An SF6 online micro-water density monitoring system

Abstract

The present invention relates to the technical field of SF6 online monitoring, and specifically to an SF6 online micro-water density monitoring system, comprising a plurality of transmitters, a data processing module and an alarm module. The transmitter is used to collect air pressure data, temperature data and relative humidity data in real time; the data processing module comprises a preprocessing module, a density monitoring module and a micro-water content monitoring module; the preprocessing module is used to select representative data and calculate the average data based on the monitoring period; the density monitoring module and the micro-water content monitoring module are respectively used to calculate the gas density and micro-water content in the SF6 gas chamber according to the preprocessed relative humidity data, temperature data and air pressure data. The present invention obtains representative data by screening the data and taking the average, thereby greatly reducing the computational complexity of the online monitoring of SF6 micro-water density, and reduces the demand for the hardware performance of the equipment while retaining the monitoring accuracy and timeliness, thereby effectively reducing the detection cost.

Description

Technical Field

[0001] The present invention relates to the technical field of SF6 online monitoring, and in particular to an SF6 online micro-water density monitoring system. Background Art

[0002] SF6 gas is a commonly used insulating medium gas used in high-voltage switchgear and substations. Micro-water content refers to the moisture content in SF6 gas, usually expressed in ppm or ppb. When SF6 electrical equipment is in operation, it is inevitable that SF6 gas will leak outward, causing its density to decrease. At the same time as SF6 gas leaks, moisture outside the equipment will also penetrate into the equipment, causing the micro-water content in the SF6 gas inside the equipment to increase. When the micro-water content in SF6 gas is too high, it will cause safety hazards to high-voltage electrical equipment.

[0003] Traditional SF6 gas leakage monitoring uses pressure gauges or density relays, which cannot fully compensate for the impact of temperature on pressure, and the accuracy and reliability are not ideal. In addition, the SF6 gas micro-water content monitoring process and equipment are relatively complex, and offline detection is generally used, and the measurement results cannot objectively reflect the micro-water content. By online monitoring of the micro-water content and density in SF6 gas, leaks and abnormal conditions can be discovered in time, the accuracy and reliability of monitoring can be improved, and the occurrence of equipment failures and accidents can be reduced. In addition, online monitoring can also realize real-time monitoring of the micro-water content and density of SF6 gas, helping operation and maintenance personnel to take timely measures to extend the service life of equipment and improve the reliability and safety of equipment.

[0004] Existing monitoring systems often need to collect a large amount of data, such as temperature, air pressure, etc. The collected data needs to undergo relatively complex calculations to obtain the corresponding micro-water content or gas density. If all the data are calculated, not only will the amount of calculation be large, the performance requirements of the monitoring equipment will be high, and the calculation rate will be reduced, lacking real-time performance and reliability. Summary of the invention

[0005] Based on this, it is necessary to propose an SF6 online micro-water density monitoring system to address the problems that the existing SF6 gas density and micro-water content are difficult to monitor online, resulting in poor real-time performance, low reliability, and high detection cost.

[0006] The present invention is implemented through the following technical scheme: an SF6 online micro-water density monitoring system comprises a plurality of transmitters, a data processing module and an alarm module.

[0007] The transmitter is used to collect real-time data on air pressure, temperature and relative humidity in the SF6 gas chamber.

[0008] The data processing module includes a preprocessing module, a density monitoring module, and a micro-water content monitoring module. The preprocessing module is used to preprocess the temperature data, relative humidity data, and air pressure data collected in real time, select representative data, and calculate the average data based on the monitoring period. The density monitoring module is used to calculate the SF6 gas density based on the preprocessed temperature data and air pressure data. The micro-water content monitoring module is used to calculate the micro-water content of SF6 gas based on the preprocessed relative humidity data and air pressure data.

[0009] The alarm module is used to determine whether the SF6 gas density and trace water content exceed the preset standards, and if so, an alarm signal is issued.

