Double-circuit line mutual inductance on-line monitoring system based on distributed sensor

Abstract

The invention provides a double-circuit line mutual inductance online monitoring system based on a distributed sensor. The system comprises a distributed intelligent sensing node cluster module, a data processing module, an energy and communication module, a dynamic pilot frequency excitation control module and a mutual inductance parameter monitoring module. The distributed intelligent sensing node cluster module deploys one intelligent sensing node every 500 meters along the double-circuit line and is used for collecting mechanical data, environmental data and pilot frequency excitation response data of the double-circuit line in real time; the data processing module is used for receiving the original data transmitted by the distributed intelligent sensing node cluster module and preprocessing the original data; the energy and communication module is used for providing a power supply and a data transmission channel for each module; the dynamic pilot frequency excitation control module is used for dynamically adjusting a pilot frequency excitation signal according to the power frequency interference intensity; and the mutual inductance parameter monitoring module is used for constructing a line electromagnetic-mechanical coupling simulation model and calculating mutual inductance parameters of the double-circuit line.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an online monitoring system for mutual inductance of double-circuit lines based on distributed sensors. Background Art

[0002] Double-circuit lines are widely used in power transmission, and accurate measurement of their mutual inductance parameters is crucial for stable system operation. With the expansion and structural optimization of power grids, there is an increasing trend of operating one circuit while building another circuit on the same tower or in parallel. This necessitates testing the mutual inductance parameters of double-circuit lines in a partial power outage environment. Traditional measurement methods recommended by DL / T1583-2016, "Guidelines for the Measurement of Power-Frequency Electrical Parameters of AC Transmission Lines," require a complete power outage of both lines for measurement. This complete outage results in channel power loss, and the change in operating mode reduces the grid's risk tolerance.

[0003] Early methods for measuring zero-sequence mutual inductance on double-circuit lines during partial power outages were the power frequency interference method and the zero-sequence increment method. The testing principle used the zero-sequence current increment generated when a single-phase tripping occurs on the operating line, along with the zero-sequence induced voltage component generated on the adjacent infrastructure line, to calculate the line mutual inductance. During the test, a program controller was connected to the operating line circuit breaker control circuit to open and close the circuit breaker on phases A, B, and C, respectively. However, technical issues arose: analyzing transient zero-sequence current signals sampled from current transformers was difficult, resulting in significant errors in the calculated results. Existing technologies, such as the power frequency interference method and the zero-sequence increment method, suffer from large measurement errors and poor anti-interference capabilities. Furthermore, traditional monitoring systems rely on manual inspections, resulting in low timeliness and high costs. Summary of the Invention

[0004] The object of the present invention is to provide an online monitoring system for mutual inductance of double-circuit lines based on distributed sensors to solve the problems raised in the above background technology.

[0005] The present invention is achieved through the following technical solutions:

[0006] A distributed sensor-based online monitoring system for double-circuit line mutual inductance, comprising: a distributed intelligent sensor node cluster module, a data processing module, an energy and communication module, a dynamic frequency-differential excitation control module, and a mutual inductance parameter monitoring module;

[0007] The distributed intelligent sensor node cluster module deploys an intelligent sensor node every 500 meters along the double-circuit line to collect the mechanical data and environmental data of the double-circuit line and the different-frequency excitation response data in real time;

[0008] The data processing module is used to receive the original data transmitted by the distributed intelligent sensor node cluster module and perform pre-processing;

[0009] The energy and communication module is used to provide power supply and data transmission channels for each module;

[0010] The dynamic inter-frequency excitation control module is used to dynamically adjust the inter-frequency excitation signal according to the intensity of the power frequency interference;

[0011] The mutual inductance parameter monitoring module is used to construct a line electromagnetic-mechanical coupling simulation model and calculate the mutual inductance parameters of the double-circuit line.

