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Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC-SMES

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 3039-3056页

作者： Dahiya, Preeti Sharma, Veena Naresh, R. Natl Inst Technol Elect Engn Dept Hamirpur 177005 India

In this article, disrupted oppositionallearned gravitational search algorithm (DOGSA)-optimized sliding mode control (SMC) is proposed for the load frequency regulation problem in deregulated power system including flexible alternating current transmission system devices, namely thyristor-controlled series capacitor (TCSC) and superconducting magnetic energy storage (SMES) without and with doubly fed induction generator (DFIG) wind turbine. Initially, the performance of the proposed control scheme is compared with genetic algorithm-tuned integral controller reported in the literature for two-area interconnected power system with TCSC in tie-line under deregulated environment. Further, the SMES unit is also considered in one of the areas and the system performance is analyzed and found to be better with the combination of optimized SMC and TCSC-SMES for unilateral, bilateral as well as contract violation cases in comparison with system with optimal SMC and TCSC and also with the controller reported in the literature. The potential of the proposed schemes is further analyzed and studied in the presence of nonlinear constraints, namely generation rate constraint, governor deadband and time delay which are present in the real-time power system. From the simulation results, it is deduced that the optimal SMC-generated control signal eliminates the chattering problem in the controller output. Also, the combination of TCSC and SMES improves the transient performance of the system. Thus, the frequency regulation in deregulated power system has been performed using the DOGSA-tuned SMC and also investigated the coordinated action of TCSC-SMES with inclusion of DFIG wind turbine in both the control areas of the system.

关键词： DFIG wind turbine FACTS devices gravitational search algorithm Sliding mode control

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An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2019年第0期49卷 440-464页

作者： Nayak, Chandan Saha, Suman Kumar Kar, Rajib Mandal, Durbadal NIT Raipur Dept Elect & Telecommun Engn Raipur 492010 Chhattisgarh India NIT Durgapur Dept Elect & Commun Engn Durgapur 713209 W Bengal India

Globally the human death rate is accelerating day by day due to the cardiovascular diseases (CVDs), and it will be elevated shortly. In this scenario, the QRS complex detection of electrocardiogram (ECG) signal is considered as a simple, non-invasive, inexpensive, and preliminary diagnosis method used to assess the cardiac health of a patient. In this paper, an optimally designed Integer Order Digital Differentiator (IODD) based preprocessor is proposed for the accurate estimation of R-peak locations in the ECG signal. IODD, one of the major constituents of the preprocessor, is designed most proficiently by using a metaheuristic evolutionary optimization method called gravitational search algorithm (GSA). In GSA, as the number of iteration increases the exploration capability fades out, and the exploitation capability fades in, which help it to avoid the local optima stagnation problem and results in faster convergence. The IODD based preprocessor accentuates the QRS complexes of the ECG signal irrespective of its abnormal morphology. The employed detector is a simple threshold independent R-peak decision logic designed by utilizing the properties of the Hilbert transform. In order to emphasize the superiority of the proposed research work the proposed IODD based QRS detection approach is validated on the first channel records of MIT/BIH Arrhythmia database (MBAD), QT database (QTDB), MIT/BIH noise stress test database (NSTDB), atrial fibrillation termination challenge database (AFTDB), and MIT/BIH ST change database (STDB). The sensitivity (Se) and positive Predictivity (+P) values for MBAD, QTDB, NSTDB, AFTDB, and STDB are Se=99.92% and +P=99.92%, Se=99.98% and +P=99.96%, Se=95.23% and +P=94.41%, Se=99.03% and +P=99.76%, and Se=99.93% and +P=99.90%, respectively. These performance metrics ensure the accuracy of the proposed R-peak detection technique for a wide variety of QRS morphologies and thereby affirm the applicability of the proposed IODD for the efficient

关键词： Electrocardiogram (ECG) gravitational search algorithm Integer order digital differentiator Hilbert transform QRS complex detection

