光伏系統故障診斷是提高光伏系統可靠性和安全性的基本任務。由於光伏系统輸出存在高度非線性特性，且容易受到外界環境因素的影響，光伏系統直流側故障難以被傳統保護裝置檢測。光伏系統故障發生不僅會損害光伏模組，致使光伏系統發電量降低，嚴重時甚至會導致安全問題和火災隱患。本論文通過模擬建模分析在標準測試條件下的光伏系統故障電流-電壓曲線，進而選取有效的故障診斷特徵參數。為了排除光照與溫度的影響，本論文提出了一種提出了基於混合粒子群優化和信賴域仿射最優化演算法的非線性最小二乘法進行參數標準化，將光伏系統的輸出特徵參數轉化到標準測試條件下，進而歸一化後得到光伏故障的識別特徵量。再者，採用多類指數損失函數的逐步添加模型，以回歸分類決策樹作為基礎分類器的集成學習分類演算法，作為光伏故障的診斷演算法，並通過定期利用光伏系統正常運行的資料更新參數標準化方程，使故障診斷演算法能適應於光伏系統的自然老化，保持故障分類演算法的長期有效性。本論文所提診斷演算法的特徵量所具備的泛化能力，能適應於不同的光伏模組，即使在故障樣本不足時，亦能採用其它案場的故障樣本建立故障診斷模型。更進一步，本論文針對光伏模組運行於外界環境最容易存在之灰塵因素，分析了積灰下光伏系統電流-電壓曲線的輸出特徵，並針對舊有光伏系統無法滿足監督學習大量標籤資料的要求，提出了一種利用人工蜂群演算法優化半監督極限學習機的機器學習演算法，結合光伏串列輸出電流-電壓曲線的特徵參數標準化方法，所提診斷演算法能利用少量模擬標籤資料與大量光伏案場歷史無標籤資料進行故障診斷，利用低成本資料進而降低了人力和時間成本，此外，對於積灰狀態的監控能預警系統廠商進行有效清洗，增加光伏系統發電收益。本論文數值模擬和實驗測試採用3.51kWp多晶矽太陽能板和3.9kWp單晶太陽能板的光伏串列驗證所提故障診斷演算法的泛化能力、精確度和可靠性。

Fault diagnosis of a photovoltaic (PV) system is an essential task for improving its reliability and safety. PV faults at the dc side are difficult to detect by traditional protective devices, which may reduce power conversion efficiency and even lead to safety matters and fire disaster. This thesis investigates intelligent fault diagnostic methods for a PV system. First, optimal faulty features are extracted by analyzing I-V curves from different faults including hybrid faults of a PV system under the standard test condition (STC). Moreover, the trust-region-reflective (TRR) deterministic algorithm combined with the particle-swarm-optimization (PSO) metaheuristic algorithm is proposed to standardize faulty features into the ones under the STC. In addition, a multi-class adaptive boosting (AdaBoost) algorithm, which is the stage-wise additive modeling using multi-class exponential (SAMME) loss function based on the classification and regression tree (CART) as the weak classifier, is utilized to establish the fault diagnostic model. The effectiveness of the fault diagnostic model could long-term maintain by periodically updating the feature standardization equations to standardize the fault features into the ones under the STC. On the other hand, PV systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. I-V characteristics of PV strings under soiling condition are also analyzed in this thesis. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine (ABC-SSELM) is proposed to handle this problem. Combining with the parameter normalization method, the proposed ABC-SSELM algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Furthermore, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 kWp and 3.9 kWp are used to verify the proposed diagnostic methods. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technologies.

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