1. Introduction
SF
6 gas possesses many excellent characteristics, such as high dielectric strength, strong chemical inertness, and low toxicity, which significantly improve the reliability and stability of equipment and reduce the labor intensity of the routine inspection and operation maintenance of equipment, and thus, SF
6 has been widely used in GIS [
1,
2].
Few decomposition products are generated during the normal operation of GIS. In the case of a discharge or overheating fault in GIS, SF
6 will decompose and produce a series of low-fluorine sulfides (S
xF
6−x, 1 ≤ x ≤ 5). These low-fluorine sulfides will repolymerize into SF
6 after the faults disappear. However, when trace amounts of water or oxygen are present in SF
6, these low-fluorine sulfides will react with the impurities to produce more compounds, such as HF, SO
2, H
2S, CO, CF
4, CS
2, SO
2F
2, SOF
2, etc. [
3].
Van Brunt [
4] from the National Bureau of Standards of the United States proposed a three-region model to explain the SF
6 decomposition mechanism during discharge, which is widely recognized. In his theory, the SF
6 discharge region can be divided into three zones, as shown in
Figure 1: the glow zone, ion migration zone, and main gas chamber zone. In the presence of water and oxygen, SF
6, water, and oxygen are dissociated under electron collision in the glow region to form low-fluorine sulfide, O, OH, and F particles. These particles will react in the glow region, and the reaction processes are shown in Formulas (1)–(8) [
3].
In the ion migration zone, some ions react with the compounds produced in the glow zone, and the reaction in this zone has little influence on the final product. The main reaction process is shown in Formulas (9)–(11).
When the stable products produced in the glow zone are diffused to the main gas chamber zone, they will react with the water and oxygen in the main gas chamber region to generate more stable products. The main reaction process is shown in Formulas (12)–(15).
According to the discharge mechanism of SF
6, HF is one of the main decomposition products. HF is very active. It can strongly corrode metal materials, which brings great security risks to the safe operation of GIS. IEC60480-2020 clearly states that SF
6 will produce HF under discharge and high-temperature faults, and it stipulates that the maximum allowable concentration of HF in SF
6 gas insulation is 25 μL/L [
5].
The discharge and overheat faults in GIS will produce HF, so HF is a very important fault characteristic gas in GIS [
6]. The chemical properties of HF are very active, which leads to HF’s existence for a short time. Therefore, it is difficult to detect HF in the offline detection of gas in GIS.
The traditional HF gas detection methods mainly include infrared absorption spectroscopy, detection tubes, and carbon nanotube gas sensors. Infrared spectroscopy has wide spectral lines, dense absorption peaks, and poor anti-interference ability, and it is easily affected by environmental gases and makes it difficult to detect trace gases. The detection tube method can realize the detection of HF gas by using the principle of chemical color reaction, but it is easily affected by the environment and there is gas interference. The accuracy of the carbon nanotube method is low, and it makes it difficult to realize online detection. Fourier transform infrared absorption spectroscopy can also be used to detect HF gas, but its volume is large, the sensitivity is low, and it is not suitable for online monitoring [
7,
8]. Tunable Diode Laser Absorption Spectroscopy (TDLAS) has also been used to detect HF [
8,
9,
10]. In recent years, the detection of HF gas by photoacoustic spectroscopy has been rare. Teemu Tomberg et al. adopted the cantilever beam-enhanced photoacoustic spectroscopy technique based on an optical parametric oscillator to detect the strong absorption of spectral lines of HF at 2475.8836 nm and found that the
LOD of HF was below nL/L [
11]. All of these studies focused on HF standard gases. At present, there is no research on the online detection of HF gas generated during simulated GIS discharge.
Gas photoacoustic spectroscopy is a non-background gas detection technology with high accuracy, good long-term stability, and high sensitivity. Resonance photoacoustic spectroscopy can detect flowing gas [
12,
13,
14,
15,
16]. Therefore, resonance photoacoustic spectroscopy was used in this research to monitor the HF generated during a simulated discharge fault. The research results are of great significance for the safe operation of GIS.
2. Principle of Resonant Photoacoustic Spectroscopy Detection Technology
Gas photoacoustic spectroscopy is a kind of detection technology based on the photoacoustic effect, which is caused by the periodic non-radiative relaxation (thermal effect) caused by the absorption of changing light energy by gas molecules. The sound pressure generated by gas molecules in photoacoustic cells can be expressed as the wave formula [
17,
18,
19,
20].
