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    Wyświetlanie 1-14 z 14
    Tytuł:
    Coordinated control strategy for microgrid stability maintenance under isolated island operation
    Autorzy:
    Wu, Pan
    Xu, Xiaowei
    Powiązania:
    https://bibliotekanauki.pl/articles/1841281.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    coordinated control
    isolated island operation
    microgrid
    particle swarm optimization
    Opis:
    In this study, the inverter in a microgrid was adjusted by the particle swarm optimization (PSO) based coordinated control strategy to ensure the stability of the isolated island operation. The simulation results showed that the voltage at the inverter port reduced instantaneously, and the voltage unbalance degree of its port and the port of point of common coupling (PCC) exceeded the normal standard when the microgrid entered the isolated island mode. After using the coordinated control strategy, the voltage rapidly recovered, and the voltage unbalance degree rapidly reduced to the normal level. The coordinated control strategy is better than the normal control strategy.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 2; 285-295
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fault diagnosis of power transformer based on improved particle swarm optimization OS-ELM
    Autorzy:
    Li, Yuancheng
    Ma, Longqiang
    Powiązania:
    https://bibliotekanauki.pl/articles/140428.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    power transformer
    fault diagnosis
    improved particle swarm optimization
    OS-ELM
    parameter optimization
    Opis:
    A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
    Źródło:
    Archives of Electrical Engineering; 2019, 68, 1; 161-172
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Economic dispatch in power system networks including renewable energy resources using various optimization techniques
    Autorzy:
    Hafiz, Abrar Mohamed
    Abdelrahman, M. Ezzat
    Temraz, Hesham
    Powiązania:
    https://bibliotekanauki.pl/articles/1841222.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Economic Dispatch (ED)
    Particle Swarm Optimization (PSO)
    Sine-Cosine
    Algorithm (SCA)
    Photovoltaic (PV)
    Opis:
    Economic dispatch (ED) is an essential part of any power system network. ED is how to schedule the real power outputs from the available generators to get the minimum cost while satisfying all constraints of the network. Moreover, it may be explained as allocating generation among the committed units with the most effective minimum way in accordance with all constraints of the system. There are many traditional methods for solving ED, e.g., Newton-Raphson method Lambda-Iterative technique, Gaussian-Seidel method, etc. All these traditional methods need the generators’ incremental fuel cost curves to be increasing linearly. But practically the input-output characteristics of a generator are highly non-linear. This causes a challenging non-convex optimization problem. Recent techniques like genetic algorithms, artificial intelligence, dynamic programming and particle swarm optimization solve nonconvex optimization problems in a powerful way and obtain a rapid and near global optimum solution. In addition, renewable energy resources as wind and solar are a promising option due to the environmental concerns as the fossil fuels reserves are being consumed and fuel price increases rapidly and emissions are getting higher. Therefore, the world tends to replace the old power stations into renewable ones or hybrid stations. In this paper, it is attempted to enhance the operation of electrical power system networks via economic dispatch. An ED problem is solved using various techniques, e.g., Particle Swarm Optimization (PSO) technique and Sine-Cosine Algorithm (SCA). Afterwards, the results are compared. Moreover, case studies are executed using a photovoltaic-based distributed generator with constant penetration level on the IEEE 14 bus system and results are observed. All the analyses are performed on MATLAB software.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 3; 643-655
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Parameter estimation of photovoltaic module relied on golden jackal optimization
    Autorzy:
    Nguyen, Thuan Thanh
    Powiązania:
    https://bibliotekanauki.pl/articles/27309949.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    golden jackal optimization
    henry gas solubility optimization
    particle swarm optimization
    PV parameter estimation
    single diode model
    Opis:
    Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 4; 987--1003
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    PSO based optimal location and sizing of SVC for novel multiobjective voltage stability analysis during N – 2 line contingency
    Autorzy:
    Mangaiyarkarasi, S. P
    Sree Renga Raja, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/140430.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    N – 2 contingency analysis
    voltage severity
    particle swarm optimization (PSO)
    SVC
    power system planning
    Opis:
    In this paper voltage stability is analysed based not only on the voltage deviations from the nominal values but also on the number of limit violating buses and severity of voltage limit violations. The expression of the actual state of the system as a numerical index like severity, aids the system operator in taking better security related decisions at control centres both during a period of contingency and also at a highly stressed operating condition. In contrary to conventional N – 1 contingency analysis, Northern Electric Reliability Council (NERC) recommends N – 2 line contingency analysis. The decision of the system operator to overcome the present contingency state of the system must blend harmoniously with the stability of the system. Hence the work presents a novel N – 2 contingency analysis based on the continuous severity function of the system. The study is performed on 4005 possible combinations of N – 2 contingency states for the practical Indian Utility 62 bus system. Static VAr Compensator is used to improve voltage profile during line contingencies. A multi- objective optimization with the objective of minimizing the voltage deviation and also the number of limit violating bus with optimal location and optimal sizing of SVC is achieved by Particle Swarm Optimization algorithm.
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 4; 535-550
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new technique to design planar dipole antennas by using Bezier curve and Particle Swarm Optimization
    Autorzy:
    Homsup, N.
    Silabut, W.
    Kesornpatumanum, V.
    Boonek, P.
