages. This
makes the theory on optimal change-point detection suitable to identify line
outages via PMUs with fast and accurate sampling. However, existing change
point detection methods require post-outage voltage distribution unknown in
distribution systems. Therefore, we design a maximum likelihood-based method to
directly learn the distribution parameters from PMU data. We prove that
the estimated parameters-based detection still achieves the optimal
performance, making it extremely useful for distribution grid outage
identifications. Simulation results show highly accurate outage identification
in eight distribution grids with 14 configurations with and without DERs using
PMU data.Comment: 9 page
VADER: Visualization and Analytics for Distributed Energy Resources
Enabling deep penetration of distributed energy resources (DERs) requires
comprehensive monitoring and control of the distribution network. Increasing
observability beyond the substation and extending it to the edge of the grid is
required to achieve this goal. The growing availability of data from
measurements from inverters, smart meters, EV chargers, smart thermostats and
other devices provides an opportunity to address this problem. Integration of
these new data poses many challenges since not all devices are connected to the
traditional supervisory control and data acquisition (SCADA) networks and can
be novel types of information, collected at various sampling rates and with
potentially missing values. Visualization and analytics for distributed energy
resources (VADER) system and workflow is introduced as an approach and platform
to fuse these different streams of data from utilities and third parties to
enable comprehensive situational awareness, including scenario analysis and
system state estimation. The system leverages modern large scale computing
platforms, machine learning and data analytics and can be used alongside
traditional advanced distribution management system (ADMS) systems to provide
improved insights for distribution system management in the presence of DERs
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Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
A significant portion of the literature on fault localization assumes (more or less explicitly) that there are sufficient reliable measurements to guarantee that the system is observable. While several heuristics exist to break the observability barrier, they mostly rely on recognizing spatio-temporal patterns, without giving insights on how the performance are tied with the system features and the sensor deployment. In this paper, we try to fill this gap and investigate the limitations and performance limits of fault localization using Phasor Measurement Units (PMUs), in the low measurements regime, i.e., when the system is unobservable with the measurements available. Our main contribution is to show how one can leverage the scarce measurements to localize different type of distribution line faults (three-phase, single-phase to ground,..) at the level of sub-graph, rather than with the resolution of a line. We show that the resolution we obtain is strongly tied with the graph clustering notion in network science
Low-Resolution Fault Localization Using Phasor Measurement Units with Community Detection
A significant portion of the literature on fault localization assumes (more
or less explicitly) that there are sufficient reliable measurements to
guarantee that the system is observable. While several heuristics exist to
break the observability barrier, they mostly rely on recognizing
spatio-temporal patterns, without giving insights on how the performance are
tied with the system features and the sensor deployment. In this paper, we try
to fill this gap and investigate the limitations and performance limits of
fault localization using Phasor Measurement Units (PMUs), in the low
measurements regime, i.e., when the system is unobservable with the
measurements available. Our main contribution is to show how one can leverage
the scarce measurements to localize different type of distribution line faults
(three-phase, single-phase to ground, ...) at the level of sub-graph, rather
than with the resolution of a line. We show that the resolution we obtain is
strongly tied with the graph clustering notion in network science.Comment: Accepted in IEEE SmartGridComm 2018 Conferenc
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics
In modern smart grids deployed with various advanced sensors, e.g., phasor
measurement units (PMUs), bad (anomalous) measurements are always inevitable in
practice. Considering the imperative need for filtering out potential bad data,
this paper develops a novel online bad PMU data detection (BPDD) approach for
regional phasor data concentrators (PDCs) by sufficiently exploring
spatial-temporal correlations. With no need for costly data labeling or
iterative learning, it performs model-free, label-free, and non-iterative BPDD
in power grids from a new data-driven perspective of spatial-temporal nearest
neighbor (STNN) discovery. Specifically, spatial-temporally correlated regional
measurements acquired by PMUs are first gathered as a spatial-temporal time
series (TS) profile. Afterwards, TS subsequences contaminated with bad PMU data
are identified by characterizing anomalous STNNs. To make the whole approach
competent in processing online streaming PMU data, an efficient strategy for
accelerating STNN discovery is carefully designed. Different from existing
data-driven BPDD solutions requiring either costly offline dataset
preparation/training or computationally intensive online optimization, it can
be implemented in a highly cost-effective way, thereby being more applicable
and scalable in practical contexts. Numerical test results on the Nordic test
system and the realistic China Southern Power Grid demonstrate the reliability,
efficiency and scalability of the proposed approach in practical online
monitoring
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Diverse fault types, fast re-closures, and complicated transient states after
a fault event make real-time fault location in power grids challenging.
Existing localization techniques in this area rely on simplistic assumptions,
such as static loads, or require much higher sampling rates or total
measurement availability. This paper proposes a faulted line localization
method based on a Convolutional Neural Network (CNN) classifier using bus
voltages. Unlike prior data-driven methods, the proposed classifier is based on
features with physical interpretations that improve the robustness of the
location performance. The accuracy of our CNN based localization tool is
demonstrably superior to other machine learning classifiers in the literature.
To further improve the location performance, a joint phasor measurement units
(PMU) placement strategy is proposed and validated against other methods. A
significant aspect of our methodology is that under very low observability (7%
of buses), the algorithm is still able to localize the faulted line to a small
neighborhood with high probability. The performance of our scheme is validated
through simulations of faults of various types in the IEEE 39-bus and 68-bus
power systems under varying uncertain conditions, system observability, and
measurement quality.Comment: 11 pages, 8 figure
Online Measurement-Based Estimation of Dynamic System State Matrix in Ambient Conditions
In this paper, a purely measurement-based method is proposed to estimate the
dynamic system state matrix by applying the regression theorem of the
multivariate Ornstein-Uhlenbeck process. The proposed method employs a
recursive algorithm to minimize the required computational effort, making it
applicable to the real-time environment. One main advantage of the proposed
method is model independence, i.e., it is independent of the network model and
the dynamic model of generators. Among various applications of the estimated
matrix, detecting and locating unexpected network topology change is
illustrated in details. Simulation studies have shown that the proposed
measurement-based method can provide an accurate and efficient estimation of
the dynamic system state matrix under the occurrence of unexpected topology
change. Besides, various implementation conditions are tested to show that the
proposed method can provide accurate approximation despite measurement noise,
missing PMUs, and the implementation of higher-order generator models with
control devices.Comment: 11 pages, 13 figure
A Survey of Data Fusion in Smart City Applications
The advancement of various research sectors such as Internet of Things (IoT),
Machine Learning, Data Mining, Big Data, and Communication Technology has shed
some light in transforming an urban city integrating the aforementioned
techniques to a commonly known term - Smart City. With the emergence of smart
city, plethora of data sources have been made available for wide variety of
applications. The common technique for handling multiple data sources is data
fusion, where it improves data output quality or extracts knowledge from the
raw data. In order to cater evergrowing highly complicated applications,
studies in smart city have to utilize data from various sources and evaluate
their performance based on multiple aspects. To this end, we introduce a
multi-perspectives classification of the data fusion to evaluate the smart city
applications. Moreover, we applied the proposed multi-perspectives
classification to evaluate selected applications in each domain of the smart
city. We conclude the paper by discussing potential future direction and
challenges of data fusion integration.Comment: Accepted and To be published in Elsevier Information Fusio
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