Wind power generation line state sensing and fault detecting and positioning system

Abstract

The invention discloses a state sensing and fault detecting and positioning system for a wind power generation line. The state sensing and fault detecting and positioning system comprises a distributed state sensing unit, an intelligent data transmission network, a cloud comprehensive processing platform and a multi-dimensional interaction terminal. The distributed state sensing unit collects line operation parameters through a multi-dimensional sensor group; the intelligent data transmission network realizes data layered transmission and edge preprocessing; the cloud comprehensive processing platform adopts a data fusion module, a fault diagnosis module, a positioning calculation module and the like, and realizes fault identification, positioning and evaluation in combination with various formulas; and the multi-dimensional interaction terminal displays a result and supports operation. According to the system, the fault detection accuracy and timeliness are improved, accurate fault positioning and influence range quantification are realized, the operation and maintenance cost is reduced, stable operation in a complex environment is guaranteed, and the data utilization value and decision scientificity are improved.

Description

Technical Field

[0001] This invention relates to a wind power generation line status sensing fault, specifically a wind power generation line status sensing fault detection and location system. Background Technology

[0002] As an important component of clean energy, wind power transmission lines are mostly distributed in complex outdoor environments, constantly exposed to natural factors such as strong winds, temperature differences, and lightning strikes, making them prone to faults such as conductor overheating, insulation aging, and tower tilting. Traditional fault detection relies on manual inspections, which have limitations in coverage and response time, often only being discovered after the fault has escalated, leading to increased downtime losses.

[0003] In existing technologies, some detection systems use a single sensor for monitoring, resulting in insufficient data dimensions and difficulty in accurately identifying early faults. Transmission networks often rely on a single communication method, making them prone to signal interruptions in remote areas and affecting data real-time performance. Fault location depends on experience-based estimations, often with errors reaching hundreds of meters, prolonging repair time. Furthermore, the lack of quantitative assessment of line fatigue aging makes predictive maintenance difficult, leading to persistently high operation and maintenance costs.

[0004] Therefore, there is an urgent need to build a comprehensive system that can achieve multi-parameter sensing, accurate positioning, stable transmission, and predictive capabilities to meet the needs of efficient operation and maintenance of wind power lines. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned technologies and provide a wind power generation line status perception fault detection and location system.

[0006] To solve the above-mentioned technical problems, the technical solution provided by the present invention is a wind power generation line status perception fault detection and location system, which includes: a distributed status perception unit, consisting of a number of perception nodes deployed along the wind power generation line, used to collect line operating parameters and environmental parameters, including conductor temperature, three-phase current, phase-to-phase voltage, conductor vibration amplitude and tower tilt angle.

[0007] An intelligent data transmission network, connected to the distributed state sensing unit, is used to realize hierarchical transmission and edge preprocessing of sensing data;

[0008] The cloud-based integrated processing platform receives data transmitted from the intelligent data transmission network and is equipped with a fault diagnosis module, a positioning calculation module, and a line topology database to achieve fault identification and precise positioning.

[0009] The multi-dimensional interactive terminal communicates with the cloud-based integrated processing platform to display line status information, fault diagnosis results, and location data, and supports user command input.

[0010] As an improvement, the temperature acquisition of the distributed state sensing unit uses a fiber optic grating sensor, and its measurement accuracy satisfies the formula:

[0011] |T measured -T true |≤ΔT max

[0012] Among them, T measured For the sensor to measure temperature values ​​(unit: °C), T true The actual temperature value of the line (unit: °C), ΔT max This formula, representing the maximum permissible measurement error (valued at 0.5℃), is used to limit the accuracy of temperature measurements and ensure the reliability of the basic data for fault diagnosis.

[0013] As an improvement, the transmission reliability of the intelligent data transmission network meets the following requirements:

[0014]

[0015] Among them, P success N represents the data transmission success rate. received N represents the number of data packets received by the cloud platform. sent This formula represents the total number of data packets sent by the sensing node. It is used to ensure the complete transmission of critical fault data and avoid missed detections due to data loss.

