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An approach for economic design of wide area monitoring system by co-optimizing phasor measurement unit placement and associated communication infrastructure

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第8期31卷 p1-19页

作者： Patel, Chintan D. Tailor, Tarun K. Shah, Samyak S. Shrivastava, Shivam H. Nirma Univ Inst Technol Dept Elect Engn Ahmadabad 382481 Gujarat India

Wide area monitoring system has been implemented in power systems to improve control and protection aspects by carrying out the continuous synchronous measurement. It consists of phasor measurement units (PMUs) along with communication infrastructure (CI) to connect all PMUs to the phasor data concentrator (PDC). In this work, simultaneous optimization of PMU count thereby its cost and CI cost has been taken as the objective while ensuring entire system observability. To incorporate the effect of zero injection bus, an improved model has been adopted. A modified objective function has been proposed which enhances maximum observability and further improves the reliability of system monitoring. Dijkstra's algorithm has been utilized for designing optimal CI as well as to identify the location of PDC. Practical operating scenarios such as N-1 contingency and the presence of pre-installed PMU or fiber optic have also been simulated. binary dragonfly algorithm has been applied to solve the considered optimization problem. To demonstrate the efficacy of the proposed method it has been implemented on different IEEE standard test cases. A comparison of obtained results has been presented which indicates the superiority of the proposed method over other methods reported in the literature.

关键词： binary dragonfly algorithm Dijkstra's algorithm phasor measurement unit

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A novel hybrid BPSO-SCA approach for feature selection

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NATURAL COMPUTING 2021年第1期20卷 39-61页

作者： Kumar, Lalit Bharti, Kusum Kumari PDPM Indian Inst Informat Technol Design & Mfg Dumna Airport RdPO Khamaria Jabalpur MP India

Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.

关键词： binary artificial bee colony algorithm binary particle swarm optimization binary dragonfly algorithm binary moth flame optimization binary whale optimization algorithm Clustering indices Feature selection Sine cosine algorithm

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An Optimally Selective Ensemble Classifier Based on Multimodal Perturbation and Its Application

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第5期48卷 7177-7194页

作者： Wu, Qiannan Sun, Yifei Lv, Lihua Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ POB 293 MeiLong Rd 130 Shanghai 200237 Peoples R China Baoshan Iron & Steel Co Ltd Shanghai 201900 Peoples R China

The selective ensemble aims to search the optimal subset balanced accuracy and diversity from the original base classifier set to construct an ensemble classifier with strong generalization performance. A selective ensemble classifier named BRFS-BDA-SENC is proposed in this paper, which realizes the generation and selection of a set of accurate and diverse base classifiers, respectively. In the first step, a multimodal perturbation method is introduced to train distinct base classifiers. The method perturbs the sample space by bootstrap and disturbs the feature space under a newly proposed semi-random feature selection, which is a combination of the core attribute theory and the improved maximum relevance minimum redundancy criterion. Then, to search the optimal classifier subset, the binary dragonfly algorithm is utilized to adaptively select eligible base classifiers. UCI data sets and an actual data set of the blade icing of wind turbine are employed to verify the performance of BRFS-BDA-SENC. The experimental results demonstrate that BRFS-BDA-SENC has significant difference with other selective ensemble methods and improves the classification accuracy.

关键词： Selective ensemble Multimodal perturbation method Core attribute Maximum relevance minimum redundancy binary dragonfly algorithm

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Price-based demand response with renewable energy sources and peer-to-peer trading for residential microgrid with electric vehicle uncertainty

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COMPUTERS & ELECTRICAL ENGINEERING 2024年第PartB期119卷

作者： Kanakadhurga, Dharmaraj Prabaharan, Natarajan SASTRA Deemed Univ Sch Elect & Elect Engn Dept EEE Thanjavur 613401 Tamilnadu India

