Power line integrated with distributed sensing structure, power line temperature detection method, power line temperature detection device and power line temperature detection equipment

Abstract

The invention discloses a power line integrated with a distributed sensing structure and a power line temperature detection method, device and equipment, and relates to the technical field of power equipment online monitoring. A sensing core layer is integrated in the power line, and a temperature sensitive material can be embedded into an insulation protection layer of the power line in the power line preparation process; according to the power line integrated with the distributed sensing structure, the construction difficulty is greatly simplified, in addition, the special carrier waveform is designed for the pre-integrated core layer, the resolution limitation of traditional attenuation type measurement is broken through, and the power line temperature monitoring fineness is improved.

Description

Technical Field

[0001] This application relates to the technical field of on-line monitoring of power equipment, and particularly to a power line integrated with a distributed sensing structure, a power line temperature detection method, device and equipment. Background Art

[0002] Traditional power line temperature monitoring relies on later additional sensors or fiber optic distributed temperature measurement, which has problems such as complex construction and susceptibility to electromagnetic interference. Existing carrier signal temperature measurement technologies are mostly based on signal reflection feature analysis, which requires high construction precision and is difficult to distinguish temperature gradient details. Summary of the Invention

[0003] The purpose of this application is to provide a power line integrated with a distributed sensing structure, a power line temperature detection method, device and equipment, which can simplify the construction difficulty of power line temperature monitoring and improve the detection fineness.

[0004] To achieve the above purpose, this application provides the following solutions:

[0005] In the first aspect, this application provides a power line integrated with a distributed sensing structure, including a copper conductor, a sensing core layer and an insulation protection layer arranged in sequence from inside to outside, wherein the sensing core layer is integrated into the power line during the preparation process of the power line.

[0006] In the second aspect, this application provides a power line temperature detection method, which is applied to the power line integrated with the distributed sensing structure described above. The power line temperature detection method includes:

[0007] Using an FPGA to generate a composite carrier signal and inputting the composite carrier signal into the power line; the composite carrier signal includes multiple sub-carrier signals, and the temperature sensitive intervals of different sub-carrier signals are different;

[0008] Adopting an improved generalized likelihood ratio detection method to analyze the reflection signal delay spectrum of the power line to obtain a fault location result.

[0009] Optionally, the composite carrier signal is:

[0010] f n = f0 + n·Δf;

[0011] wherein, f n is the frequency of the nth sub-carrier signal in the composite carrier signal, f0 is the fundamental frequency, Δf is the frequency interval of the sub-carrier signal, Δf = 100 / d or Δf = β×k(T0) / d 2 , d is the thickness of the insulation protection layer of the power line, k(T0) is the temperature sensitivity coefficient of the sensing core layer of the power line at the base temperature T0, and β is a material characteristic constant.

[0012] Optionally, an improved generalized likelihood ratio detection method is used to analyze the time delay spectrum of the reflected signal of the power line to obtain the fault location result, specifically including:

[0013] Use the improved generalized likelihood ratio detection method to analyze the time delay spectrum of the reflected signal of the power line corresponding to each carrier signal to obtain the phase offset of each carrier signal;

[0014] According to the phase offset of each carrier signal, calculate the temperature parameters of each monitoring section of the power line;

[0015] Calculate the carrier signal transmission rate of each monitoring section according to the temperature parameters of each monitoring section;

[0016] Based on the carrier signal transmission rates of each monitoring section, calculate the fault point location.

[0017] Optionally, the calculation formula of the temperature parameter is:

[0018]

[0019] where T i is the temperature parameter of the i-th monitoring section of the power line, w n is the weight of the n-th sub-carrier signal, is the phase difference of the n-th sub-carrier signal, k is the temperature coefficient of the material dielectric constant, d is the thickness of the sensitive layer, L i is the length of the i-th monitoring section.

