Offshore complex environment wind farm power collection line fault detection method and device

Abstract

The application belongs to the field of offshore wind farms, and provides a method and device for detecting faults of a power collection line of an offshore wind farm in a complex environment. The method comprises the following steps: when the sensors at each preset monitoring point of the power collection line are normally connected, obtaining traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the power collection line, obtaining a line characteristic value and comparing the line characteristic value with a preset line fault characteristic threshold value, and judging whether a fault occurs in the corresponding power collection line according to the comparison result; if a fault occurs in the power collection line, extracting the traveling wave characteristic data of each monitoring interval of the corresponding power collection line at the time of the fault, calculating the propagation speed, the initial traveling wave voltage amplitude ratio and the initial traveling wave current amplitude ratio of the traveling wave in each monitoring interval, respectively obtaining a first fault probability, a second fault probability and a third fault probability of each monitoring interval, weightedly calculating a comprehensive fault probability of each monitoring interval, and selecting a monitoring interval corresponding to the maximum comprehensive fault probability as a fault interval.

Description

Technical Field

[0001] This invention belongs to the field of offshore wind farms, and particularly relates to a method and device for detecting faults in the power collection lines of wind farms in complex offshore environments. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Offshore wind farm collector lines are mostly located on the seabed, operating in a complex and variable environment. They are susceptible to frequent faults due to natural disasters and insulation damage. Furthermore, fault inspection and maintenance of offshore wind farm collector lines are challenging, time-consuming, and difficult. Existing methods for detecting faults in offshore wind farm collector lines consider only the current signal on the lines. However, this approach lacks environmental adaptability, introduces errors in the detected data, and results in low accuracy of fault detection results. Summary of the Invention

[0004] In order to solve the technical problems existing in the background art, the present invention provides a method and device for detecting faults in the collector lines of wind farms in complex offshore environments. It can quickly locate faults in the collector lines of offshore wind farms and determine the fault area while the protection trips.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for detecting faults in the power collection lines of wind farms in complex offshore environments.

[0006] A method for fault detection of power collection lines in wind farms operating in complex offshore environments includes: When the sensors at each preset monitoring point of the collector line are connected normally, the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line are acquired. Based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, the line characteristic value is obtained and compared with the preset line fault characteristic threshold. Based on the comparison result, it is determined whether the corresponding collector line has a fault. If a fault occurs in the collector line, the traveling wave characteristic data of each monitoring section of the collector line at the time of the fault are extracted, and the propagation speed of the traveling wave, the initial traveling wave voltage amplitude ratio, and the initial traveling wave current amplitude ratio in each monitoring section are calculated. The first fault probability, the second fault probability, and the third fault probability of each monitoring section are obtained respectively. The comprehensive fault probability of each monitoring section is then calculated by weighted summation. The monitoring section corresponding to the highest comprehensive fault probability is selected as the fault section.

[0007] As one implementation method, the process for determining whether the sensors at each preset monitoring point of the power collection line are properly connected is as follows: Acquire the sensor connection status dataset, which includes electrical connection data and signal connection data; Calculate the electrical connection deviation rate signal factor based on electrical connection data; calculate the signal connection deviation rate signal factor based on signal connection data; The electrical connection deviation rate signal factor is compared with the preset electrical deviation rate threshold. If the electrical deviation rate is greater than the preset electrical deviation rate threshold, the sensor connection is abnormal. Otherwise, the signal connection deviation rate signal factor is compared with the preset signal deviation rate threshold. If the signal connection deviation rate signal factor is greater than the signal deviation rate threshold, the sensor connection is abnormal. Otherwise, the connection status evaluation value is obtained by combining the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor. The connection status assessment value is compared with the preset connection status assessment threshold. If the connection status assessment value is greater than the connection status assessment threshold, the sensor connection is abnormal; if the connection status assessment value is not greater than the connection status assessment threshold, the sensor connection is normal.

[0008] As one implementation method, the formulas for calculating the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor are as follows: ; ; ; In the formula, For electrical connection deviation rate signal factor, For signal connection deviation rate signal factor, This is the standard error value of the resistor. This is the standard error value of the voltage. This is the standard error value of the current; The signal-to-noise ratio, The signal amplitude, For signal frequency, For reference signal-to-noise ratio, For the reference signal amplitude, For the reference signal frequency, This is the noise ratio error value. This is the amplitude error value. This represents the frequency error value.

[0009] As one implementation method, the formula for calculating the connection status evaluation value is: ; In the formula, This is a connection status assessment value. for Weighting factors for Weighting factors It is a natural constant; For electrical connection deviation rate signal factor, This is the signal connection deviation rate signal factor.

