Oil-immersed transformer distributed temperature measurement method based on fluorescent optical fiber sensor

Abstract

The application relates to the technical field of power equipment state monitoring, in particular to a distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors. The method comprises the following steps: arranging fluorescent optical fiber sensors in a partitioned and layered three-dimensional topological structure in key temperature rise areas inside a transformer to form a distributed temperature measurement network; exciting a fluorescent signal through a pulsed laser to inject excitation light; collecting the signal through a double-channel lock-in amplification technology, demodulating the temperature in combination with a double-weight adaptive attenuation model and an oil flow coupling compensation function; reconstructing a dynamic temperature field based on a three-dimensional thermodynamic inversion algorithm to mark high-gradient hot spots; and fusing temperature, load current and oil flow rate data to realize hierarchical alarm. The application breaks through the limitation of traditional single-point monitoring, has excellent strong electromagnetic interference resistance, can accurately reconstruct a global temperature field and dynamically give early warning, and significantly improves the operation safety and operation and maintenance efficiency of the transformer.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment state monitoring, in particular to a distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors. BACKGROUND

[0002] Oil-immersed transformers are the core equipment of power systems, and their operating state directly affects the safety and stability of the power grid. When the equipment is running, internal windings, cores and other components generate heat due to energy loss. If the local temperature is too high or the hot spot exceeds the tolerance range of the insulation material, it may accelerate the aging of the insulation, cause short circuits and even damage the equipment. Therefore, it is crucial to monitor the temperature of the internal key areas in real time and accurately. The current transformer temperature measurement technology has many limitations and cannot meet the needs of full-area and high-precision monitoring:

[0003] Limitations of traditional contact temperature measurement technology:

[0004] The mainstream contact sensors such as thermocouples and thermal resistors are limited by installation conditions and can only be placed at a few discrete points, which cannot cover the key temperature rise areas such as the interlayer of high-voltage windings and the surface of the core, making it difficult to capture local micro-scale overheating. At the same time, such sensors have weak anti-electromagnetic interference ability and are prone to signal distortion in the complex electromagnetic environment inside the transformer. Moreover, their materials and insulation layers are affected by oil immersion and high temperature for a long time, which may cause aging and safety hazards.

[0005] Limitations of non-contact and indirect temperature measurement technology:

[0006] Non-contact methods such as infrared temperature measurement and oil surface temperature measurement can only monitor the surface or oil surface temperature of the equipment and cannot reflect the real temperature of internal components such as windings and cores. The difference between the two is significant. Thermal simulation calculation based on load current relies on empirical parameters and cannot handle sudden conditions such as cooling system abnormalities and local structural defects, which lacks accuracy.

[0007] Adaptability issues of existing optical fiber temperature measurement technology:

[0008] To solve the problem of electromagnetic interference, the optical fiber temperature measurement technology explored by the industry still has obvious shortcomings: limited sensitivity and accuracy, affected by environmental noise, and poor performance in high-temperature and high-gradient scenarios. The interference of internal oil flow and other factors in the transformer is not fully considered, which further affects the accuracy of temperature measurement. Moreover, it can only provide discrete point temperature data and cannot reconstruct the internal global temperature distribution, making it difficult to locate the spatial position of the hot spot and the diffusion trend, resulting in delayed early warning.

[0009] Therefore, the oil-immersed transformer distributed temperature measurement method based on fluorescent optical fiber sensors is proposed to solve the above problems. SUMMARY

[0010] The oil-immersed transformer distributed temperature measurement method based on the fluorescent optical fiber sensor aims to solve the problems in the background.

[0011] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0012] The oil-immersed transformer distributed temperature measurement method based on the fluorescent optical fiber sensor comprises the following steps:

[0013] S1, sensor arrangement: the fluorescent optical fiber sensor is arranged in a partitioned and layered three-dimensional topological structure in the key temperature rise area inside the oil-immersed transformer, including the high-voltage winding interlayer, the low-voltage winding interlayer, the surface of the iron core and the hot spot area of the heat dissipation oil duct; the sensor is composed of a distributed temperature measurement network formed by a plurality of fluorescent optical fibers connected in series through anti-electromagnetic interference optical fiber jumpers, and the length of each optical fiber is customized according to the spatial characteristics of the monitoring area;

[0014] S2, excitation signal injection: a pulsed excitation light with a wavelength of 380 nm to 420 nm is injected into the fluorescent optical fiber sensor through a pulsed laser to excite the fluorescent substance to generate a fluorescent afterglow signal;

[0015] S3, signal acquisition and demodulation: the fluorescent afterglow signal is collected in real time, the ambient noise is separated by using a double-channel synchronous lock-in amplification technology, and the temperature measurement value is obtained by calculating the fluorescent decay time constant;

[0016] S4, temperature field reconstruction: the dynamic temperature field distribution inside the transformer is reconstructed based on the thermodynamic inversion algorithm in combination with the three-dimensional spatial coordinates of the sensor and the real-time temperature data, and the hot spot area with a temperature gradient exceeding 5 degrees Celsius per centimeter is identified and marked;

[0017] S5, time sequence correlation analysis of the temperature data and the transformer load current and the cooling oil flow rate, when the temperature change rate exceeds the dynamic threshold and does not match the load change trend, a hierarchical alarm mechanism is started.

[0018] As a preferred scheme, the sensor arrangement in step S1 comprises:

[0019] In the winding interlayer, a spiral winding type arrangement is adopted, the outside of the optical fiber is coated with an oil-resistant polyimide protective layer with a thickness of not more than 0.2 mm, and is fixed to the winding insulation support through a micro ceramic buckle;

[0020] In the hot spot area of the heat dissipation oil duct, a double-redundancy cross arrangement is adopted, and two optical fibers are fixed to the inlet and outlet positions of the oil duct at an angle of 30 degrees to 60 degrees.

[0021] As a preferred scheme, the signal demodulation of step S3 adopts a double-weight adaptive attenuation model:

[0022] The effective decay time constant is first calculated as the product of the time constant of the fast fluorescent decay component and the real-time signal-to-noise ratio dynamic weight, plus the product of the time constant of the slow fluorescent decay component and its complementary weight, plus the oil flow disturbance compensation;

[0023] wherein the real-time signal-to-noise ratio dynamic weight is equal to the real-time signal-to-noise ratio value divided by the sum of the real-time signal-to-noise ratio and the ambient noise coefficient;

[0024] The oil flow disturbance compensation is obtained by integration, the integral term containing the product of the transformer oil density and the square of the velocity gradient of the oil flow speed in the normal direction of the optical fiber, plus the product of the temperature-dependent dynamic viscosity and the square of the oil flow speed curl, the integration being along the segment length of the optical fiber element, the result being multiplied by the optical fiber fluid sensitivity coefficient.

[0025] As a preferred solution, the temperature calculation employs an oil flow coupling compensation function:

[0026] The temperature value is equal to the calibration coefficient a of the fluorescent material multiplied by the square of the effective decay time constant, plus the coefficient b multiplied by the effective decay time constant, plus the constant term c, finally superimposed with the oil flow influence correction factor d multiplied by the logarithm value of the oil flow resultant speed;

[0027] wherein the oil flow resultant speed is obtained by calculating the square root of the sum of the squares of the three-dimensional velocity components, and adding a small offset to prevent zero speed singularity.

[0028] As a preferred solution, the three-dimensional thermodynamic inversion algorithm of step S4 comprises:

[0029] The heat conduction and convection coupling equation is established, which contains the product of the transformer oil heat capacity and the temperature time derivative equal to the divergence of the heat conductivity and the spatial second-order derivative of the temperature, plus the winding heat source power, minus the dot product of the oil flow speed and the temperature gradient;

[0030] The regularized least squares inversion method is employed, the objective function being the sum of the squares of the deviations of the measured temperature values of all sensors from the calculated values, plus the smoothing constraint term of the integral of the squares of the spatial second-order derivatives of the temperature field in the entire domain, the temperature field distribution being solved by minimizing the objective function.

[0031] As a preferred solution, the hierarchical alarm triggering condition of step S5 is:

[0032] The first-level alarm triggering condition is that the temperature time change rate is greater than the sum of the power function term of the load current and the exponential decay term of the cooling state, and the oil flow speed-up lags behind the load current speed-up;

[0033] The second-level shutdown protection triggering condition is that the hot spot area time change rate is greater than the dynamic diffusion threshold, and the maximum temperature gradient exceeds 8 degrees Celsius per centimeter;

[0034] Wherein, the dynamic diffusion threshold is equal to the basic diffusion rate multiplied by the linear function of the relative change proportion of the load current relative to the rated value, plus the thermal accumulation factor.

[0035] As a preferred solution, the fluorescent optical fiber sensor adopts europium ion doped silica-zirconia composite fluorescent material, and the temperature sensitivity is 0.85 percent per degree Celsius of the relative change of the decay time constant, and the temperature measurement accuracy is better than plus or minus 0.5 degrees Celsius; and the surface of the optical fiber is coated with a nano-aluminum oxide insulation layer, and the thickness is controlled within the range of plus or minus 5 nanometers.

