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PV Output Power Enhancement using whale optimization algorithm under Normal and Shading Conditions

INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2020年第3期10卷 1536-1543页

作者： Maniraj, B. Fathima, A. Peer Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

This paper deals with whale hunting behaviour inspired whale optimization algorithm (WOA) for tracking maximum power from the solar photovoltaic (PV) system. Maximum power point tracking (MPPT) controller has become an essential requirement for tracking the actual power present in solar PV system. In literature, various optimization techniques have been proposed for normal and partial shading conditions (PSC). But the problem in the conventional methods is tracking peak power under shading conditions is not assurance due to the presence of many peaks in a shading conditions. Because the local peaks are presented very close to global peaks. The results of conventional algorithms get failed with local peaks instead of tracking global peak power particularly in shading conditions. In this paper, a new WOA algorithm has been proposed which has the ability to reach the peak power presented in solar PV panel under different climatic conditions. In further, the proposed WOA algorithm has been investigated in MATLAB/Simulink model and comparison has been made with different MPPT algorithms, namely, Perturb and Observe (PO), grey wolf optimization (GWO). The results clearly demonstrated the proposed WOA algorithm giving more than 99.6% efficiency with high tracking speed and minimum payback period under PSC.

关键词： Maximum power point tracking Photovoltaic Partial shading conditions whale optimization algorithm

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An intelligent lung tumor diagnosis system using whale optimization algorithm and support vector machine

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2020年第2期11卷 374-384页

作者： Vijh, Surbhi Gaur, Deepak Kumar, Sushil Amity Univ Dept Comp Sci & Engn Sect 125 Noida Uttar Pradesh India Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal Telangana India

Medical image processing technique are widely used for detection of tumor to increase the survival rate of patients. The development of computer-aided diagnosis system shows improvement in observing the medical image and determining the treatment stages. The earlier detection of tumor reduces the mortality of lung cancer by increasing the probability of successful treatment. In this paper, the intelligent lung tumor diagnosis system is developed using various image processing technique. The simulated steps involve image enhancement, image segmentation, post-processing, feature extraction, feature selection and classification using support vector machine (SVM) kernel. Gray level co-occurrence matrix method is used for extracting the 19 texture and statistical features of lung computed tomography (CT) image. whale optimization algorithm (WOA) is considered for selection of best prominent feature subset. The contribution provided in this paper is the development of WOA_SVM to automate the aided diagnosis system for determining whether the lung CT image is normal or abnormal. An improved technique is developed using whale optimization algorithm for optimal feature selection to obtain accurate results and constructing the robust model. The performance of proposed methodology is evaluated using accuracy, sensitivity and specificity and obtained as 95%, 100% and 92% using radial bias function support vector kernel.

关键词： Lung tumor Global thresholding Gray level co-occurrence matrix whale optimization algorithm Support vector machine

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Binary whale optimization algorithm and Binary Moth Flame optimization with Clustering algorithms for Clinical Breast Cancer Diagnoses

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JOURNAL OF CLASSIFICATION 2020年第1期37卷 66-96页

作者： Sayed, Gehad Ismail Darwish, Ashraf Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Egypt Sci Res Grp Egypt SRGE Cairo Egypt Helwan Univ Sci Res Grp Egypt SRGE Fac Sci Cairo Egypt

Models based on machine learning algorithms have been developed to detect the breast cancer disease early. Feature selection is commonly applied to improve the performance of these models through selecting only relevant features. However, selecting relevant features in unsupervised learning is much difficult. This is due to the absence of class labels that guide the search for relevant information. This kind of the problem has rarely been studied in the literature. This paper presents a hybrid intelligence model that uses the cluster analysis algorithms with bio-inspired algorithms as feature selection for analyzing clinical breast cancer data. A binary version of both moth flame optimization and whale optimization algorithm is proposed. Two evaluation criteria are adopted to evaluate the proposed algorithms: clustering-based measurements and statistics-based measurements. The experimental results positively demonstrate that the capability of the proposed bio-inspired feature selection algorithms to produce both meaningful data partitions and significant feature subsets.