[0012] Specifically, the distributed intelligent sensor node cluster module includes a broadband electromagnetic coupling unit, an optical fiber strain monitoring unit and a micro-environment sensing unit;

[0013] The broadband electromagnetic coupling unit synchronously collects zero-sequence voltage and current in both power frequency and differential frequency bands;

[0014] The optical fiber strain monitoring unit uses a fiber Bragg grating array to collect the conductor axial strain and vibration frequency in real time at a sampling rate of 10kHz;

[0015] The micro-environment sensing unit collects temperature and humidity data in real time through temperature and humidity sensors, and collects wind speed data in real time through wind speed sensors.

[0016] Specifically, the data processing module includes an edge computing unit and a data dimension reduction unit;

[0017] The edge computing unit uses an adaptive digital notch filter to dynamically filter out power frequency harmonic interference from the heterodyne excitation response data, and uses a Kalman filter to eliminate noise from the mechanical data and environmental data, and generates a high-confidence original data set;

[0018] The mechanical and environmental data are subjected to Kalman filtering to eliminate noise and generate a high-confidence original data set. The data dimension reduction unit receives the original data set generated by the edge computing unit and compresses the data dimensionally using an improved sparse principal component analysis algorithm.

[0019] Specifically, the energy and communication module includes a high-frequency CT power supply unit and a dual-channel communication network unit;

[0020] The high-frequency CT power acquisition unit obtains the induced current through magnetic induction coupling and converts the induced current into direct current through a rectifier circuit;

[0021] The dual-channel communication network unit uses a low-power wide area network and a 5G slice emergency channel dual mode to transmit the collected data.

[0022] Specifically, the dynamic frequency-different excitation control module includes a data receiving unit, an adaptive frequency conversion strategy unit, and a PID closed-loop control unit;

[0023] The data receiving unit receives the processed data set from the data processing module, and the adaptive frequency conversion strategy unit establishes an impedance-strain-environment joint matrix based on the data set, and uses the environmental factor compensation algorithm to perform parameter correction on the power frequency zero-sequence impedance to select the optimal frequency difference point;

[0024] The PID closed-loop control unit performs PID closed-loop control on the amplitude of the different-frequency excitation signal according to the optimal different-frequency point.

[0025] Specifically, the specific process of establishing the impedance-strain-environment joint matrix is ​​as follows:

[0026]

[0027] Where G represents the impedance-strain-environment joint matrix, λ i represents the weight coefficient of the i-th parameter, z0 represents the heterofrequency zero-sequence impedance matrix, ε represents the mechanical strain tensor, and E represents the environmental factor matrix.

[0028] Specifically, the specific process of using the environmental factor compensation algorithm to correct the parameters of the power frequency zero-sequence impedance is as follows:

[0029] z 0,c =z0-(αΔT+βε+γv 2 )

[0030] Among them, z 0,c represents the corrected power frequency zero-sequence impedance, α represents the temperature coefficient, ΔT represents the temperature change, αΔT represents the compensation term for temperature change on impedance, β represents the strain coefficient, βε represents the compensation term for strain on impedance, γ represents the wind speed coefficient, v represents the wind speed, γv 2 It represents the compensation term of wind speed to impedance.

[0031] Specifically, the specific process of the PID closed-loop control unit performing PID closed-loop control on the amplitude of the inter-frequency excitation signal according to the optimal inter-frequency point is as follows:

[0032] First, calculate the equivalent impedance Z(f test ):

[0033]

[0034] Among them, f test Indicates the optimal frequency point, f N represents the power frequency, and ρ represents the empirical coefficient of the conductor;

[0035] Second, according to the line protection setting, set the upper limit of the excitation voltage V max =0.6V set ;

[0036] Third, inject the initial frequency excitation current I inf :

[0037]

[0038] Fourth, calculate the total adjustment amount ΔI of PID closed-loop control:

[0039]

[0040] Among them, K p Represents the proportionality coefficient, K i Integration coefficient, K d Represents the differential coefficient, e represents the voltage deviation, e=V max -V couple ,V couple Represents the coupling voltage measured by the distributed sensor;

[0041] Fifth, adjust the amplitude I′ of the different-frequency excitation current according to the total regulation amount inf :I′ inf =I inf +ΔI.