[0016] As an improvement, the fault diagnosis module achieves preliminary fault identification through feature quantity threshold judgment, satisfying:

[0017]

[0018] Where X is the normalized value of the monitoring parameter, X th1 X is the warning threshold. th2 S is the fault threshold. normal S warning S fault These represent three states: normal, warning, and fault. This formula is used to classify and judge abnormal parameters, thereby improving the accuracy of fault identification.

[0019] As an improvement, the positioning calculation module adopts the dual-end traveling wave positioning method, and the positioning formula is:

[0020]

[0021] Among them, L fault L is the distance from the fault point to the beginning of the line (in meters). total The total length of the line is given in meters (m), and v is the traveling wave velocity (value 2.9 × 10⁻⁶). 8The formula uses time difference calculation to accurately locate the fault point. The time difference is calculated by the time difference between the travel wave front and the line head.

[0022] As an improvement, a vibration fatigue assessment unit is also included to calculate the cumulative damage of the conductor:

[0023]

[0024] Where D is the cumulative damage degree, N i N represents the actual number of cycles of the conductor under the i-th level of vibration stress. f,i The fatigue life under this stress level is defined as follows: when D≥1, it is considered that there is a risk of fatigue failure. This formula is used to assess the potential failure of the conductor due to long-term vibration.

[0025] As an improvement, the sensing node is powered by solar energy, and its battery life meets the following requirements:

[0026]

[0027] Among them, T endure The driving range during continuous rainy weather (in hours), C batt Battery capacity (unit: Ah), U batt The rated voltage of the battery is η (V), where η is the power conversion efficiency and P is the voltage of the battery. avg The average power consumption of the node (in W) is used to ensure the system's continuous operation under extreme weather conditions.

[0028] As an improvement, the cloud-based integrated processing platform also includes a data fusion module, which employs a weighted fusion algorithm:

[0029]

[0030] Among them, X fusion The parameter values ​​after fusion, ω i The weighting coefficients for the i-th sensor (satisfying) ), X i Let be the measurement value of the i-th sensor. This formula is used to reduce the impact of single sensor error on fault diagnosis and improve data reliability.

[0031] As an improvement, an insulation aging assessment module is also included, which calculates the insulation aging index using the following formula:

[0032]

[0033] Among them, K aging E represents the insulation aging index, where tanδ(T) and tanδ(T0) are the dielectric loss tangents at temperature T and reference temperature T0, respectively.a The activation energy is k, and the Boltzmann constant is k. This formula is used to assess the aging degree of the line insulation layer and predict the risk of insulation failure.

[0034] As an improvement, the multi-dimensional interactive terminal is also equipped with a fault impact range calculation unit, the calculation formula of which is:

[0035] S influence =π·(r0+k·I) fault ) 2

[0036] Among them, S influence Area affected by the fault (unit: m) 2 ), r0 is the reference radius of influence, k is the current influence coefficient, I fault The fault current amplitude (unit: A) is used to quantify the potential range of a fault and help determine emergency repair priorities.

[0037] The advantages of this invention compared with the prior art are: the system can accurately and timely detect faults. With the help of a multi-dimensional sensor group and strict temperature measurement accuracy limits, combined with three-level threshold judgment, it can capture subtle changes in circuit parameters, distinguish between normal, warning and fault states, and avoid missed detection and misjudgment.

[0038] It can accurately locate faults and quantify the scope of impact, reduce errors through dual-end traveling wave positioning, and calculate the affected area of ​​the fault using formulas, which helps in the priority of emergency repairs and shortens the troubleshooting and repair time.

[0039] The system can reduce operation and maintenance costs and extend line life. By using vibration fatigue assessment and insulation aging assessment, it can realize the transformation from "post-event repair" to "pre-event prevention", reducing downtime losses and resource waste.

[0040] In complex environments, the system ensures stable power supply and data transmission. Optimized power configuration guarantees operation under extreme weather conditions, and high-reliability design ensures critical data transmission and enhances anti-interference capabilities.

