Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Dehghani, Majid; Riahi-Madvar, Hossien; Hooshyaripor, Farhad; Mosavi, Amir; Shamshirband, Shahaboddin; Zavadskas, Edmundas Kazimieras; Chau, Kwok Wing

doi:10.3390/en12020289

Cited by 180 publications

(81 citation statements)

References 35 publications

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“…The ANFIS has shown promising results in a wide range of applications for developing prediction models [106][107][108]. However, optimization of the model parameters can dramatically improve the quality and accuracy of modeling [78].…”

Section: Model Developmentmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Section: Model Developmentmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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“…Optimization of the axial turbomachinery as a prevalent activity in the enhancement ofthe efficiency of the blade commonly is performed in recent year. Using multi-disciplinary design optimization (MDO) [1], [2], [13][14][15] which found in the ANSYS Workbench as a new conventional method and other turbomachinery analysis modules, axial turbomachinery blade concept can approach to its best performance and mechanical properties. Geometrical parameters consisting of stack line position (thickness and Sweep angle) can be considered as our design variable to maximize stage characteristics.…”

Section: Design Variable and Objective Functionmentioning

confidence: 99%

Aeromechanical Optimization of First Row Compressor Test Stand Blades

Ghalandari¹,

Ziamolki²,

Mosavi³

et al. 2019

Preprint

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In this study, an optimization of the first blade in new test rig presented. Blade tuning is conducted using 3D geometrical parameters. Sweep and dihedral play an essential role in this study. Compressor characteristics and blades vibrational behavior are the main objective of the evaluation. Here, the attachments are designed to isolate blade dynamics from Disk. So, the Vibrational behaviors of the one's blade are tuned based on the self-excited and forced vibration phenomenon. Using a semi analytical MATLAB code instability conditions are satisfied. The code takes advantages of whitehead and force response theory to predict classically and stall flutter speeds. Beside, Forced vibrations instability is controlled using a theory presented by Campbell. Aerodynamics of new blade geometry determined using multistage simulations Computational fluid dynamics (CFD) software. Numerical results show increasing performance near the surge line and working interval along with increasing mass flow.

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“…The power generation of renewable energy (like wind and solar) is usually affected by the weather situation [5][6][7]. Compared with the above energies, hydropower is seen as one of the most important environment-friendly and pollution-free energy sources since it has the advantages of low operating cost and multiple comprehensive benefits (like flood control, power generation, water supply, shipping and irrigation) [8][9][10]. Meanwhile, the working conditions of hydropower plants can rapidly change in a very short period of time [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

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In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

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Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Cited by 180 publications

References 35 publications

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Aeromechanical Optimization of First Row Compressor Test Stand Blades

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

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