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检索条件"主题词=whale optimization algorithms"

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Fractional Order PID Control Applied to a Pasteurization Plant

Fractional Order PID Control Applied to a Pasteurization Pla...

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IEEE Biennial Congress of Argentina (ARGENCON)

作者： Luis Caiza Marco Herrera Noel Pérez-Pérez Fabricio Yépéz Diego S. Benítez Oscar Camacho Colegio de Ciencias e Ingenierí as Universidad San Francisco de Quito USFQ Quito Ecuador

ISBN: (数字)9798350365931

ISBN: (纸本)9798350365948

Milk pasteurization is complex and nonlinear, challenging to manage with traditional controllers such as ON-OFF or PID. This study examines fractional-order PID (FO-PID) control due to its ability to handle numerous variables effectively. The FO-PID controller ensures performance and robustness to maintain the required temperature limits. Using the Armfield Process Plant Trainer, PCT23MKII, a realistic small-scale model of a pasteurizing process is established. This system includes high-/low-level switches and two-way solenoid valves for tank filling operations. The results indicate that FO-PID extends the functionality of classical PID controllers by enhancing the delivery of the control signal.

关键词： Transmitters Process control Valves whale optimization algorithms Robustness Temperature control Solenoids Particle swarm optimization Tuning Testing

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Fault Prediction Method Based on Improved Bidirectional Long Short-Term Memory Combined with Sample Entropy for Battery

Fault Prediction Method Based on Improved Bidirectional Long...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Song Ren Jing Sun Xiaodong Zhang Yiwei Liu Diantao Wang School of Information and Electronic Engineering Shandong Technology and Business University Yantai China YanTai DongFang Wisdom Electric Co. Ltd Yantai China

Fast and accurate prediction of battery failures is essential to improve the safety and reliability of electric vehicles (EVs). In this paper, a fault prediction method based on improved bidirectional long short-term memory (IBiLSTM) combined with sample entropy (SampEn) is proposed. The parameters of the BiLSTM are optimized using the whale optimization algorithm (WOA), and attention mechanism is used to assign weights to the prediction values of the BiLSTM to obtain the optimal prediction values. The SampEn can detect the abnormal degree of the sequence containing the predicted values in order to achieve fault prediction. Finally, the accuracy and practicality of the proposed fault prediction method are verified by algorithmic principles and experimental studies.

关键词： Fault diagnosis Time series analysis Prediction methods Prediction algorithms Electric vehicles Entropy whale optimization algorithms

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Feature Selection Method Based On GWO-PSO for Coronary Artery Disease Classification

Feature Selection Method Based On GWO-PSO for Coronary Arter...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Eluri Rama Krishna Nagaraju Devarakonda School of Computer Science & Engineering VIT-AP University Andhra Pradesh India

In this analysis, optimization of Hybrid Grey Wolf technique for Feature Selection analysis is implemented. In many areas standard performance is given by Grey Wolf Optimizer's (GWO). To enhance the performance different GWO factors are signified GWO mainly based on the two conflicting concepts. This will exploit the optimal solutions. The performance of search algorithms is improved by balancing the both exploration and exploitation. For continuous search space problems are obtained from the original hybrid approach which is superior to performance. The main binary problem is fromfeature selection. Hence hybrid PSOGWO is also known as BGWOPSO. This is the best feature subset. To produce the best results, Euclidean separation matrix and K-Nearest Neighbor's classifier are used. Performance is evaluated using one common benchmark dataset from the UCI repository. According to the findings, the whale optimization algorithm, binary genetic algorithm, binary PSO, binary genetic algorithm, and Binary GWO (BGWO) provide the highest levels of performance in terms of accuracy, the selection of the most effective optimal features, and the amount of time required to compute.

关键词： Support vector machines Sensitivity and specificity Feature extraction Search problems whale optimization algorithms Classification algorithms Particle swarm optimization

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A Novel Technique Based on Crow Search Algorithm to Solve Optimal Control Problem of a Single-Link Rigid Robotic Manipulator

A Novel Technique Based on Crow Search Algorithm to Solve Op...

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Power Electronics and Energy (ICPEE), International Conference on

作者： Venkata Satya Durga Manohar Sahu Padarbinda Samal Chinmoy Kumar Panigrahi School of Electrical Engineering Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India

This study shows how Meta-heuristic optimization programming may be implemented to solve constrained optimal control problems. With a single-link rigid manipulator (SLM) model as an example, the proposed Crow search algorithm (CSA) is contrasted with the differential evolution algorithm (DE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) in terms of performance index, which has demonstrated the notable outcomes on another algorithm.

关键词： Metaheuristics Optimal control Manipulators Search problems whale optimization algorithms Power electronics Performance analysis

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Red Deer Algorithm-Based Optimal Total Harmonic Distortion Minimization for Multilevel Inverters

Red Deer Algorithm-Based Optimal Total Harmonic Distortion M...

