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Island-based crow search algorithm for solving optimal control problems

APPLIED SOFT COMPUTING 2020年 90卷 106170-000页

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Eliiyi, Deniz Tursel Ege Univ Fac Engn Dept Mech Engn Izmir Turkey Izmir Bakircay Univ Fac Engn & Architecture Dept Mech Engn Izmir Turkey Izmir Bakircay Univ Fac Engn & Architecture Dept Ind Engn Izmir Turkey

crow search algorithm (crow) is one of the members of recently developed swarm-based meta-heuristic algorithms. Literature includes different applications of this algorithm on engineering design problems. However, this optimization method suffers from some drawbacks such as premature convergence and trapping into local optima at the early phase of iterations. In order to conquer this algorithm specific inabilities, many research studies have been conducted in the literature dealing with the improvements and enhancements on the search mechanism of crow. Structured population mechanism plays a vital role in preserving and controlling diversity, and thus increases the solution efficiency in evolutionary algorithms. Among the different types of methods used in structured algorithms, the island model is one of the widely applied solution strategies, in which the population individuals are subdivided into a predefined number of subpopulations. Migration mechanism is the key factor increasing population diversity, which takes place between independently running subpopulations during iterations to exchange valuable and useful solution information. This study embeds the fundamentals of the island model concepts into the crow search algorithm to improve its probing capabilities of the search domain, by means of the periodically interacting subpopulations on the course of iterations. In addition, four different hierarchical migration topologies have been proposed, and their search effectiveness have been evaluated and compared over 45 optimization test functions. The optimization function test set includes classic benchmark optimization problems and CEC 2015 benchmark functions. Furthermore, each hierarchical island model is applied for solving six different optimal control problems in order to investigate their efficiencies on multi-dimensional real world optimization problems. The investigated optimal control problems are parallel reaction, continuous stirred tank reactor, b

关键词： crow search algorithm Island model Hierarchical structured population Optimal control

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Multi-Objective Optimal Location and Sizing of Hybrid Photovoltaic System in Distribution Systems Using crow search algorithm

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY REsearch 2019年第4期9卷 1681-1692页

作者： Pandey, Anand Kumar Kirmani, Sheeraz Jamia Millia Islamia Dept Elect Engn Fac Engn & Technol New Delhi 110025 India

The optimal size and location of Distributed generation (DG) in a distribution system can minimize the power loss and improve the voltage profile of the distribution system. But the random placement of DG will not solve the problem of power loss and voltage profile. Therefore optimization algorithms are used to find the optimal size and location of DG to minimize the power loss and to improve the voltage profile. This research paper introduces a new metaheuristic algorithm which is the crow search algorithm (CSA) to calculate the optimal size and location of multiple solar photovoltaic (PV) units for reducing the power loss and improving the voltage profile. This paper also discusses the complete mathematical modeling of probabilistic PV generation, time-varying load modeling, loss calculation, voltage stability, and optimal PV size calculation. The method proposed in this paper is tested on IEEE 30 and 57 bus test systems and compared with other existing methods like Genetic algorithm (GA), Particle swarm optimization (PSO) and Hybrid GA-PSO method. CSA has only 2 settings parameters while GA and PSO have 6 and 3 settings parameters. Because of this, computation time and complexity of the algorithm are less in comparison to other methods and it gives a better result. Therefore by using the CSA method, the optimal size and location of the DG can be found which gives less power loss and improved voltage profile, less computational time and easy to implement compared to other existing methods proposed here.

