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Multiobjective distribution system operation with demand response to optimize solar hosting capacity, voltage deviation index and network loss

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Loji, Kabulo Sharma, Sachin Sharma, Gulshan Rawat, Tanuj Durban Univ Technol Dept Elect Power Engn ZA-4001 Durban South Africa Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal Inst Technol Manipal Karnataka India Univ Johannesburg Dept Elect & Elect Engn Technol ZA-2006 Johannesburg South Africa GE Renewable Energy Noida India

In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (VDev) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually. To assess the efficacy of the solution, different multi-objective case studies are scrutinised based on the combinations of (i) HC and Loss, (ii) HC and VDev, (iii) Loss and VDev, (iv) HC Loss and VDev simultaneously with the effect of demand response. The multi-objective research problem is formulated as non-linear and non-convex programming approach. To solve this complex problem, the modified crow search optimization (MCSO) is proposed. The MCSO achieved the 0.0714 MW of network loss with the optimal integration of distributed generation and is comparable to the well-established optimization algorithms available in literature. From the simulation results, it is found that HC is 3322.31 kW, VDev is 0.4982 p.u and system losses is 1314.86 kWh with demand response program when all the objectives are simultaneously optimized. The simulation outcomes highlight the superiority of the MCSO over others. The application results show the benefits and the beauty of proposed research work.

关键词： Multi-objective optimization crow search optimization algorithm Solar photovoltaic Demand response

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MCSO: MANTA crow search optimization-ENABLED ACTOR CRITIC NEURAL NETWORK FOR ALZHEIMER CLASSIFICATION USING 3D MRI IMAGES

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JOURNAL OF MECHANICS IN MEDICINE AND BIOLOGY 2024年第3期24卷 2350033-2350033页

作者： Neethu, M. Jayasingh, J. Roopa Karunya Inst Technol & Sci Dept Elect Commun Engn Karunya Nagar Coimbatore Tamil Nadu India Nehru Coll Engn & Res Ctr Trichur Kerala India

A form of dementia is Alzheimer's syndrome, also known as a neurodegenerative syndrome, damages the neuron cells in a selective mode. The count of patients that is increased gradually here by the disease is considered a worldwide concern, which may cause death in more cases. The rapid and accurate detection and categorization of Alzheimer's disease have obtained enormous awareness from researchers due to the studies of a deep model. However, the productive detection and categorization of Alzheimer's disease with reliable biomarkers are more challenging tasks. In this paper, Manta crow search optimization (MCSO)-based Actor Critic Neural Network (ACNN) is devised for the Alzheimer classification process. The Region of Interest (ROI) is extracted based on the thresholding process, and the preprocessing is done using the Type 2 Fuzzy and Cuckoo search (T2FCS) filter. Moreover, the Sparse Fuzzy C-means (Sparse FCM) model has been in employment to segment preprocessed images. The feature extraction is a productive process for classifying Alzheimer's disease. The ACNN classifier is utilized for performing Alzheimer's disease classification process. Furthermore, the ACNN classifier is skilled by a devised optimization algorithm named the MCSO model. The projected MCSO-based ACNN outperformed other existing techniques with a testing accuracy of 0.9295, sensitivity of 0.9378, and specificity of 0.9354.

关键词： Alzheimer's disease actor critic neural network manta ray foraging optimization sparse fuzzy C-means algorithm crow search optimization algorithm

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A new two-degree of freedom combined PID controller for automatic generation control of a wind integrated interconnected power system

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Protection and Control of Modern Power Systems 2022年第1期7卷 300-315页

作者： Appala Naidu Karanam Binod Shaw Department of Electrical Engineering National Institute of TechnologyRaipurIndia

Frequency control of an interconnected power system in the presence of wind integration is complex since wind speed/power variations also affect system frequency in addition to load ***,improving existing control schemes is necessary to maintain a stable frequency in such complex power system *** this paper,a new 2-degree of freedom combined proportional-integral and derivative control scheme is applied to a wind integrated interconnected power *** designing the controller,several inputs used for a secondary frequency control loop are considered along with the merits of the existing *** combined controller provides better control action than existing controllers in the presence of wind as is evidenced by the wide variety of results *** tuning of the controller gains,a crow search optimization algorithm(CRSOA)is *** are obtained via the MATLAB/Simulink software.

