On-load tap-changing transformer voltage monitoring method and system fused with optical fiber sensing

Abstract

The invention relates to the technical field of transformer regulation, and particularly discloses an on-load tap-changing transformer voltage monitoring method and system fused with optical fiber sensing, and the method comprises the steps: obtaining multi-mode monitoring data outputted by an optical fiber sensor, the multi-modal monitoring data comprises voltage fluctuation waveform data, vibration spectrum data, temperature distribution data and a load current change rate; acquiring real-time power grid disturbance data according to the voltage fluctuation waveform data; acquiring mechanical resonance phase data according to the vibration spectrum data; and establishing a winding hot-spot temperature change gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hot-spot temperature change gradient. According to the invention, through multi-mode monitoring data acquisition, the operation state of the transformer is comprehensively mastered, the problem of single traditional monitoring data is solved, and cognition of the overall condition of the transformer is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer regulation, and in particular to a method and system for monitoring voltage of an on-load tap-changing transformer integrating optical fiber sensing. Background Art

[0002] With the development of society and the economy, the number of various electrical devices has increased dramatically, and the requirements for power quality are becoming increasingly stringent. Precision instruments in industrial production, electronic equipment in the information industry, and smart homes in residents all require a stable voltage supply. Once voltage fluctuations exceed the allowable range, they not only affect the normal operation of equipment and reduce production efficiency, but may also damage the equipment and cause economic losses.

[0003] Traditional monitoring methods rely heavily on conventional electrical parameters like voltage and current, failing to capture multimodal data such as vibration spectrum, temperature distribution, and load current change rate. This makes it difficult for operations and maintenance personnel to fully understand the transformer's operating status, and potential faults such as localized winding overheating and loose mechanical components go undetected. For example, vibration anomalies caused by mechanical problems within the transformer are difficult to detect based solely on voltage and current data. If these anomalies are not addressed promptly, they can develop into serious faults, impacting the reliability of the power system. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing, so as to solve the technical problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing, comprising: Acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; Acquiring real-time grid disturbance data according to the voltage fluctuation waveform data; Acquiring mechanical resonance phase data according to the vibration spectrum data; Establishing a winding hotspot temperature change gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hotspot temperature change gradient; Obtaining a voltage regulation priority parameter based on the real-time power grid disturbance data, mechanical resonance phase data, and a temperature-load correlation coefficient; acquiring voltage regulation trend data based on the real-time grid disturbance data; Obtaining a regulation difference value according to the voltage regulation priority parameter and the voltage regulation trend data, and determining whether the regulation difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

[0006] Preferably, the step of acquiring real-time grid disturbance data according to the voltage fluctuation waveform data includes: Perform fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; Mark the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; Obtain the historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; Extract voltage fluctuation amplitude and frequency offset according to disturbance type; Dynamically correct the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamic correction disturbance data; The dynamically corrected disturbance data is used as real-time power grid disturbance data, wherein the real-time power grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

[0007] Preferably, the step of obtaining mechanical resonance phase data according to the vibration spectrum data includes: Perform wavelet packet decomposition on the vibration spectrum data to decompose the vibration signal into multiple different frequency bands; Obtaining the fundamental frequency of each of the frequency bands; Obtaining a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; Determining whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; Obtaining the voltage fundamental wave phase, and obtaining the fundamental wave phase difference value according to the fundamental frequency component phase angle and the voltage fundamental wave phase; Determining whether the fundamental wave phase difference value exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; Generate a mechanical condition degradation index based on the duration of the abnormality; Get voltage fluctuation rate; A correlation analysis is performed on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

[0008] Preferably, the step of establishing a winding hotspot temperature change gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hotspot temperature change gradient includes: The temperature data measured at different positions on the winding are obtained according to the temperature distribution data, and the continuous temperature distribution field of the winding is obtained through the interpolation algorithm; Identify the highest temperature area in the temperature distribution field and mark it as the winding hot spot area; Obtaining hotspot temperature values of the winding hotspot region at multiple consecutive sampling moments, and generating temperature change gradients at adjacent moments based on the hotspot temperature values at the multiple consecutive sampling moments; Obtain the load current change rate at each continuous sampling moment, and dynamically correlate it with the temperature change gradient to obtain dynamic correlation result data; Generate temperature-load correlation coefficients based on dynamic correlation result data.

[0009] Preferably, the step of acquiring voltage regulation trend data according to the real-time power grid disturbance data includes: Obtaining grid disturbance records for multiple preset time periods; Obtaining a predicted fluctuation range for a preset time period based on the power grid disturbance record; generating a voltage deviation probability distribution map based on the plurality of power grid disturbance records and the predicted fluctuation range; Acquire multiple voltage distribution peaks according to the voltage deviation probability distribution graph; Obtain voltage distribution peaks that are greater than a preset threshold and mark them as voltage high deviation risk ranges; generating a voltage deviation trend change graph according to the voltage high deviation risk range; Voltage regulation trend data is generated according to the voltage deviation trend change diagram, wherein the voltage regulation trend data includes a voltage change direction and a voltage change amplitude.

[0010] Preferably, the step of generating on-load voltage regulation information according to the regulation difference value includes: Obtaining an adjustment direction of the on-load tap changer according to the positive or negative value of the adjustment difference value, wherein the adjustment direction includes lowering the voltage and raising the voltage; Obtaining the switch adjustment step length according to the numerical value of the adjustment difference value, and establishing a mapping relationship between the adjustment difference value and the adjustment step length; Obtaining a tap adjustment limit of the transformer, wherein the tap adjustment limit includes a tap adjustment range and an adjustment interval time; Generate control instructions for the on-load tap changer based on the adjustment direction and switch adjustment step.

