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Strategic management of stochastic power losses in smart transmission grids

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第8期29卷

作者： Koloushani, Sayed Mahdi Nasri, Mehdi Rezaei, Mohammad Mahdi Islamic Azad Univ Dept Elect Engn Khomeinishahr Branch Esfahan Iran

This paper contributes to the development of a smart wide-area controller for the management of stochastic power losses in smart transmission grids while power system reliability is met. A comprehensive strategic management model is used for designing a smart grid management strategy. The proposed wide-area control algorithm enables continuous communication and control, allowing suppliers to optimize active power losses based on price preference and system technical issues. Wide-area control variables are continuously controlled by solving an optimization problem with an active intent to minimize power losses. A gravitational search algorithm (GSA) is presented to solve the optimization problem. In this paper, the wide-area control system is combined with local controllers and a hybrid controller is proposed. Proposed algorithms are tested on the standard Institute of Electrical and Electronics Engineers (IEEE) 9-bus and IEEE 118-bus test systems. Because of the stochastic behavior of the power system, various and random contingencies are considered. Simulation results demonstrate the efficiency of the hybrid algorithm.

关键词： active power loss minimization gravitational search algorithm optimal power flow smart grid management wide-area control

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Hybrid chemometric approach for estimating the heat of detonation of aromatic energetic compounds

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HELIYON 2019年第7期5卷 e02035-e02035页

作者： Adeyemo, Hayatullahi B. Owolabi, Taoreed O. Suleiman, Muhammad A. Akande, Kabiru O. Alhiyafi, Jamal Fayose, Sola Olatunji, Sunday O. King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia Usmanu Danfodiyo Univ Dept Math Comp Sci Unit PMB 2346 Sokoto Nigeria Adekunle Ajasin Univ Phys & Elect Dept Akungba Akoko 342111 Ondo State Nigeria Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci POB 1982 Dammam 31441 Saudi Arabia King Fahd Univ Petr & Minerals Grad Studies Dhahran 31261 Saudi Arabia Univ Edinburgh Sch Engn Inst Digital Commun Edinburgh EH8 9AB Midlothian Scotland

This work presents an elegant technique for estimating the heat of detonation (HD) of thirty organic energetic compounds by combining support vector regression (SVR) and gravitational search algorithm (GSA). The work shows that numbers of nitrogen and oxygen atoms as well as the compound molar mass are sufficient as descriptors. On the basis of three performance measuring parameters, the hybrid GSA-SVR outperforms Mortimer and Kamlet (1968), Mohammad and Hamid (2004) and Mohammad (2006) models with performance improvement of 93.951%, 86.197%, and 47.104%, respectively. The superior performance demonstrated by the proposed method would be of immense significance in containing the potential damage of the explosives through quick estimation of HD of organic energetic compounds without loss of experimental precision.

关键词： Materials science Heat of detonation Organic energetic compounds Support vector regression gravitational search algorithm

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Optimal location management in LTE networks using evolutionary techniques

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2019年第15期32卷 e4100-e4100页

作者： Aqeeli, Emad Hashim, Hashim A. Haque, Anwar Shami, Abdallah Western Univ Elect & Comp Engn London ON N6A 5B9 Canada

Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users' mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC-based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large-scale environment.

关键词： artificial bee colony gravitational search algorithm location management LTE MME pooling paging particle swarm optimization SON tracking area list update

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Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2018年第2期25卷 157-172页

作者： Ayinde, Babajide O. Hashim, Hashim A. Univ Louisville Dept Elect & Comp Engn Louisville KY 40203 USA Univ Western Ontario Dept Elect & Comp Engn London ON N6A 5B9 Canada

Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using artificial bee colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques - gravitational search algorithm (GSA) and differential evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.

关键词： Artificial bee colony gravitational search algorithm Differential evolution Constrained optimization Relay deployment Laplacian Lifetime enhancement Graph-based deployment

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A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9319-9324页

作者： Yin, Minghao Hu, Yanmei Yang, Fengqin Li, Xiangtao Gu, Wenxiang NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

Clustering is used to group data objects into sets of disjoint classes called clusters so that objects within the same class are highly similar to each other and dissimilar from the objects in other classes. K-harmonic means (KHM) is one of the most popular clustering techniques, and has been applied widely and works well in many fields. But this method usually runs into local optima easily. A hybrid data clustering algorithm based on an improved version of gravitational search algorithm and KHM, called IGSAKHM, is proposed in this research. With merits of both algorithms, IGSAKHM not only helps the KHM clustering escape from local optima but also overcomes the slow convergence speed of the IGSA. The proposed method is compared with some existing algorithm on seven data sets, and the obtained results indicate that IGSAKHM is superior to KHM and PSOKHM in most cases. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Clustering K-harmonic means gravitational search algorithm IGSAKHM algorithm

