Wind turbine generation performance monitoring with Jaya algorithm

Jin, Rui; Wang, Long; Huang, Chao; Jiang, Shancheng

doi:10.1002/er.4382

Cited by 9 publications

(9 citation statements)

References 20 publications

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“…b) Requirement of a Large Amount of SCADA Data. The methods devised in [4,10,14,15] require a large amount of SCADA data from established wind farms. However, this may not suit specific applications.…”

Section: Limitations Of Previous Methodsmentioning

confidence: 99%

“…Recently, alternatives to evolutionary optimization proposed by [10,14] started to appear. For example, Jin et al [15] collect SCADA data from a wind farm in Jiangsu Province, China. They then employ Jaya algorithm as a means to estimate the parameters of the four-parameter logistic function.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When such condition is satisfied, the size of the trust region is too large and must be shrunk. Whether the satisfied inequality is Equation ( 14) or (15), Δ k must be updated from one iteration to the next. Refs.…”

Section: The Six-parameter Logistic Function (6ple)mentioning

confidence: 99%

“…Another option is to impose constraints on the parameters of the logistic function in such a way that the logistic function is guaranteed to be monotonic (which is what we achieve in this paper). b) Requirement of a Large Amount of SCADA Data . The methods devised in [4, 10, 14, 15] require a large amount of SCADA data from established wind farms. However, this may not suit specific applications.…”

Section: Introductionmentioning

confidence: 99%

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A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets

Aldaoudeyeh

Alzaareer

Harasis

et al. 2021

IET Renewable Power Gen

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The literature contains different methods for estimating logistic functions parameters to fit the power-speed characteristics of a wind turbine (WT). However, their disadvantages are: (1) they require a large amount of supervisory control and data acquisition data;(2) the parameter range needed for constrained optimization is not systematically determined; and/or (3) they do not guarantee monotonically increasing relationship between wind speed and predicted WT output power. This paper proposes a systematic approach to fitting the five-and the six-parameter logistic functions to power-speed data of WTs. The authors introduce new limits on the parameters of these functions, which guarantee their monotonicity. Most of these limits are determined analytically and are obtained from manufacturer datasheets. Afterwards, these limits are passed as optimization constraints for subspace trust region algorithm. The results show that the authors' method generally provides better accuracy with mean absolute percentage error values below 0.02 for the five-parameter logistic function and below 0.005 for the six-parameter logistic function. The authors present accurate fits for a group of WTs of different ratings (from 275 kW to 3000 kW) and 12 unique manufacturers, which proves the versatility of the authors' method.

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Section: Limitations Of Previous Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: The Six-parameter Logistic Function (6ple)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets

Aldaoudeyeh

Alzaareer

Harasis

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…To define the value for output power P rwind , linear interpolation calculation is introduced by the HOMER simulation software [ 13 , 14 ], as shown in the following equation:

…”

Section: Mathematical Modelmentioning

confidence: 99%

An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System

Zhu

Zhao

Wang

et al. 2020

Computational Intelligence and Neuroscience

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This study introduces a supercapacitor hybrid energy storage system in a wind-solar hybrid power generation system, which can remarkably increase the energy storage capacity and output power of the system. In the specific solution, this study combines the distributed power generation system and the hybrid energy storage system, while using the static reactive power compensation system and the conductance-fuzzy dual-mode control method to increase output power in stages. At the same time, the optimal configuration model of the wind-solar hybrid power generation system is established using MATLAB/Simulink software. The output power of the microgrid to the wind-photovoltaic hybrid power generation system is calculated by simulation, and the optimization process of each component of the system is simulated. This study mainly uses the static reactive power compensation system and the conductance-fuzzy dual-mode control method to optimize the wind-solar hybrid power generation system. Using MATLAB software simulation verifies the feasibility and rationality of the optimal configuration of the system.

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4E analysis of the horizontal axis wind turbine with LCA consideration for different climate conditions

Jahromi

Moosavian

Yaghoubirad

et al. 2022

Energy Science & Engineering

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Rising concerns about greenhouse gas emissions from fossil fuels have made it important to pay attention to renewable energy sources. In this regard, using wind turbines to use the potential of wind energy as a source of clean energy is being developed. This study developed a numerical model of a horizontal axis wind turbine in MATLAB software. A multicriteria analysis based on energy, exergy, economic, and environmental analysis has been performed to policymaking and evaluate the potential of wind energy systems. Six cities of Buenos Aires, Harare, Madrid, Melbourne, Shiraz, and Washington have also been analyzed as a case study to examine the impact of different climates on the result of these criteria. According to energy analysis, Melbourne is the best choice with Temperate Oceanic Climates and average energy efficiency of 17.1%. From an exergy point of view, Harare, which has Oceanic Subtropical Highland Climates, is the best choice, with an average exergy efficiency of 11.6%. In terms of the Levelized cost of energy, Melbourne is also known as the most economical city, with an energy cost of $0.14 per kWh. Also, based on the environmental aspect of the analysis, which represents the carbon footprint, Shiraz, with the Cold-Summer Mediterranean Climate and the lowest carbon emission per year, is the most optimal option.

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Wind turbine generation performance monitoring with Jaya algorithm

Cited by 9 publications

References 20 publications

A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets

A new method to fit logistic functions with wind turbines power curves using manufacturer datasheets

An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System

4E analysis of the horizontal axis wind turbine with LCA consideration for different climate conditions

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