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Stability Improvement of Sulselrabar System With Integrated Wind Power Plant Using Multi-Band PSS3C Based mayfly optimization algorithm

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IEEE ACCESS 2024年 12卷 76707-76734页

作者： Djalal, Muhammad Ruswandi Robandi, Imam Prakasa, Mohamad Almas Inst Teknol Sepuluh Nopember Dept Elect Engn Surabaya 60111 Indonesia

The introduction of additional controllers is essential in modern electric power systems to enhance their stability, particularly during disturbances. One effective method is the implementation of power system stabilizers (PSS). However, precise coordination of PSS equipment is necessary to determine the optimal locations and parameters. This study focuses on the optimal analysis of Multi-Band PSS3C (MB-PSS3C) coordination in integrated Wind Power Plant (WPP) systems in South, Southeast, and West Sulawesi (Sulselrabar). An artificial intelligence approach, utilizing the mayfly optimization algorithm (MOA), is suggested for optimizing both the location and parameters of the PSS. Comparative investigations were conducted to assess the efficacy of MB-PSS3C in comparison with SB-PSS1A and MB-PSS2B, based on previous research. The performance analysis employed the time domain simulation method, reviewing the speed deviation response, field voltage response, PSS output voltage response, and rotor angle response for each generator. Eigenvalue analysis was performed for each control scheme. Load changes were applied to generators 1 (BAKARU) and 11 (WPP SIDRAP) to evaluate the performance of the system. The application of the MOA-based MB-PSS3C results in an increased damping ratio, improved speed response, and a more optimal rotor angle. MB-PSS3C provides a larger additional damping signal to the generator exciter, as indicated by the increase in the field voltage on the generator.

关键词： Power system stability Stability analysis Oscillators Damping Numerical stability Generators Rotors Wind power generation optimization methods Stability multi-band PSS3C wind power plant mayfly optimization algorithm Sulselrabar

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A novel optimal PI^λ1I^λ2D^μ1D^μ2 controller using mayfly optimization algorithm for automatic voltage regulator system

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NEURAL COMPUTING & APPLICATIONS 2023年第27期35卷 19899-19918页

作者： Cavdar, Bora Sahin, Erdinc Akyazi, Omur Nuroglu, Fatih Mehmet Karadeniz Tech Univ Dept Energy Syst Engn Trabzon Turkiye Giresun Univ Dept Comp Engn Giresun Turkiye Karadeniz Tech Univ Dept Elect & Elect Engn Trabzon Turkiye

This paper presents a novel (PIIDD mu 2)-I-lambda 1-D-lambda 2-D-mu 1 controller for the automatic voltage regulator (AVR) system. The AVR system controls the terminal voltage of synchronous generators. In this way, it contributes to voltage stability in power systems. The parameters of the proposed controller are optimized with the recently developed mayfly algorithm for various objective functions. These objective functions are Zwe-Lee Gaing, the integral of time multiplied absolute error, the integral of squared error, the integral of time multiplied squared error and the integral of absolute error (IAE). The best result in terms of transient response parameters (settling time, rise time, and maximum overshoot) at terminal voltage is compared to controllers developed in recent years. Both time domain analysis and frequency domain analysis is used in this comparison. From the comparison results, it has been observed that the proposed controller provided better performance as compared to existing controllers in the literature. In addition to time and frequency domain analysis, robustness, nonlinear effect, and external disturbance rejection performances of the proposed controller are analyzed for the AVR system. As a result of all these analyses, it is seen that the proposed controller displays outstanding performance.