[0010] As one implementation method, the formula for calculating the line characteristic value is: ; In the formula, For line characteristic values, The initial traveling wave amplitude, Instantaneous amplitude, For the arrival time of the traveling wave, For reference traveling wave amplitude, For reference instantaneous amplitude, For reference, the arrival time of the traveling wave, This is the allowable deviation value for the traveling wave amplitude. This is the allowable deviation value for instantaneous amplitude. This is the allowable deviation value for the arrival time of the traveling wave.

[0011] As one implementation method, the formulas for calculating the first failure probability, the second failure probability, the third failure probability, and the combined failure probability are as follows: ; ; ; ; In the formula, For the first The first failure probability in each interval For the first The second failure probability in each interval For the first The probability of the third failure in each interval; For the first The traveling wave velocity in each interval, This is the normal maximum traveling wave velocity. This is the normal minimum traveling wave velocity; For the first The ratio of the initial traveling wave voltage amplitude in each interval This is the ratio of the normal maximum initial traveling wave voltage amplitude. This represents the normal minimum initial traveling wave voltage amplitude ratio; For the first The ratio of the initial traveling wave current amplitude in each interval This is the ratio of the normal maximum initial traveling wave current amplitude. This is the normal minimum initial traveling wave current amplitude ratio; For the first The length of the line in each section, For the first The time difference between intervals; For the first The overall failure probability corresponding to each interval Stored in the database Weighting factors Stored in the database Weighting factors Stored in the database Weighting factors.

[0012] A second aspect of the present invention provides a fault detection device for wind farm collection lines in complex offshore environments.

[0013] A fault detection device for wind farm collector lines in complex offshore environments, comprising: The line data acquisition module is used to acquire traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the power collection line when the sensor connection at each preset monitoring point of the power collection line is normal. The line fault judgment module is used to obtain line characteristic values ​​based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, and compare them with the preset line fault characteristic threshold. Based on the comparison result, it determines whether the corresponding collector line has a fault. The fault range determination module is used to extract the traveling wave characteristic data of each monitoring range of the collector line when a fault occurs, calculate the propagation speed of the traveling wave, the initial traveling wave voltage amplitude ratio, and the initial traveling wave current amplitude ratio in each monitoring range, and obtain the first fault probability, second fault probability, and third fault probability for each monitoring range respectively. Then, the comprehensive fault probability of each monitoring range is calculated by weighted summation, and the monitoring range corresponding to the highest comprehensive fault probability is selected as the fault range.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for detecting faults in wind farm collector lines in complex offshore environments.

[0016] A fourth aspect of the present invention provides a computer program product.

[0017] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps in the above-described method for detecting faults in wind farm collector lines in complex offshore environments.

[0018] A fifth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for detecting faults in wind farm collector lines in complex offshore environments.

[0020] Compared with the prior art, the beneficial effects of the present invention are: This invention avoids misjudgments by balancing the electrical and signal connection states, ensuring accurate sensor connection status assessment and preventing any impact on the accuracy of sensor-based data acquisition. It also fully considers the line environment and usage status, dynamically adjusting the allowable deviation data of traveling wave characteristics to effectively enhance adaptability to complex and variable marine environments. This reduces misjudgments and missed judgments caused by changes in environmental and line parameters. Furthermore, it can divide the power collection line into multiple sections, deeply calculating the fault probability of each section from multiple dimensions, thereby comprehensively determining the faulty line, obtaining corresponding handling solutions, improving the accuracy of fault location, avoiding inaccurate fault location due to considering only one factor, and enhancing the location capability under multiple fault scenarios.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of a method for detecting faults in wind farm collection lines in complex offshore environments according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a wind farm power collection line fault detection device in a complex offshore environment according to an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, 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 invention pertains.

[0026] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0027] Example 1 like Figure 1 As shown, this embodiment of the invention provides a method for fault detection of wind farm collector lines in complex offshore environments, including: S101: When the sensors at each preset monitoring point of the collector line are connected normally, acquire the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line.

[0028] Specifically, the process for determining whether the sensors at each preset monitoring point of the power collection line are connected normally is as follows: (1) Obtain the sensor connection status dataset, which includes electrical connection data and signal connection data; the electrical connection data includes resistance value, voltage value and current value, and the signal connection data includes signal-to-noise ratio, signal amplitude and signal frequency.

[0029] (2) Calculate the electrical connection deviation rate signal factor based on electrical connection data; calculate the signal connection deviation rate signal factor based on signal connection data.

