Abstract This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual estimations from Micro Phasor Measurement Unit (Micro-PMU). The high-dimensional high-frequency multivariate characteristics are nonlinear time-varying where computational efficiency becomes crucial to realize online adaptive FD. The adaptive assumption-free method is developed through Principal Component Analysis (PCA) for dimension reduction and feature extraction with reduced complexity. Novel fault indicators D x ( t ) and discrimination index AD ( t ) are developed using Kullback–Leibler Divergence (KLD) for an accurate evaluation of Transformed Components (TCs) through recursive Smooth Kernel Density Estimation (KDE). The algorithm is developed through extensive data with 2.2 × 10 6 measurements from a GPV system under Maximum PPT (MPPT) and Intermediate PPT (IPPT) switching modes. The validation scenarios include seven faults: open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements.

中文翻译：

---

通过基于在线 PCA-KDE 的多变量 KL 散度，使用 PMU 和高频多传感器数据在 MPPT 下实时检测光伏系统故障

摘要 本文考虑在大变化期间在功率点跟踪 (PPT) 模式下并网光伏 (GPV) 系统中基于数据的实时自适应故障检测 (FD)。PPT 模式下的故障长时间未被检测到，给系统带来了新的保护挑战和威胁。通过实时多传感器测量和来自微相量测量单元 (Micro-PMU) 的虚拟估计，开发了一种智能 FD 算法。高维高频多元特性是非线性时变的，计算效率对于实现在线自适应 FD 变得至关重要。自适应无假设方法是通过主成分分析 (PCA) 开发的，用于降维和特征提取并降低复杂性。新的故障指标 D x ( t ) 和鉴别指数 AD ( t ) 是使用 Kullback-Leibler 散度 (KLD) 开发的，用于通过递归平滑核密度估计 (KDE) 准确评估转换组件 (TC)。该算法是根据来自 GPV 系统在最大 PPT (MPPT) 和中间 PPT (IPPT) 切换模式下的 2.2 × 10 6 测量的大量数据开发的。验证场景包括七种故障：开路、电压骤降、部分遮蔽、逆变器、电流反馈传感器和升压转换器故障中的 MPPT/IPPT 控制器。自适应算法被证明在计算上是有效的，并且对于在大温度和辐照度变化以及噪声测量下成功的 FD 非常准确。