In Formula (16), p represents the sound pressure of the gas, and c represents the sound velocity in the gas; γ = CP/CV, which represents the specific heat ratio of the gas; H represents the thermal power density generated by the gas absorption-modulated light energy. If the incident light intensity is I, H = αI, α is the absorption coefficient of the gas molecule.
In a cylindrical coordinate system, considering gas heat conduction loss and viscosity loss, the amplitude
Aj(
ω) in normal mode
j can be expressed as follows:
In Formula (17),
ωj represents the resonant angular frequency under normal mode
j;
VC represents the cavity volume;
Qj represents the quality factor under normal mode
j;
represents the conjugate complex number of sound pressure in simple positive mode
j. In actual use,
ω =
ωj is usually used to ensure that the photoacoustic cell works in a simple positive mode, in which case, the sound pressure of the photoacoustic cell can be expressed as follows:
In Formula (18), P0 represents the power of the light source; , and C represents the concentration of the gas; is called the photoacoustic cell constant, expressed as Ccell, which is a parameter related only to the structure, material, and size of the photoacoustic cell.
The sensitivity of the microphone is denoted as
Ms, and then, the photoacoustic signal
Spas can be denoted as follows:
According to Formula (19), when Ms, Ccell, α, and P0 are constant, the photoacoustic signal is proportional to the concentration of the gas to be measured, which is the theoretical basis of photoacoustic detection.
4. Experiment and Analysis
4.1. The Modulating Sinusoidal Wave Frequency of the DFB Laser
When the frequency of a modulating sinusoidal wave is half of the photoacoustic cell resonance frequency, the photoacoustic second-harmonic signal will be the strongest. Therefore, before the formal experiment, it was necessary to determine the resonance frequency of the photoacoustic cell.
To find the resonance frequency of the photoacoustic cell, the resonant frequency of the photoacoustic cell was tested using the system shown in
Figure 2. In the test, H
2S/SF
6 mixed gas was used as the sample gas. The DFB laser (PL-DFB-1578-AA81-SA, LD-PD INC) had a central wavelength of 1578.12 nm. In order to enhance the photoacoustic signal, the output optical power of the DFB laser was amplified by using an Erbium-Doped Fiber Amplifier (EDFA). The output optical power of the EDFA was 1W. The peak-to-peak value of the modulating sinusoidal wave was 63.4 mV. In order to eliminate the influence of temperature change on the photoacoustic signal, the temperature control system was used to control the temperature of the photoacoustic cell at 30 °C.
The photoacoustic cell was filled with 100 μL/L H
2S/SF
6 standard gas. The gas pressure in the photoacoustic cell was 1 atmosphere. The amplitude of the modulating sinusoidal wave was 31.7 mV. The output power of the EDFA was set to 1 W. The frequency of the modulating sinusoidal wave was adjusted from 300 Hz to 345 Hz, and the value of the photoacoustic second-harmonic signal was recorded. The photoacoustic second-harmonic signal was fitted using a Lorentz curve. The fitting result is shown in
Figure 9. As can be seen from
Figure 9, when the frequency of the modulating sinusoidal wave was 321.8 Hz, the signal was the strongest. Therefore, the first-order longitudinal resonance frequency of the photoacoustic cell was 643.6 Hz.
4.2. Calibration of HF Concentration
To calibrate the detection sensitivity of the system, 525 μL/L HF/SF
6 standard gas was used. Before the test, the photoacoustic cell was purged 5 times with the standard gas to passivate the aluminum alloy. The standard gas was quickly tested after intake, and the test time was 40 s. The test was repeated 12 times. The test results are shown in
Figure 10.
Figure 10 shows the mean and standard deviation of each test.
As can be seen from
Figure 10, the photoacoustic signal gradually increased and became stable with the increase in the number of test times. This was due to the passivation of aluminum alloy to reduce the loss of HF. After 12 tests, the photoacoustic signal no longer increased. The result of the 12th test was used to calculate the detection sensitivity of the system to HF gas.
Figure 11 shows the photoacoustic second-harmonic wave of the 12th test. The mean value of the photoacoustic signal in the 12th test was 233.750 μV. By calculation, the detection sensitivity of the test system was 0.445 μV/(μL/L).
