Article
Engineering, Electrical & Electronic
Waseem Haider, S. Jarjees Ul Hassan, Arif Mehdi, Arif Hussain, Gerardo Ondo Micha Adjayeng, Chul-Hwan Kim
Summary: Efficient network reconfiguration with the integration of distributed generation units plays a crucial role in mitigating power loss and voltage instability in distribution systems. The optimal placement and sizing of DGs, determined through a multi-objective particle swarm optimization algorithm, significantly improve system stability, reliability, and efficiency. Simulation results on an IEEE-33 bus radial distribution system demonstrate a substantial reduction in power loss and voltage deviation with the inclusion of DG units.
Article
Energy & Fuels
Muhammad Bachtiar Nappu, Ardiaty Arief, Willy Akbar Ajami
Summary: With the growing power grid and the need for higher system efficiency due to the increasing number of renewable energy penetrations, power system operators require a fast and efficient method of operating the power system. One of the main problems in modern power system operation is optimal power flow (OPF), which aims to minimize the total production cost of power plants while maintaining system stability, security, and reliability. This paper proposes a new method, incremental particle swarm optimization (IPSO), to solve OPF. IPSO modifies the particle swarm optimization (PSO) structure by increasing the particle size, allowing the optimization process to become faster. The results obtained by the IPSO method show superior performance in terms of energy generation costs, system voltage stability, and losses compared to the conventional PSO method.
Article
Computer Science, Artificial Intelligence
Qisong Song, Liya Yu, Shaobo Li, Naohiko Hanajima, Xingxing Zhang, Ruiqiang Pu
Summary: In this study, particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm were optimized to improve the comprehensive performance of energy dispatching between different sites. A new improved PSO-ACO algorithm was proposed based on hybrid algorithm to address the issue of poor energy dispatching efficiency between sites. The algorithm introduced multiobjective performance indicators, vitality factor, transformation of PSO routes into ant colony enhancement pheromone, angle guidance function, and high-quality pheromone update rule to enhance the optimization capability and convergence speed. Simulation experiments were conducted to compare the algorithm with other methods, and the results demonstrated that the improved PSO-ACO algorithm achieved shorter routes, lower time consumption, and higher security in energy dispatching optimization.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Li, Ziping Wei, Jingjing Wu, Shuai Yu, Tian Zhang, Chunli Zhu, Dezhi Zheng, Weisi Guo, Chenglin Zhao, Jun Zhang
Summary: Evolutionary computation has achieved impressive results in solving complex problems, but there is no theoretical guarantee for reaching the global optimum. To address this challenge, researchers have proposed an evolutionary computation framework called EVOLER, aided by machine learning, which enables theoretically guaranteed global optimization of complex non-convex problems. This is achieved by learning a low-rank representation of the problem and exploring a small attention subspace using evolutionary computation methods to reliably avoid local optima.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Thermodynamics
Shengping Xu, Guojiang Xiong, Ali Wagdy Mohamed, Houssem R. E. H. Bouchekara
Summary: This paper presents an improved comprehensive learning particle swarm optimization (CLPSO) named FV-ICLPSO to solve the optimization problem of economic dispatch in power systems. The proposed method demonstrates a significant advantage in convergence speed and is validated in multiple practical cases.
Article
Engineering, Electrical & Electronic
Tamer M. Sobhy Ibrahim, Tomas Tinoco De Rubira, Alberto Del Rosso, Mahendra Patel, Swaroop Guggilam, Ahmed Mohamed
Summary: This paper proposes an optimization approach to improve voltage security in transmission networks, formulated as a multi-period optimal reactive power dispatch problem. The goal is to maximize the dynamic reactive power reserve of the system by minimizing the reactive power supplied by synchronous generators, providing preventive control schedules.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yasir Muhammad, Muhammad Asif Zahoor Raja, Muhammad Altaf, Farman Ullah, Naveed Ishtiaq Chaudhary, Chi-Min Shu
Summary: This paper presents a new computing paradigm based on fractional order comprehensive learning particle swarm optimization for solving the reactive power dispatch problems in power systems. The method is tested and verified to be stable, effective, and reliable.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Electrical & Electronic
Frank Rodrigues, Yuri Molina, Clivaldo Silva, Zocimo Naupari
Summary: This paper introduces a new algorithm for simultaneous tuning of AVR and PSS optimal parameters using PSO-OED technique, applied to both SMIB and 9-Bus Power Systems, showing improved dynamic stability and convergence rate in simulations.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Chemical
Whei-Min Lin, Chia-Sheng Tu, Sang-Jyh Lin, Ming-Tang Tsai
Summary: This paper explores the operation and dispatch of power units in a power system by considering the horizon year load and carbon taxes. It uses the modified particle swarm optimization method to solve the objective function, taking into account fuel costs and carbon taxes. The study demonstrates the efficiency and ability of the proposed method using a real 345KV system, and analyzes the impacts of different carbon taxes on unit dispatch.
