Using a genetic algorithm for parameter identification of transformer R-L-C-M model

Rashtchi, Vahid; Rahimpour, Ebrahim; Rezapour, Esmaeil Mashhadi

doi:10.1007/s00202-005-0303-5

Cited by 67 publications

(25 citation statements)

References 10 publications

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“…The CPU times presented in Table I have been obtained by using an Intel Core i5-2430M processor, corresponding to the evaluation of the ability of any individual using the model without auxiliary circuit. The estimation procedure described in the previous chapter has been tested using a high-voltage winding of a power transformer with the same physical characteristics presented in [22], [32], obtaining the results in Table II and Figs. 8 and 9.…”

Section: Evaluation and Results Using A Power Transformer Windingmentioning

confidence: 99%

New global parameter estimation method for a transformer detailed model

Gómez

Mombello

2014

2014 IEEE PES Transmission &Amp; Distribution Conference and Exposition - Latin America (PES T&D-La)

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The RLCM parameters of the ladder network detailed model of a transformer winding are obtained using a new estimation method based on frequency response measurements on transformer terminals. The estimation problem is reformulated from electromagnetic formulations used in the model evaluation algorithm. A multi-objective global search for solutions is performed by means of a genetic algorithm in a space of reduced and fixed dimension. The coupling characteristic is intrinsic to the model, thereby avoiding inductive inequality constraints. The proposed method is characterized by its simplicity, high physical cohesion, good conditioning and computational efficiency. Keywords-Parameter estimation; RLCM model for transformer windings; frequency response analysis (FRA); multiobjective optimization; genetic algorithms; filament method; computational efficiencyI.

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Section: Evaluation and Results Using A Power Transformer Windingmentioning

confidence: 99%

New global parameter estimation method for a transformer detailed model

Gómez

Mombello

2014

2014 IEEE PES Transmission &Amp; Distribution Conference and Exposition - Latin America (PES T&D-La)

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“…Compared with more recently developed particle swarm optimization (PSO), GA has a better chance of finding a more qualified solution, since the mutation operation can make the population cluster around several "good" solutions instead of one "good" solution [17]. Moreover it has been demonstrated that GA is robust in the parameter identification problem and can achieve good results [18,19]. GA processes are well explained elsewhere [20], and we present the identification process of GA in Figure 3.…”

Section: Parameter Identificationmentioning

confidence: 99%

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Liang

et al. 2018

Energies

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Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.

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“…Assuming the temperature of coal is constant, so the heat brought into the coal mill by the raw coal is proportional to the mass flow, therefore Q coal is denoted as K 3 W C ; when the temperature of primary air is constant, the heat brought into coal mill by inlet air is proportional to the mass flow of primary air, while there is a linear relationship between the heat capacity and the temperature of primary air, therefore Q air is denoted as [K 1 T in þ K 2 ]W air ; the heat generated by the coal grinding is proportional to the coal mill power, therefore K 9 P is selected, where P is the total amount of current consumed by the coal mill, which is defined in Eq. (12). Similarly, the heat brought out by the coal mill through the mixture of air and pulverized coal Q P.F.…”

Section: Nonlinear Model Of Coal Millmentioning

confidence: 99%