微電網虛擬同步發電機之智慧型控制;Intelligent Control of Virtual Synchronous Generator for Microgrid

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题名:	微電網虛擬同步發電機之智慧型控制;Intelligent Control of Virtual Synchronous Generator for Microgrid
作者:	曾梓渝;Tseng, Tzu-Yu
贡献者:	電機工程學系
关键词:	虛擬同步發電機;下垂控制;搖擺方程式;虛擬慣量;儲能系統;太陽光發電系統;派翠機率小波模糊類神經網路;分散式能源;virtual synchronous generator;droop control;swing equation;virtual inertia;storage system;photovoltaic;Petri probabilistic wavelet fuzzy neural network;distributed resources
日期:	2021-08-19
上传时间:	2021-12-07 13:09:57 (UTC+8)
出版者:	國立中央大學
摘要:	本論文提出一種虛擬同步發電機的控制策略，利用虛擬慣量估測器來估測虛擬同步發電機的虛擬慣量，並減少在負載變化下分散式發電機的實功率輸出和頻率的振盪。此外，為了提高虛擬慣量估測器的性能與分散式發電機的實功率輸出和頻率的暫態響應，提出了一種線上訓練的派翠機率小波模糊神經網絡（PPWFNN）控制器來取代傳統的比例積分(PI)控制器。本文也介紹了派翠機率小波模糊神經網絡的結構和倒傳遞調整的線上學習算法。另外，也提出了下垂控制與低頻負載卸除的控制策略。微電網由儲能系統、太陽能（PV）系統和負載組成。低頻負載卸除是防止連續頻率下降和防止停電的主要措施。本研究為了克服傳統下垂控制在負載變化下的暫態響應緩慢與抗擾性差等缺點，也應用了線上訓練的派翠機率小波模糊神經網絡控制器來取代傳統的PI控制器。最後，根據模擬和實驗結果，使用所提出的虛擬慣量估測器和派翠機率小波模糊神經網絡控制器，可以實現在負載變化下提升實功率輸出的暫態響應、頻率響應和快速卸載的性能。;In this thesis, a novel virtual inertia estimation methodology is proposed to estimate the suitable virtual inertia of the virtual synchronous generator (VSG) and to reduce the oscillations of the active power output and frequency of the distributed generator (DG) under load variation. Moreover, to improve the performance of the proposed virtual inertia estimator and the transient responses of the active power output and frequency of the DG, an online trained Petri probabilistic wavelet fuzzy neural network (PPWFNN) controller is proposed to replace the conventional proportional-integral (PI) controller. The network structure and the online learning algorithm using backpropagation (BP) of the proposed PPWFNN are represented in detail. Furthermore, a microgrid based on droop control with under-frequency load shedding (UFLS) algorithm is also researched in this study. The microgrid is composed of a storage system, a photovoltaic (PV) system and the load. The UFLS is the principal measure to prevent successive frequency declination and blackouts. In this study, to overcome the drawbacks of the traditional droop control such as slow transient response and poor disturbance rejection under load variation. The online trained PPWFNN controller is also proposed to replace the conventional PI controller in microgrid. Finally, according to the simulation and experimental results, superior performance of the active power output, frequency response and rapid load shedding under load variation can be achieved by using the proposed virtual inertia estimator and the intelligent PPWFNN controller.
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