关键词： Automatic voltage regulator Fractional controller mayfly optimization algorithm Time and frequency domain analysis

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The Static Adjustment Method of Secondary Suspension Load for Six-Axle Locomotives via Modified mayfly optimization algorithm

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9165-9174页

作者： Xiao, Yuqi Wu, Yongjun West Anhui Univ Sch Mech & Vehicle Engn Luan 237012 Anhui Peoples R China Chongqing Jiaotong Univ Sch Traff & Transportat Chongqing 400074 Peoples R China

The static distribution of the static secondary suspension load has a significant impact on the adhesion, rim wear and passenger comfort of the locomotive. At present, the locomotive secondary suspension load adjustment is mainly realized by artificial padding to adjust the height of the spring. This process is not predictable, so it is difficult to complete the adjustment at one time, and it is limited by the operating space, technician proficiency, weight and volume of equipment, etc., so the efficiency of the whole industrialization process needs to be improved. Therefore, it is necessary to study the relationship between the shimming quantity, shimming position and load variance, and establish an expert system to guide the spring adjustment process, so as to avoid the waste of time, space and human resources caused by repeated operation. Based on the analysis of the supporting structure and force balance relationship, the general theoretical model of the load adjustment of the secondary suspension design with flexicoil suspension springs of six-axle locomotive is established in this study. The mayfly algorithm (MOA), which is characterized by high efficiency and precision, is introduced into the field of the locomotive spring load adjustment process for the first time in this study. However, the standard MOA still faces the problems of low efficiency of late iteration, sample degradation and algorithm prematurity. Therefore, this study optimized the standard MOA from the parameter control, offspring mutation strategy and weighted optimization strategy of the position updating process. The experimental analysis of an HXD1C locomotive proves that the proposed method is a great improvement in terms of efficiency and accuracy.

关键词： Springs Load modeling Axles Wheels Rail transportation Suspensions (mechanical systems) optimization Locomotive secondary suspension design with flexicoil suspension springs static load distribution mayfly optimization algorithm

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Optimal Design of IIR-Type Fractional Order Digital Integrator Using mayfly optimization algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第2期42卷 913-942页

作者： Dey, Souvik Roy, Provas Kumar Chakraborty, Sudipta Bengal Inst Technol Dept Elect & Commun Engn Kolkata 700150 W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani 741235 W Bengal India Techno Main Saltlake Dept Elect & Commun Engn Kolkata 700091 W Bengal India

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers.

关键词： Digital signal processing Infinite impulse response Fractional order integrator Whale optimization algorithm Chaotic oppositional whale optimization algorithm mayfly optimization algorithm

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MPPT control based on improved mayfly optimization algorithm under complex shading conditions

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2021年第6期22卷 661-674页

作者： He, Xingmin He, Baina Zhao, Yunwei Cui, Rongxi Zhang, Jingru Dong, Yanchen Jiang, Renzhuo Shandong Univ Technol Coll Elect & Elect Engn Zibo Peoples R China Shandong Vocat Coll Ind Dept Elect Engn Zibo Peoples R China Smart Grid Rizhao Power Supply Co Rizhao Peoples R China

The output power curve of the photovoltaic array presents a multi-peak characteristic under partial shading conditions, which causes the traditional maximum power point tracking technology to fail to guarantee the maximum power output of the photovoltaic cell. In response to this problem, this paper proposes to apply the improved mayfly optimization algorithm (MA) to the maximum power tracking control. By introducing the gravity factor and limiting the search space of male mayfly, the optimization accuracy of the algorithm is enhanced, the vibration of the algorithm near the MPP is reduced, and the occurrence of premature phenomenon is avoided. Three test functions are selected to verify the algorithm, and under the conditions of rapid irradiance changes and complex shadow occlusion, an MPPT model based on the Boost circuit is established to verify the effectiveness of the algorithm. The simulation results show that the improved MA algorithm can effectively converge to MPP under complex shading conditions, and the output efficiency of photovoltaic arrays can be maintained above 99.96%. The average tracking time for different shading patterns is about 0.15 s.

