35,600 research outputs found
Ensemble Methods of Classification for Power Systems Security Assessment
One of the most promising approaches for complex technical systems analysis
employs ensemble methods of classification. Ensemble methods enable to build a
reliable decision rules for feature space classification in the presence of
many possible states of the system. In this paper, novel techniques based on
decision trees are used for evaluation of the reliability of the regime of
electric power systems. We proposed hybrid approach based on random forests
models and boosting models. Such techniques can be applied to predict the
interaction of increasing renewable power, storage devices and swiching of
smart loads from intelligent domestic appliances, heaters and air-conditioning
units and electric vehicles with grid for enhanced decision making. The
ensemble classification methods were tested on the modified 118-bus IEEE power
system showing that proposed technique can be employed to examine whether the
power system is secured under steady-state operating conditions.Comment: 6 pages, 4 figures, 4 tables. Submitted to PSS
Application of EOS-ELM with binary Jaya-based feature selection to real-time transient stability assessment using PMU data
Recent studies show that pattern-recognition-based transient stability
assessment (PRTSA) is a promising approach for predicting the transient
stability status of power systems. However, many of the current well-known
PRTSA methods suffer from excessive training time and complex tuning of
parameters, resulting in inefficiency for real-time implementation and lacking
the online model updating ability. In this paper, a novel PRTSA approach based
on an ensemble of OS-extreme learning machine (EOSELM) with binary Jaya
(BinJaya)-based feature selection is proposed with the use of phasor
measurement units (PMUs) data. After briefly describing the principles of
OS-ELM, an EOS-ELM-based PRTSA model is built to predict the post-fault
transient stability status of power systems in real time by integrating OS-ELM
and an online boosting algorithm, respectively, as a weak classifier and an
ensemble learning algorithm. Furthermore, a BinJaya-based feature selection
approach is put forward for selecting an optimal feature subset from the entire
feature space constituted by a group of system-level classification features
extracted from PMU data. The application results on the IEEE 39-bus system and
a real provincial system show that the proposal has superior computation speed
and prediction accuracy than other state-of-the-art sequential learning
algorithms. In addition, without sacrificing the classification performance,
the dimension of the input space has been reduced to about one-third of its
initial value.Comment: Accepted by IEEE Acces
A multifeature fusion approach for power system transient stability assessment using PMU data
Taking full advantage of synchrophasors provided by GPS-based wide-area
measurement system (WAMS), a novel VBpMKL-based transient stability assessment
(TSA) method through multifeature fusion is proposed in this paper. First, a
group of classification features reflecting the transient stability
characteristics of power systems are extracted from synchrophasors, and
according to the different stages of the disturbance process they are broken
into three nonoverlapped subsets; then a VBpMKL-based TSA model is built using
multifeature fusion through combining feature spaces corresponding to each
feature subset; and finally application of the proposed model to the IEEE
39-bus system and a real-world power system is demonstrated. The novelty of the
proposed approach is that it improves the classification accuracy and
reliability of TSA using multifeature fusion with synchrophasors. The
application results on the test systems verify the effectiveness of the
proposal.Comment: Accepted by Mathematical Problems in Engineerin
Delay Aware Intelligent Transient Stability Assessment System
Transient stability assessment is a critical tool for power system design and
operation. With the emerging advanced synchrophasor measurement techniques,
machine learning methods are playing an increasingly important role in power
system stability assessment. However, most existing research makes a strong
assumption that the measurement data transmission delay is negligible. In this
paper, we focus on investigating the influence of communication delay on
synchrophasor-based transient stability assessment. In particular, we develop a
delay aware intelligent system to address this issue. By utilizing an ensemble
of multiple long short-term memory networks, the proposed system can make early
assessments to achieve a much shorter response time by utilizing incomplete
system variable measurements. Compared with existing work, our system is able
to make accurate assessments with a significantly improved efficiency. We
perform numerous case studies to demonstrate the superiority of the proposed
intelligent system, in which accurate assessments can be developed with time
one third less than state-of-the-art methodologies. Moreover, the simulations
indicate that noise in the measurements has trivial impact on the assessment
performance, demonstrating the robustness of the proposed system
Automatic Emotion Recognition (AER) System based on Two-Level Ensemble of Lightweight Deep CNN Models
Emotions play a crucial role in human interaction, health care and security
investigations and monitoring. Automatic emotion recognition (AER) using
electroencephalogram (EEG) signals is an effective method for decoding the real
emotions, which are independent of body gestures, but it is a challenging
problem. Several automatic emotion recognition systems have been proposed,
which are based on traditional hand-engineered approaches and their
performances are very poor. Motivated by the outstanding performance of deep
learning (DL) in many recognition tasks, we introduce an AER system (Deep-AER)
based on EEG brain signals using DL. A DL model involves a large number of
learnable parameters, and its training needs a large dataset of EEG signals,
which is difficult to acquire for AER problem. To overcome this problem, we
proposed a lightweight pyramidal one-dimensional convolutional neural network
(LP-1D-CNN) model, which involves a small number of learnable parameters. Using
LP-1D-CNN, we build a two level ensemble model. In the first level of the
ensemble, each channel is scanned incrementally by LP-1D-CNN to generate
predictions, which are fused using majority vote. The second level of the
ensemble combines the predictions of all channels of an EEG signal using
majority vote for detecting the emotion state. We validated the effectiveness
and robustness of Deep-AER using DEAP, a benchmark dataset for emotion
recognition research. The results indicate that FRONT plays dominant role in
AER and over this region, Deep-AER achieved the accuracies of 98.43% and 97.65%
for two AER problems, i.e., high valence vs low valence (HV vs LV) and high
arousal vs low arousal (HA vs LA), respectively. The comparison reveals that
Deep-AER outperforms the state-of-the-art systems with large margin. The
Deep-AER system will be helpful in monitoring for health care and security
investigations
Is Machine Learning in Power Systems Vulnerable?