[0020] Optionally, after calculating the temperature parameters of each monitoring section of the power line according to the phase offset of each carrier signal, it further includes:

[0021] When the absolute value of the difference between the temperature parameter of the i-th monitoring section and the temperature parameter of the i-1-th monitoring section > 15 °C, then use the following formula to perform median filtering on the temperature parameter of the i-th monitoring section:

[0022] T′ i = med(T i-2 , T i-1 , T i+1 , T i+2 );

[0023] where T' i is the temperature parameter of the i-th monitoring section after median filtering, T i-2 , T i-1 , T i+1 and T i+2 are the temperature parameters of the i-2-th, i-1-th, i+1-th and i+2-th monitoring sections respectively, and med( ) is the median filtering parameter;

[0024] When the temperature parameter of the i-th monitoring section > 120 °C, the temperature parameter of the i-th monitoring section is compensated using the following formula;

[0025] T” i = T i + 0.07(T i - 80) 1.2 ;

[0026] Where, T” i is the compensated temperature parameter of the i-th monitoring section.

[0027] Optionally, the formula for calculating the carrier signal transmission rate of each monitoring section based on the temperature parameter of each monitoring section is:

[0028]

[0029]

[0030] Where, v(T i ) is the carrier signal transmission rate of the i-th monitoring section, ε r (T i ) is the dielectric property parameter, c is the speed of light, T i is the temperature parameter of the i-th monitoring section of the power line, and k(T0) is the temperature sensitivity coefficient of the sensing core layer of the power line at the base temperature T0.

[0031] Optionally, based on the carrier signal transmission rate of each monitoring section, calculate the fault point location, specifically including:

[0032] Under the condition of satisfying the formula , determine the maximum value of n; where, n is the n-th monitoring section, L i is the length of the i-th monitoring section, v(T i ) is the carrier signal transmission rate of the i-th monitoring section, and τ 测量 is the signal time delay obtained by measurement;

[0033] Based on the maximum value of n, calculate the fault point location using the following formula;

[0034]

[0035] Where, L is the distance from the starting position of the power line to the fault point position, is the carrier signal transmission rate of the n max + 1-th monitoring section, τ total is the total time delay of the first n max monitoring sections, and n max is the maximum value of n.

[0036] In a third aspect, the present application provides a power line temperature detection device, which applies the above-mentioned power line temperature detection method. The power line temperature detection device includes:

[0037] A composite carrier signal transmitting module, configured to generate a composite carrier signal using an FPGA and input the composite carrier signal into the power line; the composite carrier signal includes a plurality of sub-carrier signals, and the temperature sensitive intervals of different sub-carrier signals are different;

[0038] A fault location module, configured to analyze the reflection signal delay spectrum of the power line by using an improved generalized likelihood ratio detection method to obtain a fault location result.

[0039] In a fourth aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned power line temperature detection method.

[0040] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0041] The present application provides a power line, a power line temperature detection method, a device and a device integrated with a distributed sensing structure. A sensing core layer is integrated in the power line of the present application, and temperature sensitive materials can be embedded inside the insulation protection layer of the power line during the preparation process of the power line, forming the power line integrated with the distributed sensing structure of the present application, which greatly simplifies the construction difficulty. In addition, the present application designs a dedicated carrier waveform for the pre-integrated core layer, breaks through the resolution limit of traditional attenuation measurement, and improves the fineness of power line temperature monitoring. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0043] Figure 1 It is a schematic structural diagram of a power line integrated with a distributed sensing structure provided by an embodiment of the present application;

[0044] Figure 2 It is a flowchart of a power line temperature detection method provided by an embodiment of the present application;

[0045] Figure 3 It is a schematic diagram of the python code of the noise reduction algorithm provided by an embodiment of the present application;

[0046] Figure 4 Schematic diagram of the matlab code for phase unwrapping processing provided in an embodiment of the present application;

[0047] Figure 5 Diagram showing relevant parameters for phase-weighted generalized cross-correlation calculation provided in an embodiment of the present application;

[0048] Figure 6 Schematic diagram of the fault point location principle provided in an embodiment of the present application;

[0049] Figure 7 Schematic diagram of the python code for the AMFTR precise positioning algorithm provided in an embodiment of the present application;

[0050] Figure 8 Diagram showing relevant parameters for time reversal correlation calculation provided in an embodiment of the present application;

[0051] Figure 9 Diagram showing relevant parameters for the temperature-wave velocity synchronous correction coefficient provided in an embodiment of the present application;

[0052] Figure 10 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0053] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0054] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0055] In an exemplary embodiment, a power line integrated with a distributed sensing structure is provided, as Figure 1 shown, including a copper conductor 101, a sensing core layer 102, and an insulation protection layer 103 arranged in sequence from the inside to the outside.

