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An efficient way to schedule dispersed generators for a microgrid system's economical operation under various power market conditions and grid involvement

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第5期14卷 1799-1809页

作者： Misra, Srikant Panigrahi, P. K. Dey, Bishwajit GIET Univ Dept EEE Gunupur Odisha India IIT Indian Sch Mines Dept EE Dhanbad India Adani Univ Dept Elect Engn Ahmadabad India

A microgrid is a collection of distributed energy resources (DERs) It might encompass both traditional and unconventional energy sources. The way a microgrid functions determines whether it is categorized as either islanded or grid-connected. The focus of this study is to explore five different optimal scheduling operation scenarios for Distributed Energy Resources (DERs) with the aim of achieving economic benefits for a low voltage microgrid system. The scenarios being studied are developed using several approaches to grid participation and pricing strategies within the power market. The optimization tool used in the research was the crow search algorithm (CSA), a widely recognized and recently developed algorithm known for its speed and efficiency. The results of the study indicate that the microgrid's most profitable operational scenario involved active participation of the grid in the purchase and sale of electricity, utilizing a dynamic pricing approach based on time-of-usage (TOU). Based on numerical data, the crow search algorithm method produced solutions of superior quality in less time compared to several other algorithms with similar designs that were employed in the study. The CSA approach was able to achieve this consistently, regardless of the scale of the problem being addressed.

关键词： crow search algorithm Renewable energy sources Time of usage Microgrid energy management Electricity market price

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An approach to attain a balanced trade-off solution for dynamic economic emission dispatch problem on a microgrid system

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第4期14卷 1300-1311页

作者： Dey, Bishwajit Raj, Saurav Babu, Rohit Chhualsingh, Tapas Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad India Gandhi Inst Engn & Technol Dept Elect & Elect Engn Gunupur Odisha India Inst Chem Technolog Dept Elect Engn Marathwada Campus Jalna India Alliance Univ Alliance Coll Engn & Design Bengaluru 562106 Karnataka India

Electricity generation involves the release of hazardous gases into the environment by fossil-fueled generators. Along with promoting the renewable energy sources, power engineers must come up with a compromise solution that results in reduced harmful gas emissions when electricity is generated economically. The goal of this paper is to structure a balanced trade-off approach for solving the problem of environment constrained economic dispatch (ECED).novel comparison of the proposed ECED method, existing price-penalty-factor (PPF), and fractional programming (FP) methods for solving CEED problems is carried out on a dynamic 3-unit test system to evaluate which technique provides a healthier trade-off solution in terms of cost as well as toxic gases emitted. By combining the greedy JAYA algorithm with an algorithm based on a crow's food seeking approach, a robust hybrid algorithm is developed that used as the optimization tool. When comparing the suggested ECED technique to the PPF and FP based CEED solutions, the amount of released pollutants and generating cost were considerably nearer to the emission dispatch and economic dispatch respectively. Furthermore, statistical research backs up the suggested hybrid optimizer's superiority over existing algorithms in the literature.

关键词： Microgrid Energy management Dynamic economic emission dispatch crow search algorithm Jaya algorithm

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A Novel Fault Diagnostic Approach for DC-DC Converters Based on CSA-DBN

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IEEE ACCESS 2018年 6卷 6273-6285页

作者： Sun, Quan Wang, Youren Jiang, Yuanyuan Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China Anhui Univ Sci & Technol Coll Elect & Informat Engn Huainan 232001 Peoples R China

Effective fault diagnosis for mission-critical and safety-critical systems has been an essential and mandatory technique to reduce failure rate and prevent unscheduled shutdown. In this paper, to realize fault diagnosis for a closed-loop single-ended primary inductance converter, a novel optimization deep belief network (DBN) is presented. First, wavelet packet decomposition is adopted to extract the energy values from the voltage signals of four circuit nodes, as the fault feature vectors. Then, a four-layer DBN architecture including input and output layers is developed. Meanwhile, the number of neurons in the two hidden layers is selected by the crow search algorithm (CSA) with training samples. Not only the hard faults such as open-circuit faults and short-circuit faults but also the soft faults such as the component degradation of power MOSFET, inductor, diode, and capacitor are considered in this study. Finally, these fault modes are isolated by CSA-DBN. Compared with the back-propagation neural network and support vector machine fault diagnosis methods, both simulation and experimental results show that the proposed method has a higher classification accuracy that proves its effectiveness and superiority to the other methods.

