An intelligent management system for power supply facilities based on data analysis

Abstract

The present invention belongs to the technical field of intelligent management of transformers, and discloses an intelligent management system for power supply facilities based on data analysis, the system including a monitoring period division module, a transformer mechanical performance abnormality analysis module, a transformer electrical performance abnormality analysis module, a transformer operation abnormality analysis module, a load adjustment power target value analysis module, a transformer switching analysis module and a cloud storage library. When analyzing the abnormal electrical performance of each operating transformer in the target substation, the present invention takes into account the input voltage fluctuation and input frequency fluctuation of the power grid to which the target substation is connected, which is closer to the actual operation situation and improves the accuracy of the judgment. The present invention obtains the load adjustment power target value based on a comprehensive analysis of the required load power of the target power grid, the rated load power provided by the abnormal transformer, and the historical customary required load power of the target substation in the next monitoring period, and can realize personalized and accurate load management.

Description

Technical Field

[0001] The present invention belongs to the technical field of transformer intelligent management, and relates to an intelligent management system for power supply facilities based on data analysis. Background Art

[0002] Power supply facilities are important basic equipment to ensure power supply, usually including power generation equipment, transmission equipment and distribution equipment. The distribution equipment distributes the electric energy transmitted by the transmission line to each user terminal, mainly including transformers, distribution cabinets, distribution boxes, etc. The transformer is the core equipment in the substation. The switching management of the substation transformer is crucial to ensure the stable operation of the power grid, ensure equipment safety, optimize resource allocation and promote technological innovation. Through the switching operation of the transformer, the power supply capacity can be flexibly adjusted. Therefore, a transformer intelligent management based on data analysis is of great significance.

[0003] In the prior art, there are also related solutions for intelligent management of transformers. For example, the transformer operation and maintenance management method and device with publication number CN117670287A include: obtaining operating data of various parts of the transformer; determining health status assessment results of various parts of the transformer based on the operating data of various parts of the transformer; analyzing the operating data of various parts of the transformer based on a preset fault condition data set to determine the fault diagnosis results of various parts of the transformer; determining maintenance plan adjustment data of various parts of the transformer based on the health status assessment results of various parts of the transformer, the fault diagnosis results of various parts of the transformer and the operating data of various parts of the transformer, so that the user can perform maintenance tests on various parts of the transformer based on the maintenance plan adjustment data of various parts of the transformer.

[0004] The above scheme still has certain limitations: on the one hand, the existing transformer intelligent management process is usually only based on the collected transformer operation data when judging the abnormal state of the transformer, ignoring the impact of the operating state of the power grid to which the transformer is connected on the operating state of the transformer. This analysis method does not take into account the operating state of the power grid, which may lead to misjudgment or omission of abnormal states of the transformer, and fail to promptly discover and deal with potential problems of the transformer caused by the power grid, which may reduce the reliability and stability of the entire power system.

[0005] On the other hand, the existing intelligent management process of transformers lacks analysis of the switching demand of abnormal transformers and analysis of the impact of the customary voltage load in the next monitoring period on the switching demand of transformers. This analysis method does not take into account the changes in the customary voltage load in the future period, which may lead to a lack of foresight in transformer switching decisions. When the voltage load changes significantly, if the transformer fails to switch in time, it may cause overload, undervoltage and other problems, affecting the stability and reliability of the power system. At the same time, failure to reasonably adjust the switching state of the transformer according to the load demand may cause some transformers to be in a heavy or light load state for a long time, resulting in waste of resources or excessive wear of equipment. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, an intelligent management system for power supply facilities based on data analysis is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A data analysis-based intelligent management system for power supply facilities, including: a monitoring period division module, which is used to divide the operating time of the target substation into a number of monitoring periods.

[0008] The transformer mechanical performance anomaly analysis module is used to collect the mechanical performance monitoring data of each operating transformer in the target substation and analyze the mechanical performance anomalies of each operating transformer in the target substation.

[0009] The transformer electrical performance anomaly analysis module is used to collect the electrical performance monitoring data of each operating transformer in the target substation and analyze the electrical performance anomalies of each operating transformer in the target substation.