[0042] Specifically, the mutual inductance parameter monitoring module uses a finite element network to construct a line electromagnetic-mechanical coupling simulation model, and the specific process of calculating the mutual inductance parameters of the double-circuit line in combination with the PID closed-loop control results is as follows:

[0043] Calculate the change in magnetic flux induced in loop 2 by a unit current change in loop 1:

[0044]

[0045] Among them, Φ 12 represents the total magnetic flux generated by the current I1 in loop 1 and passing through loop 2, C2 represents the closed path of loop 2, and B represents the magnetic induction intensity generated by the current I1 in three-dimensional space;

[0046] Calculate the mutual inductance M:

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention provides a distributed sensor-based online monitoring system for the mutual inductance of a double-circuit line. This system effectively avoids power frequency interference and improves measurement reliability through dynamic, heterodyne excitation control. It accurately compensates for the influence of environmental factors on impedance, ensuring that the excitation signal can effectively stimulate the inherent response of the line under different operating conditions, thereby enhancing the ability to identify and detect mutual inductance parameters. Furthermore, the system combines an electromagnetic-mechanical coupling simulation model to perform parameter inversion and fitting, calculate the mutual inductance parameters of the double-circuit line, dynamically track changes in the mutual inductance of the line, and monitor potential faults in the double-circuit line. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 This is a structural diagram of a double-circuit line mutual inductance online monitoring system based on distributed sensors provided by the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more apparent, exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0052] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art are not described to avoid confusion with the present invention.

[0053] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0054] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0055] In order to fully understand the present invention, a detailed structure will be provided in the following description to illustrate the technical solution proposed by the present invention. Optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may also have other implementations.

[0056] See also Figure 1 , an online monitoring system for mutual inductance of double-circuit lines based on distributed sensors, the system comprising: a distributed intelligent sensor node cluster module, a data processing module, an energy and communication module, a dynamic frequency-differential excitation control module, and a mutual inductance parameter monitoring module;

[0057] The distributed intelligent sensor node cluster module deploys an intelligent sensor node every 500 meters along the double-circuit line to collect the mechanical data and environmental data of the double-circuit line and the different-frequency excitation response data in real time;

[0058] The data processing module is used to receive the original data transmitted by the distributed intelligent sensor node cluster module and perform pre-processing;

[0059] The energy and communication module is used to provide power supply and data transmission channels for each module;

[0060] The dynamic inter-frequency excitation control module is used to dynamically adjust the inter-frequency excitation signal according to the intensity of the power frequency interference;

[0061] The mutual inductance parameter monitoring module is used to construct a line electromagnetic-mechanical coupling simulation model and calculate the mutual inductance parameters of the double-circuit line.

[0062] Exemplarily, the distributed intelligent sensor node cluster module is deployed at the following locations: an intelligent sensor node is set up every 500 meters along the double-circuit line to monitor the mechanical state (strain) of the line, environmental conditions (temperature, humidity, etc.) and the response caused by the heterofrequency excitation signal in real time. These intelligent sensor nodes have perception and preliminary processing capabilities to ensure that the collected data is accurate and uploaded in a timely manner.

[0063] The energy and communication module provides stable power supply for each module and establishes a reliable data transmission channel. Data is transmitted from the sensor node to the main data processing unit at the back end through wireless or wired paths.

[0064] The data processing module receives raw multi-channel data from sensor nodes, filters out noise, performs format unification and data calibration, and prepares data sets for subsequent analysis.

[0065] The dynamic inter-frequency excitation control module dynamically adjusts the amplitude and frequency of the inter-frequency excitation signal based on real-time monitoring of the on-site power frequency interference intensity. This ensures that the excitation signal can effectively stimulate the inherent response of the line under different operating conditions, enhancing the ability to identify and detect mutual inductance parameters.

