检索结果-内蒙古大学图书馆

Double closed-loop control of PMSM based on improved model reference adaptive strategy and sine-cosine algorithm

ELECTRICAL ENGINEERING 2025年 1-17页

作者： Feng, Chengyang Yang, Hengzhan Tan, Bo Qian, Fucai Xian Technol Univ Dept Elect Informat Engn Xian Peoples R China Xian Univ Technol Dept Automat & Informat Engn Xian Peoples R China

Permanent magnet synchronous motor is a high-order nonlinear and uncertain system. Aiming at the problem that its conventional feedback control is vulnerable to various disturbance factors of the system, resulting in insufficient anti-disturbance performance, based on the mathematical model of the permanent magnet synchronous motor and the basic principle of the model reference adaptive algorithm, this paper designs a double closed-loop adaptive controller by using the generalized output error signal of the system combined with the input quantity of the forward path and the output feedback quantity of the controlled object, and utilizes the improved sine-cosine optimization algorithm to optimize the variable parameters in the controller. Finally, an experimental platform is built, and the classic PI control and the traditional model reference adaptive control are selected as the comparison groups. The experimental results verify that the fluctuation generated by the control system when disturbed is smaller, the speed of restoring stability is faster, and the anti-disturbance ability is stronger.

关键词： Permanent magnet synchronous motor Double closed-loop system Model reference adaptive control sine-cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

SCSO: snake optimization with sine-cosine algorithm for parameter extraction of solar photovoltaic models

引用

DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-38页

作者： Li, Qingrui Zhou, Yongquan Luo, Qifang Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Solar power generation is a clean and sustainable energy source. To ensure the efficient operation of photovoltaic (PV) systems, it is essential to develop accurate equivalent models of PV cells and precisely determine their unknown parameters. However, due to the nonlinear and multimodal characteristics of PV systems, accurately extracting PV parameters remains a significant challenge. This paper proposes a hybrid Snake Optimization combined with a sine-cosine algorithm (SCSO) to address the PV parameter extraction problem. The proposed algorithm incorporates three key improvements: (1) integration of the sine-cosine algorithm to enhance the bio-inspired Snake Optimization, balancing exploration and exploitation;(2)The parameters C1 and C2 are adaptively adjusted, and the Newton-Raphson method is introduced to accelerate the algorithm's convergence speed which accelerates convergence;and (3) application of a lens imaging reverse learning strategy to improve exploration capabilities and population diversity, preventing the algorithm from becoming trapped in local optima. First, the performance of the SCSO algorithm is qualitatively analyzed using the CEC2022 test functions. Then, the algorithm is applied to extract parameters for three different PV modules. Finally, two commercial models (TFST 40 and MCSM 55) are tested under varying environmental conditions to validate the algorithm's accuracy. Experimental results demonstrate that SCSO outperforms several state-of-the-art metaheuristic algorithms, achieving higher precision and faster convergence.

关键词： PV modules Parameter extraction Snake optimization algorithm sine-cosine algorithm Hybrid metaheuristic optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Sizing and Location of TSCs in Medium-Voltage Distribution Networks via the sine-cosine algorithm

Optimal Sizing and Location of TSCs in Medium-Voltage Distri...

引用

IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

作者： Danilo Montoya, Oscar Garrido-Arevalo, Victor M. Gil-Gonzalez, Walter Univ Dist Francisco Jose de Caldas Fac Ingn Bogota 110231 Colombia Univ Pamplona Programa Ingn Elect Pamplona 760042 Colombia Univ Tecnol Pereira Dept Elect Engn Pereira 660003 Colombia

ISBN: (纸本)9798331516918;9798331516901

This paper addresses the optimal siting and sizing of thyristor-switched capacitors (TSCs) in medium-voltage distribution networks (MVDNs) to minimize the expected grid operating costs associated with energy losses by taking into account the annualized investments in TSCs. The sine-cosine algorithm (SCA) is applied to determine the location and sizing of the TSCs for reactive power compensation in MVDNs. The SCA approach is an effective math-inspired population-based metaheuristic optimizer that explores and exploits the solution space using sine and cosine trigonometric functions. A discrete-continuous codification is used to represent the location and sizing problem of the TCSC in MVDNs. The discrete part is related to the nodes where the compensators must be located. In contrast, the continuous aspect of codification is linked to the sizes of the TCSC. The proposed methodology was evaluated in the 33-bus grid system and compared to three metaheuristic algorithms: the Chu & Beasley Genetic algorithm (CBGA), the Particle Swarm Optimizer (PSO), and the Black Widow Optimizer (BWO). Computational validations show that the BWO and SCA algorithms reach the best solution with a reduction of the objective function by around 11.22%. All the numerical validations are carried out in the MATLAB programming software using version 2022b.