[0041] In addition, the system enhances the value of data and the scientific nature of decision-making, reduces errors through the fusion of multi-source data, and helps to summarize fault patterns, optimize maintenance strategies, and improve the level of intelligent management. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the wind power generation line status perception fault detection and location system of the present invention. Detailed Implementation

[0043] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0045] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of the other element or feature will be oriented "over" the other element or feature. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations, such as being rotated 90 degrees or other orientations, and the spatial descriptive terms used herein will be interpreted accordingly.

[0046] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0047] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0048] Referring to the attached diagram, the wind power generation line status perception fault detection and location system includes: a distributed status perception unit, which consists of several perception nodes deployed along the wind power generation line, used to collect line operating parameters and environmental parameters, including conductor temperature, three-phase current, phase-to-phase voltage, conductor vibration amplitude and tower tilt angle.

[0049] An intelligent data transmission network, connected to the distributed state sensing unit, is used to realize hierarchical transmission and edge preprocessing of sensing data;

[0050] The cloud-based integrated processing platform receives data transmitted from the intelligent data transmission network and is equipped with a fault diagnosis module, a positioning calculation module, and a line topology database to achieve fault identification and precise positioning.

[0051] The multi-dimensional interactive terminal communicates with the cloud-based integrated processing platform to display line status information, fault diagnosis results, and location data, and supports user command input.

[0052] As an improvement, the temperature acquisition of the distributed state sensing unit uses a fiber optic grating sensor, and its measurement accuracy satisfies the formula:

[0053] |T measured -T true |≤ΔT max

[0054] Among them, T measured For the sensor to measure temperature values ​​(unit: °C), T true The actual temperature value of the line (unit: °C), ΔT max This formula, representing the maximum permissible measurement error (valued at 0.5℃), is used to limit the accuracy of temperature measurements and ensure the reliability of the basic data for fault diagnosis.

[0055] As an improvement, the transmission reliability of the intelligent data transmission network meets the following requirements:

[0056]

[0057] Among them, P success N represents the data transmission success rate. received N represents the number of data packets received by the cloud platform. sent This formula represents the total number of data packets sent by the sensing node. It is used to ensure the complete transmission of critical fault data and avoid missed detections due to data loss.

[0058] As an improvement, the fault diagnosis module achieves preliminary fault identification through feature quantity threshold judgment, satisfying:

[0059]

[0060] Where X is the normalized value of the monitoring parameter, X th1 X is the warning threshold. th2 S is the fault threshold. normal S warning S faultThese represent three states: normal, warning, and fault. This formula is used to classify and judge abnormal parameters, thereby improving the accuracy of fault identification.

[0061] As an improvement, the positioning calculation module adopts the dual-end traveling wave positioning method, and the positioning formula is:

[0062]

[0063] Among them, L fault L is the distance from the fault point to the beginning of the line (in meters). total The total length of the line is given in meters (m), and v is the traveling wave velocity (value 2.9 × 10⁻⁶). 8 The formula uses time difference calculation to accurately locate the fault point. The time difference is calculated by the time difference between the travel wave front and the line head.

[0064] As an improvement, a vibration fatigue assessment unit is also included to calculate the cumulative damage of the conductor:

[0065]

[0066] Where D is the cumulative damage degree, N i N represents the actual number of cycles of the conductor under the i-th level of vibration stress. f,i The fatigue life under this stress level is defined as follows: when D≥1, it is considered that there is a risk of fatigue failure. This formula is used to assess the potential failure of the conductor due to long-term vibration.

[0067] As an improvement, the sensing node is powered by solar energy, and its battery life meets the following requirements:

[0068]

[0069] Among them, T endure The driving range during continuous rainy weather (in hours), C batt Battery capacity (unit: Ah), U batt The rated voltage of the battery is η (V), where η is the power conversion efficiency and P is the voltage of the battery. avg The average power consumption of the node (in W) is used to ensure the system's continuous operation under extreme weather conditions.

[0070] As an improvement, the cloud-based integrated processing platform also includes a data fusion module, which employs a weighted fusion algorithm:

[0071]

[0072] Among them, X fusion The parameter values ​​after fusion, ω i The weighting coefficients for the i-th sensor (satisfying) ), X i Let be the measurement value of the i-th sensor. This formula is used to reduce the impact of single sensor error on fault diagnosis and improve data reliability.