引用

Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Taha A. Taha Mohd Khair Hassan Noor Izzri Abdul Wahab Hussein I. Zaynal Department of Electrical and Electronics Engineering University Putra Malaysia (UPM) Selangor Malaysia Computer Engineering Technology Department Al-Kitab University Kirkuk Iraq

This study offers a new Red Deer Algorithm (RDA) optimization technique for total harmonic distortion (THD) minimization in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To test the angles determined via RDA optimization, a single-phase seven-level cascade multi-level inverter with symmetrical DC sources was used in this study. The switching angles for the optimally minimized total harmonic distortion (OMTHD) were computed via RDA optimization. RDA optimization was compared to meta-heuristic algorithms like the Improved whale optimization Algorithm (IWOA), whale optimization Algorithm (WOA), Sunflower Optimizer (SFO), Particle Swarm optimization (PSO), Krill Herd (KH), Grey Wolf Optimizer (GWO), Galactic Swarm optimization (GSA), Genetic Algorithm (GA), Fruit Fly optimization Algorithm (FFO) and Artificial Greyctric Field Algorithm (AEFA). The RDA optimization outperformed the ten algorithms used as benchmark frameworks in this study as it can find angles with minimum THD for the modulation index in 0–1.

关键词： Total harmonic distortion Switches Benchmark testing Multilevel inverters Minimization whale optimization algorithms Particle swarm optimization

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Multi-objective optimal configuration of two-stage reactive power compensation in power grids with power loss index

Multi-objective optimal configuration of two-stage reactive ...

引用

Electrical Engineering, Big Data and algorithms (EEBDA), IEEE International Conference on

作者： Guo Ting Xu Liangde Chen Zhonghao Yang Fan Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China Guangzhou Electric Power Design Institute Co. Ltd. Guangzhou China

In view of the fact that the current distribution network reactive power optimization problem has poor solution time and is easily trapped in a local optimum search, this paper carries out a study on the multi-objective optimal configuration of two-stage reactive power compensation for power loss indices. A multi-objective optimal configuration model for two-stage reactive power compensation devices in power grids based on the power loss index is developed with the objective functions of minimizing loss cost, purchase cost, installation cost, and operation cost, and introducing penalty functions to solve the optimal installation location and capacity of reactive power compensation devices in power grids under constrained conditions using the remora optimization algorithm. The proposed method is validated by simulation based on IEEE-34 and PG-69 systems. The simulations of the proposed method are validated by IEEE-34 and PG-69 systems. The simulation results show that the method proposed in this paper has advantages over the multi-objective optimization without power loss index in reducing loss, improving node voltage distribution, reducing cost, and optimizing time, and the remora optimization algorithm can achieve the global search optimization better than particle swarm optimization algorithm and whale optimization algorithm. The accuracy and applicability of multi-objective optimal configuration of reactive power compensation in power grid are improved.

关键词： Reactive power Costs Simulation Voltage Search problems Power grids whale optimization algorithms

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Intelligent Reflecting Surfaces Assisted UAV Reliable Communication

Intelligent Reflecting Surfaces Assisted UAV Reliable Commun...

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IEEE Conference on Wireless Communications and Networking

作者： Haiying Peng Yufeng Zheng Peng He Yaping Cui Ruyan Wang Dapeng Wu Luo Chen School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing China

In this paper, we investigate the reliability of intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications in the case of limited UAV energy. Under constraints of the UAV energy and the channel decoding error rate, we formulate a reliability maximization problem by jointly optimizing the IRS’s scheduling, the UAV’s trajectory, the IRS’s phase shift, and the UAV’s transmit power. Since the partial constraints of the problem are strictly nonconvex and its variables are coupling, the problem is difficult to convert to a nonconvex problem. Therefore, we propose a chaotic adaptation hybrid whale optimization algorithm (CAHWOA) to solve the problem. CAHWOA is implemented by using alternately the chaotic adaptation whale optimization algorithm (CAWOA) and the binary optimization algorithm (BWOA). Simulation results demonstrate that the joint optimization of IRS and UAV can improve the system communication reliability by almost 32% compared with the two baseline schemes. CAHWOA can improve the convergence rate by nearly 20% and enhance the optimization-seeking accuracy by about 0.04 compared with the three baseline algorithms.

关键词： Couplings Chaotic communication Error analysis Simulation Autonomous aerial vehicles whale optimization algorithms Trajectory

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Optimizing Synthesized Comfort Index through Occupancy Driven in IoT based Smart Buildings

Optimizing Synthesized Comfort Index through Occupancy Drive...