[0036] As a preferred solution, the winding heat source power is dynamically updated through an electro-thermal coupling model:

[0037] The heat source power is equal to the product of the winding resistivity and the square of the current density, plus the product of the core loss coefficient and the time derivative of the magnetic flux density;

[0038] Wherein, the winding resistivity changes inversely with the increase of temperature, and the resistivity decreases by 0.4 percent for every 1 degree Celsius increase in temperature.

[0039] As a preferred solution, the thermal accumulation factor is calculated by a recursive algorithm with a forgetting factor:

[0040] The current thermal accumulation factor is equal to the value of the last time multiplied by the exponential decay factor, plus the current temperature deviation value multiplied by the thermal accumulation coefficient;

[0041] Wherein, the exponential decay factor is determined by the thermal decay coefficient and the sampling time interval, and the thermal accumulation coefficient is related to the thermal decay coefficient.

[0042] As can be seen from the above technical solutions provided by the present application, the oil-immersed transformer distributed temperature measurement method based on the fluorescent optical fiber sensor provided by the present application has the following beneficial effects:

[0043] 1. Distributed accurate temperature measurement of the whole area, breaking through the limitations of traditional single-point monitoring:

[0044] The fluorescent optical fiber sensor is arranged in a partitioned and layered three-dimensional topological structure, covering key temperature rise areas such as high-voltage winding interlayers, low-voltage winding interlayers, core surfaces and heat dissipation oil channels, realizing full-area distributed monitoring of the three-dimensional space inside the transformer, and solving the problem that traditional thermocouples / infrared temperature measurement can only obtain local discrete point temperatures;

[0045] Combined with a double-weight adaptive attenuation model and an oil flow coupling compensation function, the temperature measurement accuracy is significantly improved, and through oil flow disturbance compensation, the temperature calculation deviation caused by oil flow impact is effectively eliminated;

[0046] 2. High reliability in strong electromagnetic environment, adapting to extreme working conditions of the transformer:

[0047] The fluorescent optical fiber sensor adopts a special composite material, is coated with a high insulation layer on the surface, and is designed to resist electromagnetic interference, so that the sensor can stably work in a strong electric field and a mechanical vibration environment inside a transformer, and solves the problem of signal distortion caused by electromagnetic interference of a traditional electronic sensor.

[0048] The sensor has oil resistance and high temperature resistance, and is suitable for a harsh environment for long-term operation of an oil-immersed transformer.

[0049] 3. Three-dimensional dynamic temperature field reconstruction, accurate positioning of high gradient hot spots:

[0050] Based on a heat conduction-convection coupled finite element model and a regularization least square inversion algorithm, real-time reconstruction of a three-dimensional temperature field inside the transformer is realized, and micro-scale hot spots (such as local overheating between winding layers) that are difficult to identify by traditional methods can be captured.

[0051] Through maximum temperature gradient monitoring and hot spot area diffusion rate analysis, potential faults can be identified in advance, and the response time can be greatly shortened compared with the traditional way of relying on artificial inspection.

[0052] 4. Multi-source fusion hierarchical early warning, reducing the risk of false alarm and missed alarm:

[0053] Fusion of temperature, load current, oil flow rate and other multi-source data, construction of “temperature change rate-load correlation threshold-oil flow response” linkage criterion, effective control of first-level alarm false alarm rate, and improvement of second-level shutdown accuracy;

[0054] The thermal accumulation factor adopts a dynamic calculation method, and the hot spot diffusion threshold is adaptively adjusted, so that “normal load temperature rise” and “abnormal fault temperature rise” can be accurately distinguished, and false action caused by single parameter fluctuation can be avoided.

[0055] 5. Full life cycle cost optimization, adaptive engineering application:

[0056] The fluorescent optical fiber sensor has a long service life and matches the operation cycle of the transformer, and the operation and maintenance cost is reduced compared with the traditional sensor; the distributed network design connects multiple regions through a single optical fiber, reduces the wiring complexity, and saves the hardware cost;

[0057] The system supports standardization protocol and edge computing gateway integration, can directly access the power dispatching system, does not need additional modification, adapts to the existing transformer substation operation and maintenance system, and has low engineering landing difficulty.

[0058] In summary, the present application provides a high-precision, high-reliability and full-coverage temperature measurement solution for oil-immersed transformers through the whole-chain innovation of “distributed sensing-anti-interference transmission-accurate calculation-full reconstruction-intelligent early warning”, which can significantly improve the equipment operation safety and reduce the operation and maintenance cost, and has important engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The figure is a flow chart of the steps of the distributed temperature measurement method for oil-immersed transformers based on fluorescent fiber optic sensors of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0062] like Figure 1 As shown, an embodiment of the present invention provides a distributed temperature measurement method for an oil-immersed transformer based on a fluorescent optical fiber sensor, comprising the following steps:

[0063] S1. Sensor Layout: Fluorescent fiber optic sensors are deployed in a partitioned and layered three-dimensional topology in key temperature-rise areas within the oil-immersed transformer, including between high-voltage and low-voltage winding layers, on the core surface, and in hot spots in the heat dissipation oil channel. The sensors consist of multiple sections of fluorescent optical fiber connected in series via electromagnetic interference-resistant fiber optic patch cords to form a distributed temperature measurement network. The length of each optical fiber section is customized based on the spatial characteristics of the monitoring area.

[0064] S2. Excitation signal injection: A pulsed laser is used to inject a pulsed excitation light with a wavelength of 380 nm to 420 nm into the fluorescent fiber sensor to excite the fluorescent substance to generate a fluorescence afterglow signal.

[0065] S3, signal acquisition and demodulation: real-time acquisition of fluorescence afterglow signals, use of dual-channel synchronous phase-locked amplification technology to separate environmental noise, and obtain temperature measurements by calculating the fluorescence decay time constant;

[0066] S4, Temperature Field Reconstruction: Combining the sensor's three-dimensional spatial coordinates with real-time temperature data, the dynamic temperature field distribution inside the transformer is reconstructed based on a thermodynamic inversion algorithm, identifying and marking hot spots where the temperature gradient exceeds 5 degrees Celsius per centimeter;

[0067] S5. Perform time-series correlation analysis on the temperature data, transformer load current, and cooling oil flow rate. When the temperature change rate exceeds the dynamic threshold and does not match the load change trend, activate the graded alarm mechanism.

[0068] In this embodiment, the sensor deployment in step S1 includes:

[0069] The optical fiber is laid in a spiral winding pattern between winding layers. The outer side of the optical fiber is covered with an oil-resistant polyimide protective layer with a thickness of no more than 0.2 mm and fixed to the winding insulation support bar by micro ceramic clips.

[0070] In the heat dissipation oil channel hot spot area, a double-redundancy cross layout method is adopted, and two sections of optical fibers are crossed and fixed at the inlet and outlet positions of the oil channel at an angle of 30 degrees to 60 degrees;

[0071] Further, the role of step S1 is to ensure full coverage monitoring of the key temperature rise area inside the oil-immersed transformer by precise layout of the partitioned and layered three-dimensional topological structure, while avoiding interference with the insulation performance and oil flow field of the transformer, providing a reliable spatial coordinate reference and signal transmission channel for subsequent temperature collection and analysis. The following are the detailed steps:

[0072] Step S1-1: Three-dimensional coordinate mapping of key temperature rise area:

[0073] Area identification basis: According to the transformer design drawings (including winding structure, core size, oil channel layout) and operation procedures (such as GB / T1094.2-2013), determine the high-voltage winding interlayer, low-voltage winding interlayer, core surface, and heat dissipation oil channel hot spot area as the core monitoring area; among them, the hot spot area needs to be further accurately positioned in combination with the heat flow simulation results (such as the area with heat flow density > 500 W / m² under rated load calculated by ANSYS);

[0074] Three-dimensional scanning and coordinate modeling: use an industrial-grade three-dimensional laser scanner (accuracy ±0.05mm) to scan the transformer internal monitoring area, obtain the spatial dimensions of each area (such as winding interlayer gap 0.5-2cm, oil channel cross-sectional size 3-8cm), curvature radius (average winding curvature 15-30cm), and adjacent component spacing (such as core and winding spacing 5-10cm); based on the scanning data, construct a three-dimensional coordinate system (with the center of the transformer oil tank as the origin, the axial direction as the Z axis, and the radial direction as the X axis), and output the boundary coordinate matrix of each monitoring area , wherein, is the boundary coordinate matrix of the monitoring area, , , are the , , , coordinates of the th boundary point in the three-dimensional coordinate system,

[0075] Environmental parameter recording: simultaneously record the electromagnetic environment of each area (such as the power frequency electric field intensity near the high-voltage winding 10-50kV / m), oil flow path (obtain the oil channel internal flow velocity vector field and mechanical vibration frequency (10-100Hz) through CFD simulation, provide a basis for subsequent fiber selection and fixation method design;

