GPVS-Faults: Experimental Data for fault scenarios in grid-connected PV systems under MPPT and IPPT modes

Overview The Grid-connected PV System Faults (GPVS-Faults) data are collected from lab experiments of faults in a PV microgrid system. There are 16 data files in ‘.mat’ and also ‘.csv’, each for one experiment scenario, including photovoltaic array faults; inverter faults; grid anomalies; feedback sensor fault; and MPPT controller faults of various severity. GPVS-Faults data can be used to design/ validate/ compare various algorithms of fault detection/ diagnosis/ classification for PV system protection and reactive maintenance. Description The faults were introduced manually halfway during the experiments. The high-frequency measurements are noisy; with disturbances and variations of temperature and insolation during and between the experiments; MPPT/IPPT modes have adverse effects on the detection of low-magnitude faults. After critical faults, the operation is interrupted and the system may shut-down; the challenge is to detect the faults before a total failure. We refer interested researchers to read and cite the following references for detailed descriptions of: - GPVS-Faults scenarios, experiments, and data collection procedures are described in [1] with results for comparisons. - System description, energy management system, control/ communication of the PV system are detailed in [2]. -The description of the static multiblock version of the fault detection algorithm was presented in [3]. References [1]. A. Bakdi, W. Bounoua, A. Guichi, S. Mekhilef, (2020). Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL Divergence. International Journal of Electrical Power & Energy Systems. [2]. A. Guichi, A. Talha, E. M. Berkouk, S. Mekhilef, S. Gassab, (2018). A new method for intermediate power point tracking for PV generator under partially shaded conditions in hybrid system. Sol. Energy. 170, 974-987. https://doi.org/10.1016/j.solener.2018.06.027. [3]. A. Bakdi, W. Bounoua, S. Mekhilef, L. M. Halabi, (2019). Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV. Energy, 116366. https://doi.org/10.1016/j.energy.2019.116366. Structure GPVS-Faults data files are labelled as “Fxy”, where: x∈{0,1,…,7} represents the fault scenario: '0' is a fault-free experiment. '1',…,'7' are the 7 types of faults. y∈{'L','M'} is the operation mode: 'L' is Limited power mode (IPPT) 'M' is Maximum power mode (MPPT) e.g. “F4M” is fault F4 in MPPT mode, “F1L” is fault F1 in IPPT mode. Each data file includes the following columns: Time: Time in seconds, average sampling T_s=9.9989 μs. Ipv: PV array current measurement. Vpv: PV array voltage measurement. Vdc: DC voltage measurement. ia, ib, ic: 3-Phase current measurements. va, vb, vc: 3-Phase voltage measurements. Iabc: Current magnitude. If: Current frequency. Vabc: Voltage magnitude. Vf: Voltage frequency.

# 概述
并网光伏系统故障数据集（Grid-connected PV System Faults, GPVS-Faults）采集自某光伏微电网系统的故障实验室实验。数据集包含16个.mat与.csv格式的数据文件，每个文件对应一种实验场景，涵盖光伏阵列故障、逆变器故障、电网异常、反馈传感器故障以及不同严重程度的最大功率点跟踪（Maximum Power Point Tracking, MPPT）控制器故障。GPVS-Faults数据集可用于设计、验证、对比各类用于光伏系统保护与事后维护的故障检测、诊断及分类算法。

# 数据集说明
实验过程中手动于中途引入故障。高频测量数据带有噪声，且实验过程中及实验间隙存在温度与日照强度的扰动与变化；MPPT/中间功率点跟踪（Intermediate Power Point Tracking, IPPT）模式会对低幅值故障的检测产生不利影响。当发生严重故障后，系统运行会被中断甚至停机，因此研究难点在于在系统完全失效前检测出故障。我们建议感兴趣的研究者参阅以下文献以获取详细说明：
- GPVS-Faults的场景设置、实验流程与数据采集方法详见文献[1]，并附带对比实验结果。
- 光伏系统的系统架构、能量管理系统、控制与通信细节详见文献[2]。
- 静态多块故障检测算法的相关说明详见文献[3]。

# 参考文献
[1] A. Bakdi, W. Bounoua, A. Guichi, S. Mekhilef. (2020). Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL Divergence. *International Journal of Electrical Power & Energy Systems*.
[2] A. Guichi, A. Talha, E. M. Berkouk, S. Mekhilef, S. Gassab. (2018). A new method for intermediate power point tracking for PV generator under partially shaded conditions in hybrid system. *Sol. Energy*, 170, 974-987. https://doi.org/10.1016/j.solener.2018.06.027.
[3] A. Bakdi, W. Bounoua, S. Mekhilef, L. M. Halabi. (2019). Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV. *Energy*, 116366. https://doi.org/10.1016/j.energy.2019.116366.

# 数据结构
GPVS-Faults数据文件以"Fxy"格式命名，其中：
1. x∈{0,1,…,7}代表故障场景：x=0为无故障实验；x=1至x=7共7种故障类型。
2. y∈{'L','M'}代表运行模式：'L'为限功率模式（中间功率点跟踪，IPPT）；'M'为最大功率点跟踪模式（MPPT）。
例如，"F4M"代表MPPT模式下的F4故障，"F1L"代表IPPT模式下的F1故障。

每个数据文件包含以下字段：
- Time：时间，单位为秒，平均采样周期T_s=9.9989 μs。
- Ipv：光伏阵列电流测量值。
- Vpv：光伏阵列电压测量值。
- Vdc：直流母线电压测量值。
- ia、ib、ic：三相电流测量值。
- va、vb、vc：三相电压测量值。
- Iabc：三相电流幅值。
- If：电流频率。
- Vabc：三相电压幅值。
- Vf：电压频率。

搜集汇总

背景与挑战

背景概述

该数据集是用于并网光伏系统故障研究的实验数据，包含16个文件，覆盖光伏阵列、逆变器、电网异常等7类故障场景，并在MPPT和IPPT两种操作模式下收集。数据可用于开发和验证故障检测、诊断及分类算法，支持光伏系统保护和预防性维护，但数据具有高频噪声，并受温度、光照变化影响，增加了低幅度故障检测的挑战。