[0056] Among them, the sensing core layer is integrated into the power line during the power line manufacturing process, and its manufacturing process is as follows: A conductive-temperature sensitive composite material layer is coated on the surface of the copper conductor 101 as the sensing core layer 102, and then the insulation protection layer 103 is covered to form the power line integrated with the distributed sensing structure of the present application. Specifically, after the copper wire is drawn into shape, it is continuously coated through a three-layer coextrusion device:

[0057] The first layer: a conductive composite material containing silicon carbide / PVDF (thickness 50 μm).

[0058] The second layer: a cross-linked polyethylene insulation layer (thickness 2 mm).

[0059] The third layer: a metal copper wire or a conductive medium.

[0060] The detailed parameters of each layer are as follows: a copper conductor with a diameter of 10 mm, successively coated with a sensing core layer (weight ratio of silicon carbide / PVDF 70%:30%, thickness 50 μm) and an insulation protection layer (cross-linked polyethylene thickness 2.0 mm).

[0061] The temperature response mechanism of this power line is as follows: the dielectric constant ε r (T) of the sensing core layer 102 has a non-linear relationship with temperature T (ε r (T) = k·T 2 ), resulting in the phase shift of the carrier signal varying with temperature, and the sensitivity is 3 times higher than that of traditional resistive temperature measurement. Exemplarily, k is the temperature coefficient, k = 0.0032 - 0.0041 / °C.

[0062] In another exemplary embodiment, the material of the above-mentioned sensing core layer 102 is composed of silicon carbide nanoparticles and polyvinylidene fluoride in a mass ratio of 60 - 75%:25 - 40%.

[0063] As shown in Table 1 and Table 2, the power line integrated with a distributed sensing structure has the following advantages:

[0064] 1. Subversive process innovation: The pre-integrated core layer enables the factory-produced wire to have temperature measurement capabilities without modification, and the cost is only 12 - 18% of that of the retrofitted sensor system.

[0065] 2. Strong anti-interference ability: The built-in design of the core layer avoids external electromagnetic interference, and the signal-to-noise ratio (SNR) is increased by more than 15 dB;

[0066] 3. Full life cycle monitoring: The sensing core layer ages synchronously with the wire, avoiding monitoring failures caused by the mismatch of the service life of traditional sensors.

[0067] Table 1 Comparison table of budget costs

[0068]

[0069] Table 2 Comparison of temperature detection accuracy of the pre-integrated process

[0070]

[0071] In another exemplary embodiment, a power line temperature detection method is provided. The power line temperature detection method is applied to the power line integrated with the distributed sensing structure described above, and the power line temperature detection method includes the following steps 101 and 102.

[0072] Step 101: Use an FPGA to generate a composite carrier signal and input the composite carrier signal into the power line; the composite carrier signal includes multiple sub-carrier signals, and the temperature-sensitive intervals of different sub-carrier signals are different.

[0073] Step 102: Analyze the reflection signal delay spectrum of the power line by using an improved generalized likelihood ratio detection method to obtain a fault location result.

[0074] In another exemplary embodiment, as Figure 2 shown, the composite carrier signal of the present application uses a multi-band frequency hopping carrier signal, and its frequency selection range satisfies: at a reference temperature of 20 °C, the frequency of the nth sub-carrier signal is:

[0075] f n = f0 + n·Δf, (1 ≤ n ≤ N);

[0076] where, f n is the frequency of the nth sub-carrier signal in the composite carrier signal, f0 is the fundamental frequency, and its value range is 6 - 10 MHz, Δf is the frequency interval of the sub-carrier signal, (unit: MHz / mm), Δf = 100 / d or Δf = β×k(T0) / d 2 , d is the thickness of the insulation protection layer of the power line, k(T0) is the temperature sensitivity coefficient of the sensing core layer of the corresponding power line at the base temperature T0, and its value range is 0.0032 - 0.0041 / °C, β is the material characteristic constant, and its value range is 0.018 - 0.022 MHz·mm^2 / °C.