关键词： Intelligent systems Breast cancer Feature selection whale optimization algorithm Moth flame optimization WBCD

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Optimal network reconfiguration and DG allocation using adaptive modified whale optimization algorithm considering probabilistic load flow

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 192卷 106909-106909页

作者： Uniyal, Ankit Sarangi, Saumendra Motilal Nehru Natl Inst Technol Allahabad Allahabad Uttar Pradesh India Natl Inst Technol Srinagar Uttarakhand India

The optimal DG allocation (ODGA) and network reconfiguration (NR) are significant tools to improve voltage stability and minimize power losses in a distribution network. In a real power distribution network loads are random variables, thus ODGA and NR processes are incorporated using a probabilistic load flow (PLF). The reactive power capability of DGs has a significant impact on the network parameters which can be studied using PLF. In the present work, an ODGA and NR processes have been incorporated to improve the voltage stability and loss profile of the distribution system considering probabilistic loads and DGs which are operated at varying pfs. The optimization problem has been solved using an adaptive modified whale optimization algorithm (AMWOA). A novel technique based on depth first search algorithm has been employed to check radiality constraints. The effectiveness of proposed technique has been tested on two standard distribution networks.

关键词： Active power loss Distribution system Distributed generation Network reconfiguration Point estimate methods Probabilistic load flow whale optimization algorithm

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Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2021年第5期17卷

作者： Yue, Yinggao You, Hairong Wang, Shuxin Cao, Li Wenzhou Univ Oujiang Coll Wenzhou 325035 Zhejiang Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine-cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

关键词： Heterogeneous wireless sensor networks whale optimization algorithm coverage optimization sine and cosine algorithm energy balance

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Micro-vibration response analysis and its application of electronic workshop raw land based on whale optimization algorithm

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Journal of Measurement Science and Instrumentation 2022年第4期13卷 390-397页

作者： YU Caizhi LU Yutai WANG Peng SUN Changku State Key Laboratory of Precision Measuring Technology and Instruments Tianjin UniversityTianjin 300072China

Environmental micro-vibration is one of the key factors impacting the running of electronic *** frequency micro-vibration has a significant influence on the normal operation of high precision machining and testing equipment,and even causes irreversible damage to the ***-vibration testing and response analysis are important to guide the vibration isolation design and ensure the stable operation of various precision equipment in the *** of Davidenkov model are fitted based on whale swarm optimization algorithm,and its applicability is *** the same time,taking the testing project of an electronic workshop raw land as an example,the micro-vibration response is *** results show that the nonlinear constitutive model constructed by whale optimization algorithm can simulate the dynamic nonlinear behavior of soil under the action of micro-vibration *** with the traditional equivalent linearization method,the nonlinear constitutive model based on the whale optimization algorithm has a smaller acceleration response *** can effectively suppress the“virtual resonance effect”produced by the equivalent linearization method.

关键词： micro-vibration response nonlinear dynamic constitutive model whale optimization algorithm electronic workshop raw land

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Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks

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COMPUTER NETWORKS 2021年 197卷 108250-108250页

作者： SureshKumar, K. Vimala, P. Annamalai Univ Dept Elect & Commun Engn Chidambaram 608002 Tamil Nadu India

Energy efficiency is a key challenging task while designing routing model in Wireless Sensor Network (WSN). Various energy efficiency routing approaches are designed to transfer the data packets in a secure path through cluster head (CH), but to increase the lifespan of network and to maintain high scalability poses a complex task in WSN environment. Hence, an energy efficient and trust based routing model is designed using proposed Exponentially-Ant Lion whale optimization (E-ALWO) algorithm to route the data packets to receiver. However, the E-ALWO algorithm is derived by the integration of Exponentially Weighted Moving Average (EWMA) concept with Ant Lion optimization (ALO) and whale optimization algorithm (WOA), respectively. However, the proposed model performs the routing process through CH such that the selection of CH is made using ALWO algorithm based on the constraints, namely energy and delay. The optimal and the secure route used for the data transferring process is computed using proposed E-ALWO algorithm based on the fitness measure. The fitness function considers the factors, namely energy, trust, delay, and distance. Moreover, the path with maximal fitness value is accepted as the routing path, by which the data is forwarded to sink node through CH. The average performance measure achieved by the proposed method by considering without attack scenario in terms of delay, residual energy, throughput, and trust is 0.1484sec, 0.4998J, 42984kbps, and 0.5885.