[0011] The present invention also provides a voltage monitoring system for an on-load tap-changing transformer integrating optical fiber sensing, comprising: A first acquisition module is used to acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; A second acquisition module is used to acquire real-time power grid disturbance data according to the voltage fluctuation waveform data; a third acquisition module, configured to acquire mechanical resonance phase data according to the vibration spectrum data; a fourth acquisition module, configured to establish a winding hotspot temperature change gradient according to the temperature distribution data, and generate a temperature-load correlation coefficient according to the winding hotspot temperature change gradient; A fifth acquisition module is configured to acquire a voltage regulation priority parameter based on the real-time power grid disturbance data, mechanical resonance phase data, and a temperature-load correlation coefficient; a sixth acquisition module, configured to acquire voltage regulation trend data according to the real-time grid disturbance data; an adjustment module, configured to obtain an adjustment difference value according to the voltage adjustment priority parameter and the voltage adjustment trend data, and determine whether the adjustment difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

[0012] Preferably, the second acquisition module includes: A transformation unit is used to perform fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; A marking unit, used to mark the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; A matching unit is used to obtain a historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; An extraction unit, used to extract the voltage fluctuation amplitude and frequency offset according to the disturbance type; A correction unit, configured to dynamically correct the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamically corrected disturbance data; The first acquisition unit is configured to use the dynamically corrected disturbance data as real-time grid disturbance data, wherein the real-time grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

[0013] Preferably, the third acquisition module includes: A decomposition unit, used to perform wavelet packet decomposition on the vibration spectrum data, and decompose the vibration signal into multiple different frequency bands; A second acquiring unit, configured to acquire a fundamental frequency of each of the frequency bands; A third acquiring unit, configured to acquire a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; A first judging unit, configured to judge whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; a fourth acquiring unit, configured to acquire a voltage fundamental wave phase, and acquire a fundamental wave phase difference value according to the fundamental frequency component phase angle and the voltage fundamental wave phase; A second judging unit, configured to judge whether the fundamental wave phase difference exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; a first generating unit, configured to generate a mechanical state degradation index according to an abnormality duration; a fifth acquiring unit, configured to acquire a voltage fluctuation rate; The analyzing unit is configured to perform a correlation analysis on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

[0014] Preferably, the fourth acquisition module includes: a sixth acquisition unit, configured to acquire temperature data measured at different positions on the winding according to the temperature distribution data, and obtain a continuous temperature distribution field of the winding through an interpolation algorithm; An identification unit, used to identify the highest temperature area in the temperature distribution field and mark it as a winding hot spot area; a seventh acquiring unit, configured to acquire hotspot temperature values of the winding hotspot region at a plurality of consecutive sampling moments, and generate a temperature change gradient at adjacent moments according to the hotspot temperature values at the plurality of consecutive sampling moments; an eighth acquisition unit, configured to acquire a load current change rate at each continuous sampling moment, and dynamically correlate it with a temperature change gradient to obtain dynamic correlation result data; The second generating unit is used to generate a temperature-load correlation coefficient according to the dynamic correlation result data.

[0015] The beneficial effects of the present application are as follows: the present invention comprehensively grasps the operating status of the transformer through multi-modal monitoring data collection, solves the problem of single traditional monitoring data, and improves the understanding of the overall condition of the transformer. In the data processing and analysis link, key parameters such as real-time grid disturbance value, mechanical resonance phase data, temperature-load correlation coefficient, etc. are accurately obtained, and potential fault hazards are effectively discovered, providing strong support for the accurate assessment of the operating status of the transformer. According to these parameters, voltage regulation priority parameters and trend curve slopes are generated, and then the adjustment difference value is determined, so as to achieve precise control of the on-load tap changer, ensure voltage stability, improve power quality, and reduce damage to electrical equipment caused by voltage fluctuations. At the same time, real-time monitoring and abnormal judgment of the transformer operating status, as well as adjustment of control instructions according to abnormal situations, ensure the safety and reliability of the on-load tap changer operation, avoid improper voltage regulation under abnormal circumstances, and prevent the expansion of faults. Overall, the method and system ensure the stable operation of the power system, reduce operation and maintenance costs, extend the service life of the transformer, and improve the safety and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of a method flow chart according to an embodiment of the present application.

[0017] Figure 2 This is a schematic diagram of the system structure of an embodiment of the present application.

[0018] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, the present application provides a method for monitoring voltage of an on-load tap-changing transformer by integrating optical fiber sensing, which is applied to a circuit component feature database, including: S1. Acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; S2. acquiring real-time grid disturbance data according to the voltage fluctuation waveform data; S3. Acquire mechanical resonance phase data according to the vibration spectrum data; S4. Establishing a winding hotspot temperature change gradient based on the temperature distribution data, and generating a temperature-load correlation coefficient based on the winding hotspot temperature change gradient; S5. Obtaining a voltage regulation priority parameter according to the real-time grid disturbance value, the mechanical resonance phase data, and the temperature-load correlation coefficient; S6. Acquiring voltage regulation trend data according to the real-time grid disturbance data; S7. Obtaining an adjustment difference value according to the voltage adjustment priority parameter and the slope of the trend curve, and determining whether the adjustment difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