[0076] Step S1-2: Design of partitioned hierarchical stereoscopic topology structure:

[0077] Partitioning rule: divide the monitoring area according to the dual dimensions of "electrical characteristics + thermal sensitivity":

[0078] High-voltage winding area (A area): contains winding layers of different voltage levels such as 110kV / 220kV / 500kV, and sets a monitoring sub-area for every 2-3 winding layers;

[0079] Low-voltage winding area (B area): for the concentration of Joule heat caused by large current on the low-voltage side, sub-areas are set by segmenting the winding axis (each segment length ≤1m);

[0080] Core area (C area): focuses on covering the surfaces of core columns, yokes, and clamps, and sets sub-areas by "column + plane" block division;

[0081] Heat dissipation oil channel area (D area): includes oil inlet, oil outlet, and oil channel bends (areas with significant eddy current effect), and sets upstream and downstream sub-areas according to the oil flow direction;

[0082] Layered logic setting: along the radial direction of the transformer (X-axis), set "inner-middle-outer" three layers: the inner layer is close to the winding conductor (5-10mm from the conductor surface), the middle layer is between the insulation paper layers, and the outer layer is close to the oil channel; along the axial direction (Z-axis), set a horizontal layer every 0.5m in height to ensure that there is no blind area for vertical direction thermal gradient monitoring;

[0083] Topology structure verification: simulate the fiber layout path by computer simulation to verify whether it meets:

[0084] Each hot spot area has at least 2 overlapping fiber sections;

[0085] The total length of the optical fiber is ≤ the available wiring space inside the transformer (usually ≤50m);

[0086] The minimum spacing between adjacent fiber sections is ≥5cm (to avoid signal crosstalk);

[0087] Step S1-3: Fluorescent fiber selection and environmental resistance pretreatment:

[0088] Optical fiber core parameter selection:

[0089] Fluorescent material: select Eu 3+ doped SiO2-ZrO2 composite optical fiber to ensure that the temperature sensitivity meets

[0090] where, is the temperature sensitivity, which represents the ratio of the relative change rate of fluorescence lifetime to the temperature change rate, is the fluorescence decay time constant, is the temperature;

[0091] Optical fiber diameter: bare fiber diameter 50-100 μm (considering flexibility and mechanical strength), total diameter ≤200 μm after covering with oil-resistant polyimide protective layer (to avoid affecting the winding insulation distance);

[0092] Insulation performance: coated with a 50±5 nm Al2O3 insulation layer, tested insulation resistance ≥10 14 Ω·cm (complies with DL / T2348-2021 standard);

[0093] Environmental resistance pretreatment process:

[0094] Optical fiber cleaning:

[0095] Anhydrous ethanol ultrasonic cleaning (30 kHz, 5 min) is used to remove surface impurities, and after nitrogen blowing and drying, it is placed in a 120°C oven for 2h;

[0096] Protective layer curing: heat curing treatment (200°C, 1h) is performed on the polyimide protective layer to improve the resistance to transformer oil (25# mineral oil) immersion performance (after 1000h immersion test, no swelling and cracking);

[0097] End processing: the optical fiber is cut and flattened at both ends (end surface roughness ≤0.5 μm), and an electromagnetic interference resistant optical fiber jumper is fused (the jumper is made of stainless steel armor, with shielding effectiveness ≥80 dB@1 MHz-1 GHz);

[0098] Step S1-4: implementation of region-specific fixation method:

[0099] Winding interlayer fixation (A / B area):

[0100] Laying method: spiral winding type, winding 1 turn every 10 cm along the winding axis, winding radius 5 mm larger than winding outer diameter (to avoid extruding the insulation layer);

[0101] Selection of fixing member: micro ceramic buckle (size 3×2×1 mm, high temperature resistance 200°C) is used, which is pasted on the winding insulation paper through high temperature resistant epoxy glue (shear strength ≥15 MPa), with buckle spacing ≤5 cm, to ensure that the optical fiber does not swing and shake;

[0102] Stress control: the winding tension is controlled at 5-10 N (real-time monitored by a tension meter), to avoid excessive stretching of the optical fiber leading to increased attenuation (tests show that when the tension is >15 N, the signal attenuation increases by ≥0.5 dB / m);

[0103] Heat dissipation oil channel fixation (D area):

[0104] Layout: Adopting double-redundant cross structure, two segments of optical fiber are laid along the length and width direction of the oil duct respectively, with a crossing angle of 30°-60° (adjustable according to the cross-sectional size of the oil duct, 45° for rectangular oil duct);

[0105] Fixed position: The inlet segment of optical fiber is 5 cm away from the inlet of the oil duct, and the outlet segment of optical fiber is 5 cm away from the outlet of the oil duct, fixed on the inner wall of the oil duct by a polytetrafluoroethylene clamp (oil-resistant and low-friction), with a 0.1 mm thick silicone rubber pad (buffering oil flow impact) between the clamp and the optical fiber;

[0106] Redundancy design: Two segments of optical fiber are independently laid, ensuring that there is a backup signal when a single segment of optical fiber fails (redundancy ≥ 2);

[0107] Surface fixation of iron core (C area):

[0108] Layout: Linearly along the axial direction of the iron core column, and along the circular curve at the iron yoke, fitting the surface profile of the iron core (bending radius ≥ 5 cm, to avoid excessive bending leading to signal loss);

[0109] Fixing method: Fixed every 10 cm with flexible high-temperature resistant adhesive tape (temperature resistance 150°C, adhesive force ≥ 0.5 N / cm), tape width ≤ 5 mm (to reduce the impact on the heat dissipation of the iron core);

[0110] Step S1-5: Distributed network series connection and connectivity verification:

[0111] Optical fiber segment length customization: According to the spatial characteristics of each monitoring area, the optical fiber segment length is customized: inter-winding layer segment length = winding height × π × winding turns (e.g. for a 1 m high winding with 5 turns, segment length ≈ 15.7 m); oil duct segment length = oil duct length + 0.5 m redundancy on both ends; iron core segment length = iron core unfolded length + 0.3 m redundancy;

[0112] Series connection method: Connect each area optical fiber segment in series through anti-electromagnetic interference optical fiber jumpers (length 0.5-1 m), connect the jumpers and optical fiber segments with fusion splicing (splicing loss ≤ 0.1 dB), and cover the splicing points with stainless steel protection tubes (diameter 3 mm) and fix them in non-hot spot areas;

[0113] Connectivity and signal testing: After series connection, test the total optical loss of the network (total loss ≤ 5 dB) through optical time domain reflectometer (OTDR) to ensure no broken fiber and false connection; inject 400 nm pulse light (power 10 mW) to verify that each segment of optical fiber can normally output fluorescent signal (signal-to-noise ratio ≥ 30 dB); simulate the vibration (10-100 Hz, amplitude 0.1 mm) during transformer operation, and test the signal after 30 minutes without abnormal fluctuation (fluctuation amplitude ≤ 1%).

[0114] In this embodiment, step S2 is used to precisely control the parameters and transmission path of the pulsed excitation light to efficiently excite the characteristic afterglow signal of the fluorescent substance in the fluorescent fiber, while suppressing electromagnetic interference and environmental noise, thereby providing a high signal-to-noise ratio original excitation source for subsequent signal demodulation. The following are the detailed steps:

[0115] Step S2-1: Pulsed laser parameter customization and performance benchmark calibration:

[0116] Core parameter matching design: for Eu 3+ The energy level structure of the doped SiO2-ZrO2 composite fluorescent material (its 4f-4f transition has a strong absorption peak at 394nm) uses an InGaN-based semiconductor pulse laser to ensure:

[0117] Wavelength coverage: 380nm–420nm (central wavelength: 400±5nm, excitation efficiency can be maintained at ≥80% when the deviation from the material absorption peak is ≤10nm);

[0118] Pulse width 5–50 ns (should be shorter than the fast decay component of fluorescence The typical value is 100ns, to avoid the overlap of the excitation light and the fluorescence signal in the time domain. After testing, a 50ns pulse can reduce the overlapping interference to ≤5%).

[0119] Single pulse energy 10–50 μJ (too low energy will result in insufficient fluorescence intensity, while too high energy will cause photobleaching - after 1 hour of continuous 50 μJ irradiation, a decrease in fluorescence intensity of ≤3% is considered acceptable);

[0120] Wavelength stability calibration: In a constant temperature environment of 25°C (±0.1°C), the output wavelength is calibrated using a high-precision spectrometer (resolution 0.05nm). The wavelength drift is controlled within ±0.5nm / °C using the laser's built-in temperature control module (total drift ≤5nm when the ambient temperature fluctuates by 10°C). After calibration, the wavelength-temperature response curve is recorded for subsequent real-time compensation.

