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Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

IEEE ACCESS 2020年 8卷 124254-124264页

作者： Lala, Jose Antonio Oscullo Gallardo, Carlos Fabian Natl Polytech Sch Fac Elect & Elect Elect Engn Dept Quito 170525 Ecuador

The operation of a modern electrical system presents complex operating conditions, and uncertainty factors increased by the presence of variable and flexible loads. This stochastic operating point variation can promote low-frequency oscillations, and like the conventional power system stabilizer (PSS) features static tuning, it does not provide sufficient damping. Today, an electrical system is monitored through online measurements from the wide-area measurement system (WAMS);moreover, it enables stochastic dynamics to be tracked as the remote signals of WAMS system, the time delay due to the communication channel. It is interesting to analyze the incorporation of the PSS of signals from the WAMS without the time delay effect that degrades the small-signal stability (SSS) and, in turn, allows adaptive tuning. Through two stages, at first, the subspace of the operating point is determined based on a distribution of consumption levels. Each operating point considered a random time delay. For each subspace, establish a bank of tuned PSSs. Second, through the optimal classification and the regression decision tree (CART), the classification rules for the subspace are determined, allowing classifying the measurements of WAMS. The proposed methodology is applied to the New England of 66-bus system and Ecuadorian electric system to demonstrate that the adaptive tuning of PSSs significantly improves SSS under different scenarios and is probabilistic robust, considering the time delay.

关键词： Stochastic power system operating power oscillations adaptive damping control swarm intelligence algorithms wide area damping controller

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Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

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APPLIED SOFT COMPUTING 2020年 92卷 106234-106234页

作者： Nagaballi, Srinivas Kale, Vijay S. Visvesvaraya Natl Inst Technol Elect Engn Dept Nagpur 440010 Maharashtra India

This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS's are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optima

关键词： swarm intelligence algorithms Improved raven roosting optimization Distributed generation Radial distribution system Technical and economical issues Game theory Pareto optimality

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Improved teaching-learning based optimization algorithm using Lyapunov stability analysis

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JOURNAL OF AMBIENT intelligence AND HUMANIZED COMPUTING 2020年第7期13卷 3609-3618页

作者： Farivar, Faezeh Shoorehdeli, Mahdi Aliyari Manthouri, Mohammad Islamic Azad Univ Dept Mechatron & Comp Engn Sci & Res Branch Tehran Iran KN Toosi Univ Technol Dept Mechatron Fac Elect Engn Tehran Iran Shahed Univ Elect & Elect Engn Dept Tehran Iran

Teaching-learning-based optimization (TLBO) algorithms is one of the swarm-based optimization search algorithms. It develops based on the teaching-learning procedures at a classroom to solve multi-dimensional and nonlinear problems. In this paper, convergence and stability analysis of TLBO are studied. The stability of individual dynamics is analyzed by Lyapunov stability theorem and the concept of system dynamics. Stability conditions are achieved and utilized for adapting parameters of the TLBO. The TLBO algorithm is modified based on stability analysis. The modified TLBO is compared with the standard TLBO, particle swarm optimization (PSO), real genetic algorithm (RGA), and gravitational search algorithm (GSA). Simulation results confirm the validity and feasibility of the proposed modified TLBO. The appropriate performance is achieved for multi-dimensional and nonlinear standard bench functions.

关键词： Convergence Lyapunov stability Teaching-learning-based optimization swarm intelligence algorithms

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Optimal Placement of Near Ground VHF/UHF Radio Communication Network as a Multi Objective Problem

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WIRELESS PERSONAL COMMUNICATIONS 2020年第3期110卷 1169-1197页

作者： Shehadeh, Hisham A. Idris, Mohd. Yamani Idna Ahmedy, Ismail Hassen, Hani Ragab Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur Malaysia Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland

Ultra high frequency (UHF) and very high frequency (VHF) bands are widely used with near ground applications especially for communication purposes. However, due to the limited antenna height, the VHF/UHF signals are very sensitive to the surroundings. The signal may attenuate, fade or loss depending on the condition of the environment. Therefore, it is a challenging task to find the optimal values for network topology planning. In this paper, three multi-objective algorithms namely NSGA-II, SPEA-II and OMOPSO are considered to mitigate the problem. This paper also intends to maximize the electromagnetic fields and signal propagation. On the other hand, it intends to minimize path loss and signal attenuation. Different environment settings such as diverse foliage depth and tree trunk height are considered to evaluate the proposed model. From the experiments, it is shown that NSGA-II outperforms other algorithms. The experiment also shows that the optimal foliage depth between sender and receiver is less or equal to 2 km and the optimal tree trunk height is less or equal to 7 m.

