9,946 research outputs found
3D Power-map for Smart Grids---An Integration of High-dimensional Analysis and Visualization
Data with features of volume, velocity, variety, and veracity are challenging
traditional tools to extract useful analysis for decision-making. By
integrating high-dimensional analysis with visualization, this paper develops a
3D power-map animation as an effective solution to the challenge. An
architecture design, with detailed data processing procedure, is proposed to
realize the integration. Two of the most important components in the
architecture are presented: the Single-Ring Law for random matrices as solid
mathematic foundation, and the proposed statistical index MSR as
high-dimensional data for visualization. The whole procedure is easy in logic,
fast in speed, objective and even robust against bad data. Moreover, it is an
unsupervised machine learning mechanism directly oriented to the raw data
rather than logics or models based on simplifications and assumptions. A case
study validates the effectiveness and performance of the developed 3D power-map
in analysis extraction.Comment: 5 pages, 7 figures, submitted to PESGM 2015. arXiv admin note:
substantial text overlap with arXiv:1502.0006
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid
Power systems are developing very fast nowadays, both in size and in
complexity; this situation is a challenge for Early Event Detection (EED). This
paper proposes a data- driven unsupervised learning method to handle this
challenge. Specifically, the random matrix theories (RMTs) are introduced as
the statistical foundations for random matrix models (RMMs); based on the RMMs,
linear eigenvalue statistics (LESs) are defined via the test functions as the
system indicators. By comparing the values of the LES between the experimental
and the theoretical ones, the anomaly detection is conducted. Furthermore, we
develop 3D power-map to visualize the LES; it provides a robust auxiliary
decision-making mechanism to the operators. In this sense, the proposed method
conducts EED with a pure statistical procedure, requiring no knowledge of
system topologies, unit operation/control models, etc. The LES, as a key
ingredient during this procedure, is a high dimensional indictor derived
directly from raw data. As an unsupervised learning indicator, the LES is much
more sensitive than the low dimensional indictors obtained from supervised
learning. With the statistical procedure, the proposed method is universal and
fast; moreover, it is robust against traditional EED challenges (such as error
accumulations, spurious correlations, and even bad data in core area). Case
studies, with both simulated data and real ones, validate the proposed method.
To manage large-scale distributed systems, data fusion is mentioned as another
data processing ingredient.Comment: 12 pages, 11 figures, submitted to IEEE Transactions on Smart Gri
Spatio-Temporal Big Data Analysis for Smart Grids Based on Random Matrix Theory: A Comprehensive Study
A cornerstone of the smart grid is the advanced monitorability on its assets
and operations. Increasingly pervasive installation of the phasor measurement
units (PMUs) allows the so-called synchrophasor measurements to be taken
roughly 100 times faster than the legacy supervisory control and data
acquisition (SCADA) measurements, time-stamped using the global positioning
system (GPS) signals to capture the grid dynamics. On the other hand, the
availability of low-latency two-way communication networks will pave the way to
high-precision real-time grid state estimation and detection, remedial actions
upon network instability, and accurate risk analysis and post-event assessment
for failure prevention.
In this chapter, we firstly modelling spatio-temporal PMU data in large scale
grids as random matrix sequences. Secondly, some basic principles of random
matrix theory (RMT), such as asymptotic spectrum laws, transforms, convergence
rate and free probability, are introduced briefly in order to the better
understanding and application of RMT technologies. Lastly, the case studies
based on synthetic data and real data are developed to evaluate the performance
of the RMT-based schemes in different application scenarios (i.e., state
evaluation and situation awareness).Comment: Book chapter#23 for the book "Transportation and Power Grid in Smart
Cities: Communication Networks and Services". arXiv admin note: text overlap
with arXiv:1302.0885 by other author
Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices
Future power grids are fundamentally different from current ones, both in
size and in complexity; this trend imposes challenges for situation awareness
(SA) based on classical indicators, which are usually model-based and
deterministic. As an alternative, this paper proposes a statistical indicator
system based on linear eigenvalue statistics (LESs) of large random matrices:
1) from a data modeling viewpoint, we build, starting from power flows
equations, the random matrix models (RMMs) only using the real-time data flow
in a statistical manner; 2) for a data analysis that is fully driven from RMMs,
we put forward the high-dimensional indicators, called LESs that have some
unique statistical features such as Gaussian properties; and 3) we develop a
three-dimensional (3D) power-map to visualize the system, respectively, from a
high-dimensional viewpoint and a low-dimensional one. Therefore, a statistical
methodology of SA is employed; it conducts SA with a model-free and data-driven
procedure, requiring no knowledge of system topologies, units operation/control
models, causal relationship, etc. This methodology has numerous advantages,
such as sensitivity, universality, speed, and flexibility. In particular, its
robustness against bad data is highlighted, with potential advantages in cyber
security. The theory of big data based stability for on-line operations may
prove feasible along with this line of work, although this critical development
will be reported elsewhere.Comment: 8 pages, 8 figures, 3 table
A Random Matrix Theoretical Approach to Early Event Detection Using Experimental Data
In this paper, High-dimensional data analysis methods are proposed to deal
with random matrix which is composed by the real data from power network before
and after the fault. The mean spectral radius (MSR) of non-Hermitian random
matrices is defined as a statistic analytic for the fault detection. By
analyzing the characteristics of random matrices and observing the changes of
the spectral radius of random matrices, grid failure detection will be
achieved. This paper describes the basic mathematical theory of this big data
method, and the real-world data of a certain China power grid is used to verify
the methods.Comment: 4 pages, 6 figure
Internet of Things for Residential Areas: Toward Personalized Energy Management Using Big Data
Intelligent management of machines, particularly in a residence area, has
been of interest for many years. However, such system design has always been
limited to simple control of machines from a local area or remotely from the
Internet. In this report, for the first time, an intelligent system is
proposed, where not only provides intelligent control ability of machines to
user, but also utilizes big data and optimization techniques to provide
promotional offers to the user to optimize energy consumption of machines.
