# Long-Lifetime Event-Driven Wireless Monitoring System for Pole-Mounted Transformers
## 1 Introduction  
Power transformers are one of the most vital components in power systems for substation operations and safety of electricity [1]. Figure 1 shows several physical quantities caused by various electrical faults in power transformers carrying high AC voltages and large currents [2, 3, 4, 5]. A sudden failure can be very costly, not only for replacement, but also in terms of power outage-related problems for consumers [6]. As smart grids develop rapidly, enhancement of their reliability increases in demand. Thus, it is necessary to monitor and diagnose the state of transformers continuously, to timeously detect incipient transformer problems, and to plan maintenance and repair or replacement [7].  
Conventional monitoring systems for large-scale power transformers have been developed using wired monitoring components. Wired sensors are widely adopted for high-speed continuous data acquisition (DAQ) of raw data from physical quantities of transformers such as bushing failures [8, 9], winding deformation [10, 11], partial discharge [12], and valve malfunctions [13]. For pole-mounted transformers, however, a high-speed DAQ system can be a bulky and very expensive solution [14]. Remote methods using infrared thermographs or ultrasonic fault detection have been introduced for monitoring pole-mounted transformers with limited accessibility or safety hazards [15, 16]. Even though the equipment for remote monitoring can be expensive, multiple transformers at different locations can be monitored using a single remote monitoring unit. However, skilled specialists need to be present on-site to analyze the performance, which eventually leads to higher operation and maintenance (O&M) costs [17]. Recently, battery-powered wireless real-time monitoring systems have been widely developed for monitoring pole-mounted power transformers at a lower cost and with a smaller size [18, 19, 20, 21, 22]. As the sampling rate required to acquire the physical quantities as raw data can be too high for low-costdevices, the raw signal from a sensor needs to be converted into a root mean square (RMS) value that quantifies the physical or electrical parameters of the input signal [14; 23]. An RMS criterion is directly related to the load applied to the sensor and provides a suitable measurement quantity for any monitoring application [14]. In [23], an RMS value from a piezoelectric sensor quantified the amount of partial discharge (PD) in power transformers. However, continuous sampling and data transmission of the converted RMS value using conventional monitoring systems severely shorten battery lifetime, owing to the large energy requirement.  
To overcome this problem, this paper proposes a long-lifetime wireless monitoring system that provides low-cost real-time solutions. The proposed event controller (EC) monitors the abnormality of the input and allows for event-driven sampling when an abnormality is present in a transformer. The maximum RMS value for a given abnormality is obtained using an RMS converter followed by a peak detector. The proposed EC enables or disables each block and determines when to sample and transmit data. Without an abnormal signal, most of the components fall into sleep mode, waiting for the next abnormality. As a result, the battery lifetime of the proposed monitoring system can be two orders of magnitude higher than that of a conventional monitoring system without an EC.