[0066] The mutual inductance parameter monitoring module uses preprocessed data in combination with an electromagnetic-mechanical coupling simulation model to perform parameter inversion and fitting, calculate the mutual inductance parameters of the line, dynamically track changes in the mutual inductance of the line, and monitor potential faults in the double-circuit line.

[0067] Specifically, the distributed intelligent sensor node cluster module includes a broadband electromagnetic coupling unit, an optical fiber strain monitoring unit and a micro-environment sensing unit;

[0068] The broadband electromagnetic coupling unit synchronously collects zero-sequence voltage and current in both power frequency and differential frequency bands;

[0069] The optical fiber strain monitoring unit uses a fiber Bragg grating array to collect the conductor axial strain and vibration frequency in real time at a sampling rate of 10kHz;

[0070] The micro-environment sensing unit collects temperature and humidity data in real time through temperature and humidity sensors, and collects wind speed data in real time through wind speed sensors.

[0071] Exemplarily, the broadband electromagnetic coupling unit utilizes broadband sensing elements to monitor electromagnetic field changes in the circuit in real time. It simultaneously acquires the zero-sequence voltage and current of the circuit at both the power frequency (50 Hz or 60 Hz) and the differential frequency (excitation frequency). The collected voltage and current signals are amplified and filtered by the analog front end to ensure that signals from different frequency bands are captured.

[0072] The fiber optic strain monitoring unit uses a fiber Bragg grating array (FBG) sensor to monitor the axial strain and vibration frequency of the conductor in real time at a sampling rate of up to 10kHz. The FBG sensor changes the wavelength of the reflected wave based on strain deformation, and an optical demodulator reads the strain value at high speed. The vibration frequency is collected to reflect the cable's condition and vibration characteristics, which can be used for anomaly detection.

[0073] The microenvironment sensing unit collects environmental parameters in real time, including temperature, humidity, and wind speed. Temperature and humidity sensors output these environmental parameters via electrical signals. Wind speed sensors (such as pitot tubes or ultrasonic anemometers) measure wind speed, reflecting the impact of natural environmental changes on the line.

[0074] Specifically, the data processing module includes an edge computing unit and a data dimension reduction unit;

[0075] The edge computing unit uses an adaptive digital notch filter to dynamically filter out power frequency harmonic interference from the heterodyne excitation response data, and uses a Kalman filter to eliminate noise from the mechanical data and environmental data, and generates a high-confidence original data set;

[0076] The mechanical and environmental data are subjected to Kalman filtering to eliminate noise and generate a high-confidence original data set. The data dimension reduction unit receives the original data set generated by the edge computing unit and compresses the data dimensionally using an improved sparse principal component analysis algorithm.

[0077] For example, the edge computing unit receives mechanical data, environmental data, and response data caused by heterodyne excitation from the distributed intelligent sensor node cluster module, and uses an adaptive digital notch filter to filter out the power frequency (50 / 60Hz) and its harmonic interference to improve the purity of the excitation response signal. A Kalman filter (for mechanical and environmental data) is used to eliminate sensor noise and extract the true trend of motion and environmental signals. The data dimensionality reduction unit receives a high-confidence raw data set generated by the edge computing unit.

[0078] An improved sparse principal component analysis (PCA) algorithm is used to identify the most representative and relevant data features. This algorithm leverages sparsity to highlight important features while mitigating the effects of noise and redundant information. The resulting compressed yet information-rich low-dimensional feature set facilitates storage, transmission, and subsequent analysis. This reduces data dimensionality while preserving key information.

[0079] Specifically, the energy and communication module includes a high-frequency CT power supply unit and a dual-channel communication network unit;

[0080] The high-frequency CT power acquisition unit obtains the induced current through magnetic induction coupling and converts the induced current into direct current through a rectifier circuit;

[0081] The dual-channel communication network unit uses a low-power wide area network and a 5G slice emergency channel dual mode to transmit the collected data.

