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pathfinder algorithm optimized fractional order tilt-integral-derivative (FOTID) controller for automatic generation control of multi-source power system

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MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS 2021年第1期27卷 23-35页

作者： Priyadarshani, Sonali Subhashini, K. R. Satapathy, J. K. Natl Inst Technol Dept Elect Engn Rourkela 769008 Odisha India

This paper introduces a fractional order tilt-integral-derivative (FOTID) controller which is structurally analogous to fractional order proportional-integral-derivative controller in a power system for solving automatic generation control (AGC) problem. It is optimized by a recent metaheuristic optimizer called pathfinder algorithm (PFA). An interconnected two-area power system model comprising of multi-sources like thermal, hydro and gas generating units including physical constraints namely, governor dead band (GDB) and generation rate constraint (GRC) are taken into consideration for the study. The efficiency of the proposed controller for AGC is shown by comparing it with PFA optimized tilt-integral-derivative (TID) and proportional-integral-derivative (PID) controllers with integral of time multiplied absolute error (ITAE) taken as the objective function. Simulation study supports the claim that the proposed controller provides better dynamic responses as compared to the others. Sensitivity and robust analyses are done to demonstrate the effectiveness of the proposed PFA optimized FOTID controller to a wide variation in system parameters, at different step load and random load disturbances.

关键词： Automatic generation control pathfinder algorithm Fractional order controller Tilt-integral-derivative controller Proportional-integral-derivative controller Multi-source power system Governor dead band Generation rate constraint

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APFA:Ameliorated pathfinder algorithm for Engineering Applications

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Journal of Bionic Engineering 2024年第3期21卷 1592-1616页

作者： Keyu Zhong Fen Xiao Xieping Gao Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal UniversityChangsha410081China School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China School of Computer Science Xiangtan UniversityXiangtan411105China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China

pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food *** algorithm has the characteristics of a simple structure and high ***,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation *** paper proposes an Ameliorated pathfinder algorithm called APFA to solve complex engineering optimization ***,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the ***,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst ***,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence *** three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the *** performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic *** numerical experimental results demonstrated the superiority of APFA.

关键词： pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems

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A new meta-heuristic optimizer: pathfinder algorithm

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APPLIED SOFT COMPUTING 2019年 78卷 545-568页

作者： Yapici, Hamza Cetinkaya, Nurettin Konya Tech Univ Grad Inst Konya Turkey Konya Tech Univ Elect & Elect Engn Dept Konya Turkey

This paper proposes a new meta-heuristic algorithm called pathfinder algorithm (PFA) to solve optimization problems with different structure. This method is inspired by collective movement of animal group and mimics the leadership hierarchy of swarms to find best food area or prey. The proposed method is tested on some optimization problems to show and confirm the performance on test beds. It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively. Also, PFA is designed for multi-objective problems (MOPFA). The results obtained show that it can approximate to true Pareto optimal solutions. The proposed PFA and MPFA are implemented to some design problems and a multi-objective engineering problem which is time consuming and computationally expensive. The results of final case study verify the superiority of the algorithms proposed in solving challenging real-world problems with unknown search spaces. Furthermore, the method provides very competitive solutions compared to well-known meta-heuristics in literature, such as particle swarm optimization, artificial bee colony, firefly and grey wolf optimizer. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization Optimization techniques Metaheuristics Multi-objective optimization pathfinder algorithm

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Solution of optimal reactive power dispatch problem using pathfinder algorithm

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ENGINEERING OPTIMIZATION 2021年第11期53卷 1946-1963页

作者： Yapici, Hamza Necmettin Erbakan Univ Eregli Vocat Sch Konya Turkey

The optimal reactive power dispatch (ORPD) problem, as a subproblem of optimal power flow, has significant effects in providing reliability and economic operation. In this article, a modified version of the pathfinder algorithm (PFA), which is inspired by the collective movement of a swarm led by one member, is proposed for solving the ORPD problem. The objective of this study is to minimize the power losses by adjusting the control variables. Numerical analyses are performed on 57-bus and 118-bus power systems. To show the performance and effectiveness of the modified pathfinder algorithm (mPFA), some well-known methods are used for comparison. Statistical tests are performed to assess the consistency and ranking of the proposed method. The results show that the mPFA achieved competitive results and obtained a competitive ranking with statistical analyses. The simulations show that the proposed method could be a superior algorithm for solving the ORPD problem.

关键词： Minimization of power loss optimal reactive power dispatch pathfinder algorithm statistical tests

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Convolutional Sparse Coding Using pathfinder algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

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IEEE SENSORS JOURNAL 2021年第16期21卷 18132-18145页

作者： Zhou, Qiuyang Zhang, Yuhui Yi, Cai Lin, Jianhui He, Liu Hu, Qiwei Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 260061 Peoples R China China Railway Elect Survey Design & Res Inst Co L Tianjin 300250 Peoples R China

Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.

