Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

Lee, Chun‐Yao; Chen, Po-Hung; Shen, Yi-Xing

doi:10.1016/j.eswa.2011.02.054

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…The adaptive technique updates the neural network weights in time domain. The radial basis network function network employed has better capability of approximation and faster learning speed [16]. A modified particle swarm method generates the optimized nominal weights of the neural network.…”

Section: Introductionmentioning

confidence: 99%

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

Rahim

Khan²

2014

Arab J Sci Eng

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Permanent magnet synchronous generators are becoming increasingly popular as utility-scale wind generators. While their performances are satisfactorily under normal conditions, they may be degraded under wind gusts as well as in extremely low grid voltage conditions. An adaptive control of superconducting magnetic energy storage (SMES) system for efficient wind energy transfer as well as dynamic performance improvement is proposed in this article. A radial basis function neural network has been employed to determine the controller parameter values. The nominal weights of the neural network are obtained from training of a large inputoutput data set generated through an improved swarm optimization procedure. These weights are then updated through a novel method of tracking of system outputs in time domain. Tests carried out with the adaptive controller show that the improved particle swarm-based radial basis network SMES controller delivers wind energy to grid efficiently and at the same time exhibit very good damping profile.

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Section: Introductionmentioning

confidence: 99%

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

Rahim

Khan²

2014

Arab J Sci Eng

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“…Literatürde maksimum güç noktasını bulunması ve sistemin daha verimli çalışması için; ortamdan rüzgar hızı ölçülerek elde edilen türbin rüzgar güç eğrilerini kullanarak tepe güç noktasının tespiti ve sistemin bu noktada tutulmaya çalışılması [7,8] ve rüzgar güç üretim sistemlerinde kullanılan doğrultucu, evirici ve kıyıcı gibi güç elektroniği çeviricilerinin değişik metotlarla kontrolü [9,10] gibi teknikler kullanılmaktadır. Ayrıca bulanık mantık [11,12], parçacık sürü optimizasyonu ve yapaya sinir ağları [13,14] Tek faz reaktif güç= Q/3=875Var…”

Section: Gi̇ri̇ş (Introduction)unclassified

Küçük Güçlü Rüzgar Türbi̇nleri̇ İçi̇n Farkli Bi̇r Bulanik Mantik Karar Veri̇ci̇li̇ Mgni̇ Metodu

Mengi¹,

Altaş²

2015

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖZETBu çalışmada rüzgar türbinlerinden maksimum verimin elde edilebilmesi için kullanılan farklı bir Maksimum Güç Noktası İzleyici (MGNİ) tekniği anlatılmaktadır. Deneysel olarak kurulan ve testleri yapılan sistem; biri motor diğeri generatör olarak çalışan birbirine akuple edilmiş asenkron makinalardan oluşan rüzgar türbin emülatörü, transformatör, yüksek akımlı doğrultma devresi, kıyıcı, evirici, veri aktarım kartı ve bilgisayar ünitesi, akım ve gerilim algılayıcıları, yükler ve MATLAB/Simulink ortamında tasarlanan bulanık mantık tabanlı bir MGNİ yazılımdan oluşmaktadır. Sistemde rüzgardan elde edilebilecek tepe güç değeri bilgisayar tarafından sürekli olarak hesaplanarak o anki ortam koşullarında beslenebilecek maksimum yük miktarı tespit edilebilmektedir. Bu sistem; ortamdan rüzgar hızı, basınç, sıcaklık, nem, v. b. bilgileri kullanmadan sadece sisteme ait akım ve gerilim bilgileri ile tepe güç noktası izleme yapması ve tasarlanan Bulanık Mantık Sonuçlandırıcı (BMS) bu akım ve gerilim bilgilerini kullanarak tepe güç değerini sürekli olarak hesaplayabilmesi bakımından yapılan diğer çalışmalardan ayrılmaktadır.Anahtar Kelimeler: Maksimum güç noktası izleyici, rüzgar türbini, bulanık mantık sonuçlandırıcı, yenilenebilir enerji A DIFFERENT FUZZY DECISION MAKING MPPT METHOD FOR A MICRO POWER WIND TURBINE ABSTRACTIn this study, a different Maximum Power Point Tracking (MPPT) technique to obtain maximum efficiency from wind turbines is mentioned. The system, which is experimentally established and tested, consists of a wind turbine emulator, which consists of asynchronous machines coupled to each other and one of them acts as the motor while the other is the generator, transformer, rectifier with high current, chopper, invertor, data acquisition card and computer unit, current and voltage sensors, loads and a fuzzy logic based MPPT software designed in MATLAB/Simulink environment. Maximum load to be fed in the case of existing environmental conditions can be defined by continuously calculating the maximum power value to be obtained from the wind by the computer. This system differs from the other studies in that it tracks maximum power point with only the current and voltage date of the system without taking wind power, pressure, heat, humidity etc into consideration; besides, the designed Fuzzy Logic Reasoning (FLR) can continuously calculate maximum power point by using this current and voltage data.

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“…In recent years, the global wind industry has grown rapidly, wind energy has become one of the most important renewable energy sources, installed capacity growth rate of wind turbines has become more than 25% annually, and wind energy has been widely applied to water irrigation [1], urban power supply [2], and many other fields. However, there are still some difficult issues in wind energy control technology: how to get maximum power output of wind turbine is one of the main concerns.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data-Driven Control

Feng

Zhao³

2012

Journal of Control Science and Engineering

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Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and data-driven controller for the WECS is designed based on the Markov model. The neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured.

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Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

Cited by 30 publications

References 21 publications

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

A Swarm-Based Adaptive Neural Network SMES Control for a Permanent Magnet Wind Generator

Küçük Güçlü Rüzgar Türbi̇nleri̇ İçi̇n Farkli Bi̇r Bulanik Mantik Karar Veri̇ci̇li̇ Mgni̇ Metodu

Neural Network Compensation Control for Output Power Optimization of Wind Energy Conversion System Based on Data-Driven Control

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