ANALYSIS OF WIND- AND SHIP-INDUCED SEDIMENT RESUSPENSION IN DULUTH-SUPERIOR HARBOR1

ABSTRACT: Suspended solids and ammonium concentration profiles measured at five locations in Duluth-Superior Harbor during July-October 1985 were analyzed to quantify wind and ship effects on sediment resuspension and resulting harbor water quality. Wind components from the SE quadrant correlated strongly with depth-averaged suspended solids concentrations that were unaffected by ship passage or thermal stratification. Winds from that quadrant have the largest fetch in the harbor. The highest correlation (r²= 0.93) was with the 6-hour average of the ESE wind velocity component. Multiple linear regression analysis of data from post-ship passage concentration profiles yielded numerical estimates of settling velocities of 0.08 to 0.25 cm s^?1, typical of ship-resuspended sediments, and vertical eddy diffusivities of 4 to 13 cm² s¹. The results suggest that ambient vertical eddy diffusivities in the harbor are less than 4 cm² s^?1 in the absence of ship passages and with winds less than 5 m s^?1 (10 knots).     

“一一·二四”海难渤海风场的数值模拟

用中尺度数值模式 ＭＭ５对 １９９９年 １１月 ２４日烟台附近发生重大海难事故的渤海风场进行了数值模 拟，探讨了该次冷锋大风的风场特征和影响机制．结果表明：用９０ｋｍ的粗网格可以成功地模拟地面冷高压 与锋面的发展和移动，以及高空环流形势的演变．而通过３０ｋｍ细网格的模拟发现，在冷锋后的行星边界层 中．存在着一条宽度为 ２００ ｋｉｎ的中尺度强风带，最大风速位于 ９２５ｈＰａ，它与高空的极锋急流并不相连；细 网格还模拟出了渤海中尺度低压的发展过程，它使近地面层大风的强度显著加强．模拟还表明，海陆差异对 近地面的风场分布有重要的影响，强风带移入渤海后，在渤海海域形成一个 ２００—３００ ｋｍ的中尺度强风 区．因此，采用具有较高分辨率的中尺度数值模式，对提高渤海大风的预报水平，避兔海难事故的发生更具 有重要意义．     

涉及电动振动台选型的结构与技术的评价和分析

通过振动台系统组成部分（振动台体，磁场电源和功率放大器）涉及的结构与技术的分析，对振动台系统技术指标的先进性，结构设计的合理性及振动台用于振动试验的准确性和安全性的评价提供依据，可供选择振动台系统参考。     

中国东南沿海重点城市台风危险性分析

中国东南沿海地区是中国经济最发达，人口最稠密的地区，同时也是世界上受台风影响最严重的地区之一。本文以上海经9个沿海重点为例，通过对《台风年鉴》数据的分析处理，提取了台风关键参数并进行了统计分析与建模；利用Monte Carlo抽样和成熟台风风场模型模拟，得到了极值风速序列，对年极值风速序列进行极值风速统计分析，得到了不同重现期和地貌的极值风速，结果与实测风速比较吻合。     

A convective wind resource model for Solar Vortex power generation

ABSTRACT

Climate change has increased the need for clean, nonpolluting energy sources to decrease dependence on fossil fuels. Alternative energy sources, mainly solar and horizontal wind, have been the primary focus for producing clean energy. New technologies are being developed, such as the Solar Vortex (SoV), which was developed at the Georgia Institute of Technology, and relies on a vertical wind resource to generate power. The National Renewable Energy Lab (NREL) has resource models representing solar and horizontal wind resources across the 48 United States. This research developed a vertical wind resource model that is comparable in resolution to NREL’s solar and horizontal wind resource models and uses the model for estimating power output for the SoV. This model complements NREL’s existing resource models and supports the deployment of an additional clean energy generation technology. The model was applied to Mesa, Arizona to find feasible sites for a small-scale vertical wind farm.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

Investigation of sustainable energy alternatives for powering remote communities in northern Ontario

ABSTRACT

Remote communities in the North of Ontario survive in isolation as their proximity to the southern industrial sector of the province limits their accessibility to the major grid. The lack of grid connection has led to antiquated methods of power generation which pollute the environment and deplete the planet of its natural resources. Aside from the primary means of electricity generation being by diesel generators, generation infrastructure is deteriorating due to age and the stagnation of the power supply has led to communities facing load restrictions. These challenges may be resolved by introducing clean energy alternatives and providing a fuel blend option. The primary energy sources investigated in this research are solar, wind, and hydrogen. To assess the viability of these energy production methods in Northern communities, an exergy analysis is employed as it utilizes both the first and second law of thermodynamics to determine systems’ efficiency and performance in the surroundings. Local weather patterns were used to determine the viability of using wind turbines, solar panels and/or hydrogen fuel cells in a remote community. Through analysis of the resources available at the community, it was determined that the hydrogen fuel cell was best suited to provide clean energy to the community. Wind resulted in low efficiency in the range of 2–3% while solar efficiencies resulted in ranges of 18 – 19%, as the seasonal variations between the three years is not very great. Due to the higher operating efficiencies observed of the PV panels it would also be an attractive alternative to diesel generators however, the lack of consistent operation above 30% efficiency throughout the year, resulted in hydrogen fuel cells being a better alternative.     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.