关键词： Battery energy storage units distribution networks sine-cosine algorithm CO2 emissions

来源：评论

学校读者我要写书评

暂无评论

Optimal Solution of Reactive Power Dispatch in Transmission System to Minimize Power Losses Using sine-cosine algorithm

引用

IEEE ACCESS 2022年 10卷 20223-20239页

作者： Saddique, Muhammad Shahzar Habib, Salman Haroon, Shaikh Saaqib Bhatti, Abdul Rauf Amin, Salman Ahmed, Emad M. Univ Engn & Technol UET Dept Elect Engn Taxila 47050 Pakistan Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Govt Coll Univ Faisalabad GCUF Dept Elect Engn & Technol Faisalabad 38000 Pakistan Jouf Univ Coll Engn Dept Elect Engn Sakaka 72388 Aljouf Saudi Arabia Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt

Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching-learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms.

关键词： Optimization Reactive power Particle swarm optimization Heuristic algorithms Standards Electrical engineering Probability density function Electric power system optimal reactive power dispatch electrical transmission system sine-cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

Illumination correction model with sine-cosine algorithm to optimize gray wolf population for least-squares support vector regression

引用

COLOR RESEARCH AND APPLICATION 2022年第1期47卷 92-106页

作者： Yang, Junyi Zhao, Haitao Chen, Sheng Hangzhou Dianzi Univ Sch Mech Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat Engn Hangzhou Peoples R China

The color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA-GWO-LSSVR) based on the sine-cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least-squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine-cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

关键词： color constancy gray wolf optimization algorithm least-squares support vector regression sine-cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm

引用

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第10期60卷 2931-2949页

作者： Xu, Binfeng Martin, Diego Khishe, Mohammad Boostani, Reza Guangdong Food & Drug Vocat Coll Guangzhou 510520 Guangdong Peoples R China Univ Politecn Madrid ETSI Telecomunicac Av Complutense 30 Madrid 28040 Spain Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Shiraz Univ Elect & Comp Engn Fac CSE & IT Dept Shiraz Iran

The prevalence of the COVID-19 virus and its variants has influenced all aspects of our life, and therefore, the precise diagnosis of this disease is vital. If a polymerase chain reaction test for a subject is negative, but he/she cannot easily breathe, taking a computed tomography (CT) image from his/her lung is urgently recommended. This study aims to optimize a deep convolution neural network (DCNN) structure to increase the COVID-19 diagnosis accuracy in lung CT images. This paper employs the sine-cosine algorithm (SCA) to optimize the structure of DCNN to take raw CT images and determine their status. Three improvements based on regular SCA are proposed to enhance both the accuracy and speed of the results. First, a new encoding approach is proposed based on the internet protocol (IP) address. Then, an enfeebled layer is proposed to generate a variable-length DCNN. The suggested model is examined over the COVID-CT and SARS-CoV-2 datasets. The proposed method is compared to a standard DCNN and seven variable-length models in terms of five known metrics, including sensitivity, accuracy, specificity, F1-score, precision, and receiver operative curve (ROC) and precision-recall curves. The results demonstrate that the proposed DCNN-IPSCA surpasses other benchmarks, achieving final accuracy of (98.32% and 98.01%), the sensitivity of (97.22% and 96.23%), and specificity of (96.77% and 96.44%) on the SARS-CoV-2 and COVID-CT datasets, respectively. Also, the proposed DCNN-IPSCA performs much better than the standard DCNN, with GPU and CPU training times, which are 387.69 and 63.10 times faster, respectively.

关键词： DCNNs Chest CT scans COVID-19 Internet protocol address sine-cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

Parameter identification of solar photovoltaic models by multi strategy sine-cosine algorithm

引用

ENERGY SCIENCE & ENGINEERING 2024年第4期12卷 1422-1445页

作者： Zhou, Ting-ting Shang, Chao Maanshan Univ Architectural Engn Inst Maanshan 243100 Peoples R China Nanjing Tech Univ Pujiang Inst Nanjing Peoples R China

