Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Bilal, Boudy; Adjallah, Kondo H.; Sava, Alexandre; Yetilmezsoy, Kaan; Kıyan, Emel

doi:10.1016/j.energy.2021.122089

Cited by 11 publications

(8 citation statements)

References 31 publications

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“…The wind power output is determined by the stochastic wind speed [29][30][31], which is modeled using the widely used two-parameter Weibull distribution function. For each interval, the Cumulative distribution function (CDF) and Probability density function (PDF) of the wind speed is expressed as in the following two equations, respectively.…”

Section: Wind Power Modelingmentioning

confidence: 99%

“…In the early stage, HMCR has a large value, and the elite-learning operator has a high probability of being chosen. Thus, as shown in Equation (30), the elite solution is used to guide the population to move to the optimal region rapidly, and the convergence ability can be enhanced. Meanwhile, a differential vector is also added to increase the exploration ability and avoid premature convergence.…”

Section: Multiple Learningmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

et al. 2022

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Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (DEEDIWEv) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the DEEDIWEv model.

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Section: Wind Power Modelingmentioning

confidence: 99%

Section: Multiple Learningmentioning

confidence: 99%

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

et al. 2022

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“…[5][6][7][8][9][10][11][12] Fault pattern identification is derived from fault diagnosis, which aims to identify and label the raw fault data without a priori knowledge and to establish a targeted fault pattern database of the wind turbine performance process. 10,[13][14][15][16][17] Traditional fault pattern identification is mainly divided into statistical-based methods and artificial intelligence-based methods, 18,19 such as fuzzy classifiers, [20][21][22] random forests (RFs), 23,24 and clustering methods including k-nearest neighbor (KNN) and spectral clustering. [25][26][27][28] These traditional methods often include two steps of offline training and 1 online diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A novel self-learning framework for fault identification of wind turbine drive bearings

Yuan

Liang

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis–based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity–based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating.

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“…To decompose signals and pre-processing data, evaluate the upcoming total of wind turbines energy production, and optimize the fuzzy GMDH neural network parameters, literature [ 20 ] proposed a mutual prediction model based on empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. Author in [ 21 ] provides an innovative adaptive neuro-fuzzy inference method to estimate the yield power of a wind turbine based on wind power inputs such as wind speed, turbine rotational swiftness, and mechanical-to-electrical power converter temperature.…”

Section: Introductionmentioning

confidence: 99%

Active power control strategy for wind farms based on power prediction errors distribution considering regional data

et al. 2022

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One of the renewable energy resources, wind energy is widely used due to its wide distribution, large reserves, green and clean energy, and it is also an important part of large-scale grid integration. However, wind power has strong randomness, volatility, anti-peaking characteristics, and the problem of low wind power prediction accuracy, which brings serious challenges to the power system. Based on the difference of power prediction error and confidence interval between different new energy power stations, an optimal control strategy for active power of wind farms was proposed. Therefore, we focus on solving the problem of wind power forecasting and improving the accuracy of wind power prediction. Due to the prediction error of wind power generation, the power control cannot meet the control target. An optimal control strategy for active power of wind farms is proposed based on the difference in power prediction error and confidence interval between different new energy power stations. The strategy used historical data to evaluate the prediction error distribution and confidence interval of wind power. We use confidence interval constraints to create a wind power active optimization model that realize active power distribution and complementary prediction errors among wind farms with asymmetric error distribution. Combined with the actual data of a domestic (Cox’s Bazar, Bangladesh) wind power base, a simulation example is designed to verify the rationality and effectiveness of the proposed strategy.

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Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study

Cited by 11 publications

References 31 publications

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

A novel self-learning framework for fault identification of wind turbine drive bearings

Active power control strategy for wind farms based on power prediction errors distribution considering regional data

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