[0010] The transformer operation abnormality analysis module is used to determine whether there is transformer operation abnormality in the target substation based on the abnormal mechanical performance and electrical performance of each operating transformer in the target substation. If there is, it is recorded as an abnormal transformer and the abnormal transformer is cut off.

[0011] The load power target value analysis module is used to collect the required load power of the target power grid connected to the target substation, and then analyze the rated load power provided by the abnormal transformer and the historical customary required load power of the target substation in the next monitoring period to obtain the load power target value.

[0012] The transformer switching analysis module is used to determine whether transformer switching operation is required based on the load power target value, and to identify the specific switching direction if necessary.

[0013] Cloud storage repository for storing historical oil level change data and historical customary demand load power.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) When managing the operating transformers of the target substation, the present invention analyzes the abnormal mechanical properties and electrical properties of each operating transformer of the target substation, and then determines whether there is any transformer operation abnormality in the target substation. This analysis method combines mechanical and electrical information to form a more comprehensive and accurate judgment of the operating status of the transformer, reducing misjudgments or missed judgments caused by single-dimensional analysis. By deeply analyzing the abnormal data of mechanical and electrical properties, the problem of the transformer can be more accurately located, providing strong support for subsequent troubleshooting and maintenance.

[0015] (2) The present invention takes into account the input voltage fluctuation and input frequency fluctuation of the power grid to which the target substation is connected when analyzing the abnormal electrical performance of each operating transformer in the target substation. This analysis method can more accurately understand the electrical environment in which the transformer is located. This real-time data feedback makes the analysis results closer to the actual operating conditions and improves the accuracy of the judgment. According to the fluctuation of the input voltage and frequency of the power grid, the load distribution of the transformer can be dynamically adjusted to ensure the stable operation of the power grid. This helps to reduce voltage fluctuations and frequency offsets caused by load imbalance and improve the overall stability of the power grid.

[0016] (3) The present invention obtains the target value of load power regulation based on the comprehensive analysis of the target power grid's demand load power, the rated load power provided by the abnormal transformer, and the historical customary demand load power of the target substation in the next monitoring period. This analysis method can formulate a load regulation plan that is closer to the actual operating conditions by comprehensively considering the real-time demand load power of the target power grid, the rated load capacity of the abnormal transformer, and the historical customary demand load of the substation, and realize personalized and accurate load management. Based on the comprehensive analysis of big data and historical data, the load regulation decision is made more scientific and reasonable. By considering the historical customary demand load, it is possible to predict the future load change trend and make preparations for load regulation in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0019] Figure 2 The present invention is a logic flow chart for determining the existence of transformer operation abnormality.

[0020] Figure 3 The present invention is a logic flow chart for determining the switching demand of a transformer. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the present invention provides an intelligent management system for power supply facilities based on data analysis, the system comprising a monitoring period division module, a transformer mechanical performance abnormality analysis module, a transformer electrical performance abnormality analysis module, a transformer operation abnormality analysis module, a load adjustment power target value analysis module, a transformer switching analysis module and a cloud storage library, wherein the monitoring period division module is respectively connected to the transformer mechanical performance abnormality analysis module and the transformer electrical performance abnormality analysis module, the transformer mechanical performance abnormality analysis module and the transformer electrical performance abnormality analysis module are both connected to the transformer electrical performance abnormality analysis module, the transformer electrical performance abnormality analysis module is connected to the load adjustment power target value analysis module, the load adjustment power target value analysis module is connected to the transformer switching analysis module, and the cloud storage library is respectively connected to the transformer mechanical performance abnormality analysis module and the load adjustment power target value analysis module.

[0023] The monitoring period division module is used to divide the operation time of the target substation into a plurality of monitoring periods.

[0024] It should be explained that the reason for dividing the operating time of the target substation into monitoring periods is to more precisely grasp its operating status, optimize the operation and maintenance strategy, and promptly discover and deal with potential problems, thereby ensuring the safe and stable operation of the power grid and efficient energy supply.

