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Solving energy management of renewable integrated microgrid systems using crow search algorithm

SOFT COMPUTING 2020年第14期24卷 10433-10454页

作者： Dey, Bishwajit Bhattacharyya, Biplab Srivastava, Apoorv Shivam, Kumar Indian Sch Mines Dept Elect Engn Indian Inst Technol Dhanbad Bihar India

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic-emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper.

关键词： Combined economic-emission dispatch Penalty factor Microgrid Grey wolf optimization Teaching-learning-based optimization Sine cosine algorithm crow search algorithm

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A Novel crow search algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation

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IEEE ACCESS 2020年 8卷 27807-27820页

作者： Farh, Hassan M. H. Al-Shaalan, Abdullah M. Eltamaly, Ali Mohamed Al-Shamma'a, Abdullrahman A. King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura 35516 Egypt Taiz Univ Coll Engn Dept Mechatron Engn Taizz 6803 Yemen

The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

关键词： crow search algorithm particle swarm optimization renewable distributed generation sizing solar allocation photovoltaic wind farm

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An Improved crow search algorithm Based on Spiral search Mechanism for Solving Numerical and Engineering Optimization Problems

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IEEE ACCESS 2020年 8卷 92363-92382页

作者： Han, Xiaoxia Xu, Quanxi Yue, Lin Dong, Yingchao Xie, Gang Xu, Xinying Taiyuan Univ Technol Coll Elect & Power Engn Dept Automat Taiyuan 030024 Peoples R China Taiyuan Univ Technol Sch Elect & Informat Engn Taiyuan 030024 Peoples R China

The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm & x2019;s slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance.

关键词： Optimization Spirals search problems Perturbation methods Convergence Mathematical model Benchmark testing crow search algorithm weight coefficient spiral search mechanism engineering design optimization

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Sine-cosine crow search algorithm: theory and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第12期32卷 7725-7742页

作者： Khalilpourazari, Soheyl Pasandideh, Seyed Hamid Reza Concordia Univ Dept Mech Ind & Aerosp Engn MIAE Montreal PQ Canada Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran

In this paper, we propose a new hybrid algorithm called sine-cosine crow search algorithm that inherits advantages of two recently developed algorithms, including crow search algorithm (CSA) and sine-cosine algorithm (SCA). The exploration and exploitation capabilities of the proposed algorithm have significantly improved. Performance of the so-called SCCSA was evaluated in unimodal, multimodal, fixed-dimensional multimodal and composite benchmark functions using robust measures. Based on in-depth analyses and statistical information, we showed that the suggested methodology could provide promising solutions comparing to other state-of-the-art algorithms.

关键词： Sine-cosine crow search algorithm Global optimization crow search algorithm Sine-cosine algorithm

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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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crow search algorithm Based on Neighborhood search of Non-Inferior Solution Set

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IEEE ACCESS 2019年 7卷 52871-52895页

作者： Qu, Chiwen Fu, Yanming Baise Univ Sch Informat Engn Baise 533000 Peoples R China Guangxi Univ Comp & Elect Informat Coll Nanning 530004 Peoples R China

Characterized by less parameter settings, easy implementation, and strong optimization capacity, the crow search algorithm has been successfully applied to solve the optimization problem. As the basic crow search algorithm is a new kind of swarm intelligent algorithm only based on the crow's memory foraging mode, it also contains defects like slow search speed and low optimization precision in later iterations, which are especially obvious for the optimization of high-dimensional functions. In order to overcome these shortcomings, a new crow search algorithm based on neighborhood search of non-inferior solution set (NICSA) is proposed. The proposed algorithm makes the crow individual choose the memory search mode or neighborhood search mode automatically in the course of evolution by the determination factor of non-inferior solution. With this strategy, the local exploitation and the global exploration of the algorithm became more balanced. In the neighborhood search, the selectivity factor is used to guide non-inferior solutions to adaptively execute neighborhood search of Levy flight or Gaussian flight, to enhance the neighborhood searchability of the algorithm and improve the optimization precision. The result of simulation experiments with 23 benchmark test functions verifies that the proposed algorithm has good optimization effect in the aspects of search veracity, convergence rate, and robustness.

