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Comprehensive Optimization of PID Controller Parameters for DC Motor Speed Management Using a Modified jellyfish search algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第1期46卷 320-342页

作者： Guven, Aykut Fatih Mengi, Onur Ozdal Elseify, Mohamed A. Kamel, Salah Yalova Univ Dept Elect & Elect Engn Yalova Turkiye Giresun Univ Dept Elect & Elect Engn Giresun Turkiye Al Azhar Univ Fac Engn Dept Elect Engn Qena Egypt Aswan Univ Dept Elect Engn Aswan Egypt

The proportional integral derivative (PID) controller plays a crucial role in various industrial applications. However, conventional methods often fail to provide optimal parameter values. Therefore, this study proposes a novel version of the jellyfish search (JS) algorithm, the modified jellyfish search (mJS) algorithm, for optimally tuning PID controllers to regulate the speed of a direct current (DC) motor. The original JS algorithm is known for its nature-inspired approach;however, it has limitations in terms of exploitation power. To address these issues, the proposed mJS incorporates quasi-dynamic opposed-based learning and a Weibull probability distribution. The objective function minimizes the integral of the time-weighted absolute error (ITAE). The effectiveness of mJS was validated by solving the 23rd classical benchmark function, followed by a comparative analysis with contemporary optimization algorithms using stringent statistical criteria. Then mJS is then applied to optimize the PID parameters of three different DC motor models. Results show that the mJS outperforms the standard JS, gray wolf optimization (GWO), JAYA, and golden jackal optimization (GJO) for various performance indicators. Specifically, the best ITAE values were 3.274e6 for DC motor 1 using the JS algorithm, 0.002737 for DC motor 2 using the GJO algorithm, and 0.02785 for DC motor 3 using the mJS algorithm. Additionally, the mJS and JS algorithms reduced the settling time to 0.005 s for DC motor 1, whereas the best settling time of 0.325 s for DC motor 2 was achieved by the mJS algorithm, and a settling time of 1.19 s was recorded for DC motor 3 using both the mJS and JS algorithms. These findings underscore the practical importance of the developed optimizer in engineering applications and its significant contribution to the literature.

关键词： jellyfish search algorithm modified jellyfish search algorithm PID controller quasi-dynamic learning Weibull probability distribution

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jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition

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RENEWABLE ENERGY 2023年第1期204卷 197-217页

作者： Yang, Bo Wu, Shaocong Li, Qiang Yan, Yingjie Li, Danyang Luo, Enbo Zeng, Chunyuan Chen, Yijun Guo, Zhengxun Shu, Hongchun Li, Zilin Wang, Jingbo Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China China Southern Power Grid Co Ltd EHV Power Transmiss Co Dali Bur Dali 671000 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming 650217 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

Thermoelectric generators (TEGs) can convert heat to electricity based on the temperature difference between their hot and cold sides. However, during practical operations, low thermoelectric conversion efficiency is usually caused by non-uniform temperature difference (NTD) conditions;thus, reliable and effective maximum power point tracking (MPPT) under NTD conditions is imperative for TEG arrays. Therefore, in this study, a jellyfish search (JS) algorithm-based TEG array reconfiguration strategy is proposed to realise MPPT under NTD conditions, upon which the overall power loss is effectively decreased, the energy conversion efficiency is efficiently enhanced, and the MPPT speed is improved. In particular, the simulation results demonstrate that the JS algorithm-based TEG array reconfiguration using a random pattern can enhance the maximum output power by 23.32% for 9 x 9 symmetric TEG arrays and 15.78% for 10 x 15 asymmetric TEG arrays. Finally, a hardware-inthe-loop (HIL) test was conducted on the RTLAB platform to verify its feasibility for hardware implementation.

