During each PD sampling process, the RF antenna contactlessly senses the PD signals in the air and conveys them to the RSA 306B, which sets up an intermediate frequency (IF) carrier signal to shift the ultra-high frequency PD signals to a lower spectrum (i.e., IF modulation). The modulated signal will then be sampled, quantified and digitized by high-speed analog-to-digital converter (ADC), and the data stream will be transmitted to the computer via USB-3 connection (Figure 4). Here, we call the data within one sample duration (i.e., 10 milliseconds in this study) a data or sample frame. The computer reads the data frame via Tektronix application programing interface (API) [35], and calculates the data in both time domain in-phase/quadrature (I/Q) stream form (i.e., quadrature modulation) (Figure 5) [36] and spectrum form (Figure 6) using Fourier Transform, Equation (1). The power is calculated as Equation (2).

Figure 4. Signal capturing, modulation and sampling in RSA 306B.

Figure 5. I/Q modulation for data in time domain [36].

Figure 6. RSA API and data flow in computer [36].

If the system detects any signal power exceeding a pre-set threshold (i.e., −50 dB in this study) during each sample frame, the I/Q data and spectrum information of that frame will be recorded in .r3f and siq format (Figure 7), which can be further decoded to the time sequence .csv files. The fundamental functions of data acquisition are fulfilled by C/C++ based on RSA 306B API, and a python interface is implemented on top of the C/C++ code and RSA API for the simplicity of user interaction and process control.

Figure 7. r3f and .siq file for data storage [36].

As the RSA has the sampling rate of 112 MHz and its signal sampling bandwidth of 40 MHz, for each complete PD scanning process, the system will iteratively set RSA’s IF modulation carrier frequency from 100 MHz to 2500 MHz (i.e., one iteration), with an incremental step of 40 MHz, to cover the PD signal frequency of interest (Figure 8). In the end of each iteration (i.e., 61 samples in 60 iterations of 600 milli-seconds), the system will stitch the power-spectrum diagram of each sample frame for a complete diagram, and remove I/Q data that does not include any PD signals. If any PD signals are captured, the corresponding I/Q data and power-spectrum diagrams will be kept and named according to the timestamp and detected PD frequency. In this way, users can easily observe and trace back the records of PD occurrences, and the system storage can be more efficiently utilized.

With the expansion of widely spread high-speed rail network, internet of things (IoT) technology should be added to local PD detection and monitoring system at each switchgear/transformer station for the remote accessibility and potentially instant responsiveness to any alert messages it may generate. To achieve this goal, the google cloud service (e.g., Google Drive, Firebase, etc.) is employed and a

Figure 8. (a) Iterative sampling and spectrum analysis; (b) Iterative scanning to cover the entire PD spectrum.

smartphone application (APP) is developed. The local PD DAS will exploit Rclone [37] to set up a new cloud storage node, and continuously synchronize the newly detected PD I/Q data and full power-spectrum diagram to the cloud storage at the end of each PD scanning iteration. On the other hand, when the user activates the APP, it will periodically request data synchronization with the cloud server via Google drive API, so that the user can see the updated PD monitoring on their fingertips. In addition, whenever the cloud receives new files, it sends out a notification email to alert the user for newly detected PD events.

In this section, the system evaluation results are presented. The system has been tested under the lab environment (Figure 9), by applying a configurable arbitrary waveform generator (AWG) with a broad bandwidth of 2GHz [38]. The AWG output is connected with a 50 Ω resistive load, which is parallel connected with a 2 G bandwidth oscilloscope [39] with spectrum analyzer to measure the actual signal waveform, amplitude and frequency that is applied onto the resistor. When the AWG signal passes through the resistor, the circuit generates RF signals as a simulation of PD signals within 100 - 2500 MHz range. Numerical test scenarios have been proceeded, and here we illustrate two representative cases, single-signal and dual-signal detection, to validate the functionalities.

