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Records with Keyword: Particle Swarm Optimization
Showing records 108 to 132 of 136. [First] Page: 1 2 3 4 5 6 7 Last
Solar Hydrogen Variable Speed Control of Induction Motor Based on Chaotic Billiards Optimization Technique
Basem E. Elnaghi, M. N. Abelwhab, Ahmed M. Ismaiel, Reham H. Mohammed
February 22, 2023 (v1)
Subject: Optimization
Keywords: chaotic billiards optimization, electrolysis, field-oriented control, hydrogen production, Particle Swarm Optimization, solar–hydrogen induction motor drive
This paper introduces a brand-new, inspired optimization algorithm (the chaotic billiards optimization (C-BO) approach) to effectively develop the optimal parameters for fuzzy PID techniques to enhance the dynamic response of the solar−hydrogen drive of an induction motor. This study compares fuzzy-PID-based C-BO regulators to fuzzy PID regulators based on particle swarm optimization (PSO) and PI-based PSO regulators to provide speed control in solar−hydrogen, induction-motor drive systems. The model is implemented to simulate the production and storage of hydrogen while powering an induction-motor drive which provides a great solution for the renewable energy storage problem in the case of solar pumping systems. MATLAB/Simulink 2021a is used to simulate and analyze the entire operation. The laboratory prototype is implemented in real time using a DSP-DS1104 board. Based on the simulation and experimental results, the proposed fuzzy-PID-based C-BO has reduced speed peak overshoot by 45... [more]
Stability Enhancement of Wind Energy Conversion Systems Based on Optimal Superconducting Magnetic Energy Storage Systems Using the Archimedes Optimization Algorithm
Heba T. K. Abdelbadie, Adel T. M. Taha, Hany M. Hasanien, Rania A. Turky, S. M. Muyeen
February 21, 2023 (v1)
Subject: Optimization
Keywords: Archimedes optimization algorithm, Genetic Algorithm, Particle Swarm Optimization, PI controller, superconducting magnetic energy storage system, wind energy
Throughout the past several years, the renewable energy contribution and particularly the contribution of wind energy to electrical grid systems increased significantly, along with the problem of keeping the systems stable. This article presents a new optimization technique entitled the Archimedes optimization algorithm (AOA) that enhances the wind energy conversion system’s stability, integrated with a superconducting magnetic energy storage (SMES) system that uses a proportional integral (PI) controller. The AOA is a modern population technique based on Archimedes’ law of physics. The SMES system has a big impact in integrating wind generators with the electrical grid by regulating the output of wind generators and strengthening the power system’s performance. In this study, the AOA was employed to determine the optimum conditions of the PI controller that regulates the charging and discharging of the SMES system. The simulation outcomes of the AOA, the genetic algorithm (GA), and pa... [more]
Particle Swarm Optimization Algorithm-Tuned Fuzzy Cascade Fractional Order PI-Fractional Order PD for Frequency Regulation of Dual-Area Power System
Mokhtar Shouran, Aleisawee Alsseid
February 21, 2023 (v1)
Subject: Optimization
Keywords: dual-area power system, fuzzy cascade fractional order proportional-integral and fractional order proportional-derivative, load frequency control, Particle Swarm Optimization
This study proposes a virgin structure of Fuzzy Logic Control (FLC) for Load Frequency Control (LFC) in a dual-area interconnected electrical power system. This configuration benefits from the advantages of fuzzy control and the merits of Fractional Order theory in traditional PID control. The proposed design is based on Fuzzy Cascade Fractional Order Proportional-Integral and Fractional Order Proportional-Derivative (FC FOPI-FOPD). It includes two controllers, namely FOPI and FOPD connected in cascade in addition to the fuzzy controller and its input scaling factor gains. To boost the performance of this controller, a simple and powerful optimization method called the Particle Swarm Optimization (PSO) algorithm is employed to attain the best possible values of the suggested controller’s parameters. This task is accomplished by reducing the Integral Time Absolute Error (ITAE) of the deviation in frequency and tie line power. Furthermore, to authenticate the excellence of the proposed F... [more]
