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Color Image Edge Detection Method Based on the Improved whale optimization Algorithm

IEEE ACCESS 2023年 11卷 5981-5989页

作者： Liu, Dujin Zhou, Shiji Shen, Rong Luo, Xuegang Sichuan Univ Arts & Sci Sch Intelligent Mfg Dazhou 635000 Peoples R China Panzhihua Univ Sch Math & Comp Panzhihua 617000 Peoples R China

A high level of computation is required for edge detection in color images captured by unmanned aerial vehicles (UAVs) to address issues, such as noise, distortion, and information loss. Thus, an edge detection method for UAV-captured color images based on the improved whale optimization algorithm (WOA) is proposed in this study. In this method, the color image pixels are represented by quaternions, and the global random position variables and information exchange mechanism are introduced into the random walk foraging formula of the WOA. Further, a random disturbance factor is also introduced into the predator-prey formula of the spiral bubble net. The proposed improved WOA is then used to obtain the preliminary edge of the UAV-captured color image. An edge-point classification method using the radius of the shortest distance between the whale and the current global optimum in each iteration is presented to enhance a preliminary edge. The experimental results show that the proposed edge detection method has the advantages of strong denoising, fast speed, and good quality.

关键词： Image edge detection Autonomous aerial vehicles Quaternions whale optimization algorithms Image color analysis Spirals Classification and purification edge detection quaternion unmanned aerial vehicle (UAV) whale optimization algorithm (WOA)

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Comprehensive Technical Review of Recent Bio-Inspired Population-Based optimization (BPO) algorithms for Mobile Robot Path Planning

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IEEE ACCESS 2024年 12卷 20942-20961页

作者： Saleh, Izzati Borhan, Nuradlin Yunus, Azan Rahiman, Wan Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia CL Solut Sdn Bhd Wisma Rampai Kuala Lumpur 59200 Malaysia Univ Sains Malaysia Cluster Smart Port & Logist Technol COSPALT Nibong Tebal 14300 Malaysia Daffodil Int Univ Dept Comp Sci & Engn Daffodil Robot Lab Dhaka 1216 Bangladesh

Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper's focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, whale optimization Algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.

关键词： optimization methods Path planning Mathematical models whales Spirals Mobile robots Bio-inspired computing Metaheuristics Particle swarm optimization whale optimization algorithms Grasshopper optimization algorithm grey wolf optimization metaheuristic optimization path planning salp swarm optimization sparrow search optimization whale optimization

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Enhanced Speed Control for BLDC Motors Using WOA-Integrated PID Controller optimization

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IEEE ACCESS 2024年 12卷 162465-162475页

作者： Krishnamoorthy, Saravana Kumar Das, Narottam Gudimetla, Prasad Emami, Kianoush Cent Queensland Univ Melbourne Sch Engn & Technol Melbourne Vic 3000 Australia Cent Queensland Univ Ctr Intelligent Syst Brisbane Qld 4000 Australia Cent Queensland Univ Bundaberg Sch Engn & Technol Bundaberg Qld 4670 Australia Cent Queensland Univ Cairns Sch Engn & Technol Cairns Qld 4870 Australia

In electrical engineering, Brushless Direct Current (BLDC) motors are frequently used in mechanical applications because of their effectiveness, strong torque, and small design. Nevertheless, reaching peak performance and making accurate adjustments to parameters can be difficult when using a simple, customized Proportional Integral Derivative (PID) controller. In the past, speed control typically included adjusting crucial factors like voltage, and current. However, manual speed regulation has drawbacks, including being time-consuming, susceptible to human error, and lacking scalability. Different traditional models have tried to enhance speed control efficiency with Artificial Intelligence (AI) but face challenges in improving Rise Time, Settling Time, Maximum Overshoot, and overall efficiency. A proposed approach to address this problem involves the implementation of a developed model using the Enhanced whale optimization Algorithm- Tuned PID (EWOA-TPID) Controller. This system utilizes the benefits of the whale optimization Algorithm (WOA) to increase convergence speed, enhance exploitation and exploration abilities, and accurate speed control by efficiently tuning the parameters of PID to decrease the steady state error and overshoot. The key performance metrics which comprise Rise Time, Settling Time, and Maximum Overshoot are utilized to assess the efficacy of this approach. Moreover, the presented system is compared with conventional models to showcase the improved effectiveness of the respective model. This innovative approach intends to contribute significantly to studies in areas like robotics, automation, electric vehicles, industrial machinery, and other systems that use BLDC motors for speed regulation.

