Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

Carneiro, Tatiane C.; Melo, Sofia P.; Carvalho, Paulo César Marques de; Braga, Arthur P. S.

doi:10.1016/j.renene.2015.08.060

Cited by 88 publications

(36 citation statements)

References 13 publications

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“…There are many different numerical methods to estimate the wind potential of a particular site. Over the past few years, many researchers have tried different techniques, but from the results of previous studies it has been clear that Weibull and Rayleigh distribution models are the most suitable for the estimation of wind potential [11][12][13]. Hennessey [14] stated that along with providing high accuracy for analyzing the wind speed distribution, Weibull model can also easily estimate mean and standard deviation of the total wind power density.…”

Section: Wind Data Collectionmentioning

confidence: 99%

Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data

2017

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Abstract:The present study deals with wind energy analysis and the selection of an optimum type of wind turbine in terms of the feasibility of installing wind power system at three locations in South Korea: Deokjeok-do, Baengnyeong-do and Seo-San. The wind data measurements were conducted during 2005-2015 at Deokjeok-do, 2001 at Baengnyeong-do and 1997 at Seo-San. In the first part of this paper wind conditions, like mean wind speed, wind rose diagrams and Weibull shape and scale parameters are presented, so that the wind potential of all the locations could be assessed. It was found that the prevailing wind directions at all locations was either southeast or southwest in which the latter one being more dominant. After analyzing the wind conditions, 50-year and 1-year extreme wind speeds (EWS) were estimated using the graphical method of Gumbel distribution. Finally, according to the wind conditions at each site and international electro-technical commission (IEC) guidelines, a set of five different wind turbines best suited for each location were shortlisted. Each wind turbine was evaluated on the basis of technical parameters like monthly energy production, annual energy production (AEP) and capacity factors (CF). Similarly, economical parameters including net present value (NPV), internal rate of return (IRR), payback period (PBP) and levelized cost of electricity (LCOE) were considered. The analysis shows that a Doosan model WinDS134/3000 wind turbine is the most suitable for Deokjeok-do and Baengnyeong-do, whereas a Hanjin model HJWT 87/2000 is the most suitable wind turbine for Seo-San. Economic sensitivity analysis is also included and discussed in detail to analyze the impact on economics of wind power by varying turbine's hub height.

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Section: Wind Data Collectionmentioning

confidence: 99%

Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data

2017

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“…In the proposed method, a new mutation method was performed to improve the global searching ability and restrained the premature convergence to local minima to achieve higher accuracy in electrical demand forecasting. Carneiro et al [34] applied PSO to estimate the Weibull parameters for wind speed, and PSO was demonstrated to be a valuable technique for characterizing the particular wind conditions. Bahrami et al [35] used PSO to enhance the generation coefficient of the grey model, which played an effective role in improving the accuracy of short-term electric load forecasting.…”

Section: Intelligent Forecasting Methodsmentioning

confidence: 99%

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Liu

2016

Energies

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Abstract:Big data mining, analysis, and forecasting play vital roles in modern economic and industrial fields, especially in the energy system. Inaccurate forecasting may cause wastes of scarce energy or electricity shortages. However, forecasting in the energy system has proven to be a challenging task due to various unstable factors, such as high fluctuations, autocorrelation and stochastic volatility. To forecast time series data by using hybrid models is a feasible alternative of conventional single forecasting modelling approaches. This paper develops a group of hybrid models to solve the problems above by eliminating the noise in the original data sequence and optimizing the parameters in a back propagation neural network. One of contributions of this paper is to integrate the existing algorithms and models, which jointly show advances over the present state of the art. The results of comparative studies demonstrate that the hybrid models proposed not only satisfactorily approximate the actual value but also can be an effective tool in the planning and dispatching of smart grids.

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“…Carneiro et al . () use PSO to find optimal parameters in the modelling of wind‐speed frequency distribution to evaluate the wind energy potential in the region of interest. Wang et al .…”

Section: Introductionmentioning

confidence: 99%

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

Wilson

Gross

2019

Geophysical Prospecting

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The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater.

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Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region

Cited by 88 publications

References 13 publications

Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data

Techno-Economic Assessment of Wind Energy Potential at Three Locations in South Korea Using Long-Term Measured Wind Data

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

Reflection‐constrained 2D and 3D non‐hyperbolic moveout analysis using particle swarm optimization

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