    Kuhirun, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/140461.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    planar dipole antenna
    Bézier curve
    particle swarm optimization (PSO)
    small antenna
    dual band antenna
    Opis:
    This research presents a new technique which includes the principle of a Bezier curve and Particle Swarm Optimization (PSO) together, in order to design the planar dipole antenna for the two different targets. This technique can improve the characteristics of the antennas by modifying copper textures on the antennas with a Bezier curve. However, the time to process an algorithm will be increased due to the expansion of the solution space in optimization process. So as to solve this problem, the suitable initial parameters need to be set. Therefore this research initialized parameters with reference antenna parameters (a reference antenna operates on 2.4 GHz for IEEE 802.11 b/g/n WLAN standards) which resulted in the proposed designs, rapidly converted into the goals. The goal of the first design is to reduce the size of the antenna. As a result, the first antenna is reduced in the substrate size from areas of 5850 mm2 to 2987 mm2 (48.93% approximately) and can also operates at 2.4 GHz (2.37 GHz to 2.51 GHz). The antenna with dual band application is presented in the second design. The second antenna is operated at 2.4 GHz (2.40 GHz to 2.49 GHz) and 5 GHz (5.10 GHz to 5.45 GHz) for IEEE 802.11 a/b/g/n WLAN standards.
    Źródło:
    Archives of Electrical Engineering; 2016, 65, 3; 513-525
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
    Autorzy:
    Pandiarajan, K.
    Babulal, C. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/141059.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    non-dominated sorting genetic algorithm
    generation rescheduling
    particle swarm optimization (PSO)
    differential evolution
    overload index
    Opis:
    This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 3; 367-384
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid method for the optimal reactive power dispatch and the control of voltagesin an electrical energy network
    Autorzy:
    Benchabira, Aissa
    Khiat, Mounir
    Powiązania:
    https://bibliotekanauki.pl/articles/140740.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    electrical energy network
    interior point method (IPM)
    optimal reactive power dispatch (ORPD)
    particle swarm optimization (PSO)
    Opis:
    This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.
    Źródło:
    Archives of Electrical Engineering; 2019, 68, 3; 535-551
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system
    Autorzy:
    Kumar, V. E.
    Jerome, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/141105.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    inverted pendulum
    LQR controller
    particle swarm optimization (PSO)
    genetic algorithm
    adaptive inertia weight factor
    state feedback control
    Opis:
    This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 3; 345-365
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Performance Study on Synchronous and Asynchronous Update Rules for A Plug-In Direct Particle Swarm Repetitive Controller
    Autorzy:
    Ufnalski, B.
    Grzesiak, L. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/141272.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    repetitive process control
    particle swarm optimization (PSO)
    synchronous and asynchronous update rules
    dynamic optimization problem
    repetitive disturbance rejection
    optimal control
    Opis:
    In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO) to solve in on-line mode a dynamic optimization problem (DOP) related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs), is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.
    Źródło:
    Archives of Electrical Engineering; 2014, 63, 4; 635-646
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation
    Autorzy:
    He, Ping
    Dong, Jie
    Wu, Xiaopeng
    Yun, Lei
    Yang, Hua
    Powiązania:
    https://bibliotekanauki.pl/articles/27309934.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    BP neural network
    photovoltaic power generation
    PSO–GWO model
    PSO–GWO–BP prediction model
    particle swarm optimization
    gray wolf optimization
    back propagation
    standard grey wolf algorithm
    Opis:
    At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.
    Źródło:
    Archives of Electrical Engineering; 2023, 72, 3; 613--628
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms
    Autorzy:
    Król, K.
    Machczyński, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/141588.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    power line
    electric field
    magnetic field
    optimization
    genetic algorithm
    particle swarm algorithm
    Opis:
    The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.
    Źródło:
    Archives of Electrical Engineering; 2018, 67, 4; 829-843
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model
    Autorzy:
    Knypiński, Ł.
    Nowak, L.
    Sujka, P.
    Radziuk, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/141610.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    optymalizacja
    histereza
    model Jilesa-Athertona
    optimization
    hysteresis
    Jiles-Atherton model
    particle swarm optumization method
    Opis:
    In the paper an algorithm and computer code for the identification of the hysteresis parameters of the Jiles-Atherton model have been presented. For the identification the particle swarm optimization method (PSO) has been applied. In the optimization procedure five design variables has been assumed. The computer code has been elaborated using Delphi environment. Three types of material have been examined. The results of optimization have been compared to experimental ones. Selected results of the calculation for different material are presented and discussed.
    Źródło:
    Archives of Electrical Engineering; 2012, 61, 2; 139-148
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Allocation of real power generation based on computing over all generation cost: an approach of Salp Swarm Algorithm
    Autorzy:
    Devarapalli, Ramesh
    Sinha, Nikhil Kumar
    Rao, Bathina Venkateswara
    Knypiński, Łukasz
    Lakshmi, Naraharisetti Jaya Naga
    García Márquez, Fausto Pedro
    Powiązania:
    https://bibliotekanauki.pl/articles/1841291.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    economic load dispatch
    heuristic algorithms
    optimization
    Particle Swarm
    Algorithm
    Salp Swarm Algorithm
    ekonomiczna wysyłka ładunku
    algorytmy heurystyczne
    optymalizacja
    rój cząstek
    algorytm
    Opis:
    Economic Load Dispatch (ELD) is utilized in finding the optimal combination of the real power generation that minimizes total generation cost, yet satisfying all equality and inequality constraints. It plays a significant role in planning and operating power systems with several generating stations. For simplicity, the cost function of each generating unit has been approximated by a single quadratic function. ELD is a subproblem of unit commitment and a nonlinear optimization problem. Many soft computing optimization methods have been developed in the recent past to solve ELD problems. In this paper, the most recently developed population-based optimization called the Salp Swarm Algorithm (SSA) has been utilized to solve the ELD problem. The results for the ELD problem have been verified by applying it to a standard 6-generator system with and without due consideration of transmission losses. The finally obtained results using the SSA are compared to that with the Particle Swarm Optimization (PSO) algorithm. It has been observed that the obtained results using the SSA are quite encouraging.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 2; 337-349
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-14 z 14

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