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A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction

ENVIRONMENTAL POLLUTION 2020年第Part3期266卷 115216-115216页

作者： Sun, Wei Huang, Chenchen North China Elect Power Univ Dept Econ Management Baoding 071000 Peoples R China

Acid rain is a serious threat to terrestrial ecosystems. To provide more accurate early warning information for acid rain prevention, urban planning, and travel planning, a novel air pollutant prediction model was proposed in this paper to predict NO2 and SO2. First, the data were decomposed into several sub-sequences by a complete ensemble empirical mode decomposition with adaptive noise. Second, the subsequences are reconstructed by variational mode decomposition and sample entropy. Then, the new subsequences are predicted by the extreme learning machine combined with the whale optimization algorithm. The empirical analysis was carried out through 8 data sets. According to the experimental results, three main conclusions can be drawn. First, the proposed model in this paper has excellent prediction performance and robustness. In all the comparison experiments, the R-2 and RMSE of the proposed model are the best among all the models. Second, data preprocessing is very necessary. After adding the decomposition algorithm, the average improvement levels of R-2 and RMSE were 897.57% and 50.78%, respectively. Third, the re-decomposition of IMF1 is an effective method to improve prediction accuracy. After the re-decomposition of IMF1, R-2 can be improved by 13.64% on average on the original basis, and RMSE can be reduced by 31.99% on average. The results of this study can provide a valuable reference for the research of air pollutant prediction. In future work, the application of the proposed model in other air pollutants or other regions can be explored. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Air pollutant prediction Extreme learning machine whale optimization algorithm Sample entropy Complete ensemble empirical mode decomposition with adaptive noise Acid rain

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Optimal variable structure control with sliding modes for unstable processes

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Journal of Central South University 2021年第10期28卷 3147-3158页

作者： KUMAR Satyendra AJMERI Moina Department of Electrical Engineering National Institute of TechnologyPatna 800005India

In this work,a variable structure control(VSC)technique is proposed to achieve satisfactory robustness for unstable *** values of unknown parameters of VSC are obtained using whale optimization algorithm which was recently reported in *** analysis has been done to verify the suitability of the proposed structure for industrial *** proposed control strategy is applied to three different types of unstable processes including non-minimum phase and nonlinear systems.A comparative study ensures that the proposed scheme gives superior performance over the recently reported VSC ***,the proposed method gives satisfactory results for a cart inverted pendulum system in the presence of external disturbance and noise.

关键词： variable structure control sliding mode control whale optimization algorithm robustness non-linear system

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Enhancement of low voltage ride-through ability of the photovoltaic array aided by the MPPT algorithm connected with wind turbine

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第4期54卷 503-527页

作者： Madan, Charanjeet Kumar, Naresh DCR Univ Sci & Technol Dept Elect Engn Sonipat India

Purpose By means of the massive environmental and financial reimbursements, wind turbine (WT) has turned out to be a satisfactory substitute for the production of electricity by nuclear or fossil power plants. Numerous research studies are nowadays concerning the scheme to develop the performance of the WT into a doubly fed induction generator-low voltage ride-through (DFIG-LVRT) system, with utmost gain and flexibility. To overcome the nonlinear characteristics of WT, a photovoltaic (PV) array is included along with the WT to enhance the system's performance. Design/methodology/approach This paper intends to simulate the control system (CS) for the DFIG-LVRT system with PV array operated by the MPPT algorithm and the WT that plays a major role in the simulation of controllers to rectify the error signals. This paper implements a novel method called self-adaptive whale with fuzzified error (SWFE) design to simulate the optimized CS. In addition, it distinguishes the SWFE-based LVRT system with standard LVRT system and the system with minimum and maximum constant gain. Findings Through the performance analysis, the value of gain with respect to the number of iterations, it was noted that at 20th iteration, the implemented method was 45.23% better than genetic algorithm (GA), 50% better than particle swarm optimization (PSO), 2.3% better than ant bee colony (ABC) and 28.5% better than gray wolf optimization (GWO) techniques. The investigational analysis has authenticated that the implemented SWFE-dependent CS was effectual for DFIG-LVRT, when distinguished with the aforementioned techniques. Originality/value This paper presents a technique for simulating the CS for DFIG-LVRT system using the SWFE algorithm. This is the first work that utilizes SWFE-based optimization for simulating the CS for the DFIG-LVRT system with PV array and WT.