[0021] As described in steps S1-S7 above, the present invention obtains multimodal monitoring data output by an optical fiber sensor. Leveraging the characteristics of the optical fiber sensor, it is installed at a key location on the transformer, enabling it to simultaneously collect voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate. Next, real-time grid disturbance data is obtained based on the voltage fluctuation waveform data. Then, the disturbance type matching the current harmonic distortion level is found in the historical grid disturbance database. The voltage fluctuation amplitude and frequency offset are extracted from the disturbance type-related data. These data are then dynamically corrected in combination with the load current change rate to ultimately obtain real-time grid disturbance data. Mechanical resonance phase data is then obtained based on the vibration spectrum data. After obtaining the voltage fundamental phase, the difference between the two is calculated to obtain the fundamental phase difference. If this difference exceeds a preset threshold, a mechanical resonance anomaly is determined, the anomaly duration is recorded, and a mechanical condition degradation index is generated. Finally, a correlation analysis is performed between the mechanical condition degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data. Subsequently, the winding hotspot temperature gradient is established based on the temperature distribution data, and a temperature-load correlation coefficient is generated. The load current change rate at each sampling moment is simultaneously acquired and dynamically correlated with the temperature gradient to generate a temperature-load correlation coefficient. Subsequently, the voltage regulation priority parameter is derived based on the real-time grid disturbance value, mechanical resonance phase data, and the temperature-load correlation coefficient. Next, voltage regulation trend data is obtained based on the real-time grid disturbance data. A voltage deviation trend graph is plotted within this range to generate voltage regulation trend data, including the direction and magnitude of voltage change. Finally, a regulation difference value is obtained based on the voltage regulation priority parameter and the slope of the trend curve, and a determination is made as to whether it exceeds a preset threshold. The difference between the voltage regulation priority parameter and the slope of the trend curve is calculated to obtain the regulation difference value. The regulation difference value is compared with a preset threshold. If it exceeds the threshold, the sign of the regulation difference value determines whether the on-load tap changer should increase or decrease the voltage. The adjustment step size is determined based on the difference value and a corresponding relationship is established. The transformer tap adjustment limit is obtained and, based on the adjustment direction and step size, a control command for the on-load tap changer is generated. Before issuing the command, the transformer operating status is checked and the command is adjusted if any abnormality is detected. The on-load tap changer is then adjusted based on the command to ensure voltage stability.

[0022] In one embodiment, the step of acquiring real-time grid disturbance data according to the voltage fluctuation waveform data includes: S201, performing fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; S202, marking the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; S203: Obtain a historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; S204, extracting voltage fluctuation amplitude and frequency offset according to disturbance type; S205, dynamically correcting the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamically corrected disturbance data; S206: Using the dynamically corrected disturbance data as real-time grid disturbance data, wherein the real-time grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

[0023] As described in steps S201-S206 above, the present invention performs a fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and third harmonic component. The collected voltage fluctuation waveform data is input into a specific algorithm program, which can convert the complex, time-varying voltage fluctuation waveform from the time domain to the frequency domain. In the frequency domain, the program analyzes and calculates the components of different frequencies to determine the amplitude of the fundamental wave and the third harmonic component. This makes the different frequency components, which were originally difficult to distinguish in the time domain, clearly distinguishable in the frequency domain. Next, the harmonic distortion level is labeled based on the fundamental wave amplitude and the energy distribution of the third harmonic component. The energy of the fundamental wave and the third harmonic is calculated by accumulating power over a period of time. The ratio of the third harmonic energy to the fundamental wave energy is then calculated. Based on this ratio and in accordance with pre-set standards, the harmonic distortion is classified into different levels, such as mild, moderate, and severe. A database of historical power grid disturbances is then accessed and the disturbance type corresponding to the current harmonic distortion level is matched. A database containing detailed information about various past grid disturbance events is established, acting like a "grid disturbance dictionary." Once the current harmonic distortion level is determined, matching records are searched in the database. By comparing historical disturbance events that most closely match the current harmonic distortion level, the likely disturbance type, such as a short circuit or sudden load change, is identified. The voltage fluctuation amplitude and frequency offset are then extracted based on the disturbance type. Different disturbance types affect grid voltage and frequency in different ways. For each identified disturbance type, the historical or theoretical voltage and frequency changes are analyzed. By monitoring the voltage and frequency changes before and after the disturbance, the voltage fluctuation amplitude (i.e., the magnitude of the voltage change) is calculated. The frequency offset (i.e., the difference between the grid frequency and the normal frequency) is also determined. The fluctuation amplitude and frequency offset are then dynamically corrected based on the load current change rate, generating dynamically corrected disturbance data. Current sensors are used to monitor load current changes in real time, and the load current change rate (i.e., the degree of load current change per unit time) is calculated. The load current change rate is then linked to the previously determined voltage fluctuation amplitude and frequency offset using a pre-established relationship model or empirical rules. Based on this correlation, the voltage fluctuation amplitude and frequency offset are adjusted and corrected to better reflect the actual grid operating status. Finally, the dynamically corrected disturbance data is used as real-time grid disturbance data. The voltage fluctuation amplitude and frequency offset data, corrected for the load current rate of change, are integrated to generate real-time grid disturbance data. This data is promptly transmitted to the grid monitoring system for real-time viewing and analysis by operations and maintenance personnel. This real-time data enables operations and maintenance personnel to quickly understand the grid's real-time operating status, identify potential problems promptly, and take appropriate measures to ensure stable grid operation.

