在全球能源危機與環境污染的背景下，分散式發電(Distributed Generation)因其對環境友好的特性，成為解決環境問題的選項之一。另一方面，微型電網(Micro Grid)作為利用可再生能源的一種切實可行方案，可以連接分散式發電與配電網，並進一步降低分散式發電對電網的影響。由於分散式發電單元多是以電力電子轉換器為介面，因此微型電網智慧逆變器(Smart Inverter)的優化表現尤為重要。
本文首先針對LCL型併網逆變器在弱電網下的穩定性問題，提出離散型的反步滑動模式控制(Discrete-Time Backstepping Sliding-Mode Control）方法。首先，本文對離散時間下的三階動態系統模型進行推導，進而設計離散型反步控制級聯滑動模式控制器，並進一步對其進行李雅普諾夫穩定(Lyapunov Stability)證明。同時，利用時變映射關係對系統的狀態方程進行轉換，克服離散型反步控制設計中的非因果問題。此外，利用對高階LCL型逆變器的遞迴子系統進行設計，可以通過逐步虛擬控制(Virtual Control)的設計來保證系統的漸近穩定性，因此系統不需要額外的有源阻尼(Active Damping)控制算法設計。並且提出的控制演算法可以結合反步控制與滑動模式控制的各自優點，因此LCL型併網逆變器系統在有電網阻抗以及電網阻抗改變的情況下，仍能維持系統穩定以及實現較好的控制效果。
再者，為了解決微型電網中的低慣性問題以及智慧逆變器的實虛功率耦合問題，本文更提出一種基於虛擬同步電機(Virtual Synchronous Generator)的線上訓練自我調整模糊類神經網路(Fuzzy Neural Network)功率解耦演算法。首先，本文針對微型電網之虛擬同步電機的功率耦合行為進行分析，並對提出演算法的系統動態模型進行推導。另外，為了實現功率解耦控制強健性以及動態特性快的特點，本文設計全域滑動模式控制器。同時，進一步通過自我調整模糊神經網路控制器來繼承全域滑動模式控制律，並解決全域滑動模式控制依賴具體系統資訊的缺點。藉由投影演算法(Project Algorithm)與李雅普諾夫穩定性定理，提出神經網路參數的自我調整調節律，以保證神經網路的收斂以及實現系統的完全功率解耦。本文所提出各式演算法的有效性和優越性將通過數值模擬和實驗驗證。

In the background of the global environmental pollution and energy crisis, distributed generation (DG) is an attractive option for solving environmental issues because of its environmental friendly feature. Moreover, a microgrid (MG) is a practical solution to utilize renewable and sustainable energy sources, and as a connection to the distribution network for reducing the impact of DGs to the power grid. In addition, most of DGs are employed through the power electronic interface, and thus the performance of smart inverters in the MG is critical.
A discrete-time backstepping sliding-mode control (DTBSMC) method for an LCL-type grid-connected inverter is further designed in this dissertation. Firstly, the dynamic model of a discrete-time three-order system is derived, and a discrete-time backstepping control method cascading with the sliding-mode control theory is designed via Lyapunov stability verification. Moreover, the system state equation is transformed into a special form by using a time-varying mapping for overcoming the difficulty of a non-causal problem. Besides, through the recursively subsystem design for the high-order LCL-type inverter, the asymptotic system stability can be ensured by step-by-step virtual control designs without the requirement of additional active damping (AD) method design. In addition, the proposed method can combine both the advantages of the backstepping control method and sliding-mode control theory. Therefore, the LCL-type grid-connected inverter system can maintain the system stability and have strong robustness under the condition of a power grid with varied grid impedances.
In order to address the low inertia problem and the power coupling between the active power and the reactive power, this dissertation further proposes an online-trained adaptive fuzzy-neural-network power decoupling (AFNNPD) strategy for a virtual synchronous generator (VSG) control in the MG. Firstly, the mechanism of the power coupling for the VSG control in a MG is analyzed, and the system dynamic model for the proposed power decoupling method is derived. Then, a total sliding-mode controller is designed for the power decoupling with the characteristics of the strong robustness and fast dynamic response. Moreover, an adaptive fuzzy-neural-network (AFNN) control is designed to mimic the TSMC law for relaxing the requirement of the detail system information in the TSMC. In addition, adaptive tuning laws for network parameters are derived according to the projection algorithm and the Lyapunov stability theorem for guaranteeing the network convergence as well as the totally power decoupling performance. Numerical simulations and experimental results are provided to verify the effectiveness and the superiority of all the proposed methods in this dissertation.

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