A Novel Exponential-Weighted Method of the Antlion Optimization Algorithm for Improving the Convergence Rate
Szu-Chou Chen, Wen-Chen Huang, Ming-Hsien Hsueh, Chieh-Yu Pan, Chih-Hao Chang
February 21, 2023 (v1)
Subject: Optimization
Keywords: antlion optimization, metaheuristic, Particle Swarm Optimization
The antlion optimization algorithm (ALO) is one of the most effective algorithms to solve combinatorial optimization problems, but it has some disadvantages, such as a long runtime. As a result, this problem impedes decision makers. In addition, due to the nature of the problem, the speed of convergence is a critical factor. As the size of the problem dimension grows, the convergence speed of the optimizer becomes increasingly significant. Many modified versions of the ALO have been developed in the past. Nevertheless, there are only a few research articles that discuss better boundary strategies that can increase the diversity of ants walking around an antlion to accelerate convergence. A novel exponential-weighted antlion optimization algorithm (EALO) is proposed in this paper to address slow convergence rates. The algorithm uses exponential functions and a random number in the interval 0, 1 to increase the diversity of the ant’s random walks. It has been demonstrated that by optimiz... [more]
Optimization of Sour Water Stripping Unit Using Artificial Neural Network−Particle Swarm Optimization Algorithm
Ye Zhang, Zheng Fan, Genhui Jing, Mohammed Maged Ahemd Saif
February 21, 2023 (v1)
Keywords: artificial neural network, Particle Swarm Optimization, sensitivity analysis, sour water stripping
Sour water stripping can treat the sour water produced by crude oil processing, which has the effect of environmental protection, energy saving and emission reduction. This paper aims to reduce energy consumption of the unit by strengthening process parameter optimization. Firstly, the basic model is established by utilizing Aspen Plus, and the optimal model is determined by comparative analysis of back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) models. Then, the sensitivity analysis of Sobol is used to select the operating variables that have a significant influence on the energy consumption of the sour water stripping system. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the operating conditions of the sour water stripping unit. The results show that the RBFNN model is more accurate than other models. Its network structure is 5-66-1, and the expected value has an appro... [more]
A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks
Koon Meng Ang, Cher En Chow, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Faten Khalid Karim, Doaa Sami Khafaga, Sew Sun Tiang, Wei Hong Lim
February 21, 2023 (v1)
Keywords: artificial neural network, Machine Learning, Particle Swarm Optimization, training algorithm, two-level learning phases
Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the a... [more]
Design of Soft-Sensing Model for Alumina Concentration Based on Improved Deep Belief Network
Xiangquan Li, Bo Liu, Wei Qian, Guoyong Rao, Lijuan Chen, Jiarui Cui
February 21, 2023 (v1)
Subject: Optimization
Keywords: alumina concentration, aluminum electrolysis, empirical model decomposition, Particle Swarm Optimization, soft-sensing model
Alumina concentration is an important parameter in the production process of aluminum electrolysis. Due to the complex production environment in the industrial field and the complex physical and chemical reactions in the aluminum reduction cell, nowadays it is still unable to carry out online measurement and real-time monitoring. For solving this problem, a soft-sensing model of alumina concentration based on a deep belief network (DBN) is proposed. However, the soft-sensing model may have some limitations for different cells and different periodic working conditions such as local anode effect, pole changing, and bus lifting in the same cell. The empirical mode decomposition (EMD) and particle swarm optimization (PSO) with the DBN are combined, and an EMD−PSO−DBN method that can denoize and optimize the model structure is proposed. The simulation results show that the improved soft-sensing model improves the accuracy and universality of prediction.
