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This article investigates the impacts of proper modeling of the wake effects and wind speed delays, between different wind turbines' rows, on the dynamic performance accuracy of the wind farms models. Three different modeling scenarios were compared to highlight the impacts of wake effects and wind speed time-delay models. In the first scenario, wind wake effect and time delay are ignored. Consequently, all wind turbines are assumed subjected to the same wind speed profile. The second scenario considers the wind wake effect, and it was modeled by reducing the incident wind speed values by 0.5 m/s from one row to the next row facing the wind. The last scenario, the proposed model, used the wind wake effect model developed for wind turbines micrositting applications and considered the effect of the delay time between the wind speed profiles incident on the different wind turbines' rows. (Contains 17 figures.)

Abstract Background In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented. Methods A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation. Results A total average similarity of 98.76%(± 0.68) with gold standards was achieved. Conclusion The proposed approach represents a robust and accurate approach to prostate segmentation.

This paper proposes a novel stochastic planning model for AC-DC hybrid distribution systems (DSs). Taking into account the possibility of each line/bus being AC or DC, the model finds the optimal AC-DC hybrid configuration of buses and lines in the DS. It incorporates consideration of the stochastic behavior of load demands and renewable-based distributed generators (DGs). The stochastic variations are addressed using a Monte-Carlo simulation technique. The objective of the planning model is the minimization of DS installation and operation costs. The optimal planning solution is obtained by dividing the hybrid planning problem into two nested optimization problems: 1) the main problem is formulated using a genetic algorithm (GA) to search for the optimal AC-DC configuration and 2) the subproblem is used for determining the optimal power flow solution for each configuration generated by the GA. The proposed model has been employed for finding the optimal configuration for a suggested case study that included photovoltaic panels, wind-based DG, and electric vehicle charging stations. The same case study was also solved using a traditional AC planning technique in order to evaluate the effectiveness of the proposed model and the associated cost-savings. The results demonstrate the advantages offered by the proposed model. The proposed framework represents an effective technique that can be used by DS operators to identify the optimal AC-DC network configuration of future DSs. [ABSTRACT FROM PUBLISHER]

Smart grids have been emerging nowadays as an initiative to operate modern distribution systems in a more economic and efficient way. Energy storage systems (ESSs) are one of the promising technologies that can achieve the goals of smart grids via facilitating the connection of renewable sources, improving system reliability, and controlling the net demand through peak load shaving, etc. In this paper, a comprehensive planning framework is introduced for ascertaining the most cost effective siting and sizing of ESSs that maximize their benefits in distribution networks. A probabilistic approach is further adopted that includes the consideration of the stochastic nature of system components. Such approach allows determining the optimal operation of ESS at each load state. Moreover, contingency planning decisions, in the form of load points to be shed during contingencies, are identified through the approach proposed. [ABSTRACT FROM PUBLISHER]