[0025] The transformer mechanical performance anomaly analysis module is used to collect mechanical performance monitoring data of each operating transformer in the target substation and analyze the mechanical performance anomaly of each operating transformer in the target substation.

[0026] In a preferred embodiment of the present invention, the mechanical performance monitoring data of each operating transformer includes noise anomaly index, vibration frequency anomaly index, oil level and appearance quality evaluation data of each operating transformer.

[0027] It should be noted that the reasons for selecting the noise anomaly index, vibration frequency anomaly index, oil level and appearance quality evaluation data as the mechanical performance monitoring data of the operating transformer are as follows: 1. When there are faults such as loose core, deformation of winding or poor contact inside the transformer, the noise will increase abnormally. By monitoring the noise anomaly index, these potential problems can be discovered in time to avoid further deterioration of the fault. 2. Vibration monitoring technology can capture the tiny vibration signals generated by the transformer during operation. By analyzing the changes in vibration frequency, the faulty components inside the transformer, such as the core and winding, can be accurately located. This is of great significance for rapid diagnosis and repair of faults. 3. The transformer oil plays an insulating and cooling role in the transformer. The stability of the oil level is crucial to ensure the insulation performance and heat dissipation effect of the transformer. By monitoring the changes in the oil level, problems such as oil leakage, excessive or low oil level can be discovered in time, and corresponding treatment measures can be taken. 4. The appearance quality evaluation data can intuitively reflect the mechanical properties and operating environment of the transformer, providing intuitive judgment basis for operation and maintenance personnel.

[0028] Furthermore, the appearance quality evaluation data includes the defect position and defect area corresponding to each defect of each operating transformer.

[0029] In a preferred embodiment of the present invention, the analysis of the abnormal mechanical performance of each operating transformer in the target substation requires the construction of the abnormal mechanical performance index of each operating transformer in the target substation. The specific analysis method is as follows: extract the oil level of each operating transformer in the target substation, recorded as ,in Indicates the number of each operating transformer in the target substation, ,in Indicates the number of operating transformers in the target substation.

[0030] The previous monitoring period corresponding to the current monitoring period is recorded as the reference monitoring period. The oil level data of the reference monitoring period is extracted from the historical oil level change data stored in the cloud storage library. Then, the oil level change curve of each operating transformer is drawn with time as the horizontal coordinate and oil level as the vertical coordinate. Several monitoring points are evenly arranged for the oil level change curve of each operating transformer. The oil level corresponding to each monitoring point of the oil level change curve of each operating transformer is obtained, and then the oil level change evaluation index of each operating transformer of the target substation is obtained by analysis. .

[0031] It is necessary to explain the reason for drawing the oil level change curve of each operating transformer: the oil level change curve can intuitively show the change trend of the transformer oil level over time. By observing the fluctuation of the curve, the operation and maintenance personnel can quickly understand whether the oil level is stable and whether there are abnormal fluctuations. This intuitiveness helps to quickly identify potential problems and provide a basis for subsequent troubleshooting and processing.

[0032] The noise anomaly index and vibration frequency anomaly index of each operating transformer in the target substation are extracted and recorded as , .

[0033] It should be added that the analysis method of the noise anomaly index of each operating transformer is as follows: the noise intensity of each operating transformer is detected multiple times by a noise detection device to obtain each noise intensity, and then the average is calculated to obtain the monitored noise intensity of each operating transformer, and the monitored noise intensity of each operating transformer is compared with a preset noise intensity threshold to obtain the noise intensity deviation value of each operating transformer, and then the ratio is calculated with the noise intensity threshold to obtain the noise anomaly index of each operating transformer.

[0034] It should be noted that the analysis method of the vibration frequency anomaly index of each operating transformer refers to the analysis method of the noise anomaly index.

[0035] Extract the defect locations and defect areas corresponding to each defect of each operating transformer in the target substation, and analyze and obtain the appearance quality evaluation index of each transformer in the target substation .