This article deals with the impact of demand response (DR) with peer-to-peer (P2P) energy trading for a residential smart home consisting of smart appliances, an electric vehicle, a battery energy storage system (BESS), and renewable energy-based distributed generation (DG) such as solar PV and wind. The proposed work deals with two stages, such as DR implementation and P2P energy trading. The first stage of implementation deals with the optimal scheduling of smart appliances in a residential home using a binary dragonfly algorithm (BDA). Each appliance in the smart home is scheduled based on the slab tariff, Time of Day (ToD), and Real-Time Pricing (RTP) with DR to identify the most economical tariff structure for the residential home. Five different cases are examined to identify the best smart home for effectively utilizing DGs by reducing grid dependency and electricity *** RTP tariff is proved to be more beneficial than the slab tariff and ToD tariff because the electricity cost of the smart home is very minimal for all the cases with RTP, especially in case 5 cost is reduced by (sic)12.0613 than slab tariff and (sic) 8.414 than ToD. Hence, the RTP tariff is chosen as the optimal tariff structure for the next stage of the proposed *** second stage of implementation deals with the P2P trading between the smart homes with a proposed enhanced bidding strategy to reduce the grid dependency and electricity cost of the individual smart homes. The proposed enhanced bidding strategy involves a double auction mechanism to determine the trading decision and trading cost for the benefit of both consumers and prosumers. The electricity cost reduction achieved by consumer 1 and consumer 2 from the enhanced smart bidding strategy are (sic) 92.7615 and (sic) 15.1525, respectively. The results also proved that prosumer 1 and prosumer 2 obtained a profit of (sic) 78.2170 and (sic) 28.6970, respectively. The EV uncertainty analysis is also examined in smart homes to v

关键词： Smart home Demand response Peer-to-peer energy trading Renewable energy Battery Electric vehicle binary dragonfly algorithm

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An improved dragonfly algorithm for feature selection

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KNOWLEDGE-BASED SYSTEMS 2020年 203卷 106131-106131页

作者： Hammouri, Abdelaziz, I Mafarja, Majdi Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu-Doush, Iyad Al Balqa Appl Univ Dept Comp Informat Syst Al Salt 19117 Jordan Birzeit Univ Dept Comp Sci Birzeit Palestine Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Yarmouk Univ Comp Sci Dept Irbid Jordan Amer Univ Kuwait Comp Dept Salmiya Kuwait

dragonfly algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Feature selection binary dragonfly algorithm Selected features Classification accuracy V-shaped transfer function Optimization

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Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data

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ANALYTICAL BIOCHEMISTRY 2021年 627卷 114242-114242页

作者： Pashaei, Elnaz Pashaei, Elham Istanbul Aydin Univ Dept Software Engn Istanbul Turkey Istanbul Gelisim Univ Dept Comp Engn Istanbul Turkey

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNASeq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.

关键词： Gene selection binary black hole algorithm binary dragonfly algorithm Gene expression Cancer classification

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A New Approach to Determine the Optimal Number and Arrangement of Power Quality Monitors for Voltage Sag Detection

A New Approach to Determine the Optimal Number and Arrangeme...

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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Raif Aličić Senad Smaka Electricity Distribution Company ED Sarajevo Public Enterprise Electric Utility of Bosnia and Herzegovina Sarajevo Bosnia and Herzegovina Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9781728138169

This paper presents a new approach to determine the optimal number and arrangement of power quality monitors (PQMs) for voltage sag detection. It is necessary to determine the optimal number and arrangement of PQMs since their installation at all buses in a network is an uneconomical option due to relatively high price of PQMs. The appropriate mathematical model, that describes the considered optimization problem, is created by using the concept of topological monitor reach area. A new definition of the cost function is presented in the paper in order to simultaneously determine the required number of PQMs and their best arrangement. Also, the effect of setting different values of monitor's coverage control parameter on the obtained results is analyzed. Four optimization methods are implemented to solve the considered problem: binary Bat algorithm, binary dragonfly algorithm, binary Particle Swarm Optimization and Genetic algorithm. The presented approach is tested on the IEEE 34-node test system. Simulations proved that the binary Bat algorithm has the best performance in terms of computational time, convergence and the probability rate of finding the global optimum.

关键词： binary optimization binary bat algorithm binary dragonfly algorithm binary particle swarm optimization Genetic algorithm Power quality monitors Topological monitor reach area Voltage sags voltage sags Particle swarm optimization Genetic algorithm (GA) Lexical permutation sorting MONITOR optimum number Power quality optimum performance Cost functions binary global optimum

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