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Optimal day-ahead scheduling in micro-grid with renewable based DGs and smart charging station of EVs using an enhanced manta-ray foraging optimisation

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IET RENEWABLE POWER GENERATION 2022年第11期16卷 2413-2428页

作者： Bastawy, Mostafa Ebeed, Mohamed Ali, Abdelfatah Shaaban, Mostafa F. Khan, Baseem Kamel, Salah Aswan Univ Dept Elect Engn Aswan Egypt Sohag Univ Dept Elect Engn Sohag Egypt South Valley Univ Dept Elect Engn Qena Egypt Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Hawassa Univ Dept Elect & Comp Engn Hawassa Ethiopia

Micro-grids (MGs) are small parts of the electrical power system that work along with the electric system or autonomously based on environmental or economic conditions. The renewable-based distributed generators (RDGs) and electric vehicle charging stations (EVCSs) are wildly incorporated in MGs. Optimal day-ahead scheduling of the MG is ahead corner of energy management for cost reduction. In addition, solving the economic load dispatch and day-ahead scheduling of the MG is a complex optimisation problem, especially considering the RDGs, EVCS, and uncertainties in the electrical system. This paper aims to optimise the day-ahead scheduling of the MG with and without a smart charging strategy for electric vehicles. An enhanced manta-ray foraging optimisation (EMRFO) algorithm is proposed to solve this optimisation problem. EMRFO depends upon boosting population diversity and the searching ability of the standard MRFO using strategies. The proposed strategies are based on quasi-oppositional-based learning and local chaotic mutation. The studied MG consists of wind turbines, fuel cells, and diesel generators. The day-ahead scheduling of the MG is solved with and without considering the uncertainties of the load demand and the wind speed. The proposed algorithm for day-ahead scheduling of the MG is compared to well-known algorithms such as anti lion optimisation, particle swarm optimisation, whale optimisation algorithm, sine cosine algorithm, and harmony search algorithm. The simulation results demonstrate that the proposed algorithm is superior to these algorithms for solving the optimisation problem. The results show that the generation cost is reduced considerably from 77,745.61 $ to 76,984.2 $ by applying the smart operation strategy.

关键词： enhanced manta-ray foraging optimisation algorithm battery powered vehicles power engineering computing distributed power generation load dispatching electric vehicle charging smart charging strategy power generation economics optimisation cost reduction renewable-based distributed generators electrical power system particle swarm optimisation metaheuristics renewable based DGs power grids electric vehicle whale optimisation algorithm wind turbines complex optimisation problem electrical system search problems electric system power generation scheduling optimal day-ahead scheduling ahead corner

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Research on Short-term Power Load Forecasting Using the WOA-BiLSTM-Attention Model 6

Research on Short-term Power Load Forecasting Using the WOA-...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Dai, Lei Xiao, Chengcheng Tang, Guohua China University of Geosciences Beijing School of Science Beijing China

ISBN: (纸本)9798350313444

The foundation of power system operation is short-term power load forecasting, and precise load forecasting can guarantee the power system's safe and stable operation. This paper chooses the bidirectional long- and short-term memory network model of the attention mechanism under the whale Optimization algorithm (WOA-BiLSTM-Attenion) for the evaluation of electric power forecasting in order to address the issues of low precision and poor accuracy of short-term load forecasting. Bi-directional long-short-term memory network modeling attentional mechanisms can lessen the loss of past information and boost the effect of important information. In order to minimize human intervention, the whale Optimization algorithm (WOA) is then applied for hyperparameter selection. The experimental results demonstrate that the WOA-BiLSTM-Attention model outperforms BP, LSTM, BiLSTM, and BiLSTM-Attention in terms of short-term power load forecasting, with an RMSE value of 5913.9446 and an R2 value of 0.9477. Its greater accuracy has some repercussions for other fields' forecasting research. © 2023 IEEE.

关键词： Attention mechanism BiLSTM deep learning load forecasting whale optimisation algorithm

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Experimental investigation on similar and dissimilar alloys of stainless steel joints by laser beam welding

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ADVANCES IN MATERIALS AND PROCESSING TECHNOLOGIES 2022年第Supp 1期8卷 13-28页

作者： Bijivemula, Narayana Reddy Hema, P. Padmanabhan, G. SV Univ Dept Mech Engn Tirupati 517502 Andhra Pradesh India

Alloy and Stainless steel plates have to be welded to meet the present generation of smart manufacturing by Laser Beam Welding. Laser Beam Welding (LBW) plays an important role in joining of metals or welding the parts of similar/dissimilar metals. Due to its high-speed weldability with high laser beam power result in high strength and low heat affected zones with better appearance and quality. AISI Stainless steels which are widely used in various industries due to their resistance to corrosion. There is often a need to study the effect of process parameters, while fabricating stainless steel structures and/or when weld stainless steel to low-alloy steels. A solemn attempt is made in the present research work to join similar and dissimilar AISI 4130 Alloy and AISI 309 Stainless Steel joints by CO2 Laser Beam Welding with an objective to investigate the effects of process parameters. The weld joints are studied based on their Bead Width and Micro Hardness. The performance characteristic curves are drawn and analysed the influence of the process parameters on the weld joints. The whale optimisation algorithm is used to identify the optimal combination of process parameters based on the experimental output results of Bead Width and Micro Hardness.