[0024] In one embodiment, the step of acquiring mechanical resonance phase data according to the vibration spectrum data includes: S301, performing wavelet packet decomposition on the vibration spectrum data to decompose the vibration signal into multiple different frequency bands; S302, obtaining the fundamental frequency of each frequency band; S303, obtaining a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; S304, determining whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; S305, obtaining the voltage fundamental phase, and performing a difference calculation based on the fundamental frequency component phase angle and the voltage fundamental phase to obtain a fundamental phase difference; S306, determining whether the fundamental wave phase difference exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; S307. Generate a mechanical state degradation index according to the abnormality duration; S308, obtaining voltage fluctuation rate; S309 , performing correlation analysis on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

[0025] As described in the above steps S301-S309, the present invention performs wavelet packet decomposition on the vibration spectrum data. The complex vibration spectrum data is "disassembled" into small parts, and processed multiple times through specific filters to divide the vibration signal into multiple frequency bands according to different frequency ranges. Then, the fundamental frequency and third harmonic component of each frequency band are obtained. After multiple frequency bands are obtained through wavelet packet decomposition, the lowest frequency is obtained for each frequency band. At the same time, the frequency corresponding to the third harmonic is identified, and the component corresponding to this frequency is extracted. Then, the energy ratio of the third harmonic component is obtained based on the fundamental frequency and the third harmonic component. To calculate the energy ratio of the third harmonic component, the energy of the third harmonic component and the fundamental frequency component are first calculated respectively, and then it is determined whether the energy ratio of the third harmonic component exceeds the preset threshold. The calculated ratio is compared with a pre-set value. This preset threshold is determined based on the historical operating data of the equipment and industry experience. If the ratio does not exceed the threshold, the mechanical components in the corresponding frequency band are operating normally and the machine is marked as safe. If the ratio exceeds the threshold, a mechanical looseness warning is issued. The fundamental frequency component phase angle is extracted, and then the voltage fundamental phase is obtained. The difference between the fundamental frequency component phase angle and the voltage fundamental phase is calculated. The voltage fundamental phase is first obtained by performing phase analysis on the voltage signal. The fundamental frequency component phase angle extracted from the frequency band is then subtracted from the voltage fundamental phase (or vice versa, depending on the analysis requirements). The resulting difference is the fundamental phase difference, which reflects the relationship between mechanical vibration and voltage. Next, a determination is made as to whether the fundamental phase difference exceeds a preset threshold. The calculated fundamental phase difference is compared with a pre-set threshold. If it exceeds the threshold, a mechanical resonance anomaly is detected. The duration from the abnormality detection to the disappearance of the abnormality or the initiation of the next step is recorded as the anomaly duration. This is used to assess the severity and development trend of the fault. A mechanical condition degradation index is then generated based on the anomaly duration. Using a pre-established mathematical model, the system uses the anomaly duration as input to calculate the mechanical condition degradation index (MCDI). The anomaly duration refers to the duration of mechanical resonance anomalies and other issues that occur in the transformer. The MDI is a quantitative indicator that visually reflects the degree of deterioration in the equipment's mechanical condition. As the anomaly duration increases, the MDI increases accordingly, indicating a gradual deterioration in the equipment's mechanical condition. This relationship is typically implemented through a mathematical function or algorithm, using linear or nonlinear calculations to accurately reflect the changing trends in the equipment's mechanical condition over time. For example, this model was applied to monitor a critical transformer at a large substation. When the transformer's vibration spectrum data indicates a mechanical resonance anomaly, the system begins recording the anomaly duration. As the anomaly duration increases over time, the model calculates the MDI based on the pre-defined relationship.Assume that the initial abnormality lasts for one hour and the mechanical condition degradation index is 0.1 (the index ranges from 0 to 1, with 0 indicating good mechanical condition and 1 indicating complete mechanical deterioration). When the abnormality lasts for five hours, the mechanical condition degradation index rises to 0.3. Based on the change in the index, the operation and maintenance personnel determine that the transformer's mechanical condition is deteriorating, possibly due to loose or worn mechanical components. Next, the voltage fluctuation rate is obtained. By collecting the maximum, minimum, and average voltage values over a period of time, the voltage fluctuation rate is calculated by subtracting the minimum from the maximum value and dividing it by the average. This provides an understanding of voltage fluctuations and determines their correlation with mechanical faults. Finally, a correlation analysis is performed between the degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data. Using correlation analysis methods such as Pearson correlation analysis, the mechanical condition degradation index and voltage fluctuation rate are analyzed, and the correlation coefficient between them is calculated to determine the correlation between the two. Based on the correlation analysis results and combined with previously acquired data, the mechanical resonance phase data is corrected and optimized to obtain more accurate mechanical resonance phase data, providing a reliable basis for assessing the transformer's operating condition.

[0026] In one embodiment, the step of establishing a winding hotspot temperature variation gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hotspot temperature variation gradient includes: S401, acquiring temperature data measured at different positions on the winding according to the temperature distribution data, and obtaining a continuous temperature distribution field of the winding through an interpolation algorithm; S402, identifying the highest temperature area in the temperature distribution field and marking it as a winding hotspot area; S403, obtaining hotspot temperature values of the winding hotspot region at multiple consecutive sampling moments, and generating temperature change gradients at adjacent moments based on the hotspot temperature values at the multiple consecutive sampling moments; S404, obtaining the load current change rate at each continuous sampling moment, and dynamically correlating it with the temperature change gradient to obtain dynamic correlation result data; S405. Generate a temperature-load correlation coefficient based on the dynamic correlation result data.