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Disruption: A new operator in gravitational search algorithm

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SCIENTIA IRANICA 2011年第3期18卷 539-548页

作者： Sarafrazi, S. Nezamabadi-pour, H. Saryazdi, S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

To improve the exploration and exploitation abilities of the standard gravitational search algorithm (GSA), a novel operator called "Disruption", originating from astrophysics, is proposed. The disruption operator is inspired by nature and, with the least computation, has improved the ability of GSA to further explore and exploit the search space. The proposed improved GSA has been evaluated on 23 nonlinear benchmark functions and compared with standard GSA, the genetic algorithm and particle swarm optimization. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Numerical function optimization Heuristic search algorithm Swarm intelligence gravitational search algorithm Disruption operator

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Filter modeling using gravitational search algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第1期24卷 117-122页

作者： Rashedi, Esmat Nezamabadi-pour, Hossien Saryazdi, Saeid Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

This paper is devoted to the presentation of a new linear and nonlinear filter modeling based on a gravitational search algorithm (GSA). To do this, unknown filter parameters are considered as a vector to be optimized. Examples of infinite impulse response (IIR) filter design, as well as rational nonlinear filter, are given. To verify the effectiveness of the proposed GSA based filter modeling, different sets of initial population with the presence of different measurable noises are given and tested in simulations. Genetic algorithm (GA) and particle swarm optimization (PSO) are also used to model the same examples and some simulation results are compared. Obtained results confirm the efficiency of the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Heuristic algorithms gravitational search algorithm Filter modeling System approximation

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Heuristic optimization based choice of LQR weighting matrices for a rotary inverted pendulum

Heuristic optimization based choice of LQR weighting matrice...

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IEEE International Conference on Recent Trends in Electrical, Control and Communication (RTECC)

作者： Nath, Krishanu Dewan, Lillie NIT Kurukshetra Dept Elect Engn Kurukshetra Haryana India

ISBN: (纸本)9781538643105

The choice of weighting matrices of the linear quadratic regulator (LQR) is one such decision which has a great effect on the dynamics of the system. There exists a nonlinear relationship between the dynamics of plant and their numerical values. Problems where such nonlinear relation exists, heuristic have served very useful to extract a better solution. In this paper, an attempt is made for the optimal choice of LQR weighting matrices using heuristic optimization techniques for a rotary inverted pendulum such that the settling time of the system is minimized.

关键词： Linear quadratic regulator Teaching learning-based optimization Seeker optimization algorithm gravitational search algorithm

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Speech Emotion Recognition Based on Kernel Principal Component Analysis and Optimized Support Vector Machine 8

Speech Emotion Recognition Based on Kernel Principal Compone...

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Chen, Chuang Chellali, Ryad Xing, Yin Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing Peoples R China Guilin Univ Technol Coll Geomat & Geoinformat Guilin Peoples R China

ISBN: (纸本)9781538682463

Speech emotion recognition is a hot topic nowadays. In order to improve recognition rates, we propose a new solution based on kernel principal component analysis (KPCA) and optimized SVM. KPCA reduces features dimension, while the gravity search algorithm (GSA) is used to optimize support vector machine (SVM) classification performances. We show that KPCA with Gaussian kernel function applied to the Berlin emotional speech database, KPCA outperforms the classical principal. On the other hand, we present experimental results showing that the GSA-SVM model achieves higher recognition rates and runs faster than two classical optimized SVM namely the SVM-particle swarm and the SVM-genetic algorithm. Combined together the KPCA-GSA/SVM achieves good recognition rates.

关键词： kernel principal component analysis gravitational search algorithm support vector machine speech emotion recognition

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A New PSOGSA Inspired Convolutional Echo State Network for Long-term Health Status Prediction

A New PSOGSA Inspired Convolutional Echo State Network for L...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Zhang, Gege Zhang, Chao Li, Zheqing Zhang, Weidong Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Sun Yat Sen Univ Affiliated Hosp 6 Dept Informat Ctr Guangzhou 510655 Guangdong Peoples R China

ISBN: (纸本)9781728103778

This paper develops a long-term time series prediction model based on echo state networks for health monitoring. Convolutional dynamics are built by extending randomly connected reservoirs to convolutional structures in the input-to-state transition. Also, a new particle swarm optimization-gravitational search algorithm is put forward to make the convolutional reservoir near the chaotic edge, in which memory information-based decision-making and l` evy flight random walk are utilized to improve its local and global search capabilities, respectively. Finally, the validity of the model is verified by the real health index data set. The experiment illustrates that the system can exhibit powerful computing ability by adopting the evolutionary algorithm close to the chaotic edge. These results also show that our proposed algorithm exceeds the test error of the least squares support vector machine by 50% on average.

关键词： Long-term Time Series Prediction Echo State Networks Computing Ability Particle Swarm Optimization gravitational search algorithm

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