Figure 9. Lab test setup for PD DAS (including antenna, RSA 306B, a portable computer/embedded system), an AWG, a 50-ohm resistor (i.e., PD stimulator), and an oscilloscope.

We connect the 50 Ω resistor with AWG via BNC-alligator cable, and placed the PD DAS with its antenna about one foot away from the resistor. Once the PD DAS is turned on and initiated with the program to scan RF/PD signals in the air, we set up the AWG to generate a 315 MHz sine waveforms of 1V peak-to-peak. Then, the DAS captures the RF signals that exceed the preset threshold (Figure 10), and generates one spreadsheet (i.e., .csv file) of signal value in time-domain (i.e., I/Q ADC values), and a power-spectrum diagram in frequency domain (Figure 11(a) and Figure 11(b)). In the end of that particular scanning iteration, a complete power-spectrum diagram is drawn from 100 MHz to 2500 MHz (Figure 11(c)). Another test results of 768 MHz RF/PD signal are demonstrated in Figure 11(d)-(f). One can see that although there exist some relatively low frequency noises, most less than 1 MHz and −60 dB, RF/PD signals of interest can be clearly captured without much interference. In addition, the IF modulation

Figure 10. Capturing signals exceeding the power threshold.

Figure 11. (a) Capturing 315 MHz RF/PD signals with ADC values in time domain; (b) Impulse at 315 MHz in frequency domain for one frame; (c) Impulse at 315 MHz in complete scanning iteration; (d) Capturing 768 MHz RF/PD signals with ADC values in time domain; (e) Impulse at 768 MHz in frequency domain for one frame; (f) Impulse at 768 MHz in complete scanning iteration.

can be observed in Figure 11(a) and especially in Figure 11(d), because of the higher IF carrier frequency.

We also have run the dual RF/PD detection test to confirm that if multiple PD events occur approximately at the same time (i.e., in one scanning iteration), the PD DAS can capture both of them. We first create a Bluetooth signal (i.e., about 2400 MHz) by turning on two Bluetooth devices and transmitting large files from one device to the other. Meanwhile, we create another 768 MHz signal with AWG as aforementioned, and turn on the PD DAS. It is worth noting that we have to adjust the RF/PD detection threshold to −70 dB so as to catch the low-power Bluetooth signal. As depicted in Figure 12, one can see that the DAS can clearly capture both 768 MHz and the Bluetooth signals.

The confirmation of capturing multiple RF signals of various frequency demonstrates the capability and adaptivity of detecting a variety of PD signals simultaneously. Yet, the power threshold for signal detection needs to be carefully selected as it balances the detection rate and false alarms. Meanwhile, various signal filter techniques (e.g., band-pass and band-stop filters, wavelet filtering, etc.) can be applied to distinguish the hazard PD signals from the routine RF traffic with known spectrums.

Figure 12. (a) No test signal generated (only noises); (b) Only with a 768 MHz generated by AWG; (c) Only with a Bluetooth signal at about 2400 MHz; (d) With both 768 MHz and Bluetooth signals in the air.

The cloud service is established on the DAS via Google drive and Rclone, so that any files created and edited within this particular Google drive folder will be automatically synchronized in all terminals that use the same Google drive account. Once the user logs in with proper credentials (Figure 13(a)) on a remote device (e.g., a smartphone), one can see the updated I/Q data and spectrum diagrams (Figure 13(b)), and corresponding file attribute details (Figure 13(c)). In addition to the real-time synchronization from the local PD DAS to the cloud and granting remote accessibility, the cloud also sends a notification email to the user whenever any new PD signals are detected (Figure 13(d)). In that case, personnel who is in charge of monitoring the switchgears and transformers in high-speed

Figure 13. (a) APP login; (b) APP access to files; (c) File attribute details; (d) Email notifications of detected PD events.

rail can be alerted at early stage to minimize the potential damage and maintenance cost PD may cause.