[0077] In another exemplary embodiment, the detection method of the embodiment of the present application applies multi-band dynamic frequency hopping technology. The specific acquisition method of each sub-carrier signal is: divide 16 sub-channels in the 1 - 30 MHz frequency band. The N sub-carriers are divided into three frequency bands according to temperature sensitivity:

[0078] Frequency band 1 (low temperature region < 80 °C): f1 - f n1 , n1 = 4;

[0079] Frequency band 2 (medium temperature region 80 - 120 °C): f n1+1 - f n2 , n2 = 10;

[0080] Frequency band 3 (high temperature region > 120 °C): f n2+1 - f N, N = 16;

[0081] The temperature sensitivities corresponding to each frequency band are: 0.83 ± 0.05% / °C, 1.12 ± 0.08% / °C, 1.45 ± 0.12% / °C.

[0082] The carrier signal is modulated by OFDM-QAM and then injected into the wire. By measuring the carrier phase frequency shift in different frequency bands, distributed detection of the temperature along the line is realized. At the same time, combined with the improved generalized cross-correlation algorithm and the matched-field time reversal technology, accurate positioning of the fault point can be achieved, and the positioning accuracy can reach 0.5 meters.

[0083] In an exemplary embodiment, an FPGA is used to generate a composite carrier signal, and a single cycle includes:

[0084] Preamble: A Gold sequence (length 31) is used for synchronization.

[0085] Data segment: 8 OFDM subcarriers (bandwidth 10 MHz).

[0086] The carrier signal parameters are set as: reference frequency f0 = 8 MHz; number of subcarriers N = 16, Δf = 5 MHz (when d = 2 mm, Δf = 100 / d). And the system sets normal and abnormal temperature gradients to facilitate detecting abnormal power carriers caused by temperature anomalies during the temperature transfer of the receiving wire, and at the same time detecting and comparing deviations from the normal wave frequency range. Within the set carrier range, real-time monitoring is carried out to achieve real-time conversion between power carriers and temperature, timely transmit the abnormal temperature in the wire using power carriers, obtain the carrier wave frequency sizes of each part of the wire, and detect the real-time temperature of this section of the wire.

[0087] In an exemplary embodiment, step 101 above can be implemented through the following steps:

[0088] Step 201, the FPGA generates a composite carrier signal, including 16 OFDM subcarriers, and the frequency band covers 6 - 26 MHz (assuming Δf = 5 MHz, d = 2 mm).

[0089] Step 202, each subcarrier uses QAM-64 modulation, and the preamble is a Gold sequence (code length 31 bits).

[0090] In another exemplary embodiment, OFDM-QAM (Orthogonal Frequency Division Multiplexing - Quadrature Amplitude Modulation) joint modulation is adopted to inversely deduce the temperature distribution of the insulating layer through the frequency response characteristics. The impedance spectrum fingerprint matching algorithm is used to establish a relationship model between the attenuation coefficient α(f) of the carrier signal and the temperature gradient, and the temperature anomaly point, i.e., the fault point, is located through the spectral shift characteristics, as shown in Table 3 and Table 4.

[0091] Table 3 Mapping relationship between carrier signal frequency band division and temperature sensitivity

[0092] Sub - carrier number Frequency range (MHz) Temperature sensitivity (℃) 1-4,6-10 0.83±0.05 Low temperature region (<80°C) 5-10,11-20 1.12±0.08 Medium - temperature range (80 - 120℃) 11-16,21-28 1.45±0.12 High - temperature range (>120℃)

[0093] Table 4 Comparison and positioning of impedance spectrum shift caused by temperature gradient

[0094]

[0095] In another exemplary embodiment, the above step 102 can be replaced by the following steps 301 - 303.

[0096] Step 301: Calculate the temperature parameters of each monitoring section of the power line according to the phase offset of each carrier signal.

[0097] Step 302: Calculate the carrier signal transmission rate of each monitoring section according to the temperature parameters of each monitoring section.

[0098] Step 303: Calculate the location of the fault point based on the carrier signal transmission rate of each monitoring section.

[0099] In another exemplary embodiment, the acquisition method of the reflected signal is as follows:

[0100] First, the receiving end captures the reflected signal at a sampling rate of 1 GS / s and synchronously extracts the subcarrier response signals Sn (f,t) (n = 1 - 16) (f - corresponding wave frequency; t - corresponding temperature).

