Analysis of Static Frequency Characteristics Based on Curve Fitting

2013 ◽  
Vol 339 ◽  
pp. 602-607
Author(s):  
Chun Li Song ◽  
Di Chen Liu ◽  
Jun Wu ◽  
Fei Fei Dong ◽  
Lian Tu ◽  
...  
Keyword(s):  
Power System ◽  
Least Squares ◽  
Curve Fitting ◽  
Least Squares Method ◽  
Frequency Characteristics ◽  
Frequency Deviation ◽  
New Method ◽  
Calculation Error ◽  
Restriction Point

Identification and calculation of static frequency characteristics is of great significance for power system to maintain its stability. In this paper, coefficient of static frequency characteristics is fitted by the least squares method. Frequency deviation restriction point under different capacitances is forecasted by the fitted trend of coefficient of static frequency characteristics. Moreover, the new method is simulated and its calculation error is also compared.

Calculation and Forecast on Coefficient of Static Frequency Characteristics Based on Curve Fitting

2012 ◽  
Vol 433-440 ◽  
pp. 2817-2822
Author(s):  
Fei Fei Dong ◽  
Di Chen Liu ◽  
Jun Wu ◽  
Lin Zhang ◽  
Xiao Ming Wang ◽  
...  
Keyword(s):  
Power System ◽  
Finite Number ◽  
Curve Fitting ◽  
Frequency Characteristics ◽  
Traditional Methods ◽  
K Value

It is commonly used to represent static frequency characteristics of a power system that coefficient K reflects the frequency excursion caused by load changes. As the parameter for dispatching branch to arrange measure or design equipment when frequency excursion occurs, K is of great importance to dispatching branch. However, the traditional methods can only get the range of coefficient K, but not the K corresponded to the specific disturbance. In this paper, curve fitting is used to obtain the trend of K-value by a finite number of measured points of coefficient of static frequency characteristics. And then, the predicted value is compared to the simulated result to judge whether it meets the permitted error. This is a breakthrough in the study of K-value.

Accurate power series for eigenvalues of spheroidal angle functions and their convergence radii

2011 ◽  
Vol 89 (11) ◽  
pp. 1083-1099
Author(s):  
Tam Do-Nhat
Keyword(s):  
Power Series ◽  
Least Squares ◽  
Complex Plane ◽  
Branch Point ◽  
Upper Bound ◽  
Least Squares Method ◽  
Radius Of Convergence ◽  
New Method ◽  
Recursion Relations

In this paper, the radius of convergence of the spheroidal power series associated with the eigenvalue is calculated without using the branch point approach. Studying the properties of the power series using the recursion relations among its coefficients in the new method offers some insights into the spheroidal power series and its associated eigenfunction. This study also used the least squares method to accurately compute the convergence radii to five or six significant digits. Within the circle of convergence in the complex plane of the parameter c = kF, where k is the wavenumber and F is the semifocal length of the spheroidal system, the extremely fast convergent spheroidal power series are computed with full precision. In addition, a formula for the magnitude of the upper bound of the error is obtained.

Curve-Fitting by the Method of Grouping

1952 ◽  
Vol 5 (2) ◽  
pp. 238
Author(s):  
PG Guest
Keyword(s):  
Least Squares ◽  
Curve Fitting ◽  
Least Squares Method ◽  
Normal Equations

A method of fitting polynomials is described in which the "normal" equations are obtained much more rapidly than the corresponding equations in the least-squares method. Efficiencies are found to be about 90 per cent. The method is illustrated by an example.

Research of Oilfield Production Forecast Based on Least Squares Fitting and Improved BP Neural Network

2013 ◽  
Vol 333-335 ◽  
pp. 1456-1460 ◽  
Author(s):  
Wen Bo Na ◽  
Zhi Wei Su ◽  
Ping Zhang
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Curve Fitting ◽  
Bp Neural Network ◽  
Least Squares Method ◽  
Forecast Error ◽  
Production Forecast ◽  
Least Squares Fitting ◽  
Weak Points ◽  
Lm Algorithm

A new method which is least squares fitting combined with improved BP neural network based on LM algorithm was put forward. In order to overcome the weak points that easy to fall into local minimum, slow convergence of traditional BP neural network, we use LM algorithm to improve it. Least-squares curve fitting can be used to reflect the overall trend of the data changes, so we adopted least squares method firstly to make curve fitting for sample data firstly. Then, we corrected the fitting error by the improved BP Neural Network which has the advantages that reflecting external factors. Finally, the fitted values and error correction values were added to get oilfield production forecast. The results show that the oilfield production forecast error is significantly lower than the single curve fitting, BP Neural Network or LMBP.

scholarly journals A Least Squares Estimation Method for the Linear Learning Model

1978 ◽  
Vol 15 (1) ◽  
pp. 145-153
Author(s):  
Berend Wierenga
Keyword(s):  
Least Squares ◽  
Simulation Study ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Estimation Method ◽  
Learning Model ◽  
New Method ◽  
Data Sets ◽  
Goodness Of Fit Test ◽  
Chi Square

The author presents a new method for estimating the parameters of the linear learning model. The procedure, essentially a least squares method, is easy to carry out and avoids certain difficulties of earlier estimation procedures. Applications to three different data sets are reported, as well as results from a goodness-of-fit test. A simulation study was carried out to validate the method. The outcomes are compared with those obtained from the minimum chi square estimation method. The results of the new method appear to be satisfactory.

The least-squares triangle: a new method for evaluation of solid phase compositions from solubility measurements

1981 ◽  
Vol 59 (21) ◽  
pp. 3076-3083 ◽  
Author(s):  
John W. Lorimer
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Solid Phase ◽  
Extrapolation Method ◽  
New Method ◽  
Initial Composition ◽  
Hypothesis Tests ◽  
Error Analyses ◽  
Solid Phases

A least-squares method is described for calculating compositions of equilibrium solid phases from data on solubilities and either wet residues or initial compositions for systems of three or more thermodynamic components. The method minimizes the squares of areas of triangles formed by the solubility, wet residue (or initial composition), and solid composition points. Full descriptions of error analyses and hypothesis tests are given, along with an illustrative example and detailed comparisons with the traditional extrapolation method.

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