[0082] Exemplarily, when the line current exceeds a threshold, the high-frequency CT power supply unit utilizes magnetic induction coupling to induce current from the conductor for power supply. This magnetic field is induced by a high-frequency converter (such as a high-frequency transformer or a magnetic induction coil) to obtain the induced current. The collected induced current is converted into stable direct current by a rectifier circuit and supplied to each module. This enables self-generated power supply without the need for an external power supply, improves the convenience and intelligence of node deployment, and ensures continuous power supply when the line load is heavy, thereby ensuring stable operation of the equipment.

[0083] The dual-channel communication network unit realizes efficient and secure data transmission, ensuring the real-time performance of remote monitoring and data analysis.

[0084] Low-power wide area networks (LPWANs) use technologies such as NB-IoT and LoRaWAN, which are suitable for long-distance, low-speed data transmission, low power consumption, and extended battery life.

[0085] The 5G slice emergency channel utilizes dedicated 5G slices to provide high-speed, low-latency emergency data transmission during network congestion or emergencies. During routine monitoring, transmission is prioritized over low-power networks to conserve energy. During critical moments or unusual events, the 5G emergency channel is switched to ensure reliable and timely information delivery.

[0086] Specifically, the dynamic frequency-different excitation control module includes a data receiving unit, an adaptive frequency conversion strategy unit, and a PID closed-loop control unit;

[0087] The data receiving unit receives the processed data set from the data processing module, and the adaptive frequency conversion strategy unit establishes an impedance-strain-environment joint matrix based on the data set, and uses the environmental factor compensation algorithm to perform parameter correction on the power frequency zero-sequence impedance to select the optimal frequency difference point;

[0088] The PID closed-loop control unit performs PID closed-loop control on the amplitude of the different-frequency excitation signal according to the optimal different-frequency point.

[0089] Specifically, the specific process of establishing the impedance-strain-environment joint matrix is ​​as follows:

[0090]

[0091] Where G represents the impedance-strain-environment joint matrix, λ i represents the weight coefficient of the i-th parameter, z0 represents the heterofrequency zero-sequence impedance matrix, ε represents the mechanical strain tensor, and E represents the environmental factor matrix.

[0092] Specifically, the specific process of using the environmental factor compensation algorithm to correct the parameters of the power frequency zero-sequence impedance is as follows:

[0093] z 0,c =z0-(αΔT+βε+γv 2 )

[0094] Among them, z 0,c represents the corrected power frequency zero-sequence impedance, α represents the temperature coefficient, α=0.0041 / ℃, ΔT represents the temperature change, αΔT represents the compensation term for the temperature change on the impedance, β represents the gauge factor, β=1.2×10 -5 Ω / μ, ε represents strain, βε represents the compensation term of strain to impedance, γ represents the wind speed coefficient, γ=3.7×10 -6 / (m / s) 2 , v represents wind speed, γv 2 It represents the compensation term of wind speed to impedance.

[0095] According to the power frequency interference intensity and the joint matrix analysis results, the best frequency point is selected from the preset rule library, as shown in the following table:

[0096] Level of interference Recommended frequency (HZ) Priority Low 55HZ 1 middle 45HZ or 65HZ 2 high 75HZ 3

[0097] Specifically, the specific process of the PID closed-loop control unit performing PID closed-loop control on the amplitude of the inter-frequency excitation signal according to the optimal inter-frequency point is as follows:

[0098] First, calculate the equivalent impedance Z(f test ):

[0099]

[0100] Among them, f test Indicates the optimal frequency point, f N represents the power frequency, ρ=0.2 represents the empirical coefficient of the conductor;

[0101] Second, according to the line protection setting, set the upper limit of the excitation voltage V max =0.6V set ;

[0102] Third, inject the initial frequency excitation current I inf :

[0103]

[0104] Fourth, calculate the total adjustment amount ΔI of PID closed-loop control:

[0105]

[0106] Among them, K p =0.5 represents the proportional coefficient, K i =0.1 integral coefficient, K d =0.05 represents the differential coefficient, e represents the voltage deviation, e=V max -V couple ,V couple Represents the coupling voltage measured by the distributed sensor;

[0107] Fifth, adjust the amplitude I′ of the different-frequency excitation current according to the total regulation amount inf :I′ inf =I inf +ΔI.