[0101] Then, the signal is preprocessed and wavelet denoised. After denoising, the effective bandwidth of the signal is increased by 17% (signal-to-noise ratio ≥ 36 dB), and the phase measurement error is controlled within ±0.03 radians. The program code is as Figure 3 shown.

[0102] In another exemplary embodiment, after the reflected signal is wavelet denoised, the temperature parameters are obtained through phase change resolution and frequency domain integration.

[0103] The phase change resolution is as follows:

[0104] 1. Calculate the phase offset of each subcarrier signal: Among them, is the reference phase, Sn is the reflected signal of the nth subcarrier signal, is the phase difference of the nth subcarrier signal.

[0105] 2. Phase unwrapping process: When the phase difference between adjacent frequency points exceeds π, perform ±2π compensation. The core logic of the algorithm using Matlab is as Figure 4 shown.

[0106] Extract the phase offset of the characteristic frequency point Calculate the temperature parameters of each monitoring section according to the following preset formula.

[0107]

[0108] Among them, T i is the temperature parameter of the ith monitoring section of the power line, w n is the weight of the nth subcarrier signal, 0.15 in the low temperature band, 0.22 / 0.35 in the medium temperature, and 0.28 in the high temperature. is the phase difference of the nth subcarrier signal, d is the thickness of the sensitive layer, 50 ± 5 μm, L i is the length of the ith monitoring section, divided by 100 m intervals, k is the temperature coefficient of the dielectric constant of the material, 0.0036 ± 0.0002 / °C.

[0109] In the embodiment of the present application, a calibration reference point (interval ≤ 200 m) needs to be set at the power line joint, and the power line is divided into multiple monitoring sections.

[0110] In another exemplary embodiment, the time difference of arrival of the signal is calculated by the Generalized Cross-Correlation (GCC) algorithm, and the fault distance is calculated in combination with the wave velocity model. After actual measurement, the temperature detection sensitivity reaches 1.2 °C / MHz, and the positioning error ≤ 0.53 m (test conditions: IEC60287 standard ambient temperature 25 °C). After detecting the abnormal power carrier signal above, through the temperature parameter inversion algorithm, the fault location is located and transmitted. The following is the improvement of the temperature parameter inversion and positioning algorithm.

[0111] 1. Temperature parameter inversion algorithm

[0112] Temperature field reconstruction based on frequency domain integration:

[0113]

[0114] denoised_signal = wavelet_denoise(freq_response, wavelet='db8', level=5);

[0115] # Integration reconstruction formula (corresponding to ε_r(T)=kT 2 model);

[0116] phase_shift = np.unwrap(np.angle(denoised_signal));

[0117] T = np.sqrt((phase_shift * d) / (3e8 * k * L)) # d: Core layer thickness, L: Line length

[0118] # Multi-band weighted fusion;

[0119] weights = [0.15, 0.22, 0.35, 0.28] # 6 / 12 / 18 / 24MHz weights

[0120] return np.dot(T_freq_bands, weights)

[0121] 2. Improvement of positioning algorithm

[0122] / / Improved generalized cross-correlation algorithm implemented on FPGA:

[0123] module GCC_enhanced(

[0124] input clk,

[0125] input[15:0] ref_signal,

[0126] input[15:0] echo_signal,

[0127] output reg[31:0] tau_est);

[0128] / / Complex Morlet wavelet convolution kernel;

[0129] parameter WAVELET_REAL[0:15] = {16'h3C00, 16'h1A23,..., 16'hFF8D};

[0130] parameter WAVELET_IMAG[0:15] = {16'h0000, 16'h2D45,..., 16'h0042};

[0131] always@(posedge clk)begin

[0132] / / Wavelet domain cross-power spectrum calculation;

[0133] complex_mult(ref_fft,echo_fft_conj,cross_spectrum);

[0134] / / Phase transformation weighting;

[0135] apply_phase_weighting(cross_spectrum);

[0136]

[0137] The embodiments of the present application synchronously implement temperature inversion and fault location, where temperature detection uses the frequency-domain cross-correlation algorithm and location uses the improved matched-field time-reversal algorithm.

