Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after t...
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Based on the scenario of high-penetration distributed photovoltaic connected to an AC/DC distribution network, this paper analyzes the dynamic characteristics of frequency and voltage in a distribution network after the blocking failure of the flexible interconnection channel. In order to enhance the transient stability of the system after the fault, this paper comprehensively considers the active regulation ability of photovoltaic units, and puts forward an emergency coordinated control strategy for a single-ended distribution network with flexible interconnection channel blocking. Firstly, the non-fault channel is overloaded for a short time, then the comprehensive influence of factors such as electrical distance, response time and adjustment cost on the frequency modulation effect of the system is quantitatively evaluated;according to the evaluation results, the photovoltaic and synchronous units are controlled by "control instead of tripping", and finally, the high-frequency tripping is carried out, based on the principle of "photovoltaics first". After the frequency control is completed, the reactive power optimization model of the system is established, and the improved tabu-particle swarm optimization algorithm is used to solve it, so as to optimize the voltage of the distribution network nodes. Finally, an equivalent simulation model is established to verify the coordinated control strategy.
Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning ...
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Multi-unmanned aerial vehicle (UAV) collaborative task planning and distribution path planning are the core content of agricultural UAV logistics distribution. In this study, the multi-UAV collaborative task planning and the distribution path planning were discussed, and such constraint conditions as UAV load capacity, battery capacity and flight time were comprehensively considered, aiming to reduce the number of UAVs and their power consumption. To ensure the safe and efficient completion of multi-UAV logistics distribution tasks, 3D agricultural ultralow space was subjected to environment modeling, and a bilevel planning model for collaborative planning of UAV distribution route and flight path was constructed. Then, an improved particle swarm optimization (pso) algorithm with the improved learning factor and inertia coefficient was designed on the basis of pso framework, and the global optimal solution in the current iteration was improved using variable neighborhood descent search. The feasibility of the proposed algorithm was verified by analyzing a practical case. With the central city area of XX City as the study area, 1 logistics & freight transportation center was taken as the central warehouse (coordinates: 50, 50, unit: km) and 50 intelligent express cabinets as the express cabinets of UAVs. The obtained results were comparatively analyzed with those acquired through the basic psoalgorithm. The results manifest that the proposed algorithm performs better than the compared algorithms. The improved pso algorithm is superior to the basic psoalgorithm in aspects of total UAV flight distance, number of UAVs used and algorithm convergence time, indicating that the model and algorithm established in this study are feasible and effective.
In this paper, three-axis measuring system calibration problems, according to the ideal measurement system projection orthogonal system of thought, through the vector field measurement system to map the relationship b...
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ISBN:
(纸本)9781479900305
In this paper, three-axis measuring system calibration problems, according to the ideal measurement system projection orthogonal system of thought, through the vector field measurement system to map the relationship between ideal orthogonal system, establish measurement system deviation parameter model;psoalgorithm will introduce self-learning mechanism and thus solve the optimal mapping parameters, the measurement system quadrature error between axes, as well as gain deviation between axes simultaneously zero drift is corrected to achieve a measurement system to map the relationship between ideal orthogonal system parameter identification. Simulation results show that this method can be very effective in achieving an effective measurement system for three-axis correction.
Hysteresis which is inherent in smart sensors or actuators may severely deteriorate system performance such as giving rise to undesirable inaccuracies or oscillations, even leading to instability. Therefore, it is nec...
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ISBN:
(纸本)9789881563811
Hysteresis which is inherent in smart sensors or actuators may severely deteriorate system performance such as giving rise to undesirable inaccuracies or oscillations, even leading to instability. Therefore, it is necessary to find a model describing the behavior of hysteresis. In this method, an improved hysteretic operator is proposed to describe the change tendency and extract the dynamic property of rate-dependent hysteresis. Then a dynamic hysteresis model is established with introduction of such hysteresis operator combined with a linear system. An improvedpso (Particle Swarm Optimization) algorithm is used for identification of parameters. The established model has a brief structure and few parameters. Finally, the method of modeling hysteresis nonlinearity is applied in piezoelectric actuators and compared with PI model. The results indicate that this model is able to accurately describe the characteristics of hysteresis nonlinearity in the actual system.
Aiming at the problem of the task location and the crowdsourcing pricing of the task package under the bilateral mechanism,a multi-objective optimization(MOP)model was established,and the particle swarm optimization a...
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Aiming at the problem of the task location and the crowdsourcing pricing of the task package under the bilateral mechanism,a multi-objective optimization(MOP)model was established,and the particle swarm optimization algorithm based on the task point perspective and the user perspective based greedy strategy was adopted as the fitness function(pso)to *** analysis of the pricing law of the task and the judgment of the reasons for the unfinished task are ***,we visualize the task and member's feature information,conduct preliminary analysis,and then further analyze the pricing law through K-means ***,we construct the Logit two-dimensional selection model,and through the function form and regression effect of the model,the impact task is not *** reason for the completion is mainly caused by factors such as insufficient credit and time at the time of pricing
An improved particle swarm optimization(pso) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of pso, and it is used to solve the problem of logistics route optimization. Thi...
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An improved particle swarm optimization(pso) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of pso, and it is used to solve the problem of logistics route optimization. This algorithm can not only improve the convergence speed, but also avoid falling into local optimum. In the process of improving the standard algorithm, two methods are proposed to adjust the inertia weight value according to the number of iterations. One is piecewise linear decreasing, another is linear decreasing. The results show that linear decline is better than piecewise linear decline to achieve the purpose of optimization, which is more conducive to accelerate the convergence rate and enhance the ability of optimization. Through the simulation experiment of the specific vehicle routing optimization problem, the results show that after the improvement, the optimization performance is enhanced, the optimization speed is accelerated, and the complexity is not increased, which greatly improves the performance of the original algorithm.
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