[0010] The above detection system obtains representative data by screening and averaging the continuous air pressure, temperature and relative humidity collected in real time. It is not necessary to calculate each collected data to obtain the corresponding micro-water content and gas density, which greatly reduces the computational complexity of SF6 micro-water density online monitoring. Under the premise of retaining the monitoring accuracy and timeliness, it reduces the demand for equipment hardware performance and effectively reduces the detection cost. In addition, by monitoring the temperature, air pressure and relative humidity in the SF6 gas chamber, the dew point and saturated water vapor pressure are calculated, and the final micro-water content is converted into an equivalent micro-water content at 20°C, which not only improves the detection accuracy of the gas density and micro-water content of the SF6 gas chamber, but also facilitates the comparison of the gas density and micro-water content with the national industry standards, making the judgment results clearer.

[0011] Furthermore, the transmitter includes a body, a temperature sensor, a humidity sensor, an air pressure sensor, a fan and two electronic valves. An inlet and an outlet are provided on the body, and the inlet and the outlet are connected to the SF6 gas chamber through pipes respectively. Electronic valves are installed at the inlet and the outlet. The fan is installed in the body and is used to transport the gas in the body from the inlet to the outlet. The temperature sensor, the humidity sensor and the air pressure sensor are all installed in the body and are used to detect the temperature data, the relative humidity data and the air pressure data in the SF6 gas chamber respectively.

[0012] Furthermore, the processing method of the preprocessing module is as follows:

[0013] 1. Obtain the corresponding temperature data, relative humidity data, and air pressure data according to the sampling period, and establish data sets respectively. The temperature data set is recorded as T = (t1, t2, ..., t m ), relative humidity data is recorded as RH = (rh1, rh2, ..., rh s ), the air pressure data set is recorded as P = (p1, p2, ..., p n ).

[0014] Second, use the random average method to select representative data in the data set. Specifically, divide the data set evenly into a intervals, and randomly select a data in each interval. The selected temperature data set is recorded as: temperature data set T k =(t i1 , t i2 ,…,t ia ), relative humidity dataset RH k =(rh i1 , rh i2 ,…,rh ia ), air pressure dataset P k =(p i1 , p i2 ,…,p ia ). i = 1, 2, ..., b, where b is the maximum number of data in each interval.

[0015] 3. Calculate the average of multiple filtered data.

[0016] The mean value of temperature data is expressed as:

[0017]

[0018] In the formula, k represents the time, is the mean temperature of the kth sampling period. ij ∈T j , j=1,2,…,a.

[0019] The mean of the relative humidity data is expressed as:

[0020]

[0021] In the formula, is the mean humidity value of the kth sampling period, rh ij ∈RH j .

[0022] The mean of the air pressure data after random average screening is expressed as:

[0023]

[0024] In the formula, is the mean air pressure of the kth sampling period, p ij ∈P j .

[0025] Furthermore, the processing method of the density monitoring module is as follows:

[0026] Get the SF6 gas state equation, the formula is expressed as:

[0027] P = 0.57 × 10-4 ρt(1+B)-ρ 2 A

[0028] In the formula, P is the air pressure, t is the temperature, ρ is the gas density, and the coefficients A and B are:

[0029] A=0.75×10 -4 (1-0.727×10 -3 ρ)

[0030] B = 2.51 × 10 -3 ρ(1-0.84×10 -3 ρ)

[0031] Substituting coefficients A and B into the original gas state equation, we get the cubic equation about ρ:

[0032]

[0033] By solving the above equation using Newton's iteration algorithm, the SF6 gas density ρ can be calculated.

[0034] Furthermore, the processing method of the trace water content monitoring module is as follows:

[0035] Calculate SF6 gas dew point T based on relative humidity data d , the formula is:

[0036]

[0037] Where a1 and b1 are Magnus coefficients.