[0138] In another exemplary embodiment, a process for adaptively correcting the temperature distribution is provided, specifically:

[0139] When the absolute value of the difference between the temperature parameter of the i-th monitoring segment and the temperature parameter of the (i-1)-th monitoring segment > 15 °C, the temperature parameter of the i-th monitoring segment is median-filtered using the following formula:

[0140] T′ i =med(T i-2 ,T i-1 ,T i+1 ,T i+2 );

[0141] Where T' i is the temperature parameter of the i-th monitoring segment after median filtering, and T i-2 , T i-1 , T i+1 and T i+2 are the temperature parameters of the (i-2)-th, (i-1)-th, (i+1)-th, and (i+2)-th monitoring segments respectively, and med() is the median filtering parameter;

[0142] When the temperature parameter of the i-th monitoring segment > 120 °C, the temperature parameter of the i-th monitoring segment is compensated using the following formula;

[0143] T” i =T i +0.07(T i -80) 1.2 ;

[0144] Where T” iis the compensated temperature parameter for the i-th monitoring section, and 0.07 is the proportionality coefficient.

[0145] In another exemplary embodiment, the positioning algorithm is improved.

[0146] Analysis of the defects of the original algorithm to support the necessity of improvement is as follows:

[0147] The problems of traditional Generalized Cross-Correlation (GCC) are: the multi-path effect of power lines causes pseudo-peaks in the time-delay spectrum, the misjudgment rate > 35%, and the sensitivity-immunity contradiction caused by a fixed threshold (the missed detection rate reaches 28% when the signal-to-noise ratio < 20 dB).

[0148] The limitations of conventional Matched Field Localization are: a large amount of channel models need to be pre-stored, the computational complexity reaches O(N 2 ), and there is a model mismatch under time-varying working conditions, and the positioning error fluctuates by ±2.1 m.

[0149] Based on the above defects, the following improvements are made in this application.

[0150] Improvement point 1: Phase Weighted Generalized Cross-Correlation (PW-GCC).

[0151] The calculation formula of Phase Weighted Generalized Cross-Correlation is:

[0152]

[0153] The parameters of Phase Weighted Generalized Cross-Correlation are as Figure 5 shown.

[0154] Specifically, in the embodiment of this application, α = 0.7 is set to effectively suppress the strong noise frequency band, and in addition, a complex Morlet wavelet convolution kernel is added to improve the time-frequency resolution.

[0155] Improvement point 2: Adaptive Matched Field Time Reversal (AMFTR), implementation steps:

[0156] 1. Establish a parametric model of the propagation path: Establish a temperature-dielectric constant-wave speed transfer chain:

[0157] v(T) = v0 * sqrt(εr(20°C) / εr(T))

[0158] = 2e8 * sqrt(3.6 / (0.0036 * T 2 )) [m / s];

[0159] In the formula: ε_r(T) = 0.0036T 2 + 2.8, material properties (dielectric constant and temperature).

[0160] 2. Generate a dynamic matched field library: temperature gradient → dielectric constant distribution → wave speed field.

[0161] 3. Inversion focusing algorithm, the time delay cumulative formula for each segment is:

[0162]

[0163] where τ 时延 is the cumulative time delay, L i is the length of the i-th monitoring segment, and v(T i ) is the temperature of the i-th monitoring segment.

[0164] As Figure 6 shown, the fault point location method is:

[0165] Under the condition of satisfying the formula , determine the maximum value of n; where n is the n-th monitoring segment, L i is the length of the i-th monitoring segment, v(T i ) is the carrier signal transmission rate of the i-th monitoring segment, and τ 测量 is the signal time delay obtained by measurement;

[0166] Based on the maximum value of n, calculate the fault point location using the following formula;

[0167]

[0168]

[0169] where L is the distance from the starting position of the power line to the fault point position, is the carrier signal transmission rate of the (n max + 1)-th monitoring segment, τ total is the total time delay of the first n max monitoring segments, and n max is the maximum value of n.

[0170] In another exemplary embodiment, in order to illustrate the implementation process of the technical solution of the present application, the following example is set:

[0171] Step 1: Signal preprocessing.

[0172] Collect the reflected signal srx(t)srx(t), sampling rate ≥ 1 GHz

[0173] Wavelet packet decomposition for denoising (db8 wavelet, 5-layer decomposition).

[0174] Obtain the dielectric constant and temperature field.

[0175] Step 2: PW-GCC time delay estimation.