关键词： Solar PV Distributed Generation crow search algorithm Genetic algorithm Particle swarm optimization hybrid GA-PSO method and optimal size

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Optimal power scheduling in real-time distribution systems using crow search algorithm for enhanced microgrid performance

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Selvaraj, Ganesh Rajangam, Kanimozhi Vishnuram, Pradeep Bajaj, Mohit Zaitsev, Ievgen Grace Coll Engn Dept Elect & Elect Engn Thoothukudi 628005 Tamil Nadu India Anna Univ Univ Coll Engn Dept Elect & Elect Engn BIT Campus Tiruchirappalli 620024 Tamilnadu India SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 603203 India Graph Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Univ Business & Technol Coll Engn Jeddah 21448 Saudi Arabia Natl Acad Sci Ukraine Inst Electrodynam Dept Theoret Elect Engn & Diagnost Elect Equipment Beresteyskiy 56 UA-03680 Kyiv 57 Ukraine Natl Acad Sci Ukraine Ctr Informat Analyt & Tech Support Nucl Power Faci Akad Palladina Ave 34-A Kyiv Ukraine

Microgrids (MGs) have gained significant attention over the past two decades due to their advantages in service reliability, easy integration of renewable energy sources, high efficiency, and enhanced power quality. In India, low-voltage side customers face significant challenges in terms of power supply continuity and voltage regulation. This paper presents a novel approach for optimal power scheduling in a microgrid, aiming to provide uninterrupted power supply with improved voltage regulation (VR). To address these challenges, a crow search algorithm is developed for effective load scheduling within the distribution system. The proposed method minimizes the total operating cost (TOC) and maximizes VR under varying loading conditions and distributed generation (DG) configurations. A case study in Tamil Nadu, India, is conducted using a microgrid composed of three distributed generation sources (DGs), modeled and simulated using the Electrical Transient Analyzer Program (ETAP) environment. The proposed approach is tested under three operational scenarios: grid-connected mode, islanded mode, and grid-connected mode with one DG outage. Results indicate that the crow search algorithm significantly optimizes load scheduling, leading to a substantial reduction in power loss and enhancement in voltage profiles across all scenarios. The islanded mode operation using the crow search algorithm demonstrates a remarkable reduction in TOC and maximizes voltage regulation compared to other modes. The main contributions of this work include: (1) developing a new meta-heuristic approach for power scheduling in microgrids using the crow search algorithm, (2) achieving optimal power flow and load scheduling to minimize TOC and improve VR, and (3) successfully implementing the proposed methodology in a real-time distribution system using ETAP. The findings showcase the effectiveness of the crow search algorithm in microgrid power management and its potential for application in other

关键词： Microgrid Load scheduling crow search algorithm Voltage regulation Distributed generation Total operating cost Power management ETAP simulation

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A Combined Fractional Order Proportional Integral Derivative Controller for Automatic Generation Control Integrated with Wind and Small Hydropower Plant Using crow search algorithm

ELECTRICA

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ELECTRICA 2023年第3期23卷 584-596页

作者： Karanam, Appala Naidu Shaw, Binod Nayak, Jyoti Ranjan Natl Inst Technol Dept Elect Engn Visakhapatnam Andhra Pradesh India Vignans Inst Informat Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India

Automatic generation control (AGC) is essential for maintaining the frequency stability of power systems. However, designing an optimal controller for AGC remains a challenge. To overcome this deficiency, a novel two-degree-of-freedom-combined fractional order PID (2DOF-combined FOPID) controller is proposed and tested on two-area interconnected power systems consisting of reheat thermal and hydro units. The controller's performance is compared with conventional PID, FOPID, 2-DOF-PID, and 2DOF-FOPID controllers. The objective is to reduce the area control error. Optimal controller gain parameters are determined using the crow search algorithm (CSA) to minimize the integral time absolute error objective function. The dynamic behavior of the system is investigated in response to a 0.01 p.u. load disturbance in area-1. The designed controller's performance is also explored using wind integration and a CSA-optimized small hydropower plant. Results show that the 2DOF-combined FOPID controller outperforms the other controllers in terms of stability indices, including overshoot (Osh), undershoot (Ush), and settling time response (Ts) in the dynamic responses of the power system.