关键词： LVRT Doubly fed induction generators (DFIGs) Wind turbine PV array Maximum power point tracking whale optimization algorithm

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A Fault Classification Method for Rolling Bearing Based on Multisynchrosqueezing Transform and WOA-SMM

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IEEE ACCESS 2020年 8卷 215355-215364页

作者： Zheng, Jinde Gu, Mingen Pan, Haiyang Tong, Jinyu Anhui Univ Technol Sch Mech Engn Maanshan 243032 Peoples R China

To solve the problem that the limited time-frequency features cannot fully represent the deep-seated state information of rolling bearing, the time-frequency analysis method, whale optimization algorithm (WOA) and support matrix machine (SMM) are combined, and a fault diagnosis model based on multisynchrosqueezing transform (MSST) and WOA-SMM is proposed. First, the time-frequency trait of the original signal is extracted by MSST. Then, using the time-frequency spectrum processed by MSST as the input of SMM, MSST solves the problem of state information loss when constructing a characteristic matrix. Finally, the parameters of the SMM are optimized by WOA and the ideal parameters can be obtained adaptively, and the problem of setting parameters subjectively is solved. The experimental analysis of the two datasets shows that WOA-SMM is superior not only to other classifiers in classification performance, but also has higher in convergence accuracy and speed for rolling bearing fault diagnosis.

关键词： Time-frequency analysis Fault diagnosis optimization whales Rolling bearings Transforms Feature extraction WOA-SMM whale optimization algorithm fault diagnosis rolling bearing multisynchrosqueezing transform

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Research on New Adaptive whale algorithm

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IEEE ACCESS 2020年 8卷 90165-90201页

作者： Chen, Xuan Zhejiang Ind Polytech Coll Design & Art Shaoxing 312000 Peoples R China

Bionic algorithms have always played an important role in industrial, agricultural, and scientific research. The optimization of bionics algorithms has always been the focus of scholars in various countries. A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm. The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population. Optimizing the adaptive convergence factor is able to balance the local and global optimization ability. The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability. In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm. In the simulation, IWOA, Ant Colony optimization, Particle Swarm optimization, whale optimization algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions. The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.

关键词： optimization whales Convergence Sociology Statistics Search problems Prediction algorithms whale optimization algorithm convergence factor Levy behavior triggering rule

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Multi-target QSAR modelling of chemo-genomic data analysis based on Extreme Learning Machine

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷 104977-104977页

作者： Anter, Ahmed M. Moemen, Yasmine S. Darwish, Ashraf Hassanien, Aboul Ella Shenzhen Univ Hlth Sci Ctr Sch Biomed Engn Shenzhen 518060 Peoples R China Beni Suef Univ Fac Comp & Informat Bani Suwayf 62511 Egypt Menoufia Univ Natl Liver Inst Clin Pathol Dept Menoufia Egypt Helwan Univ Fac Sci Cairo Egypt Cairo Univ Fac Comp & Informat Cairo Egypt SAGE Cairo Egypt

This paper presents a new Quantitative Structure-Activity Relationship (QSAR) model based on Extreme Learning Machine (ELM) to predict the biological activity of the benchmark Escape-Data sets compounds in order to provide an effective learning solution for regression analysis. The pre-processing phase of this model has been performed for the chemo-genomics datasets using the k-Nearest Neighbours (k-NN) algorithm to predict missing values of the dataset. In the second phase, the Genetic algorithm hybrid with Binary whale optimization algorithm (GBWOA) is adapted to determine the significance and the optimized features in feature selection phase. The minmax method is used in the third phase to transform all features to binary form in order to increases the efficiency of the proposed model by smoothing the data points and reducing fluctuation among features. ELM is used in the final phase as regression algorithm to predict chemo-genomics chemical compound. Different experiments have been performed in this paper on datasetwhich has been collected from ExCAPE chemo-genomics database project composed of 43509 compounds, 1134 targets besides biological activity and 40 chemical descriptors. The experimental results show that the proposed model is efficient in improving the level of prediction based on some statistical measurements. Also, ELM produced satisfactory results when the number of hidden nodes is greater than or equal to 1000 L. Moreover, the proposed model achieved high accuracy using R-2 measure (approximate to 0.971) which outperforms the other algorithms in literature such as (WOA, ALO, BAT and CSA) with accuracies (approximate to 0.673, approximate to 0.753, approximate to 0.680, and approximate to 0.897) respectively. In addition, the docking results succeeded in validating the current QSAR model. In the current research, 41686 (95.81%) compounds are lead compound and 36965 (84.95%) compounds are a candidate for multi-target genes. (C) 2019 Elsevier B.V. All

关键词： Machine learning whale optimization algorithm Genetic algorithm Extreme learning machine QSAR Molecular docking ExCAPE chemo-genomics database

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Optimal probabilistic reliable hybrid allocation for system reconfiguration applying WT/PV and reclosures

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AIN SHAMS ENGINEERING JOURNAL 2020年第1期11卷 109-118页

作者： Swief, R. A. El-Amary, Noha H. Ain Shams Univ Fac Engn Cairo 11566 Egypt AASTMT Cairo 2033 Egypt