[0030] The formulas for calculating the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor are as follows: ; ; ; In the formula, For electrical connection deviation rate signal factor, For signal connection deviation rate signal factor, This is the standard error value for resistance, representing the normal permissible error range of resistance parameters in the sensor's electrical connection. It is used to determine whether the deviation between the actual measured resistance value and the reference value is within a reasonable range. This is the standard error value for voltage, representing the normal permissible error range of the voltage parameter. It is used to assess the degree of deviation of the actual voltage measurement value. This is the standard error value for current, representing the normal allowable error range of the current parameter, used to determine whether the actual current measurement value conforms to the standard. , and Before leaving the factory, the standard value is determined through calibration experiments. For example, standard resistance, voltage, and current signals are applied to the sensor, the deviation between the measured value and the true value is recorded, and the maximum permissible error in a statistical sense is taken as the standard value. This is the standard error value of the noise ratio. This is the standard error value of the amplitude. This represents the standard error value for the frequency.

[0031] This is the actual resistance error value. This is the actual voltage error value. This is the actual error value of the current, obtained by subtracting the measured value from the standard value.

[0032] The signal-to-noise ratio, The signal amplitude, For signal frequency, For reference signal-to-noise ratio, For the reference signal amplitude, For the reference signal frequency, This is the noise ratio error value. This is the amplitude error value. This represents the frequency error value.

[0033] The electrical connection deviation rate signal factor is calculated, transforming electrical connection data (resistance, voltage, and current values) into an indicator that intuitively reflects the degree of deviation from the standard electrical connection state. This avoids the ambiguity of determining normality when relying solely on raw data observation. By comparing the result with electrical deviation rate thresholds stored in the database, it objectively determines whether the sensor's current electrical connection is within an acceptable range, reducing false positives and ensuring the reliability of the judgment. If the electrical deviation rate exceeds the threshold, electrical connection anomalies can be quickly located, saving time spent investigating other irrelevant factors.

[0034] The formula clearly outlines complex multi-data relationships using mathematical logic, highlighting the cumulative effect of the error-to-standard-error ratio in logarithmic function form. This ensures computational stability while emphasizing the overall deviation after relative error accumulation, allowing for precise measurement of deviation. Regardless of the sensor's application scenario or hardware environment, a unified measurement standard is maintained, enhancing the algorithm's versatility. Considering the standard error values ​​of different electrical parameters, it captures the impact of subtle changes in each electrical parameter on the overall electrical connection status, ensuring accurate calculation of the final electrical connection deviation rate signal factor and providing a high-quality data foundation for subsequent judgments.

[0035] (3) Compare the electrical connection deviation rate signal factor with the preset electrical deviation rate threshold. If the electrical deviation rate is greater than the preset electrical deviation rate threshold, the sensor connection is abnormal. Otherwise, compare the signal connection deviation rate signal factor with the preset signal deviation rate threshold. If the signal connection deviation rate signal factor is greater than the signal deviation rate threshold, the sensor connection is abnormal. Otherwise, combine the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor to obtain the connection status evaluation value.

[0036] The electrical deviation rate threshold was determined by analyzing numerous real-world operational cases, specifically when sensor electrical connections malfunctioned (e.g., poor contact, insulation damage). Value distribution, setting the threshold as the critical value that can reliably distinguish between normal and fault states. For example, statistics show that when If over 90% of the cases correspond to actual electrical connection faults, then the threshold is set to 1.0. The same signal deviation rate threshold can also be determined in this way.

[0037] This provides an additional dimension for judging sensor connection status from a signal perspective. Not only must the electrical connection of the sensor be normal, but the quality of signal transmission (signal-to-noise ratio, signal amplitude, and signal frequency) is equally crucial. Ensuring the absence of abnormalities in the signal connection links prevents sensor data acquisition or transmission failures due to signal issues. If problems arise in signal connection judgment, the focus can quickly shift to the relevant data acquisition and processing stages, improving troubleshooting efficiency.

[0038] By adopting a combination of square root and natural logarithm, the cumulative relative errors of each signal parameter are comprehensively considered. The square root weakens the excessive influence of extreme errors on the results, making the signal connection deviation rate signal factor more robust in reflecting the overall signal connection quality. As a key intermediate quantity, it serves the final connection status assessment together with the electrical connection deviation rate signal factor.

[0039] The formula for calculating the connection status assessment value is as follows: ; In the formula, This is a connection status assessment value. for Weighting factors for Weighting factors It is a natural constant; For electrical connection deviation rate signal factor, This is the signal connection deviation rate signal factor.