[0121] Power consistency verification: Use photodiode array (response time ≤ 1ns) to monitor continuous The energy fluctuation of each pulse must have a coefficient of variation of ≤2% (i.e., 99.7% of the pulse energies must fall within ±3σ of the mean). If this exceeds the limit, closed-loop drive current regulation is initiated (drive current 50–200 mA, with an adjustment accuracy of 0.1 mA) to ensure that output stability complies with the standard of "excitation light power fluctuation ≤±2%" in DL / T2348-2021.

[0122] Step S2-2: Dynamic adaptive adjustment of pulse timing and energy:

[0123] Adaptive repetition rate control: based on the effective fluorescence decay time (typical value 1-5 μs), set laser repetition frequency satisfy (reserve 100% safety margin) (where, is the laser repetition frequency, is the fluorescence effective decay time constant); for example:

[0124] when s, kHz, actual selection 1-10 kHz (avoiding fluorescence superposition caused by high frequency pulse, test shows kHz, the amount of residual fluorescence before ;

[0125] Through real-time data linkage with temperature field decreases with temperature rise), dynamically adjust (temperature rises , can be increased by 10%);

[0126] Energy gradient distribution strategy: different distribution of pulse energy according to the signal attenuation characteristics of different monitoring areas:

[0127] High-voltage winding area (strong electromagnetic interference, signal attenuation ≥20%): single pulse energy 30-50 μJ, ensure that the energy reaching the fiber end is ≥10 μJ;

[0128] Oil channel area (oil flow scattering causes signal loss of about 15%): energy 20-30 μJ;

[0129] Iron core area (signal attenuation ≤10%): energy 10-20 μJ (avoiding material aging caused by excessive excitation);

[0130] Peak power constraint: control the peak power in the range of 5-50 W through the formula (where, is the peak power, is the single pulse energy, is the pulse width):

[0131] When

[0132] When and ;

[0133] Step S2-3: Anti-strong electromagnetic interference optical path integrated design:

[0134] Low-loss optical chain construction: uses the transmission path of "laser → optical isolator → collimating lens → coupler → sensing fiber". Key component parameters:

[0135] Optical isolator: operating wavelength 380–420 nm, isolation ≥ 35 dB (prevents light reflected from the fiber end face (typical reflectivity 4%) from being fed back into the laser, reducing output fluctuation from ±5% to within ±1%);

[0136] Collimating lens: focal length 8mm, numerical aperture , collimate the laser beam into parallel light with a diameter of 1.5mm (divergence angle ≤ 0.5mrad) to reduce transmission loss;

[0137] Fiber coupler: GRIN lens type, coupling efficiency ≥85% (for 50-100μm core diameter sensing fiber, ), coupling loss ≤ 1.2dB;

[0138] Electromagnetic shielding system construction:

[0139] The laser and optical components are encapsulated in a brass shielding box (3mm thick), the inner wall of which is covered with carbonyl iron absorbing material (for Frequency band attenuation ≥70dB), can resist 50kV / m power frequency electric field interference inside the transformer;

[0140] Fiber optic patch cord mm stainless steel bellows, both ends are grounded through 360° electromagnetic sealing joints (grounding resistance , suppress common mode interference;

[0141] The drive circuit power supply uses an isolation transformer (isolation voltage 22kV) and a low-pass filter (cut-off frequency 1kHz) to reduce conducted interference;

[0142] Temperature drift compensation: Because the laser wavelength drifts with temperature (0.08nm / °C), the shielding box has a built-in TEC semiconductor cooler, which is monitored in real time by a Pt100 temperature sensor (accuracy ±0.1°C). The PID control temperature is stabilized at 25±0.3°C (at this time, the wavelength drift is ≤±0.024nm, ensuring a match of ≥95% with the material absorption peak);

[0143] Step S2-4: Excitation efficiency verification and closed-loop feedback optimization:

[0144] Fluorescence characteristic spectrum detection: The fluorescence spectrum at the output end of the sensing fiber is collected by a micro-spectrometer (resolution 0.5nm), which must meet the following requirements:

[0145] Characteristic peak intensity ratio: 615nm ( 5 D0→ 7 F2 transition) and 590nm ( 5D0→ 7 F1 transition) peak intensity ratio ≥ 3:1 (when the ratio is stable, the material excitation state is normal);

[0146] Stimulation efficiency (in, for the excitation efficiency)( When the coupler is aligned, check the accuracy of the coupler alignment or the damage of the optical fiber);

[0147] Spatial uniformity verification: Fluorescence intensity is tested along the length of the sensing fiber (sampling every 1m), and the relative deviation is ≤±8%:

[0148] If the deviation of a certain section is greater than 10%, check whether there is excessive bending (when the bending radius is less than 5cm, mode loss will cause the local strength to drop by ≥15%).

[0149] The signal difference of the two optical fibers cross-laid in the oil channel area should be ≤5% to ensure the effectiveness of the redundant signal;

[0150] Real-time feedback adjustment: Building a closed loop of "spectrometer → MCU → laser driver":

[0151] when When the pulse energy is increased by 5 μJ / step (upper limit 50 μJ);

[0152] If the fluorescence intensity drops by ≥5% within 1 hour (a sign of photobleaching), the repetition rate will be automatically reduced by 20% and the decay rate will be recorded (a maintenance alert will be triggered if it exceeds 0.5% / h);

[0153] Step S2-5: Timing synchronization and interference source suppression:

[0154] Multi-module clock alignment: achieved through synchronous trigger signal (TTL level, rising edge ≤ 5ns):

[0155] The laser pulse emission time is , signal acquisition module delay (pulse width) and then start (such as ns, the data is collected from ns start);

[0156] The acquisition window duration is set to (Make sure to cover the full attenuation curve, e.g. s, the window length is 10 μs), which matches the integration time of the subsequent lock-in amplifier;

[0157] Background light filtering: A 380-420nm bandpass filter (bandwidth ±10nm) is inserted into the optical path to attenuate the autofluorescence (400-500nm) of the transformer oil-paper insulation material by 295%, increasing the fluorescence signal SNR to ≥35dB.

[0158] Pulse shape optimization: shaping the rectangular pulse into a Gaussian pulse The smooth edges can reduce electromagnetic radiation (1MHz radiation intensity is reduced by 225dB), avoiding interference with the relay protection signal inside the transformer;

[0159] Through the above steps, the pulse light meeting the excitation requirements of the fluorescent material can be stably output in a harsh environment of strong electromagnetic field, high temperature and oil immersion, ensuring the intensity and characteristic stability of the fluorescent afterglow signal, and providing reliable input for subsequent temperature demodulation.

[0160] In this embodiment, the signal demodulation of step S3 adopts a double-weight adaptive attenuation model:

[0161] First, the effective attenuation time constant is calculated, which is the product of the time constant of the fluorescent fast decay component and the real-time signal-to-noise ratio dynamic weight, plus the product of the time constant of the fluorescent slow decay component and its complementary weight, plus the oil flow disturbance compensation;

[0162] Among them, the real-time signal-to-noise ratio dynamic weight is equal to the real-time signal-to-noise ratio value divided by the sum of the real-time signal-to-noise ratio and the environmental noise coefficient;

[0163] The oil flow disturbance compensation is obtained by integral calculation, and the integral term contains the product of the transformer oil density and the velocity gradient square of the oil flow speed in the normal direction of the optical fiber, plus the product of the temperature-dependent dynamic viscosity and the square of the oil flow speed, and the integral is along the length of the optical fiber element segment. The result is multiplied by the optical fiber fluid sensitivity coefficient;

[0164] The temperature calculation adopts an oil flow coupling compensation function:

[0165] The temperature value is equal to the calibration coefficient a of the fluorescent material multiplied by the square of the effective attenuation time constant, plus the coefficient b multiplied by the effective attenuation time constant, plus the constant term c, and finally superimposed with the oil flow influence correction factor d multiplied by the logarithmic value of the oil flow combined speed;

[0166] Among them, the oil flow combined speed is obtained by calculating the square root of the sum of the square of the three-dimensional velocity components, and adding a small offset to prevent zero speed singularity;

[0167] Further, the function of step S3 is to extract the decay characteristics of the fluorescent afterglow from the strong noise background through high-precision signal acquisition and intelligent demodulation algorithm, accurately calculate the temperature-sensitive decay time constant, and provide high signal-to-noise ratio raw data for temperature field reconstruction; The following are the detailed steps:

[0168] Step S3-1: Fluorescent afterglow signal acquisition system construction:

[0169] Optical detection module selection and configuration:

[0170] A back-illuminated InGaAs photodetector (response band 450-850 nm, matching with Eu 3+ fluorescence emission peak (615 nm), quantum efficiency ≥80% @ 615 nm, dark current ≤10 pA (25℃), which can effectively capture weak fluorescence signals (minimum detectable power ≤-80 dBm);

[0171] A 3 nm bandwidth 615 nm bandpass filter (cut-off depth OD6) is connected in series in front of the detector, which filters out the residual 380-420 nm excitation light (decay ≥10 6 times) and transformer oil autofluorescence (400-550 nm), and improves the fluorescence signal purity to ≥99%;

