## 1 Introduction
The study [13] investigated the performance analysis of a 954 MWp PV array in Mauritania; the monthly performance ratio varied from 61% in August to 71% in November because of the high temperature difference between different seasons. Furthermore, in Djibouti, the average performance ratio of a 302.4 kWp PV system operating under a dusty desert maritime climate [14] is 84%. For every 1 \({}^{\circ}\)C increases in ambient temperature, the performance ratio decreases by 0.7%. Beyond this, the above-mentioned standard has also stimulated the development of technologies that enable the performance of photovoltaic system to be estimated or monitored. [PERSON] et al. [15] implemented a wireless Raspberry Pi for a PV module monitoring system based on the IEC 61724 standard. [PERSON] et al. [16] developed the methodology, including performance analyses of few PV systems and the degradation rate studies using NREL/RdTools. Although the standard IEC61724 is widely used in different types of photovoltaic systems, it has proved to be well evaluated. Rather than describing the performance of individual components, it concentrates on assessing the performance of the array as part of the system, and providing a summary of performance suitable for comparing PV installations of various sizes operating in different climatic conditions [17]. Furthermore, some studies in module level monitoring were applied in performance evaluation and fault diagnosis in the PV system. In order to localize the faulty PV module, a wireless sensor network connecting to each module is presented to compare the measured and predicted maximum power of PV string through an innovative maximum power point algorithm [18]. [PERSON] et al. [19] employed an intelligent multi-sensor architecture to detect the causes of efficiency losses for the monitoring of photovoltaic systems at the panel level, which is equipped with voltage, current, irradiance, temperature and inertial sensors. Although it may be effective to detect which module is underperforming by comparing estimated and measured parameters, these methods have the disadvantages of the inability to detect and locate faults in a timely manner, as unautomated methods for fault diagnosis without human intervention. They also need a large number of sensors which can entail high costs. [PERSON] et al. [20] presented an online fault diagnosis technique with optimized voltage sensor locations, which used the low voltage and high voltage sections to locate the faulty module position. This method still requires a number of voltage sensors to be installed in the PV strings, which makes it particularly difficult for large-scale PV array. The new contribution of this study is to propose an approach to automatically evaluate the real-time status of each module and locate the anomalies at module level in a smart PV system. The performance evaluation is based on the performance indicators in current and voltage that are defined and calculated under field conditions. Section 2 briefly introduces the test site including the used commercial PV modules, meteorological sensors, electrical sensors and monitoring system to form an advanced PV system. Section 3 demonstrates the key parts for performance evaluation of the proposed module-level performance evaluation approach, which comprises input data filtering and data matrix, defining performance indicators based on I-V characteristics, a thermal model to evaluate module temperature and the process of performance evaluation. Section 4 provides the outdoor experiments to verify the effectiveness of proposed approach and discussion. Section 5 includes the conclusions and future work.