KR101519929B1 - An intelligent deterioration diagnosing system for a distributing board with optic fiber temperature sensor and the method thereof - Google Patents
An intelligent deterioration diagnosing system for a distributing board with optic fiber temperature sensor and the method thereof Download PDFInfo
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Abstract
The present invention relates to an intelligent deterioration diagnosis system and method for a switchgear with an optical fiber temperature sensor, and more particularly, to a sensor system having a sensor unit including a plurality of optical fiber sensors for detecting an internal space temperature of the housing, ; And diagnosing the deterioration state inside the housing based on the internal space temperature detected by the sensor unit and the contact temperature of each facility, and diagnosing the internal state of the housing according to the deteriorated state information in the diagnosed housing, And a monitoring device for controlling or generating an alarm signal based on the internal space temperature of the housing received from the sensor unit and the contact temperature of each facility, Based on the contact temperature information, the spatial temperature information, the temperature rise information, and the temperature tilt information received from the feature extracting unit, the feature extraction unit extracting the temperature rise information, the temperature rise information, A reasoning unit for calculating a result value from which the state can be inferred; To provide a configuration including a determining of determining the deterioration state of the inside.
By using the optical fiber temperature sensor according to the above-described method, it is possible to reduce the error caused by electromagnetic interference (EMI) in temperature detection, to make the system compact or lightweight, and to stably maintain the temperature even under particularly harsh conditions It is possible to discriminate according to a learning and systematic method while taking into consideration all the temperature factors affecting the abnormal state such as the temperature and the change according to the time, and thus it is possible to judge the abnormality of the switchboard more accurately.
Description
The present invention relates to a system and method for intelligent deterioration diagnosis of a switchboard using an optical fiber sensor and an optical fiber temperature sensor for diagnosing a deterioration state of a housing including a water tank, an electric power distribution panel, an electric motor control panel, .
In particular, the present invention relates to an optical fiber temperature probe for detecting a temperature change by directly attaching to a position to be measured such as a contact portion of a power facility, a switch, a transformer, A fiber optic interferometer that sets the path until it is detected by the photodetector, a signal processing circuit that applies a driving signal to the light source and processes the electrical signal converted by the photodiode to detect the temperature value and output it as an analog value, The present invention relates to an intelligent deterioration diagnosis system for a switchgear with an optical fiber temperature sensor constituted by a signal processing device for transmitting a signal and a method thereof.
Further, the present invention detects the space temperature inside the housing of the switchboard and the contact temperature of each facility, extracts the contact temperature information, the space temperature information, the contact temperature change information, and the space temperature change information, The present invention relates to an intelligent deterioration diagnosis system for a switchgear with an optical fiber temperature sensor for inferring an internal deterioration state and a method thereof.
Generally, power facilities such as transformers, electric motors, power cables, bus bars, etc. are deteriorated by various causes, but in most cases, irrespective of the cause, they are expressed as abnormal phenomena such as rising sunrise or fire. Therefore, the system that monitors the thermal changes of the electric power facilities and warns the fire early can play an important role in preventing the safety accident of electric power facilities and reducing the spread of damage in case of an accident.
Safety diagnosis of electric power facilities In order to improve reliability, precise monitoring and detection of thermal changes of equipment should be preceded. In particular, considering the fact that the use of large capacity electric power facilities is increasing, it is urgent to develop this highly reliable safety diagnosis technology and early warning system.
Several techniques have been developed and used to diagnose overheating of power facilities including high-voltage switchboard, low-voltage switchboard, distribution board, and switchboard. Typical examples are electric thermal contact temperature sensors, infrared temperature sensors and thermal cameras.
However, the existing electric contact temperature sensors of high and low pressure parts frequently cause frequent errors due to electromagnetic waves and incapability of operation, and also cause adverse effects such as deterioration of the function and performance of the sensor due to aging of the sensor material have. It is also difficult to simultaneously measure the local temperature change and the overall temperature change slope. In the case of the infrared thermal detector, it is suitable for the purpose of analyzing the accurate thermal distribution. However, there is a problem in economical efficiency and long-term reliability for the purpose of constant monitoring of electric power equipment for overheating and fire alarm. Therefore, developed countries are developing technologies that can solve these problems, but so far, they are still dependent on conventional technologies.
Electricity facilities, which have a large impact on society at large, are becoming larger and higher in pressure day by day, and industrial, economic and social losses accompanying the occurrence of accidents in electric power facilities tend to be very large. There is a need for a surveillance diagnostic system that can automatically detect and shut down faults in power facilities such as transformers, switches, breakers, transformers, and busbars. In order to prevent accidents caused by abnormal overheating, substation staff periodically perform visual inspection or diagnosis using an infrared camera [
Accordingly, there is an increasing demand for an online monitoring system capable of constantly monitoring the contact portion of the electric power equipment and the temperature of the bus bar and the bushing portion in the switchboard. However, the conventional RTD sensor has a disadvantage that it is highly influenced by the surrounding electromagnetic field and can not be directly attached to the measurement object due to the insulation problem. In addition, it is difficult to implement a surveillance system using such a discoloration tape or an IR camera because of the disadvantage that it is impossible to measure the temperature inside a sealed cabinet [
On the other hand, in the case of the optical fiber temperature sensor, the influence of the electric noise can be minimized without being influenced by the electromagnetic field, and since the optical fiber itself is composed of the insulating material, it can be attached without any additional insulation.
Transformers are among the most important and expensive equipment among electric power facilities in power distribution facilities. The critical importance in high-voltage transformers is safety and insulation. However, many of the mechanical, thermal, and electrical stresses that these transformers have to undergo throughout their lifetime often provide an increase in performance degradation of the isolation system.
Thermal errors (hot spots) and electrical faults (partial discharge or PD) can lead to premature insulation breakdown and catastrophic transformer failure. They are also two major mechanisms for safety and financial disaster caused by unexpected transformer failures or outages.
The hot spots inside the transformer are caused by overheating of the maximum use time or by local overheating as the cooling efficiency is reduced. Hot spots accelerate the aging of insulating oil inside the transformer, causing transformer failure. By monitoring the hot spot temperature, it is possible to estimate the life of the transformer.
On the other hand, difficulties caused by electromagnetic interference (EMI) in temperature detection in a high voltage transformer can be solved by using an optical fiber sensor. Fiber-optic sensors made from dielectric materials such as fused-silica glass and sapphire are inherently unaffected by EMI.
In addition, many optical fiber sensors have the advantages of small size, light weight and high sensitivity. Therefore, these sensors can be mounted inside the transformer tank without affecting the performance of the sensor and the insulation of the transformer. This is very important for both hotspot temperature sensing. The approximation of the hot spot temperature is more accurate when the temperature measurement is performed near the winding.
