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Optimization of wind turbine energy and power factor with an evolutionary computation algorithm

ENERGY 2010年第3期35卷 1324-1332页

作者： Kusiak, Andrew Zheng, Haiyang Univ Iowa Dept Mech & Ind Engn Seamans Ctr 3131 Iowa City IA 52242 USA

An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controllable and non-controllable variables of a 1.5 MW wind turbine. An evolutionary strategy algorithm solves the data-derived optimization model and determines optimal control settings. computational experience has demonstrated opportunities to improve the power factor and the power output by optimizing set points of blade pitch angle and generator torque. It is shown that the pitch angle and the generator torque can be controlled to maximize the energy capture from the wind and enhance the quality of the power produced by the wind turbine with a DFIG generator. These improvements are in the presence of reactive power remedies used in modern wind turbines. The concepts proposed in this paper are illustrated with the data collected at an industrial wind farm. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Wind turbine Power factor Power output Power quality Data mining Neural network Dynamic modeling Multi-objective optimization evolutionary computation algorithm

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Multi-objective optimization of HVAC system with an evolutionary computation algorithm

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ENERGY 2011年第5期36卷 2440-2449页

作者： Kusiak, Andrew Tang, Fan Xu, Guanglin Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables supply air temperature and supply air duct static pressure set points are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Data-driven models evolutionary computation algorithm HVAC system optimization Energy savings

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Research on rotary crane control using a neural network optimized by an improved bat algorithm

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ARTIFICIAL LIFE AND ROBOTICS 2025年 1-7页

作者： Fujii, Hiroyuki Nakazono, Kunihiko Oshiro, Naoki Kinjo, Hiroshi Univ Ryukyus Grad Sch Engn & Sci Okinawa Japan Univ Ryukyus Fac Engn Okinawa Japan

In this paper, we propose a three-layered neural network controller (NC) optimized using an improved bat algorithm (BA) for a rotary crane system. In our previous study, the simulation results showed that an NC optimized using the original BA exhibits good control and evolutionary performance. However, the simulation execution time was long. Therefore, to address this problem, we propose an improved BA that reduces the execution time. We show that the NC optimized by the improved BA exhibits the same control performance as that optimized via conventional methods. It is also shown that the time for evolutionary calculations can be reduced.

关键词： Bat algorithm Neural network evolutionary computation algorithm

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Dynamic control of wind turbines

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RENEWABLE ENERGY 2010年第2期35卷 456-463页

作者： Kusiak, Andrew Li, Wenyan Song, Zhe Univ Iowa Dept Mech & Ind Engn Iowa City IA 52242 USA

The paper presents an intelligent wind turbine control system based on models integrating the following three approaches: data mining, model predictive control, and evolutionary computation. To enhance the control strategy of the intelligent system, a multi-objective model is proposed. The model involves five different objectives with different weights controlling the wind turbine performance. These weights are adjusted in response to the variable wind conditions and operational requirements. Three control factors, wind speed, turbulence intensity, and electricity demand are considered in eight computational scenarios. The performance of each scenario is illustrated with numerical results. Published by Elsevier Ltd.

关键词： Wind turbine Wind energy Data mining Model predictive control evolutionary computation algorithm Control strategy optimization

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Multi-source, multi-object and multi-domain (M-SOD) electromagnetic interference system optimised by intelligent optimisation approaches

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NATURAL COMPUTING 2020年第4期19卷 713-732页

作者： Hu, Yihua Li, Minle Liu, Xiangyu Tan, Ying Natl Univ Def Technol Coll Elect Engn Hefei 230037 Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China

With the wide use of electromagnetic information equipment, a larg number of wireless radiation systems coexisting in the same region produce intentional or unintentional interference on electronic receivers. For the purpose of intentional electromagnetic interference, it is necessary to realisethe efficient suppression of other receivers at little cost. When multipletransmitting sources are used to interfere with multiple receivers, the parameters of multiple transmitting sources are required to be comprehensivelyoptimised and set so as to achieve a desired high-efficiency ***, we propose a novel method to optimise the setting of parameters of a multi-source, multi-object and multi-domain (M-SOD) interference system based on intelligent optimisation approaches. Furthermore, this study also builds anintelligent optimisation model, which contains multiple transmitters andreceivers which involved many parameters include position, direction of space domain, frequency, bandwidth, and power. Then the model is abstracted to theproblem of single-objective optimisation with constraints and optimised through a traditional GA and an improved FWA method. The extensive experiments andcomparisons show that the proposed algorithm is an effective approach forsetting the parameters of an M-SOD electromagnetic interference system andsuperior to the conventional method.

