The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information an...
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The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets' trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%-30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%-50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm's target loss rate and tracking errors are about 0 similar to 10% and 1.5%similar to 2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequen
The new power system integrates multiple entities such as energy suppliers, load aggregators and energy stations. There are information privacy and technical barriers between subjects, which are independent and interr...
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ISBN:
(纸本)9798350306194;9798350306187
The new power system integrates multiple entities such as energy suppliers, load aggregators and energy stations. There are information privacy and technical barriers between subjects, which are independent and interrelated, and some subjects can achieve two-way interaction and energy trading. The new power system for areas includes multiple distributed power sources and multiple energy supply units, and each energy supply unit is independent and coupled to each other. Aiming at the lowest power supply cost, the operation cost model of each equipment in the system is established, and the consistency algorithm is used to optimize the operation state of each equipment in the system. Experiments show that the consensus algorithm can respond quickly and effectively to the changes in the system load to ensure the economy of the system operation.
Lighting is a part of social infrastructure construction. In modern urban planning and infrastructure development, the lighting load is a significant portion of the electrical load. The demand for lighting load is inc...
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The independent and stable operation of micro-grid provides technical guarantee for Self-Sustained Highway. At present, there is a contradiction between voltage and power regulation in traditional secondary control. I...
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ISBN:
(数字)9781665450669
ISBN:
(纸本)9781665450669
The independent and stable operation of micro-grid provides technical guarantee for Self-Sustained Highway. At present, there is a contradiction between voltage and power regulation in traditional secondary control. In this paper, a virtual impedance adaptive adjustment based on consistency algorithm is proposed to solve the above problems. Firstly, adjacent DGs communicate to exchange information of real-time power. Calculating compensation from adaptive impedance layer with consistency algorithm. The deviation of primary control is adjusted by dynamic virtual impedance. This method can distribute active power according to capacity, and reduce the voltage deviation at the same time. The effectiveness of the method is validated by the simulation and experimental results.
The islanded DC microgrid undertakes its voltage control and power management alone because of its independency from the grid. The line impedance brings difficulties for the droop control strategy to improve the contr...
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ISBN:
(数字)9781665408530
ISBN:
(纸本)9781665408530;9781665408523
The islanded DC microgrid undertakes its voltage control and power management alone because of its independency from the grid. The line impedance brings difficulties for the droop control strategy to improve the control of the bus voltage and the power management of battery storage units (BSUs) at the same time. Therefore, a secondary coordinated control strategy, including the BSU power management and the bus voltage correction, is proposed in this paper, which improves the conventional droop control strategy. The droop control is the primary control, leading to the decline of bus voltages. The secondary control is a distributed control based on the consistency algorithm to correct the bus voltages, by increasing the system load until the BSUs reach the same state of charge (SoC). The effectiveness of the proposed strategy is verified by experiments in the Matlab/Simulink.
In order to ensure that under the influence of input saturation, a safe distance between adjacent locomotives and adjacent trains in multiple heavy haul trains (HHTS) is main-tained, an anti-saturation sliding mode co...
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In order to ensure that under the influence of input saturation, a safe distance between adjacent locomotives and adjacent trains in multiple heavy haul trains (HHTS) is main-tained, an anti-saturation sliding mode consistency (ASMC) control algorithm is proposed. First, a multitrain and multiparticle dynamic model (MMDM) based on multitrain single particle that considers nonlinear coupling force and external disturbance effect is established. Next, a dynamic auxiliary compensation (DAC) system combined with sliding mode surface that can rapidly reduce the saturation deviation is designed and consistency algorithm of the simplified control structure is introduced to construct the ASMC control algorithm. Then, theoretical derivation proved that the algorithm can ensure the convergence of the tracking distance between adjacent locomotives and between adjacent trains to a bounded safe range whilst overcoming the influence of input saturation on each train. Lastly, the simulink and RT-LAB simulation results are used to verify the effectiveness of the design algorithm. & COPY;2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
In active distribution network (ADN), there exist significant differences in the characteristics of different types of energy storage, leading to coordination challenges. This makes it difficult to effectively address...