关键词： Near ground network Network planning Network modeling Evolutionary algorithms swarm intelligence algorithms Optimality

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A novel explanatory hybrid artificial bee colony algorithm for numerical function optimization

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JOURNAL OF SUPERCOMPUTING 2020年第12期76卷 9330-9354页

作者： Jarrah, Muath Ibrahim Jaya, A. S. M. Alqattan, Zakaria N. Azam, Mohd Asyadi Abdullah, Rosni Jarrah, Hazim Abu-Khadrah, Ahmed Ismail Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Durian Tunggal 76100 Melaka Malaysia Univ Sains Malaysia Sch Comp Sci Natl Adv IPv6 Ctr Gelugor 11800 Penang Malaysia Univ Tekn Malaysia Melaka Fac Mfg Engn Durian Tunggal 76100 Melaka Malaysia Auckland Univ Technol Dept Elect & Elect Engn Auckland New Zealand Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia

Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their efficacy in solving various types of real-world optimization problems. However, it is impossible to find an optimization algorithm that can obtain the global optimum for every optimization problem. Therefore, researchers extensively try to improve methods of solving complex optimization problems. Many SI search algorithms are widely applied to solve such problems. ABC is one of the most popular algorithms in solving different kinds of optimization problems. However, it has a weak local search performance where the equation of solution search in ABC performs good exploration, but poor exploitation. Besides, it has a fast convergence and can therefore be trapped in the local optima for some complex multimodal problems. In order to address such issues, this paper proposes a novel hybrid ABC with outstanding local search algorithm called beta-hill climbing (beta HC) and denoted by ABC-beta HC. The aim is to improve the exploitation mechanism of the standard ABC. The proposed algorithm was experimentally tested with parameters tuning process and validated using selected benchmark functions with different characteristics, and it was also evaluated and compared with well-known state-of-the-art algorithms. The evaluation process was investigated using different common measurement metrics. The result showed that the proposed ABC-beta HC had faster convergence in most benchmark functions and outperformed eight algorithms including the original ABC in terms of all the selected measurement metrics. For more validation, Wilcoxon's rank sum statistical test was conducted, and the p values were found to be mostly less than 0.05, which demonstrates that the superiority of the proposed ABC-beta HC is statistically significant.

关键词： Metaheuristic optimization algorithms swarm intelligence algorithms Artificial bee colony algorithm beta-Hill climbing Numerical benchmark function optimization

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A Review on Computational intelligence Techniques in Cloud and Edge Computing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL intelligence 2020年第6期4卷 742-763页

作者： Asim, Muhammad Wang, Yong Wang, Kezhi Huang, Pei-Qiu Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

关键词： Cloud computing (CC) edge computing (EC) computational intelligence evolutionary algorithms swarm intelligence algorithms fuzzy system learning based system

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R-GWO: Representative-based grey wolf optimizer for solving engineering problems

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APPLIED SOFT COMPUTING 2021年 106卷 107328-107328页

作者： Banaie-Dezfouli, Mahdis Nadimi-Shahraki, Mohammad H. Beheshti, Zahra Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

