###### Abstract
The equivalent series resistance of the filter capacitor can be obtained by measuring the dielectric loss angle of the capacitor, which can be calculated by the windowed Fourier transform, intelligent algorithm transform, or wavelet transform [16]. Infrared temperature sensors can only measure the temperature of the outermost winding of dry-type reactors [17]. Distributed optical fiber sensors can continuously measure the temperature of reactors to obtain the temperature distribution [18]. The inter-turn short-circuit fault status of the reactor can be judged by detecting the internal magnetic field signal of the coil. In the case of a short-circuit fault between turns, the magnetic field signal is no longer symmetrical [19]. If the reactor or capacitor of the PPF is abnormal (e.g., detuned), then the method of harmonic impedance identification can be used to analyze the changes in the harmonic impedance of the PPF. Additionally, a support vector machine algorithm can be applied to determine the faulty element based on the change rate of the harmonics current. Moreover, the detuning of the element can be determined by calculating the ratio of the variation rate of harmonics current [20; 21]. Benefiting from the development of sensors, computer networks, and component technologies, researchers have proposed numerous techniques for monitoring and controlling industrial system operations. In recent years, numerous intelligent systems for diagnostic analysis have been studied and developed. The industrial remote monitoring system monitors the running state of industrial equipment through computer software and hardware. The existing remote monitoring system is not limited to the local area network. Industrial equipment can be connected to the internet to transmit data via the Internet of Things (IoT), enabling remote monitoring of industrial equipment through the internet. Elastic Compute Service has the advantages of high efficiency, reliability, and elastic scalability, and has consequently been widely deployed in remote monitoring systems. For example, [PERSON] et al. [22] proposed a remote robot monitoring system which transmits image data to the Alibaba ECS via the TCP/IP protocol, enabling remote monitoring; the system can be accessed through a web interface. [PERSON] et al. [23] proposed a panoramic monitoring system for start-up and debugging in substations. This system utilizes tunnel communication programs deployed on the ECS to monitor data from multiple substations. Reliability and safety requirements are crucial to the operations of DTPPFs. To prevent potential disasters or economic losses, efforts have been devoted to supervising the running state of DTPPFs. At present, DTPPF performance is mainly measured and diagnosed by a combination of visual inspection and manual maintenance. However, this approach suffers from several shortcomings, such as high costs and imprecision. To address this issue, it is more desirable to explore an automated approach to monitor the running state of the DTPPF. DTPPFs are incapable of maintaining the system on their own without technical support from the manufacturer. Consequently, a DTPPF manufacturer must provide technical support throughout the product lifecycle for routine operation. This study aims to meet DTPPF manufacturers' needs and to improve the service quality of DTPPFs. In general, most monitoring and fault diagnosis approaches are domain-specific and provide decision support to particular end users. Therefore, the study of monitoring and fault diagnosis is usually highly related to a specific application. In this paper, a more general fault identification method for the core devices of DTPPFs is proposed. A cloud server-assisted remote monitoring and fault identification system for DTPPFs is developed. This system enables fault alarms, fault recall, fault diagnosis, and real-time viewing of various states of the DTPPF via the internet. The main contributions of this paper are as follows:  
1.