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In active distribution network (ADN), there exist significant differences in the characteristics of different types of energy storage, leading to coordination challenges. This makes it difficult to effectively address power fluctuation issues caused by the substantial integration of renewable energy sources (RESs). To this end, a day-ahead and intraday joint optimal dispatch method in ADN, which includes an energy storage coordination strategy and a scheduling framework, is proposed in this paper. The energy storage coordination strategy can schedule centralized and distributed energy storage (CES and DES) according to their differences in capacity and response speed. CES is used as energy-type energy storage to take part in the peak shaving of ADN, while DES is used as power-type energy storage to smooth out the rapid power fluctuation of RES. The scheduling framework is divided into two stages: day-ahead and intraday. In the day-ahead stage, the operation state of CES and other slow-response resources is optimized and determined. In the intraday stage, the rough output of the fast-response DES, is optimized based on the short-term prediction. Subsequently, a consistency algorithm is utilized for optimizing the rough output of DES to obtain the precise output state. Further, the scheduling framework uses the schedulable margin of DES as a feedback signal to optimize the output status of CES. A case study on an IEEE 33-bus system verifies the effectiveness of the proposed method.
Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, co...
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Most of the previous SOC equalization methods for microgrid energy storage target DC microgrids and use centralized control structures, while in recent years many researchers have begun to focus on a decentralized, communication-based implementation of distributed control structures. In this paper, based on the existing research, we use the multi-agent system (MAS) structure and dynamic consistency algorithm (DCA) to realize the estimation of the SOC mean value of microgrid energy storage obtained by each agent in the system. For the problems of the iterative convergence that exists in the distributed control, such as being too slow, too frequent communication, stability, etc., we optimally select the parameters of the consistency iterative summation to improve convergence. In addition, we use the event-driven method to further reduce the unnecessary communication frequency. Finally, a numerical simulation model of the AC microgrid is established by Matlab to verify the effectiveness of the method, which reduces the communication volume by about 50% while maintaining the effect of the control strategy.
The purpose of this study is to enhance the economic applicability, reliability, and flexibility of energy supply by developing a novel Hybrid Energy System (HES). We establish a hybrid power generation model that int...
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The purpose of this study is to enhance the economic applicability, reliability, and flexibility of energy supply by developing a novel Hybrid Energy System (HES). We establish a hybrid power generation model that integrates wind power generation (PW), photovoltaic power generation (PV), concentrated solar power (CSP), thermal power generation (TP), and wind and solar power curtailment (WSC) energy storage to enable flexible energy utilization and improve overall energy efficiency. To address the challenges of integrated regulation across multiple energy sources, we introduced an event-triggered multi-agent consensus algorithm with a PID controller (PEMCA) and applied it to the HES. PEMCA is employed to regulate the power output of each energy source to maximize the economic benefits of HES operation. The event-triggered mechanism within PEMCA significantly reduces the communication frequency, while the PID controller effectively mitigates the communication interference fluctuations, eliminates errors, and ensure smooth operation of the system. Verification results indicate that, compared to the traditional multi-agent incremental consensus algorithm (MCA), PEMCA substantially decreases the number of communications within the hybrid energy system, resulting in a 93.6 % reduction in communication space utilization. To evaluate the impact of communication interference within the HES, we simulated three different levels of interference. The results demonstrate that PEMCA outperforms the MCA algorithm in terms of stability and anti-interference capability, effectively suppressing error fluctuations and exhibiting greater robustness and reliability. When the HES system is combined with the PEMCA strategy, the total system cost is reduced by 1.4% and 4.2 %, respectively, compared to using the Gurobi mathematical solver and the consideration of secondary energy. These findings validate the effectiveness of the algorithm.
Traditional droop control allocates distributed generation (DG) power based on capacity proportion, which leads to high system operating costs. To address this issue, this study proposes a distributed economic control...
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Traditional droop control allocates distributed generation (DG) power based on capacity proportion, which leads to high system operating costs. To address this issue, this study proposes a distributed economic control strategy for microgrids based on reinforcement pinning (RP) control. To minimize operating costs, reinforcement learning (RL) continuously updates the Q-value table through reward feedback to obtain the optimal strategy. The optimal action determined by RL is set as the pinning reference value and transmitted to the pinning agent. Other agents achieve marginal cost consistency through the pinning information iteration matrix. To correct frequency deviations, proportional-integral (PI) control is used to ensure frequency stability for the system's operation. Simulations under different scenarios were conducted in MATLAB/Simulink. The results show that the proposed approach can coordinate the output of distributed power sources, reduce the operating costs of the microgrid, and maintain frequency stability.
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