[0073] As an improvement, an insulation aging assessment module is also included, which calculates the insulation aging index using the following formula:

[0074]

[0075] Among them, K aging E represents the insulation aging index, where tanδ(T) and tanδ(T0) are the dielectric loss tangents at temperature T and reference temperature T0, respectively. a The activation energy is k, and the Boltzmann constant is k. This formula is used to assess the aging degree of the line insulation layer and predict the risk of insulation failure.

[0076] As an improvement, the multi-dimensional interactive terminal is also equipped with a fault impact range calculation unit, the calculation formula of which is:

[0077] S influence =π·(r0+k·I) fault ) 2

[0078] Among them, S influence Area affected by the fault (unit: m) 2 ), r0 is the reference radius of influence, k is the current influence coefficient, I fault The fault current amplitude (unit: A) is used to quantify the potential range of a fault and help determine emergency repair priorities.

[0079] I. System Overall Architecture:

[0080] The system comprises four core modules: a distributed state sensing unit, an intelligent data transmission network, a cloud-based integrated processing platform, and a multi-dimensional interactive terminal. These modules work collaboratively.

[0081] Distributed state sensing units collect line operating parameters and environmental parameters;

[0082] The intelligent data transmission network transmits the sensed data to the cloud in layers;

[0083] The cloud-based integrated processing platform analyzes the data to achieve fault diagnosis and location;

[0084] The multi-dimensional interactive terminal displays the results and supports user operations.

[0085] II. Specific implementation details of each module:

[0086] (I) Distributed State Awareness Unit:

[0087] This unit consists of sensing nodes deployed every 500-800 meters along the wind power generation line. Each node integrates multiple types of sensors and a local processing module to collect the following parameters:

[0088] Conductor temperature (using a fiber Bragg grating sensor);

[0089] Three-phase current (Rogowski coil sensor), phase-to-phase voltage (capacitive voltage divider sensor);

[0090] Conductor vibration amplitude (piezoelectric vibration sensor);

[0091] Tower tilt angle (tilt sensor);

[0092] Ambient temperature and humidity (integrated temperature and humidity sensor).

[0093] Temperature measurement accuracy control:

[0094] The measurement accuracy of fiber Bragg grating sensors satisfies the formula:

[0095] |T measured -T true |≤ΔT max

[0096] in:

[0097] T measured The measured temperature value (unit: °C) output by the sensor;

[0098] T true The actual temperature value of the conductor (unit: °C);

[0099] ΔT max This represents the maximum permissible error (0.5℃ in this embodiment).

[0100] This formula ensures that temperature data provides a reliable basis for fault diagnosis (such as overheating faults) by limiting the deviation between the measured value and the true value.

[0101] Power supply design for sensing nodes:

[0102] The node uses a hybrid power supply of solar energy and lithium batteries, and its battery life meets the following requirements:

[0103]

[0104] in:

[0105] T endure The driving time during continuous rainy weather (unit: h);

[0106] C batt =100Ah (lithium battery capacity);

[0107] U batt =12V (battery rated voltage);

[0108] η = 0.85 (power conversion efficiency);

[0109] P avg =5W (average power consumption of a node).

[0110] Substitution (Approximately 8.5 days) to ensure the system continues to operate under extreme weather conditions.

[0111] (II) Intelligent Data Transmission Network:

[0112] The network adopts a layered transmission architecture of "short-distance Mesh + long-distance private network":

[0113] Short-range transmission: Sensing nodes communicate via a wireless mesh network based on the IEEE 802.15.4g standard to achieve local data exchange and aggregation;

[0114] Long-distance transmission: Data is aggregated and uploaded to the cloud via a 5G private network, with a transmission rate of ≥1Mbps;

[0115] Edge preprocessing: The edge gateway filters (removes outliers) and compresses data to reduce cloud computing power consumption.

[0116] Transmission reliability guarantee:

[0117] Data transmission success rate meets the following requirements:

[0118]

[0119] in:

[0120] P success To improve transmission success rate;

[0121] N received This represents the number of data packets received from the cloud.

[0122] N sent The total number of data packets sent to the sensing node.