Thus, temperature sensing is very important in most monitoring applications of power facilities. Most temperature measurement tasks can be performed using conventional electronic temperature sensors, but existing temperature sensors have a limited range of measurements. On the other hand, even under particularly harsh conditions, fiber optic temperature sensors show advantages over existing instrumentation.
In addition, many of the material properties show strong temperature dependence. Measurement is required to use or supplement the temperature effect. Examples of such temperature dependencies include dew point, density, electrical conductivity, refractive index, intensity and diffusion.
In industrial applications and research, most measurements can be measured using conventional temperature sensors such as thermocouples, junction temperature sensors, resistance temperature sensors, or thermistors. However, existing temperature sensors have limitations in the following cases.
- If you need to cover a large area in highly scattered measurements
- If a large number of sensors need to be integrated to monitor distant temperatures such as power cables
- when the electromagnetic interference significantly reduces the signal to noise ratio
- The use of electronic devices is restricted due to the explosive environment.
- If a lightweight structure monitoring device with low mass impact is required
- Temperature in a place where the environment such as dust, water, and high temperature is poor.
Especially in this environment, fiber optic temperature sensors have great advantages.
An object of the present invention is to solve the above-mentioned problems, and an object of the present invention is to solve the above-mentioned problems, and an object of the present invention is to provide a method and apparatus for detecting a temperature change of a power unit, such as a transformer, a switch, a breaker, A fiber optic interferometer for setting a path until an optical signal output from a light source is detected by a photodetector through an optical sensor, a signal processing circuit for applying a driving signal to a light source and a signal processing of an electrical signal converted by the photodiode, And outputting an analog value or transmitting it to an upper-level system, and an optical fiber temperature sensor comprising the optical fiber temperature sensor and the method.
It is also an object of the present invention to detect a space temperature inside a housing of a switchboard and a contact temperature of each facility to extract contact temperature information, space temperature information, contact temperature change information, and space temperature change information, And an optical fiber temperature sensor for estimating a deterioration state of the interior of the housing.
It is another object of the present invention to provide an optical fiber temperature sensor which is a sensor for measuring the phase change using a phase change of light generated when a change in refractive index or optical path length of an optical fiber occurs, Mode optical fiber or a polarization-maintaining optical fiber, and an optical fiber temperature sensor using a laser having excellent monochromaticity as a light source, and a method thereof.
In order to accomplish the above object, the present invention provides an intelligent deterioration diagnosis system for a switchgear having an optical fiber temperature sensor for diagnosing a deterioration state of a housing including a water tank, an electric distribution panel, an electric motor control panel, a high pressure tank, A sensor unit comprising a plurality of optical fiber sensors for detecting an internal space temperature of the housing and a contact temperature in a facility inside the housing; And diagnosing the deterioration state inside the housing based on the internal space temperature detected by the sensor unit and the contact temperature of each facility, and diagnosing the internal state of the housing according to the deteriorated state information in the diagnosed housing, And a monitoring device for controlling or generating an alarm signal based on the internal space temperature of the housing received from the sensor unit and the contact temperature of each facility, Based on the contact temperature information, the spatial temperature information, the temperature rise information, and the temperature tilt information received from the feature extracting unit, the feature extraction unit extracting the temperature rise information, the temperature rise information, A reasoning unit for calculating a result value from which the state can be inferred; It characterized in that it comprises a determination of determining the deterioration state of the inside.
According to another aspect of the present invention, there is provided an intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, wherein the optical fiber sensor is composed of two cavity surfaces having a predetermined distance, And an optical fiber interferometer for extracting a difference between the optical path and the optical path, wherein the sensor measures the temperature by detecting a temperature change from the difference between the extracted phase difference and the optical path.
According to another aspect of the present invention, there is provided an intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, wherein the sensor unit comprises a laser diode (LD) light source having a variable wavelength of 1.3 to 1.5 占 퐉, a photo detector, A plurality of optical fiber sensors, and a 3-dB coupler. Light emitted from the laser diode passes through a 3-dB coupler to a fiber optic interferometer. Light reaching the interferometer interferes with each other and uses two peak wavelengths And extracts a light spectrum with respect to a temperature change. The extracted light spectrum is subjected to discrete signal processing and filtering to demodulate the signal, thereby extracting a light path difference.
Further, the present invention is characterized in that in the intelligent deterioration diagnosis method of a switchboard having an optical fiber temperature sensor, the phase difference DELTA Phi with respect to the temperature change DELTA T is obtained by the following [Expression 1].
[Equation 1]
Where L is the length of the interferometer,? Is the wavelength of the light, and? = 1 × 10 -5 / ° C.
Further, the present invention is characterized in that, in the intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, the difference Di of the optical path is obtained by the following [Expression 2].
[Equation 2]
However, λ p, m and λ p, m + 1 represent the wavelengths of two adjacent peaks, respectively.
According to another aspect of the present invention, there is provided an intelligent deterioration diagnostic method for a switchboard having an optical fiber temperature sensor, wherein the sensor unit converts a mixed optical signal sensed by the optical fiber sensor into a wave number region by a linear interpolation method, In order to analyze a frequency component, a peak frequency of each sensor is evaluated by evaluating a discrete Fourier transform (DFT), and a peak frequency of the extracted sensor signal is filtered by a low-pass filter to extract a peak wavelength.
According to another aspect of the present invention, there is provided an intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, wherein the inferring unit comprises: A first half module and a polynomial-type mathematical expression model for determining a belonging matrix and a cluster center value for the environment information using at least one environment information (feature point) FCM (Fuzzy C-means) Weighted Least Square Estimator) to determine a coefficient of the polynomial equation, and calculate a resultant value that can infer the deterioration state in the housing based on the membership matrix and the cluster center value for the environment information determined in the first half module And a second half module for calculating by using a fuzzy clustering based RBF Neural Network (FRBFNN).
The present invention also relates to an intelligent deterioration diagnostic method for a switchgear having an optical fiber temperature sensor for diagnosing a deterioration state of a housing including a water tank, a distribution board, a motor control panel, a high pressure tank, Detecting a contact temperature and a space temperature inside the housing by an optical fiber sensor; (b) detecting environmental information by extracting contact temperature information, space temperature information, temperature rise information, and temperature gradient information inside the housing using the contact temperature and the space temperature inside the housing; (c) Calculating deterioration state information inside the housing based on the detected environment information, and (d) determining a deterioration state inside the housing based on the calculated deterioration state information, wherein the step (c (C1) extracting contact temperature information, space temperature information, temperature rise information, and temperature tilt information inside the housing based on the contact temperature and the space temperature inside the housing; (c2) calculating a result value from which the contact temperature information, the spatial temperature information, the temperature rise information, and the temperature gradient information can be inferred; And (c3) comparing the resultant value with a preset reference to determine a deterioration state inside the housing.