[0036] Using the formula The mechanical performance abnormality index of each operating transformer in the target substation is obtained by analysis ,in They represent the influence weight factors corresponding to the noise anomaly index and vibration frequency anomaly index of the transformer in operation of the target substation, They respectively represent the influencing weight factors corresponding to the oil level change evaluation index and the appearance quality evaluation index of the operating transformer of the target substation.

[0037] It should be noted that the influence weight factors corresponding to the noise anomaly index and vibration frequency anomaly index of each operating transformer in the target substation are set based on the following: different types of transformers have different noise and vibration characteristics during operation. For example, there are differences in the noise and vibration performance of oil-immersed transformers and dry-type transformers. For transformer types with higher noise, the weight of the noise anomaly index may be appropriately increased when setting the weight factor to more accurately reflect its impact on the overall status.

[0038] It should be noted that the influencing weight factors corresponding to the oil level change evaluation index and appearance quality evaluation index of each operating transformer in the target substation are set based on the following: 1. The characteristics and degree of influence of oil level changes may be different for transformers of different types and structures. For example, the oil level change of an oil-immersed transformer may be closely related to factors such as sealing performance and heat dissipation requirements. When setting the weight factor, it is necessary to consider the specific type and structural characteristics of the transformer, and appropriately increase the weight for transformer types that are prone to oil level change problems. 2. As the transformer is used for a longer time, aging phenomena such as rust and paint peeling may appear on its appearance. For transformers with more severe aging, the weight of the appearance quality evaluation index should be higher to remind operation and maintenance personnel to perform maintenance and repairs in a timely manner.

[0039] For example, , .

[0040] In a preferred embodiment of the present invention, the analysis obtains the oil level change evaluation index of each operating transformer of the target substation, and the specific analysis is as follows: extract the oil level of each monitoring point corresponding to the oil level change curve of each operating transformer, and record it as ,in Indicates the number of the monitoring point. ,in Indicates the number of monitoring points.

[0041] Using the formula The oil level change evaluation index of each operating transformer in the target substation is obtained by analysis ,in represents a natural constant, Indicates the preset reference oil level, Indicates the allowable difference between the oil level at the preset monitoring point and the reference oil level.

[0042] In a preferred embodiment of the present invention, the analysis obtains the appearance quality evaluation index of each transformer of the target substation, and the specific analysis is as follows: extract the defect position and defect area corresponding to each defect of each operating transformer of the target substation, classify each defect of each operating transformer according to the structural component where the defect position is located to obtain each defect corresponding to each structural component of each operating transformer, and obtain the number of defects corresponding to each structural component of each operating transformer by counting, which is recorded as ,in Indicates the number of the structural component. ,in Indicates the number of structural components.

[0043] Exemplarily, the structural components may be explosion-proof membranes, boxes and porcelain bushings.

[0044] It should be noted that the reasons for discussing the defects of different structural components separately are: 1. Different structural components have different functions and working principles, and the causes of their defects are often different. Separate discussions can more accurately determine the specific location and root cause of the defects. 2. The defects of different structural components have different degrees of impact on the overall performance and safe operation of the equipment. Separate discussions can evaluate the severity of the defects of each component and determine the priority of maintenance.

[0045] The defect areas corresponding to the defects of each structural component of each operating transformer are accumulated and calculated to obtain the defect areas corresponding to each structural component of each operating transformer, which are recorded as .

[0046] Using the formula The appearance quality evaluation index of each transformer in the target substation is obtained by analysis ,in represents the reference defect area of ​​each structural component, Indicates the preset reference defect quantity of each structural component.

[0047] The transformer electrical performance anomaly analysis module is used to collect electrical performance monitoring data of each operating transformer in the target substation and analyze electrical performance anomalies of each operating transformer in the target substation.

[0048] In a preferred embodiment of the present invention, the electrical performance monitoring data of each operating transformer includes an input voltage, an output voltage, an output current and an input frequency of each operating transformer.