[0172] A low-noise preamplifier (gain 10 4 –10 6 times, noise factor ≤1.5 dB @ 1 kHz) is used to convert the photocurrent signal (nA level) into a voltage signal (mV level), and the bandwidth is set to 10 Hz-1 MHz (covering the fast and slow component frequency range of fluorescence decay);

[0173] Timing synchronous acquisition control:

[0174] Taking the trigger signal (t=0) of the pulsed laser as the reference, a synchronous control signal is generated by FPGA: starting acquisition after the end of the laser pulse (delay ns), and the acquisition window duration is set to 10 ( The typical value of the fast decay component is 100 ns, so the window duration is 1 us), which ensures complete recording of the whole process from fluorescence excitation to decay to baseline;

[0175] The sampling rate is set to 100 MS / s (sampling interval 10 ns), and each signal has 1000 data points for single acquisition (covering 10 us duration), and the fast / slow decay component signals are synchronously acquired by a dual-channel ADC (16-bit resolution, ENOB ≥14 bits), with a quantization error ≤0.02%;

[0176] Parallel acquisition of multi-channel signals:

[0177] For N segments of optical fiber in a distributed temperature measurement network (N ≤32), a 1×32 optical switch (switching time ≤5 ms, insertion loss ≤1.5 dB) is used for polling acquisition, and each segment of optical fiber is acquired 200 times per second (sampling frequency 200 Hz), ensuring that the capture bandwidth of temperature dynamic change is ≥10 Hz (can identify temperature rise fluctuations within 50 ms);

[0178] An optical attenuator (adjustable 0–30 dB) is connected in series between the optical switch and the detector. When the fluorescence intensity is too high (exceeding the linear range of the detector by 1 V), it automatically attenuates the signal to avoid signal saturation (linearity error ≤ 0.5%).

[0179] Step S3-2: Dual-channel synchronous phase-locked amplification and noise separation:

[0180] Phase-locked amplifier core parameter matching:

[0181] Reference signal: at the laser pulse frequency (1-10kHz) as a reference, a square wave reference signal with the same frequency (duty cycle 50%) is generated to synchronize with the periodic excitation characteristics of the fluorescence signal;

[0182] Dual-channel configuration: Channel 1 focuses on the fast decay component ns), the integration time is set to 200 ns (matching the time scale of fast decay); channel 2 focuses on the slow decay component ( ns), the integration time is set to 1 μs (to cover the slow decay process), and the phase difference between the two channels is locked to 0° (to ensure that the signals are superimposed in phase);

[0183] Suppression ratio: Through a 24th-order Butterworth low-pass filter (roll-off rate 120dB / dec), the suppression ratio of the power frequency harmonics (50Hz×200) at 10kHz is ≥120dB, which can reduce the noise power from -50dBm to below -170dBm;

[0184] Noise separation and signal-to-noise ratio improvement:

[0185] To address the electromagnetic noise (10kHz–1MHz) and thermal noise (white noise) inside the transformer, a “correlation detection + differential amplification” combination is used: the lock-in amplifier only responds to the fluorescent signal with the same frequency and phase as the reference signal, and the suppression ratio of random noise is ≥10 4 :1;

[0186] Real-time calculation of signal-to-noise ratio (SNR) (where SNR is the real-time signal-to-noise ratio). When SNR < 20dB (such as when the electromagnetic interference in the high-voltage winding area is strong), the integration time of the lock-in amplifier is automatically increased (to a maximum of 5μs). At this time, the SNR can be increased to dB(Demodulation Error ;

[0187] Adopting an adaptive filtering algorithm (RLS recursive least squares), the noise model is estimated in real time through a noise reference channel (collecting electromagnetic signals from the transformer housing), eliminating common-mode interference from the collected signal and further improving the SNR by 3-5dB.

[0188] Signal preprocessing:

[0189] Baseline correction: record baseline signal (dark current + ambient noise) without excitation once every 100 acquisitions, eliminate baseline drift (drift amount (where, is the corrected signal, is the original signal, is the baseline signal) by formula mV / °C);

[0190] Pulse interference rejection: identify abnormal pulses (amplitude more than 3 times the standard deviation of the mean) using criteria, replace by adjacent data point interpolation (error , avoid distortion of decay curve caused by accidental interference;

[0191] Step S3-3: dual-weight adaptive decay model solving:

[0192] Fluorescence decay curve fitting:

[0193] The corrected fluorescence signal is fitted using a double exponential decay model:

[0194] (where, , is the amplitude of the fast / slow decay component, , is the corresponding time constant, is the fitting residual);

[0195] Using the Levenberg-Marquardt nonlinear least squares algorithm (iteration times ≤ 50 times, convergence accuracy 1e-6), extract (fitting error ≤ 2 ns) and (fitting error ≤ 5 ns) in a 1 μs acquisition window, and calculate the sum of squared residuals ( is the fitted signal), when fitting failure is determined, triggering re-sampling;

[0196] Dual-weight effective decay time calculation:

[0197] Weight coefficient dynamic adjustment: calculate weight , (where, is the ambient noise coefficient, when SNR dB, , prefer to trust the fast decay component; when SNR=10 dB, , enhance the weight of the slow component);

[0198] Basic effective decay time: (Integrate the temperature-sensitive characteristics of fast and slow components to reduce the impact of single component fluctuations);

[0199] Oil flow disturbance compensation: by formula

[0200] Calculate the fluid disturbance correction term, where s m / kg is the optical fiber fluid sensitivity coefficient, is the transformer oil density (895kg / m³@25℃), is the temperature-dependent dynamic viscosity ( Pa·s@ , decreases linearly with increasing temperature). is the oil flow shear rate, is the oil flow vortex, and the final effective decay time (Compensate for the decay time drift caused by oil flow shock. Tests show that the error can be reduced from ±2.5°C to ;

[0201] Step S3-4: Temperature calculation based on oil flow coupling compensation:

[0202] Temperature-decay time calibration curve construction:

[0203] The fluorescent fiber was calibrated over the entire temperature range (20-120°C) in a constant temperature oil bath (accuracy ±0.05°C), and data were collected every 5°C. , get the original data pair

[0204] Use a quadratic polynomial to fit the base curve:

[0205] (in, , , is the calibration coefficient of the fluorescent material, and the goodness of fit ;

[0206] Oil flow velocity coupling compensation:

[0207] Introducing the oil flow velocity model (in, is the modulus of the oil flow velocity vector, 、 、 The oil flow velocity vector is 、 、 Directional component) (real-time acquisition by the oil channel flow meter, accuracy ±0.01m / s), the correction formula is: (in, The correction factor is affected by the oil flow, the higher the flow rate The higher the value is; The minimum value is to prevent zero speed singular point);

[0208] Compensation effect: when the oil flow rate increases from 0.1 m / s to 0.5 m / s, the uncompensated temperature error is ±3℃, and after compensation, the error is ≤±0.6℃ (verified on an oil circulation experiment platform of a certain institute of electric science);

[0209] Real-time temperature calculation and filtering:

[0210] Single temperature calculation time ≤1ms (satisfies 200Hz sampling rate requirement), sliding average filtering is used for continuous 10 times calculation results (window size 10), smooths high frequency noise (such as transient fluctuation caused by electromagnetic pulse), but retains ≥0.5℃ / s temperature rise trend (avoids excessive filtering to cover the real hot spot);

[0211] Outlier rejection: when the deviation of a certain calculation temperature from the mean value of adjacent 5 times is >3℃, it is determined as an outlier (such as local contamination of optical fiber), and the previous 3 times mean value is automatically replaced (outlier ratio ≤0.1%);

[0212] Through the above steps, in the complex environment of strong electromagnetic interference (50kV / m) and oil flow disturbance (flow rate 0-1m / s), the temperature measurement accuracy of ±0.8℃ (20-120℃) can be realized, and high-fidelity point temperature data is provided for subsequent three-dimensional temperature field reconstruction.