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International Conference on Engineering Technology and Technopreneuship (ICE2T)

作者： Ghulam Fizza Kushsairy Kadir Haidawati Nasir Electrical and Electronic Engineering British Malaysian Institute Universiti Kuala Lumpur Selangor Malaysia Computer Engineering Technology Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (数字)9798350304107

ISBN: (纸本)9798350304114

Smart buildings are evolving rapidly with the integration of advanced technologies to enhance occupant comfort and sustainability. A central challenge in this domain is the optimization of comfort, dynamically aligned with occupancy detection. This research introduces a distinctive approach by leveraging Internet of Things (IoT) technologies, a key enabler for smart buildings, to facilitate real-time occupancy detection and environmental control. Utilizing a C4.5 decision tree, precise occupancy detection was achieved with metrics such as precision 97.87%, recall 98.93% and an F1 score 98.83%. The integration of an enhanced whale optimization algorithm with a synthesized comfort index, resulting in a value of 0.9960, demonstrates superior performance over conventional algorithms. By connecting IoT-enabled occupancy detection with comfort index optimization, this work pioneers a new paradigm in occupant comfort within smart buildings, with far-reaching implications for smart and sustainable building management.

关键词： Measurement Smart buildings whale optimization algorithms Real-time systems Classification algorithms Indexes Internet of Things

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An Innovative Method for Energy Intensive Routing and Transmission Network Positioning in Integrated Wireless Detector Networks

An Innovative Method for Energy Intensive Routing and Transm...

引用

Recent Trends in Electronics and Communication (ICRTEC), International Conference on

作者： Rakheeba Taseen Haseeba Yaseen Niranjan L Gadige Radha Mahesh B Neelagar Shwetha N Dept. of ISE CMR Institute of Technology Bengaluru India Dept. of I.T Vasavi College of Engineering Bengaluru India Dept. of ECE CMR Institute of Technology Bengaluru India Dept. of ECE VTU Belagavi Bengaluru India Dept. of ECE Dr.Ambedkar Institute of Technology Bengaluru India

Sensor nodes in Integrated Wireless Detector Networks (IWDN) may malfunction as a result of abrupt damage or running out of battery power. As a result, IWDN is divided into numerous different networks. In order to restore wireless connectivity on the network, new relay nodes must be added to a partitioned IWDN. This process is known as relay node installation. In IWDN, energy conservation and network lifetime extension are made possible through energy-efficient routing. The placement of relay nodes and energy-efficient routing issues for IWDN are addressed in this work. Rarely have these issues been examined together in the literature. The initial step in this paper is to build a mathematical model for each issue. It is believed that the IWDN comprises an unreachable region where sensor nodes cannot be positioned in order to solve the placement issue for relay nodes. It is converted to the wireless communication path length for energy-efficient routing. A whale optimizer technique is used because the problem is asynchronous polynomial (AP) hard. The report examines the outcomes of three adaptive techniques using the whale optimizer approach. The proposed method for IWDN is put to the test using mathematical simulations. The analysis and discussion highlight the benefits of the recommended strategy for resolving the placement of AP-hard base stations and power concerns for IWDN.

关键词： Wireless communication Wireless sensor networks Simulation Energy conservation Detectors Routing whale optimization algorithms

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Optimal Tuning of Fractional Order PID Controller with Metaheuristic algorithms for High Efficiency High Gain DC-DC Boost Converter

Optimal Tuning of Fractional Order PID Controller with Metah...

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Power Electronics and Energy (ICPEE), International Conference on

作者： Dhruvkumar Patel Srishti P K Padhy Department of ECE PDPM IIITDM Jabalpur Jabalpur India

This paper discusses the design and control of a high-efficiency and high-gain DC-DC Boost Converter which is used for power conditioning between Photovoltaic cells and load. DC-DC converter offers limited gain, and their efficiency decreases with an increasing gain due to very high duty cycles, which causes high voltage stress on switches. By using a coupled inductor and a clamped circuit, higher gains are achieved without loss in efficiency. A fractional-order PID (FOPID) controller is suggested for maintaining the constant voltage at the load end. A FOPID controller has advantages over a PID controller and offers better performance. For tuning the FOPID controller, different optimization algorithms have been used. Integrated Time Weighted Absolute Error (ITAE) of the system has been taken as performance criteria and has been minimized by optimization algorithms to obtain parameter values for the FOPID controller. Namely, four different algorithms have been used, i.e., Genetic Algorithm (GA), Particle Swarm optimization Algorithm (PSO), whale optimization Algorithm (WOA), and Grey Wolf optimization Algorithm (GWO). MATLAB Simulink has been used for design and simulation of the boost converter and controller, and the results obtained for the FOPID controller by different optimization algorithms along with a PID controller have been compared and discussed.

关键词： Power conditioning Software packages Photovoltaic cells Metaheuristics whale optimization algorithms Voltage control Inductors

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