[0040] (4) Compare the connection status assessment value with the preset connection status assessment threshold. If the connection status assessment value is greater than the connection status assessment threshold, the sensor connection is abnormal; if the connection status assessment value is not greater than the connection status assessment threshold, the sensor connection is normal. The method for determining the connection status assessment threshold is the same as the method for determining the electrical deviation rate threshold.

[0041] S102: Based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, obtain the line characteristic value and compare it with the preset line fault characteristic threshold. Based on the comparison result, determine whether the corresponding collector line has a fault.

[0042] In the specific implementation process, the line characteristic value is compared with the line fault characteristic threshold stored in the database. If the line characteristic value is greater than the line fault characteristic threshold stored in the database, then the collector line has a fault; if the line characteristic value is not greater than the line fault characteristic threshold stored in the database, then the collector line has not a fault.

[0043] The formula for calculating the line characteristic value is as follows: ; In the formula, For line characteristic values, The initial traveling wave amplitude, Instantaneous amplitude, For the arrival time of the traveling wave, For reference traveling wave amplitude, For reference instantaneous amplitude, For reference, the arrival time of the traveling wave, This is the allowable deviation value for the traveling wave amplitude. This is the allowable deviation value for instantaneous amplitude. This is the allowable deviation value for the arrival time of the traveling wave.

[0044] and It can be obtained by using current transformers and voltage transformers in conjunction with a high-speed data acquisition system. This can be detected using a traveling wave sensor. By comparing the actual parameters with reference parameters, it can be determined whether the changes in the actual parameters exceed the normal range, thereby inferring whether a fault has occurred in the corresponding line.

[0045] By tracking the line status in real time, faults can be detected instantly upon occurrence, allowing for rapid activation of response mechanisms and reducing the impact of faults on the power collection system. The real-time recorded data provides the foundation for subsequent precise analysis of the fault location, facilitating rapid fault diagnosis.

[0046] Formula integration , , By calculating with their respective reference values ​​and allowable deviations, a comprehensive assessment is made as to whether a fault has occurred in the line, avoiding the dominance of a single factor in obtaining the first deviation matching dataset. The first deviation matching dataset includes several first deviation matching data, including temperature matching values, humidity matching values, and seawater flow velocity matching values. The ambient temperature and humidity of the line environment where the collector line is located are compared with the first deviation matching data to obtain the environmental matching coefficient. Obtain the second deviation matching dataset, which includes several second deviation matching data points, including transmission power matching values, dielectric loss factor matching values, and insulation resistance matching values. Compare the transmission power, dielectric loss factor, and insulation resistance of the current collector line under operating conditions with the second deviation matching data to obtain the operating condition matching coefficient. Perform a comprehensive analysis of the environmental matching coefficient and the operating condition matching coefficient to obtain the comprehensive matching coefficient. Determine the first and second deviation matching data points corresponding to the smallest comprehensive matching coefficient. Obtain the permissible deviation data of the traveling wave characteristics corresponding to the first and second deviation matching data points from the database.

[0047] The formula for calculating the environmental matching coefficient is: ; In the formula, For environmental matching coefficients, For ambient temperature, For ambient humidity, For temperature matching values, This is the humidity matching value.

[0048] For the collector lines of offshore wind farms, changes in these environmental parameters can directly or indirectly affect the operating status and performance of the collector lines. For example, An increase in resistance will lead to an increase in the cable's resistance. Adding it will reduce the insulation performance.

[0049] The formula will , To avoid the limitations of focusing solely on the impact of a single environmental factor on the collector line, a comprehensive environmental coefficient is used to reflect the overall difference between the external environmental values ​​and the matching values ​​of the collector line. The formula for calculating the state matching coefficient is as follows: ; In the formula, To use state matching coefficients, For transmission power, For dielectric loss factor, For insulation resistance, For transmission power matching value, This is the matching value for the dielectric loss factor. This is the insulation resistance matching value.

[0050] Dielectric loss is a physical quantity that measures the energy loss characteristics of insulating materials under the influence of an electric field due to processes such as dielectric polarization and conductivity. It is obtained using a dielectric loss meter and represents the ratio of electrical energy consumed to stored electrical energy per unit time in an alternating current electric field. In current collector circuits, the insulating material... It directly affects the power transmission efficiency and insulation reliability of the line. Insulation resistance refers to the ability of an insulating material to impede the flow of electric current. It is an important indicator for measuring the insulation performance of a current collector circuit and is obtained using an insulation resistance tester. During long-term use, the circuit will undergo normal aging, causing... Get smaller and The closer the match, the closer the current usage status is to the matching status in the database.