[0213] In this embodiment, the three-dimensional thermodynamic inversion algorithm of step S4 includes:

[0214] The heat conduction and convection coupling equation is established, which includes the product of transformer oil heat capacity and temperature time derivative equal to the divergence of thermal conductivity and temperature spatial second-order derivative, plus winding heat source power, minus the dot product of oil flow velocity and temperature gradient;

[0215] The regularization least square inversion method is used, the objective function is the sum of squares of deviation of all sensor measured temperature values and calculated values, plus the smoothing constraint term of integral of square of temperature field spatial second-order derivative in the whole domain, and the temperature field distribution is solved by minimizing the objective function;

[0216] The winding heat source power is dynamically updated through the electro-thermal coupling model:

[0217] The heat source power is equal to the product of winding resistivity and current density square, plus the product of iron core loss coefficient and magnetic flux density time derivative;

[0218] Among them, the winding resistivity changes inversely with temperature, and the resistivity decreases by 0.4% for every 1 degree Celsius temperature rise;

[0219] Furthermore, step S4 uses the discrete temperature data and spatial coordinate information from the distributed sensors to reconstruct the complete dynamic temperature field inside the transformer using a three-dimensional thermodynamic inversion algorithm. This accurately identifies high-gradient hotspots and provides a global thermal distribution basis for equipment status assessment. The detailed steps are as follows:

[0220] Step S4-1: Temperature-coordinate data preprocessing and spatiotemporal alignment:

[0221] Sensor space coordinate mapping:

[0222] Based on the three-dimensional coordinate system constructed in step S1-1 (with the center of the tank as the origin and the axial direction as the Axis, radial Axis), the temperature measurement point space coordinate matrix of each section of fluorescent optical fiber (in, is the sensor space coordinate matrix, 、 、 For the The three-dimensional coordinates of the temperature measurement points, is the total number of temperature measurement points, usually Corresponding temperature data (in, is the measured temperature vector, For the The measured temperature of the point) is associated to generate a temperature dataset with coordinate labels ;

[0223] Coordinate accuracy calibration: The coordinates of three fixed reference points (such as the marking points on the inner wall of the fuel tank) are measured by a laser tracker (accuracy ±0.03mm) to calibrate the sensor coordinate matrix. Perform affine transformation (translation + rotation + scaling) to correct installation errors (ensure coordinate deviation mm);

[0224] Temperature data spatiotemporal purification:

[0225] Outlier elimination: Use the local outlier factor (LOF) algorithm to eliminate outliers at each temperature measurement point. Calculate the temperature deviation between it and the five neighboring points. If the LOF value is greater than 1.5, it is considered abnormal (such as a jump caused by local optical fiber damage) and replaced by Kriging interpolation (spatial correlation weighting). The interpolation error is ≤ 0.3°C.

[0226] Time series alignment: Due to the 5ms time difference in multi-channel polling acquisition, all temperature measurement point data are aligned to the same time axis through FPGA timestamp (accuracy 10ns) (in, For the Sampling time, , sampling interval 100 ms), ensuring time synchronization of dynamic temperature field (time difference ;

[0227] Spatial density enhancement: For temperature points in sparse areas (e.g., inter-winding layer gaps > 2 cm), radial basis function (RBF) interpolation is used to supplement virtual temperature measurement points (density ≥ 1 point / cm³ , providing more intensive constraint conditions for inversion algorithm;

[0228] Step S4-2: Construction of three-dimensional thermodynamic coupling model:

[0229] Geometric model and meshing:

[0230] Based on transformer design drawings (including three-dimensional dimensions of winding, core, oil duct, and oil tank), a 1:1 geometric model is constructed in finite element software (such as ANSYS), with key area meshing: tetrahedral elements (edge length 1-3 mm) for inter-winding layers, hexahedral elements (edge length 2-5 mm) for oil ducts, and shell elements (thickness 5 mm) for oil tank walls. The total number of elements is about 50-100 million (balancing calculation precision and efficiency);

[0231] Mesh quality verification: Ensure that the element distortion rate is ≤15% and the aspect ratio is ≤5 (through mesh quality evaluation tools) to avoid calculation errors (error ≤2%) caused by mesh distortion;

[0232] Temperature-dependent modeling of material parameters:

[0233] Winding (copper): density , specific heat capacity at constant pressure (where is the specific heat capacity at constant pressure of copper, is the temperature) (20-120°C), thermal conductivity (where is the thermal conductivity of copper);

[0234] Core (silicon steel sheet):

[0235] , (where is the specific heat capacity at constant pressure of silicon steel sheet), (where is the thermal conductivity of silicon steel sheet) along the rolling direction;

[0236] Transformer oil (25# mineral oil): (where is the density of transformer oil), (where is the specific heat capacity at constant pressure of transformer oil), (Where, is the thermal conductivity of transformer oil);

[0237] Insulating paper: , (Where, is the specific heat capacity of insulating paper at constant pressure), (Where, is the thermal conductivity of insulating paper) (isotropic);

[0238] Thermal conduction-convection coupling control equation:

[0239] Using the thermal conduction-convection coupling model: Where, is the material density, is the specific heat capacity at constant pressure, is the temperature, is the time, is the thermal conductivity, is the gradient operator, is the winding Joule heat source and the core eddy current heat source, is the oil flow velocity vector;

[0240] Boundary condition setting:

[0241] Oil tank outer wall: natural convection heat dissipation, convective heat transfer coefficient W / ( ℃) (Where, is the convective heat transfer coefficient, is the wall and ambient temperature difference), ambient temperature ℃ (real-time acquisition);

[0242] Winding and core contact interface: thermal resistance ℃ / W (Where, is the contact thermal resistance) (considering the influence of the insulating layer);

[0243] Oil duct inlet and outlet: known oil flow velocity (provided by the oil flow velocity sensor) and inlet oil temperature (measured value), set as Dirichlet boundary;

[0244] Step S4-3: Implementation of three-dimensional thermodynamic inversion algorithm:

[0245] Forward problem solving:

[0246] Based on the above model and control equation, the finite volume method is used to solve the transient temperature field: time step ms (matching the sensor sampling frequency), each time step is solved by iteration (convergence criterion: residual , output the calculated temperature field ,in, To calculate the temperature field, is the spatial coordinate, For time;

[0247] Dynamic update of heat source items:

[0248] Joule heat source of winding ,in, is the Joule heat source of the winding, is the conductivity, is the current density, where the conductivity ,in, S / m is the conductivity of copper at 20°C, current density ,in, is the load current, is the number of parallel conductors, is the cross-sectional area of ​​a single conductor;

[0249] Core eddy current heat source ,in, is the core eddy current heat source, W s / (m T) is the iron loss coefficient, is the magnetic flux density, W·s / (m·T) is the iron loss coefficient, is the magnetic flux density, obtained from electromagnetic simulation;

[0250] Total heat source ,in, is the total heat source, updated every 5 time steps (in response to changes in load current);

[0251] Regularized least squares inverse optimization:

[0252] Objective function construction: The inversion goal is to minimize the deviation between the measured and calculated temperatures while constraining the smoothness of the temperature field: ,in, For the The measured temperature of each sensor, For the model in sensor coordinates The calculated temperature at is the smoothness constraint factor, is the Laplace operator of the temperature field (characterizing the curvature), is the volume of the computational domain;

[0253] Optimization algorithm: The conjugate gradient method is used for iterative solution. The initial temperature field is set to uniform distribution (25°C). The temperature field parameters (such as heat source intensity and material thermal conductivity correction term) are updated in each iteration until the objective function converges (iteration number difference drops to below 1% of the initial value);

[0254] Step S4-4: Real-time reconstruction of dynamic temperature field and time evolution:

[0255] Real-time reconstruction trigger mechanism:

[0256] Each set of temperature data (about 1s) starts once the inversion calculation, using GPU parallel acceleration (such as NVIDIA A100, calculation time ≤500ms), to ensure that the reconstruction result lags behind the measured time ≤1s (to meet the dynamic monitoring requirements);

[0257] History data cache: save the temperature field sequence of the last 300s (3000 time steps) for analyzing the temperature evolution trend (such as the heat point diffusion speed);

[0258] Temperature field spatio-temporal interpolation and visualization:

[0259] For the discrete grid temperature obtained by inversion, Kriging spatial interpolation (variogram model: Gaussian model, search radius 5cm) is used to generate a global continuous temperature field, with an interpolation accuracy of ≤0.5℃ (compared with the verification point);

[0260] Output form: three-dimensional cloud chart (isothermal surface interval 5℃), cross-sectional temperature distribution (axial / radial / circumferential), key path temperature curve (such as winding central axis), real-time display through visualization platform (such as Paraview);

[0261] Step S4-5: Hot spot area identification and gradient verification:

[0262] Temperature gradient calculation:

[0263] For the reconstructed temperature field, the spatial gradient is calculated by the central difference method: (Where, is the temperature gradient, , , are the partial derivatives of temperature in x, y, z directions), and the gradient module value , is the temperature gradient module value) (unit: °C / cm);

[0264] Hot spot area determination and marking:

[0265] Hot spot definition: meet and the temperature of the connected region, the hot spot boundary is extracted by region growing algorithm (accuracy ±2mm), and its volume ( is the hot spot volume), maximum temperature ​ Maximum temperature of hot spot and center coordinates

[0266] Classification of hot spots: primary hot spot (5-8℃ / cm), secondary hot spot (>8℃ / cm), marked with different colors (e.g. yellow / red) in the visualization interface, and output of hot spot feature parameter table (position, gradient, volume, duration);

[0267] Verification of reconstruction accuracy:

[0268] Evaluate accuracy using reserved verification points (sensors not involved in inversion): calculate the average error of the verification points (where, is the calculated temperature, is the measured temperature), and require an average error and a maximum error ;

[0269] If the error exceeds the limit, adjust the smoothing constraint factor (±0.1) or the grid density (local encryption), and re-invert until the accuracy requirements are met;

[0270] Through the above steps, high-precision reconstruction of the three-dimensional dynamic temperature field inside the transformer (spatial resolution ±5mm, time resolution 1s) can be achieved, and high-gradient hot spot areas can be accurately identified, providing direct thermal distribution basis for equipment overheating fault warning.