[0049] It should be noted that the reasons for selecting input voltage, output voltage, output current and input frequency as the electrical performance monitoring data for operating transformers are: 1. Monitoring of input voltage can reflect the stability of power supply of the power grid and whether there is overvoltage or undervoltage, which is crucial to the normal operation of the transformer, because abnormal input voltage may cause problems such as transformer insulation damage and increased heating. 2. Monitoring of output voltage can ensure that the transformer provides a stable and reliable power supply to the load, and its fluctuation can directly reflect the operating status of the transformer. For example, winding failure may cause abnormal output voltage. 3. The output current reflects the size of the load carried by the transformer. By monitoring it, abnormal conditions such as overload can be discovered in time to avoid damage to the transformer due to overload. 4. Monitoring of input frequency helps to determine the stability of the power grid frequency. Frequency fluctuations may affect the performance and life of the transformer, and can also provide a reference for the stable operation of the entire power system.

[0050] In a preferred embodiment of the present invention, the analysis of the abnormal electrical performance of each operating transformer in the target substation requires the construction of an abnormal electrical performance index of each operating transformer. The specific analysis method is as follows: extract the input voltage and input frequency of each operating transformer in the current monitoring period, and then analyze and obtain the input voltage fluctuation and input frequency fluctuation of each operating transformer in the current monitoring period, which are recorded as , .

[0051] It should be added that the analysis method of the input voltage fluctuation and the input frequency fluctuation refers to the analysis method of the output voltage fluctuation index.

[0052] It should be noted that the present invention takes into account the input voltage fluctuation and input frequency fluctuation of the power grid to which the target substation is connected when analyzing the abnormal electrical performance of each operating transformer of the target substation. This analysis method can more accurately understand the electrical environment in which the transformer is located. This real-time data feedback makes the analysis result closer to the actual operation situation and improves the accuracy of the judgment. According to the fluctuation of the input voltage and frequency of the power grid, the load distribution of the transformer can be dynamically adjusted to ensure the stable operation of the power grid. This helps to reduce voltage fluctuations and frequency offsets caused by load imbalance and improve the overall stability of the power grid.

[0053] The output voltage of each operating transformer in the current monitoring period is extracted, and then the output voltage fluctuation curve of each operating transformer in the current monitoring period is drawn with time as the horizontal axis and output voltage as the vertical axis. Then, a number of output voltage monitoring points are arranged at equal intervals on the output voltage fluctuation curve of each operating transformer, and the output voltage corresponding to each output voltage monitoring point on the output voltage fluctuation curve of each operating transformer is obtained, which is recorded as ,in Indicates the number of the output voltage monitoring point, ,in Indicates the number of output voltage monitoring points.

[0054] Using the formula Analyze and obtain the output voltage fluctuation index of each operating transformer during the current monitoring period ,in Indicates the preset reference output voltage, Indicates the permissible difference between the preset output voltage of the operating transformer and the reference output voltage.

[0055] Similarly, the output current fluctuation index of each operating transformer during the current monitoring period can be obtained: .

[0056] Using the formula Analyze and obtain the electrical performance abnormality index of each operating transformer in the target substation ,in They respectively represent the influence weight factors corresponding to the output voltage fluctuation index and the output current fluctuation index of the preset operating transformer.

[0057] It should be noted that the setting basis of the influence weight factors corresponding to the output voltage fluctuation index and output current fluctuation index of each operating transformer is: for different types of transformers, their output characteristics and sensitivity to voltage and current fluctuations may be different. For example, dry-type transformers and oil-immersed transformers have differences in heat dissipation methods and insulation structures, which may cause them to react differently to output fluctuations. If a certain type of transformer is more susceptible to output fluctuations, the fluctuation index weight associated with it can be appropriately increased.

[0058] See also Figure 2 As shown, the transformer operation abnormality analysis module is used to determine whether there is transformer operation abnormality in the target substation based on the abnormal mechanical performance and electrical performance of each operating transformer in the target substation. If so, it is recorded as an abnormal transformer and the abnormal transformer is cut off.

[0059] In a preferred embodiment of the present invention, the specific analysis for determining whether there is any transformer operation abnormality in the target substation is as follows: extract the mechanical performance abnormality index and electrical performance abnormality index of each operating transformer in the target substation, and then sum them up according to the weights to obtain the operation abnormality index of each operating transformer in the target substation.

