Prediction of flicker produced by wind turbines

Bossanyi, Ervin; Saad‐Saoud, Z.; Jenkins, Nick

doi:10.1002/(sici)1099-1824(199809)1:1<35::aid-we11>3.0.co;2-j

Cited by 33 publications

(13 citation statements)

References 4 publications

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“…The Kalman filter method only needs observation data in a limited time, and the recursive algorithm is simple, but it can only be used for linear signal processes, and the noise signal must obey a Gaussian distribution. This method assumes that the statistical properties of noise, which it is not easy to estimate in practice, are known, and this is absolutely a flaw that should not be overlooked [4].…”

Section: ) Time Series Modelmentioning

confidence: 99%

Survey of Wind Power Output Power Forecasting Technology

Zou¹

2023

HSET

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Because of the randomness of wind energy and the non-linearity of power system, there are many dubious variables that should be noticed when forecasting the output power of the wind power. Physical method is often used in the medium-term forecasting, as its model does not require the historical data of the wind farm. The statistical method is simple and requires a small amount of data. It can be applied in those situations where data acquisition is difficult. The artificial intelligence model is suitable in the random or non — linear system as it does not rely on the accurate mode of the objective. The combined forecasting model maximizes favorable factors and minimizes unfavorable ones as contained in above-mentioned methods. This article gives out a brief summary and proposes some improvement measures against the main existing problems in prediction field.

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Section: ) Time Series Modelmentioning

confidence: 99%

Survey of Wind Power Output Power Forecasting Technology

Zou¹

2023

HSET

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“…Besides, Kalman filter‐based prediction improved by using a time‐step of 1 min to predict the wind speed. [ 35 ] Authors in ref. [36,37] also proposed ARMA model, which is a slightly improved version presented in ref.…”

Section: Impacts On System Operationmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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“…In view of all the above, it seems advisable to use the approximate expression (5) with some reservations. Some authors (Bossanyi et al, 1998) evaluate the error when using approximate methods for prediction of P st up to 20%, so it is recommended to use the exact method, according to equation (4).…”

Section: B Fast Voltage Variations (Flicker)mentioning

confidence: 99%