关键词： Electromagnetic interference Transmitting sources Parameter setting Intelligent optimisation evolutionary computation algorithm

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evolutionary Learning of Differential Morphological Profile Structure for Shape Feature Enabled Faster R-CNN

Evolutionary Learning of Differential Morphological Profile ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on evolutionary computation (IEEE CEC)

作者： Hurt, J. Alex Keller, James Scott, Grant J. Univ Missouri Elect Eng & Comp Sci Columbia MO 65211 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Recently, computer vision tasks such as classification and object detection have been dominated by deep neural network (DNN) approaches. As DNN methodologies have matured, researchers have found that some of the most common DNN techniques result in models that are highly dependent upon the textures and colors of the imagery, rather than the shape, leading to suboptimal network performance. This problem can be especially problematic in the remote sensing domain, where the discrimination of objects for classification or detection may rely heavily on their shape. To combat this lack of shape bias in DNNs, a network was developed to integrate the Differential Morphological Profile (DMP), an image processing technique for shape extraction, with standard convolutional DNNs for performing computer vision tasks on High Resolution Remote Sensing Imagery (HR-RSI). Previously, this network, known as DMPNet, has been applied to both classification and object detection in HR-RSI with high levels of success. However, the hyper-parametric nature of DMPNet structure required researchers to carefully select the parameters of shape extraction, a choice that could greatly help or hinder DMPNet performance. In this study, we utilize a evolutionary computation algorithm (ECA) to learn the parameters of shape extraction from the data presented to the DMPNet for object detection. Our results show that our DMP-enabled detection models perform better object detection in HR-RSI using an ECA to learn shape extraction parameters than manually selected parameters on the same dataset.

关键词： Deep learning differential morphological profile evolutionary computation algorithm object detection neural networks

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Investigation and Improvement of Distributed Differential Evolution algorithm Cloudde 13

Investigation and Improvement of Distributed Differential Ev...

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13th International Conference on Advanced computational Intelligence (ICACI)

作者： Luo, Liu-Yue Shi, Lin Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

ISBN: (纸本)9781665412544

As a kind of new emerging optimization technology, distributed evolutionary computation (DEC) algorithms have fast developed in recent years. The DEC algorithms, which make use of multiple computers or resources to enhance the optimization capabilities of algorithms, have received widespread attention. Among the DEC algorithms, a cloud-based distributed differential evolution (Cloudde) algorithm has shown excellent performance. The Cloudde has a double-layered heterogeneous distribution structure, which can run different differential evolution (DE) variants with various parameters andJor operators in different populations. Moreover, the Cloudde can adaptively migrate individuals among the populations to make best use of the computational resources among multiple populations. However, since the proposal of the Cloudde, there are still some questions remained to he discussed. The first is how to choose the basic DE algorithms to form various DE variants (i.e., the various populations). The second is how to evaluate the performance of different populations of individuals hence we can rank the populations. The third is how to design an efficient migration strategy to make full use of computing resources among multiple populations. This paper makes investigation on these issues and studies the performance of Cloudde variants with various configurations for these three aspects. The experimental results in this paper are useful for researchers who want to conduct further research on Cloulde and other related DEC algorithms. Moreover, based on the investigation results, an improved Cloudde (1-Cloudde) is proposed and the experimental results show the superiority of 1-Cloudde when compared with Cloudde.

关键词： Differential evolution Cloudde evolutionary computation algorithm adaptive migration strategy topology structure

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Adaptive Optimization in evolutionary Reinforcement Learning Using evolutionary Mutation Rates

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IEEE ACCESS 2024年 12卷 165384-165394页

作者： Zhao, Y. Ding, Y. Pei, Y. Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Univ Aizu Comp Sci Div Aizu Wakamatsu Fukushima 9658580 Japan

Deep reinforcement learning (DRL) has achieved notable success in continuous control tasks. However, it faces challenges that limit its applicability to a wider array of tasks, including sparse rewards and limited exploration. Recently, the integration of evolutionary algorithms (EAs) with deep reinforcement learning has emerged as a significant area of research. evolutionary reinforcement learning (ERL) methods can help address specific challenges inherent in conventional reinforcement learning algorithms. However, the introduction of evolutionary computation algorithms increases the number of hyperparameters, and sensitivity to these hyperparameters continues to pose a significant challenge. This paper proposes an evolutionary reinforcement learning method incorporating evolutionary mutation rates. This method integrates a self-adaptive mutation rate mechanism into the ERL framework, which maintains two populations: one consisting of individuals (agents) and the other one comprising mutation rates. This represents our original contribution to this research. The actor population is categorized into several groups, each assigned a specific mutation rate. After mutation, the mutation rate of the population evolves based on the performance of the mutations within the actor population. This approach addresses the challenge of selecting mutation rates in ERL. Experimental results demonstrate superior performance compared to the standard ERL framework across six continuous control tasks.

关键词： Optimization Standards evolutionary computation Evolution (biology) Heuristic algorithms Deep reinforcement learning Convergence Sensitivity Robustness Neural networks evolutionary computation algorithm evolutionary reinforcement learning self-adaptive mutation optimization

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