According to another aspect of the present invention, there is provided an intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, wherein the optical fiber sensor is composed of two cavity surfaces having a predetermined distance, And a fiber optic interferometer for extracting a difference between the optical path and the optical path, wherein the sensor measures the temperature by detecting a temperature change from a difference between the extracted phase difference and an optical path. In the step (a) The light emitted from the laser diode is passed through a 3-dB coupler to a fiber optic interferometer using a 3-dB coupler, a plurality of optical fiber sensors constituting a fiber optic interferometer, a laser diode (LD) The light reaching the interferometer interferes with each other, extracts the optical spectrum of the temperature change using the two peak wavelengths, The extracted optical spectrum is characterized by extracting the difference between the optical path by demodulating the signal after the discrete signal processing and filtering.
The present invention further provides an intelligent deterioration diagnosis method for a switchboard having an optical fiber temperature sensor, wherein the step (c2) comprises: (c21) detecting contact temperature information, space temperature information, Determining a membership matrix and a cluster center value for the environment information using FCM (Fuzzy C-means) algorithm for at least one environment information (feature point) among temperature rise information, temperature rise information, and temperature gradient information; and (c22) (WLSE) to determine the coefficients of the polynomial equation, and to infer the cleanliness within the housing based on the membership matrix and the cluster center value for the environment information And calculating a result value using a fuzzy clustering based RBF Neural Network (FRBFNN).
As described above, according to the intelligent deterioration diagnosis system and method of a switchgear with an optical fiber temperature sensor according to the present invention, by using an optical fiber temperature sensor, errors caused by electromagnetic interference (EMI) The system can be downsized or lightened, and the temperature can be stably measured even under particularly harsh conditions, so that the deterioration diagnosis can be performed more accurately.
In addition, according to the intelligent deterioration diagnosis system and method of a switchgear with an optical fiber temperature sensor according to the present invention, various factors affecting an abnormal state such as temperature and its change over time are considered, Therefore, it is possible to more accurately determine whether or not there is an abnormality in the switchboard.
1 is a block diagram of a configuration of an intelligent deterioration diagnosis system for a switchboard having an optical fiber temperature sensor according to an embodiment of the present invention.
2 is a view illustrating a head structure of an optical fiber interferometer sensor according to an embodiment of the present invention.
3 is a block diagram of an optical fiber temperature sensor system according to an embodiment of the present invention.
4 is a graph showing the response spectrum of the optical fiber temperature sensor according to an embodiment of the present invention.
5 is a configuration diagram of an FP sensor having a scanning interferometer according to an embodiment of the present invention.
6 is a graph illustrating an example of a spectral signal processing algorithm using two peaks according to an embodiment of the present invention.
7 is a comparison of an inherent sensor signal and a filtered signal in accordance with an embodiment of the present invention.
8 is a graph of the optical spectrum after filtering according to an embodiment of the present invention.
9 is a graph showing the wavelength displacement sensitivity to a measured temperature according to an embodiment of the present invention.
10 is a block diagram showing a configuration of a monitoring apparatus according to an embodiment of the present invention;
11 is a block diagram showing a configuration of an abnormality determination unit according to an embodiment of the present invention;
12 is a block diagram of a hardware configuration of a controller of a monitoring apparatus according to an embodiment of the present invention;
13 is a block diagram of a hardware configuration of a switch encoder input unit according to an embodiment of the present invention.
14 is a block diagram of a hardware configuration of an Ethernet communication controller according to an embodiment of the present invention;
15 is a block diagram of a hardware configuration of a TFT LCD I / F unit according to an embodiment of the present invention;
16 is a graph showing a captured waveform of an output voltage of a back light controller according to an exemplary embodiment of the present invention.
17 is a block diagram of a hardware configuration of an external memory unit according to the present invention.
18 is a block diagram showing a configuration of a reasoning unit of a deterioration diagnosis system for a switchboard according to an embodiment of the present invention;
19 is an exemplary view showing learning data for learning a reasoning unit of a deterioration diagnosis system for a switchboard according to an embodiment of the present invention;
20 is a flowchart illustrating an intelligent deterioration diagnosis method of a switchboard having an optical fiber temperature sensor according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the drawings.
In the description of the present invention, the same parts are denoted by the same reference numerals, and repetitive description thereof will be omitted.
First, a configuration of an intelligent deterioration diagnosis system for a switchboard having an optical fiber temperature sensor according to an embodiment of the present invention will be described with reference to FIG.
1, an intelligent deterioration diagnosis system for a switchboard having an optical fiber temperature sensor according to an embodiment of the present invention includes a
The
In addition, the switchgear is installed in a building or a factory using a large-capacity electric power, and is divided into a water main, an electric switchboard, a distribution board and the like depending on its use. In order to distribute electric power to the inside of the
The equipment or equipment (or electric power equipment) 11 installed inside the
The deterioration diagnosis system of the switchboard according to the present invention is provided with sensing means for sensing the state of each device in the
Meanwhile, the
The
Next, the
That is, the
Preferably, the
The
The
Meanwhile, the
Further, the
Next, the structure of the optical
As shown in FIG. 2, the sensor head of the optical
That is, when the distance between the two object planes constituting the interferometer is L, the phase difference Φ between the reflected waves reflected from the two surfaces is Φ = 4πrLv / ν when the refractive index r of the optical fiber core, the optical frequency v, c. The coherence length of the light source is much larger than 2rL and the power reflectance of the interferometer plane is expressed by Equation (1).
[Equation 1]
At this time, the interference output optical power Pr and the phase are expressed by the following equations (2) and (3).
&Quot; (2) "
&Quot; (3) "
Where R is the reflectance of the two surfaces, Pi is the input optical power, r is the index of refraction of the optical fiber, L is the length of the interferometer, and?
When the temperature around the optical fiber sensor changes, the refractive index of the optical fiber and the length of the interferometer change, causing a phase change of the output optical signal. That is, the temperature increases to change the refractive index and the length to cause the phase change of the interferometer output signal.
The phase change with respect to the ambient temperature change of the interferometer is represented by the following equation (4).
&Quot; (4) "
In the above equation, the change of the refractive index due to the temperature change is larger than the change of the length of the interferometer, so that the phase change due to the length change is ignored. Where α = 1 × 10 -5 / ° C.
Next, the configuration of the temperature sensor system of the
3, the
When a driving current is supplied to the LD in the driving circuit and a pulse is applied to the LD by using a function (pulse) generator, pulse-modulated light is emitted from the LD. The light emitted from the LD goes through the 3-dB coupler into the interferometer. The light arriving at the interferometer interferes with each other and returns to the PD via a 3-dB coupler. The interference light returned to the PD is converted into an electric signal from the PD and input to the PC. The PC measures the phase change of the interference output signal of the interferometer from the electric signal sent from the PD.