[0038] The saturated water vapor pressure is calculated based on the gas dew point. The formula is:

[0039]

[0040] In the formula, E s0 is the saturated water vapor pressure at 0℃, a2 and b2 are coefficients.

[0041] The real-time water content H in the SF6 gas chamber is s Expressed as:

[0042]

[0043] Convert the real-time trace water content to the trace water content H at 20°C 20 , the formula is:

[0044]

[0045] Where P 20 E is the pressure of SF6 gas converted to 20℃. 20is the saturated water vapor pressure at 20°C.

[0046] Furthermore, the preset standards of the alarm module include gas density range, water content threshold, and continuous growth rate threshold. The calculation method of the continuous growth rate is as follows:

[0047] Calculate the growth rate of each stage, the formula is expressed as:

[0048]

[0049] In the formula, K ρk is the growth rate of SF6 gas density in the kth sampling period, K Rk is the growth rate of trace water content in the kth sampling period.

[0050] The growth rate of each stage is mapped to a plane coordinate system to obtain multiple coordinate points, and the corresponding curve function F(x) is obtained by curve fitting method. The slope of the curve function F(x) is calculated as the continuous growth rate. The calculation formula of the slope is: K'=F'(x).

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention obtains representative data by screening and averaging the continuous air pressure, temperature and relative humidity collected in real time. It is not necessary to calculate each collected data to obtain the corresponding micro-water content and gas density, which greatly reduces the computational complexity of online monitoring of SF6 micro-water density. Under the premise of retaining monitoring accuracy and timeliness, it reduces the demand for equipment hardware performance and effectively reduces the detection cost. In addition, by monitoring the temperature, air pressure and relative humidity in the SF6 gas chamber, the dew point and saturated water vapor pressure are calculated, and the final micro-water content is converted into an equivalent micro-water content at 20°C, which not only improves the detection accuracy of the gas density and micro-water content of the SF6 gas chamber, but also facilitates the comparison of the gas density and micro-water content with the national industry standards, making the judgment result clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the overall structure of the SF6 online micro-water density monitoring system in Example 1 of the present invention;

[0054] Figure 2 This is the operation flow chart of the SF6 online micro-water density monitoring system;

[0055] Figure 3 for Figure 1 Schematic diagram of the structure of the transmitter;

[0056] Figure 4 A step diagram of the processing method of the preprocessing module. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is considered to be "fixed to" another component, it may be directly fixed on the other component or there may be a central component at the same time.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0060] Example 1: Please refer to Figure 1-Figure 2 , this embodiment provides a SF6 online micro-water density monitoring system, including multiple transmitters, a data processing module and an alarm module.

[0061] Please combine Figure 3 The transmitter is used to collect the air pressure and temperature in the SF6 gas chamber in real time. The transmitter includes a body, a temperature sensor, a humidity sensor, an air pressure sensor, a fan, a connecting pipe, and multiple electronic valves. The temperature sensor, humidity sensor, and air pressure sensor are all installed in the body, and are used to detect the temperature data, relative humidity data, and air pressure data in the SF6 gas chamber, respectively. An inlet and an outlet are provided on the body, and the inlet and the outlet are connected to the SF6 gas chamber through pipes, respectively, and electronic valves are installed at the inlet and the outlet. The electronic valve adopts a one-way valve. The one-way valve at the inlet guides the body, and the gas can only flow from the SF6 gas chamber into the body. The one-way valve at the outlet guides the gas chamber, and the gas can only flow from the body into the SF6 gas chamber. The fan is installed in the body, and is used to transport the gas in the body from the inlet to the outlet.

[0062] Please combine Figure 4 ,The data processing module includes a preprocessing module, a density monitoring module, and a trace water content monitoring module.

[0063] The preprocessing module is used to preprocess the real-time collected temperature data, relative humidity data, and air pressure data, select representative data, and calculate the average data based on the monitoring period.

