scholarly journals Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 206
Author(s):  
Mansoor Khan ◽  
Muhammad Rashid Naeem ◽  
Essam A. Al-Ammar ◽  
Wonsuk Ko ◽  
Hamsakutty Vettikalladi ◽  
...  
Keyword(s):  
Wind Power ◽  
Transfer Learning ◽  
Large Scale ◽  
Wind Farms ◽  
Weather Conditions ◽  
Absolute Error ◽  
Climatic Conditions ◽  
Green Energy ◽  
Source Power ◽  
Power Forecasting

Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.

Study of Wind Power Forecasting Assessment Standard Based on Unit Commitment and Spinning Reserve Optimization Model

2012 ◽  
Vol 608-609 ◽  
pp. 547-552
Author(s):  
Chun Jie Gao ◽  
Peng Wang
Keyword(s):  
Wind Power ◽  
Power Output ◽  
Large Scale ◽  
North China ◽  
Wind Farms ◽  
Spinning Reserve ◽  
Wind Power Forecasting ◽  
Assessment Standard ◽  
North China Area ◽  
Power Forecasting

After large-scale wind power integrate into the system, there is a great impact for the system dispatching operation and the unit maintenance and repair of the wind power , so it's extremely necessary to forecast wind power output and assess its level of forecasting. This paper mainly focusing on the containing wind power system, studies the wind power output fluctuation in the demand for system reserve, and analyse the rationality of the wind power forecasting assessment standard in North China area wind power integration operation management implementing regulations by combining with the status of wind power in North China area, that is, whether the assessment mechanism can promote wind farms raising the forecasting level.

Comparative Exploration of Mini Vertical Axis Breeze-Driven Generator

2014 ◽  
Vol 670-671 ◽  
pp. 964-967
Author(s):  
Shu Hua Bai ◽  
Hai Dong Yang
Keyword(s):  
Natural Gas ◽  
Wind Turbine ◽  
Wind Power ◽  
North Africa ◽  
Large Scale ◽  
Solar Power ◽  
Vertical Axis ◽  
Green Energy ◽  
Energy Crisis ◽  
Fossil Energy

Nowadays, energy crisis is becoming increasingly serious. Coal, petroleum, natural gas and other fossil energy tend to be exhausted due to the crazy exploration. In recent decades, several long lasting local wars broke out in large scale in Mideast and North Africa because of the fighting for the limited petroleum. The reusable green energy in our life like enormous wind power, solar power, etc is to become the essential energy. This article is to conduct a comparative exploration of mini wind turbine, with the purpose of finding a good way to effectively deal with the energy crisis.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

scholarly journals Resonance Stability Analysis of Large-Scale Wind Power Bases with Type-IV Wind Generators

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5220
Author(s):  
Facai Xing ◽  
Zheng Xu ◽  
Zheren Zhang ◽  
Yangqing Dan ◽  
Yanwei Zhu
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farms ◽  
Negative Resistance ◽  
Resonance Mode ◽  
Resonance Structure ◽  
Power Bases ◽  
Impedance Model ◽  
Wind Generators ◽  
Type Iv

To guarantee the reliable and efficient development of wind power generation, oscillation problems in large-scale wind power bases with Type-IV generators are investigated from the view of resonance stability in this paper. Firstly, the transfer characteristics of disturbances in Type-IV wind generators are analyzed to establish their impedance model, based on the balance principle of frequency components. Subsequently, considering the dynamic characteristics of the transmission network and the interaction among several wind farms, the resonance structure of a practical wind power base is analyzed based on the s-domain nodal admittance matrix method. Furthermore, the unstable mechanism of the resonance mode is further illustrated by the negative-resistance effect theory. Finally, the established impedance model of the Type-IV wind generator and the resonance structure analysis results of the wind power bases are verified through the time-domain electro-magnetic transient simulation in PSCAD/EMTDC. Case studies indicate that there is a certain resonance instability risk in large-scale wind power bases in a frequency range of 1–100 Hz, and the unstable resonance mode is strongly related to the negative-resistance effect and the capacitive effect of Type-IV wind generators.

scholarly journals A Self-Regulation Strategy for the Power Fluctuation of the Islanded Voltage Source Converter (VSC) Station Delivering Large-Scale Wind Power