The interferometer sensor is the optical
More specifically, the operating temperature of the laser diode (LD) used as a light source in the
The optical fiber temperature sensor of the interferometer is represented by the change of the phase difference between the reference arm and the sensing arm of the interferometer due to the change of the measured amount. Since the difference of the measured amount is represented only by the difference of the scale factor, A processor can be used.
Therefore, when the sensor system of the optical
In the optical multiplexing sensor system, the output signals from the sensor array are in a state where the sensor signals are separated on the time axis or spectrum and mixed with each other. Therefore, an optical multiplexing method is used to separate signals of specific sensors.
3, when a cavity type interferometer is adopted as a sensor in a multi-channel sensor system configuration, a signal processing device and a sensor array can be connected by a single optical fiber, It is possible to minimize the number of required optical parts and also has an advantage of excellent optical efficiency. 3 is a block diagram of the optical fiber temperature sensor system.
In the case of the Fabry-Perot sensor array, when the reflectance of the sensor near the light source changes, the intensity of the incident light to the rear sensor changes, resulting in interference between the sensors. In order to solve this problem, the problem of crosstalk is solved by constructing in parallel as shown in Fig.
The Mach-Zehnder interferometer, which is mainly used in multiplexed interferometric sensors, is complicated in structure due to the use of a separate phase modulator for signal processing, and a signal processing algorithm such as a synthetic heterodyne Which is difficult to implement.
As described above, the optical fiber temperature device of the
When a driving current is supplied from a laser diode (LD) light source and a pulse is applied, pulse-modulated light is emitted from the laser diode (LD). The light emitted from the LD goes through a 3-dB coupler to the fiber interferometer. The light arriving at the interferometer interferes with each other and extracts the optical spectrum of the temperature change using two peak wavelengths. This optical spectrum extracts the optical path difference by demodulating the signal through discrete signal processing and filtering.
The demodulated interference light returned to the sensor photodetector (PD) via a 3-dB coupler is converted into an electrical signal in the PD and processed by the arm core processor.
The hardware related to the signal processing is designed to calculate the amount of phase change due to the change of the measurement signal along with the direction of change of the measurement signal, while emphasizing the general purpose and constructing the basic system of the arm core processor. To this end, the hardware related to the signal processing operates synchronously with the signal of the system synchronizing part, and performs functions such as control of LD modulation and switching through the control program, acquisition and display of the sensor signals by position along the sensor array, and interrupt control .
In the arm core processor, the phase change of the interference output signal of the interferometer is measured from the electric signal sent from the PD. As described above in Equation (4), the ambient temperature change of the interferometer is calculated through the measured phase change.
Further, two peak tracking methods using a light source spectrum can be applied in order to detect the signal variation of the length difference Di of the optical path of the interferometer sensor according to the temperature change.
At the peak wavelength, the phase difference between the interfering rays is an integer of 2 ?, which corresponds to constructive interference. At the wavelength of the valley, the phase difference between the interfering rays is an integer of 2? To which? Is added, corresponding to destructive interference. The separation between the peak position [lambda] p and the two adjacent peaks is called the free spectral range, and the reflection spectrum (FSR) is given by:
&Quot; (5) "
&Quot; (6) "
Where n is the reflectivity of the cavity and L is the distance of the interferometer cavity. D i is the optical path length difference in the cavity, and λ p is the wavelength of the light at the peak.
In the interferometer sensor, the cavity length L and the optical path difference Di are encoded with a reflection spectrum and can be inferred by analyzing the change in the reflection spectrum of the sensor. Several techniques for detecting sensor spectrum changes can be taken, such as measuring the reflected power at a given wavelength and tracking the spectral peak shift to obtain a phase delay.
Two peaks are detected between the spectrum of the incident light and the reflected light, and the absolute Di (difference in optical path) of the sensor is calculated using two adjacent peak wavelengths in equation (7).
&Quot; (7) "
Here,? P, m and? P, m + 1 represent the wavelengths of two adjacent peaks, respectively.
Therefore, this method has a larger dynamic range in the Di measurement.
As long as the prediction error does not exceed a separate half between adjacent peaks, the original peak is located precisely. The relative Di change is calculated with high precision.
As shown in FIG. 4, the spectral response of the sensor to the reflected light is shown using a coated film on the end surface of the polarization maintaining optical fiber. This response shows a sufficiently high signal-to-noise ratio (SNR) of about 15 dB and a relatively low loss of 14 dB. 4 is a graph showing the response spectrum of the optical fiber temperature sensor.
This makes it easy to observe peak wavelength displacement of light during sensing. In the case of the sensor according to the invention, the theoretical FSR is determined to be 8.24 nm by equation 6, which is in good agreement with the test results.
Next, the optical signal processing method in the
The sensor requires a spectral response to changes in the interference signal and external parameters.
In order to infer the information of the external parameter, the signal change of the sensor must be detected. In general, a scanning interferometer is used to detect a change in a sensor signal, but a resolution and a separate interferometer device are required. In addition, there is a limit to a high temperature thermometer because the system stability and the surrounding environment are easily affected. Therefore, in the present invention, the scanning interferometer is excluded and the difference between the optical paths is estimated using two peak spectra. And extracts the interference spectrum from the mixed optical signal through signal processing of discrete Fourier transform.
First, a scanning interferometer method will be described.
The scanning interferometer is a conventional method for detecting the difference Di of the optical path of the interferometer sensor, and basically consists of a detection interferometer and a receiving interferometer with a control function.
The difference in the path of light of the two interferometers is arranged several times larger than the coherent length of the light source so that the interference fringes can be observed with one interferometer when individually illuminated. Both the reflection mode and the transmission mode are configured in this way.
Figure 5 shows the principle of a generic scanning interferometer system. Low interference light from a broadband light source is coupled to the first received interferometer and phase-modulated light reaches the detection interferometer through a 3 dB coupler. 5 is a configuration diagram of an FP sensor having a scanning interferometer.
The photodiode detector is used to detect the intensity of the light reflected from the detection interferometer and it is necessary to scan the difference in the optical path of the receiving interferometer to find the difference in the optical path of the sensing interferometer. Assuming that the light source has the optical power spectrum of P (λ), the optical power received by the photodiode detector is given by the following equation.
&Quot; (16) "
Where T is the power transmittance of the receiving interferometer and R is the power reflectivity.
The receiving interferometer may be configured in other ways, such as a piezo scanning device, an electronic scanning method, or a Michelson interferometer by a FP interferometer.
One of the most prominent advantages of a scanning interferometer system is the use of a low coherent light source by using a light source such as a narrow band laser. However, not only the resolution and the size of the interferometer but also the hardware cost due to the configuration of the interferometer device are added, and the system stability and the surrounding environment are easily affected, resulting in a limitation in the temperature measurement and accuracy of the high temperature.