关键词： Motors Velocity control Torque Artificial intelligence optimization Regulation PI control MATLAB whale optimization algorithms Pulse width modulation Brushless DC motors Artificial intelligence (AI) brushless direct current (BLDC) motor enhanced whale optimization algorithm (EWOA) proportional integral derivative (PID) pulse width modulation (PWM)

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Optimal Design of a PIDD² Controller for an AVR System Using Hybrid whale optimization Algorithm

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IEEE ACCESS 2024年 12卷 128525-128540页

作者： Dai, Fei Gao, Song Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Many meta-heuristic algorithms have been used in designing PID controllers to control automatic voltage regulator (AVR) system. PIDD2 controller is a PID controller with second-order derivative gain. The fireworks whale optimization algorithm is an advanced metaheuristic algorithm that combines fireworks and whale optimization algorithms. It has good exploratory properties because it explores a large potential space and avoids premature local optima. This paper presents a PIDD2 controller for improving the performance of the AVR system using the hybrid whale optimization algorithm. The simulation results, divided into two parts based on whether the limitation of the excitation voltage is considered or not, include a detailed analysis of the proposed controller in terms of step response, stability analysis, trajectory tracking, parameter uncertainty, and rejection of disturbance. To demonstrate the effectiveness of the proposed controller of the AVR system, it was compared with the controllers in recent literature in each of the aforementioned terms. The proposed controller, in most comparisons, provides better results than other controllers. All these results demonstrate that the performance and robustness of the proposed controller are competitive.

关键词： Voltage control Generators whale optimization algorithms Transfer functions Tuning Synchronous generators Automatic voltage control Optimal control Automatic voltage regulator PIDD2 controller optimal control hybrid whale optimization algorithm

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Fuzzy Automatic Disturbance Rejection Control of Quadrotor UAV Based on Improved whale optimization Algorithm

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IEEE ACCESS 2023年 11卷 69117-69130页

作者： Shan, Zebiao Wang, Yuhang Liu, Xiaosong Wei, Changbin Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China Jilin Univ Coll Commun Engn Changchun 130022 Peoples R China Changchun Meteorol Instrument Res Inst Changchun 130102 Peoples R China

With the aim of improving the rapidity and accuracy of quadrotor UAV positional trajectory tracking, this paper proposes a fuzzy automatic disturbance rejection control of quadrotor UAV based on improved whale optimization algorithm (IWOA-Fuzzy-ADRC). (1) To address the problems of low accuracy and slow convergence of the traditional whale algorithm, a combination of logistic chaos mapping and skew tent mapping is applied to improve the diversity of the initial population. (2) The global search capability is strengthened to avoid falling into local optimum through introducing the cross operator and Gaussian variational operators. (3) The IWOA is employed to iteratively optimize the adjustment coefficients of the fuzzy active disturbance rejection controller, the calculus gain of the nonlinear state error feedback control (NLSEF), and the error correction coefficients of the expansive state observer (ESO). The simulation results demonstrate that compared with the traditional ADRC controller and PID controller, the proposed IWOA-Fuzzy-ADRC controller can effectively improve the control performance of the system, accurately track the desired flight trajectory, accelerate the dynamic response of the control system, reduce the steady-state error, and enhance the anti-interference capability.

关键词： Quadrotors Control systems Heuristic algorithms Observers Autonomous aerial vehicles whale optimization algorithms Rotors Fuzzy systems Chaotic communication INDEX TERMS Gaussian processes UAV trajectory tracking fuzzy ADRC whale optimization algorithm chaotic mapping cross-operator Gaussian variant operators

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Optimal Placement of FACTS Devices Using Modified whale optimization Algorithm for Minimization of Transmission Losses

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IEEE ACCESS 2024年 12卷 130816-130831页

作者： Kar, Manoj Kumar Kanungo, Sanjeet Alsaif, Faisal Ustun, Taha Selim Tolani Maritime Inst Dept Elect & Elect Pune 410507 Maharashtra India Tolani Maritime Inst Marine Engn Dept Pune 410507 Maharashtra India King Saud Univ Coll Engn Dept Elect Engn Riyadh 11421 Saudi Arabia AIST FREA Fukushima Renewable Energy Inst Koriyama 9630298 Japan