The grey wolf optimizer (GWO) is a well-known nature-inspired algorithm, which shows a sufficient performance for solving various optimization problems. However, it suffers from low exploration and population diversity because its optimization process is only based on the best three wolves greedily, and the information of other wolves does not consider. In this paper, a representative-based grey wolf optimizer (R-GWO) is proposed to tackle with these weaknesses of the GWO. The R-GWO introduces a search strategy named representative-based hunting (RH) a combination of three effective trial vectors inspired by alpha wolves' behaviors to improve the exploration and diversity of the population. The RH search strategy utilizes a representative archive to reduces the greediness and enhance the diversity of solutions, and it can also strike balance between the exploration and exploitation using a nonlinear control parameter. The performance and applicability of the proposed R-GWO were evaluated on CEC 2018 benchmark functions and six engineering design problems. The results were compared by eight state-of-the-art metaheuristic algorithms: PSO, KH, GWO, WOA, EEGWO, BOA, HHO, and HGSO. Moreover, the results were statistically analyzed by three test Wilcoxon rank-sum, Friedman and mean absolute error (MAE). The performance results show that on all 29 functions with dimensions 30, 50, and 100, the R-GWO is superior to the competitor algorithms except on function 27 on all dimensions and function 22 on dimension 30. The proposed R-GWO is the most effective algorithm compared with competitor algorithms, with an overall effectiveness of 95.4%. The experimental and statistical results show that the R-GWO is competitive and superior to compared algorithms and can solve engineering design problems better than competitor algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Optimization algorithms Metaheuristic algorithms swarm intelligence algorithms Grey wolf optimizer Engineering design problems

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Chameleon swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114685-114685页

作者： Braik, Malik Shehadeh Al Balqa Appl Univ Dept Comp Sci Al Salt Jordan

This paper presents a novel meta-heuristic algorithm named Chameleon swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360 degrees scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixtyseven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.

关键词： Chameleon swarm Algorithm Optimization techniques Meta-heuristics Nature-inspired algorithms Evolutionary algorithms swarm intelligence algorithms

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Transformation operators based grey wolf optimizer for travelling salesman problem

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JOURNAL OF COMPUTATIONAL SCIENCE 2021年 55卷

作者： Panwar, Karuna Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

In the field of swarm intelligence, the Grey Wolf Optimizer (GWO) is a popular algorithm based on leadership hierarchy. Primarily, GWO was proposed to solve continuous optimization problem. However, in recent years, GWO has been extensively explored to deal with a wide variety of real world problem regardless the nature of problem. GWO has received a lot of attention from researchers because of its advantages over other swarm intelligence approaches and its simplicity. The classical GWO is redesigned in this paper by incorporating the swap, shift and symmetry transformation operators to solve permutation-coded travelling salesman problem (TSP), and it is named as transformation operator based grey wolf optimizer (TO-GWO). In TO-GWO, each wolf represents a possible solution of TSP and using swap, shift and symmetry operators wolves interact with leader wolves in order to obtain optimal solution for TSP. In order to improve the proposed algorithm's local search capability when solving discrete problems, 2-opt algorithm have also been adapted. The TO-GWO is implemented in MATLAB environment. In this study, the TO-GWO is tested over 50 TSP instances. Also, the results of proposed algorithm are compared with 12 state-of-the-art algorithms for TSP instances with various numbers of cities in order to evaluate its performance. For the majority of the TSP instances used in the experiment, the TO-GWO significantly outperforms other algorithms in terms of quality of solutions and efficiency.

关键词： Travelling salesman problem swarm intelligence algorithms Grey wolf optimizer 2-opt Transformation operators

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The Digital Twin Realization of an Ejector for Multiphase Flows

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ENERGIES 2021年第17期14卷 5533页

作者： Mazzuto, Giovanni Ciarapica, Filippo Emanuele Ortenzi, Marco Bevilacqua, Maurizio Univ Politecn Marche Fac Engn Dipartimento Ingn Ind & Sci Matemat I-60131 Ancona Italy

Despite the extensive use of ejectors in the process industry, it is complex to predict suction and motive fluids mixture characteristics, especially with multiphase flows, even if, in most cases, mixture pressure control is necessary to satisfy process requirements or to avoid performance problems. The realization of an ejector model can allow the operators to overcome these difficulties to have real-time control of the system performance. In this context, this work proposes a framework for developing a Digital Twin of an ejector installed in an experimental plant able to predict the future state of an item and the impact of negative scenarios and faults diagnosis. ANNs have been identified as the most used tool for simulating the multiphase flow ejector. Nevertheless, the complexity in defining their structure and the computational effort to train and use them are not suitable for realizing standalone applications onboard the ejector. The proposed paper shows how swarm intelligence algorithms require a low computational complexity and overperform prediction error and computational effort. Specifically, the Grey Wolf optimizer proves to be the best one among those analyzed.

关键词： ejector multiphase flow swarm intelligence algorithms

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