关键词： Thermoelectric generator array reconfiguration Non-uniform temperature distribution jellyfish search algorithm Hardware-in-the-loop

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jellyfish search algorithm-based optimum tuning of PI controller for a front-end converter in a DFIG-based wind energy conversion system

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SOFT COMPUTING 2024年第11-12期28卷 7287页

作者： Rauth, Sheshadri Shekhar Kastha, Debaprasad Bajpai, Prabodh Indian Inst Technol Kharagpur Dept Elect Engn Kharagpur 721302 West Bengal India

This paper proposes a jellyfish search algorithm (JSA)-based offline method to design a DC voltage controller of AC/DC front-end converter (FEC) in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). First, a multi-objective tuning problem is formulated using a full-order converter model to achieve maximum utilization of converter parameters in controller design. Next, a normalized mono-objective problem is developed using a utopia-tracking approach to reduce the computational complexity in design. Finally, the mono-objective problem is solved using JSA to enhance the controller performance by obtaining optimal gain values for the controller. A comparative study is performed with other metaheuristic optimization techniques to justify the use of JSA in this application. The results confirm that the accuracy of the JSA-based approach is higher, whereas the execution time of the method is lower than the other methods. Furthermore, to validate the performance of the proposed approach, a DFIG-based WECS is modeled and simulated using OPAL-RT real-time simulator. A grid voltage-oriented vector control is used as the overall control algorithm for the FEC. Unlike the conventional pole-zero cancellation method, the proposed approach provides no voltage overshoot during voltage development. Compared to the published literature, it also ensures 40.65% lesser voltage deviation without any overcurrent, irrespective of wind speed and load variations.

关键词： Controller design Front-end AC/DC converter jellyfish search algorithm Pole zero cancellation Doubly fed induction generator Wind energy conversion system

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Torque ripple minimization of bridgeless CUK converter-based BLDC motor using Improved jellyfish search algorithm

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ISA TRANSACTIONS 2023年 136卷 374-389页

作者： Rajesh, A. Saravanan, A. Gnana SCAD Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India Francis Xavier Engn Coll Dept Elect & Elect Engn Tirunelveli Tamil Nadu India

This paper proposed an improved jellyfish search (ImpJS) technique for torque ripple minimization on CUK converter based BLDC motor. In this paper, crossover and mutation operator are utilized to improve searching behavior of the JS algorithm. Hence, it is named as improved jellyfish algorithm (ImpJS). At first, BLDC motor is considered along with the Cuk converter is improved through the switched inductor. Simultaneously, the execution of the BLDC motor operation includes speed and torque control strategy is also analyzed. In order to improve these two strategies, the proposed ImpJS system is introduced. The best gain parameter is tuned to upgrade the controller operation considering the objective function. Finally, the proposed technique-based BLDC motor is performed on MATLAB/Simulink platform in order to analyze the performance is compared with other existing system to determine the effectiveness. The existing techniques like particle swarm optimization (PSO), ant lion optimizer (ALO) and salp swarm algorithm (SSA). In case 1 and case 2, the MSE for proposed technique achieves the value of 0.01093 and 0.01095. In case 1 and case 2, the voltage deviation for the proposed system achieves the value of 2 and 2 (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Torque ripple jellyfish search algorithm Cross over Mutation Cuk converter Speed control Torque control BLDC motor

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Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by jellyfish search algorithm

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IEEE ACCESS 2021年 9卷 92053-92069页

作者： Shaheen, Abdullah M. Elsayed, Abdullah M. Ginidi, Ahmed R. Elattar, Ehab E. El-Sehiemy, Ragab A. Suez Univ Fac Engn Dept Elect Engn Suez 43518 Egypt Damietta Univ Fac Engn Dept Elect Engn Dumyat 22052 Egypt Taif Univ Coll Engn Dept Elect Engn At Taif 21944 Saudi Arabia Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafr Al Sheikh 33516 Egypt

Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on jellyfish search algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem's complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

关键词： Control systems Automation Reactive power Optimization Performance evaluation Economics Sensitivity Distribution systems automation static VAr compensators reconfiguration distributed generation units jellyfish search algorithm