A novel modified whale optimization algorithm (MWOA) is presented in this study to minimize transmission losses. The proposed method is used to determine the best control variables, such as reactive power generation, transformer tap settings, and reactive power sources. First, the power flow analysis method is used to determine where the flexible AC transmission system (FACTS) devices should be placed. To achieve the intended objectives, the suggested MWOA approach is applied on multiple IEEE standard test bus systems (IEEE-30, -57, and-118) at different active and reactive loading conditions. The shunt compensation was handled by a Static VAR compensator (SVC) while the series compensation was handled by a thyristor-controlled series compensator (TCSC). The results of applying the MWOA approach are shown and contrasted with those of other promising optimization techniques, including the sine cosine algorithm (SCA), whale optimization algorithm (WOA), moth flame optimization (MFO), grey wolf optimization (GWO), and particle swarm optimization (PSO). The proposed method significantly reduces the active power loss, i.e., 11.11 % in IEEE 30, 50.32 % in IEEE 57 and 15.17% in IEEE 118 bus system at base loading. Ultimately, a comprehensive study of the statistical data was conducted to validate the precision and robustness of the suggested methodology.

关键词： optimization Mathematical models Power system stability Reactive power whale optimization algorithms Propagation losses Thyristors Active power loss load flow analysis modified whale optimization algorithm optimal placement SVC TCSC

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Exploring LBWO and BWO algorithms for Demand Side optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

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IEEE ACCESS 2024年 12卷 28831-28852页

作者： Youssef, Heba Kamel, Salah Hassan, Mohamed H. Mohamed, Ehab Mahmoud Belbachir, Nasreddine Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Minist Elect & Renewable Energy Cairo 11517 Egypt Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Addawasir Dept Elect Engn Wadi Ad Dawasir 11991 Saudi Arabia Univ Mostaganem Dept Elect Engn Mostaganem 27000 Algeria

Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic (PV) panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. Simulation results indicate that in case 2, LBWO and BWO achieved a remarkable reduction in electricity cost by 61% and 51% respectively. In case 3, the reduction was even more significant, with LBWO and BWO lowering the cost by 76% and 64% respectively. Moreover, LBWO generated a revenue of 154 (cents), while BWO generated a revenue of 118 (cents). The results confirm the effectiveness and robustness of the suggested scheme in reducing electricity costs and the PAR (Peak to Average Ratio), while simultaneously increasing profits for electricity consumers.

关键词： optimization Costs Renewable energy sources Smart homes Peak to average power ratio Energy storage Scheduling whale optimization algorithms Demand side management Energy management Storage management Beluga whale optimization demand side management leader beluga whale improvement mini-grid renewable energy source storage system

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PI Parameters Tuning for Frequency Tracking Control of Wireless Power Transfer System Based on Improved whale optimization Algorithm

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IEEE ACCESS 2024年 12卷 13055-13069页

作者： Yang, Xiong Guan, Jiamin State Grid Jiangsu Elect Power Co Ltd Distribut Network Technol Dept Res Inst JSEPRI Nanjing 211103 Peoples R China Nanjing Inst Technol Sch Mech Engn Nanjing 211167 Peoples R China

For the efficiency optimization problem of wireless power transfer system, the most important thing is that the frequency tracking control is needed to make the system work in a resonant state. The parameters of the frequency tracking controller directly determine the control performance. In this paper, we consider using an improved whale optimization algorithm to complete the PI parameter tuning of the frequency tracking controller as a way to save personnel's effort and time on the controller design. This improved whale optimization algorithm mainly has the following improvements: firstly, it uses Kent chaotic mapping to initialize the whale population, which improves the variegation of the initial solution;secondly, it introduces adaptive weight coefficients and nonlinearly improves the convergence factor, which achieves a balance between the algorithm's global and local searching ability;finally, it introduces sine-cosine algorithmic strategy and Cauchy reverse learning strategy, which avoids the problem that the algorithm is easy to mature prematurely. The superiority of the improved algorithm is verified through the testing of eight benchmark functions and the Wilcoxon rank sum test method. Finally, the algorithm is applied to the PI parameter optimization of frequency tracking control, and the phase difference curves under the control of different PI parameters are compared by simulation. The results show that the improved algorithm proposed in this paper has a better performance for the PI parameter tuning of frequency tracking control in wireless power transfer system, which can effectively help reduce the investment of human resources.