[0027] As described in steps S401-S405 above, the present invention obtains temperature data measured at different locations on the winding based on the temperature distribution data and uses an interpolation algorithm to generate a continuous temperature distribution field for the winding. Using equipment such as fiber optic sensors, temperature data is collected at multiple locations on the winding. These discrete temperature data represent the real-time temperature of different parts of the winding. Next, an interpolation algorithm is used to reasonably estimate the temperature values of other locations between these discrete data points based on the distribution patterns of the data points. This "bridges" between these discrete data points creates a continuous temperature distribution field, providing a comprehensive and intuitive understanding of the winding's temperature distribution. The highest temperature areas within the temperature distribution field are then identified and marked as winding hotspots. Within this constructed continuous temperature distribution field, the system compares all temperature data. Using a specific search method, it locates the area with the highest temperature value. To ensure accuracy, the system also checks whether the temperature in this area is significantly higher than the surrounding area and remains at the highest temperature for a certain period of time. Once these conditions are met, the area is identified as a winding hotspot. Hotspot temperature values are then acquired at multiple consecutive sampling moments. Based on these values, a temperature gradient is generated between adjacent moments. After determining the hotspot location, the data acquisition system measures and records the temperature at that location at pre-set intervals. After each measurement, the temperature value and the corresponding sampling time are stored in a database. Over time, a series of hotspot temperature data is accumulated. From this stored hotspot temperature data, the temperature values at two adjacent sampling moments are sequentially selected and the previous temperature value is subtracted from the next to obtain the temperature difference. This difference is then divided by the sampling interval to obtain the hotspot temperature gradient within that time period, which visually demonstrates the speed of hotspot temperature change. Next, the load current rate of change is acquired at each consecutive sampling moment and dynamically correlated with the temperature gradient to produce dynamic correlation data. After determining the time interval corresponding to the winding hotspot temperature gradient, current data for the same time period is acquired from the load current monitoring device. The load current's starting and ending values during this time period are determined. The starting value is subtracted from the ending value, and the resultant value is divided by the time interval to obtain the load current rate of change. Next, data analysis methods are used to correlate the load current rate of change with the temperature gradient, identifying the inherent connection and variation patterns between the two. Dynamic correlation data is then generated to establish a relationship between temperature change and load change. Finally, a temperature-load correlation coefficient is generated based on the dynamic correlation data, and the variation correlation coefficient is used as the temperature-load correlation coefficient. A specific correlation coefficient calculation method is used, using the winding hotspot temperature change and the load current rate of change from the dynamic correlation data as input data.This calculation method comprehensively considers the trend and magnitude of changes in both. Through a series of calculation steps, it produces a value between -1 and 1, which is the change correlation coefficient. This value, used as the temperature-load correlation coefficient, measures the correlation between changes in the winding hotspot temperature and changes in the load current, thereby assessing the thermal-electrical coupling status of the transformer.

[0028] In one embodiment, the step of acquiring voltage regulation trend data according to the real-time power grid disturbance data includes: S601, obtaining power grid disturbance records for multiple preset time periods; S602: Obtaining a predicted fluctuation range for a preset time period based on the power grid disturbance record; S603: generating a voltage deviation probability distribution diagram based on the plurality of grid disturbance records and the predicted fluctuation range; S604, obtaining a plurality of voltage distribution peaks according to the voltage deviation probability distribution diagram; S605: Obtain a voltage distribution peak value greater than a preset threshold, and mark it as a voltage high deviation risk range; S606: Generate a voltage deviation trend change graph according to the voltage high deviation risk range; S607 : Generate voltage regulation trend data according to the voltage deviation trend change graph, wherein the voltage regulation trend data includes a voltage change direction and a voltage change amplitude.

[0029] As described in steps S601-S607 above, the present invention obtains grid disturbance records for multiple preset time periods. The grid monitoring system collects data from various monitoring points located at various locations within the grid, such as substations and transmission lines, according to pre-set time periods. The system communicates with the equipment at these monitoring points to collect information related to grid disturbances, such as voltage fluctuations, current changes, and power anomalies. After collection, this data is organized and categorized, and stored in a dedicated database to facilitate subsequent query and analysis. Next, a predicted fluctuation range for a preset time period is obtained based on the grid disturbance records. After obtaining the historical disturbance records, different types of disturbance events are first identified, such as short-circuit faults and sudden load changes. For each disturbance event, parameters such as its frequency, duration, and the magnitude of the resulting voltage fluctuation are calculated. Then, based on the current grid operating status, such as the current load level and grid topology, as well as future load forecasts, weather information, and other related information, data analysis methods and mathematical models are used to predict the potential fluctuation range of the grid voltage within a preset time period. This prediction process comprehensively considers multiple factors to accurately estimate the voltage variation range. Then, a voltage deviation probability distribution map is generated based on multiple grid disturbance records and predicted fluctuation ranges. The collected historical disturbance records and predicted fluctuation range data are integrated to divide the voltage deviation into multiple ranges. For example, based on the rated voltage, voltage deviation ranges such as ±1% and ±2% are divided. Next, the number of times the voltage falls within each range under historical disturbances and predictions is counted, and the corresponding probability is calculated based on these counts. Finally, based on this probability data, a voltage deviation probability distribution map is plotted, clearly showing the probability of occurrence of different voltage deviation ranges. Multiple voltage distribution peaks are then identified from the voltage deviation probability distribution map. Data analysis tools are used to process the generated voltage deviation probability distribution map. Points with relatively high probability values and surrounding lower probability values are identified within the map. These points are considered voltage peaks. Once peaks are found, the corresponding voltage deviation ranges are recorded. These peaks represent ranges with a high probability of voltage deviation. Next, voltage distribution peaks exceeding a preset threshold are identified and marked as high voltage deviation risk ranges. A probability threshold is set in advance based on the tolerance capabilities of power grid equipment and historical operating data. The probability corresponding to the previously obtained voltage distribution peak is compared with this preset threshold. If the probability of a peak is greater than the threshold, the peak is marked as a high voltage deviation risk range. The relevant voltage deviation range, probability value, and power grid operation information are recorded to enable operations and maintenance personnel to quickly identify voltage deviation areas that pose a significant threat to power grid operations. A voltage deviation trend chart is then generated based on the high voltage deviation risk range. Time or other relevant factors are used as the horizontal axis, and the probability or voltage deviation value corresponding to the high voltage deviation risk range is used as the vertical axis.The voltage high deviation risk range data for different time points or related factors is plotted sequentially on a coordinate graph. These points are then connected with a curve to form a voltage deviation trend graph, which visually displays the changing trend of the voltage high deviation risk range. Finally, voltage regulation trend data is generated based on the voltage deviation trend graph. This voltage regulation trend data includes the direction and magnitude of voltage change. By analyzing the voltage deviation trend graph, the changes in the voltage high deviation risk range are observed. If the voltage high deviation risk range shows an upward trend and the current voltage is higher than the rated voltage, the voltage regulation direction is determined to be lower. Conversely, if the current voltage is lower than the rated voltage, the voltage regulation direction is determined to be higher. The voltage variation magnitude is determined based on a combination of factors such as the rate of change of the voltage deviation in the trend graph and the difference between the current voltage and the rated voltage. Ultimately, voltage regulation trend data containing both the direction and magnitude of voltage change is obtained.