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Partial shading shading mitigation of PV systems via different meta -heuristic techniques

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RENEWABLE ENERGY 2019年 130卷 1159-1175页

作者： Mohamed, Mohamed A. Diab, Ahmed A. Zaki Rezk, Hegazy Menia Univ Elect Engn Dept Fac Engn Al Minya Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia

Recently, electricity generation from solar photovoltaic (PV) has gained popularity throughout the world due to its profuse availability and eco-friendly nature. Consequently, extraction of maximum power from solar PV energy systems was the point of interest in the current researches. Various techniques have been proposed to track the maximum power point (MPP) from solar PV energy systems under variable environmental conditions. Conventional maximum power point tracking (MPPT) techniques have demonstrated the ability to track MPP with uniform solar irradiance. However, the ability of these techniques to track the accurate MPP with the condition of partial shading (PS) is not guaranteed. Hence, this paper intended to present novel optimization techniques to mitigate the PS effect and proficiently track the global maximum power point (GMPP). Grey Wolf Optimization (GWO), Moth-Flame Optimization (MFO), Salp Swarm algorithm (SSA) and Hybrid Particle Swarm Optimization-gravitational search algorithm (PSO-GSA) techniques have been proposed to handle this dilemma. The proposed techniques have been simulated and analyzed using MATIAB/SIMULINK. Furthermore, these techniques have been compared with the conventional PSO algorithm for validation. Statistical and sensitivity analysis have been established to compare the performance, check the stability, and determine the best technique out of the proposed techniques. Results showed the superiority of GWO in the speed of convergence and the time to catch GMPP. Moreover, the sensitivity analysis demonstrated the stability, successfully rate, and tracking efficiency of P50-GSA technique. Finally, this paper gives an open reference to these optimizers to attempt mass research works in PV systems under PS. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Partially shaded PV system Global MPPT Grey wolf optimization Moth-flame optimization Salp swarm algorithm gravitational search algorithm

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A New Strategy Based on GSABAT to Solve Single Objective Optimization Problem

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE REsearch 2019年第3期10卷 1-22页

作者： Sattar, H. A. Cheetar, Alaa Tareq, Iraq Univ Pendidikan Sultan Idris Tanjung Malim Malaysia Mustansiriyah Univ Baghdad Iraq Univ Baghdad Baghdad Iraq Univ Putra Malaysia Seri Kembangan Malaysia

This article proposes a new strategy based on a hybrid method that combines the gravitational search algorithm (GSA) with the bat algorithm (BAT) to solve a single-objective optimization problem. It first runs GSA, followed by BAT as the second step. The proposed approach relies on a parameter between 0 and 1 to address the problem of falling into local research because the lack of a local search mechanism increases intensity search, whereas diversity remains high and easily falls into the local optimum. The improvement is equivalent to the speed of the original BAT. Access speed is increased for the best solution. All solutions in the population are updated before the end of the operation of the proposed algorithm. The diversification feature of BAT has solved the problem of weakness in diversity observed in the algorithm by applying the parameters used in BAT. Moreover, balance is achieved through the intensification properties of the algorithms.

关键词： Bat algorithm Optimization gravitational search algorithm Hybrid algorithm Single Objective Swarm Intelligence

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Hybridized gravitational search algorithms with Real Coded Genetic algorithms for Integer and Mixed Integer Optimization Problems 1

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6th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Singh, Amarjeet Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India

ISBN: (数字)9789811033223

ISBN: (纸本)9789811033223;9789811033216

In this paper, the gravitational search algorithm (GSA) is hybridized with real coded Genetic algorithm to solve Integer and Mixed Integer programming problems. The idea is based on two earlier papers of the authors. In the first paper, the authors proposed a methodology in which the Laplace Crossover and Power Mutation were embedded in gravitational search algorithm and in the second paper, these algorithms were extended for the case of constrained optimization problems. In order to deal with integer variables, a special method is adopted. For dealing with the constraints the Deb's technique is implemented. The original GSA and three new variants are tested on a set of benchmark problems available in literature. Based on the extensive numerical and graphical analysis of results it is concluded that one of the proposed variants outperform the original GSA and the other proposed variants.