关键词： Resonant frequency whale optimization algorithms Tuning Heuristic algorithms Convergence Statistics Social factors Wireless power transfer PI control Energy efficiency frequency tracking control whale optimization algorithm

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Optimal Configuration of Distributed Generation Based on an Improved Beluga whale optimization

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IEEE ACCESS 2024年 12卷 31000-31013页

作者： Li, Jifang Zhou, Xingyao Zhou, Yifan Han, Aishan North China Univ Water Resources & Elect Power Coll Elect Engn Zhengzhou 45000 Peoples R China

Large-scale distributed generation (DG) access to the distribution network brings many uncertainties to the distribution network, considering the impact of different types of distributed power supply on the optimization results as well as the uncertainty and correlation of wind energy, photovoltaic power generation, and load. A metaheuristic algorithm the Improved Beluga whale optimization Algorithm (IBWO) is used to optimize the capacity and location of DG. This algorithm incorporates the elite reverse learning strategy and cyclone foraging strategy while adjusting the balance factor to enhance the diversity of the algorithm population and further balance local search capability with global search capability. This study optimizes the configuration of distributed energy resources considering the uncertainty and correlation of wind power, photovoltaic power, and load. The optimization objective is to reduce active power loss, improve voltage stability, and minimize investment and operating costs. By conducting simulations on IEEE 33-bus and IEEE 118-bus test cases, the active power network losses are enhanced by 55.49% and 45.39% respectively, and the algorithm outperforms other methods regarding other data, demonstrating its superiority and effectiveness.

关键词： Costs optimization Distribution networks Voltage Uncertainty Investment Power system stability Distributed power generation whale optimization algorithms Capacity planning Configuration management Distributed generation distribution network uncertainty correlation improved beluga whale optimization

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Hybrid optimization Based Harmonic Minimization in Three Phase Multilevel Inverter With Reduced Switch Topology

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IEEE ACCESS 2024年 12卷 71010-71023页

作者： Yabalar, Mehmet Halil Ercelebi, Ergun Hasan Kalyoncu Univ Dept Elect & Elect Engn TR-27010 Gaziantep Turkiye Gaziantep Univ Dept Elect & Elect Engn TR-27310 Gaziantep Turkiye

This study presents an innovative hybrid optimization approach that combines teaching-learning based optimization (TLBO) with the whale optimization algorithm (WOA) for selective harmonic elimination (SHE) technique in a modified reduced switch topology three phase multilevel inverter (MLI). The proposed topology requires fewer switches than a conventional cascaded H-bridge MLI and another reduced switch topology in a single phase MLI. Once applied to an 11-level inverter, this hybrid strategy effectively tackles the issues of harmonic reduction and total harmonic distortion (THD) on the line-to-line voltage, significantly improving the quality of the output power through the optimal determination of switching angles. The study leverages the TLBO and WOA to solve the non-linear set of equations associated with the SHE controls technique, aiming to overcome the limitations of classical methods prone to local optimal solutions and dependent on initial controlling parameters. This method has been performed in two steps, during the first step TLBO has been executed and in the next step the solutions derived from TLBO has been used as an initial guess for WOA which ensures the attainment of the precisely converged solution. By using MATLAB (R)/Simulink software environment, the performance of the hybrid TLBO with WOA method has been simulated and benchmarked against traditional standalone metaheuristic techniques. The simulation results reveal that proposed hybrid approach becomes advantageous in terms of SHE and output voltage quality across various modulation indices. The experimental results verified that the proposed algorithm has been validated through the implementation of a three-phase 11-level inverter. This study highlights the significant potential of the hybrid optimization method in progressing harmonic minimization techniques within the multilevel inverters.

关键词： Switches Harmonic analysis Topology optimization Voltage Multilevel inverters Switching loss whale optimization algorithms Teaching-learning based optimization (TLBO) whale optimization algorithm (WOA) selective harmonic elimination (SHE) reduced switch topology multilevel inverter (MLI) total harmonic distortion (THD) modulation index

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