Load Indices Based Voltage Profile Assessment of Real Time Distribution System Using Generalized Regression Neural Network

2019 ◽  
Vol 12 (4) ◽  
pp. 222
Author(s):  
A. Shiny Pradeepa ◽  
Chidambaram Vaithilingam
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Distribution System ◽  
Time Distribution ◽  
Generalized Regression Neural Network ◽  
Voltage Profile ◽  
Generalized Regression ◽  
Load Indices

scholarly journals Voltage Profile Assessment in Distribution Substation using Generalized Regression Neural Network

2019 ◽  
Vol 8 (2) ◽  
pp. 3600-3604
Keyword(s):  
Neural Network ◽  
Power System ◽  
Iterative Methods ◽  
Distribution System ◽  
Power Flow ◽  
System Stability ◽  
Convergence Time ◽  
Generalized Regression Neural Network ◽  
Voltage Profile ◽  
Generalized Regression

Power system stability is one of the major factors for the reliable operation of electric utilities. Factors resulting power system instability are the sudden increase in load or insufficient reactive power support. Efficient Voltage regulation methods enable the system to operate in a stable operating condition. Many methods reported in the literature for voltage stability assessment of the power system such as optimization method, continuation power flow method, Indices based method and Artificial Intelligence based methods. Several iterative methods are used for the solution of load flow problems. The major disadvantages of iterative methods are larger iteration and increase in convergence time which depends on size of the power system. This paper proposes new method for voltage profile assessment on distribution system using Generalized Regression Neural Network. The Power System Analysis Toolbox (PSAT) is used for Distribution power flow solution. The proposed method is tested using 52 buses, distribution system of Tirunelveli, Tamil Nadu India. The technical feasibility of the proposed method is verified by comparing the results of proposed method and PSAT

The Implementation of Pipe Climbing Robot’s Real-Time Speech Control Based on the Generalized Regression Neural Network in Embedded System

2012 ◽  
Vol 220-223 ◽  
pp. 1986-1989 ◽  
Author(s):  
Bing Hui Fan ◽  
Peng Ji ◽  
Kai Zhou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Embedded System ◽  
Real Time ◽  
Extraction Methods ◽  
Generalized Regression Neural Network ◽  
Frame Shift ◽  
Speech Control ◽  
Frame Method ◽  
Generalized Regression

This paper describes a speech pre-processing and feature extraction methods and described the principle of generalized regression neural network (GRNN). In order to use neural networks for speech recognition, this article uses the variable frame-shift average frame method to average the characteristic parameters of the collected voice frame, and the feasibility of the variable frame-shift average frame method in neural network input parameters normalization is verified by experiments. In this paper, according to this method, the speech recognition based on the generalized regression neural network (GRNN) successfully ported to an embedded system, and realized the pipe climbing robot’s real-time speech control.

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

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