关键词： Two-degree-of-freedom-combined fractional order proportional integral derivative controller automatic generation control crow search algorithm fractional order controller frequency bias tie-line control

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Simultaneous Scheduling of Machines, Tool Transporter and Tools in a Multi Machine Flexible Manufacturing System Without Tool Delay Using crow search algorithm

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2022年第3期16卷 403-419页

作者： Mareddy, Padma Lalitha Narapureddy, Sivarami Reddy Dwivedula, Venkata Ramamurthy Prayagi, S., V Annamacharya Inst Technol & Sci Elect Engn Dept Rajampet 516125 AP India Annamacharya Inst Technol & Sci Mech Engn Dept Rajampet 516125 AP India Satya Inst Technol & Management Vizianagarm 535002 AP India Yeshwantrao Chavan Coll Engn YCCE Nagpur Mech Engn Dept Nagpur 441110 Maharashtra India

This paper deals with machines, tool transporter (TT) and tools simultaneous scheduling in multi machine flexible manufacturing system (FMS) with the lowest possible number of copies of every tool type without tool delay taking into account tool transfer times to minimize makespan (MSN). The tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to determine the lowest possible number of copies of every tool variety, allocation of copies of tools to job-operations, job-operations' sequencing on machines and corresponding trip operations of TT, including the dead heading trip and loaded trip times of TT without tool delay for MSN minimization. This paper proposes nonlinear mixed integer programming (MIP) formulation to model this simultaneous scheduling problem and crow search algorithm (CSA) built on the crows' intelligent behavior to solve this problem. The results have been tabulated, analyzed and compared. (C) 2022 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved

关键词： Scheduling of machines tool transporter and tools FMS no tool delay crow search algorithm makespan optimization techniques

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Feature selection using Binary crow search algorithm with time varying flight length

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114288-114288页

作者： Chaudhuri, Abhilasha Sahu, Tirath Prasad Natl Inst Technol Raipur Dept Informat Technol Chattisgarh 492010 India

crow search algorithm (CSA) is a simple yet effective meta-heuristic algorithm that has been applied to solve many engineering problems. In CSA, fl parameter controls the search capability of crows and AP parameter balances the trade-off between exploration and exploitation. The parameter fl is initialized to a constant value in CSA. However, CSA faces the problem of being trapped in local minima. This work proposes the solution to this problem by introducing the new concept of time varying flight length in CSA. The value of fl should be large in initial stages of algorithm in order to support random exploration and it should gradually decrease in later iterations to encourage the exploitation of good solutions found so far. The proposed approach, Binary crow search algorithm with Time Varying Flight Length (BCSA-TVFL) is applied to feature selection problems in wrapper mode. Eight variants of BCSA-TVFL based on eight different transfer functions are tested. The best performing variant is then selected and compared with other state-of-the-art wrapper feature selection techniques and standard filter feature selection techniques. Performance of proposed approach is tested on 20 standard UCI datasets. Experimental result comparison shows that the proposed feature selection technique performs better than other competitors.

关键词： Feature selection Classification crow search algorithm High dimensional data Transfer function

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Resilient Wide-Area Damping Controller Design Using crow search algorithm ⁎

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IFAC-PapersOnLine 2022年第1期55卷 938-943页

作者： Murilo E.C. Bento Federal University of Rio de Janeiro Rio de Janeiro RJ 21941909 Brazil

The use of real-time data from Phasor Measurement Units (PMUs) to compose a Wide-Area Damping Controller (WADC) proved to be effective in damping inter-area power system oscillation modes. However, PMU data is vulnerable to cyber-attacks that can compromise the functioning of the WADC communication channels and the small-signal stability of the closed-loop control system. This article proposes a WADC design method based on an optimization problem to ensure the resilience of WADC to permanent loss of communication channels. The optimization problem is solved using the crow search algorithm. Case studies were done for the IEEE 68-bus system. The results of modal analysis and non-linear simulations in the time domain show the effectiveness of the proposed method.