[0108] Specifically, the mutual inductance parameter monitoring module uses a finite element network to construct a line electromagnetic-mechanical coupling simulation model. Based on the line's 3D point cloud data (Beidou coordinates and LiDAR scanning), an unstructured tetrahedral mesh is generated with a minimum mesh size of 0.1m, ensuring accurate analysis of the electric field gradient on the conductor surface. Critical areas (such as insulators and joints) are locally encrypted (mesh size 0.05m) to capture subtle defect characteristics.

[0109] The specific process of calculating the mutual inductance parameters of the double-circuit line in combination with the PID closed-loop control results is as follows:

[0110] Calculate the change in magnetic flux induced in loop 2 by a unit current change in loop 1:

[0111]

[0112] Among them, Φ 12 represents the total magnetic flux generated by the current I1 in loop 1 and passing through loop 2, C2 represents the closed path of loop 2, and B represents the magnetic induction intensity generated by the current I1 in three-dimensional space;

[0113] Calculate the mutual inductance M:

[0114] For example, the present invention employs a heterodyne excitation method, using a heterodyne power supply instead of a power-frequency power supply as the test power source to avoid power-frequency interference during the measurement process. The heterodyne zero-sequence voltage and current are extracted from the measurement signal, and then calculated to obtain the heterodyne zero-sequence impedance. This is then converted to the power-frequency zero-sequence impedance of the line. Even under complex interference conditions, the test calculations can be accurately completed, making this an optimal solution for testing mutual inductance parameters in double-circuit power lines without power outages.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed sensor-based double-circuit line mutual inductance online monitoring system, characterized in that: The system includes: a distributed intelligent sensor node cluster module, a data processing module, an energy and communication module, a dynamic frequency-differential excitation control module, and a mutual inductance parameter monitoring module; The distributed intelligent sensor node cluster module deploys an intelligent sensor node every 500 meters along the double-circuit line to collect the mechanical data and environmental data of the double-circuit line and the different-frequency excitation response data in real time; The data processing module is used to receive the original data transmitted by the distributed intelligent sensor node cluster module and perform pre-processing; The energy and communication module is used to provide power supply and data transmission channels for each module; The dynamic inter-frequency excitation control module is used to dynamically adjust the inter-frequency excitation signal according to the intensity of the power frequency interference; The mutual inductance parameter monitoring module is used to construct a line electromagnetic-mechanical coupling simulation model and calculate the mutual inductance parameters of the double-circuit line.

2. The online monitoring system for mutual inductance of a double-circuit line based on distributed sensors according to claim 1 is characterized in that: The distributed intelligent sensor node cluster module includes a broadband electromagnetic coupling unit, an optical fiber strain monitoring unit and a micro-environment sensing unit; The broadband electromagnetic coupling unit synchronously collects zero-sequence voltage and current in both power frequency and differential frequency bands; The optical fiber strain monitoring unit uses a fiber Bragg grating array to collect the conductor axial strain and vibration frequency in real time at a sampling rate of 10kHz; The micro-environment sensing unit collects temperature and humidity data in real time through temperature and humidity sensors, and collects wind speed data in real time through wind speed sensors.

3. The online monitoring system for mutual inductance of a double-circuit line based on distributed sensors according to claim 2 is characterized in that: The data processing module includes an edge computing unit and a data dimension reduction unit; The edge computing unit uses an adaptive digital notch filter to dynamically filter out power frequency harmonic interference from the heterodyne excitation response data, and uses a Kalman filter to eliminate noise from the mechanical data and environmental data, and generates a high-confidence original data set; The mechanical and environmental data are subjected to Kalman filtering to eliminate noise and generate a high-confidence original data set. The data dimension reduction unit receives the original data set generated by the edge computing unit and compresses the data dimensionally using an improved sparse principal component analysis algorithm.