This paper introduces an optimal probabilistic study based on multi objective problem integrating the stochastic behavior of the renewable resources to improve system performance properties which are represented in reducing Energy Not Served index and reducing the system power loss. The reliability indices in power system have been effected applying reconfigurations, inserting reclosures and integrating distributed generators and setting the optimal location with the appropriate size of the allocated distributed generators. The aim of the study is hugely altered according to the appropriate allocation of the reclousers and DGs with the optimal choice for the DG sizes. The achievement of the proposed methodology is to solve the technical problems while keeping distribution network with minimum pollution, so distributed generators are preferable to be renewable sources such as wind and PV energies. The volatility nature of the renewable sources, wind & PV units, makes the study crucial to embrace the probabilistic behavior of those sources. Due to the complexity of the problem which is vastly increased with the increase of the factors affecting the study, two Meta heuristic techniques are competing each other to reach the optimal solution with the optimal technique. The approached algorithm under study is examined utilizing IEEE 69 modified system. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Reconfiguration Reliability Probabilistic performance Wind PV whale optimization algorithm Gravitational search algorithm

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Feasibility study on model-based damage detection in shear frames using pseudo modal strain energy

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SMART STRUCTURES AND SYSTEMS 2020年第1期25卷 47-56页

作者： Dehcheshmeh, M. Mohamadi Hosseinzadeh, A. Zare Amiri, G. Ghodrati Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Univ Oklahoma Sch Civil Engn & Environm Sci Norman OK 73019 USA Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Sch Civil Engn Tehran Iran

This paper proposes a model-based approach for structural damage identification and quantification. Using pseudo modal strain energy and mode shape vectors, a damage-sensitive objective function is introduced which is suitable for damage estimation and quantification in shear frames. whale optimization algorithm (WOA) is used to solve the problem and report the optimal solution as damage detection results. To illustrate the capability of the proposed method, a numerical example of a shear frame under different damage patterns is studied in both ideal and noisy cases. Furthermore, the performance of the WOA is compared with particle swarm optimization algorithm, as one the widely-used optimization techniques. The applicability of the method is also experimentally investigated by studying a six-story shear frame tested on a shake table. Based on the obtained results, the proposed method is able to assess the health of the shear building structures with high level of accuracy.

关键词： damage detection shear frame pseudo modal strain energy mode shape whale optimization algorithm

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Optimal Weighted Logarithmic Transformation Converted HMOG Features for Automatic Smart Phone Authentication

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2022年第1期13卷

作者： Vinod, P. R. Anitha, A. Noorul Islam Ctr Higher Educ Kumaracoil Tamil Nadu India

This paper intends to develop an automatic behavior-based smart phone authentication model by three major phases: feature extraction, weighted logarithmic transformation, and classification. Initially, from the data related to the touches/gesture of the smartphone user, hand movement, orientation, and grasp (HMOG), features are extracted with the aid of grasp resistance and grasp stability. These extracted features are mapped within the particular range by normalizing HMOG. These normalized data are multiplied with the weights followed by logarithmic transformation in the weighted logarithmic transformation phase. As a novelty, the decision-making process related to the logarithmic and weight selection is based on the improved optimization algorithm, called modified threshold-based whale optimization algorithm (MT-WOA). The final feature vectors are fed to DBN for recognizing the authorized users. Finally, a performance-based evaluation is performed between the MT-WOA+DBN and the existing models in terms of various relevant performance measures.

关键词： HMOG Modified Threshold Function Smart Phone Authentication Weighted Logarithmic Transformation whale optimization algorithm

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A Framework for H2/H∞ Synthesis in Damping Power Network Oscillations with STATCOM

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第2期44卷 927-948页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Jharkhand India

The escalating in load demand prioritized the incorporation of additional renewable energy generation plants to the existing grid. In parallel, the necessity of reactive power balance and damping characteristics has advised the incorporation of flexible AC transmission system controllers to the existing power network. Hereby, the modern power system has been driven towards the much more composite system which in turn necessitates a healthy controller technique. The objective of this paper is to contribute recommendations to incorporate robust controller technique in the field of the electrical power system. Detailed design considerations for the H infinity controller design of a modern power network have been specified, and the same has been demonstrated with mathematical modelling of power network with static synchronous compensator (STATCOM) connected in the middle of the transmission line and in a multi-machine power system. To comment on the suitability of the controller design, a deep stability analysis has been presented and compared with the traditional power system stabilizer, power system stabilizer optimized using particle swarm optimization with time-varying acceleration coefficients algorithm and whale optimization algorithm under adverse system operating conditions. The proposed controller framework has been presented by considering the case studies of single machine infinite bus system, two-machine system model and the benchmark two-area four-generator multi-machine systems connected with STATCOM. The controller performance analysis has been verified by considering eigenvalues analysis, singular value analysis and dynamic response of system states during perturbations for the first two case studies, and system analysis under faulty condition has been investigated for the multi-machine system.

关键词： H-infinity Norm Min-max design Power system stability STATCOM Wide area monitoring systems whale optimization algorithm

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