关键词： Load frequency control 2-DOF-PID controller crow search optimization algorithm

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Automatic clustering based on crow search algorithm-Kmeans (CSA-Kmeans) and Data Envelopment Analysis (DEA)

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 1322-1337页

作者： Balavand, Alireza Kashan, Ali Husseinzadeh Saghaei, Abbas Islamic Azad Univ Sci & Res Branch Dept Ind Engn Tehran Iran Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

Cluster Validity Indices (CVI) evaluate the efficiency of a clustering algorithm and Data Envelopment Analysis (DEA) evaluate the efficiency of Decision-Making Units (DMUs) using a number of inputs data and outputs data. Combination of the CVI and DEA inspired the development of a new automatic clustering algorithm called Automatic Clustering Based on Data Envelopment Analysis (ACDEA). ACDEA is able to determine the optimal number of clusters in four main steps. In the first step, a new clustering algorithm called CSA-Kmeans is introduced. In this algorithm, clustering is performed by the crow search algorithm (CSA), in which the K-means algorithm generates the initial centers of the clusters. In the second step, the clustering of data is performed from k(min) cluster to k(max) cluster, using CSA-Kmeans. At each iteration of clustering, using correct data labels, Within-Group Scatter (WGS) index, Between-Group Scatter (BGS) index, Dunn Index (DI), the Calinski-Harabasz (CH) index, and the Silhouette index (SI) are extracted and stored, which ultimately these indices make a matrix that the columns of this matrix indicate the values of validity indices and the rows or DMUs represent the number of clustering times from k(min) cluster to k(max) cluster. In the third step, the efficiency of the DMUs is calculated using the DEA method based on the second stage matrix, and given that the DI, CH, and SI estimate the relationship within group scatter and between group scatter, WGS and BGS are used as input variables and the indices of DI, CH and SI are used as output variables to DEA. Finally, in step four, AP method is used to calculate the efficiency of DMUs, so that an efficiency value is obtained for each DMU that maximum efficiency represents the optimal number of clusters. In this study, three categories of data are used to measure the efficiency of the ACDEA algorithm. Also, the efficiency of ACDEA is compared with the DCPSO, GCUK and ACDE algorithms. According to the

关键词： Automatic Clustering K-Means crow search optimization algorithm Cluster Validity Indices Data Envelopment Analysis

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Influence of Primary Regulation on Frequency Control of an Isolated Microgrid Equipped with crow search algorithm Tuned Classical Controllers

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2021年第2期16卷 681-695页

作者： Patan, Muzeeb Khan Raja, K. Azaharahmed, Mohammed Prasad, Ch. Durga Ganeshan, P. Anna Univ Coll Engn Dindigul Dindigul 624622 Tamil Nadu India NIT Raipur Dept Elect Engn Raipur 492010 Madhya Pradesh India Sethu Inst Technol Virudunagar 626115 Tamil Nadu India

Along with the power demand variations, change in wind speed and irradiance leads to large frequency oscillations especially in case of isolated microgrid integrated with PV and wind power generating units. To minimize such frequency changes, proportional plus integral (PI) and proportional, integral, and derivative (PID) controllers are set up for controllable generating units of isolated microgrid. However, this paper introduced primary regulation concept for diesel generator (DG) along with PI/PID controller mechanism which enhances overall system stability and minimize the frequency oscillations during the initial stages of load/power changes. Also, the choice of PI and PID controllers is introduced in this paper when the DG's in microgrid equipped with primary regulation loop along secondary control and only secondary control without primary loop as extension of frequency control. The investigations are carried out in regular load change and generation change patterns along with irregular stochastic models. crow search optimization algorithm (CSOA) provide fine controller parameters in both cases and results carried out in MATLAB-SIMULINK environment

关键词： Microgrid Frequency control crow search optimization algorithm PID

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crow-penguin optimizer for multiobjective task scheduling strategy in cloud computing

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2020年第14期33卷 e4467-e4467页

作者： Singh, Harvinder Tyagi, Sanjay Kumar, Pardeep Univ Petr & Energy Studies UPES Sch Comp Sci Dept Virtualizat Dehra Dun Uttarakhand India Kurukshetra Univ Dept Comp Sci & Applict Kurukshetra Haryana India

Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade-off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade-off, this paper proposes a method, termed as crow-penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO-MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the crow search optimization algorithm (CSA) and the Penguin search optimization algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively.

关键词： cloud computing crow search optimization algorithm multiobjective functions penguin search optimization algorithm task scheduling

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Data clustering using K-Means based on crow search algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2018年第11期43卷 190-190页

作者： Lakshmi, K. Visalakshi, N. Karthikeyani Shanthi, S. Kongu Engn Coll Dept Comp Applicat Perundurai India Govt Arts & Sci Coll Dept Comp Sci Kangeyam India

Cluster analysis is one of the popular data mining techniques and it is defined as the process of grouping similar data. K-Means is one of the clustering algorithms to cluster the numerical data. The features of K-Means clustering algorithm are easy to implement and it is efficient to handle large amounts of data. The major problem with K-Means is the selection of initial centroids. It selects the initial centroids randomly and it leads to a local optimum solution. Recently, nature-inspired optimization algorithms are combined with clustering algorithms to obtain the global optimum solution. crow search algorithm (CSA) is a new population-based metaheuristic optimization algorithm. This algorithm is based on the intelligent behaviour of the crows. In this paper, CSA is combined with the K-Means clustering algorithm to obtain the global optimum solution. Experiments are conducted on benchmark datasets and the results are compared to those from various clustering algorithms and optimization-based clustering algorithms. Also the results are evaluated with internal, external and statistical experiments to prove the efficiency of the proposed algorithm.