Fig. 6 illustrates an optical signal processing algorithm of the interferometer as a spectrum extracted at each different temperature of the heating process. In Fig. 6, "initial peak " is the order m and the original position of the peak. That is, Fig. 6 is a graph illustrating an example of a spectral signal processing algorithm using two peaks.
The "last peak" is the actual final position of the same order peak after moving to a change in the measurand, the "estimate" is the estimation of the two peak tracking methods and the peak shift, the "error" is the estimation error of the peak position, Is less than half the FSR, we can correctly find the original peak.
FIG. 7 is a flowchart for explaining signal demodulation processing. The mixed optical signal captured by the sensor is converted into a wave number region by linear interpolation. Next, the DFT is evaluated and the peak frequency of each sensor is located.
A bandpass filter having a length M for restoring the optical signal of the sensor is created by moving the peak frequency of the sensor signal to a low pass filter for filtering.
The filtering is performed by multiplexing the DFT of the sensor signal and the frequency response of the band pass filter by IDFT. Finally, the filtered optical signal is shifted to the left by (M-1) / 2 to obtain a unique individual sensor optical signal.
The band pass and the next sensor observe the suppression rate of the cut-off band filter greater than 15dB. After filtering, a sinusoidal wave is obtained.
Figure 7 is a comparison of a unique sensor signal and a filtered signal. The proximity of the comparisons shows that the filtered spectra have peak positions such as the same periodicity but the signal to noise ratio with a unique spectrum is improving.
The signal demodulation is to extract the interference spectrum of the sensor from the mixed optical signal detected by the signal calling system. This is done with a dark core processor that implements digital signal processing technology.
Since the reflection spectral data obtained by the interferometer are spaced at the same wavelength, linear interpolation is employed to transform the data into the data sequence X [n] in the wavelength range.
k 0 = 4.002 rad · μm -1 in the same spacing range with frequency samples of k n = k 0 + n δk is a sample of the minimum wave number corresponding to the maximum wavelength in the light source and δk = 6.58 rad · m -1 is the wave number Sampling interval. N = 0, 1, 2, ..., N-1 (N = 20,000) is an integer representing the sampling index of the wave number. In order to analyze the frequency components of the signal, the discrete Fourier transform (DFT) of the data sequence is evaluated by the following equation.
&Quot; (17) "
The inverse discrete Fourier transform (IDFT) is as shown in equation (18)
&Quot; (18) "
The cavity length of the sensor was from 180 μm to 3 cm.
Next, the filter design will be described.
The case where the DFT of the filter's impulse response has a phase response value other than zero, i.e., the following equation.
&Quot; (19) "
In the equation, the steady-state response can be written as
&Quot; (20) "
The output sequence in the equation shows the phase delay -θ (ω) / ω associated with the input sequence.
If the phase delay is not constant, the output signal exhibits phase distortion and does not look like an input signal. In order to avoid phase distortion, the frequency response of the filter must have a linear phase characteristic in that frequency band. In this case, the shape of the signal does not change after filtering, but the overall shift given by the phase delay is made.
Based on the shift of the low-pass filter in the signal demodulation, the band-pass filter is specially designed for each sensor.
The frequency response of a
&Quot; (21) "
The band-pass filter hBP [n] is then generated by shifting the low-pass filter to the center frequency? 0 of the sensor signal selected by the equation.
&Quot; (22) "
The frequency response of the generated bandpass filter is given by the following equation.
&Quot; (23) "
This filter has a linear phase response of? (?) = (M-1) / 2?. The phase delay is constant for all ω:
&Quot; (24) "
Therefore, after filtering, the signal is shifted to the right by (M-1) / 2. The purpose of filtering is to pass all frequencies of the sensor as long as it has minimal distortion and to block system noise with all frequencies and maximum attenuation of the other sensor.
The sensor Di can be calculated by applying a signal processing algorithm to this optical spectrum. 8 is a graph of the optical spectrum after filtering.
In the present invention, the wavelength displacement with respect to the measured temperature is shown in FIG. 9 is a graph showing the sensitivity of the wavelength displacement to the measured temperature.
The slope of the straight line obtained from the data of each point in Fig. 9 shows the sensitivity of the temperature sensing, and the sensitivity is determined to be 31.8 캜. This is almost exactly the same as the theoretical result of 31.88. On the other hand, the R2 value of the heating process is 0.993, respectively, and FIG. 9 shows that the spectral response of the interferometer is very stable. The optical fiber interferometer according to the present invention can provide an extinction ratio of approximately 10 dB and an FSR of 8.24 nm.
The optical interferometer according to the present invention is small in size, easy to manufacture, and economical. Importantly, it is capable of rapid response to microscopic temperature changes and makes it suitable for high temperature applications in confined spaces in harsh environments.
Next, the configuration of the
The
10, the
The
Next, the
The
The
When the
The information collected through the optical fiber temperature sensor is stored in the database and the alarm is activated if the real time measurement temperature is higher than the threshold (average temperature or set temperature). At this time, since the temperature of the optical fiber temperature sensor and the peripheral device may be changed or the ambient temperature may be changed due to fire or the like, it is necessary to take prompt action. Therefore, when an alarm occurs, the location of the alarm should be automatically displayed on the screen and the problem can be found.
Automatically compare and analyze the information about the temperature exceeding the threshold after alarm occurrence and the history temperature information stored in the database. After comparing and analyzing the real-time temperature information and the history temperature information, confirm the location where the alarm occurred.
Next, a rough configuration of the
11, the
The
Specifically, the
In addition, the space temperature information may refer to a specific temperature in a specific space of the
Here, the
For example, the space temperature information may mean a difference between a temperature of a specific portion inside the
The
The
The
Next, a hardware configuration of the controller implemented by the
As described above, the optical fiber temperature deterioration monitoring system receives temperature data from the interferometer optical fiber temperature sensor and displays it on the TFT LCD window as an image screen. The temperature of the object is measured through the Ethernet communication line A temperature value of up to 6 channels is received from the optical fiber temperature sensor module.
Also, it receives the contact temperature of the surface, the ambient temperature, the temperature rise information, and the temperature gradient information data and displays it on the TFT LCD. At this time, the specific area of the deteriorated image is selected, the temperature change of the area is monitored and compared with the alarm set value, and an event is recorded while the alarm is raised according to the determined result. As shown in FIG. 1, for example, a sensor module (sensor unit) capable of connecting up to four sensors, a monitoring device or a controller, and a remote monitoring PC server may be used.
12, a hardware feature of the controller implemented by the
The
The circuit configuration of the switch
As shown in FIG. 13, when the number of switch inputs for switch operation for reading the recording contents of an event is 8, and the number of available ports of the microcontroller is 4, the encoder logic is used. That is, by configuring the switch input circuit as shown in FIG. 13, it is possible to detect all the desired number (for example, eight) switch inputs by only a small number of available ports (for example, four).