[0064] The specific methods of preprocessing are as follows:

[0065] 1. Obtain the corresponding temperature data, relative humidity data, and air pressure data according to the sampling period, and establish data sets respectively. The temperature data set can be recorded as T = (t1, t2, ..., t m ), the relative humidity data can be recorded as RH = (rh1, rh2, ..., rh s ), the air pressure data set can be recorded as P = (p1, p2, ..., p n ).

[0066] Second, the random average method is used to select representative data in the data set.

[0067] Specifically, the data set is evenly divided into multiple intervals, and one or more data are randomly selected in each interval. Taking the temperature data set as an example, the data set T is divided into a intervals, denoted as T1, T2, ..., T a , there are b or b+1 temperature data in each interval, where m=a*b+c, 0≤c<b.

[0068] The humidity sensor and the temperature sensor can use the same sampling frequency, or integrate the temperature and humidity sensor for synchronous detection. The relative humidity data is also divided into a interval, recorded as RH1, RH2, ..., RH a , there are b or b+1 relative humidity data in each interval, where m=a*b+c, 0≤c<b.

[0069] If the sampling frequencies of the temperature sensor and the pressure sensor are different, the same number of intervals is retained, that is, the pressure data set P is still divided into a intervals, denoted as P1, P2, ..., P a , and n=a*d+e, 0≤e<d, there are d or d+1 air pressure data in each corresponding interval.

[0070] Taking a random selection of data in each interval as an example, the temperature data set after random average screening can be recorded as T k =(t i1 , t i2 ,…,t ia ), i = 1, 2, ..., b, t ij ∈T j , j = 1, 2, ..., a. The selected humidity data set can be recorded as RH k =(rh i1 , rh i2 ,…,rhia ), i = 1, 2, ..., b, rh ij ∈RH j , j = 1, 2, ..., a. The selected air pressure data set can be recorded as P k =(p i1 , p i2 ,…,p ia ), i = 1, 2, ..., d, t ij ∈T j , j=1,2,…,a.

[0071] 3. Calculate the average of multiple filtered data.

[0072] The mean of the temperature data can be expressed as:

[0073]

[0074] In the formula, k represents the time, is the mean temperature of the kth sampling period, t ij ∈T j is the temperature data randomly selected in the jth interval.

[0075] Correspondingly, the mean of the relative humidity data can be expressed as:

[0076]

[0077] In the formula, is the mean humidity value of the kth sampling period, rh ij ∈RH j is the relative humidity data randomly selected in the jth interval.

[0078] The mean of the air pressure data after random average screening is expressed as:

[0079]

[0080] In the formula, is the mean air pressure of the kth sampling period, p ij ∈P j is the randomly selected air pressure data in the jth interval.

[0081] Since the process of sensor signal collection is periodic, when processing the data collected within a period of time, if the average screening method is adopted, the collected data will be single and cannot reflect the overall trend of the data. If the data is screened by all random screening, it is easy to miss key data and it is difficult to reflect the variability of the signal. In addition, since the sampling frequency of temperature sensors and pressure sensors is generally 1-10HZ, if the SF6 gas density or micro-water content is calculated for each sampled data, the amount of calculation is too large, and the corresponding hardware performance requirements are high. By selecting representative average data, the amount of calculation can be effectively reduced, the monitoring efficiency can be improved, and the monitoring cost can be reduced, while maintaining the monitoring accuracy.

[0082] The density monitoring module is used to calculate the SF6 gas density based on the pre-processed temperature data and pressure data. The specific calculation method is as follows:

[0083] First, obtain the SF6 gas state equation, which is expressed as:

[0084] P = 0.57 × 10 -4 ρt(1+B)-ρ 2 A

[0085] In the formula, P is the air pressure, t is the temperature, ρ is the gas density, and the coefficients A and B are:

[0086] A=0.75×10 -4 (1-0.727×10 -3 ρ)

[0087] B = 2.51 × 10 -3 ρ(1-0.84×10 -3 ρ)

[0088] Substituting coefficients A and B into the original gas state equation, we get the cubic equation about ρ:

[0089]

[0090] By solving the above equation using Newton's iteration algorithm, the SF6 gas density ρ can be calculated.