[0271] In this embodiment, the hierarchical alarm trigger condition of step S5 is:

[0272] The primary alarm trigger condition is that the temperature time rate of change is greater than the sum of the load current power function term and the cooling state exponential decay term, and the oil flow acceleration lags behind the load current acceleration;

[0273] The secondary shutdown protection trigger condition is that the hot spot area time rate of change is greater than the dynamic diffusion threshold, and the maximum temperature gradient exceeds 8 degrees Celsius per centimeter;

[0274] Wherein, the dynamic diffusion threshold is equal to the basic diffusion rate multiplied by the linear function of the relative change of the load current to the rated value, plus the thermal accumulation factor;

[0275] The thermal accumulation factor is calculated using a recursive algorithm with a forgetting factor:

[0276] The current thermal accumulation factor is equal to the value of the previous time multiplied by the exponential decay factor, plus the current temperature deviation value multiplied by the thermal accumulation coefficient;

[0277] Wherein, the exponential decay factor is determined by the thermal decay coefficient and the sampling time interval, and the thermal accumulation coefficient is related to the thermal decay coefficient;

[0278] ​Further, the role of step S5 is to build a multi-source time series correlation model by fusing the temperature data of the distributed temperature measurement system and the transformer operating state parameters (load current, oil flow rate, etc.), to realize hierarchical alarm based on dynamic threshold, accurately identify abnormal temperature rise risk and trigger the corresponding response mechanism, and ensure safe operation of equipment; The following are the detailed steps:

[0279] Step S5-1: Multi-source operating data acquisition and space-time synchronization:

[0280] Data sources and interface specifications:

[0281] Temperature data: Extract key features from the three-dimensional temperature field reconstructed in step S4, including real-time temperature of each monitoring point , hotspot area temperature , maximum temperature gradient and hotspot area (area) (sampling frequency 1 Hz, timestamp accuracy 1 ms);

[0282] Load current data: Collect three-phase current on the high-voltage side through a current transformer (accuracy 0.2 level) , , , get through effective value conversion, and normalize to (where is the rated load current, , covering 150% overload conditions);

[0283] Oil flow rate data: Collect oil flow speed vector module through an ultrasonic flowmeter built into the oil duct (accuracy ±0.01 m / s) , and record the oil flow temperature (for viscosity correction);

[0284] Data interface: Use IEC61850 standard protocol to uniformly access multi-source data to edge computing gateway, interface delay ≤10ms, data packet loss rate ≤0.1%;

[0285] Space-time synchronization calibration:

[0286] Timestamp alignment: Take the transformer GPS synchronous clock (accuracy ±1 μs) as the reference, linearly interpolate and correct the timestamps of temperature, current, and oil flow rate data , to ensure the time deviation of different source data ;

[0287] Data cleaning: A sliding average filter (with a window size of 5 points) is used on the load current data to eliminate high-frequency noise (such as current harmonic interference). A median filter (with a window size of 3 points) is used on the oil flow rate data to eliminate pulse interference (such as instantaneous jumps caused by oil flow turbulence). The signal-to-noise ratio of the processed data is ≥30dB.

[0288] Step S5-2: Time series feature extraction and multi-source correlation modeling:

[0289] Temperature dynamic feature extraction:

[0290] Temperature change rate: calculated by central difference method (in, is the rate of change of temperature with time, for The temperature value at the moment, is the sampling interval), focusing on the temperature rise rate in the hot spot area (resolution 0.1°C / s);

[0291] Hotspot diffusion characteristics: Calculate the rate of change of the hotspot area ,in, is the rate of change of the hotspot area over time, unit: , characterizes the hotspot spreading trend;

[0292] Temperature-load correlation: Calculate the Pearson correlation coefficient between temperature and load current using a sliding window (window length 60s) ,in, is the Pearson correlation coefficient between temperature and load current, for The temperature value at the moment, for The load current value at the moment, 、 They are the average values ​​of temperature and load current in the window, respectively. Under normal working conditions, , the correlation coefficient is too low ( ) indicates potential abnormalities;

[0293] Oil flow-load linkage characteristics:

[0294] Oil flow response speed: Calculate the ratio of the oil flow rate change rate to the load current change rate ,in, is the oil flow response speed coefficient, is the rate of change of oil velocity modulus, in m / s2, is the rate of change of load current, in A / s, under normal working conditions (Oil flow changes synchronously with load), when When the cooling system is delayed,

[0295] Oil flow-temperature coupling characteristics: calculated through covariance analysis

[0296] ,in, is the covariance between temperature and oil velocity, is the temperature random variable, 、 are the mathematical expectations of temperature and oil flow rate, respectively, which are negatively correlated under normal operating conditions ( ), a positive correlation indicates cooling failure;

[0297] Multi-source association model training:

[0298] Based on historical normal operation data (≥1000 hours), the temperature-load-oil flow correlation model is trained using the random forest algorithm to output the predicted value of the temperature change rate under normal conditions. ,in, is the predicted value of the temperature change rate, in °C / s, for The load current value at the moment, for Oil flow velocity model at each moment, is the time variable, and the model fit is goodness of fit ;

[0299] Calculate residuals ,in, is the predicted residual of the temperature change rate, in °C / s, and the normal residual range is determined by the 3σ criterion ,If it exceeds the range, it will be marked as a potential exception;

[0300] Step S5-3: Dynamic calculation of hierarchical alarm triggering conditions:

[0301] Level 1 alarm condition parameter calibration:

[0302] Determination of empirical coefficients: Through inversion of 100 typical fault cases (such as slight short circuit of winding, partial failure of cooler), we can obtain ,in, is the empirical coefficient of the load current's effect on the temperature change rate, in units of , (load current nonlinear coefficient), ℃ / s (cooling delay coefficient);

[0303] Oil circulation time constant (in, τo: oil circulation system time constant, representing the response delay characteristic of oil flow to temperature change, calculated based on transformer oil circulation system design parameters (oil pump flow, oil channel volume), corrected by experiment verification (oil temperature stabilization time after load mutation), error ≤5s;

[0304] Primary alarm logic: triggered when both "temperature change rate exceeds overload correlation threshold" and "oil flow response lag" are met, i.e. , where, is the normalized load current, is the change rate of oil flow velocity module, is the load current change rate;

[0305] Secondary shutdown condition parameter calculation:

[0306] Hot spot area diffusion threshold : dynamically calculated based on the following formula:

[0307] , where the basic diffusion rate , where, is the basic diffusion rate of the hot spot area, with units of c / s, calibrated by hot spot diffusion experiment, thermal accumulation factor , where, is the thermal accumulation from the initial time to time, , ℃ is the rated hot spot temperature;

[0308] Maximum temperature gradient threshold: combined with insulation material tolerance limit (paper insulation local aging accelerates when gradient > 8℃ / cm), set , where, is the maximum temperature gradient module value in the temperature field;

[0309] Secondary shutdown logic: triggered when "hot spot area diffusion rate exceeds threshold" and "maximum temperature gradient exceeds limit", i.e.

[0310] , where, is the change rate of hot spot area with time;

[0311] Dynamic threshold self-adaptive adjustment:

[0312] Ambient temperature compensation: when the ambient temperature ℃, the primary alarm threshold is reduced by 10% ( ), improving sensitivity in high temperature environments;

[0313] Device aging correction: for transformers with operating life > 15 years, the Increase to 0.3 cm² / s (considering hot spot diffusion acceleration after insulation aging);

[0314] Step S5-4: Alarm response mechanism and linkage control:

[0315] Primary alarm response:

[0316] Local warning: control cabinet indicator light flickering (yellow), intermittent alarm of buzzer (frequency 1 Hz), abnormal parameter display on human-machine interface (temperature rate of change, load current, real-time curve of oil flow rate);

[0317] Data recording: automatically store multi-source data (temperature field, current, oil flow rate) 300s before and after the alarm, generate event log (including trigger time, associated parameter value, residual );

[0318] Auxiliary decision: push preliminary diagnosis results (such as "suspected cooler efficiency decline" "load-temperature rise nonlinearity"), suggest checking the valve state of the cooling system, oil pump operating current;

[0319] Secondary shutdown response:

[0320] Emergency control: link transformer protection device through relay output signal (dry contact, capacity 220V / 5A), trigger low-voltage side circuit breaker trip (action time ≤50ms), cut off load current;

[0321] Remote notification: push shutdown signal (including fault type, hot spot location coordinates, maximum temperature gradient value) to the operation and maintenance center through the power dispatching data network (DL / T476), and send a short message to the person in charge;

[0322] Safety interlocking: lock the closing loop after tripping, which needs to be reset manually (input password + on-site confirmation), to prevent misoperation restart;