[0051] Will , and Taking these three key usage status factors into account comprehensively reflects the actual usage of the line and can quantify the degree of difference between the actual usage status and the ideal matching status of the collector line. This makes the assessment of the line's usage status more accurate.

[0052] The formula for calculating the environmental matching coefficient is: ; In the formula, For the overall matching coefficient, Stored in the database Weighting factors Stored in the database Weighting factors.

[0053] By incorporating environmental and operational factors, this approach avoids biased judgments caused by focusing only on local factors while ignoring other potential influencing factors, thus accurately grasping the actual operating conditions of the power collection line. Real-time environmental and operational parameters are acquired and compared with corresponding matching values, ensuring the evaluation process closely reflects the actual state of the power collection line under different times and operating conditions. By determining the deviation matching data corresponding to the minimum comprehensive matching coefficient, the permissible deviation data of traveling wave characteristics stored in the database can be rationally selected. The matching between the current line's actual state and various preset states is considered, making the selected permissible deviation data of traveling wave characteristics more consistent with the current line's true operating conditions, thereby improving the accuracy of fault diagnosis based on traveling wave characteristics.

[0054] The overall matching coefficient changes with variations in the line environment and operating conditions, and the selected traveling wave characteristic allowance deviation data can dynamically adapt to these changes. Under different operating conditions, the most suitable deviation data can be used to determine whether the traveling wave characteristic is abnormal, reducing misjudgments or omissions caused by fixed deviation data and improving the reliability of fault detection.

[0055] The above settings , It is obtained from the database and calculated based on historical data. , as well as ,Establish , The mapping set of its corresponding weight factors is used to obtain the current... , The following text , , All of these are obtained through a mapping set of historical data and weight factors established in the database, that is, the corresponding weight factors are obtained based on the current data. and The method of determination is the same.

[0056] S103: If a fault occurs in the collector line, extract the traveling wave characteristic data of each monitoring section of the collector line at the time of the fault, calculate the propagation speed of the traveling wave in each monitoring section, the initial traveling wave voltage amplitude ratio and the initial traveling wave current amplitude ratio, and obtain the first fault probability, the second fault probability and the third fault probability of each monitoring section respectively. Then, calculate the comprehensive fault probability of each monitoring section by weighted summation, and select the monitoring section corresponding to the largest comprehensive fault probability as the fault section.

[0057] The process involves dividing the line into several intervals based on multiple monitoring points, with each interval consisting of two adjacent monitoring points. The time difference between intervals is calculated, and the propagation speed of the traveling wave in each interval is calculated based on the line length and the corresponding time difference. A traveling wave velocity matrix is ​​constructed to obtain the first fault probability for each interval based on the traveling wave velocity matrix. The initial traveling wave voltage amplitude ratio between intervals is calculated, and an initial traveling wave voltage amplitude ratio matrix is ​​constructed to obtain the second fault probability for each interval based on the traveling wave voltage amplitude ratio matrix. The initial traveling wave current amplitude ratio between intervals is calculated, and an initial traveling wave current amplitude ratio matrix is ​​constructed to obtain the third fault probability for each interval based on the initial traveling wave current amplitude ratio matrix. The first, second, and third fault probabilities corresponding to each interval are then comprehensively analyzed to obtain the overall fault probability. The interval corresponding to the highest overall fault probability is the faulty line.

[0058] The formulas for calculating the probability of the first failure, the probability of the second failure, the probability of the third failure, and the overall probability of failure are as follows: ; ; ; ; In the formula, For the first The first failure probability in each interval For the first The second failure probability in each interval For the first The probability of the third failure in each interval; For the first The traveling wave velocity in each interval, This is the normal maximum traveling wave velocity. This is the normal minimum traveling wave velocity; For the first The ratio of the initial traveling wave voltage amplitude in each interval This is the ratio of the normal maximum initial traveling wave voltage amplitude. This represents the normal minimum initial traveling wave voltage amplitude ratio; For the first The ratio of the initial traveling wave current amplitude in each interval This is the ratio of the normal maximum initial traveling wave current amplitude. This is the normal minimum initial traveling wave current amplitude ratio; For the first The length of the line in each section, For the first The time difference between intervals; For the first The overall failure probability corresponding to each interval Stored in the database Weighting factors Stored in the database Weighting factors Stored in the database Weighting factors.

[0059] Each matrix reflects the state changes of the line section from different perspectives. For example, the traveling wave velocity reflects the influence of the line's physical characteristics on the propagation of the traveling wave, and the amplitude ratio reflects the energy changes caused by the fault, making the judgment of the fault location more accurate and reliable. Different fault types and locations often exhibit different characteristics. Multi-dimensional analysis can more meticulously distinguish these differences, thereby accurately locating the faulty line. Even when faced with complex and diverse fault situations, there is a greater probability of accurately finding the faulty section.