[0091] The micro-water content monitoring module is used to calculate the micro-water content of SF6 gas based on the pre-processed relative humidity data and air pressure data. The specific calculation method of micro-water content is as follows:

[0092] First, calculate the SF6 gas dew point T based on the relative humidity data d , the formula is:

[0093]

[0094] Wherein, a1 and b1 are Magnus coefficients. In this embodiment, a1=17.625 and b1=243.04°C are taken.

[0095] Secondly, the saturated water vapor pressure is calculated from the gas dew point, and the formula is expressed as:

[0096]

[0097] In the formula, E s0 is the saturated water vapor pressure at 0°C. In this embodiment, a2=17.269 and b2=35.86.

[0098] The real-time water content H in the SF6 gas chamber is s It can be expressed as:

[0099]

[0100] Convert the real-time trace water content to the trace water content H at 20°C 20 , the formula is:

[0101]

[0102] Where P 20 E is the pressure of SF6 gas converted to 20℃. 20 is the saturated water vapor pressure at 20°C.

[0103] The alarm module is used to determine whether the SF6 gas density and trace water content exceed the preset standards, and if so, an alarm signal is issued.

[0104] The preset standards include gas density range, water content threshold, and continuous growth rate threshold. For example, the gas density range is set to 1.4-1.5 kg / m 3 , the trace water content threshold is set at 150ppm.

[0105] The calculation method of continuous growth rate is as follows:

[0106] First, calculate the growth rate of each stage, the formula is expressed as:

[0107]

[0108] In the formula, K ρk is the growth rate of SF6 gas density in the kth sampling period, K Rk is the growth rate of trace water content in the kth sampling period.

[0109] Secondly, the growth rate of each stage is mapped to the plane coordinate system, and multiple continuous coordinate points are curve-fitted to obtain the corresponding curve function F(x). The slope of the curve function is calculated as the continuous growth rate. If the slope is always greater than 0 within a time period, an alarm signal is issued. The calculation formula of the slope is: K'=F'(x).

[0110] This embodiment obtains representative data by screening and averaging the continuous air pressure, temperature and relative humidity collected in real time. It is not necessary to calculate each collected data to obtain the corresponding micro-water content and gas density, which greatly reduces the computational complexity of online monitoring of SF6 micro-water density. Under the premise of retaining monitoring accuracy and timeliness, it reduces the demand for equipment hardware performance and effectively reduces the detection cost. In addition, by monitoring the temperature, air pressure and relative humidity in the SF6 gas chamber, and then calculating the dew point and saturated water vapor pressure, and converting the final micro-water content into an equivalent micro-water content at 20°C, it not only improves the detection accuracy of the gas density and micro-water content of the SF6 gas chamber, but also facilitates the comparison of the gas density and micro-water content with the national industry standards, making the judgment result clearer.

[0111] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An SF6 online micro-water density monitoring system, characterized in that: include: Multiple transmitters are used to collect real-time data on pressure, temperature and relative humidity in the SF6 gas chamber; The data processing module includes a preprocessing module, a density monitoring module, and a micro-water content monitoring module; the preprocessing module is used to preprocess the temperature data, relative humidity data, and air pressure data collected in real time, select representative data, and calculate average data based on the monitoring period; The density monitoring module is used to calculate the density of SF6 gas according to the pre-processed temperature data and the air pressure data; the micro-water content monitoring module is used to calculate the micro-water content of SF6 gas according to the pre-processed relative humidity data and air pressure data; The representative data include temperature data, relative humidity data, and air pressure data; the processing method of the preprocessing module is as follows:

1. Obtain the corresponding temperature data, relative humidity data, and air pressure data according to the sampling period, and establish data sets respectively; the temperature data set is recorded as T = (t1, t2, ..., t m ), relative humidity data is recorded as RH = (rh1, rh2, ..., rh s ), the air pressure data set is recorded as P = (p1, p2, ..., p n ); Second, the random average method is used to select representative data in the data set. Specifically, the data set is evenly divided into a intervals, and a data is randomly selected in each interval. The selected temperature data set is recorded as: temperature data set T k =(t i1 , t i2 ,…,t ia ), relative humidity dataset RH k =(rh i1 , rh i2 ,…,rh ia ), air pressure dataset P k =(p i1 , p i2 ,…,p ia ), i = 1, 2, ..., b, where b is the maximum number of data in each interval; 3. Calculate the average of multiple filtered data; The mean value of temperature data is expressed as: In the formula, k represents the time, is the mean temperature of the kth sampling period; t ij ∈T j , j = 1, 2, ..., a; The mean of the relative humidity data is expressed as: In the formula, is the mean humidity value of the kth sampling period, rh ij ∈RH j ; The mean of the air pressure data after random average screening is expressed as: In the formula, is the mean air pressure of the kth sampling period, p ij ∈P j ; The alarm module is used to determine whether the SF6 gas density and the trace water content exceed the preset standards, and if so, send out an alarm signal.

2. The SF6 online micro-water density monitoring system according to claim 1 is characterized in that: The transmitter includes a body, a temperature sensor, a humidity sensor, an air pressure sensor, a fan and two electronic valves; an inlet and an outlet are provided on the body, and the inlet and the outlet are connected to the SF6 gas chamber through pipelines respectively; electronic valves are installed at the inlet and the outlet; the fan is installed in the body, and is used to transport the gas in the body from the inlet to the outlet; the temperature sensor, the humidity sensor and the air pressure sensor are all installed in the body, and are used to detect the temperature data, relative humidity data and air pressure data in the SF6 gas chamber respectively.

3. The SF6 online micro-water density monitoring system according to claim 1 is characterized in that: The processing method of the density monitoring module is as follows: Get the SF6 gas state equation, the formula is expressed as: P=0.57×10 -4 ρt(1+B)-ρ 2 A In the formula, P is the air pressure, t is the temperature, ρ is the gas density, and the coefficients A and B are: A=0.75×10 -4 (1-0.727×10 -3 p) B=2.51×10 -3 p(1-0.84×10 -3 p) Substituting coefficients A and B into the SF6 gas state equation, the cubic equation about ρ is obtained: By solving the above equation using Newton's iteration algorithm, the SF6 gas density ρ can be calculated.

4. The SF6 online micro-water density monitoring system according to claim 1 is characterized in that: The processing method of the micro-water content monitoring module is as follows: Calculate SF6 gas dew point T based on relative humidity data d , the formula is: Where a1 and b1 are Magnus coefficients; The saturated water vapor pressure is calculated based on the gas dew point, and the formula is expressed as: In the formula, E s0 is the saturated water vapor pressure at 0℃, a2 and b2 are coefficients; The real-time water content H in the SF6 gas chamber is s Expressed as: Convert the real-time trace water content to the trace water content H at 20°C 20 , the formula is: Where P 20 E is the pressure of SF6 gas converted to 20℃. 20 is the saturated water vapor pressure at 20°C.

5. The SF6 online micro-water density monitoring system according to claim 1 is characterized in that: The preset standards of the alarm module include gas density range, water content threshold, and continuous growth rate threshold; wherein the continuous growth rate is calculated as follows: Calculate the growth rate of each stage, the formula is expressed as: In the formula, K ρk is the growth rate of SF6 gas density in the kth sampling period, K Rk is the growth rate of trace water content in the kth sampling period; The growth rate of each stage is mapped to a plane coordinate system to obtain multiple coordinate points, and the corresponding curve function F(x) is obtained by curve fitting method; the slope of the curve function F(x) is calculated as the continuous growth rate; the calculation formula of the slope is: K'=F'(x).

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