关键词： Laser beam welding alloy steel stainless steel bead width micro hardness ANOVA whale optimisation algorithm

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A novel method to increase the security in 5G networks using deep learning

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INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS 2025年第3期17卷 419-431页

作者： Rajasekar, A. Ramamoorthi, R. Ramya, M. Arunachalam, Vinod Sri Sairam Inst Technol Dept Artificial Intelligence & Data Sci Chennai 44 India Govt Coll Engn Dharmapuri Dept EEE Dharmapuri India SA Engn Coll Dept ECE Chennai 77 India Govt Coll Engn Dharmapuri Dept ECE Dharmapuri 636704 India

Wireless networks are being forced to handle a greater amount of data due to a variety of circumstances, and this trend is progressing at a rapid rate. The denial of service (DoS) attacks have the highest rate of growth target the expanding computational network infrastructures all over the globe. As a consequence of this, the objective of this study is to come up with an innovative model for the identification of DoS attacks on 5 G networks. The model will go through two phases: the first will be feature extraction, and the second will be attack detection. In order to successfully carry out the detection, classifiers that have long short-term memories (LSTM) are utilised. The whale optimisation algorithm (WOA) model works to optimise the weight of the LSTM. The detected output of a hybrid model that has been trained appropriately provides the accuracy rate of 96.5%

关键词： 5G deep learning dos attacks denial-of-service whale optimisation algorithm WOA

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WOATCA: A secure and energy aware scheme based on whale optimisation in clustered wireless sensor networks

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IET COMMUNICATIONS 2020年第8期14卷 1199-1208页

作者： Sharma, Richa Vashisht, Vasudha Singh, Umang Amity Univ Amity Inst Informat Technol Noida India Amity Univ Amity Sch Engn & Technol Noida India Inst Technol & Sci Ghaziabad India

Clustering is an important method for extending the network life of each node in wireless sensor networks. Owing to the untrustworthy environment, these nodes unintentionally sacrifice their energy and lost their life which is a major challenge and requires appropriate attention for smooth communication. Thus energy efficiency and network life period both are major concerns in critical and sensitive real-life applications such as military applications, landslide monitoring etc. This study reviews and analyses existing trust-based clustering routing protocols for a complete understanding of research contribution to date made by researchers of this domain. In addition, a novel trust-based energy efficient protocol based on a whale optimisation algorithm named WOATCA has been presented. This algorithm focuses on the selection of trust worthy nodes as cluster heads (CHs) by considering five parameters namely (i) node's residual energy, (ii) number of packets forwarded by a node, (iii) average cluster distance, (iv) transmission delay, and (v) node density. Simulation results and analysis demonstrate that WOATCA outperformed existing protocols by showing a percentage decrease of 87.85, 64.58 and 61.36% in the selection of compromised nodes as CH in comparison with existing trust-based schemes such as Adoptive LEACH Mobile (ALM), Topology Control algorithm for node mobility (TCM), Q12 and secure CH selection protocols, respectively.

关键词： wireless sensor networks telecommunication security protocols pattern clustering routing protocols research contribution trust-based energy efficient protocol whale optimisation algorithm WOATCA trust parameter routing protocols trust-based clustering military applications real-life applications energy efficiency smooth communication appropriate attention untrustworthy environment network life clustered wireless sensor networks secure energy secure CH selection protocols existing trust-based schemes compromised nodes node density average cluster distance parameters node trust worthy environment cluster heads

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Nature-inspired intrusion detection system for protecting software-defined networks controller

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COMPUTERS & SECURITY 2023年第1期134卷

作者： Kumar, Chandan Biswas, Soham Ansari, Md. Sarfaraj Alam Govil, Mahesh Chandra Natl Inst Technol Dept Comp Sci & Engn Sikkim 737139 India

