Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the ...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.
Fine-grained vehicle classification is a challenging task in computer vision due to the low intra-class variance. Some methods have been developed to improve the accuracy of fine-grained vehicle classification by impr...
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The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and ...
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This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic syst...
This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.
For the category of large-scale nonlinear processes that are composed of a set of linked monovariable systems and represented by discrete input-output models with unknown time-varying coefficients, the current study p...
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ISBN:
(纸本)9781665482622
For the category of large-scale nonlinear processes that are composed of a set of linked monovariable systems and represented by discrete input-output models with unknown time-varying coefficients, the current study proposes a recursive algorithm of maximum likelihood estimation based on the fuzzy inference technique. This recursive estimator employs a prediction error strategy and a maximum likelihood estimation algorithm to formulate the issue of estimating the parameters of the process under consideration. The established parameter estimation approach is enhanced by the addition of the Mamdani fuzzy inference system. A numerical simulation exemplar is used to verify the efficacy of the generated theoretical results..
This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined roa...
This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.
Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for man...
Inspired by human proteins that are synthesized from only 20 types of amino acids, the development of self-assembly methods that allow robots to be built simply by randomly stirring the parts has been explored for many years. The key challenges include how to synthesize parts in pieces into a three-dimensional functional structure in a practical time, and subsequently, achieve a controlled robotic motion, all with minimal human intervention. This study proposes a method of self-assembling a 3D robot by first self-assembling random parts into a 2D structure and then self-folding it into a 3D shape. Once self-folded, the robot, whose compositional parts contain magnets, becomes capable of performing basic tasks such as block-pushing upon an application of an external magnetic field. Self-assembly from parts into a two-dimensional structure was performed by repeatedly colliding the parts with each other, and combining them with complementary-shaped parts, like matching jigsaw puzzle pieces. Self-folding was performed by shrinking a heat-responsive film attached across the hinge of each assembly part in hot water, causing the entire 2D structure to self-fold. The experiment demonstrated a series of 13 parts self-assembling into the shape of a 3D beetle, then walking and pushing an object in 13 minutes. The self-assembly process is programmed (mechanically) to generate the same geometry even if the number of parts is greater than the necessary number for the structure, thus is capable of generating multiple structures simultaneously.
This paper treatsan indirect adaptive fuzzy fault-tolerant control using fuzzy systems for a class of uncertain SISO systems with unknown control gain sign and actuator faults. The uncertain nonlinearities of the syst...
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The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation ...
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Moisture content is one of the important indexes of food storage security. The existing detection methods are time-consuming and high cost such that it is difficult to realize online moisture detection. In this paper,...
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