关键词： crow search algorithm determination factor of non-inferior solution neighborhood search selectivity factor

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Finite element model updating using crow search algorithm with Levy flight

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2020年第13期121卷 2916-2928页

作者： Wu, Hao Wu, Pingbo Xu, Kai Li, Fansong Southwest Jiaotong Univ State Key Lab Tract Power Chengdu Sichuan Peoples R China Univ Illinois Mech Sci & Engn Urbana IL USA Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China

The accuracy of the finite element model is essential for fatigue analysis and vibration of a flexible body. This paper presents a finite element model updating (FEMU) algorithm based on crows search algorithm incorporated with Levy flight (LFCSA). The proposed algorithm is tested on the updating of two different cases: a simple structure (beam) and a complex structure (gearbox housing). To verify the performance of LFCSA, two optimization algorithm, which is the standard CSA and the particle swarm optimization algorithm with Levy flight (LFPSO), are applied to FEMU of the simple beam. The result of the comparison shows that the LFCSA has sufficient convergence speed and global search ability in the implementation of FEMU. In the case of gearbox housing, the error of frequencies and mode shapes are analyzed to indicate that the LFCSA gives a satisfactory result in a complex structure.

关键词： crow search algorithm finite element model updating gearbox housing Levy flight

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A recommender system based on a new similarity metric and upgraded crow search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第3期39卷 3167-3182页

作者： Sinha, Bam Bahadur Dhanalakshmi, R. Natl Inst Technol Nagaland Comp Sci & Engn Dimapur India Indian Inst Informat Technol Tiruchirappalli Comp Sci & Engn Tiruchirappalli India

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the crow search algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy.

关键词： Collaborative filtering similarity crow search algorithm optimization movielens jester

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Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13715-13746页

作者： Rizk-Allah, Rizk M. Hassanien, Aboul Ella Slowik, Adam Menoufia Univ Fac Engn Shibin Al Kawm Egypt Cairo Univ Fac Comp & Artificial Intelligence Cairo Egypt Koszalin Univ Technol Dept Elect & Comp Sci Koszalin Poland

Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

关键词： crow search algorithm Orthogonal Opposition Multi-objective optimization Metaheuristic Engineering designs MOORA

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Merging Lion with crow search algorithm for Optimal Location and Sizing of UPQC in Distribution Network

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 377-392页

作者： Gaddala, Kaladhar Raju, P. Sangameswara St Anns Coll Engn & Technol Chirala Chirala Chittoor India Siddhartha Inst Sci & Technol Puttur Puttur India

Unified power quality conditioner (UPQC) is exploited to alleviate the issues associated with voltage swell/dip in source voltage, and it regulates the load voltage absolutely. It is deployed to resolve the entire issues associated with current and voltage harmonics and enhance power quality. In order to solve those issues, optimal sizing and location of UPQC are adopted by the researchers, and it is still in progress. However, the power quality issues like power loss, UPQC cost and voltage stability index (VSI) are not analysed and considered. Hence, the tactics used in this paper help to design an optimal location and sizing of the UPQC in power system using a nonlinear multi-objective function. The objective function considers the minimization of power loss, UPQC cost and VSI. For attaining this objective, this paper hybridizes two well-performed optimization algorithms called lion algorithm (LA) and crow search algorithm (CSA). Since the mating of LA is carried out on the basis of CSA update, the proposed algorithm is termed as crow search mating-based lion algorithm (CSM-LA). The current experiment on localizing and sizing of UPQC is carried out in IEEE 33 and IEEE 69 benchmark test bus systems. The performance of the proposed model is distinguished with conventional methods in terms of performance and convergence analysis, and the relevant outcomes are attained which proves the superiority of the proposed model.

关键词： UPQC Distortion Voltage Power Optimal location Lion algorithm crow search algorithm

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