Analysis for action degree and influence factors of harmonic interaction in multi-wind farms

2021 ◽  
Vol 13 (4) ◽  
pp. 045301
Author(s):  
Jinhui Shi ◽  
Wei Chen ◽  
Xiping Pei ◽  
Zhanhong Wei ◽  
Xuebo Sun
Keyword(s):  
Influence Factors ◽  
Wind Farms ◽  
Harmonic Interaction

Study for Wind Farm Power Quality Based on Wind Turbine’s Characteristic Parameters

2014 ◽  
Vol 986-987 ◽  
pp. 546-550
Author(s):  
Kuo Zhao ◽  
Yi Bin Zhang ◽  
Ya Guang Wang
Keyword(s):  
Wind Power ◽  
Power Quality ◽  
Quality Index ◽  
Wind Farm ◽  
Influence Factors ◽  
Wind Farms ◽  
Characteristic Parameters ◽  
Voltage Fluctuations ◽  
Power Harmonics ◽  
Main Influence

The grid-connected wind farms bring power quality problems to power system due to the volatility and random of wind power generation. Analysis and calculation are carried out in this paper for the main influence factors of wind power harmonics, voltage fluctuations and flicker, and the assessment methods of the above power quality problems. For a certain wind farm, the corresponding power quality index limits can be calculated. Assessment conclusions can also be given by comparing the calculated power quality index values and the index limits.

scholarly journals Public reactions towards wind energy instalments. Case study: Romania and the Netherlands

2016 ◽  
Vol 11 (3) ◽  
pp. 532-543 ◽  
Author(s):  
Maria Alexandra Nichifor
Keyword(s):  
Wind Energy ◽  
Wind Turbines ◽  
Influence Factors ◽  
Wind Farms ◽  
The European Union ◽  
Economic Competition ◽  
Financial Benefits ◽  
Visual Impact ◽  
Energy Strategies

Abstract Wind energy experienced an exponential development in the past two decades, forming a main source of energy today, but also a frequently encountered issue of debate due to the increased proximity of wind turbines to citizens’ residence, especially in the case of the Western part of the European Union. Although the benefits of renewable sources of energy represent a compulsory effort towards ensuring sustainable energy strategies for the future, due to the increased pressure of balancing climate change, limitation of traditional energy resources and economic competition, the expansion of wind parks has caused strong reactions of local communities in many regions leading to the reorganization of public exposure strategies of many companies in the field. This research intends to offer a sample of public perceptions of wind turbines depending on several influence factors, based on the answers of 64 Dutch citizens and 40 Romanian respondents. Through the implementation of the Delphi method based on questionnaires and interviews, an overview of perceptions towards placement of wind turbines in the two analyzed countries has been offered, providing significant answers to the influence factors of public reactions for or against wind turbines. The main results of the research revealed the importance of financial benefits in increasing public acceptance of wind farms, as well as several subjective factors, such as the visual impact of wind turbines and onshore or offshore placement, that contribute to a positive or negative behavior of citizens towards it.

scholarly journals A New Hybrid Model Based on an Intelligent Optimization Algorithm and a Data Denoising Method to Make Wind Speed Predication

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Ping Jiang ◽  
Qingli Dong
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Fossil Fuels ◽  
Back Propagation ◽  
Influence Factors ◽  
Physical Property ◽  
Wind Farms ◽  
Denoising Method ◽  
Artificial Fish Swarm Algorithm ◽  
Wind Speed Forecasting

To mitigate the increase of anxiety resulting from the depletion of fossil fuels and destruction of the ecosystem, wind power, as the most common renewable energy, is a flourishing industry. Thus, accurate wind speed forecasting is critical for the efficient function of wind farms. However, affected by complicated influence factors in meteorology and volatile physical property, wind speed forecasting is difficult and challenging. Based on previous research efforts, an intelligent hybrid model was proposed in this paper in an attempt to tackle this difficult task. First, wavelet transform was utilized to extract the main components of the original wind speed data while eliminating noise. To make better use of the back-propagation artificial neural network, the initial parameters of the network are substituted with optimized ones, which are achieved by using the artificial fish swarm algorithm (AFSA), and the final combination model is employed to conduct wind speed forecasting. A series of data are collected from four different observation sites to test the validity of the proposed model. Through comprehensive comparison with the traditional models, the experiment results clearly indicate that the proposed hybrid model outperforms the traditional single models.

Selection of a leading edge noise prediction method for PNoise

2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez
Keyword(s):  
Wind Turbine ◽  
Turbine Blades ◽  
Prediction Method ◽  
Leading Edge ◽  
Wind Farms ◽  
Broadband Noise ◽  
Turbulence Energy ◽  
Noise Prediction ◽  
Migration Routes ◽  
Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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