[0030] In one embodiment, the step of generating on-load voltage regulation information according to the regulation difference value includes: S701, obtaining an adjustment direction of the on-load tap changer according to the positive or negative value of the adjustment difference value, wherein the adjustment direction includes lowering the voltage and raising the voltage; S702: Obtain a switch adjustment step length according to the numerical value of the adjustment difference value, and establish a mapping relationship between the adjustment difference value and the adjustment step length; S703: Obtaining a tap adjustment limit of the transformer, wherein the tap adjustment limit includes a tap adjustment range and an adjustment interval; S704: Generate a control instruction for the on-load tap changer according to the adjustment direction and the switch adjustment step.

[0031] As described in steps S701-S704 above, the present invention determines the adjustment direction of the on-load tap changer based on the sign of the adjustment difference value. The adjustment difference value is automatically detected. If the adjustment difference value is positive, it indicates that the current voltage is higher than the desired voltage. The system then determines, according to preset logic, that the on-load tap changer should perform a voltage-step-down operation. Conversely, if the adjustment difference value is negative, it indicates that the current voltage is lower than the desired voltage. The system then controls the on-load tap changer to perform a voltage-step-up operation. This determination based on the sign of the adjustment difference value makes determining the adjustment direction of the on-load tap changer quick and accurate, avoiding potential errors in manual judgment. Next, the switch adjustment step size is determined based on the value of the adjustment difference value, and a mapping relationship between the two is established. The system pre-stores a matching rule between adjustment difference values and adjustment step sizes, derived from extensive experimental data and actual operating experience. Once the adjustment difference value is obtained, the system quickly matches the corresponding adjustment step size based on this rule. For example, if the adjustment difference value is within a certain small range, the corresponding adjustment step size is one level; if the adjustment difference value increases to another range, the adjustment step size increases to two levels. This mapping relationship allows precise determination of the adjustment step size based on the degree of voltage deviation, avoiding over- or under-adjustment. The system then determines the transformer's tap adjustment limits. The system retrieves information such as the transformer's tap adjustment range and adjustment interval from the device management database. The tap adjustment range specifies the maximum and minimum positions that the on-load tap changer can adjust, ensuring that adjustment operations do not exceed the transformer's safety tolerances. The adjustment interval defines the minimum time interval between two adjustments to prevent damage to the equipment caused by frequent adjustments. The system then generates control instructions for the on-load tap changer based on the adjustment direction and the tap adjustment step size. The system integrates the determined adjustment direction and adjustment step size information and generates the corresponding control instructions according to a specific communication protocol and control logic. These instructions are signals containing the adjustment direction and adjustment step size information, which are transmitted via communication lines to the on-load tap changer's actuator. Upon receiving the instructions, the actuator drives the on-load tap changer to perform the corresponding adjustment action, regulating the transformer voltage. Finally, the system determines the transformer's current operating status and determines whether it is in an abnormal state. The system uses various sensors connected to the transformer to collect real-time operating data such as oil temperature, winding temperature, and load current. It then compares and analyzes this data against preset normal operating parameter ranges. If operating data is found to be outside the normal range, the transformer is deemed to be in an abnormal state. The system then adjusts previously generated control instructions based on the abnormality to avoid inappropriate voltage regulation operations under abnormal circumstances. The system also records relevant information about this on-load voltage regulation operation, such as the regulation direction, adjustment step size, and the transformer's operating status at the time, to facilitate subsequent operation and maintenance analysis.

[0032] like Figure 2As shown, the present invention also provides an on-load tap-changing transformer voltage monitoring system integrating optical fiber sensing, comprising: A first acquisition module 1 is used to acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; A second acquisition module 2 is used to acquire real-time power grid disturbance data according to the voltage fluctuation waveform data; A third acquisition module 3 is used to acquire mechanical resonance phase data according to the vibration spectrum data; A fourth acquisition module 4 is configured to establish a winding hotspot temperature change gradient based on the temperature distribution data, and generate a temperature-load correlation coefficient based on the winding hotspot temperature change gradient; A fifth acquisition module 5 is configured to acquire a voltage regulation priority parameter based on the real-time power grid disturbance data, mechanical resonance phase data, and a temperature-load correlation coefficient; A sixth acquisition module 6, configured to acquire voltage regulation trend data according to the real-time grid disturbance data; an adjustment module 7, configured to obtain an adjustment difference value according to the voltage adjustment priority parameter and the voltage adjustment trend data, and determine whether the adjustment difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

