Abstract With the increasing complication, compaction, and automation of distribution network equipment, a small failure will cause an outbreak chain reaction and lead to operational risk in the power distribution system, even in the whole power system.
Knowledge reduction is one of the core contents in rough set theory. In this model, the rough set theory was used to simplify and optimize the operation risk assessment indexes of distribution network equipment and the evidence D-S theory was adopted to combine the optimal indexes.
Based on Borda Number Theory, Wang put forward a fuzzy comprehensive risk assessment model of power transformer [ 12 ]. Xiao adopted the BP neural network and genetic algorithm to study the optimization method of index weights calculation in the risk assessment of transformer, which provided more accurate evidence for transformer risk management [ 9 ].
Knowledge reduction is to delete irrelevant or unimportant knowledge redundancy to simplify judgment rules with the classification capacity of knowledge base unchanged.
The main purpose of rough set is to make use of knowledge reduction to get the decision or classification rules of the problem with the classification capacity unchanged. Based on the thought of information fusion technology, and combined with the reality of fault diagnosis, Yang developed a neural network evidence fusion fault diagnosis system [ 7 ].
The rest of the paper is arranged as follows. In summary, most studies on equipment risk apply the risk assessment theory to construct evaluation index system and carried out corresponding assessment, and transformer works were the main object to study the distribution network equipment.
At last, the equipment operational risk level was obtained from the basic probability distribution decision.
If is the value of record on attribute. The discernibility matrix proposed by a Polish mathematician, A. The knowledge attributes in the knowledge base is not equal, and even some of the knowledge is redundant.
Yang deeply analyzed the special equipment risk evaluation system and used the Analytic Hierarchy Process AHP to build a hierarchical mathematical model for the special equipment risk assessment [ 6 ].
Though these studies have solved the problem of equipment risk assessment to some extent, there are still some problems for further study. Rough Set Theory As a theory of data analysis and processing, rough set theory was founded by Polish scientist Z. The methods above lay a solid theoretical foundation for the study and practice of distribution network equipment risk assessment.
Skowron, is one of the efficient algorithms for information system reduction, and it can calculate the reduction easily [ 18 ]. In the basic theory of equipment risk assessment, a variety of mathematical methods such as risk matrix method, Monte Carlo method [ 3 ], failure mode and impact analysis [ 4 ], and fault tree analysis [ 5 ] are used to describe the uncertainty and adverse consequences of various risk events.
Part two introduced the rough set theory and D-S evidence theory firstly and built an operational risk assessment model of distribution network equipment. The most widely-used methods in equipment asset management are risk matrix method and Monte Carlo method.
For real-time and objective measurement of in-service transformer failure risks, Yu proposed a transformer risk assessment method based on fuzzy AHP and artificial neural network [ 11 ].
Introduction As the basis part of the power system, the distribution network has direct contact with the power user. For example, there are multiple indexes to describe the operational risk of a distribution network device, but they play different roles in the operational risk assessment.
Therefore, the operational risk assessment of distribution network equipment is a very important topic with high theoretical and practical significance.
It proved that the method proposed in this paper is feasible, efficient, and provides a new way to assess the distribution network equipment operational risk. Taking the transformer as an instance, part three makes a detailed description of equipment operational risk assessment methods based on rough set and D-S evidence theory and carries out an example to discuss the assessment results and their application.
Let be a decision table, whereis a condition attribute set, and is a decision attribute set.
It is a critical issue to exclude the indexes that are irrelevant or unimportant from the risk assessment index system and get the accurate risk state of the equipment.Rough Set Extensions for Feature Selection Aberystwyth University September PhD Thesis.
Abstract Rough set theory. Data reduction is an important contribution of rough set theory in data analysis, data mining and machine learning.
Thesis Statement. Anyone have a MATlab code for the rough set I used your rough set functions Does Matlab have a feature of rough set theory for attribute reduction?
Rough set is a new valid mathematical theory developed in recent years, which has the ability to deal with imprecise, uncertain, and vague information. It can find valid, and potentially useful. Using Rough Set Theory to Improve Content Based Image Retrieval System Maryam Shahabi Lotfabadi This thesis is presented for the Degree of.
In classical rough set theory, an approach based on fuzzy-rough sets, fuzzy rough feature Dr. Mike Gordon for giving me his thesis and inviting me to set.Download