Article
Energy & Fuels
Khaidem Bidyanath, Sanasam Dhanabanta Singh, Shuma Adhikari
Summary: Complex power plays a crucial role in maintaining and sustaining the magnetic and electric fields. The efficiency of a power system relies on the system's loss and voltage profile. Installing capacitors helps control and reduce reactive energy levels. This paper utilizes genetic and particle swarm algorithms to determine the optimal location and size of capacitors, aiming to optimize the 132 KV Manipur Transmission System by reducing losses and improving voltage profiles. Results show that Particle Swarm Optimization (PSO) provides optimal solutions, while Genetic Algorithm (GA) is simpler in the optimization process.
Article
Mathematics, Interdisciplinary Applications
Babar Sattar Khan, Affaq Qamar, Farman Ullah, Muhammad Bilal
Summary: Researchers propose a novel method using entropy evolution and fractional calculus concepts, which is implemented in the hybrid metaheuristic computational paradigm of MFO and PSO algorithms for the ORPD problem. The proposed EMFO-FPSO algorithm demonstrates superiority in solving ORPD problem compared to well-known optimizer solvers from literature, enhancing memory effect, robustness, and stability of the MFO-PSO algorithm.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Information Systems
Emmanuel Idowu Ogunwole, Senthil Krishnamurthy
Summary: This research aims to lower the cost of active and reactive power of generators by reducing the deviation of rescheduled power from scheduled values. The innovative aspect of this study is the inclusion of reactive power rescheduling and voltage stability. The research formulates a multi-objective function, sensitivity factors, and uses a particle swarm optimization algorithm to address transmission congestion. The developed method is validated on various test systems and demonstrates effectiveness in reducing power cost and improving voltage stability.
Article
Mathematics
Olukorede Tijani Adenuga, Senthil Krishnamurthy
Summary: This paper focuses on the requirement for the integration of power plants due to the cyclical rise in electrical energy consumption. An optimization problem is formulated to minimize the operational cost while meeting network constraints and ensuring economic power dispatch and energy management system co-optimization. The developed particle swarm optimization method effectively reduces cost and improves self-consumption ratio.
Article
Thermodynamics
Guojiang Xiong, Maohang Shuai, Xiao Hu
Summary: An improved bare-bone multi-objective particle swarm optimization (IBBMOPSO) is proposed to solve the combined heat and power economic emission dispatch problems. The IBBMOPSO integrates four improved strategies to overcome the deficiencies of population diversity and premature convergence in traditional particle swarm optimization. Simulation results demonstrate that IBBMOPSO can achieve higher-quality dispatching schemes with lower generating fuel cost and less pollutant gas emission compared with other algorithms.
Article
Automation & Control Systems
Yinghao Shan, Jiefeng Hu, Huashan Liu
Summary: This article proposes a comprehensive power control and optimization strategy for microgrids. At the device level, a model predictive control combined with the droop method is utilized to achieve load sharing and flexible power dispatching. At the system level, an evolutionary particle swarm optimization algorithm is designed to generate optimal power setpoints. This scheme mitigates voltage deviations and reduces the operational cost of microgrids.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Electrical & Electronic
M. Senthil Kumar, K. Mahadevan
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2016)
Article
Computer Science, Hardware & Architecture
R. Velmurugan, K. Mahadevan
JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS
(2017)
Article
Engineering, Electrical & Electronic
M. Rajkumar, K. Mahadevan, S. Kannan, S. Baskar
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2013)
Article
Engineering, Electrical & Electronic
M. Rajkumar, K. Mahadevan, S. Kannan, S. Baskar
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2014)
Article
Engineering, Electrical & Electronic
Rajkumar Muthuswamy, Mahadevan Krishnan, Kannan Subramanian, Baskar Subramanian
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
D. Fathema Farzana, K. Mahadevan
Article
Computer Science, Hardware & Architecture
N. Mageswari, K. Mahadevan, R. Mohan Kumar
MICROPROCESSORS AND MICROSYSTEMS
(2019)
Article
Telecommunications
C. Chandravathi, K. Mahadevan
Summary: A new cross-layer technique is proposed in this paper to improve energy efficiency and address energy issues in WSN. By dynamically scheduling node energy and considering virtual end-to-end packet rate selection and congestion control, packet loss is reduced and communication performance is improved.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
D. Arun Prasad, K. Mahadevan, M. Arul Prasanna
MICROPROCESSORS AND MICROSYSTEMS
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
Rajendran Joseph Rathish, Krishnan Mahadevan
INTELLIGENT AND EFFICIENT ELECTRICAL SYSTEMS
(2018)
Article
Oceanography
M. Senthil Kumar, K. Mahadevan
INDIAN JOURNAL OF GEO-MARINE SCIENCES
(2016)
Article
Oceanography
M. Senthil Kumar, K. Mahadevan
INDIAN JOURNAL OF GEO-MARINE SCIENCES
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
S. Dhanalakshmi, S. Kannan, S. Baskar, K. Mahadevan
SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, SEMCCO 2014
(2015)
Proceedings Paper
Engineering, Mechanical
Senthil M. Kumar, K. Mahadevan
ADVANCEMENTS IN AUTOMATION AND CONTROL TECHNOLOGIES
(2014)
Proceedings Paper
Engineering, Mechanical
Senthil M. Kumar, K. Mahadevan
ADVANCEMENTS IN AUTOMATION AND CONTROL TECHNOLOGIES
(2014)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)