Smart Greenhouse Based on ANN and IOT
Medhat A. Tawfeek, Saad Alanazi, A. A. Abd El-Aziz
February 21, 2023 (v1)
Keywords: artificial neural network, dataset summarization, Internet of things, Particle Swarm Optimization, smart agriculture
The effective exploitation of smart technology in applications helps farmers make better decisions without increasing costs. Agricultural Research Centers (ARCs) are continually updating and producing new datasets from applied research, so the smart model should dynamically address all surrounding agricultural variables and improve its expertise from all available datasets. This research concentrates on sustainable agriculture using Adaptive Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANNs). Therefore, if a new related dataset is created, this new incoming dataset is merged with the existing dataset. The proposed PSO then bypasses the summarization of the dataset. It deletes the least essential and speculative records and keeps the records that are the most influential in the classification process. The summarized dataset is interposed in the training process without re-establishing the system again by modifying the classical ANN. The proposed ANN comprises an ada... [more]
The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations
Whei-Min Lin, Chia-Sheng Tu, Sang-Jyh Lin, Ming-Tang Tsai
February 21, 2023 (v1)
Subject: Optimization
Keywords: carbon tax, global warming, Particle Swarm Optimization, power system dispatch
Traditional economic dispatch methods, which are used to minimize fuel costs, have become inadequate because they do not consider the environmental impact of emissions in the optimization process. By taking into account the horizon year load and carbon taxes, this paper examines the operation and dispatch of power units in a power system. The objective function, including the cost of fuels and the cost of carbon taxes, is solved by the modified particle swarm optimization with time-varying acceleration coefficient (MPSO-TVAC) method under operational constraints. Based on different load scenarios, the influences of various carbon taxes for the dispatch of units are simulated and analyzed. The efficiency and ability of the proposed MPSO-TVAC method are demonstrated using a real 345KV system. Simulation results indicate that the average annual CO2 emissions are 0.36 kg/kwh, 0.41 kg/kwh, and 0.44 kg/kwh in 2012, 2017 and 2022, respectively. As the capacity of gas-fired plants was increase... [more]
Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm
Qingni Yuan, Ruitong Sun, Xiaoying Du
February 17, 2023 (v1)
Keywords: differential evolution algorithm and self-adaption, Particle Swarm Optimization, path planning
Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e.,... [more]
Source code for STORE model
Kildekode for STORE-modellen
Thomas A. Adams II
November 15, 2022 (v2)
Subject: Biosystems
Keywords: Computational Biology, Dynamic Modelling, Matlab, Omentum, Particle Swarm Optimization, Stochastic Modelling, Vaccine
This is the source code for the STORE (STochasic Omentum REsponse) model. The model is used to simulate how naive T-cells in the omentum will prime and multiply during the expansion period (8 days) after a T-cell vaccination in a mouse.

This is the matlab source code used in the following paper:

Christian DA, Adams TA II, Smith TA, Shallberg LA, Theisen DJ, Phan AT, Abraha M, Perry J, Ruthel G, Clark JT, Murphy KM, Kedl, RM, Hunter CA. cDC1 coordinate innate and adaptive responses in the omentum required for T cell priming and memory. Science Immunology 7, eabq7432 (2022).

This is fixed legacy code used for the paper for scientific auditing and reproduction purposes. See paper for documentation.
Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process
Titus Quah, Derek Machalek, Kody M. Powell
June 2, 2021 (v1)
Keywords: artificial neural networks, Particle Swarm Optimization, process optimization, Proximal Policy Optimization, real-time optimization, reinforcement learning
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This cas... [more]
DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO
Jui-Chung Hung
May 25, 2021 (v1)
Keywords: direction of arrival estimation, memetic algorithms, non-uniform noise, Particle Swarm Optimization, subspace maximum-likelihood
In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-un... [more]
A Joint Optimization Strategy of Coverage Planning and Energy Scheduling for Wireless Rechargeable Sensor Networks
Cheng Gong, Chao Guo, Haitao Xu, Chengcheng Zhou, Xiaotao Yuan
April 30, 2021 (v1)
Keywords: coverage optimization, Particle Swarm Optimization, queuing game, virtual force, wireless rechargeable sensor network
Wireless Sensor Networks (WSNs) have the characteristics of large-scale deployment, flexible networking, and many applications. They are important parts of wireless communication networks. However, due to limited energy supply, the development of WSNs is greatly restricted. Wireless rechargeable sensor networks (WRSNs) transform the distributed energy around the environment into usable electricity through energy collection technology. In this work, a two-phase scheme is proposed to improve the energy management efficiency for WRSNs. In the first phase, we designed an annulus virtual force based particle swarm optimization (AVFPSO) algorithm for area coverage. It adopts the multi-parameter joint optimization method to improve the efficiency of the algorithm. In the second phase, a queuing game-based energy supply (QGES) algorithm was designed. It converts energy supply and consumption into network service. By solving the game equilibrium of the model, the optimal energy distribution str... [more]
Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network