Software Defined Networks (SDN) is a new emerging networking architecture facilitated by a separate controller. It has a centralized architecture that serves network management and demand fulfillment from a single point. Architecturally, SDN provides lofty benefits, but security measures are a severe concern that prevents universal adoption. If the SDN controller gets compromised, the intruders could ensign the route of network traffic based on their requirements which causes damage to the entire network. These security limitations of SDN get rectified by employing the network intrusion detection system (NIDS). Deploying ML techniques for NIDS helps us to detect the anomalies within SDN. Selecting a minimal number of features is important as it decreases the computation overhead and accelerates the NIDS performances. For feature selection, a combination of k optimal features is selected using the hit-and-trial approach. As a result, it influences the system's durability for intrusion detection and pushes the model toward poor performance. In this study, we present a new hybrid framework for feature selection using whale optimisation algorithm (WOA), Fisher Score (FS), and Information gain (IG), named whale Optimization algorithm-Fisher Feature Importance (WOA-(FI)-I-2). WOA-(FI)-I-2 addresses the challenge of selecting the.. number of an optimal subset of features. The evaluation result manifests the WOA-F-2 I feature selection technique that enhances the performance of the NIDS model, which backs the input for various classifiers-support vector machine, Naive Bayes, logistic regression, and stochastic gradient descent. The proposed feature selection technique also provides better performance for binary class and multi-class attack detection.

关键词： Software Defined Networks Intrusion Detection System whale optimisation algorithm Fisher score Information gain Feature selection Dimension reduction

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Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

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SENSORS 2022年第12期22卷 4412-4412页

作者： Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & C Huainan 232000 Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 232000 Peoples R China Anhui Univ Sci & Technol Sch Econ & Management Huainan 232000 Peoples R China

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm's (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

关键词： coal mine safety whale optimisation algorithm LSTM CEEMDAN decomposition and reconstruction gas concentration prediction

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Protein Folding Simulation using Combinatorial whale Optimization algorithm 21

Protein Folding Simulation using Combinatorial Whale Optimiz...

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21st International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Sima, Ioan Parv, Bazil Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania

ISBN: (纸本)9781728157245

The whale Optimization algorithm (WOA) is a novel nature-inspired algorithm, being originally dedicated to continuous function optimization. This paper modifies it to address combinatorial, i.e. discrete function optimization;the new algorithm is called Combinatorial whale Optimization algorithm (cWOA). cWOA was applied to protein folding problem on the 2D HP Model, which is a well known combinatorial optimization problem. The results are encouraging for further development and expansion of experiments to 3D HP or other models.

关键词： Protein Folding Simulation HP 2D Model whale optimisation algorithm Combinatorial whale optimisation algorithm

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Efficiency of different optimisation approaches for optimal parameters of the CSM for investigating the onset field due to corona point

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第9期14卷 1751-1761页

作者： Talaat, M. Tayseer, M. El-Zein, A. Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt Shaqra Univ Elect Dept Coll Engn Dawadmi Ar Riyadh Saudi Arabia

Corona discharges require high and non-uniform field to take place. In this research, the difficulty of suggesting the required arrangement of optimum fictitious charges, their positions and values inside the conductor in charge simulation method (CSM) is solved using genetic algorithm, satin bowerbird optimisation and whale optimisation algorithm. A hybrid model based on combining CSM with proposed optimisation techniques is provided to simulate and calculate the non-uniform electric field that is created by a hyperbolic needle-to-plane configuration expressed in a dielectric medium. The accuracy of electric-field simulation results is investigated by estimating per cent potential error and the maximum deviation angle of the normal field vector at the conductor surface for each proposed optimisation algorithm, taking into account the assessment of the efficiency of all proposed optimisation algorithms. To investigate the validity and accuracy of the proposed model, a comparison is held with available previous simulation and experimental data.

关键词： electric fields genetic algorithms corona discharges (electric) optimal parameters CSM onset field corona point corona discharges nonuniform field optimum fictitious charges charge simulation method genetic algorithm satin bowerbird optimisation whale optimisation algorithm hybrid model nonuniform electric field hyperbolic needle-to-plane configuration electric-field simulation results normal field vector conductor surface

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Enabling image optimisation and artificial intelligence technologies for better Internet of Things framework to predict COVID

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IET NETWORKS 2022年第0期

作者： Allayla, Noor M. Ibraheem, Farah Nazar Jaleel, Refed Adnan Univ Mosul Dept Comp Engn Mosul Iraq Al Nahrain Univ Dept Informat & Commun Engn Baghdad 10070 Iraq

Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework. Computed Tomography (CT) image-based forecasting of COVID disease is among the important activities in medicine for measuring the severity of variability in the human body. In COVID CT images, the optimal gamma correction value was optimised using the whale optimisation algorithm (WOA). During the search for the optimal solution, WOA was found to be a highly efficient algorithm, which has the characteristics of high precision and fast convergence. whale optimisation algorithm is used to find best gamma correction value to present detailed information about a lung CT image, Also, in this study, analysis of important AI techniques has been done, such as Support Vector Machine (SVM) and Deep-Learning (Deep-Learning (DL)) for COVID disease forecasting in terms of amount of data training and computational power. Many experiments have been implemented to investigate the optimisation: SVM and DL with WOA and without WOA are compared by using confusion matrix parameters. From the results, we find that the DL model outperforms the SVM with WOA and without WOA.

关键词： COVID deep learning image processing Internet of Things Support Vector Machine whale optimisation algorithm

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