The
LOD is a very important parameter used to measure the ability of detection systems. It represents the lowest concentration of gas that can be detected by the system. The
LOD can be expressed by Formula (20).
In Formula (20), K represents the confidence coefficient, σs represents the standard deviation of the signal, and S represents the sensitivity of the detection system. According to the detection result, the standard deviation of the signal was 0.272 μV. When K is 1, LOD is 0.611 μL/L according to Formula (20).
4.3. Online Detection of HF under Simulated GIS Corona Discharge Fault
The gas tightness of the detection system was tested. Vacuum and 0.1 atm (gauge pressure) positive pressure gas tightness tests were carried out on the gas path system. Under both test conditions, there was no change in gas pressure after 12 h. This showed that the gas system was well sealed. Before each experiment, the discharge room and electrodes were scrubbed with anhydrous ethanol. Then, the gas system was connected and the gas system was cleaned five times using the vacuum pump and SF6 standard gas. Finally, the SF6 standard gas was inflated into the discharge electrical room to make the gauge pressure reach 0 atm.
Under the action of the circulating pump, the gas in the discharge room circulated in the gas path. The photoacoustic second-harmonic detection technology and wavelength modulation technology were used to test the HF concentration in the gas path. The peak-to-peak value of the scanning sawtooth wave of the DFB laser was 50 mV, and the frequency was 0.1 Hz. The peak-to-peak value of the modulating sinusoidal wave of the DFB laser was 50 mV and the frequency was 321.8 Hz.
The actual GIS corona discharge fault was simulated by the needle–plate electrode discharge. The photoacoustic spectroscopy test system shown in
Figure 2 was used for the online detection of HF generated during discharge. The plate electrode was copper, and the needle electrode material was aluminum alloy. The discharge voltage was 20 kV. The discharge lasted for 372 min. During the discharge, HF was also reduced due to chemical reactions with materials in the gas path. The concentration of HF in the gas path over time during the discharge is shown in
Figure 12. As can be seen from
Figure 12, from 0 to 150 min, the concentration of HF rose faster. This is because the HF produced by the discharge was much more than that consumed. From 150 min to 264 min, the concentration of HF was saturated. This is because the HF produced by the discharge was approximately equal to that consumed. From 264 min to 372 min, the concentration of HF showed a downward trend. This may have been due to the weakening of the discharge energy caused by the ablation of the aluminum alloy needle electrode in the discharge, resulting in less HF being produced by the discharge than consumed.
When the voltage was reduced to 0, the discharge stopped. The concentration of HF over time is shown in
Figure 13. As can be seen from
Figure 13, the concentration of HF decreased approximately linearly. This was due to the reaction of HF with materials in the gas path, resulting in a reduction in its concentration. Therefore, offline detection cannot reflect the HF concentration when a GIS fault occurs. In order to accurately measure HF concentrations in GIS, online monitoring technology is necessary.
5. Conclusions
In this research, the concentration of trace HF gas produced by GIS corona discharge was detected. An online detection platform was built, which could realize the real-time online detection of HF gas concentrations. Compared with previous studies, the platform was more consistent with real corona discharge characteristic gas-detection scenarios, which provided an important basis for GIS condition assessment. A detection system for HF gas online monitoring was built. A gas circuit system with good sealing was designed. A corona discharge fault in GIS was simulated by using needle–plate electrode discharge. Gas photoacoustic spectroscopy was used to detect HF generated in discharge in real time. In order to detect flowing gas, a resonant photoacoustic cell was designed. According to the mechanism of photoacoustic signal generation, the material of the photoacoustic cell and the size of the photoacoustic cavity were reasonably designed. By querying the HITRAN database, the absorption intensities of HF and interference gases were compared, and the absorption spectral line of HF in the near-infrared region was determined. Wavelength modulation spectroscopy and second-harmonic detection techniques were used in the detection. The online monitoring of HF generated in a simulated GIS corona discharge fault was successfully realized by using the test system. The test system was calibrated using 525 μL/L standard HF/SF6 mixed gas. The detection sensitivity of the test system for HF gas was 0.445 μV/(μL/L), and the LOD was 0.611 μL/L.
HF was found to be reduced by reacting with materials in the gas path. In the early stage of a discharge fault, the HF concentration increases rapidly. With the ablation of the discharge point, the discharge energy may weaken, resulting in a decrease in the concentration of HF. The research results of this paper have important significance in GIS online monitoring.