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jellyfish search algorithm for MPPT in Photovoltaic Systems Under Partial Shading Conditions

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FLUCTUATION AND NOISE LETTERS 2023年第2期22卷

作者： Karthikeyan, M. Manimegalai, D. Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala En Chennai India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai India

In photovoltaic systems, maximum power point tracking (MPPT) methods are used to get the maximum power out of them. The presence of several peaks in a PV array's power-voltage characteristics is due to partial shadowing conditions that increase the complexity of the tracking operation. In this paper, the Global Maximum Power Point (GMPP) is calculated using latest meta-heuristic optimization algorithm known as jellyfish search (JS). This unique MPPT approach is used to reduce PV module tracking time and improve the tracking efficiency. Using MATLAB/SIMULINK, the effectiveness of the suggested JS algorithm is assessed by contrasting it with the traditional P & O approach in terms of tracking speed and precision. The simulation findings indicate that the JS algorithm's tracking ability is better than that of the conventional P & O method. In the experimental results, the JS algorithm reduces convergence time by 56.6% when compared to the PSO algorithm. Also, the proposed JS algorithm generates output power higher than the PSO MPPT algorithm using the duty cycle ratio value at the expected peaks.

关键词： Photovoltaic systems partial shading conditions global peak jellyfish search algorithm tracking time tracking efficiency

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Optimal allocation of DG and Capacitor Units Using jellyfish search algorithm

Optimal allocation of DG and Capacitor Units Using Jellyfish...

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13th IEEE-PES Asia Pacific Power and Energy Engineering Conference (APPEEC)

作者： Tiwari, Vimal Dubey, Hari Mohan Pandit, Manjaree MITS Dept Elect Engn Gwalior India BIT Dept Elect Engn Sindri Dhanbad India

ISBN: (纸本)9781665448789

In this paper, an efficient jellyfish search algorithm (JSA) is applied to analyze the impact of the installation of distributed generation (DG) and Capacitor Bank (CB) on the radial distribution system (RDS) with the objective is to minimize real power loss and maximize the profit. Here the cost-based analysis is carried out considering the major expenses occurred due to DG and CB units installation, their operation and maintenance cost. The JSA is a new metaheuristic approach inspired by the behaviour and movement of jellyfish, to search for their food in the ocean. The efficacy of JSA is tested on the IEEE 33 bus RDS, and the outcome of simulation results are compared with reported results by other methods available in the literature. Comparison of results supports claiming the effectiveness of JSA over other methods in terms of both power loss minimization and maximizing profit.

关键词： distributed generation radial distribution system jellyfish search algorithm power loss minimization profit maximization capacitor bank

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Modeling of a solar-powered thermoelectric air-conditioning system using a random vector functional link network integrated with jellyfish search algorithm

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CASE STUDIES IN THERMAL ENGINEERING 2022年 31卷

作者： Almodfer, Rolla Zayed, Mohamed E. Abd Elaziz, Mohamed Aboelmaaref, Moustafa M. Mudhsh, Mohammed Elsheikh, Ammar H. Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Tanta Univ Fac Engn Dept Mech Power Engn Tanta 31521 Egypt Sohag Univ Fac Technol & Educ Mech Engn Dept Sohag 82524 Egypt Galala Univ Fac Comp Sci & Engn Suez 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Zagazig Univ Fac Sci Dept Math Zagazig Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt

In this study, the performance of a solar thermoelectric air-conditioning system (STEACS) is predicted using advanced optimized artificial intelligence models. A STEACS powered by solar PV panels is experimentally tested under different cooling loads varying from 65.0 to 260 W. The obtained experimental data are used to train and test the model. The model consists of a random vector functional link (RVFL) network optimized by one metaheuristic optimizer such as jellyfish search algorithm (JFSA), artificial ecosystem-based optimization (AEO), manta ray foraging optimization (MRFO), and sine cosine algorithm (SCA). The inputs of the model were time, solar irradiance, ambient temperature, wind speed, and humidity. The predicted responses of the investigated system are the input current of PV, the average temperature of the air-conditioned room, the cooling capacity, and the coefficient of performance. The accuracy of the four models is evaluated using eight statistical measures. RVFL-JFSA outperformed the other models in predicting all responses with a correlation coefficient of 0.948-0.999 and, consequently, it is recommended to use it to model STEACS system.

