A review of applications of artificial intelligent algorithms in wind farms

Wang, Yirui; Yu, Yang; Cao, Shuyang; Zhang, Xingyi; Gao, Shangce

doi:10.1007/s10462-019-09768-7

Cited by 98 publications

(38 citation statements)

References 387 publications

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“…The time series AR model proposed by Box and Jenkins is a linear parametric model which has been widely used in wind speed predictions [31,32]. The regular AR model can be expressed as…”

Section: Ar and Ar-kf Approachmentioning

confidence: 99%

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Zhao

Wei

et al. 2020

Energies

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The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

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“…The time series AR model proposed by Box and Jenkins is a linear parametric model which has been widely used in wind speed predictions [31,32]. The regular AR model can be expressed as…”

Section: Ar and Ar-kf Approachmentioning

confidence: 99%

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Zhao

Wei

et al. 2020

Energies

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“…They possess novel search mechanisms without precise mathematical calculation to find global optimal solutions. Thus, they can resolve several complicated and difficult problems, such as multi-objective optimization [4], [5], dynamic optimization [6], [7], classification and prediction [8], [9]. Their successful applications have verified their good effectiveness and efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang

Gao

et al. 2020

IEEE Access

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Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.

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“…More and more optimization algorithms have been proposed to solve complex optimization problems in recent years [1]- [3]. Most of them have complex dynamic systems including many variable parameters and some features such as rotation characteristics, high dimension, dynamical and hard to measure, and even difficult to make a mathematical model.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this paper can be summarized as follows: (1) We make first attempt to well organize SE, HS and CLS. Triple distinct search dynamics greatly improve the search ability of algorithm.…”

Section: Introductionmentioning

confidence: 99%

TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

Cai

Wang

et al. 2020

IEEE Access

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Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.

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A review of applications of artificial intelligent algorithms in wind farms

Cited by 98 publications

References 387 publications

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

A Gravitational Search Algorithm With Chaotic Neural Oscillators

TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

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