[0123] This embodiment ensures that the indicators meet the standards through a retransmission mechanism (automatically retransmitting lost data packets 3 times) and avoids the loss of critical fault data.

[0124] (III) Cloud-based integrated processing platform:

[0125] This platform is the core of the system, integrating modules such as data fusion, fault diagnosis, and location calculation, as detailed below:

[0126] Data fusion module:

[0127] A weighted fusion algorithm is used to process redundant data from multiple sensors. The formula is as follows:

[0128]

[0129] in:

[0130] X fusion These are the parameter values ​​after fusion;

[0131] m is the number of sensors participating in the fusion (m = 3 in this embodiment);

[0132] ω i The weighting coefficients for the i-th sensor (satisfying) The higher the accuracy of the sensor, the greater its weight.

[0133] X i Let be the measurement value of the i-th sensor.

[0134] For example, when fusing wire temperatures, the measured values ​​of the fiber optic grating sensor (ω1=0.6), infrared sensor (ω2=0.3), and thermocouple (ω3=0.1) are weighted and summed to reduce the influence of errors from a single sensor.

[0135] Fault diagnosis module:

[0136] Three-level status determination based on fused data:

[0137]

[0138] in:

[0139] X represents the normalized monitoring parameters (such as current and temperature);

[0140] X th1 The warning threshold is set (e.g., the current is taken as 80% of the rated value);

[0141] X th2 The fault threshold (e.g., the current is taken as 120% of the rated value);

[0142] S normal S warning S fault These represent normal, warning, and fault status, respectively.

[0143] For example, when the line current X ≥ X th2 When this occurs, it is determined to be an overcurrent fault.

[0144] Location calculation module:

[0145] The two-end traveling wave positioning method is used, and the formula is:

[0146]

[0147] in:

[0148] L fault The distance from the fault point to the beginning of the line (unit: m);

[0149] L total = 5000m (total line length);

[0150] v = 2.9 × 10 8 m / s (traveling wave velocity);

[0151] t1 = 1.2 × 10 -6 s (time it takes for the traveling wave head to reach the beginning of the wave);

[0152] t2 = 1.8 × 10 -6 s (time it takes for the traveling wavefront to reach the end).

[0153] Substitution The fault point is located at 3420 meters from the beginning.

[0154] Vibration fatigue assessment unit:

[0155] Determining the fatigue state of the conductor by assessing cumulative damage:

[0156]

[0157] in:

[0158] D represents the cumulative damage level;

[0159] n is the vibration stress level number (n = 3 in this embodiment);

[0160] N i This represents the actual number of cycles performed on the conductor under the i-th stress level.

[0161] N f,i This represents the fatigue life at this stress level (obtained experimentally).

[0162] When D ≥ 1, the conductor is considered to be at risk of fatigue fracture. For example, the N of a conductor under level 3 stress... i / N f,i The values ​​are 0.3, 0.4, and 0.3 respectively, with a total D = 1.0, triggering a fatigue warning.

[0163] Insulation aging assessment module:

[0164] Evaluating line insulation performance using insulation aging index:

[0165]

[0166] in:

[0167] K aging Insulation aging index (K) aging (≥1.5 is considered severely aged);

[0168] tanδ(T) and tanδ(T0) are the tangents of the dielectric loss angle at temperature T and reference temperature T0 = 25℃, respectively.

[0169] E a =0.8 eV (activation energy of insulating material);

[0170] k = 8.617 × 10 -5 eV / K (Boltzmann constant).

[0171] This formula, with temperature correction, more accurately reflects the degree of aging of the insulation layer.

[0172] (iv) Multi-dimensional interactive terminal:

[0173] The terminal uses a touch screen display and a remote client interface, and its functions include:

[0174] Real-time display of line status parameters, fault types, and location results;

[0175] Supports historical data query and curve comparison;

[0176] Calculation of the scope of impact of the fault:

[0177] S influence =π·(r0+k·I) fault ) 2

[0178] in:

[0179] S influence Area affected by the fault (unit: m) 2 );

[0180] r0 = 50m (reference radius of influence);

[0181] k = 0.2m / A (current influence coefficient);

[0182] I fault =1000A (fault current amplitude).

