scholarly journals Beetle Swarm Optimization Algorithm-Based Load Control with Electricity Storage

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Hengjing He ◽  
Shangli Zhou ◽  
Leping Zhang ◽  
Junhong Lin ◽  
Weile Chen ◽  
...  
Keyword(s):  
Power Consumption ◽  
Power Transmission ◽  
Scheduling Algorithm ◽  
Peak Shaving ◽  
Swarm Optimization ◽  
Intelligent Terminal ◽  
Power Load ◽  
Electric Equipment ◽  
Time Price ◽  
Filling Simulation

Based on the intelligent bidirectional interactive technology, this paper studies the flexible working mode and optimal power consumption strategy of several typical power consumption loads including energy storage equipment. Based on the real-time price scheme, the objective function and constraints are obtained, and the adaptive algorithm for beetle swarm optimization with variable whisker length is used to optimize so that the electric equipment can automatically change its power load through the intelligent terminal and even work in the way of reverse power transmission. The proposed optimal scheduling algorithm can not only maximize the interests of users but also ensure the minimum peak to average ratio so as to realize peak shaving and valley filling. Simulation results verify the effectiveness of the algorithm.

scholarly journals Synthesis of the Sparse Uniform-Amplitude Concentric Ring Transmitting Array for Optimal Microwave Power Transmission

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hua-Wei Zhou ◽  
Xue-Xia Yang ◽  
Sajjad Rahim
Keyword(s):  
Microwave Power ◽  
Large Scale ◽  
Power Transmission ◽  
Synthesis Method ◽  
Pso Algorithm ◽  
Practical Significance ◽  
Concentric Ring ◽  
Swarm Optimization ◽  
Key Factor ◽  
Feed Network

Beam capture efficiency (BCE) is one key factor of the overall efficiency for a microwave power transmission (MPT) system, while sparsification of a large-scale transmitting array has a practical significance. If all elements of the transmitting array are excited uniformly, the fabrication, maintenance, and feed network design would be greatly simplified. This paper describes the synthesis method of the sparse uniform-amplitude transmitting array with concentric ring layout using particle swarm optimization (PSO) algorithm while keeping a higher BCE. Based on this method, uniform exciting strategy, reduced number of elements, and a higher BCE are achieved simultaneously for optimal MPT. The numerical results of the sparse uniform-amplitude concentric ring arrays (SUACRAs) optimized by the proposed method are compared with those of the random-located uniform-amplitude array (RLUAA) and the stepped-amplitude array (SAA), both being reported in the literatures for the maximum BCE. Compared to the RLUAA, the SUACRA saves 32% elements with a 1.1% higher BCE. While compared to the SAA, the SUACRA saves 29.1% elements with a bit higher BCE. The proposed SUACRAs have higher BCEs, simple array arrangement and feed network, and could be used as the transmitting array for a large-scale MPT system.

scholarly journals Multi-Objective Optimization Design of an Electrohydrostatic Actuator Based on a Particle Swarm Optimization Algorithm and an Analytic Hierarchy Process

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2426 ◽  
Author(s):  
Bo Yu ◽  
Shuai Wu ◽  
Zongxia Jiao ◽  
Yaoxing Shang
Keyword(s):  
Particle Swarm Optimization ◽  
Power Consumption ◽  
Analytic Hierarchy Process ◽  
Optimization Design ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Hierarchy Process

During the last few years, the concept of more-electric aircraft has been pushed ahead by industry and academics. For a more-electric actuation system, the electrohydrostatic actuator (EHA) has shown its potential for better reliability, low maintenance cost and reducing aircraft weight. Designing an EHA for aviation applications is a hard task, which should balance several inconsistent objectives simultaneously, such as weight, stiffness and power consumption. This work presents a method to obtain the optimal EHA, which combines multi-objective optimization with a synthetic decision method, that is, a multi-objective optimization design method, that can combine designers’ preferences and experiences. The evaluation model of an EHA in terms of weight, stiffness and power consumption is studied in the first section. Then, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced to obtain the Pareto front, and an analytic hierarchy process (AHP) is applied to help find the optimal design in the Pareto front. A demo of an EHA design illustrates the feasibility of the proposed method.

A Solution to Power Load Distribution Based on Enhancing Swarm Optimization

2020 ◽  
pp. 53-63
Author(s):  
Truong-Giang Ngo ◽  
Thi-Thanh Tan Nguyen ◽  
Thi-Xuan Huong Nguyen ◽  
Trinh-Dong Nguyen ◽  
Van-Chieu Do ◽  
...  
Keyword(s):  
Load Distribution ◽  
Swarm Optimization ◽  
Power Load

Recognition Method of Abnormal Power Consumption State of Power Users Based on Strategy Gradient Algorithm

2021 ◽  
Vol 16 (7) ◽  
pp. 1090-1097
Author(s):  
Fu Bao ◽  
Yudou Gao
Keyword(s):  
Power Consumption ◽  
Gradient Algorithm ◽  
Process Testing ◽  
Power Load ◽  
Recognition Result ◽  
Abnormal Increase ◽  
Recognition Efficiency ◽  
State Of Power ◽  
Status Recognition ◽  
And Training

Because the traditional method ignores the problem of power load data preprocessing, the accuracy of the recognition result of the power consumption status is not high, the recognition efficiency is not high, and the recognition effect is not good. For this reason, a method for identifying the abnormal power consumption status of power users based on the strategy gradient algorithm is proposed. The preprocessing of power load data mainly includes the completion of missing data and the feature extraction of power load data. Based on the results of the preprocessing, the abnormal increase in user power consumption is detected. Finally, the strategy gradient algorithm is used for initial training and training process testing to complete the identification of the abnormal state of power users. The experimental results show that the accuracy of the power status recognition result of the proposed method is higher, and the recognition time is always less than 2.0 s, indicating that the recognition effect of the method is better.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

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