关键词： Thermoelectric Air conditioning Photovoltaic Random vector functional link jellyfish search algorithm Manta ray foraging optimization Artificial ecosystem-based optimization Sine cosine algorithm

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Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on jellyfish search algorithm

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ENERGIES 2022年第19期15卷 6994-6994页

作者： Shaheen, Abdullah El-Sehiemy, Ragab Kamel, Salah Selim, Ali Suez Univ Fac Engn Dept Elect Engn Suez 43533 Egypt Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafrelsheikh 33516 Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt

In this paper, the electricity network automation based on Power Network Reconfiguration (PNR) is implemented to improve the operational reliability of distribution systems using jellyfish search algorithm. For this purpose, system average interruption frequency index (SAIFI), system average interruption unavailability index (SAIUI) and total energy not supplied (TENS) are critical measures. In this paper, a new optimization technique of jellyfish search (JFS) algorithm is employed for distribution network reconfiguration for reliability improvement. It is concerned with the moving patterns of jellyfish. They are divided into three categories. The jellyfish could flow towards the ocean current or between its own swarm. Meanwhile, whenever the food supply is adequate, the jellyfishes are attracted to its location. It is formulated considering the three reliability indices of SAIFI, SAIUI and TENS, simultaneously in a multi-objective model based on the weight factors. The proposed methodology based on the JFS optimizer is implemented on an IEEE 33-node distribution network. According to the numerical results, the SAIFI, SAIUI and TENS is improved by 36.44%, 34.11% and 33.35%, compared to the initial condition. For comparison purposes, tuna swarm optimizer and tunicate swarm algorithm, besides the JFS algorithm, are implemented as well. The simulation results declare the significant outperformance of the JFS algorithm compared to TUNA and TSA in terms of the obtained improvements and the regarding convergence properties.

关键词： distribution systems operational reliability network reconfiguration jellyfish search algorithm

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Perturbation Observer Based Fractional-Order Control for SMES Systems Based on jellyfish search algorithm

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FRONTIERS IN ENERGY REsearch 2021年 9卷

作者： Luo, Ke Jiao, Yingying Zhu, Jiawei Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Changan Univ Sch Informat Engn Xian Peoples R China

The electric energy storage system (EESS) is considered as an efficient and promising tool to alleviate the power imbalance of grid-connected microgrid with distributed generation (DG). This work develops a perturbation observer-based fractional-order control (POFOC) strategy for superconducting magnetic energy storage (SMES) system. Initially, a high-gain state and perturbation observer (HGSPO) is designed for reliable estimation of the combined impact of the nonlinearities, parameter uncertainties, unmodeled dynamics, and external disturbances of SMES. Then the storage function of an SMES system is designed, which takes favorable terms into serious consideration to sufficiently utilize the physical properties of the SMES system. Moreover, a fractional-order control framework is applied for complete compensation for the estimated perturbation and adopted as the attached input to boost its dynamical responses. Furthermore, a newly proposed jellyfish search algorithm (JSA) is utilized to realize optimization and tuning of control gains of the developed strategy, upon which high-quality global optimum can be obtained to ensure prominent controlling performance. Case studies, e.g., active power and reactive power supply and system restoration capability under power grid fault effectively validate the effectiveness and reliability of the POFOC strategy compared with traditional PID control and interconnection and damping assignment passivity-based controller (IDA-PBC). In particular, the overshoot of PID is 115.264% of the rated value, while POFOC has no overshoot.

关键词： superconducting magnetic energy storage systems distributed generation fractional-order control jellyfish search algorithm perturbation observer

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