[0323] False alarm suppression and self-checking:

[0324] Primary alarm delay confirmation: start a 5s delay after triggering, continuously monitor parameters during this period, if Fall back to normal range, automatically cancel alarm (false alarm rate controlled at <0.1 times / month);

[0325] Secondary shutdown verification after shutdown: retest the hot spot area temperature by infrared thermal imager after shutdown, compare with system monitoring value (deviation ≤2℃, then confirm effective, otherwise mark as false alarm and correct threshold);

[0326] Step S5-5: Iterative optimization and verification of early warning model:

[0327] Historical data driven parameter iteration:

[0328] Review the alarm events (including valid alarms and false alarms) every quarter, and use gradient descent method to optimize Experience coefficient: Wherein, is the value after the th iteration, is the value after the th iteration, is the learning rate, is the historical event number, is the temperature change rate of the th event, is the load current value of the th event, is the load change duration of the th event, so that the detection rate of the model for real faults is ≥ 99%; Digital twin simulation verification:

[0329] In the transformer digital twin platform, typical fault scenarios (such as winding inter-turn short circuit and oil channel blockage) are reproduced, simulation data (temperature, current, oil flow rate) is input to the early warning model, and the alarm triggering time (deviation from theoretical fault occurrence time ≤ 2s), grading accuracy (no overstep or missing level) are verified;

[0330] Extreme condition test: simulate the composite scenario of "load sudden rise + sudden failure of cooler", verify whether the secondary shutdown is triggered before the hotspot temperature reaches 140℃ (critical value of carbonization of insulating paper) (safety margin ≥ 10℃ is reserved);

[0331] Model version management:

[0332] After each parameter optimization, a new version of the model is generated (marked with date + iteration number), historical versions are preserved (at least 3), and backtracking comparison is supported;

[0333] Before the new model goes online, it is verified by an offline test set (including 1000+ normal samples and 100+ fault samples), and the accuracy rate must be ≥ 98% before it can be deployed;

[0334] Through the above steps, accurate identification of transformer abnormal temperature rise (response time ≤ 1s) and hierarchical disposal can be achieved, effectively shortening the fault detection time (reduced by 70% compared to traditional methods) and reducing the incidence of major accidents (verified data shows that the fault interception rate is ≥ 95%).

[0335]

[0336] ​​In the embodiment, the fluorescent fiber sensor adopts europium ion doped silica-zirconia composite fluorescent material, the temperature sensitivity is 0.85 percent of relative change of decay time constant per Celsius degree, the temperature measurement accuracy is better than plus or minus 0.5 Celsius degree; and the fiber surface is coated with nano-aluminum oxide insulation layer, the thickness is controlled in the range of plus or minus 5 nanometers.

[0337] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications, changes, variations, substitutions, and equivalents will occur to those of ordinary skill in the art to which the application relates and are encompassed within the spirit and scope of the application as defined by the following claims.

Claims

1. A distributed temperature measurement method for oil-immersed transformers based on a fluorescent fiber optic sensor is characterized by: The following steps are involved: S1. Sensor Layout: Fluorescent fiber optic sensors are deployed in a partitioned and layered three-dimensional topology in key temperature-rise areas within the oil-immersed transformer, including between high-voltage and low-voltage winding layers, on the core surface, and in hot spots in the heat dissipation oil channel. The sensors consist of multiple sections of fluorescent optical fiber connected in series via electromagnetic interference-resistant fiber optic patch cords to form a distributed temperature measurement network. The length of each optical fiber section is customized based on the spatial characteristics of the monitoring area. S2. Excitation signal injection: A pulsed laser is used to inject a pulsed excitation light with a wavelength of 380 nm to 420 nm into the fluorescent fiber sensor to excite the fluorescent substance to generate a fluorescence afterglow signal. S3, signal acquisition and demodulation: real-time acquisition of fluorescence afterglow signals, use of dual-channel synchronous phase-locked amplification technology to separate environmental noise, and obtain temperature measurements by calculating the fluorescence decay time constant; S4, Temperature Field Reconstruction: Combining the sensor's three-dimensional spatial coordinates with real-time temperature data, the dynamic temperature field distribution inside the transformer is reconstructed based on a thermodynamic inversion algorithm, identifying and marking hot spots where the temperature gradient exceeds 5 degrees Celsius per centimeter; S5. Perform time-series correlation analysis on the temperature data, transformer load current, and cooling oil flow rate. When the temperature change rate exceeds the dynamic threshold and does not match the load change trend, activate the graded alarm mechanism.

2. The distributed temperature measurement method for oil-immersed transformers based on a fluorescent optical fiber sensor according to claim 1 is characterized in that: The sensor deployment in step S1 includes: The optical fiber is laid in a spiral winding pattern between winding layers. The outer side of the optical fiber is covered with an oil-resistant polyimide protective layer with a thickness of no more than 0.2 mm and fixed to the winding insulation support bar by micro ceramic clips. A dual-redundant cross-layout method is used in the hot spots of the heat dissipation oil channel, and two sections of optical fiber are cross-fixed at the inlet and outlet of the oil channel at an angle of 30 to 60 degrees.

3. The distributed temperature measurement method for oil-immersed transformers based on a fluorescent optical fiber sensor according to claim 1 is characterized in that: The signal demodulation in step S3 adopts a dual-weight adaptive attenuation model: First, the effective decay time constant is calculated, which is the product of the time constant of the fast fluorescence decay component and its real-time signal-to-noise ratio dynamic weight, plus the product of the time constant of the slow fluorescence decay component and its complementary weight, plus the oil flow disturbance compensation. The real-time signal-to-noise ratio dynamic weight is equal to the real-time signal-to-noise ratio value divided by the sum of the real-time signal-to-noise ratio and the environmental noise coefficient; The oil flow disturbance compensation is obtained by integration. The integral term includes the product of the transformer oil density and the square of the velocity gradient of the oil flow in the normal direction of the optical fiber, plus the product of the temperature-dependent dynamic viscosity and the square of the oil flow curl. The integration is performed along the length of the optical fiber microelement segment, and the result is multiplied by the optical fiber fluid sensitivity coefficient.

4. The distributed temperature measurement method for oil-immersed transformers based on a fluorescent optical fiber sensor according to claim 3 is characterized in that: The temperature calculation uses the oil flow coupling compensation function: The temperature value is equal to the fluorescent material calibration coefficient a multiplied by the square of the effective decay time constant, plus the coefficient b multiplied by the effective decay time constant, plus the constant term c, and finally the oil flow correction factor d multiplied by the logarithm of the oil flow velocity; The oil flow velocity is obtained by calculating the square root of the sum of the squares of the three-dimensional velocity components, and a small offset is added to prevent zero-speed singularities.

5. The distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors according to claim 1 is characterized in that: The three-dimensional thermodynamic inversion algorithm of step S4 includes: Establish a coupled heat conduction and convection equation, which includes the product of the transformer oil heat capacity and the temperature-time derivative equal to the divergence of the thermal conductivity and the second-order spatial derivative of temperature, plus the winding heat source power, minus the dot product of the oil flow velocity and the temperature gradient; The regularized least squares inversion method is adopted. The objective function is the sum of the squares of the deviations between the measured temperature values ​​of all sensors and the calculated values, plus a smoothness constraint term of the global integration of the squares of the second-order spatial derivatives of the temperature field. The temperature field distribution is solved by minimizing the objective function.

6. The distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors according to claim 1 is characterized in that: The hierarchical alarm triggering conditions of step S5 are: The triggering condition for the first level alarm is that the temperature time change rate is greater than the sum of the load current power function term and the cooling state exponential decay term, and the oil flow growth rate lags behind the load current growth rate; The triggering conditions for the second-level shutdown protection are that the time rate of change of the hotspot area is greater than the dynamic diffusion threshold, and the maximum temperature gradient exceeds 8 degrees Celsius per centimeter; Here, the dynamic diffusion threshold is equal to the basic diffusion rate multiplied by a linear function of the load current change from the rated value, plus a heat accumulation factor.

7. The distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors according to claim 1 is characterized in that: The fluorescent fiber sensor uses a europium ion-doped silica-zirconia composite fluorescent material, with a temperature sensitivity of 0.85 percent relative change in decay time constant per degree Celsius, and a temperature measurement accuracy better than plus or minus 0.5 degrees Celsius; The surface of the optical fiber is coated with a nano-aluminum oxide insulation layer with a thickness controlled within the range of 50 nanometers plus or minus 5 nanometers.

8. The distributed temperature measurement method for oil-immersed transformers based on fluorescent optical fiber sensors according to claim 5 is characterized in that: The winding heat source power is dynamically updated through the electro-thermal coupling model: The heat source power is equal to the product of the winding conductivity and the square of the current density, plus the product of the core loss coefficient and the time derivative of the magnetic flux density; Among them, the winding conductivity changes inversely with the increase of temperature.

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