关键词： Power System Control Small-Signal Stability Smart Grid Wide-Area Damping Control Cyber-Security crow search algorithm

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Identification of the photovoltaic model parameters using the crow search algorithm

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The Journal of Engineering 2018年第13期2017卷 1570-1575页

作者： Abeer Omar Hany M. Hasanien Mohamed A. Elgendy Mohamed A. L. Badr Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo 11517 Egypt School of Electrical and Electronic Engineering Newcastle University Newcastle upon Tyne NE1 7RU UK

Photovoltaic (PV) systems are widely used for several many decades. They have become an important source for green energy and they are currently used in many applications. The PV industry has grown because of the improvements in the technology of converting light into electrical energy as well as the cost reduction. This project investigates the applications of crow search algorithm (CSA) in accurately identifying the PV module parameters. CSA is a novel population-based meta-heuristic optimiser based on the intelligence crows’ exhibit in their behaviours, which helps them identify best location to state their catcher. In this study, the CSA is simulated using MATLAB environment and it is performed on the single diode and double diode PV models to estimate their parameters with minimum output power error. This error can be said to be the difference between the maximum output power and the calculated power output at a particular solar irradiance and cell temperature values.

关键词： photovoltaic power systems cost reduction power generation economics environmental factors search problems photovoltaic model parameters crow search algorithm PV systems green energy PV industry electrical energy cost reduction CSA PV module parameters population-based meta-heuristic optimiser Matlab environment double-diode PV models minimum output power error maximum output power solar irradiance cell temperature value

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Multi-objective Optimization of Combined Economic Emission Dispatch Problem in Solar PV Energy Using a Hybrid Bat-crow search algorithm

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY REsearch 2021年第1期11卷 383-391页

作者： Elbaz, Abdurazaq Guneser, Muhammet Tahir Karabuk Univ Dept Elect & Elect Engn Demir Celik Campus Karabuk Turkey

This paper deals with the multi-objective fuel cost optimization of a conventional power plant (CPP) and emission minimization in CPPs and solar PV power plants (SPVPPs) using a hybrid bat-crow search algorithm. To resolve this complicated, non-convex, and excessively nonlinear problem, a variety of meta-heuristic optimization algorithms are developed and effectively employed. To handle evolutionary multi-objective algorithms' inadequacies, such as early convergences, slowly meeting the Pareto-optimal front, and narrow trapping, applying a combination of different algorithms is unusual. This paper offers a hybrid evolutionary multi-objective optimization process based on combining the crow search optimization with the bat algorithm for dealing with the combined economic emission dispatch problem for SPVPPs. A hybrid technique combined with the proposed constriction handling method can balance exploitation and exploration tasks. Different IEEE standard bus systems were tested with the proposed hybrid method using the quadratic cost function and monitoring the transmission losses. The results of the proposed algorithm have also been compared with those of the bat, PSO, and crow search algorithms. The proposed method can be said to be effective considering the simulation results.

关键词： Bat algorithm Multi-objective optimization crow search algorithm Combined economic emission dispatch

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Solving Reactive Power Scheduling Problem using Multi-objective crow search algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第6期12卷 42-48页

作者： Salkuti, Surender Reddy Woosong Univ Dept Railrd & Elect Engn Daejeon South Korea

This paper presents the solution of multi-objective based optimal reactive power dispatch (MO-ORPD) problem by optimizing the system power losses and voltage stability enhancement index (VSEI)/L-index objectives. ORPD problem is considered as an important issue from system security and operational point of view for optimal steady-state operation of power system. Here, single-objective based ORPD problem is solved using crow search algorithm (CSA) and multi-objective based ORPD problem is solved using multi-objective CSA (MOCSA). The CSA is considered as an efficient and robust algorithm which determines the global optimal solution for solving the non-linear and discontinuous objective functions. Two standard test systems, i.e., IEEE 30 and 57 bus systems are considered to show the effectiveness, suitability and robustness of CSA and MO-CSA for solving the ORPD problem.

关键词： Optimal power flow crow search algorithm evolutionary algorithms loss minimization reactive power scheduling multi-objective optimization

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