Recent advances in Machine Learning(ML) have led to its broad adoption in a
series of power system applications, ranging from meter data analytics,
renewable/load/price forecasting to grid security assessment. Although these
data-driven methods yield state-of-the-art performances in many tasks, the
robustness and security of applying such algorithms in modern power grids have
not been discussed. In this paper, we attempt to address the issues regarding
the security of ML applications in power systems. We first show that most of
the current ML algorithms proposed in power systems are vulnerable to
adversarial examples, which are maliciously crafted input data. We then adopt
and extend a simple yet efficient algorithm for finding subtle perturbations,
which could be used for generating adversaries for both categorical(e.g., user
load profile classification) and sequential applications(e.g., renewables
generation forecasting). Case studies on classification of power quality
disturbances and forecast of building loads demonstrate the vulnerabilities of
current ML algorithms in power networks under our adversarial designs. These
vulnerabilities call for design of robust and secure ML algorithms for real
world applications.Comment: Accepted to IEEE SmartGridComm201
Random Forest Based Approach for Concept Drift Handling
Concept drift has potential in smart grid analysis because the socio-economic
behaviour of consumers is not governed by the laws of physics. Likewise there
are also applications in wind power forecasting. In this paper we present
decision tree ensemble classification method based on the Random Forest
algorithm for concept drift. The weighted majority voting ensemble aggregation
rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method.
Base learner weight in our case is computed for each sample evaluation using
base learners accuracy and intrinsic proximity measure of Random Forest. Our
algorithm exploits both temporal weighting of samples and ensemble pruning as a
forgetting strategy. We present results of empirical comparison of our method
with original random forest with incorporated "replace-the-looser" forgetting
andother state-of-the-art concept-drfit classifiers like AWE2
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection
The adoption of large-scale iris recognition systems around the world has
brought to light the importance of detecting presentation attack images
(textured contact lenses and printouts). This work presents a new approach in
iris Presentation Attack Detection (PAD), by exploring combinations of
Convolutional Neural Networks (CNNs) and transformed input spaces through
binarized statistical image features (BSIF). Our method combines lightweight
CNNs to classify multiple BSIF views of the input image. Following explorations
on complementary input spaces leading to more discriminative features to detect
presentation attacks, we also propose an algorithm to select the best (and most
discriminative) predictors for the task at hand.An ensemble of predictors makes
use of their expected individual performances to aggregate their results into a
final prediction. Results show that this technique improves on the current
state of the art in iris PAD, outperforming the winner of LivDet-Iris2017
competition both for intra- and cross-dataset scenarios, and illustrating the
very difficult nature of the cross-dataset scenario.Comment: IEEE Transactions on Information Forensics and Security (Early
Access), 201
Malware Detection at the Microarchitecture Level using Machine Learning Techniques
Detection of malware cyber-attacks at the processor microarchitecture level
has recently emerged as a promising solution to enhance the security of
computer systems. Security mechanisms, such as hardware-based malware
detection, use machine learning algorithms to classify and detect malware with
the aid of Hardware Performance Counters (HPCs) information. The ML classifiers
are fed microarchitectural data extracted from Hardware Performance Counters
(HPCs), which contain behavioral data about a software program. These HPCs are
captured at run-time to model the program's behavior. Since the amount of HPCs
are limited per processor, many techniques employ feature reduction to reduce
the amount of HPCs down to the most essential attributes. Previous studies have
already used binary classification to implement their malware detection after
doing extensive feature reduction. This results in a simple identification of
software being either malware or benign. This research comprehensively analyzes
different hardware-based malware detectors by comparing different machine
learning algorithms' accuracy with binary and multi-class classification
models. Our experimental results indicate that when compared to complex machine
learning models (e. g. Neural Network and Logistic), light-weight J48 and JRip
algorithms perform better in detecting the malicious patterns even with the
introduction of multiple types of malware. Although their detection accuracy
slightly lowers, their robustness (Area Under the Curve) is still high enough
that they deliver a reasonable false positive rate.Comment: 28 pages, 7 figures, 4 table
Towards Malware Detection via CPU Power Consumption: Data Collection Design and Analytics (Extended Version)
This paper presents an experimental design and data analytics approach aimed
at power-based malware detection on general-purpose computers. Leveraging the
fact that malware executions must consume power, we explore the postulate that
malware can be accurately detected via power data analytics. Our experimental
design and implementation allow for programmatic collection of CPU power
profiles for fixed tasks during uninfected and infected states using five
different rootkits. To characterize the power consumption profiles, we use both
simple statistical and novel, sophisticated features. We test a one-class
anomaly detection ensemble (that baselines non-infected power profiles) and
several kernel-based SVM classifiers (that train on both uninfected and
infected profiles) in detecting previously unseen malware and clean profiles.
The anomaly detection system exhibits perfect detection when using all features
and tasks, with smaller false detection rate than the supervised classifiers.
The primary contribution is the proof of concept that baselining power of fixed
tasks can provide accurate detection of rootkits. Moreover, our treatment
presents engineering hurdles needed for experimentation and allows analysis of
each statistical feature individually. This work appears to be the first step
towards a viable power-based detection capability for general-purpose
computers, and presents next steps toward this goal.Comment: Published version appearing in IEEE TrustCom-18. This version
contains more details on mathematics and data collectio
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