Eight different output levels can be created by using three output ports (OUT_PORT) of the microcontroller. Only one of the switch inputs SW1 to SW8 corresponding to each of these eight outputs can be connected to the SW_IN circuit PATH To the microcontroller input port IN_PORT.
Next, the circuit configuration of the Ethernet
In order to transmit data processed / processed by the
When the MCU and the Ethernet controller communicate with each other via SPI (Serial Peripheral Interface) communication and the data is read from the outside or the data is requested from the outside, the first signal detected is the INTx signal. It is a signal to notify that the data from the outside has arrived via the Ethernet communication while processing the work.
After this signal is detected, data is exchanged between the MCU and the Ethernet controller via SPI communication. This INTx signal is a signal that causes an interrupt to the MCU when there is a data request from the outside or when a data frame is received from the outside of the MCU. Therefore, this signal is used only when the MCU receives data.
In addition, when the data is transmitted and received, the LEDs are driven through the LEDA and LEDB outputs, thereby visually recognizing that communication is being performed properly.
On the other hand, adopts Ethernet communication UDP protocol and adopts RS-485 MODBUS communication method.
Next, the hardware configuration of the display interface unit or the TFT LCD I /
The
At this time, a configuration for signal interfacing between the LCD and the MCU is required.
LCD color R0 to R7, G0 to G7, and B0 to B7 data buses are required for data interfacing, which can control the brightness of colors and brightness of color. In addition, a pixel clock signal is required to determine which pixel point of the LCD display screen the color information transmitted through the data bus is to be displayed.
In addition, H SYNC and V SYNC signals are required to determine the coordinates of the pixel clock signal on the X and Y axes of the screen. Here, H SYNC is a signal for synchronizing with a pixel clock moving on a horizontal axis, and V SYNC is a signal for synchronizing with a pixel clock moving on a vertical axis.
The data enable signal is a signal necessary to distinguish whether the color information data of the data bus is valid data or not.
In the case of such a display device, an LCD is used to illuminate the back side of the liquid crystal display. The level of the voltage to be supplied to both ends of the LED must be about 30 V. The circuit module for generating such a voltage is a back light controller.
16 shows a captured waveform of the output voltage of the backlight controller. The output voltage of the controller has a PWM waveform, and the LCM_LEDA output waveform in a stable state outputs a PEAK value of about 30V at intervals of 6uSec as shown in FIG.
Finally, the hardware configuration of the external memory unit 350 is shown in Fig.
The NAND flash memory is used as an external memory to store executable files to be used in the microcontroller MCU, UI picture files for user interface, and control files for system control. When power is turned on, the flash memory (FLASH Memory) reads files necessary for system operation, such as executable files and UI picture files, and stores them in the RAM memory of the MCU.
At this time, the READ / WRITE operation of this series of files can be performed only while the NF_nCS line is maintained in the logic 'LOW' state, and a command must be given to distinguish whether to read or write to the NAND FLASH memory first Logic 'LOW' should be written to the NF_nCS line and the NF_CLE line should be set to logic 'HIGH', and then the data to be read or written to the memory should be determined.
Therefore, the NF_ALE address designated line must be made logic 'high' state to designate NAND FLASH memory address, and then the address value is output to NF_D0 to NF_D7 BUS.
When an address is specified in the NAND FLASH MEMORY, the data should now be READ or WRITE. When READING DATA, READ CLOCK should be output to NF_nRE line. When reading DATA from NF_D0 ~ NF_D7 BUS, Logic of NF_nRE line is set to LOW 'To' HIGH 'state and then read data on NF_D0 to NF_D7 BUS.
On the contrary, when WRITE data, WRITE clock must be given to NF_nWE line. Every time data is written to NF_D0 ~ NF_D7 BUS, the logic state of NF_nWE line is changed from LOW to HIGH and NF_D0 ~ NF_D7 BUS WRITE any DATA value.
Next, the
FIG. 18 is a block diagram showing the configuration of a reasoning unit of a deterioration diagnosis apparatus for a switchboard according to an embodiment of the present invention. FIG. 19 is a block diagram showing the configuration of a reasoning unit Fig.
18 and 19, the
The present invention can calculate the resultant value that can determine the degree of deterioration of the live part of the switchboard using each of the four minutiae points as described above. As shown in FIG. 18, the reasoning engine is a fuzzy clustering-based RBF neural network Network (Fuzzy clustering based RBF Neural Network: FRBFNN) can be used.
18,
The input module 132-1 may receive the number of feature points (x1, ..., x1) received from the
The first half module 132-2 may be a fuzzy C-means (FCM) and may use the FCM to determine the input space segmentation and the active level of the input values in each space. In the first half module 132-2, the learning of the membership function of the FRBFNN is performed by the FCM, and the membership value can be determined according to the result of the performance.
The second half module 132-3 may be a local model in each purge space, and the second half module 132-3 may be expressed by a mathematical expression model of a polynomial form. In the latter half module 132-3, learning of the polynomial can be performed by a weighted least square estimator (WLSE).
The FRBFNN may be expressed in the form of an " if-then " fuzzy rule as shown in
Also, the polynomial of the second half module 132-3 can be searched using a genetic algorithm that shows the fitness of individual chromosomes and has an optimal fitness.
&Quot; (25) "
The equation (25)
&Quot; (26) "
here,
That is, ℓ is the number of input variables, and R j is the jth fuzzy rule. Also, j = 1, ... , n in n means the number of fuzzy rules,
The FRBFNN model can be expressed by the following equation (27). In the deterioration diagnosis apparatus of the present invention, the output data of the second half module (132-3) is compared with a predetermined clean reference to determine the deterioration degree of the busbar section It can be judged.
&Quot; (27) "
Where n is the number of clusters (fuzzy rules)
In other words,
Here, the number of rules can be determined by an arbitrary experience, and in the present invention, the number of rules is set to 10, and the value can be generally set to a value between 5 and 20.
Further, in order to use FRBFNN as an inference engine of the degradation diagnosis apparatus of the present invention, learning must be performed through learning data. In the present invention, the learning data of the form shown in Fig. 19 can be used to determine the degree of deterioration of the power-transmission-side active section.
Specifically, experimental data must be acquired for learning. At this time,
For the collected data sets, the degree of deterioration, which is the output value of FRBFNN, can be defined.
For example, the output value y may be defined as 0 when the degree of deterioration is the highest, and y may be defined as 100 when the degree of overheating is lowest.
In the FRBFNN, the first half learning and the second half learning are sequentially performed. The first half learning is performed by the FCM algorithm, and the second half learning is performed by the WLSE. FCM is an algorithm based on fuzzy set theory and least squares error evaluation by improving initial C-Means clustering.