关键词： Energy Efficiency Trust Based Routing Ant Lion optimization whale optimization algorithm Cluster Head

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Estimation of Transmission Line Parameters Using Voltage-Current Measurements and whale optimization algorithm

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ENERGIES 2021年第11期14卷 3239页

作者： Hassanein, Wael S. Ahmed, Marwa M. Mosaad, Mohamed I. Abu-Siada, A. King Abdulaziz Univ Dept Ind Engn Fac Engn Jeddah 80204 Saudi Arabia King Abdulaziz Univ Dept Elect Engn Fac Engn Jeddah 80204 Saudi Arabia Yanbu Ind Coll YIC Yanbu Ind City 46452 Saudi Arabia Curtin Univ Elect & Comp Engn Discipline Bentley WA 6152 Australia

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

关键词： transmission lines voltage-current measurements parameters estimation whale optimization algorithm fault location

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Optimal techno-economic analysis of a renewable based hybrid microgrid incorporating gravity energy storage system in Indian perspective using whale optimization algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第9期31卷

作者： Dhar, Pinaki Chakraborty, Niladri Jadavpur Univ Power Engn Dept Saltlake Campus Kolkata India

A grid connected renewable energy based hybrid microgrid, incorporating solar PV, biomass generators, and Gravity Energy storage system (GESS) has been proposed in this article. This is done to provide uninterrupted energy to a hilly village of North-East India. A bi-directional energy transaction with the central grid is considered for handling uncertainties and to gain monetary benefit through energy sale. The GESS is only charged through solar PV and biomass generators, to reduce the carbon footprint. The outward energy transaction with the central grid helps in reducing the energy cost for consumers inside the microgrid. Transaction price of energy from the central grid is considered from historical data available in the website of Indian Energy Exchange Limited (IEX). For different combinations of loading pattern and GESS initial storage, the Levelized cost of energy (LCOE) is found to vary in between INR 2.71/kWh to INR 3.41/kWh, which is lower than the current Nagaland state government approved tariff of INR 3.55/kWh. Any subsidy from governmental or non-governmental organizations has not been considered in the present study while calculating the installation cost of Solar PV and Biomass generators to portray the actual scenario. whale optimization algorithm (WOA) has been used to find the optimal solution of the proposed system.

关键词： biomass gravity energy storage levelized cost of energy microgrid solar PV whale optimization algorithm

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Mobile Robot Path Planning Based Upon Updated whale optimization algorithm 10

Mobile Robot Path Planning Based Upon Updated Whale Optimiza...

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10th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Chhillar, Ankit Choudhary, Ankur Amity Univ Comp Sci & Engn Noida Uttar Pradesh India Sharda Univ SET Comp Sci & Engn Greater Noida India

ISBN: (纸本)9781728127910

Automated navigation is an important feature of any mobile robot and is a challenge for a robot to plan the path in an unknown environment. We can categorize the path planning strategy of robots in two methods, first is the Classical Methods and second is Heuristic Methods. In this paper, a modified whale optimization algorithm is proposed that ensures an optimal collision-free path. In whale optimization algorithm (WOA) the fitness of any whale will be calculated by taking in consideration the target location and the obstacles in the search space. Many simulations were run in different search spaces to find the optimal path at the end of the iterations when the individual reaches the final destination (target location). The simulation and results show, the proposed modified WOA is feasible for mobile robot path selection problems.

关键词： whale optimization algorithm Path Planning

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