Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
This paper illustrates how the penetration of electromagnetic waves in lossy media\nstrongly depends on the waveform and not only on the media involved. In particular, the so-called\ninhomogeneous plane waves are compared against homogeneous plane waves illustrating how the\nfirst ones can generate deep penetration effects. Moreover, the paper provides examples showing how\nsuch waves may be practically generated. The approach taken here is analytical and it concentrates\non the deep penetration conditions obtained by means of incident inhomogeneous plane waves\nincoming from a lossless medium and impinging on a lossy medium. Both conditions and constraints\nthat the waveforms need to possess to achieve deep penetration are analysed. Some results are\nfinally validated through numerical computations. The theory presented here is of interest in view of\na practical implementation of the deep penetration effect....
The great proliferation of wind power generation has brought about great challenges to\npower system operations. To mitigate the ramifications of wind power uncertainty on operational\nreliability, predictive scheduling of generation and transmission resources is required in the day-ahead\nand real-time markets. In this regard, this paper presents a risk-averse stochastic unit commitment\nmodel that incorporates transmission reserves to flexibly manage uncertainty-induced congestion.\nIn this two-settlement market framework, the key statistical features of line flows are extracted\nusing a high-dimensional probabilistic collocation method in the real-time dispatch, for which\nthe spatial correlation between wind farms is also considered. These features are then used to\nquantify transmission reserve requirements in the transmission constraints at the day-ahead stage.\nComparative studies on the IEEE 57-bus system demonstrate that the proposed method outperforms\nthe conventional unit commitment (UC) to enhance the system reliability with wind power integration\nwhile leading to more cost-effective operations....
In recent years, global wind power has developed rapidly to alleviate environmental\npollution and energy crisis. Due to the potential of enhancing the\nstability of power system through the application of wind power participating\nin power grid frequency regulation, the large-scale integration of wind power\nhas become a hot issue for academic research in recent years. This paper classifies\nthe frequency control problems of wind power integration and summarizes\nthe research of power system frequency regulation strategy with high\nwind power permeability. Energy storage system participating in frequency\nregulation of the power system with high wind permeability is reviewed and\nanalyzed....
A trailing-edge flap control strategy for mitigating rotor power fluctuations of a 5 MW\noffshore floating wind turbine is developed under turbulent wind inflow. The wind shear must be\nconsidered because of the large rotor diameter. The trailing-edge flap control strategy is based on\nthe turbulent wind speed, the blade azimuth angle, and the platform motions. The rotor power is\npredicted using the free vortex wake method, coupled with the control strategy. The effect of the\ntrailing-edge flap control on the rotor power is determined by a comparison with the rotor power\nof a turbine without a trailing-edge flap control. The optimal values of the three control factors are\nobtained. The results show that the trailing-edge flap control strategy is effective for improving the\nstability of the output rotor power of the floating wind turbine under the turbulent wind condition...
High-precision wind power forecast can reduce the volatility and intermittency of wind power output, which is conducive to the\nstable operation of the power system and improves the system's effective capacity for large-scale wind power consumption. In\nthe wind farm, the wind turbines are located in different space locations, and its output characteristics are also affected by wind\ndirection, wake effect, and operation conditions. Based on this, two-step ultra-short-term forecast model was proposed. Firstly,\nfuzzy C-means clustering (FCM) theory was used to cluster the units according to the out characteristics of wind turbines. Secondly,\na prediction model of RBF neural network is established for the classification clusters, respectively, and the ultra-short-term power\nforecast is performed for each unit. Finally, the above results are compared with the RBF single prediction model established by\nunclassified g wind turbines. A case study of a wind farm in northern China is carried out. The results show that the proposed\nmethod can effectively improve the prediction accuracy of wind power and prove the effectiveness of the method....
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