4. The distributed sensor-based online monitoring system for double-circuit line mutual inductance according to claim 3 is characterized in that: The energy and communication module includes a high-frequency CT power supply unit and a dual-channel communication network unit; The high-frequency CT power acquisition unit obtains the induced current through magnetic induction coupling and converts the induced current into direct current through a rectifier circuit; The dual-channel communication network unit uses a low-power wide area network and a 5G slice emergency channel dual mode to transmit the collected data.

5. The online monitoring system for mutual inductance of a double-circuit line based on distributed sensors according to claim 4 is characterized in that: The dynamic frequency-different excitation control module includes a data receiving unit, an adaptive frequency conversion strategy unit, and a PID closed-loop control unit; The data receiving unit receives the processed data set from the data processing module, and the adaptive frequency conversion strategy unit establishes an impedance-strain-environment joint matrix based on the data set, and uses the environmental factor compensation algorithm to perform parameter correction on the power frequency zero-sequence impedance to select the optimal frequency difference point; The PID closed-loop control unit performs PID closed-loop control on the amplitude of the different-frequency excitation signal according to the optimal different-frequency point.

6. The distributed sensor-based online monitoring system for double-circuit line mutual inductance according to claim 5, characterized in that: The specific process of establishing the impedance-strain-environment joint matrix is ​​as follows: Where G represents the impedance-strain-environment joint matrix, λ i represents the weight coefficient of the i-th parameter, z0 represents the heterofrequency zero-sequence impedance matrix, ε represents the mechanical strain tensor, and E represents the environmental factor matrix.

7. The online monitoring system for mutual inductance of a double-circuit line based on distributed sensors according to claim 6 is characterized in that: The specific process of using the environmental factor compensation algorithm to correct the parameters of the power frequency zero-sequence impedance is as follows: z 0,c =z0-(αΔT+βε+γv 2 ) Among them, z 0,c represents the corrected power frequency zero-sequence impedance, α represents the temperature coefficient, ΔT represents the temperature change, αΔT represents the compensation term for temperature change on impedance, β represents the strain coefficient, βε represents the compensation term for strain on impedance, γ represents the wind speed coefficient, v represents the wind speed, γv 2 It represents the compensation term of wind speed to impedance.

8. The distributed sensor-based online monitoring system for double-circuit line mutual inductance according to claim 7, characterized in that: The specific process of the PID closed-loop control unit performing PID closed-loop control on the amplitude of the different-frequency excitation signal according to the optimal different-frequency point is as follows: First, calculate the equivalent impedance Z(f test ): Among them, f test Indicates the optimal frequency point, f N represents the power frequency, and ρ represents the empirical coefficient of the conductor; Second, according to the line protection setting, set the upper limit of the excitation voltage V max =0.6V set ; Third, inject the initial frequency excitation current I inf : Fourth, calculate the total adjustment amount ΔI of PID closed-loop control: Among them, K p Represents the proportionality coefficient, K i Integration coefficient, K d represents the differential coefficient, e represents the voltage deviation, e=V max -V couple ,V couple Represents the coupling voltage measured by the distributed sensor; Fifth, adjust the amplitude I′ of the different-frequency excitation current according to the total regulation amount inf :I′ inf =I inf +ΔI.

9. The distributed sensor-based online monitoring system for double-circuit line mutual inductance according to claim 8, characterized in that: The mutual inductance parameter monitoring module uses a finite element network to construct a line electromagnetic-mechanical coupling simulation model, and the specific process of calculating the mutual inductance parameters of the double-circuit line in combination with the PID closed-loop control results is as follows: Calculate the change in magnetic flux induced in loop 2 by a unit current change in loop 1: Among them, Φ 12 represents the total magnetic flux generated by the current I1 in loop 1 and passing through loop 2, C2 represents the closed path of loop 2, and B represents the magnetic induction intensity generated by the current I1 in three-dimensional space; Calculate the mutual inductance M:

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