[0183] Substituting into S, we get influence =π·(50+0.2×1000) 2 ≈254469m 2 This assists maintenance personnel in prioritizing emergency repairs.

[0184] III. System Workflow:

[0185] The sensing nodes collect data at a frequency of 1Hz, and after local preprocessing, the data is aggregated through the Mesh network.

[0186] After filtering the aggregated data, the edge gateway uploads it to the cloud via a 5G private network;

[0187] The cloud platform integrates data and performs fault diagnosis; if a fault is identified, it initiates location calculation.

[0188] The location results and fault details are pushed to the multi-dimensional interactive terminal, triggering an audible and visual alarm.

[0189] Maintenance personnel can view information and arrange repairs through the terminal.

[0190] This invention achieves "early detection, early diagnosis, and early location" of wind power line faults through distributed sensing, intelligent algorithms, and precise positioning, significantly reducing operation and maintenance costs and improving line operation reliability.

[0191] Improving the accuracy and timeliness of fault detection: The system utilizes a multi-dimensional sensor group (such as fiber optic temperature sensors, piezoelectric vibration sensors, etc.) of a distributed state sensing unit, combined with the formula |T measured -T true |≤ΔT max Strict limitations on temperature measurement accuracy allow for real-time capture of minute parameter changes in the circuit. The fault diagnosis module is based on a three-level threshold judgment formula. It can distinguish between normal, warning and fault states, avoid missed detection or misjudgment, and achieve early detection and accurate identification of faults.

[0192] Achieving precise fault location and quantification of impact range: Employing a two-ended traveling wave location formula. This can control the fault location error to a small range, solving the problem of vague location in traditional manual inspections. Simultaneously, it utilizes the fault impact range calculation formula S... influence =π·(r0+k·I) fault ) 2 This quantifies the affected area of ​​a fault, providing data support for prioritizing repairs and shortening the time for troubleshooting and repair.

[0193] Reduce maintenance costs and extend line life: Vibration fatigue assessment unit based on cumulative damage formula The insulation aging assessment module can predict conductor fatigue risks in advance; it uses K... aging The formula assesses the condition of the insulation layer, enabling a shift from "reactive repair" to "prevention." This predictive maintenance model reduces downtime losses caused by sudden failures, while avoiding resource waste due to over-maintenance, significantly reducing operation and maintenance costs and extending the service life of line equipment.

[0194] To ensure stable operation of the system in complex environments: the sensing nodes are powered by a hybrid of solar energy and lithium batteries, utilizing a power supply formula. Optimize power supply configuration to ensure stable operation even under extreme weather conditions such as continuous rain. The intelligent data transmission network utilizes P... success With a reliability design of ≥99.9%, it ensures the complete transmission of critical data and enhances the system's anti-interference capability in the complex electromagnetic environment of wind farms.

[0195] Enhancing the value of data utilization and the scientific nature of decision-making: The data fusion module uses a weighted fusion formula. By integrating multi-source data and reducing the impact of errors from single sensors, a high-quality data foundation is provided for fault analysis. The cloud platform's ability to store and analyze historical data assists maintenance personnel in summarizing line fault patterns, optimizing maintenance strategies, and improving the scientific and intelligent level of wind power line operation and management.

[0196] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A wind power generation line condition sensing fault detection and location system, characterized in that: include: The distributed state sensing unit consists of several sensing nodes deployed along the wind power generation line, used to collect line operating parameters and environmental parameters, including conductor temperature, three-phase current, phase-to-phase voltage, conductor vibration amplitude and tower tilt angle. An intelligent data transmission network, connected to the distributed state sensing unit, is used to realize hierarchical transmission and edge preprocessing of sensing data; The cloud-based integrated processing platform receives data transmitted from the intelligent data transmission network and is equipped with a fault diagnosis module, a positioning calculation module, and a line topology database to achieve fault identification and precise positioning. The multi-dimensional interactive terminal communicates with the cloud-based integrated processing platform to display line status information, fault diagnosis results, and location data, and supports user command input.

