scholarly journals Integrated Transformer Health Estimation Methodology Based on Markov Chains and Evidential Reasoning

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Srdjan Milosavljevic ◽  
Aleksandar Janjic
Keyword(s):  
Markov Chains ◽  
Distribution System ◽  
Transition Probabilities ◽  
Diagnostic Technique ◽  
Real Data ◽  
Evidential Reasoning ◽  
Dempster Shafer Theory ◽  
The Status ◽  
Shafer Theory ◽  
Available Information

Due to the large number of power transformers (ETs) in the distribution system, there is a need for a relatively simple representation of the status of each unit in order to more easily determine where and how to allocate the budget for preventive and corrective maintenance. In recent years, the concept of the transformer health index (HI) as an integral part of resource management was adopted for the condition assessment and ranking of ETs. HI algorithms take different forms and can be determined based on a large number of specific parameters. However, the main problem in HI methodology or any modern diagnostic technique is the existence of regular measurements and inspections and accurate test results. The paper proposes a solution in the form of the upgraded HI and the novel methodology for ET ranking including the value of available information to describe ET current state. The confidence to the measurement results is calculated using evidential reasoning (ER) algorithm based on Dempster–Shafer theory. The contribution to the ER methodology is the calculation of the initial degrees of belief using Markov chains. The aging process of an ET and transition probabilities from state to state are modelled using the statistical data for the population of 300 ETs and 20 years monitoring data. The proposed methodology is tested on the real data for 110/35 kV transformer, and in the second case, compared to the sample of 30 110/x kV transformers with traditional HI calculation.

scholarly journals Power transformer health index estimation using evidential reasoning

2020 ◽  
Vol 33 (4) ◽  
pp. 571-581
Author(s):  
Srdjan Milosavljevic ◽  
Aleksandar Janjic
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Power Transformer ◽  
Real Data ◽  
Condition Assessment ◽  
Evidential Reasoning ◽  
Health Index ◽  
Power Distribution System ◽  
Index Value ◽  
Shafer Theory

Market-oriented power distribution system requires a well-planned budget with scheduled preventive and corrective maintenance during a replacement of units that are in an unsatisfactory condition. In recent years, the concept of the transformer health index as an integral part of resource management was adopted for the condition assessment and ranking of ETs. However, because of the lack of regular measurement and inspections, the confidence in health index value is greatly reduced. The paper proposes a novel methodology for the ET condition assessment and the lifetime increase through the establishment of priorities for control and maintenance. The solution is based on the upgraded health index, where the confidence to the measurement results is calculated using Evidential reasoning algorithm based on Dempster - Shafer theory. A novel, two - level hierarchical model of ET health index is proposed, with real weighting factors values. This way, the methodology for ET ranking includes the value of available information to describe ET current state. The proposed methodology is tested on real data of an installed ET and compared with the traditional health index calculation.

scholarly journals Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

Belief Function Approach to Evidential Reasoning in Causal Maps

2005 ◽  
pp. 109-141
Author(s):  
Rajendra P. Srivastava ◽  
Mari W. Buche ◽  
Tom L. Roberts
Keyword(s):  
Job Satisfaction ◽  
Information Technology ◽  
Belief Function ◽  
Evidential Reasoning ◽  
Belief Functions ◽  
Causal Maps ◽  
Dempster Shafer Theory ◽  
Evidential Reasoning Approach ◽  
Theory Of Belief Functions ◽  
Shafer Theory

The purpose of this chapter is to demonstrate the use of the evidential reasoning approach under the Dempster-Shafer (D-S) theory of belief functions to analyze revealed causal maps (RCM). The participants from information technology (IT) organizations provided the concepts to describe the target phenomenon of Job Satisfaction. They also identified the associations between the concepts. This chapter discusses the steps necessary to transform a causal map into an evidential diagram. The evidential diagram can then be analyzed using belief functions technique with survey data, thereby extending the research from a discovery and explanation stage to testing and prediction. An example is provided to demonstrate these steps. This chapter also provides the basics of Dempster-Shafer theory of belief functions and a step-by-step description of the propagation process of beliefs in tree-like evidential diagrams.