An important difference between FCM and C-Means clustering is that any data in the C-Means clustering algorithm can belong to multiple clusters with membership levels specified by membership values between 0 and 1. However, the FCM uses the objective function (cost function) to classify the cost function as a minimum while partitioning the data.
The membership matrix u may have a value between 0 and 1, and the sum of degree of belonging to which the given data belongs to each cluster may be 1 as shown in Equation (28) below.
&Quot; (28) "
Here, n is the number of clusters and m is the number of data.
Further, the cost function in the FCM can be generalized as shown in Equation (29) below.
&Quot; (29) "
here,
&Quot; (30) "
Where r is the dimension of the input space,
By using the normalized Euclidean distance, it is possible to prevent a large input variable from having a large influence on determining the center of the cluster rather than a small input variable.
On the other hand, the necessary condition for the cost function of Equation (32) to be minimized is expressed by Equation (30) and Equation (31).
&Quot; (31) "
The FCM performs iterative processing until it does not further improve the
Specifically, the FCM uses the steps to be described later,
(32)
The FCM satisfies the above expression (28), and the membership matrix having a random value between 0 and 1
If the calculation of the cost function is less than or not better than the tolerance, the calculation is stopped (step 3). Next, using the following equation (32), a new member matrix
As described above, from the FCM algorithm, the center value of each cluster
&Quot; (33) "
here,
&Quot; (34) "
The second half learning is a coefficient of the polynomial of the second half, which is performed using the WLSE. The coefficient of the polynomial is calculated so that the value of the performance evaluation function of Equation (34) is minimized. , And can be expressed by the following equation (35).
&Quot; (35) "
here,
here,
&Quot; (36) "
Here, m means the number of data.
Further, the coefficient of the polynomial, which is the local model for the jth rule, is calculated by the following equation (37).
&Quot; (37) "
The FRBFNN is learned by the FCM and the WLSE in the first half and the second half. However, the number of rules, the order of the second half polynomial, and the value of the fuzzy coefficient used in the FCM must be determined in advance.
The output of the output module 132-4 is learned such that a value between 0 and 100 is output.
The FRBFNN finally constructed through learning is Equation (27), and the degree of degradation is calculated and outputted when the
Here, the degree of deterioration can be output as a value between 0 and 100, and as shown in FIG. 19, the determination result can be displayed as a normal state, an attention state, a check state, and a dangerous state according to the range of the degree of deterioration And the degree of deterioration can be expressed by a value.
Therefore, the
For example, if the preset deterioration criterion is a steady state when the degree of deterioration is less than 20, a management state when the degree of deterioration is 20 to 70, a degree of deterioration is 80 when the degree of deterioration is greater than 70, In this case, it is judged as a dangerous state, so that the state of each of them can be activated or shut off the breaker.
Next, a method for controlling the deterioration diagnosis system of the switching center according to the present invention will be described with reference to Fig. 20 is a flowchart showing a method for controlling a deterioration diagnosis system of a switchboard according to an embodiment of the present invention.
Referring to FIG. 20, in the control method, a contact portion temperature, an ambient temperature, a contact portion temperature change and an ambient temperature change are introduced into the interior through a sensor or the like installed on one side of a switchboard, Method.
First, the contact unit temperature and the ambient temperature are sensed by the
In addition, the deterioration information inside the switchgear is calculated based on the detected state information (S110), and the degree of deterioration of the power-saving / active section can be controlled based on the calculated deterioration information (S120).
Here, the status information can be detected by a preset period and a predetermined number.
In addition, the method for controlling the deterioration diagnosis system of the switchboard may further include warning that deterioration state information inside the
Specifically, the step S110 extracts the state information inside the
Specifically, in step S110, environmental information (feature points) of at least one of a connection temperature, an ambient temperature, a connection temperature change, and an ambient temperature change in the received
Next, it is expressed as a polynomial-type mathematical expression model and is learned by WLSE (Weighted Least Square Estimator) to determine coefficients of the polynomial, and infer the cleanliness inside the housing based on the membership matrix and the cluster center value for the environment information The resultant value can be calculated using FRBFNN (Fuzzy clustering based RBF Neural Network).
Next, in step S110, the deterioration state inside the
The
Although the present invention has been described in detail with reference to the above embodiments, it is needless to say that the present invention is not limited to the above-described embodiments, and various modifications may be made without departing from the spirit of the present invention.
10: Switchgear housing 11: Components
20: sensor part 21: optical fiber temperature sensor
30: Monitoring device 31: Sensor receiving part
32: display section 33: setting section
34: abnormality determination unit 35: alarm unit
36: Storage unit 40: Remote server
131: Feature extraction unit 132: Reasoning unit
133:
Claims (10)
A sensor unit including a plurality of optical fiber sensors for detecting an internal space temperature of the housing and a contact temperature in a power facility provided in the housing; And
Diagnosing a deterioration state inside the housing based on the internal space temperature detected by the sensor unit and the contact temperature of the electric power facility, and controlling the internal state of the housing according to deterioration state information in the diagnosed housing Or a monitoring device for generating an alarm signal,
The monitoring device includes:
Space temperature information, temperature rise information, and temperature gradient information based on the internal space temperature of the housing received from the sensor unit and the contact temperature of the electric power facility,
A reasoning unit for calculating a result value that can infer the deterioration state inside the housing based on the contact temperature information, the space temperature information, the temperature rise information, and the temperature gradient information received from the feature extracting unit;
And a determination unit for determining a deterioration state inside the housing by comparing a result of the inference unit with a preset reference.
Wherein the optical fiber sensor comprises two cavity surfaces having a predetermined distance and uses an optical fiber interferometer for extracting a difference between a light path and a phase difference between incident light and reflected light through interference of light, Wherein the sensor is a sensor for measuring a temperature by detecting a temperature change from a difference in the temperature of the vehicle.
The sensor unit includes a laser diode (LD) light source having a variable wavelength of 1.3 to 1.5 占 퐉, a photo detector, a plurality of optical fiber sensors constituting the optical fiber interferometer, and a 3-dB coupler. The light emitted from the laser diode And the optical spectrum of the temperature change is extracted using the two peak wavelengths, and the extracted optical spectrum is subjected to discrete signal processing And extracting the optical path difference by demodulating the signal through filtering. The optical fiber temperature sensor of claim 1,
Wherein the phase difference DELTA phi with respect to the temperature change DELTA T is obtained by the following formula 1. < EMI ID = 1.0 >
[Equation 1]
Where L is the length of the interferometer,? Is the wavelength of the light, and? = 1 × 10 -5 / ° C.
Wherein the difference Di of the optical path is obtained by the following equation (2): < EMI ID = 2.0 >
[Equation 2]
However, λ p, m and λ p, m + 1 represent the wavelengths of two adjacent peaks, respectively.