关键词： Dictionaries Convolution Matching pursuit algorithms Fault diagnosis Vibrations Convolutional codes Encoding Bearing fault diagnosis convolutional sparse coding asymmetric Gaussian chirplet model pathfinder algorithm orthogonal matching pursuit

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A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

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SN APPLIED SCIENCES 2021年第1期3卷 118页

作者： Janamala, Varaprasad CHRIST Deemed Univ Sch Engn & Technol Dept Elect & Elect Engn Bengaluru 560074 Karnataka India

A new meta-heuristic pathfinder algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

关键词： Distributed generation Interline-photovoltaic system Power loss minimization Radial distribution system pathfinder algorithm Meta-heuristic algorithms

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Diminishing Active Power Loss and Improving Voltage Profile Using an Improved pathfinder algorithm Based on Inertia Weight

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ENERGIES 2023年第3期16卷 1270-1270页

作者： Adegoke, Samson Ademola Sun, Yanxia Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching-learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems' reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system's voltage profile.

关键词： diminished active power loss improved pathfinder algorithm ORPD pathfinder algorithm

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Extracting Unknown Parameters of Proton Exchange Membrane Fuel Cells Using Quantum Encoded pathfinder algorithm

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Li, Ning Zhou, Guo Zhou, Yongquan Deng, Wu Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China China Univ Polit Sci & Law Dept Sci & Technol Teaching Beijing Peoples R China Guangxi Univ Nationalities Xiangsihu Coll Nanning Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin Peoples R China

Proton exchange membrane fuel cell (PEMFC) is one of the most widely used fuel cell types. Accurate modeling of PEMFC can better facilitate the research of PEMFC and guide designers to design FC products that meet people's needs. The modeling problem of PEMFC can be transformed into a parametric optimization problem. In order to improve the exploration capability of the pathfinder algorithm, the concept of quantum computing is introduced and a new quantum coded pathfinder optimization algorithm (QPFA) is proposed. QPFA was applied to the extraction of parameters of NedStackPS6, BCS500W and 250W FC, and these models of PEMFC have been applied for commercial use. The experimental results were compared with seven recently proposed metaheuristics and recently published literature, showing the accuracy and high precision of QPFA in extracting PEMFC parameters.

关键词： fuel cell model extraction parameters quantum coding pathfinder algorithm metaheuristic

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Thermal-aware virtual machine placement based on multi-objective optimization (MAR, 2023)

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 17756-17757页

作者： Liu, Bo Chen, Rui Lin, Weiwei Wu, Wentai Lin, Jianpeng Li, Keqin South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

VMP (Virtual Machine Placement) is a crucial technology for energy consumption optimization of the cloud data center. Existing works mainly focus on virtual machine consolidation to increase resource utilization and reduce computing energy consumption. However, existing studies usually ignore the thermal effect that an intensive workload on IT (Information Technology) equipment can raise energy consumption by cooling systems and generate hotspots. In addition, an excessive number of virtual machine migrations increases migration costs and risks violating the SLA (Service Level Agreement) signed with users. In this paper, we present a comprehensive system model and formulate the problem as a constrained multi-objective optimization. We propose a novel thermal-aware VMP strategy to solve the problem by jointly considering virtual machines’ migration cost, energy consumption, and heat recirculation around server racks. Our strategy makes placement decisions using MOPFGA (Multi-objective algorithm based on pathfinder algorithm and Genetic algorithm) that combines classic MOPFA and GA enhanced by OBL (Opposition Based Learning) for fast convergence and avoidance of local optimum. Extensive experiments based on CloudSim using real data center workload data from PlanetLab show that our algorithm overcomes the defects of the MOPFA (multi-objective pathfinder algorithm) and GA (genetic algorithm) and significantly improves the overall efficiency of a data center. Compared with several state-of-the-art algorithms, MOPFGA on average reduces virtual machine migrations by 77.52%, increases CRAC (Computer Room Air Conditioner) supply temperature by 1.24%, and reduces cooling energy consumption by 24.78% and computational energy consumption by 23.62%.

关键词： Cloud computing Temperature Virtual machine placement pathfinder algorithm Energy efficiency Data centers

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An effective method to solve the problem of electric distribution network reconfiguration considering distributed generations for energy loss reduction

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NEURAL COMPUTING & APPLICATIONS 2021年第5期33卷 1625-1641页

作者： Nguyen, Thuan Thanh Nguyen, Thang Trung Duong, Long Thanh Truong, Viet Anh Ind Univ Ho Chi Minh City Fac Elect Engn Technol Ho Chi Minh City Vietnam Ton Duc Thang Univ Power Syst Optimizat Res Grp Fac Elect & Elect Engn Ho Chi Minh City Vietnam HCMC Univ Technol & Educ Ho Chi Minh City Vietnam

This paper proposes an effective network reconfiguration (NR) method in the presence of distributed generations (DGs) for energy loss. The proposed method uses average load and average power of DGs instead of the load and DGs' generation curves. For finding the optimal network configuration, pathfinder algorithm (PFA) is used to solve the NR problem. The effectiveness of the proposed method has been validated on two distribution network systems without and with DGs placement. The obtained results show that the proposed method has a good ability to determine the optimal configuration similar to the method based on the graphs of loads and DGs with much shorter calculated time and PFA can reach optimal solution with a much higher success rate and better obtained solution compared with particle swarm optimization and sunflower optimization algorithms. As a result, the proposed method is an effective and reliable method for solving the NR problem for energy loss reduction considering time-varying condition of loads and DGs.

关键词： Network reconfiguration Distributed generator Energy loss Time-varying load pathfinder algorithm

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