580 research outputs found
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule
Data Challenges and Data Analytics Solutions for Power Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
One-class classifiers based on entropic spanning graphs
One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The spanning graph is learned on the embedded input data and the
outcoming partition of vertices defines the classifier. The final partition is
derived by exploiting a criterion based on mutual information minimization.
Here, we compute the mutual information by using a convenient formulation
provided in terms of the -Jensen difference. Once training is
completed, in order to associate a confidence level with the classifier
decision, a graph-based fuzzy model is constructed. The fuzzification process
is based only on topological information of the vertices of the entropic
spanning graph. As such, the proposed one-class classifier is suitable also for
data characterized by complex geometric structures. We provide experiments on
well-known benchmarks containing both feature vectors and labeled graphs. In
addition, we apply the method to the protein solubility recognition problem by
considering several representations for the input samples. Experimental results
demonstrate the effectiveness and versatility of the proposed method with
respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification
Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN,
Vancouver, Canad
Data Mining Framework for Monitoring Attacks In Power Systems
Vast deployment of Wide Area Measurement Systems (WAMS) has facilitated in increased understanding and intelligent management of the current complex power systems. Phasor Measurement Units (PMU\u27s), being the integral part of WAMS transmit high quality system information to the control centers every second. With the North American Synchro Phasor Initiative (NAPSI), the number of PMUs deployed across the system has been growing rapidly. With this increase in the number of PMU units, the amount of data accumulated is also growing in a tremendous manner. This increase in the data necessitates the use of sophisticated data processing, data reduction, data analysis and data mining techniques. WAMS is also closely associated with the information and communication technologies that are capable of implementing intelligent protection and control actions in order to improve the reliability and efficiency of the existing power systems. Along with the myriad of advantages that these measurements systems, informational and communication technologies bring, they also lead to a close synergy between heterogeneous physical and cyber components which unlocked access points for easy cyber intrusions. This easy access has resulted in various cyber attacks on control equipment consequently increasing the vulnerability of the power systems.;This research proposes a data mining based methodology that is capable of identifying attacks in the system using the real time data. The proposed methodology employs an online clustering technique to monitor only limited number of measuring units (PMU\u27s) deployed across the system. Two different classification algorithms are implemented to detect the occurrence of attacks along with its location. This research also proposes a methodology to differentiate physical attacks with malicious data attacks and declare attack severity and criticality. The proposed methodology is implemented on IEEE 24 Bus reliability Test System using data generated for attacks at different locations, under different system topologies and operating conditions. Different cross validation studies are performed to determine all the user defined variables involved in data mining studies. The performance of the proposed methodology is completely analyzed and results are demonstrated. Finally the strengths and limitations of the proposed approach are discussed
Semi-supervised transfer learning methodology for fault detection and diagnosis in air-handling units
Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version
Data-Driven Methods for Managing Anomalies in Energy Time Series
With the progressing implementation of the smart grid, more and more smart meters record power or energy consumption and generation as time series. The increasing availability of these recorded energy time series enables the goal of the automated operation of smart grid applications such as load analysis, load forecasting, and load management. However, to perform well, these applications usually require clean data that describes the typical behavior of the underlying system well. Unfortunately, recorded energy time series are usually not clean but contain anomalies, i.e., patterns that deviate from what is considered normal. Since anomalies thus potentially contain data points or patterns that represent false or misleading information, they can be problematic for any analysis of this data performed by smart grid applications.
Therefore, the present thesis proposes data-driven methods for managing anomalies in energy time series. It introduces an anomaly management whose characteristics correspond to steps in a sequential pipeline, namely anomaly detection, anomaly compensation, and a subsequent application. Using forecasting as an exemplary subsequent application and real-world data with inserted synthetic and labeled anomalies, this thesis answers four research questions along that pipeline for managing anomalies in energy time series.
Based on the answers to these four research questions, the anomaly management presented in this thesis exhibits four characteristics. First, the presented anomaly management is guided by well-defined anomalies derived from real-world energy time series. These anomalies serve as a basis for generating synthetic anomalies in energy time series to promote the development of powerful anomaly detection methods. Second, the presented anomaly management applies an anomaly detection approach to energy time series that is capable of providing a high anomaly detection performance. Third, the presented anomaly management also compensates detected anomalies in energy time series realistically by considering the characteristics of the respective data. Fourth, the proposed anomaly management applies and evaluates general anomaly management strategies in view of the subsequent forecasting that uses this data. The comparison shows that managing anomalies well is essential, as the compensation strategy, which detects and compensates anomalies in the input data before applying a forecasting method, is the most beneficial strategy when the input data contains anomalies
A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF
The future evolution of technological systems dedicated to improve energy
efficiency will strongly depend on effective and reliable Energy Storage
Systems, as key components for Smart Grids, microgrids and electric mobility.
Besides possible improvements in chemical materials and cells design, the
Battery Management System is the most important electronic device that improves
the reliability of a battery pack. In fact, a precise State of Charge (SoC)
estimation allows the energy flows controller to exploit better the full
capacity of each cell. In this paper, we propose an alternative definition for
the SoC, explaining the rationales by a mechanical analogy. We introduce a
novel cell model, conceived as a series of three electric dipoles, together
with a procedure for parameters estimation relying only on voltage measures and
a given current profile. The three dipoles represent the quasi-stationary, the
dynamics and the istantaneous components of voltage measures. An Extended
Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we
propose a multi-cell EKF system based on a round-robin approach to allow the
same processing block to keep track of many cells at the same time. Performance
tests with a prototype battery pack composed by 18 A123 cells connected in
series show encouraging results.Comment: 8 page, 12 figures, 1 tabl
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
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