In Situ Parameter Estimation of Synchronous Machines Using Genetic Algorithm Method

Srinivasan, Gopalakrishnan Kalarikovilagam; Srinivasan, Hosimin Thilagar

doi:10.15598/aeee.v14i3.1707

Cited by 1 publication

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“…Figure 8 shows the initial residual representing the difference between the operational inductance frequency response giving by (26) and the experimental one. The error in the phase varies between −0.2513 and 1.589 degrees, while the error in the magnitude is within the range of 0-0.7165 dB.…”

Section: • Step 4: Fourth Pole-zero Pairmentioning

confidence: 99%

“…A major drawback of this method is that the result can converge to a local minimum depending on the initial values used and the choice of model. Other more robust alternatives are presented in some publications, such as the Maximum Likelihood Method [24], genetic algorithms [25,26], and particle swarm optimization [27], or stochastic fractal search algorithm [28]. However, their implementation is more complex and requires more computing power than the methods suggested in the IEEE 115 standard [5].…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Analytical Method for Synchronous Machine Model Design From Operational Inductance Ld(s)

Leguebedj¹,

Boukhetala²,

Tadjine³

2022

PIER B

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This paper presents an analytical method for the optimal estimation of time constants of synchronous machine from Stand Still Frequency Response Testing (SSFR). We show that the analytical method is advantageous over the conventional one since the latter is based on curve fitting representing the variation of the operational inductance as a function of the frequency and provides inaccurate and non-unique solutions. In fact, the analytical method applies the standard theory of linear systems to locate the values of poles and zeros in the frequency response and determines the optimal order of the equivalent circuit that can model the machine accurately. The proposed method is simple, practicable, and effective. However, it needs an optimisation process based on parameter differentiation to improve the values of time constants. Based on the measured data, realistic tests are given to show the advantages of the method.

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Section: • Step 4: Fourth Pole-zero Pairmentioning

confidence: 99%