Rules Mining of Power System Transient Stability Assessment Based on Clustering Analysis and Rough Set Theory

2014 ◽  
Vol 521 ◽  
pp. 418-422 ◽  
Author(s):  
Yan Xu ◽  
Xin Chen
Keyword(s):  
Power System ◽  
Set Theory ◽  
Rough Set ◽  
Clustering Analysis ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Decision Table ◽  
Final Decision ◽  
Stability Assessment ◽  
Decision Attributes

Transient Stability Assessment (TSA) aims at assessing stability of power system operation state quickly. This paper introduces rough set theory and clustering analysis to assess power system transient stability. At first, the stability operation parameters and fault places are taken as feature attributes based on the trait of power system transient ability. K-means algorithm is used to make continuous attributes among feature attributes discrete. Then feature attributes and stability types are taken as conditional attributes and decision attributes respectively. Initial decision table is established. Finally, rough set theory is used to form final decision table and rules of TSA are obtained. The IEEE 9-Bus system is employed to demonstrate the validity of the proposed approach.

scholarly journals Power system transient stability assessment based on rough set and deep residual neural network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012012
Author(s):  
Jian Chai ◽  
Xihuai Wang ◽  
Jianmei Xiao
Keyword(s):  
Neural Network ◽  
Power System ◽  
Rough Set ◽  
Transient Stability ◽  
Rough Set Theory ◽  
Assessment Method ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Stability Assessment ◽  
Combined Application

Abstract Machine learning algorithms have been widely used in power system transient stability evaluation. The combined application of data analysis and evaluation and neural network provides a new direction for power system transient stability analysis. After the actual power grid is running, there is obviously an imbalance between stable samples and unstable samples. The current deep learning network realizes the power system transient stability assessment method with too many redundant attributes, and the characteristics will inevitably be lost during the data transmission process. This leads to serious problems with the tendency of the training of the data-driven transient stability assessment model. The rough set theory algorithm is introduced to reduce the redundant attributes of power system transient data sets, which simplifies the difficulty of data training. At the same time, as the neural network deepens, the deep residual neural network model has a higher accuracy rate and effectively avoids the “gradient explosion” and “gradient dispersion” problems. Compared with the traditional neural network, it has better Evaluate performance.

Decision systems in rough set theory: A set operatorial perspective

2019 ◽  
Vol 18 (01) ◽  
pp. 1950004 ◽  
Author(s):  
G. Chiaselotti ◽  
T. Gentile ◽  
F. Infusino
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Table ◽  
Decision System ◽  
Local Consistency ◽  
Algebraic Set ◽  
Decision Systems ◽  
Set Systems ◽  
Decision Attributes

In rough set theory (RST), the notion of decision table plays a fundamental role. In this paper, we develop a purely mathematical investigation of this notion to show that several basic aspects of RST can be of interest also for mathematicians who work with algebraic and discrete methods.In this abstract perspective, we call decision system a sextuple [Formula: see text] [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text] are non-empty sets whose elements are called, respectively, objects, condition attributes, values, [Formula: see text] is a (possibly empty) set whose elements are called decision attributes, [Formula: see text] and [Formula: see text] is a map.The basic tool of our analysis is the equivalence relation [Formula: see text] on [Formula: see text], depending on the choice of a condition attribute subset [Formula: see text] and defined as follows: [Formula: see text] We denote by [Formula: see text] the equivalence class of [Formula: see text] with respect to [Formula: see text]. We interpret the classical RST notions of consistency and inconsistency for a decision table in an abstract algebraic set operatorial perspective and, in such a context, we introduce and investigate a kind of local consistency in any decision system [Formula: see text]. More specifically, we fix [Formula: see text], [Formula: see text] and try to determine in what cases all objects [Formula: see text] satisfy the condition [Formula: see text]. Then, we build a formal general framework whose basic tools are two local consistency set operators and a global closure operator, the condition attribute set [Formula: see text]. This paper provides a detailed study of these set operators, of the induced set systems and of the most relevant links between them.

Application of Rough Set Theory to Rule‐type Knowledge Discovery from Field Inspection Data on Highway Bridges

2017 ◽  
Author(s):  
Ayaho Miyamoto
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Highway Bridges ◽  
Decision Table ◽  
Field Inspection ◽  
Rule Type ◽  
Inspection Data ◽  
Contradictory Data

This paper describes an acquisitive method of rule‐type knowledge from the field inspection data on highway bridges. The proposed method is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the rough set theory to the traditional decision table reduction method. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. Instead of automatically removing all apparently contradictory data cases, the proposed method determines whether the data really is contradictory and therefore must be removed or not. The method has been tested with real data on bridge members including girders and filled joints in bridges owned and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer program is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.

Rough Set Classification Algorithms Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3119-3122
Author(s):  
Yan Xue Dong ◽  
Fu Hai Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Texture Classification ◽  
Texture Feature ◽  
Decision Table ◽  
Classification Algorithms ◽  
Reduction Algorithm ◽  
Discrete Method ◽  
Knowledge Reduction

The basic theory of rough set is given and a method for texture classification is proposed. According to the GCLM theory, texture feature is extracted and generate 32 feature vectors to form a decision table, find a minimum set of rules for classification after attribute discretization and knowledge reduction, experimental results show that using rough set theory in texture classification, accompanied by appropriate discrete method and reduction algorithm can get better classification results

Measures for evaluating the decision performance of a decision table in rough set theory

2008 ◽  
Vol 178 (1) ◽  
pp. 181-202 ◽  
Author(s):  
Yuhua Qian ◽  
Jiye Liang ◽  
Deyu Li ◽  
Haiyun Zhang ◽  
Chuangyin Dang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Table ◽  
Decision Performance

Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection Using Rough Set Theory

1998 ◽  
Vol 2 (2) ◽  
pp. 54-61
Author(s):  
Youngwan Cho ◽  
◽  
Kichul Lee ◽  
Mignon Park
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Fuzzy System ◽  
Rough Set Theory ◽  
Fuzzy Rule ◽  
Membership Functions ◽  
Linguistic Information ◽  
Input And Output ◽  
Decision Attributes ◽  
Modeling Data

The rough set theory suggested by Pawlak represents the degree of consistency between conditions and decision attributes of data pairs that have no linguistic information. In this paper, by using this representation feature, we define a measure called the occupancy degree that represents the consistency degree of a premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information. We examine the validity of the proposed method by modeling data pairs randomly generated by a fuzzy system.

Fuzzy Attribute Reduction Based on Fuzzy Similarity

2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Core Content ◽  
Fuzzy Similarity

The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

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