[0060] Dividing the power collection line into multiple sections based on monitoring points for detailed analysis allows for a more precise determination of the specific location and range of the fault. Even in long-distance power collection lines, the fault investigation scope can be quickly narrowed down by checking and analyzing each section one by one, improving fault investigation efficiency. This is particularly suitable for scenarios with long power collection lines, such as large offshore wind farms.

[0061] The calculations are performed by selecting a time range based on the moment the fault is determined, specifically within two seconds before and after the fault determination. This approach can adapt to the dynamic changes in the line at the instant the fault occurs. The propagation behavior of the traveling wave generated by the fault in different sections will vary at different times. The analysis method based on real-time dynamic data can better match various operating conditions in actual line operation, accurately capture the abnormal characteristics of each section at the instant of the fault, and thus achieve precise fault location.

[0062] Monitoring points can be set at the branch points of collection lines, the busbars of step-up substations, and the ends of branch lines. Based on the fault location of the fault line in the interval, the amplitude, polarity, and arrival time of the initial traveling wave of the fault can be monitored online. Combined with GPS / BeiDou timing technology, the accurate location of the fault point can be determined. The precise location of the fault point can be calculated based on the principle of double-ended traveling wave fault ranging.

[0063] Obtain fault characteristic data of the fault point, determine the fault type of the fault point, and obtain the corresponding fault handling solution.

[0064] Obtain the fault parameter feature data corresponding to each fault type stored in the database, compare the fault feature data with the fault parameter feature data corresponding to each fault type to obtain the similarity of each fault; the fault type corresponding to the largest fault similarity is the fault type corresponding to that fault point; the fault handling solution corresponding to that fault type stored in the database is the fault handling solution corresponding to that fault point.

[0065] The formula for calculating fault similarity is: ; In the formula, For the fault point and the first Fault similarity corresponding to each fault type The fault feature vector representation of the fault point is used to represent fault feature data. For the first stored in the database The fault parameter feature vectors corresponding to each fault type are used to represent the fault parameter feature data of each fault type, including but not limited to peak short-circuit current, power change rate, and overvoltage factor. for The model, for The model, This refers to the number of each fault type stored in the database.

[0066] The system acquires actual fault characteristic data of the fault point and compares it with fault parameter characteristic data corresponding to each fault type stored in the database. This fully utilizes accumulated data knowledge, making the judgment of fault type more scientific and accurate, and reducing misclassification due to errors in judgment. The similarity between the fault characteristic data and the characteristic data of each fault type is quantified, using specific numerical values ​​to reflect the closeness of the correlation between the two. This makes the matching of different fault types with the current fault point clear at a glance, facilitating the clear identification of the fault type that best matches the actual situation.

[0067] Once the fault type is identified, the corresponding fault handling solution stored in the database can be quickly found. This eliminates the need for manual thought or searching for solutions, significantly saving time and effort. It facilitates rapid fault response, reduces the impact of faults on power transmission lines and the entire power system, continuously improves fault handling capabilities, and, through multi-dimensional feature vector comparison, can adapt to various complex fault scenarios and accurately identify fault types.

[0068] In one specific embodiment, electrical connection error data It is 20Ω. 1V, It is 0.01A. It is 10Ω. It is 0.5V. Given 0.01A, calculate The value is 0.78, and the electrical deviation rate threshold is 1. Less than the electrical deviation rate threshold.

[0069] Signal connection error data It is 5dB. It is 0.5V. 1Hz It is 5dB. It is 0.2V. Calculated at 2Hz The value is 1.27, and the signal deviation rate threshold is 1.51. Less than the signal deviation rate threshold, It is 0.6. The value is 0.4, and the result is calculated. The score is 2.59, and the connectivity status assessment threshold is 2.74. If the value is below the connection status assessment threshold, the sensor connection is normal.

[0070] The environment of the collector line It is 25℃. 70%, It is 22℃. It is 63%, calculated as follows It is 7.52.

[0071] Line usage data of the collector line after 2 years of use It is 500W. It is 0.03. It is 1000Ω. It is 450W. It is 0.02. It is 930Ω, calculated It is 9.59. It is 0.55. The value is 0.45, and the calculation is as follows: The coefficient is 8.45. The remaining first-deviation matching data are calculated similarly to the second-deviation matching data, yielding a minimum comprehensive matching coefficient of 6.3. The corresponding deviation matching data corresponds to the traveling wave characteristic allowable deviation data stored in the database. 10V It is 8V. It takes 2ms.