[0176] 1. Calculate the weighted cross-power spectrum, using the following formula:

[0177] $G_{xy}^w(f)=\frac{X(f)Y^*(f)}{|X(f)Y(f)|^{0.7}}$;

[0178] Suppress the secondary radiation interference in the noise frequency band, with the weighting exponent $\alpha = 0.7$ (optimal when measured with SNR > 15 dB).

[0179] 2. Perform the inverse Fourier transform to obtain the time-delay spectrum.

[0180] 3. Peak detection rule: main peak height > secondary peak height × 1.8, pulse width < 3 ns.

[0181] 4. Signal processing flow: delay and interference cancellation.

[0182] Step three: Precise positioning of AMFTR.

[0183] Based on the current temperature field $T$, calculate the local wave velocity $v$ i .

[0184] Dynamically generate a matching field template set:

[0185] Stepping resolution: 0.1 m;

[0186] Coverage range: maximum time delay ± 15%;

[0187] Example code is as Figure 7 shown.

[0188] Time reversal correlation calculation (accelerated by FFT): The parameters therein are as Figure 8 shown.

[0189] If there is a need for refinement, quadratic parabola interpolation positioning can be used to refine the time delay:

[0190]

[0191] In the formula:

[0192] $\tau_p$: original maximum peak point time delay (e.g., 80.3 ns);

[0193] $\Delta\tau$: sampling interval (0.1 ns);

[0194] $\rho_{p\pm1}$: correlation values of adjacent sampling points;

[0195] Convert the time delay to distance (space), specifically:

[0196]

[0197] where $\tau$ measured $=\tau$final 。

[0198] Step Four: Error correction.

[0199] Set secondary reflection cancellation: Establish the following equation:

[0200] Multiple reflection suppression: τ valid ={τ|τ < τ direct ·1.8};

[0201] where τ valid is the time delay after error correction, and τ direact is the time delay under the assumed straight-line distance.

[0202] When the detected secondary peak intensity > 0.35 times the main peak (frequency band), temperature-wave velocity coupling correction is adopted, and the anti-temperature-wave velocity synchronization correction coefficient: Explanation of each letter in the formula, such as Figure 9 shown.

[0203] Finally, the two algorithms form a closed loop through the time-frequency two-dimensional characteristics of the OFDM-QAM signal:

[0204] ① PW-GCC provides an initial time delay estimate (accuracy ±2m); ② AMFTR combines temperature field information for spatial focusing; ③ The iterative feedback mechanism achieves a positioning error < 0.5m.

[0205] In another exemplary embodiment, a power line temperature detection device is provided. The power line temperature detection device applies the above-mentioned power line temperature detection method. The power line temperature detection device includes:

[0206] A composite carrier signal transmitting module, configured to generate a composite carrier signal using an FPGA and input the composite carrier signal into the power line; the composite carrier signal includes multiple sub-carrier signals, and the temperature-sensitive intervals of different sub-carrier signals are different.

[0207] A fault location module, configured to analyze the time delay spectrum of the reflected signal of the power line by using an improved generalized likelihood ratio detection method to obtain a fault location result.

[0208] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 10As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for detecting the temperature of a power line.

[0209] Those skilled in the art can understand that Figure 10 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0210] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0211] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0212] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0213] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0214] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0215] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of the present application; at the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A power line integrated with a distributed sensing structure, characterized in that, It includes a copper conductor, a sensing core layer, and an insulating protection layer which are arranged from the inside to the outside in sequence. Among them, the sensing core layer is integrated into the power line during the preparation process of the power line.

2. A power line temperature detection method, characterized in that, The power line temperature detection method is applied to the power line integrated with the distributed sensing structure described in claim 1. The power line temperature detection method includes: Using an FPGA to generate a composite carrier signal and inputting the composite carrier signal into the power line; the composite carrier signal includes multiple sub-carrier signals, and the temperature sensitive intervals of different sub-carrier signals are different; Adopting an improved generalized likelihood ratio detection method to analyze the delay spectrum of the reflection signal of the power line to obtain a fault location result.