关键词： maximum power point tracking mayfly optimization algorithm partial shadow photovoltaic array

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The fully informed mayfly optimization algorithm

The fully informed mayfly optimization algorithm

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International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Zhao, Juan Gao, Zheng-Ming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen Peoples R China

ISBN: (纸本)9781728196190

Considering the similarity in structures of updating equations between the mayfly optimization (MO) and particle swarm optimization (PSO) algorithms, the fully informed MO algorithm was proposed in this paper. The updating equations were re-formulated based on the explosion and stability analysis on the PSO algorithm. Results of several kinds of simulation experiments verified that such improvement could increase the capability of individuals in the MO swarm in optimization. The fully informed MO algorithm was confirmed to be another efficient way for optimization problems.

关键词： mayfly optimization algorithm benchmark function simulation experiment Monte Carlo method

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Self-organizing hierarchical mayfly optimization algorithm

Self-organizing hierarchical mayfly optimization algorithm

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International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Gao, Zheng-Ming Li, Su-Ruo Zhao, Juan Hu, Yu-Rong Jingchu Univ technol Sch Comp Engn Jingmen Peoples R China Jingchu Univ technol Sch Elect & informat Engn Jingmen Peoples R China Jingchu Univ technol Dept Sci & technol Jingmen Peoples R China

ISBN: (纸本)9781728196190

In this paper, the self-organizing hierarchical method was introduced to improve the capability of optimization for the newly proposed mayfly optimization (MO) algorithm. The weighted current velocities were removed from the updating equations and the re-initialization would be carried out if the updated velocities were turned to zero. Simulation experiments were carried out with the Monte Carlo method. Results verified the better performance of the improved MO algorithm than before.

关键词： mayfly optimization algorithm benchmark function simulation experiment Monte Carlo method

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Diagnostic Framework for Mumps Based on Deep Learning with Enhanced mayfly optimization algorithm 1

Diagnostic Framework for Mumps Based on Deep Learning with E...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mercy, S. Sudha Madhavi, S. Keerthika, A. Prathima, Ch Prabhu, R.T. R.M.K Engineering College Department of Computer Science & Design Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Department of It Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai India Department of Data Science Tirupati India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfortable. Mumps treatment centers on symptom management. The illness must take its natural course. While most people experience very moderate symptoms, some develop severe consequences. The mumps virus can be avoided with the use of the MMR vaccination. In this research, we employ a convolutional neural network (CNN) technique to better automate mumps detection. In order to automate mumps detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating machine learning (ML) technique comparisons. A mumps dataset consisting of 50×50 RGB picture patches served as the basis for all the models. Quantitative findings from each approach were put through validation testing based on their respective effectiveness metrics. To improve the efficiency of the model, the mayfly optimization algorithm is implemented. Results show that the suggested method is effective, with 97% accuracy, suggesting that it might help decrease diagnostic errors caused by human error. And the suggested system we've developed is so precise that it outperforms the CNN model, which only gets around 92% of the time. Consequently, the suggested method is 5% more accurate than the CNN Model. © 2023 IEEE.

关键词： Convolutional Neural Network mayfly optimization algorithm Mumps Classification XGBoost Classifier

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Process control of chemical dynamic system based on multi-strategy mayfly optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 7327-7352页

作者： Li, Jingyan Mo, Yuanbin Hong, Lila Gong, Rong Guangxi Minzu Univ Sch Artificial Intelligence Nanning Peoples R China Guangxi Minzu Univ Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning Peoples R China Guizhou Police Coll Guiyang Peoples R China Aba Teachers Coll Sch Comp Sci & Technol Wenchuan Peoples R China

Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.

关键词： Chemical dynamic system process control dynamic optimization mayfly optimization algorithm control vector parameterization

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A High-Performance Low Complex Design and Implementation of QRS Detector Using Modified MaMeMi Filter Optimized with mayfly optimization algorithm

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第4期32卷 2350056-2350056页

作者： Malathi, S. R. Kumar, P. Vijay Sri Venkateswara Coll Engn Dept Elect & Commun Engn Chennai 602117 Tamil Nadu India

Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as sigma and Delta are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated

关键词： Electrocardiogram baseline wander modified MaMeMi filter mayfly optimization algorithm MIT/BIH arrhythmia dataset QRS detector minimal computational cost minimal resource usage

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