Shehab Abdulhabib Alzaeemi, Saratha Sathasivam
April 29, 2021 (v1)
Keywords: 2-satisfiability based reverse analysis, artificial bee colony, artificial immune system, differential evolution, Genetic Algorithm, Particle Swarm Optimization, radial basis functions neural network
A radial basis function neural network-based 2-satisfiability reverse analysis (RBFNN-2SATRA) primarily depends on adequately obtaining the linear optimal output weights, alongside the lowest iteration error. This study aims to investigate the effectiveness, as well as the capability of the artificial immune system (AIS) algorithm in RBFNN-2SATRA. Moreover, it aims to improve the output linearity to obtain the optimal output weights. In this paper, the artificial immune system (AIS) algorithm will be introduced and implemented to enhance the effectiveness of the connection weights throughout the RBFNN-2SATRA training. To prove that the introduced method functions efficiently, five well-established datasets were solved. Moreover, the use of AIS for the RBFNN-2SATRA training is compared with the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and artificial bee colony (ABC) algorithms. In terms of measurements and accuracy, the simulation results s... [more]
A Hybrid of Particle Swarm Optimization and Harmony Search to Estimate Kinetic Parameters in Arabidopsis thaliana
Mohamad Saufie Rosle, Mohd Saberi Mohamad, Yee Wen Choon, Zuwairie Ibrahim, Alfonso González-Briones, Pablo Chamoso, Juan Manuel Corchado
December 17, 2020 (v1)
Subject: Biosystems
Keywords: Arabidopsis thaliana, Harmony Search, parameter estimation, Particle Swarm Optimization
Recently, modelling and simulation have been used and applied to understand biological systems better. Therefore, the development of precise computational models of a biological system is essential. This model is a mathematical expression derived from a series of parameters of the system. The measurement of parameter values through experimentation is often expensive and time-consuming. However, if a simulation is used, the manipulation of computational parameters is easy, and thus the behaviour of a biological system model can be altered for a better understanding. The complexity and nonlinearity of a biological system make parameter estimation the most challenging task in modelling. Therefore, this paper proposes a hybrid of Particle Swarm Optimization (PSO) and Harmony Search (HS), also known as PSOHS, designated to determine the kinetic parameter values of essential amino acids, mainly aspartate metabolism, in Arabidopsis thaliana. Three performance measurements are used in this pap... [more]
Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind−Wave Farm
Jorge Izquierdo-Pérez, Bruno M. Brentan, Joaquín Izquierdo, Niels-Erik Clausen, Antonio Pegalajar-Jurado, Nis Ebsen
March 12, 2020 (v1)
Keywords: farm layout, floating offshore energy generation, hybrid wind-wave platform, LCOE, Optimization, Particle Swarm Optimization, PSO, sustainable energy generation
Offshore floating hybrid wind and wave energy is a young technology yet to be scaled up. A way to reduce the total costs of the energy production process in order to ensure competitiveness in the sustainable energy market is to maximize the farm’s efficiency. To do so, an energy generation and costs calculation model was developed with the objective of minimizing the technology’s Levelized Cost of Energy (LCOE) of the P80 hybrid wind-wave concept, designed by the company Floating Power Plant A/S. A Particle Swarm Optimization (PSO) algorithm was then implemented on top of other technical and decision-making processes, taking as decision variables the layout, the offshore substation position, and the export cable choice. The process was applied off the west coast of Ireland in a site of interest for the company, and after a quantitative and qualitative optimization process, a minimized LCOE was obtained. It was then found that lower costs of ~73% can be reached in the short-term, and th... [more]
Optimal Design of Standalone Photovoltaic System Based on Multi-Objective Particle Swarm Optimization: A Case Study of Malaysia
Hussein Mohammed Ridha, Chandima Gomes, Hashim Hizam, Masoud Ahmadipour
February 3, 2020 (v1)
Keywords: levelized cost of energy (LCE), life cycle cost (LCC), loss of load probability (LLP), multi-objective optimization, Particle Swarm Optimization, standalone PV system
This paper presents a multi-objective particle swarm optimization (MOPSO) method for optimal sizing of the standalone photovoltaic (SAPV) systems. Loss of load probability (LLP) analysis is considered to determine the technical evaluation of the system. Life cycle cost (LCC) and levelized cost of energy (LCE) are treated as the economic criteria. The two variants of the proposed PSO method, referred to as adaptive weights PSO ( A W P S O c f ) and sigmoid function PSO ( S F P S O c f ) , are implemented using MATLAB software to the optimize the number of PV modules in (series and parallel) and number of the storage battery. The case study of the proposed SAPV system is executed using the hourly meteorological data and typical load demand for one year in a rural area in Malaysia. The performance outcomes of the proposed A W / S F P S O c f methods give various configurations at desired levels of LLP values and the corresponding minimum cost. The perform... [more]
Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques
Anjali Ramachandran, Rabee Rustum, Adebayo J. Adeloye
January 19, 2020 (v1)
Subject: Biosystems
Keywords: anaerobic digestion, ant colony optimization, artificial neural network, firefly algorithm, Genetic Algorithm, nature-inspired techniques, Particle Swarm Optimization
Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.