关键词： gravitational search algorithm Constrained optimization problems Integer and mixed integer programming problems Laplace crossover Power mutation

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Detection and Classification of High Impedance Fault in Power Distribution System using Hybrid Technique

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第8期29卷

作者： Narasimhulu, N. Kumar, D. V. Ashok Kumar, M. Vijay JNTUA Ananthapuramu Andhra Pradesh India Adm & Placements RGMCET Nandyal Andhra Pradesh India

In this paper, a hybrid strategy is introduced for detecting and classifying the High Impedance Fault in Power Distribution System. For hybridization, gravitational search algorithm is combined with Artificial Neural Network to crease the classification performance. The ANN is utilized to characterize the blame signal from the reference signal and the execution is enhanced in view of the GSA calculation. The yield of the proposed method is recognized and arranged whether it is HIF fault or no-fault. At first, the ordinary practices of the appropriation framework are assessed. After that, the deficiencies are connected and the signals are measured. At that point, these are given to the contribution of the enhanced ANN procedure, which gives the dataset to breaking down the framework exhibitions. Finally, the proposed strategy is implemented in the MATLAB/Simulink model and its execution is assessed and compared with other conventional techniques like DWT-ANFIS, DWT-RBFFN, MWT-ANFIS, and MWT-FLC based GA. From the experimental results, it shows that the proposed method achieved better performance than existing methods.

关键词： Power distribution system high impedance fault multi wavelet transforms artificial neural network gravitational search algorithm

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An Enhanced MPR OLSR Protocol for Efficient Node Selection Process in Cognitive Radio Based VANET

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WIRELESS PERSONAL COMMUNICATIONS 2019年第2期106卷 763-787页

作者： Usha, M. Ramakrishnan, B. Manonmaniam Sundaranar Univ Tirunelveli Tamilnadu India ST Hindu Coll Dept Comp Sci Nagercoil India ST Hindu Coll Res Ctr Nagercoil India

A VANET is an excellent instance of a wireless sensor network. The mobile vehicles are the nodes and communication happens between the vehicular nodes. This facility of communicating with the vehicular nodes finds varied applications ranging from entertainment to emergency services. When combined with cognitive radio techniques, VANETs are equipped with the facility of sensing the spectrum opportunistically. When the spectrum is sensed efficiently, the channel and the bandwidth can be utilized effectively. To achieve this, we have coordinated the enhanced Optimal Link State Routing Protocol (MMPR-OLSR) with the GSA-PSO (gravitational search-Particle Swarm Optimization) scheme in combination with the cognitive radio technique. This technique can be applied to the Vehicular Sensor Networks. MMPR-OLSR with GSA-PSO optimization facilitates the MMPR-OLSR protocol to select the suitable member nodes using an optimal searching technique. The GSA-PSO optimization not only helps in choosing the appropriate MMPR nodes, but also helps in reducing the unnecessary overheads due to the propagation of the control packets. By selecting the appropriate MMPR nodes. It is also possible to minimize the number of relay selector nodes used in transmission. The optimization technique also focuses on assigning the channels among all the network users. This is controlled by our proposed approach. A group of nodes are selected before the start of the actual transmission. These vehicular nodes within the communication range are used as relays in the transmission. These nodes are categorized as Multi Point Relays. Cognitive radio plays an active role by identifying the idle channels, thus enabling the usage of the unused channels. Our proposed approach works efficiently in achieving the objective of effective channel utilization combined with efficient transmission. Our proposed approach is simulated using the NS2 platform and is evaluated based on important network metrics. Our proposed metho

关键词： Optimal link state routing protocol gravitational search algorithm Particle swarm optimization Minimum multi point relay Cognitive radio

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Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

