The fuzzy regression analysis as a means of electric power losses evaluation in electrical networks

2002 ◽  
Author(s):  
V.Z. Manusov
Keyword(s):  
Regression Analysis ◽  
Electric Power ◽  
Fuzzy Regression ◽  
Electrical Networks ◽  
Power Losses ◽  
Fuzzy Regression Analysis

scholarly journals Application of fuzzy regression analysis method for determination of electric power losses in intrafactory power supply networks

2019 ◽  
Vol 11 (4) ◽  
pp. 325-331 ◽  
Author(s):  
E. I. Gracheva ◽  
O. V. Naumov
Keyword(s):  
Regression Analysis ◽  
Electric Power ◽  
Power Supply ◽  
Power Distribution ◽  
Distribution Networks ◽  
Fuzzy Regression ◽  
Power Losses ◽  
Initial Information ◽  
Fuzzy Regression Analysis ◽  
The Cost

One of the main objectives of the development of modern industry in Russia, along with an increase in the absolute volumes of electric power (EP) production, is to strengthen control over its more rational use. Saving EP and reducing the cost of its transmission along power distribution networks is of great importance for the country's energy sector. In terms of their physical nature, in terms of production, transmission and consumption, EP losses are no different from EP served to consumers. Therefore, the assessment of power losses in electrical networks is based on the same economic principles as the assessment of energy served to consumers. EP losses have a significant impact on the technical and economic parameters of the network, since the cost of losses is included in the estimated cost (reduced costs) and cost price (annual operating costs) of EP transmission. The cost component of losses in the cost of EP transmission has a large proportion. The article presents the results of research on the possibility of application of fuzzy regression analysis for problems of assessment and prediction of electric power losses in intrafactory networks. Initial information on the network is uncertain to some extent, which complicates application of traditional methods. The calculation is presented for conventional and fuzzy regression models, along with estimation of error of these models. The relevance of application of fuzzy regression analysis methods is determined by the difficulty of obtaining reliable information about the circuit and regime parameters of intrafactory networks, the probabilistic nature of change of the modes, as well as a whole complex of affecting factors, which are generally challenging for quantitative assessment. Advantages of application of fuzzy regression analysis consist in obtaining confidence intervals of required variables (value of electric power losses) for schemes of networks with uncertain initial information on their parameters, which is characteristic of intrafactory power supply systems, and enables to consider dynamics of their variation.

scholarly journals Application of Fuzzy Regression Analysis to the Evaluation of Physical Tests

1993 ◽  
Vol 5 (4) ◽  
pp. 791-799
Author(s):  
Yoshiki UEMURA ◽  
Masatoshi SAKAWA ◽  
Takeshi NAKAWADA
Keyword(s):  
Regression Analysis ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis ◽  
Physical Tests

Robust fuzzy regression analysis

2011 ◽  
Vol 181 (19) ◽  
pp. 4154-4174 ◽  
Author(s):  
Pierpaolo D’Urso ◽  
Riccardo Massari ◽  
Adriana Santoro
Keyword(s):  
Regression Analysis ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis

scholarly journals Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Pingping Gao ◽  
Yabin Gao
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Outlier Detection ◽  
Euclidean Distance ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Fuzzy Regression Analysis ◽  
Fuzzy Regression Model ◽  
Interval Type

This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.

scholarly journals Influence of resistance of contact units of switching devices on the electric power losses in shop networks of low tension

2019 ◽  
Vol 124 ◽  
pp. 02013 ◽  
Author(s):  
D. D. Micu ◽  
I. V. Ivshin ◽  
E. I. Gracheva ◽  
O. V. Naumov ◽  
A. N. Gorlov
Keyword(s):  
Electric Power ◽  
Low Voltage ◽  
Technical Condition ◽  
Electrical Networks ◽  
Power Losses ◽  
Switching Device ◽  
Low Tension ◽  
Switching Equipment ◽  
Voltage Switching ◽  
Switching Devices

This paper presents calculation of resistance of tightening contact joints of switching devices. It allows considering the technical condition of low-voltage switching equipment and to specify energy emitted in the switching device in the mode of electrical networks operation is presented in the article.

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