关键词： crow search algorithm dc-dc power converter deep belief network fault diagnosis feature extraction wavelet packets

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A new analytical index for solving capacitor placement problem of distribution networks in bi-level framework: Total loss index

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第10期30卷

作者： Montazeri, Meysam Askarzadeh, Alireza Grad Univ Adv Technol Fac Elect & Comp Engn Kerman Iran Grad Univ Adv Technol Dept Energy Management & Optimizat Inst Sci & High Technol & Environm Sci Kerman Iran

Capacitor allocation of distribution network is a complex and high-dimensional optimization problem. As a result, most often, optimization techniques are entangled in local optima. To overcome this problem, this article presents a new bi-level framework for capacitor allocation: (a) at the first level, a new analytical index, named total loss index (TLI), has been proposed to identify high potential buses;and (b), at the second level, a metaheuristic optimization, crow search algorithm (CSA), has been applied for finding the best solution among the candidate solutions. TLI, which depends on the network topology, considers the sensitivity of the network losses to reactive power injection. The proposed framework (TLI + CSA) has been tested on 33-bus and 69-bus radial distribution networks. It is observed that reduction of search space will increase the probability of finding global solution so that (a) on 33-bus network, when three high potential buses are identified at the first level, the optimizer can find the global solution at the second level and (b) on 69-bus network, when six high potential buses are identified by TLI, CSA can find the global solution. It can be drawn that using the proposed TLI + CSA leads to finding more accurate results than the other methodologies.

关键词： bi-level optimization capacitor placement crow search algorithm radial distribution network total loss index

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Indoor positioning fingerprint database construction based on CSA-DBSCAN and RCVAE-GAN

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PHYSICA SCRIPTA 2024年第5期99卷 055002-055002页

作者： Pan, Lei Zhang, Hao Zhang, Liyang Gao, Rui Zhang, Qian Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

With the increasing size of buildings, in order to achieve high-precision indoor positioning services, it is a challenging task to build an offline fingerprint database with high quality, high density and less manpower and material consumption. Aiming to solve the problem of low-quality WiFi indoor positioning fingerprint inventory constructed by traditional methods, which affects positioning accuracy and incurs high costs, this paper proposes a method for indoor positioning fingerprint database construction based on crow search algorithm Optimizes Density Clustering (CSA-DBSCAN) and Regressor Conditional VAE Generative Adversarial Network (RCVAE-GAN). Collecting only a tiny amount of sparse reference point position coordinates and RSS data makes it possible to construct a high-quality WiFi indoor positioning fingerprint database. Firstly, the method utilizes the density clustering method based on crow search algorithm Optimization (CSA-DBSCAN) to process RSS data collected from the reference point. This helps minimize the impact of abnormal RSS data on creating the fingerprint database. Secondly, the RCVAE-GAN depth generation model was developed. The model consists of an encoder E, a generator G, a discriminator D, and a regressor R. After constructing the model, the data with abnormal RSS will be removed and input into the model for pre-training and joint training, resulting in a high-quality deep-generation model. Finally, a high-quality and high-density fingerprint database is constructed by combining the collected reference points with fingerprint data generated by the depth generation model. Experimental results show that the proposed method reduces the root mean square error (RMSE) deviation of the generated fingerprint data by 38% and 12% respectively, compared to the RBF interpolation method and the CVAE-GAN method in the same experimental scenario. The constructed fingerprint database is used for positioning, improving positioning accuracy by 70% and 65%

关键词： deep learning clustering fingerprint database crow search algorithm

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Influence of Pre-Processing algorithms on Surface Water TDS Estimation Using Artificial Intelligence Models: A Case Study of the Karoon River

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第1期47卷 585-598页

作者： Panahi, Javad Mastouri, Reza Shabanlou, Saeid Islamic Azad Univ Dept Civil Engn Arak Branch Arak Iran Islamic Azad Univ Dept Water Engn Kermanshah Branch Kermanshah Iran