Vulnerability of networks of interacting Markov chains

2010 ◽  
Vol 368 (1918) ◽  
pp. 2205-2219 ◽  
Author(s):  
L. Kocarev ◽  
N. Zlatanov ◽  
D. Trajanov
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probabilities ◽  
Power Grid ◽  
Current Status ◽  
Topological Structures ◽  
Influence Model ◽  
The Status ◽  
The Eu

The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks—the network of infrastructures and the EU power grid—identifying the most vulnerable nodes of these networks.

scholarly journals Pipe burst diagnostics using evidence theory

2010 ◽  
Vol 13 (4) ◽  
pp. 596-608 ◽  
Author(s):  
Josef Bicik ◽  
Zoran Kapelan ◽  
Christos Makropoulos ◽  
Dragan A. Savić
Keyword(s):  
Distribution System ◽  
Water Distribution ◽  
Information Sources ◽  
Water Distribution System ◽  
Evidence Theory ◽  
Dempster Shafer Theory ◽  
Pipe Burst ◽  
The Individual ◽  
Shafer Theory ◽  
Theory Of Evidence

This paper presents a decision support methodology aimed at assisting Water Distribution System (WDS) operators in the timely location of pipe bursts. This will enable them to react more systematically and promptly. The information gathered from various data sources to help locate where a pipe burst might have occurred is frequently conflicting and imperfect. The methodology developed in this paper deals effectively with such information sources. The raw data collected in the field is first processed by means of several models, namely the pipe burst prediction model, the hydraulic model and the customer contacts model. The Dempster–Shafer Theory of Evidence is then used to combine the outputs of these models with the aim of increasing the certainty of determining the location of a pipe burst within a WDS. This new methodology has been applied to several semi-real case studies. The results obtained demonstrate that the method shows potential for locating the area of a pipe burst by capturing the varying credibility of the individual models based on their historical performance.

scholarly journals Some Aspects about Vagueness & Imprecision in Computer Network Fault-Tree Analysis

2010 ◽  
Vol 5 (4) ◽  
pp. 558 ◽  
Author(s):  
Daniela Elena Popescu ◽  
Madalina Lonea ◽  
Doina Zmaranda ◽  
Codruta Vancea ◽  
Cristian Tiurbe
Keyword(s):  
Computer Network ◽  
False Negative ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Upper And Lower Bounds ◽  
Dempster Shafer Theory ◽  
Computational Simplicity ◽  
Shafer Theory ◽  
Available Information ◽  
Network Fault

Based on the available information (eg.multiple functional faults or sensor errors give rise to similar alarm patterns or outcomes), some states in the behaviour of a network can not be distinguished from one another. So, the computer network’s fault tree reliability analysis frequently relies on imprecise or vague input data. The paper will use a Dempster-Shafer Theory to accommodate this vagueness and it will show how imprecision can give rise to false-negative, and false-positive inferences; there will be assigned upper and lower bounds for the probability on elements of the state space. After illustrating the computational simplicity of incorporating the Dempster-Shafer Theory probability assignments, we will apply them for analyzing the reliability of the network of our department.

Using Dempster-Shafer Theory in Data Mining

2011 ◽  
pp. 1166-1170
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Data Mining ◽  
Mathematical Theory ◽  
Evidential Reasoning ◽  
Belief Functions ◽  
Partial Knowledge ◽  
Dempster Shafer Theory ◽  
Upper And Lower Probabilities ◽  
Shafer Theory ◽  
Theory Of Evidence

The origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in his book “A Mathematical Theory of Evidence” added to Dempster’s work, including a more thorough explanation of belief functions. In summary, it is a methodology for evidential reasoning, manipulating uncertainty and capable of representing partial knowledge (Haenni & Lehmann, 2002; Kulasekere, Premaratne, Dewasurendra, Shyu, & Bauer, 2004; Scotney & McClean, 2003).

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