2. The wind power generation line condition sensing fault detection and location system according to claim 1, characterized in that: The temperature acquisition of the distributed state sensing unit uses a fiber optic grating sensor, and its measurement accuracy satisfies the formula: |T measured -T true |≤ΔT max Among them, T measured For the sensor to measure temperature values ​​(unit: °C), T true The actual temperature value of the line (unit: °C), ΔT max This formula, representing the maximum permissible measurement error (valued at 0.5℃), is used to limit the accuracy of temperature measurements and ensure the reliability of the basic data for fault diagnosis.

3. The wind power generation line condition sensing fault detection and location system according to claim 2, characterized in that: The transmission reliability of the intelligent data transmission network meets the following requirements: Among them, P success N represents the data transmission success rate. received N represents the number of data packets received by the cloud platform. sent This formula represents the total number of data packets sent by the sensing node. It is used to ensure the complete transmission of critical fault data and avoid missed detections due to data loss.

4. The wind power generation line condition sensing fault detection and location system according to claim 3, characterized in that: The fault diagnosis module achieves preliminary fault identification through feature quantity threshold judgment, satisfying the following: Where X is the normalized value of the monitoring parameter, X th1 X is the warning threshold. th2 S is the fault threshold. normal S warning S fault These represent three states: normal, warning, and fault. This formula is used to classify and judge abnormal parameters, thereby improving the accuracy of fault identification.

5. The wind power generation line condition sensing fault detection and location system according to claim 4, characterized in that: The positioning calculation module uses the dual-end traveling wave positioning method, and the positioning formula is: Among them, L fault L is the distance from the fault point to the beginning of the line (in meters). total The total length of the line is given in meters (m), and v is the traveling wave velocity (value 2.9 × 10⁻⁶). 8 The formula uses time difference calculation to accurately locate the fault point. The time difference is calculated by the time difference between the travel wave front and the line head.

6. The wind power generation line condition sensing fault detection and location system according to claim 5, characterized in that: It also includes a vibration fatigue assessment unit for calculating the cumulative damage to the conductor: Where D is the cumulative damage degree, N i N represents the actual number of cycles of the conductor under the i-th level of vibration stress. f,i The fatigue life under this stress level is defined as follows: when D≥1, it is considered that there is a risk of fatigue failure. This formula is used to assess the potential failure of the conductor due to long-term vibration.

7. The wind power generation line condition sensing fault detection and location system according to claim 6, characterized in that: The sensing node is powered by solar energy, and its battery life meets the following requirements: Among them, T endure The driving range during continuous rainy weather (in hours), C batt Battery capacity (unit: Ah), U batt The rated voltage of the battery is η (V), where η is the power conversion efficiency and P is the voltage of the battery. avg The average power consumption of the node (in W) is used to ensure the system's continuous operation under extreme weather conditions.

8. The wind power generation line condition sensing fault detection and location system according to claim 7, characterized in that: The cloud-based integrated processing platform also includes a data fusion module, which employs a weighted fusion algorithm: Among them, X fusion The parameter values ​​after fusion, ω i The weighting coefficients for the i-th sensor (satisfying) ), X i Let be the measurement value of the i-th sensor. This formula is used to reduce the impact of single sensor error on fault diagnosis and improve data reliability.

9. The wind power generation line condition sensing fault detection and location system according to claim 8, characterized in that: It also includes an insulation aging assessment module, which calculates the insulation aging index using the following formula: Among them, K aging E represents the insulation aging index, where tanδ(T) and tanδ(T0) are the dielectric loss tangents at temperature T and reference temperature T0, respectively. a The activation energy is k, and the Boltzmann constant is k. This formula is used to assess the aging degree of the line insulation layer and predict the risk of insulation failure.

10. The wind power generation line condition sensing fault detection and location system according to claim 9, characterized in that: The multi-dimensional interactive terminal is also equipped with a fault impact range calculation unit, and the calculation formula is as follows: S influence =π·(r0+k·I fault ) 2 Among them, S influence Area affected by the fault (unit: m) 2 ), r0 is the reference radius of influence, k is the current influence coefficient, I fault The fault current amplitude (unit: A) is used to quantify the potential range of a fault and help determine emergency repair priorities.

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