Estimation of location and coordinated tuning of PSS based on mean-variance mapping optimization

Rueda, José L.; Cepeda, Jaime C.; Erlich, I.

doi:10.1109/pesgm.2012.6345025

Cited by 14 publications

(13 citation statements)

References 25 publications

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“…MVMO has successfully been applied for the solution of different power system optimization problems. Furthermore, Numerical comparisons between MVMO and evolutionary algorithms have shown that MVMO exhibits a better performance, especially in terms of convergence speed [57][58][59]. In this case, the MVMO tool is used and the OF is defined as:…”

Section: Determination Of Ai Optimal Parametersmentioning

confidence: 99%

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

2020

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This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.

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Section: Determination Of Ai Optimal Parametersmentioning

confidence: 99%

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

2020

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“…Para más detalles sobre cómo ajustar los dos diferentes factores, el de forma y el de escala, revisar (Cepeda et al, 2012). El MVMO se ha aplicado satisfactoriamente para la solución de diferentes problemas de optimización en sistemas potencia tales como: el despacho de potencia reactiva óptima , la identificación de equivalentes dinámicos (Cepeda et al, 2012), ubicación óptima y sintonización coordinada de controladores de amortiguamiento (Rueda et al, 2012), el control óptimo en granjas eólicas e identificación de parámetros de modelo de demanda (González-Longatt et al,2012).…”

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Estimación de Parámetros del Suelo de dos Capas usando el Algoritmo de Optimización de Mapeo Media-Varianza

2019

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ResumenEn este artículo se analiza la aplicación de la técnica de optimización de mapeo media-varianza en el problema de estimación de los parámetros del modelo de suelo de dos capas horizontales. El problema consiste en determinar los parámetros del modelo del suelo a partir de las mediciones experimentales de resistividad aparente obtenidas con el método de Wenner, minimizando el error medio cuadrático entre valores de las curvas de resistividad experimental y teórica, calculadas con expresiones matemáticas y parámetros del suelo obtenidos por la técnica de optimización de mapeo media-varianza. Se realizaron varias pruebas con medidas de resistividad que corresponden a diferentes tipos de suelos, comparando los resultados obtenidos con los reportados en la literatura técnica. En conclusión, de acuerdo a los resultados, se encontró que el desempeño de la técnica de optimización de mapeo media-varianza es superior a las otras técnicas de optimización analizadas. Palabras clave: algoritmos metaheurísticos; resistividad del suelo; mapeo media-varianza; suelo de dos capasAbstract This paper presents an analysis of applying the optimization of mean-variance mapping technique in the problem of estimating the parameters of the soil model of two horizontal layers. This problem consists of determining the parameters of the soil model from experimental measurements of apparent resistivity obtained with Wenner method, minimizing the mean square error between values of the experimental and theoretical resistivity curves, which are calculated with mathematical expressions and soil parameters acquired through the optimization of mean-variance mapping technique. Several tests were carried out with resistivity measurements that correspond to different soil types, the results were contrasted with those reported in the technical literature and metaheuristics implemented. In conclusion, according to the results, the performance of the optimization of mean-variance mapping technique was found to be superior than other analyzed optimization techniques.

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“…creation of new candidate solutions) based on the mean and variance derived from the set of n-best solutions attained so far and saved in a continually updating archive. MVMO is also characterized for being a single-particle approach whose tradeoff between search diversification and intensification translates into fast progress rates with reduced risk of premature convergence as evidenced by preliminary tests on well-known benchmark functions and power system optimization problems [28][29][30].…”

Section: Mvmomentioning

confidence: 99%

Performance comparison of heuristic optimization methods for optimal dynamic transmission expansion planning

Gavela

Rueda

Vargas

et al. 2013

Int. Trans. Electr. Energ. Syst.

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SUMMARY Modern heuristic optimization algorithms have gained considerable attention in different research fields due to their conceptual simplicity and their ability to deal with different complex optimization problems. Motivated by promising results from the application of these emerging tools on other power system optimization problems, this paper presents a comparative study on the capabilities of three heuristic optimization algorithms for solving the problem of optimal dynamic centralized transmission expansion planning (CTEP). Among the studied algorithms are: Evolutionary particle swarm optimization, differential evolution, and a newly developed tool named mean–variance mapping optimization. Considering a predefined indicative generation expansion plan, which is usually contemplated in regulations for the power system planning in most Latin American countries, modified versions of the well‐known Garver's system and the IEEE‐118 bus multi‐area system are used to evaluate the performance of each algorithm for solving the dynamic CTEP with a medium‐term planning horizon of 10 years. Copyright © 2013 John Wiley & Sons, Ltd.

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Estimation of location and coordinated tuning of PSS based on mean-variance mapping optimization

Cited by 14 publications

References 25 publications

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

Estimación de Parámetros del Suelo de dos Capas usando el Algoritmo de Optimización de Mapeo Media-Varianza

Performance comparison of heuristic optimization methods for optimal dynamic transmission expansion planning

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