Since a high traffic communication is involved among the machines and the
optimization-big data core of system, the communication core of the proposed
system is designed based on cloud, where many challenging issues such as
spectrum assignment and resource management are involved. To deal with that,
the communication network in the home area network (HAN) is designed based on
the cognitive radio system, where a new spectrum assignment method based on the
ant colony optimization (ACO) algorithm is proposed to perform spectrum
assignment to the machines in the HAN. Performance evaluation of the proposed
spectrum assignment method shows its performance in fair spectrum assignment
among machines.Comment: Draft of technical report. Limited version under preparation for
submissio
A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model
Multi-event detection and recognition in real time is of challenge for a
modern grid as its feature is usually non-identifiable. Based on factor model,
this paper porposes a data-driven method as an alternative solution under the
framework of random matrix theory. This method maps the raw data into a
high-dimensional space with two parts: 1) the principal components (factors,
mapping event signals); and 2) time series residuals (bulk, mapping
white/non-Gaussian noises). The spatial information is extracted form factors,
and the termporal infromation from residuals. Taking both spatial-tempral
correlation into account, this method is able to reveal the multi-event: its
components and their respective details, e.g., occurring time. Case studies
based on the standard IEEE 118-bus system validate the proposed method.Comment: 7 pages, 2 figure
A Correlation Analysis Method for Power Systems Based on Random Matrix Theory
The operating status of power systems is influenced by growing varieties of
factors, resulting from the developing sizes and complexity of power systems;
in this situation, the modelbased methods need be revisited. A data-driven
method, as the novel alternative, on the other hand, is proposed in this paper:
it reveals the correlations between the factors and the system status through
statistical properties of data. An augmented matrix, as the data source, is the
key trick for this method; it is formulated by two parts: 1) status data as the
basic part, and 2) factor data as the augmented part. The random matrix theory
(RMT) is applied as the mathematical framework. The linear eigenvalue
statistics (LESs), such as the mean spectral radius (MSR), are defined to study
data correlations through large random matrices. Compared with model-based
methods, the proposed method is inspired by a pure statistical approach,
without a prior knowledge of operation and interaction mechanism models for
power systems and factors. In general, this method is direct in analysis,
robust against bad data, universal to various factors, and applicable for
real-time analysis. A case study, based on the standard IEEE 118-bus system,
validates the proposed method.Comment: 9 pages, 9 figures, Accepted by IEEE Trans on Smart Gri
Invisible Units Detection and Estimation Based on Random Matrix Theory
Invisible units mainly refer to small-scale units that are not monitored by,
and thus are not visible to utilities. Integration of these invisible units
into power systems does significantly affect the way in which a distribution
grid is planned and operated. This paper, based on random matrix theory (RMT),
proposes a statistical, data-driven framework to handle the massive grid data,
in contrast to its deterministic, model-based counterpart. Combining the
RMT-based data-mining framework with conventional techniques, some heuristics
are derived as the solution to the invisible units detection and estimation
task: linear eigenvalue statistic indicators (LESs) are suggested as the main
ingredients of the solution; according to the statistical properties of LESs,
the hypothesis testing is formulated to conduct change point detection in the
high-dimensional space. The proposed method is promising for anomaly detection
and pertinent to current distribution networks---it is capable of detecting
invisible power usage and fraudulent behavior while even being able to locate
the suspect's location. Case studies, using both simulated data and actual
data, validate the proposed method.Comment: 10 pages,Accepted by IEEE Transaction on Power System
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
The widespread popularity of smart meters enables an immense amount of
fine-grained electricity consumption data to be collected. Meanwhile, the
deregulation of the power industry, particularly on the delivery side, has
continuously been moving forward worldwide. How to employ massive smart meter
data to promote and enhance the efficiency and sustainability of the power grid
is a pressing issue. To date, substantial works have been conducted on smart
meter data analytics. To provide a comprehensive overview of the current
research and to identify challenges for future research, this paper conducts an
application-oriented review of smart meter data analytics. Following the three
stages of analytics, namely, descriptive, predictive and prescriptive
analytics, we identify the key application areas as load analysis, load
forecasting, and load management. We also review the techniques and
methodologies adopted or developed to address each application. In addition, we
also discuss some research trends, such as big data issues, novel machine
learning technologies, new business models, the transition of energy systems,
and data privacy and security.Comment: IEEE Transactions on Smart Grid, 201
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