[0033] In one embodiment, the second acquisition module 2 includes: A transformation unit is used to perform fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; A marking unit, used to mark the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; A matching unit is used to obtain a historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; An extraction unit, used to extract the voltage fluctuation amplitude and frequency offset according to the disturbance type; A correction unit, configured to dynamically correct the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamically corrected disturbance data; The first acquisition unit is configured to use the dynamically corrected disturbance data as real-time grid disturbance data, wherein the real-time grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

[0034] In one embodiment, the third acquisition module 3 includes: A decomposition unit, used to perform wavelet packet decomposition on the vibration spectrum data, and decompose the vibration signal into multiple different frequency bands; A second acquiring unit, configured to acquire a fundamental frequency of each of the frequency bands; A third acquiring unit, configured to acquire a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; A first judging unit, configured to judge whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; a fourth acquiring unit, configured to acquire a voltage fundamental wave phase, and acquire a fundamental wave phase difference value according to the fundamental frequency component phase angle and the voltage fundamental wave phase; A second judging unit, configured to judge whether the fundamental wave phase difference exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; a first generating unit, configured to generate a mechanical state degradation index according to an abnormality duration; a fifth acquiring unit, configured to acquire a voltage fluctuation rate; The analyzing unit is configured to perform a correlation analysis on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

[0035] In one embodiment, the fourth acquisition module 4 includes: a sixth acquisition unit, configured to acquire temperature data measured at different positions on the winding according to the temperature distribution data, and obtain a continuous temperature distribution field of the winding through an interpolation algorithm; An identification unit, used to identify the highest temperature area in the temperature distribution field and mark it as a winding hot spot area; a seventh acquiring unit, configured to acquire hotspot temperature values of the winding hotspot region at a plurality of consecutive sampling moments, and generate a temperature change gradient at adjacent moments according to the hotspot temperature values at the plurality of consecutive sampling moments; an eighth acquisition unit, configured to acquire a load current change rate at each continuous sampling moment, and dynamically correlate it with a temperature change gradient to obtain dynamic correlation result data; The second generating unit is used to generate a temperature-load correlation coefficient according to the dynamic correlation result data.

[0036] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, value library or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM).

[0037] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0038] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring voltage of an on-load tap-changing transformer by integrating optical fiber sensing, characterized in that: include: Acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; Acquiring real-time grid disturbance data according to the voltage fluctuation waveform data; Acquiring mechanical resonance phase data according to the vibration spectrum data; Establishing a winding hotspot temperature change gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hotspot temperature change gradient; Obtaining a voltage regulation priority parameter based on the real-time power grid disturbance data, mechanical resonance phase data, and a temperature-load correlation coefficient; acquiring voltage regulation trend data based on the real-time grid disturbance data; Obtaining a regulation difference value according to the voltage regulation priority parameter and the voltage regulation trend data, and determining whether the regulation difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

2. The method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing according to claim 1, characterized in that: The step of acquiring real-time grid disturbance data according to the voltage fluctuation waveform data comprises: Perform fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; Mark the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; Obtain the historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; Extract voltage fluctuation amplitude and frequency offset according to disturbance type; Dynamically correct the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamic correction disturbance data; The dynamically corrected disturbance data is used as real-time power grid disturbance data, wherein the real-time power grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

3. The method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing according to claim 2, characterized in that: The step of obtaining mechanical resonance phase data according to the vibration spectrum data comprises: Perform wavelet packet decomposition on the vibration spectrum data to decompose the vibration signal into multiple different frequency bands; Obtaining the fundamental frequency of each of the frequency bands; Obtaining a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; Determining whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; Obtaining the voltage fundamental wave phase, and obtaining the fundamental wave phase difference value according to the fundamental frequency component phase angle and the voltage fundamental wave phase; Determining whether the fundamental wave phase difference value exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; Generate a mechanical condition degradation index based on the duration of the abnormality; Get voltage fluctuation rate; A correlation analysis is performed on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

4. The method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing according to claim 2, characterized in that: The step of establishing a winding hotspot temperature change gradient according to the temperature distribution data, and generating a temperature-load correlation coefficient according to the winding hotspot temperature change gradient includes: The temperature data measured at different positions on the winding are obtained according to the temperature distribution data, and the continuous temperature distribution field of the winding is obtained through the interpolation algorithm; Identify the highest temperature area in the temperature distribution field and mark it as the winding hot spot area; Obtaining hotspot temperature values of the winding hotspot region at multiple consecutive sampling moments, and generating temperature change gradients at adjacent moments based on the hotspot temperature values at the multiple consecutive sampling moments; Obtain the load current change rate at each continuous sampling moment, and dynamically correlate it with the temperature change gradient to obtain dynamic correlation result data; Generate temperature-load correlation coefficients based on dynamic correlation result data.

5. The method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing according to claim 4, characterized in that: The step of acquiring voltage regulation trend data according to the real-time power grid disturbance data comprises: Obtaining grid disturbance records for multiple preset time periods; Obtaining a predicted fluctuation range for a preset time period based on the power grid disturbance record; generating a voltage deviation probability distribution map based on the plurality of power grid disturbance records and the predicted fluctuation range; Acquire multiple voltage distribution peaks according to the voltage deviation probability distribution graph; Obtain voltage distribution peaks that are greater than a preset threshold and mark them as voltage high deviation risk ranges; generating a voltage deviation trend change graph according to the voltage high deviation risk range; Voltage regulation trend data is generated according to the voltage deviation trend change diagram, wherein the voltage regulation trend data includes a voltage change direction and a voltage change amplitude.