[0060] Exemplarily, the weights corresponding to the mechanical performance abnormality index and electrical performance abnormality index of each operating transformer are: .

[0061] The operation abnormality index of each operating transformer of the target substation is compared with the preset operation abnormality index threshold. If the operation abnormality index of a certain operating transformer is greater than the operation abnormality index threshold, the operating transformer is judged to be abnormal; otherwise, the operating transformer is judged to be normal.

[0062] It should be noted that the basis for setting the operation abnormality index threshold is: 1. Transformer manufacturers usually provide the normal operating parameter range of the equipment and some recommended abnormality index thresholds. These recommendations are based on the design, materials and performance testing of the equipment and can serve as an important reference for setting the abnormality index threshold. 2. The technical specifications of the transformer, including parameters such as rated capacity, voltage level, insulation level, etc. These specifications can help determine the expected performance range of the equipment under normal operating conditions.

[0063] Exemplarily, the operation abnormality index threshold is .

[0064] When there is an abnormal operation of the transformer in the target substation, it is determined that there is an abnormal operation of the transformer in the target substation; otherwise, it is determined that there is no abnormal operation of the transformer in the target substation.

[0065] It should be noted that the present invention performs analysis based on the abnormal mechanical and electrical performance of each operating transformer in the target substation when managing the operating transformer of the target substation, and then determines whether there is any transformer operation abnormality in the target substation. This analysis method combines mechanical and electrical information to form a more comprehensive and accurate judgment on the operating status of the transformer, reducing misjudgments or missed judgments caused by single-dimensional analysis. By in-depth analysis of abnormal data on mechanical and electrical performance, the problem of the transformer can be more accurately located, providing strong support for subsequent troubleshooting and maintenance.

[0066] The load power target value analysis module is used to collect the required load power of the target power grid connected to the target substation, and then analyze the rated load power provided by the abnormal transformer and the historical customary required load power of the target substation in the next monitoring period to obtain the load power target value.

[0067] In a preferred embodiment of the present invention, the specific method of analyzing and obtaining the target value of load power is as follows: extract the required load power of the target power grid connected to the target substation, the rated load power provided by the abnormal transformer, and the historical customary required load power of the target substation in the next monitoring period, and record them as , and .

[0068] It should be explained that the next monitoring period refers to the next monitoring period relative to the current monitoring period.

[0069] It should be added that the analysis method of the historical customary demand load power for the next monitoring period is: extract the historical customary demand load power from the cloud storage library, and then match it with the next monitoring period to obtain several historical customary demand load powers corresponding to the next monitoring period, and then perform average calculation to obtain the historical customary demand load power for the next monitoring period.

[0070] The load power target value is obtained based on the demand load power of the target power grid connected to the target substation and the historical customary demand load power analysis for the next monitoring period.

[0071] It should be added that the load power target value is obtained by: taking the required load power of the target power grid connected to the target substation and the historical customary demand load power for the next monitoring period Substitute into the formula Analyze and obtain the load power target value ,in They respectively represent the weights of the target power grid demand load power to which the target substation is connected and the historical customary demand load power in the next monitoring period.

[0072] The load power target value is adjusted based on the load power target value, the load power in the current monitoring period and the rated load power provided by the abnormal transformer.

[0073] It should be added that the specific method of analyzing and adjusting the load power target value is: , Load power during the current monitoring period and abnormal transformer provides rated load power Substitute into the analysis formula Analyze and obtain the target value of load power .

[0074] It should be noted that the present invention obtains the target value of load power adjustment based on the comprehensive analysis of the target power grid's demand load power, the rated load power provided by the abnormal transformer, and the historical customary demand load power of the target substation in the next monitoring period. This analysis method can formulate a load adjustment plan that is closer to the actual operating conditions by comprehensively considering the real-time demand load power of the target power grid, the rated load capacity of the abnormal transformer, and the historical customary demand load of the substation, and realize personalized and accurate load management. Based on the comprehensive analysis of big data and historical data, load adjustment decisions are made more scientific and reasonable. By considering the historical customary demand load, future load change trends can be predicted and load adjustment preparations can be made in advance.

