## 1 Introduction  
The capacity expansion of renewable energy sources is among the sustainable methods that can mitigate global warming issues [1]. Photovoltaic (PV)-based energy generation systems are broadly deployed due to their affordability and the growing concerns with regard to the environment [2]. The PV system, which has a scalability characteristic, can be installed in various areas, such as households, buildings, and utilities. Various approaches have been investigated to effectively accept such PV systems, including studies on energy forecasting [3; 4], optimization [5; 6], and monitoring [7] approaches. In particular, developing PV health monitoring systems is essential for the stable management of PV systems, as PV faults may cause energy losses or fatal accidents. A site in the UK experienced energy losses of 18.9% in a year due to PV system faults [8]. The faults often cause fires and can cause fatal problems in power grids [9; 10]. Various fault detection approaches for PV systems have been studied based on the following approaches: modeling [11; 12; 13; 14; 15], electrical signal analysis [16; 17; 18; 19; 20], and electrical circuit simulation [21; 22; 23; 24]. Each approach has its pros and cons according to user perspectives. In particular, model-based approaches are often employed because their simplicity, compared with electrical signal analysis or circuit analysis approaches. The model-based approaches can be typically divided into end-to-end and threshold methods. First, the end-to-end methods directly decide whether faults occur or not, which requires a huge amount of fault-labeled data to train fault detection models, such as deep neural network (DNN). Recently, to mitigate this issue, semi-supervised learning schemes were studied for PV fault detection and showed comparable performance with those of fully supervised learning methods [25; 26]. Second, the threshold methods estimate the power generation of PV systems, and compare the estimations with actually measured power generation. By analyzing the differences, users detect different abnormalities, such as shading effects [27], snow accumulation [28], maximum power point tracking error [29], and faulty conditions of DC-AC converters [15; 30; 31]. The threshold methods have the advantage of working well even without fault-labeled data. However, the thresholds are typically predetermined [31], or determined by monitoring data profiles [30], which has room for improvement. The accurate detection of faults and the reduction of false alarms are crucial for the effective operation of PV systems. Therefore, properly setting threshold confidence intervals is among the essential processes of the model-based approaches. However, renewable energy sources, such as wind or PV systems, are significantly affected by local weather conditions. Thus, users experience difficulties in manually setting thresholds under various weather conditions. To deal with this problem, in this paper, we propose a deep reinforcement learning (DRL)-based dynamic threshold scheme, in which an AI agent continuously updates thresholds depending on weather changes. We employed a proximal policy optimization (PPO) agent and used various fields of weather data, including irradiance, ambient temperature, humidity, and wind speed. The agent consists of continuous state and action spaces. A reward function was designed to learn proper thresholds without fault labels, which is useful, since fault-labeled data is very limited. To evaluate the proposed scheme, we used the PV data of the National Institute of Standards and Technology (NIST) [32], which is publicly accessible. The proposed model was compared to the existing static and dynamic threshold schemes. To the best of our knowledge, this is the first study in which a DRL agent with continuous action space was used as a label-free fault detection scheme to detect inverter faults in PV systems.