Therefore, scientific assessment of power distribution equipment operation risk is significant to the security of power distribution system. Therefore, the operational risk assessment of distribution network equipment is a very important topic with high theoretical and practical significance.
The construction of smart grid leads to a wider application of new distribution network equipment such as transformers, SF6 circuit breakers, vacuum circuit breakers, and box-type substations. The rest of the paper is arranged as follows. So, applying the attribute reduction function of rough set and D-S evidence theory, this paper proposed an operational risk assessment model of distribution network equipment based on rough set and D-S evidence theory to further study the operational risk management of distribution network equipment.
Though these studies have solved the problem of equipment risk assessment to some extent, there are still some problems for further study. With the increasing complication, compaction, and automation of distribution network equipment, a small fault somewhere in the equipment may outbreak chain reaction, causing catastrophic damages to the distribution network system, even in the entire power system, namely, operational risk [ 12 ].
The knowledge attributes in the knowledge base is not equal, and even some of the knowledge is redundant. 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 ].
Part four gives the conclusion of this paper. 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 ].
The most widely-used methods in equipment asset management are risk matrix method and Monte Carlo method. Knowledge reduction is one of the core contents in rough set theory.
The methods above lay a solid theoretical foundation for the study and practice of distribution network equipment risk assessment.
Rough Set Theory As a theory of data analysis and processing, rough set theory was founded by Polish scientist Z. 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.
Currently, the rough set theory has been widely used in the field of information science, medicine, chemistry, machinery, and management science [ 14 — 17 ] and has become an important tool for knowledge discovery. 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.
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.
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.
It is a theoretical method to study the representation, learning, and induction of incomplete and uncertain knowledge and data [ 13 ] and has been widely studied and applied to the classification and knowledge acquisition of imprecise, uncertain, and incomplete information.
For the problem that the single assessment method cannot make full use of the various operational information of the secondary equipment in kV grid, Wang proposed a state assessment method for secondary equipment in kV grid based on information fusion technology [ 8 ].
At last, the equipment operational risk level was obtained from the basic probability distribution decision. With further study of equipment risk assessment, new methods like AHP and information fusion algorithm have been introduced.
If is the value of record on attribute. Skowron, is one of the efficient algorithms for information system reduction, and it can calculate the reduction easily [ 18 ]. In order to get the satisfactory assessment conclusions from the complete and incomplete information and improve the assessment level, an operational risk assessment model of distribution network equipment based on rough set and D-S evidence theory was built.
This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Knowledge reduction is to delete irrelevant or unimportant knowledge redundancy to simplify judgment rules with the classification capacity of knowledge base unchanged.
Let be a decision table, whereis a condition attribute set, and is a decision attribute set. 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 ].
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.
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. 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 ]. Taking the transformer as an example, this paper compared the assessment result obtained from the method proposed in this paper with that from the ordinary Rogers ratio method and discussed the application of the proposed method.
The discernibility matrix proposed by a Polish mathematician, A. Introduction As the basis part of the power system, the distribution network has direct contact with the power user.Dimensionality Reduction: Rough Set Based Feature Rough set theory has been used as such a dataset pre processor with much success, but current methods are.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): A rapid growth of interest in rough set theory  and its. Data reduction is an important contribution of rough set theory in data analysis, data mining and machine learning.
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?
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. 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.Download