Accurate modeling and parameter identification of photovoltaic (PV) cells is a difficult task due to the nonlinear characteristics of PV cells. The goal of this paper is to propose a multi strategy sine-cosine algorithm (SCA), named enhanced sine-cosine algorithm (ESCA), to evaluate nondirectly measurable parameters of PV cells. The ESCA introduces the concept of population average position to increase the population exploration ability, and at the same time introduces the personal destination agent mutation mechanism and competitive selection mechanism into SCA to provide more search directions for ESCA while ensuring the search accuracy and diversity maintenance. To prove that the proposed ESCA is the best choice for extracting nondirectly measurable parameters of PV cells, ESCA is evaluated by the single-diode model, the double-diode model, the three-diode model, and the photovoltaic module model (PVM), and compared with eight existing popular methods. Experimental results show that ESCA outperforms similar methods in terms of diversity maintenance, high efficiency, and stability. In particular, the proposed ESCA method is less than the SCA by 0.081, 0.144, and 0.578 in the standard deviation statistics metrics of the three PVM models (PV-PWP201, STM6-40/36, and STP6-120/36), respectively. Therefore, the proposed ESCA is an accurate and reliable method for parameter identification of PV cells. Figure 6. The flowchart of enhanced sine-cosine ***

关键词： mutation mechanism parameter identification photovoltaic cells sine-cosine algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-strategy and self-adaptive differential sine-cosine algorithm for multi-robot path planning

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期232卷

作者： Akay, Rustu Yildirim, Mustafa Yusuf Erciyes Univ Dept Mechatron Engn Kayseri Turkiye Erciyes Univ Inst Nat & Appl Sci Kayseri Turkiye

Researchers have conducted studies using various metaheuristic algorithms for multi-robot path planning recently. One of these algorithms, sine-cosine algorithm (SCA) cannot produce satisfactory results in path planning problems, due to a single update strategy. It is necessary to adopt multiple update strategies and improve its performance for a wider set of problems. We have proposed a new multi-strategy self-adaptive differential sine-cosine algorithm (sdSCA) that uses a pool of strategies and allows for more frequent selection of strategies that lead to better solutions. Thus, dependency of SCA on a single strategy has been removed and it has become a more stable for a wider set of problems and convergence of SCA is improved. Firstly, effectiveness of sdSCA was tested in CEC2015 benchmark functions and CEC2020 real-world optimization problems. Performance of sdSCA at these tests is satisfactory. Secondly, sdSCA was applied to online multi-robot path planning in complex environments with static and dynamic obstacles. In this path planning simulation, the proposed algorithm achieved an average improvement of 42% compared to SCA. It also appears to produce results superior to state-of-the-art metaheuristic algorithms.

关键词： Multi-robot Path planning sine-cosine algorithm Multi-strategy Self-adaptive

来源：评论

学校读者我要写书评

暂无评论

Solving 0-1 Knapsack Problems Using sine-cosine algorithm

Solving 0-1 Knapsack Problems Using Sine-Cosine Algorithm

引用

Palestinian International Conference on Information and Communication Technology (PICICT)

作者： Mahfouz, Khaled Al-Betar, Mohammed Azmi Ali, Sharaz Awadallah, Mohammed A. Ajman Univ Coll Engn & Informat Technol Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates

ISBN: (纸本)9781665436519

The task of optimization is no easy task and from a computational point of view, it often involves scanning a large search space to find the best solution that adheres to all the constraints and desired specifications. Designing a customized algorithm to solve several optimization problems is also a challenging task, therefore scientists and engineers utilize metaheuristic algorithms that can provide an optimal solution within a reasonable time. This optimal solution may or may not be the best solution in the search space, but it is usually good enough to satisfy the requirements without spending a lot of computational resources or time. The 0-1 knapsack problem is an constraint-based optimization problem in which a number of items have to be packed into a container by maximizing the value of the items in the container while also adhering to the weight limit of the container. In this paper, sine-cosine algorithm (SCA) is adopted to solve 0-1 knapsack problems. The proposed algorithm is called binary sine-cosine algorithm (BSCA). Due to the binary nature of 0-1 knapsack problem, the SCA is manipulated using a mapping function. The performance of the proposed BSCA is evaluated using 15 well-known datasets. Furthermore, the performance of the proposed BSCA is compared with other comparative algorithms (i.e., GA, PSO, and BFPA) from the literature using the same datasests. It can be observed from the results that the performance of the proposed BSCA is similar to other algorithms by obtaining the optimal results on 10 datasets. While the results of the proposed BSCA are convergent with others for the remaining five datasets.

关键词： Optimization Constrained optimization sine-cosine algorithm Meta-heuristic Population-based algorithm Knapsack problem

来源：评论

学校读者我要写书评

暂无评论

A comprehensive survey on the sine-cosine optimization algorithm

引用

ARTIFICIAL INTELLIGENCE REVIEW 2023年第6期56卷 4801-4858页

作者： Rizk-Allah, Rizk M. Hassanien, Aboul Ella Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Cairo Univ Fac Comp & AI Cairo Egypt

Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine-cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm's destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers' attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the sine-cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included.

关键词： Metaheuristics optimization sine-cosine algorithm Modifications Hybridization Applications

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：