[0072] Traveling wave characteristic data of collector lines 100V It is 80V. It takes 5ms. 90V It is 75V. Calculate if it is 0. The value is 1.11, and the line fault characteristic threshold is 0.96. If the value is greater than 0.96, then a fault has occurred in the collector line. The traveling wave characteristic data at each monitoring point of the collector line corresponding to the fault time is obtained from the recorded traveling wave characteristic data. , , To simplify the calculation, we obtain 0.23, 0.2 It is 0.18. It is 0.3. It is 0.4. The value is 0.3, and the result is calculated. The overall fault probability is 0.2. The overall fault probability of the other intervals is calculated similarly, and the maximum overall fault probability is 0.53. The interval corresponding to the maximum overall fault probability is the faulty line, and the fault point is obtained.

[0073] Fault characteristic data of the fault point and the first The fault parameter characteristic data corresponding to each fault type are compared. To simplify the calculation, we obtain The similarity score is 0.92. Other fault types are similarly calculated, and the fault similarity score corresponding to the fault type with the highest score is 0.94. The fault type corresponding to this fault type is the fault type of the fault point, and the fault handling solution corresponding to this fault type stored in the database is the fault handling solution corresponding to the fault point.

[0074] Example 2 like Figure 2 As shown in the figure, this embodiment of the invention provides a fault detection device for wind farm collection lines in complex offshore environments, which specifically includes the following modules: The line data acquisition module 201 is used to acquire traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the power collection line when the sensor connection at each preset monitoring point of the power collection line is normal. The line fault judgment module 202 is used to obtain line characteristic values ​​based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, and compare them with the preset line fault characteristic threshold, and judge whether the corresponding collector line has a fault based on the comparison result. The fault range determination module 203 is used to extract the traveling wave characteristic data of each monitoring range of the collector line when a fault occurs, calculate the propagation speed of the traveling wave, the initial traveling wave voltage amplitude ratio and the initial traveling wave current amplitude ratio in each monitoring range, and obtain the first fault probability, the second fault probability and the third fault probability of each monitoring range respectively. Then, the comprehensive fault probability of each monitoring range is calculated by weighted summation, and the monitoring range corresponding to the largest comprehensive fault probability is selected as the fault range.

[0075] It should be noted that each module in the embodiments of the present invention corresponds one-to-one with each step in the above embodiments, and their specific implementation processes are the same, which will not be described in detail here.

[0076] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the above-described method for detecting faults in wind farm collector lines in complex offshore environments.

[0077] Example 4 A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps in the above-described method for detecting faults in wind farm collector lines in complex offshore environments.

[0078] Example 5 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for detecting faults in wind farm collection lines in complex offshore environments.

[0079] The electronic device of this embodiment includes a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0080] The following components are connected to the I / O interface: input sections including keyboards, mice, etc.; output sections including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; storage sections including hard disks; and communication sections including network interface cards such as local area network (LAN) cards and modems. The communication sections perform communication processing via networks such as the Internet. Drives are also connected to the I / O interface as needed. Removable media, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive as needed so that computer programs read from them can be installed into the storage section as required.

[0081] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs the various functions defined in the apparatus of this application.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for fault detection of power collection lines in wind farms operating in complex offshore environments, characterized in that, include: When the sensors at each preset monitoring point of the collector line are connected normally, the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line are acquired. Based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, the line characteristic value is obtained and compared with the preset line fault characteristic threshold. Based on the comparison result, it is determined whether the corresponding collector line has a fault. If a fault occurs in the collector line, the traveling wave characteristic data of each monitoring section of the collector line at the time of the fault are extracted, and the propagation speed of the traveling wave, the initial traveling wave voltage amplitude ratio, and the initial traveling wave current amplitude ratio in each monitoring section are calculated. The first fault probability, the second fault probability, and the third fault probability of each monitoring section are obtained respectively. The comprehensive fault probability of each monitoring section is then calculated by weighted summation. The monitoring section corresponding to the highest comprehensive fault probability is selected as the fault section. The formulas for calculating the probability of the first failure, the probability of the second failure, the probability of the third failure, and the overall probability of failure are as follows: ； ； ； ； In the formula, For the first The first failure probability in each interval For the first The second failure probability in each interval For the first The probability of the third failure in each interval; For the first The traveling wave velocity in each interval, This is the normal maximum traveling wave velocity. This is the normal minimum traveling wave velocity; For the first The ratio of the initial traveling wave voltage amplitude in each interval This is the ratio of the normal maximum initial traveling wave voltage amplitude. This represents the normal minimum initial traveling wave voltage amplitude ratio; For the first The ratio of the initial traveling wave current amplitude in each interval This is the ratio of the normal maximum initial traveling wave current amplitude. This is the normal minimum initial traveling wave current amplitude ratio; For the first The length of the line in each section, For the first The time difference between intervals; For the first The overall failure probability corresponding to each interval Stored in the database Weighting factors Stored in the database Weighting factors Stored in the database Weighting factors.

