Wind power in modern power systems

Chen, Zhe

doi:10.1007/s40565-013-0012-4

Cited by 33 publications

(17 citation statements)

References 18 publications

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“…Parzen window estimation method is a very effective non-parametric estimation method, which is also named as kernel density estimation [5]. Its general expression is:…”

Section: Parzen Window Estimation Methodsmentioning

confidence: 99%

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Wind Power Interval Forecasting Based on Confidence Interval Optimization

Zhang

Zang³

et al. 2018

Energies

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Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.

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“…Parzen window estimation method is a very effective non-parametric estimation method, which is also named as kernel density estimation [5]. Its general expression is:…”

Section: Parzen Window Estimation Methodsmentioning

confidence: 99%

“…Traditional forecast methods are mainly point forecasts with a certain degree of error. Interval forecasts can effectively describe the uncertainty of the wind power, so they have attracted more attention in recent years [4,5].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Interval Forecasting Based on Confidence Interval Optimization

Zhang

Zang³

et al. 2018

Energies

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“…The stability analysis of WECS with PMSG is another critical issue in designing a controller which extracts the maximum energy from the wind turbine [10]. The small-signal stability analysis of WECS with the two-mass model is introduced in [11].…”

Section: Introductionmentioning

confidence: 99%

Optimal Estimation and Tracking Control for Variable-speed Wind Turbine with PMSG

Bakhtiari

Nazarzadeh

2020

Journal of Modern Power Systems and Clean Energy

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Designing an optimal tracking controller and system observer is a nonlinear problem in the variable-speed wind energy conversion system (WECS). In this paper, an adjusted feedforward and feedback optimal controller with extended Kalman filter (EKF) is introduced to estimate and control the permanent magnet synchronous generator (PMSG) for the maximum power point tracking (MPPT) with several disturbances of the wind speed. An augmented model of wind turbine and PMSG is expanded, and then the parameters of the optimal controller and estimator are obtained. The dynamic stability of the closed-loop system with feedback-feedforward controller (FFC), EKF and speed controller are analyzed. To compare the dynamic performance of EKF and FFC with the conventional controllers, numerical results are demonstrated with the disturbances of the wind speed and faults in power grid. Index Terms-Permanent magnet synchronous generator (PMSG), maximum power point tracking (MPPT), extended Kalman filter (EKF), optimal tracking control.

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“…It is acknowledged widely that accurate WPF significantly reduces the risks of incorporating wind power in power supply systems [11]. Generally, the WPF results are in the deterministic form (i.e., point forecast).…”

Section: Introductionmentioning

confidence: 99%

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

et al. 2019

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Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

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Wind power in modern power systems

Cited by 33 publications

References 18 publications

Wind Power Interval Forecasting Based on Confidence Interval Optimization

Wind Power Interval Forecasting Based on Confidence Interval Optimization

Optimal Estimation and Tracking Control for Variable-speed Wind Turbine with PMSG

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

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