One of the essential indicators of surface water quality is total dissolved solids (TDS) that is prominent for both short- and long-term water resources management. In this study, in order to resolve the non-stationarity challenges faced by water quality parameters (WQPs), variational mode decomposition (VMD) combined with two unsupervised artificial intelligence (AI) models, artificial neural network (ANN) and extreme learning machine (ELM), was used. crow search algorithm (CSA) was recruited to train the ANN and ELM networks and find the optimum weight and bias values. To validate the constructed hybrid VMD-ANN/ELM-CSA models, Molasani station in Karoon River was considered to model TDS at monthly timescale. The WQPs time-series data are simultaneously factorized into respective intrinsic mode functions (IMFs) using VMD algorithm. The proposed VMD-ELM-CSA model shows the best performance at considered station, in comparison with the comparative standalone and hybrid models, with maximum values of R-2 = 0.97 and E-ns = 0.96 at testing period. Results show that using VMD could reduce RMSE about 37 and 12% when coupled with ANN-CSA and ELM-CSA, respectively, at Molasani station. It was concluded that the CSA optimization algorithm and VMD data decomposition technique could improve the prediction accuracy of the AI models and may be recommended for accurate WQPs prediction.

关键词： Surface water TDS Water quality parameters Data decomposition algorithm crow search algorithm Hybrid artificial intelligence model

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Performance evaluation and comparative analysis of crowWhale-energy and trust aware multicast routing algorithm

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WEB INTELLIGENCE 2023年第3期21卷 271-291页

作者： Shende, Dipali K. Angal, Yogesh S. PCCOER Pune Maharashtra India Bhivarabai Sawant Inst Technol & Res Wagholi Pune India

Multipath routing helps to establish various quality of service parameters, which is significant in helping multimedia broadcasting in the Internet of Things (IoT). Traditional multicast routing in IoT mainly concentrates on ad hoc sensor networking environments, which are not approachable and vigorous enough for assisting multimedia applications in an IoT environment. For resolving the challenging issues of multicast routing in IoT, crowWhale-energy and trust-aware multicast routing (crowWhale-ETR) have been devised. In this research, the routing performance of crowWhale-ETR is analyzed by comparing it with optimization-based routing, routing protocols, and objective functions. Here, the optimization-based algorithm, namely the SpiderMonkey Optimization algorithm (SMO), Whale Optimization algorithm (WOA), Dolphin Echolocation Optimization (DEO) algorithm, Water Wave Optimization (WWO) algorithm, crow search algorithm (CSA), and, routing protocols, like Ad hoc On-Demand Distance Vector (AODV), CTrust-RPL, Energy-Harvesting-Aware Routing algorithm (EHARA), light-weight trust-based Quality of Service (QoS) routing, and Energy-awareness Load Balancing-Faster Local Repair (ELB-FLR) and the objective functions, such as energy, distance, delay, trust, link lifetime (LLT) and EDDTL (all objectives) are utilized for comparing the performance of crowWhale-ETR. In addition, the performance of crowWhale-ETR is analyzed in terms of delay, detection rate, energy, Packet Delivery Ratio (PDR), and throughput, and it achieved better values of 0.539 s, 0.628, 78.42%, 0.871, and 0.759 using EDDTL as fitness.

关键词： Spider monkey optimization dolphin echolocation water wave optimization crow search algorithm whale optimization algorithm

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Improved Interleaved Single-Ended Primary Inductor-Converter forSingle-Phase Grid-Connected System

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Intelligent Automation & Soft Computing 2023年第3期35卷 3459-3478页

作者： T.J.Thomas Thangam K.Muthu Vel Noorul Islam Centre for Higher Education Nagarkovil629180TamilnaduIndia