The sensor unit converts the mixed optical signal sensed by the optical fiber sensor into a wave number region by a linear interpolation method, evaluates a discrete Fourier transform (DFT) to analyze a frequency component of the signal, And extracting a peak wavelength by filtering the peak frequency of the extracted sensor signal with a low-pass filter. The intelligent deterioration diagnosis system for a switchgear with an optical fiber temperature sensor, comprising:
The reasoning unit,
(Feature point) of at least any one of the contact temperature information, the spatial temperature information, the temperature rise information, and the temperature gradient information received from the feature extracting unit using FCM (Fuzzy C-means) A first half module for determining a membership matrix and a cluster center value for information, and
Based on a matrix of belonging to the environment information determined in the first half module and a cluster center value determined by the first half module, And a second half module for calculating a result of inferring the internal degradation state using a Fuzzy Clustering based RBF Neural Network (FRBFNN).
(a) sensing a contact temperature and a space temperature inside the housing by an optical fiber sensor;
(b) detecting environmental information by extracting contact temperature information, space temperature information, temperature rise information, and temperature gradient information inside the housing using the contact temperature and the space temperature inside the housing,
(c) calculating deterioration state information in the housing based on the detected environment information, and
(d) determining a deterioration state inside the housing based on the calculated deterioration state information,
The step (c)
(c1) extracting contact temperature information, space temperature information, temperature rise information, and temperature gradient information inside the housing based on the contact temperature and the space temperature inside the housing;
(c2) calculating a result value from which the deterioration state inside the housing can be inferred from the contact temperature information, the space temperature information, the temperature rise information, and the temperature gradient information; And
and (c3) comparing the resultant value with a preset reference to determine a deterioration state inside the housing. The optical fiber temperature sensor of claim 1,
Wherein the optical fiber sensor comprises two cavity surfaces having a predetermined distance and uses an optical fiber interferometer for extracting a difference between a light path and a phase difference between incident light and reflected light through interference of light, And the temperature is measured by sensing the temperature change from the vehicle to the vehicle,
In the step (a), a laser diode (LD) light source having a variable wavelength of 1.3 μm to 1.5 μm, a photo detector, a plurality of optical fiber sensors constituting the optical fiber interferometer, and a 3-dB coupler, The light emitted from the optical fiber interferometer is passed through a 3-dB coupler, the light reaching the interferometer interferes with each other, extracts a light spectrum with respect to a temperature change using two peak wavelengths, Wherein the optical path difference is extracted by demodulating a signal through discrete signal processing and filtering.
The step (c2)
(c21) the environment information (minutiae) of at least one of the contact temperature information, the space temperature information, the temperature rise information, and the temperature slope information inside the housing, using FCM (Fuzzy C-means) Determining a membership matrix and a cluster center value for < RTI ID = 0.0 >
(c22) a polynomial expression of a mathematical expression model, which is learned by a weighted least square estimator (WLSE) to determine coefficients of the polynomial, and based on the membership matrix and the cluster center value for the environment information, And calculating a resultant value that can infer cleanliness using a fuzzy clustering based RBF neural network (FRBFNN).
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| KR101713832B1 (en) * | 2016-11-11 | 2017-03-09 | 주식회사 일산전기 | The smart temperature system of fiber bragg grating in power distributing board |
| KR20190042218A (en) * | 2017-10-16 | 2019-04-24 | (주)파이브테크 | Apparatus for detecting light temperature using polarization maintaining optical fiber |
| KR102085346B1 (en) * | 2019-04-26 | 2020-03-05 | 신호원 | Power supply control device |
| KR102244683B1 (en) * | 2020-10-29 | 2021-04-26 | 주식회사 케이디티 | Apparatus and method for diagnosing deterioration using array temperature sensor |
| KR102321734B1 (en) * | 2021-04-20 | 2021-11-04 | 주식회사 티오솔루션 | Distribution Panel Temperature Monitoring System Using AI |
| CN113639891A (en) * | 2020-09-03 | 2021-11-12 | 深圳阿珂法先进科技有限公司 | High-speed optical fiber temperature sensing demodulation method based on equivalent wavelength |
| KR20220075072A (en) * | 2020-11-27 | 2022-06-07 | (주)노티스 | Temperature measurement device using a light and method thereof |
| KR102462081B1 (en) | 2022-04-20 | 2022-11-03 | 지투파워(주) | Temperature monitoring system for electrical equipment using temperature sensor of fiber optic probe type |
| KR20230059124A (en) * | 2021-10-25 | 2023-05-03 | 주식회사 정우계전 | Deivce and method of anomaly detection in switchboard based on artificial intelligence |
| WO2024092980A1 (en) * | 2022-11-01 | 2024-05-10 | 中车永济电机有限公司 | Temperature sensing apparatus and method, and storage medium |
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| KR101713832B1 (en) * | 2016-11-11 | 2017-03-09 | 주식회사 일산전기 | The smart temperature system of fiber bragg grating in power distributing board |
| KR20190042218A (en) * | 2017-10-16 | 2019-04-24 | (주)파이브테크 | Apparatus for detecting light temperature using polarization maintaining optical fiber |
| KR102045831B1 (en) | 2017-10-16 | 2019-11-18 | (주)파이브테크 | Apparatus for detecting light temperature using polarization maintaining optical fiber |
| KR102085346B1 (en) * | 2019-04-26 | 2020-03-05 | 신호원 | Power supply control device |
| CN113639891A (en) * | 2020-09-03 | 2021-11-12 | 深圳阿珂法先进科技有限公司 | High-speed optical fiber temperature sensing demodulation method based on equivalent wavelength |
| KR102244683B1 (en) * | 2020-10-29 | 2021-04-26 | 주식회사 케이디티 | Apparatus and method for diagnosing deterioration using array temperature sensor |
| KR20220075072A (en) * | 2020-11-27 | 2022-06-07 | (주)노티스 | Temperature measurement device using a light and method thereof |
| KR102549446B1 (en) | 2020-11-27 | 2023-06-30 | (주)노티스 | Temperature measurement device using a light and method thereof |
| KR102321734B1 (en) * | 2021-04-20 | 2021-11-04 | 주식회사 티오솔루션 | Distribution Panel Temperature Monitoring System Using AI |
| KR20230059124A (en) * | 2021-10-25 | 2023-05-03 | 주식회사 정우계전 | Deivce and method of anomaly detection in switchboard based on artificial intelligence |
| KR102600365B1 (en) * | 2021-10-25 | 2023-11-10 | 주식회사 정우계전 | Deivce and method of anomaly detection in switchboard based on artificial intelligence |
| KR102462081B1 (en) | 2022-04-20 | 2022-11-03 | 지투파워(주) | Temperature monitoring system for electrical equipment using temperature sensor of fiber optic probe type |
| WO2024092980A1 (en) * | 2022-11-01 | 2024-05-10 | 中车永济电机有限公司 | Temperature sensing apparatus and method, and storage medium |
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