2. The method for fault detection of wind farm collector lines in complex offshore environments as described in claim 1, characterized in that, The process for determining whether the sensors at each preset monitoring point of the power collection line are properly connected is as follows: Acquire the sensor connection status dataset, which includes electrical connection data and signal connection data; Calculate the electrical connection deviation rate signal factor based on electrical connection data; calculate the signal connection deviation rate signal factor based on signal connection data; The electrical connection deviation rate signal factor is compared with the preset electrical deviation rate threshold. If the electrical deviation rate is greater than the preset electrical deviation rate threshold, the sensor connection is abnormal. Otherwise, the signal connection deviation rate signal factor is compared with the preset signal deviation rate threshold. If the signal connection deviation rate signal factor is greater than the signal deviation rate threshold, the sensor connection is abnormal. Otherwise, the connection status evaluation value is obtained by combining the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor. The connection status assessment value is compared with the preset connection status assessment threshold. If the connection status assessment value is greater than the connection status assessment threshold, the sensor connection is abnormal; if the connection status assessment value is not greater than the connection status assessment threshold, the sensor connection is normal.

3. The method for fault detection of wind farm collector lines in complex offshore environments as described in claim 2, characterized in that, The formulas for calculating the electrical connection deviation rate signal factor and the signal connection deviation rate signal factor are as follows: ； ； ； In the formula, For electrical connection deviation rate signal factor, For signal connection deviation rate signal factor, This is the standard error value of the resistor. This is the standard error value of the voltage. This is the standard error value of the current; The signal-to-noise ratio, The signal amplitude, For signal frequency, For reference signal-to-noise ratio, For the reference signal amplitude, For the reference signal frequency, This is the noise ratio error value. This is the amplitude error value. This represents the frequency error value.

4. The method for fault detection of wind farm collector lines in complex offshore environments as described in claim 3, characterized in that, The formula for calculating the connection status assessment value is: ； In the formula, This is a connection status assessment value. for Weighting factors for Weighting factors It is a natural constant; For electrical connection deviation rate signal factor, This is the signal connection deviation rate signal factor.

5. The method for fault detection of wind farm collector lines in complex offshore environments as described in claim 1, characterized in that, The formula for calculating the characteristic value of the line is: ； In the formula, For line characteristic values, The initial traveling wave amplitude, Instantaneous amplitude, For the arrival time of the traveling wave, For reference traveling wave amplitude, For reference instantaneous amplitude, For reference, the arrival time of the traveling wave, This is the allowable deviation value for the traveling wave amplitude. This is the allowable deviation value for instantaneous amplitude. This is the allowable deviation value for the arrival time of the traveling wave.

6. A fault detection device for wind farm collector lines in complex offshore environments, characterized in that, The method for detecting faults in wind farm collector lines in complex offshore environments, as described in any one of claims 1-5, includes: The line data acquisition module is used to acquire traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the power collection line when the sensor connection at each preset monitoring point of the power collection line is normal. The line fault judgment module is used to obtain line characteristic values ​​based on the traveling wave characteristic data, traveling wave reference characteristic data and traveling wave characteristic allowable deviation data of the collector line, and compare them with the preset line fault characteristic threshold. Based on the comparison result, it determines whether the corresponding collector line has a fault. The fault range determination module is used to extract the traveling wave characteristic data of each monitoring range of the collector line when a fault occurs, calculate the propagation speed of the traveling wave, the initial traveling wave voltage amplitude ratio, and the initial traveling wave current amplitude ratio in each monitoring range, and obtain the first fault probability, second fault probability, and third fault probability for each monitoring range respectively. Then, the comprehensive fault probability of each monitoring range is calculated by weighted summation, and the monitoring range corresponding to the highest comprehensive fault probability is selected as the fault range.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the method for detecting faults in wind farm collector lines in complex offshore environments as described in any one of claims 1-5.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps in the method for detecting faults in wind farm collector lines in complex offshore environments as described in any one of claims 1-5.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for detecting faults in wind farm collector lines in complex offshore environments as described in any one of claims 1-5.

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