The generation of electricity based on renewable energy sources,parti-cularly Photovoltaic(PV)system has been greatly increased and it is simply insti-gated for both domestic and commercial *** power generated from the PV system is erratic and hence there is a need for an efﬁcient converter to perform the extraction of maximum *** improved interleaved Single-ended Primary Inductor-Converter(SEPIC)converter is employed in proposed work to extricate most of power from renewable *** proposed converter minimizes ripples,reduces electromagnetic interference due toﬁlter elements and the contin-uous input current improves the power output of PV panel.A crow search Algo-rithm(CSA)based Proportional Integral(PI)controller is utilized for controlling the converter switches effectively by optimizing the parameters of PI *** optimized PI controller reduces ripples present in Direct Current(DC)vol-tage,maintains constant voltage at proposed converter output and reduces over-shoots with minimum settling and rise *** voltage is given to single phase grid via 1�Voltage Source Inverter(VSI).The command pulses of 1�VSI are produced by simple PI *** response of the proposed converter is thus improved with less input *** implementing CSA based PI the efﬁciency of proposed converter obtained is 96%and the Total Harmonic Distor-tion(THD)is found to be 2:4%.The dynamics and closed loop operation is designed and modeled using MATLAB Simulink tool and its behavior is performed.

关键词： Improved interleaved DC-DC SEPIC converter crow search algorithm PI controller voltage source inverter PV array single phase grid

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Stock market forecasting using intrinsic time-scale decomposition in fusion with cluster based modified CSA optimized ELM

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 8777-8793页

作者： Das, Sudeepa Sahu, Tirath Prasad Janghel, Rekh Ram Natl Inst Technol Dept Informat Technol Raipur Chhattisgarh India

Stock price prediction is a significant index which helps to achieve maximum benefit with minimum risk by increasing the decision making capability of financial investigators and investors. However, the problem of short term stock price prediction is a complex task due to its uncertainty, discontinuity, and random nonlinear nature. In this paper, a prediction model is proposed to predict random nonlinear stock market price using Intrinsic Time-Scale Decomposition (ITD), Cluster based Modified crow search algorithm (CMCSA), and Optimized Extreme Learning Machine (OELM). ITD is adopted to decompose the non-stationary stock price data into some Proper-Rotation-Components (PRCs) and a residual component. ITD is used to convert non-stationary stock price data to stationary data which are simpler and steady for analysis. The CMCSA is proposed by modifying crow search algorithm (CSA) with better capability to select optimal weight and biases of ELM. Thereafter, the optimized ELM is used to predict the PRCs and residual component individually which are then incorporated to predict closing price of short term stocks. The effectiveness of proposed CMCSA is tested and validated by solving benchmark problems. The experimental study indicates that the proposed ITD-CMCSA-OELM model outperforms the CMCSA-OELM, CSA-OELM, DE-OELM (Differential Evolution optimized ELM) and ANN models. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： crow search algorithm Extreme learning machine Decomposition method Stock market Cluster based Modified crow search algorithm (CMCSA) Friedman test

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Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier

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NETWORK-COMPUTATION IN NEURAL SYSTEMS 2022年第1-2期33卷 95-123页

作者： Kalaivani, K. Maheswari, N. Uma Venkatesh, R. Sree Vidyanikethan Engn Coll Tirupati Andhra Pradesh India Psna Coll Engn & Technol Dept Comp Sci & Engn Dindigul India Psna Coll Engn & Technol Dept Informat Technol Dindigul India

Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy. To solve this, the present study proposes a hybridized Ant Lion crow search Optimization Genetic algorithm (ALCSOGA) to perform effective feature selection. This hybrid optimization encompasses Ant Lion, crow search and Genetic algorithm. Ant lion algorithm determines the elite position. While, the crow search algorithm utilizes the phenomenon of position and memory of each crow for evaluating the objective function. Both these algorithms are fed into Genetic algorithm to improve the performance of feature selection process. Then, Stochastic Learning rate optimized Long Short Term Memory (LSTM) is proposed to classify the extracted optimized features. Finally, comparative analysis is performed in terms of accuracy, recall, F1-score, and precision. Moreover, statistical analysis is performed with respect to Sum of Squares (SS), degree of freedom (df), F Critical (F crit), F Statistics (F), p, and Mean Square (MS) value. Analytical results revealed the efficiency of proposed system over conventional methods and thereby confirming its efficiency for predicting heart disease.

关键词： Index Terms- Ant Lion algorithm crow search algorithm Genetic algorithm Lstm Neural Network CVD Diagnosis

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