3. The power line temperature detection method according to claim 2, characterized in that, The composite carrier signal is: f n = f0 + n·Δf; Among them, f n is the frequency of the nth subcarrier signal in the composite carrier signal, f0 is the fundamental frequency, Δf is the frequency interval of the subcarrier signal, Δf = 100 / d or Δf = β×k(T0) / d 2 , d is the thickness of the insulating protective layer of the power line, k(T0) is the temperature sensitivity coefficient of the sensing core layer of the power line at the base temperature T0, and β is the material characteristic constant.

4. The power line temperature detection method according to claim 2, characterized in that Adopting an improved generalized likelihood ratio detection method to analyze the delay spectrum of the reflection signal of the power line to obtain a fault location result, specifically including: Adopting an improved generalized likelihood ratio detection method to analyze the delay spectrum of the reflection signal of the power line corresponding to each carrier signal to obtain the phase offset of each carrier signal; Calculating the temperature parameters of each monitoring section of the power line according to the phase offset of each carrier signal; Calculating the transmission rate of the carrier signal of each monitoring section according to the temperature parameters of each monitoring section; Calculating the fault point location based on the transmission rate of the carrier signal of each monitoring section.

5. The power line temperature detection method according to claim 4, characterized in that, The calculation formula of the temperature parameter is: Among them, T i is the temperature parameter of the i-th monitoring section of the power line, w n is the weight of the n-th subcarrier signal, Δφ n is the phase difference of the n-th subcarrier signal, k is the temperature coefficient of the material dielectric constant, d is the thickness of the sensitive layer, L i is the length of the i-th monitoring section.

6. The power line temperature detection method according to claim 4, characterized in that, After calculating the temperature parameters of each monitoring section of the power line according to the phase offset of each carrier signal, it further includes: When the absolute value of the difference between the temperature parameter of the i-th monitoring section and the temperature parameter of the (i - 1)-th monitoring section > 15°C, median filtering is performed on the temperature parameter of the i-th monitoring section using the following formula: T′ i = med(T i-2 , T i-1 , T i+1 , T i+2 ); Among them, T' i is the temperature parameter after median filtering for the i-th monitoring segment, T i-2 , T i-1 , T i+1 and T i+2 are the temperature parameters of the (i - 2)-th, (i - 1)-th, (i + 1)-th, and (i + 2)-th monitoring segments respectively, and med() is the median filtering parameter; When the temperature parameter of the i-th monitoring section > 120°C, compensation is performed on the temperature parameter of the i-th monitoring section using the following formula; T” i = T i + 0.07(T i - 80) 1.2 ; where, T” i is the compensated temperature parameter of the i-th monitoring section.

7. The power line temperature detection method according to claim 4, wherein The formula for calculating the transmission rate of the carrier signal of each monitoring section according to the temperature parameters of each monitoring section is: where v(T i ) is the carrier signal transmission rate of the i-th monitoring section, ε r (T i ) is the dielectric characteristic parameter, c is the speed of light, T i is the temperature parameter of the i-th monitoring section of the power line, and k(T0) is the temperature sensitivity coefficient of the sensing core layer of the power line at the base temperature T0.

8. The power line temperature detection method according to claim 4, characterized in that, Calculating the fault point location based on the transmission rate of the carrier signal of each monitoring section, specifically including: Under the condition of satisfying the formula , determine the maximum value of n; where n is the nth monitoring section, L i is the length of the ith monitoring section, v(T i ) is the carrier signal transmission rate of the ith monitoring section, and τ 测量 is the signal time delay obtained by measurement; Based on the maximum value of n, calculating the fault point location using the following formula; Among them, L is the distance from the starting position of the power line to the fault point position. is the carrier signal transmission rate of the (n max + 1)-th monitoring section, and τ total is the total time delay of the first n max monitoring sections, and n max is the maximum value of n.

9. A power line temperature detection device, characterized in that, The power line temperature detection device applies the power line temperature detection method described in any one of claims 2 - 8. The power line temperature detection device includes: A composite carrier signal transmitting module for using an FPGA to generate a composite carrier signal and inputting the composite carrier signal into the power line; the composite carrier signal includes multiple sub-carrier signals, and the temperature sensitive intervals of different sub-carrier signals are different; A fault location module for adopting an improved generalized likelihood ratio detection method to analyze the delay spectrum of the reflection signal of the power line to obtain a fault location result.

10. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the power line temperature detection method described in any one of claims 2 - 8.

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