6. The method for monitoring voltage of an on-load tap-changing transformer integrated with optical fiber sensing according to claim 1, characterized in that: The step of generating on-load voltage regulation information according to the regulation difference value includes: Obtaining an adjustment direction of the on-load tap changer according to the positive or negative value of the adjustment difference value, wherein the adjustment direction includes lowering the voltage and raising the voltage; Obtaining the switch adjustment step length according to the numerical value of the adjustment difference value, and establishing a mapping relationship between the adjustment difference value and the adjustment step length; Obtaining a tap adjustment limit of the transformer, wherein the tap adjustment limit includes a tap adjustment range and an adjustment interval time; Generate control instructions for the on-load tap changer based on the adjustment direction and switch adjustment step.

7. A voltage monitoring system for an on-load tap-changing transformer integrating optical fiber sensing, characterized in that: include: A first acquisition module is used to acquire multimodal monitoring data output by the optical fiber sensor, wherein the multimodal monitoring data includes voltage fluctuation waveform data, vibration spectrum data, temperature distribution data, and load current change rate; A second acquisition module is used to acquire real-time power grid disturbance data according to the voltage fluctuation waveform data; a third acquisition module, configured to acquire mechanical resonance phase data according to the vibration spectrum data; a fourth acquisition module, configured to establish a winding hotspot temperature change gradient according to the temperature distribution data, and generate a temperature-load correlation coefficient according to the winding hotspot temperature change gradient; A fifth acquisition module is configured to acquire a voltage regulation priority parameter based on the real-time power grid disturbance data, mechanical resonance phase data, and a temperature-load correlation coefficient; a sixth acquisition module, configured to acquire voltage regulation trend data according to the real-time grid disturbance data; an adjustment module, configured to obtain an adjustment difference value according to the voltage adjustment priority parameter and the voltage adjustment trend data, and determine whether the adjustment difference value exceeds a preset threshold; If exceeded, on-load tap adjustment information is generated according to the adjustment difference value, and the on-load tap adjustment switch of the on-load tap adjustment transformer is adjusted according to the on-load tap adjustment information, wherein the on-load tap adjustment information includes the switch adjustment step and direction instruction.

8. The on-load tap-changing transformer voltage monitoring system integrated with optical fiber sensing according to claim 7, characterized in that: The second acquisition module includes: A transformation unit is used to perform fast Fourier transform on the voltage fluctuation waveform data to obtain the fundamental wave amplitude and the third harmonic component; A marking unit, used to mark the harmonic distortion level according to the fundamental wave amplitude and the energy distribution of the third harmonic component; A matching unit is used to obtain a historical power grid disturbance database and match the disturbance type corresponding to the current harmonic distortion level; An extraction unit, used to extract the voltage fluctuation amplitude and frequency offset according to the disturbance type; A correction unit, configured to dynamically correct the fluctuation amplitude and frequency offset based on the load current change rate to obtain dynamically corrected disturbance data; The first acquisition unit is configured to use the dynamically corrected disturbance data as real-time grid disturbance data, wherein the real-time grid disturbance data includes the corrected fluctuation amplitude and frequency offset.

9. The on-load tap-changing transformer voltage monitoring system integrated with optical fiber sensing according to claim 7, characterized in that: The third acquisition module includes: A decomposition unit, used to perform wavelet packet decomposition on the vibration spectrum data, and decompose the vibration signal into multiple different frequency bands; A second acquiring unit, configured to acquire a fundamental frequency of each of the frequency bands; A third acquiring unit, configured to acquire a third harmonic component energy ratio according to the fundamental frequency and the third harmonic component; A first judging unit, configured to judge whether the energy proportion of the third harmonic component exceeds a preset threshold; If it does not exceed, the corresponding frequency band is marked as mechanically safe; If it exceeds, the corresponding frequency band is marked as mechanical looseness warning, and the phase angle of the fundamental frequency component is extracted; a fourth acquiring unit, configured to acquire a voltage fundamental wave phase, and acquire a fundamental wave phase difference value according to the fundamental frequency component phase angle and the voltage fundamental wave phase; A second judging unit, configured to judge whether the fundamental wave phase difference exceeds a preset threshold; If it exceeds, it is determined to be a mechanical resonance abnormality and the abnormality duration is obtained; a first generating unit, configured to generate a mechanical state degradation index according to an abnormality duration; a fifth acquiring unit, configured to acquire a voltage fluctuation rate; The analyzing unit is configured to perform a correlation analysis on the mechanical state degradation index and the voltage fluctuation rate to obtain mechanical resonance phase data.

10. The on-load tap-changing transformer voltage monitoring system integrated with optical fiber sensing according to claim 7, characterized in that: The fourth acquisition module includes: a sixth acquisition unit, configured to acquire temperature data measured at different positions on the winding according to the temperature distribution data, and obtain a continuous temperature distribution field of the winding through an interpolation algorithm; An identification unit, used to identify the highest temperature area in the temperature distribution field and mark it as a winding hot spot area; a seventh acquiring unit, configured to acquire hotspot temperature values of the winding hotspot region at a plurality of consecutive sampling moments, and generate a temperature change gradient at adjacent moments according to the hotspot temperature values at the plurality of consecutive sampling moments; an eighth acquisition unit, configured to acquire a load current change rate at each continuous sampling moment, and dynamically correlate it with a temperature change gradient to obtain dynamic correlation result data; The second generating unit is used to generate a temperature-load correlation coefficient according to the dynamic correlation result data.

Images