Ultra-short-term forecast of wind speed and wind power based on morphological high frequency filter and double similarity search algorithm

Hong, D. Y.; Ji, Tianyao; Li, M.S.; Wu, Q. H.

doi:10.1016/j.ijepes.2018.07.061

Cited by 57 publications

(13 citation statements)

References 29 publications

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“…To ensure the realisation of proposed algorithm, this paper makes the following reasonable assumptions: (i) the communication between WTs in the algorithm is bidirectional, the topology of the WF communication system is undirected; (ii) WTs can obtain the necessary information from the units around it by cable or wireless communication, and only some selected units (determined by location or some other factors) can obtain GDC from dispatching centre and the active power information of WF; (iii) WTs have power forecasting ability, can forecast ultra‐short‐term wind speed [26, 30, 31]. Here, we assume that the ultra‐short‐term power prediction results in this paper are completely accurate. …”

Section: Masca For the Realisation Of Apdmentioning

confidence: 99%

Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

Yao

Chen

et al. 2019

IET Renewable Power Generation

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With the increase of wind power penetration in the power system, wind farm (WF) needs to limit active power and accurately track the instructions from the dispatch centre. Since a WF has many distributed wind turbines (WTs), it is a crucial issue to reasonably distribute power reference values to WTs. In this study, a novel active power dispatch (APD) strategy based on dynamic grouping of WTs is proposed. This strategy considers the characteristics and operating conditions of WTs, which can smoothen the power reference values to WTs and reduce fluctuations of key parameters of WTs. Then, a distributed dispatch strategy based on multi-agent system consistency algorithm (MASCA) is applied for APD, which provides a dispatch strategy for WTs that does not require a centralised control centre. And the segmental virtual consistency algorithm is presented as an improvement of MASCA, which innovatively allows MASCA to support the grouping strategy for APD. Finally, the simulations show that the proposed strategy can enable WTs to obtain smoother reference power to track the dispatching instruction while reducing fluctuations of rotor speed and pitch angle, which is helpful to alleviate the fatigue of WTs. The dispatch strategy also shows good robustness when some communication is interrupted.

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Section: Masca For the Realisation Of Apdmentioning

confidence: 99%

Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

Yao

Chen

et al. 2019

IET Renewable Power Generation

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“…This volatile nature of WP limits the plant owners to actively participate in the short‐term energy market and maximize their operating economy just like any other conventional energy sources. Therefore, to mitigate this issue, accurate forecasts of wind speed are paramount to enable the WP plants compatible to participate in the short‐term energy market 12,13 …”

Section: Introductionmentioning

confidence: 99%

A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

Das

Malakar

2021

Int Trans Electr Energ Syst

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SUMMARY The ever‐increasing demand for green energy sources encourages wind farms to participate in the energy market through long‐term contracts. This paper discusses the existing frameworks of the wind energy market and presents the strategies to integrate wind energy into the grid through the valuable power purchase agreement (PPA). Critical observations reveal that the opportunities of the short‐term energy market remain unexplored for wind farms till date. To promote more investments and to enhance the operating economy, the wind energy sector must adapt to the sporadic nature of generation in the day‐ahead energy market. The selection of accurate PPA for wind farms is a potent area of research and has encouraged the authors to explore this area vividly. In view of this, the main objective of the article is to develop a day‐ahead sleeved PPA model and to evaluate its effectiveness in the Indian energy market. To correlate the proposed model with practicality aspects, seasonal wind speed scenarios are forecasted using the sARIMA model for a practical wind farm. The performance of the employed sARIMA model is evaluated through proper comparative assessment with other models. Thereafter to validate the importance of the developed PPA, a detailed comparative analysis is carried out with the existing long‐term PPA. Further, the potentiality of the proposed sleeved PPA is compared with its other counterparts. Result analysis confirms the usefulness of the proposed PPA model in maximizing the economy of the renewable energy market entities.

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“…On this basis, some scholars use the method of preprocessing the wind power time series to reduce the impact of the non-stationarity of the original time series on the prediction accuracy. Wind power data are preprocessed by filtering, decomposition and other methods, and then the processed time series is input to the prediction model to obtain a more accurate prediction result [16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Liu

Zhao

et al. 2020

Energies

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Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

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Ultra-short-term forecast of wind speed and wind power based on morphological high frequency filter and double similarity search algorithm

Cited by 57 publications

References 29 publications

Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

Active power dispatch strategy of the wind farm based on improved multi‐agent consistency algorithm

A day‐ahead sleeved power purchase agreement model for estimating the profit of wind farms in the Indian energy market

A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

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