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IET SMART GRID 2019年第2期2卷 192-202页

作者： Murty, Vallem Veera Venkata Satya Narayana Kumar, Ashwani NIT Kurukshetra Elect Dept Kurukshetra Haryana India

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi-objective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

关键词： distribution networks renewable energy sources distributed power generation genetic algorithms power generation economics power distribution economics power engineering computing DG units general algebraic modelling system optimal network reconfiguration IEEE 33-bus test system optimal DG integration hybrid optimisation renewable energy sources renewable energy usage renewable-based distribution generations fossil fuels energy security greenhouse gases microgrid system analysis hybrid energy sources microgrid zones small hydro-based DGs renewable power generation optimisation problem multiobjective optimisation method optimal integration renewable-based DGs power loss maximise annual cost savings global climate change mitigation distribution system solar-based DG realistic time varying load model hydro-based DG gravitational search algorithm algebraic modelling system time varying distribution system CO2

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Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach

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TOURISM MANAGEMENT 2021年 82卷 104208-104208页

作者： Xie, Gang Qian, Yatong Wang, Shouyang Chinese Acad Sci Acad Math & Syst Sci MDIS CFS Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

After more than ten years of exponential development, the growth rate of cruise tourist in China is slowing down. There is increasingly financial risk of investing in homeports, cruise ships and promotional activities. Therefore, forecasting Chinese cruise tourism demand is a prerequisite for investment decision-making and planning. In order to enhance forecasting performance, a least squares support vector regression model with gravitational search algorithm (LSSVR-GSA) is proposed for forecasting cruise tourism demand with big data, which are search query data (SQD) from Baidu and economic indexes. In the proposed model, hyper-parameters of the LSSVR model are optimized with GSA. By comparing these models with various settings, we find that LSSVR-GSA with selected mobile keywords and economic indexes can achieve the highest forecasting performance. The results indicate the proposed framework of the methodology is effective and big data can be helpful predictors for forecasting Chinese cruise tourism demand.

关键词： Cruise Big data gravitational search algorithm Tourism demand forecasting

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Memetic Particle Gravitation Optimization algorithm for Solving Clustering Problems

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IEEE ACCESS 2019年 7卷 80950-80968页

作者： Huang, Ko-Wei Wu, Ze-Xue Peng, Hsing-Wei Tsai, Ming-Chia Hung, Yu-Chieh Lu, Yu-Chin Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung 807 Taiwan

Data clustering is a well-known data analysis technique for organizing unlabeled datapoints into clusters on the basis of similarity measures. The real-world applications of data clustering include bioinformatics, vector quantization, data mining, geographical information systems, pattern recognition, image processing, and wireless sensors. The data in a cluster are similar (minimizing the intra-cluster distance) and differ from the data in other clusters (maximizing the inter-cluster distance). The cluster problem has been proven to be NP-hard, but can be solved using meta-heuristic algorithms, such as ant colony optimization, genetic algorithms, gravitational search algorithm (GSA), and particle swarm optimization (PSO). This paper proposes a memetic clustering algorithm with efficient search and fast convergence, respectively, based on PSO and GSA, called the memetic particle gravitation optimization (MPGO) algorithm. The two main mechanisms of MPGO are hybrid operation and diversity enhancement. The former involves the exchange of individuals from two subpopulations after a predefined number of function evaluations (FEs), whereas the latter involves an enhancement operator, which is similar to the crossover process of differential evolution, for enhancing the diversity of each system. Individuals from the PSO and GSA systems are selected for the exchange of solutions by using the roulette-wheel approach. The performance of the proposed algorithm was evaluated on 52 benchmark test functions, six UCI machine learning benchmarks, and image segmentation of six well-known images. A comparison with existing algorithms verified the superior performance of the proposed algorithm in terms of a fitness value, an accuracy rate, and a peak signal-to-noise ratio.

关键词： Data clustering gravitational search algorithm image segmentation memetic algorithm particle swarm optimization

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