关键词： Data mining cluster analysis K-Means Particle Swarm optimization crow search optimization algorithm

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A Hybrid Fuzzy-crow Optimizer for Unconstrained and Constrained Engineering Design Problems

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2022年 12卷 1-24页

作者： Gafar, Mona Gamal El-Sehiemy, Ragab A. Sarhan, Shahenda Prince Sattam Bin Abdulaziz Univ Al Sulail Coll Sci & Humanities Dept Comp Sci Kharij Saudi Arabia Kafrelsheikh Univ Fac Artificial Intelligence Dept Machine Learning & Informat Retrieval Kafrelsheikh Egypt Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafrelsheikh Egypt Mansoura Univ Fac Comp & Informat Sci Mansoura Egypt

Constrained search space is considered a problematic field for engineers. It requires finding solutions for problems satisfying a number of predefined constraints while solving uncertain and ambiguous situations that realistic problems exhibit. As all feasible solutions should have degrees of truth to accommodate real design problems. The fuzzy set theory is able to handle uncertainty issues in real-time problems. In this paper, we introduce a hybrid fuzzy-crow framework for providing an optimal design of constrained and unconstrained engineering problems. The fuzzy-crow framework works on an initial population of fuzzy numbers for problem solutions. It benefits Zadeh extension principle for calculating problem fitness functions and constraints in addition to their membership degrees. The main features of the proposed framework are merging the merits of fuzzy logic and crow search optimization. The fuzzified objective and constraints are incorporated to obtain a fine-tuned solution at fast convergence of the non-dominated solutions. The proposed framework was evaluated based on statistical and convergence analysis using 10 benchmark test functions and five constrained engineering problems against some of the state of art. The results indicated the superiority of the proposed framework over the state of art in finding fine-tuned non-dominated optimized solutions in fuzzy search space.

关键词： crow search optimization algorithm Fuzzy System Engineering Design Problem Constrained and Unconstrained optimization Problems

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Optimal Scheduling Strategy for Power Systems Containing Offshore Wind Farms Considering Wind Power Uncertainty

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第5期32卷 2350090-2350090页

作者： Zhang, Jinhua Zhu, Yuerong Zhao, Zhengyang North China Univ Water Resources & Elect Power Sch Elect Engn Zhengzhou 450011 Peoples R China

The wind power interval prediction of offshore wind farms and power plan arrangement of conventional thermal power units are of vital importance in the consumption of offshore wind power, the reduction of greenhouse gas impact on the environment, and the electric power system safe and economic operating. With the purpose of selecting the appropriate Copula function on the basis of the results of wind speed and wind power normal test, establish the mathematical model of wind-fire joint optimal scheduling, and optimize coal-fired power units power generation after comparing the convergence performance of particle swarm optimization method and crow search algorithm. Results indicate that the selected Copula function meets the expected criteria, and the optimized thermal unit climbs more smoothly and through the optimization of CSA the complete economic consumption of running is lessened. An idea is presented by this paper, which considers the uncertainties of offshore wind power generation, and the basis for the operational performance of CSA over PSO, and which provides a joint wind-thermal economic optimal dispatch strategy.

关键词： Wind power uncertainty Copula theory crow search optimization algorithm wind farm optimal scheduling

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Research on Gas Emission Prediction Based on KPCA-ICSA-SVR

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PROCESSES 2024年第12期12卷 2655-2655页

作者： Liu, Li Dai, Linchao Mao, Xinyi Chen, Yutao Jing, Yongheng Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Peoples R China State Key Lab Coal Mine Disaster Prevent & Control Chongqing 400037 Peoples R China Chongqing Res Inst China Coal Technol & Engn Grp Chongqing 400037 Peoples R China

In the context of deep mining, the uncertainty of gas emission levels presents significant safety challenges for mines. This study proposes a gas emission prediction model based on Kernel Principal Component Analysis (KPCA), an Improved crow search algorithm (ICSA) incorporating adaptive neighborhood search, and Support Vector Regression (SVR). Initially, data preprocessing is conducted to ensure a clean and complete dataset. Subsequently, KPCA is applied to reduce dimensionality by extracting key nonlinear features from the gas emission influencing factors, thereby enhancing computational efficiency. The ICSA is then employed to optimize SVR hyperparameters, improving the model's optimization capabilities and generalization performance, leading to the development of a robust KPCA-ICSA-SVR prediction model. The results indicate that the KPCA-ICSA-SVR model achieves the best performance, with RMSE values of 0.17898 and 0.3071 for the training and testing sets, respectively, demonstrating superior robustness and generalization capability.

关键词： gas emission prediction data preprocessing prediction metrics crow search optimization algorithm

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