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 560
Author(s):  
Juanjuan Sun ◽  
Hui Wang ◽  
Xiaomin Zhu ◽  
Qian Pu
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Self Regulation ◽  
Wind Farms ◽  
The Self ◽  
Voltage Source ◽  
Time Variability ◽  
Frequency Regulation ◽  
Voltage Source Converter ◽  
Power Fluctuation

When the power source of a voltage source converter (VSC) station at the sending end solely depends on wind power generation, the station is operating in an islanding mode. In this case, the power fluctuation of the wind power will be entirely transmitted to the receiving-end grid. A self-regulation scheme of power fluctuation is proposed in this paper to solve this problem. Firstly, we investigated the short-time variability characteristic of the wind power in a multi-terminal direct-current (MTDC) project in China. Then we designed a virtual frequency (VF) control strategy at the VSC station based on the common constant voltage constant frequency (CVCF) control of VSC station. By cooperating with the primary frequency regulation (PFR) control at the wind farms, the self-regulation of active power pooling at the VSC station was realized. The control parameters of VF and PFR control were carefully settled through the steady-state analysis of the MTDC grid. The self-regulation effect had been demonstrated by a twenty-four-hour simulation. The results showed that the proposed scheme could effectively smoothen the power fluctuation.

A Wind-Thermal Hydropower Coordination Control Strategy Based on Multiple Constraints of Large-Scale Wind Power Integration

2013 ◽  
Vol 724-725 ◽  
pp. 463-468
Author(s):  
Jian Bo Wang ◽  
Wen Ying Liu ◽  
Wei Zheng ◽  
Chen Liang
Keyword(s):  
Wind Power ◽  
Control Strategy ◽  
Large Scale ◽  
Thermal Power ◽  
Wind Farms ◽  
System Stability ◽  
Maximum Output ◽  
Multiple Constraints ◽  
Operating Characteristics ◽  
Output Constraints

Due to the fluctuations and intermittency of wind power, large-scale wind farms integration will cause adverse impact on the safety and stability of the system,such as harmonic pollution, bad power quality, system stability destruction.On the basis of multiple constraints, including hydropower’s and thermal power’s operating characteristics, determination of reserve capacity considering wind power forecasting bias, climbing speed constraints, and maximum output constraints, this paper proposed a control strategy of joint coordination of wind, hydropower and thermal power, which suppressed the fluctuations of wind power effectively. At last, the article give a simulation to verify the feasibility of the control strategy to stabilize system frequency.

Study on a Service Mechanism that Wind/Photovoltaic/Energy Storage Power Station Help Wind Farms Tracking Generation Schedule Curve

2013 ◽  
Vol 860-863 ◽  
pp. 1909-1913
Author(s):  
Hai Xiang Xu ◽  
Peng Wang ◽  
Xiao Meng Ren
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Station ◽  
Forecasting Accuracy ◽  
Wind Power Forecasting ◽  
Assessment Standard ◽  
The Impact ◽  
Power Forecasting

At present, the technology of wind power forecasting isn‘t mature enough in china, so some grid-connected wind farms will be assessed when theirs power forecasting accuracy cant reach the assessment standard. In response to the situation, combined with the characteristics of WPSPS and wind farms, this paper designs a service mechanism that WPSPS help wind farms tracking generation schedule curve, namely, encouraging WPSPS to supply output compensation service for wind farm by market means to increase the accuracy of wind power forecasting. By this mechanism, not only WPSPS and wind farms will achieve win-win, but also the impact on the grid caused by fluctuations of wind powers output will reduce.

Mechanism Research about Impact of Large-Scale Wind Power Integration on Grid Loss

2014 ◽  
Vol 1070-1072 ◽  
pp. 200-203
Author(s):  
Ze Tian Wei ◽  
Wen Ying Liu ◽  
Fu Chao Liu ◽  
Jian Zong Zhuo
Keyword(s):  
Wind Power ◽  
Transmission Lines ◽  
Power Flow ◽  
Large Scale ◽  
Wind Farms ◽  
Active Power ◽  
Equivalent Model ◽  
Factors Affecting ◽  
Main Factors ◽  
Grid Loss

This paper firstly analyzes the mechanism of transmission line and transformer loss and illustrates the equivalent model and calculating method. Then creates a simple three-node model and discusses the main factors which affect the grid loss with adequate formula. At last, we draw a concise conclusion that there are several factors affecting grid loss. The main factors are the location of wind power access, the active power flow of transmission lines, the active power output of wind farms and the voltage level of wind power access.

Sign in / Sign up

Export Citation Format

Share Document