The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review
Gad Shaari, Neyre Tekbiyik-Ersoy, Mustafa Dagbasi
December 10, 2019 (v1)
Keywords: Particle Swarm Optimization, solar, thermal, unit commitment, Wind
Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also develo... [more]
Finding better limit cycles of semicontinuous distillation. Part 1: Back-stepping design Methodology
Pranav Bhaswanth Madabhushi, Thomas Adams II
July 9, 2019 (v1)
Keywords: Hybrid dynamic system, Limit Cycle, Particle Swarm Optimization, Process Design, Semicontinuous Distillation
Semicontinuous ternary zeotropic distillation is a periodic process that is carried out
in a single distillation column and a tightly integrated external middle vessel. In the
state-of-the-art design procedure of this process, a continuous distillation process that
separates the top and bottoms products to the desired purity is used to generate an arbitrary
initial state for simulating the dynamics of the semicontinuous distillation process.
Although this method is useful in estimating the limit cycle, it was later found that the
operation of the process in this limit cycle was economically sub-optimal. In this study,
a new algorithmic design procedure, called the back-stepping design methodology, is
proposed to find better limit cycles for zeotropic ternary semicontinuous distillation
using the aspenONE Engineering suite. The proposed methodology was applied to two
different case studies using feed mixtures with different chemical components. A comparison
with the current d... [more]
Finding better limit cycles of semicontinuous distillation
Pranav Bhaswanth Madabhushi, Thomas Adams II
March 22, 2019 (v1)
There are three different ways of operating the distillation process based on production requirements and operational flexibility. Semicontinuous distillation of multicomponent mixtures is a cost-effective technology in the intermediate production range when compared with traditional batch and continuous distillation processes. The process, which has both continuous and discrete dynamics, operates in a limit cycle (an isolated periodic orbit). Design of this process entails finding the system’s time-invariant parameters, for example, equipment design parameters, reflux rate etc., to operate in a limit cycle having acceptable performance. In semicontinuous distillation studies, the performance metric chosen is the separation cost, which is defined as the total annualized cost-per-production. The state-of-the-art design procedure involves determining an initial state for estimating the limit cycle through the dynamic simulation of the process and is found to be effective. However, it lac... [more]
Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network
Cheng-Ming Lee, Chia-Nan Ko
February 27, 2019 (v1)
Keywords: adaptive annealing learning algorithm, Particle Swarm Optimization, radial basis function neural network, short-term load forecasting, support vector regression
A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using an SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from the Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision compared to various... [more]
A Current Control Approach for an Abnormal Grid Supplied Ultra Sparse Z-Source Matrix Converter with a Particle Swarm Optimization Proportional-Integral Induction Motor Drive Controller
Seyed Sina Sebtahmadi, Hanieh Borhan Azad, Didarul Islam, Mehdi Seyedmahmoudian, Ben Horan, Saad Mekhilef
January 31, 2019 (v1)
Keywords: induction motor drives, matrix converter, Particle Swarm Optimization, Z-source network
A rotational d-q current control scheme based on a Particle Swarm Optimization- Proportional-Integral (PSO-PI) controller, is used to drive an induction motor (IM) through an Ultra Sparse Z-source Matrix Converter (USZSMC). To minimize the overall size of the system, the lowest feasible values of Z-source elements are calculated by considering the both timing and aspects of the circuit. A meta-heuristic method is integrated to the control system in order to find optimal coefficient values in a single multimodal problem. Henceforth, the effect of all coefficients in minimizing the total harmonic distortion (THD) and balancing the stator current are considered simultaneously. Through changing the reference point of magnitude or frequency, the modulation index can be automatically adjusted and respond to changes without heavy computational cost. The focus of this research is on a reliable and lightweight system with low computational resources. The proposed scheme is validated through bot... [more]
Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization
Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou, Hui Qin
November 28, 2018 (v1)
Subject: Optimization
Keywords: comprehensive learning, hydropower reservoir system, multi-objective optimal operation, multi-swarm, Particle Swarm Optimization
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing h... [more]
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