In weak grid, feedforward of grid voltage control is widely used to effectively suppress grid-side current distortion of inverters caused by harmonics in point of common coupling (PCC) voltage. However, due to its int...
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In weak grid, feedforward of grid voltage control is widely used to effectively suppress grid-side current distortion of inverters caused by harmonics in point of common coupling (PCC) voltage. However, due to its introduction of a positive feedback loop related to the grid impedance, it results in a significant reduction in the system phase margin. In view of this, in this paper, the output impedance of a three-phase LCL grid-connected inverter under a quasi-proportional resonant (QPR) controller is first modeled. Instead of the traditional grid voltage feedforward control strategy, a band-pass filter is added to the grid voltage feedforward channel. Secondly, a multi-objective constraint method is proposed to make improvements to the feedforward function. Then, a multi-objective constraint function is established with the constraints of base-wave current tracking performance, system stability margin, and low-frequency amplitude, and the feasibility of its function optimization design method is verified. Theoretical analysis shows that the optimized grid voltage feedforward control strategy can effectively reshape the phase characteristics of the system output impedance, which greatly broadens the adaptation range of the system to the grid impedance. Finally, the effectiveness of the proposed control strategy is verified by building a semi-physical simulation experimental platform based on RT-LAB OP4510.
A task scheduling algorithm is an effective means to ensure multi-core processor system efficiency. This paper defines the task scheduling problem for multi-core processors and proposes a multi-objective constraint ta...
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A task scheduling algorithm is an effective means to ensure multi-core processor system efficiency. This paper defines the task scheduling problem for multi-core processors and proposes a multi-objective constraint task scheduling algorithm based on artificial immune theory (MOCTS-AI). The MOCTS-AI uses vaccine extraction and vaccination to add prior knowledge to the problem and performs vaccine selection and population updating based on the Pareto optimum, thereby accelerating the convergence of the algorithm. In the MOCTS-AI, the crossover and mutation operators and the corresponding use probability for the task scheduling problem are designed to guarantee both the global and local search ability of the algorithm. Additionally, the antibody concentration in the the MOCTS-AI is designed based on the bivariate entropy. By designing the selection probability in consideration of the concentration probability and fitness probability, antibodies with high fitness and low concentration are selected, thereby optimizing the population and ensuring its diversity. A simulation experiment was performed to analyze the convergence of the algorithm and the solution diversity. Compared with other algorithms, the MOCTS-AI effectively optimizes the scheduling length, system energy consumption and system utilization.
Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol...
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Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as significant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identified that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efficiency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.
In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. C...
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In the context of Industry 4.0 and based on the demand of digital logistics construction of industrial enterprises, this paper integrates the concept of digital twin into Refined Logistics Supply Chain construction. Considering the constraints of multi-distribution center, heterogeneous vehicle performance, distribution cost, quasi-shipment certificate and humanized management, Refined Logistics Supply Chain System (RLSCS) and cross-regional scheduling optimization model of logistics vehicles with multi-distribution center were established. The designed model can minimize the transportation cost, reduce the transportation time, and improve the vehicle load rate. An adaptive elite honey badger target algorithm based on cubic mapping mechanism (IHBA), is designed to solve the model. Further, performance evaluation by optimizing test functions, the convergence performance of IHBA algorithm was demonstrated. Finally, the simulation experiment is carried out according to the actual business data and it is compared to eight other optimistic algorithms. The experimental results show that the proposed algorithm is more effective and more robust, and the related models and algorithms can provide research basis for industrial products digital supply chain system.
Terrorist attacks have contributed significantly to using wireless technologies to identify concrete destruction survivors. A dynamic ad hoc mobile network (MANET) consists of wireless linked nodes, which route hop-by...
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Terrorist attacks have contributed significantly to using wireless technologies to identify concrete destruction survivors. A dynamic ad hoc mobile network (MANET) consists of wireless linked nodes, which route hop-by-hop without the support of a fixed infrastructure acquires information from trapped survivors. The energy efficiency that extends the lifespan of the network is an essential prerequisite of MANET. Researchers have suggested many strategies to accomplish this purpose and a cluster of these techniques in MANETs are used to provide an energy-efficient approach. In this paper we are proposing a red deer multi-objective constraint applied for an energy efficient QoS routing (RD-MOCER) algorithm to the number of clusters in an ad hoc network and the energy dispensing in nodes to provide an energy effective solution and to minimise network traffic. Intracluster and intercluster traffic is handled by the cluster heads in the proposed approach. The algorithm suggested takes account of mobile nodes' node degrees, transmitting capacity and battery power usage. This approach gives a variety of options at a time, the key benefit of which is that the ideal Pareto front results in these solutions. We correlate the findings with two other well-known methods of clustering;MOPSO and MOEAQ-based clustering with different results. We conduct detailed simulations to demonstrate that the solution proposed is an effective and stronger solution to clustering in ad hoc cell networks than the other two techniques.
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