[0075] See also Figure 3 As shown, the transformer switching analysis module is used to determine whether a transformer switching operation is required based on the load power target value, and to identify the specific switching direction if necessary.

[0076] In a preferred embodiment of the present invention, the specific method for judging whether a transformer switching operation is required is as follows: extracting the target value of the regulating load power, and then comparing it with the pre-set regulating load power threshold range. If the target value of the regulating load power is within the regulating load power threshold range, it is judged that the transformer switching operation is not required; otherwise, it is judged that the transformer switching operation is required.

[0077] In a preferred embodiment of the present invention, the analysis for identifying the specific switching direction is as follows: extract the target value of the regulating load power, and then compare it with the upper limit value and the lower limit value corresponding to the preset regulating load power threshold range. If the target value of the regulating load power is greater than the upper limit value corresponding to the regulating load power threshold range, it is determined that a switching operation is required; if the target value of the regulating load power is less than the lower limit value corresponding to the regulating load power threshold range, it is determined that a switching operation is required.

[0078] It should be noted that the basis for setting the load power threshold range is: the load power threshold range needs to be set according to the voltage stability requirements. By analyzing the voltage adjustment characteristics of the transformer and the voltage fluctuation requirements of the power grid, the voltage changes under different load powers can be determined, so as to set a suitable threshold range to ensure voltage stability.

[0079] The cloud storage repository is used to store historical oil level change data and historical customary demand load power.

[0080] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent management system for power supply facilities based on data analysis, characterized in that: include: A monitoring period division module is used to divide the operation time of the target substation into a number of monitoring periods; The transformer mechanical performance abnormality analysis module is used to collect the mechanical performance monitoring data of each operating transformer in the target substation and analyze the mechanical performance abnormalities of each operating transformer in the target substation; The transformer electrical performance anomaly analysis module is used to collect the electrical performance monitoring data of each operating transformer in the target substation and analyze the electrical performance anomaly of each operating transformer in the target substation; The transformer operation abnormality analysis module is used to determine whether there is any transformer operation abnormality in the target substation based on the abnormal mechanical performance and electrical performance of each operating transformer in the target substation. If there is, it is recorded as an abnormal transformer and the abnormal transformer is cut off; The load power target value analysis module is used to collect the required load power of the target power grid connected to the target substation, and then analyze the rated load power provided by the abnormal transformer and the historical customary required load power of the target substation in the next monitoring period to obtain the load power target value; The transformer switching analysis module is used to determine whether a transformer switching operation is required based on the load power target value, and if necessary, identify the specific switching direction; Cloud storage repository for storing historical oil level change data and historical customary demand load power; The specific method of analyzing and obtaining the target value of load power is as follows: Extract the required load power of the target power grid connected to the target substation, the rated load power provided by the abnormal transformer, and the historical customary required load power of the target substation in the next monitoring period; The load power target value is obtained based on the demand load power of the target power grid connected to the target substation and the historical customary demand load power of the next monitoring period; The load power target value is adjusted based on the load power target value, the load power in the current monitoring period and the rated load power provided by the abnormal transformer.

2. The intelligent management system for power supply facilities based on data analysis according to claim 1, characterized in that: The mechanical performance monitoring data of each operating transformer includes the noise abnormality index, vibration frequency abnormality index, oil level and appearance quality evaluation data of each operating transformer; The appearance quality evaluation data includes the defect location and defect area corresponding to each defect of each operating transformer; The electrical performance monitoring data of each operating transformer includes the input voltage, output voltage, output current and input frequency of each operating transformer.

3. The intelligent management system for power supply facilities based on data analysis according to claim 2, characterized in that: The analysis of the abnormal mechanical performance of each operating transformer in the target substation requires the construction of the abnormal mechanical performance index of each operating transformer in the target substation, and the specific analysis method is as follows: Extract the oil level of each operating transformer in the target substation and record it as ,in Indicates the number of each operating transformer in the target substation, ,in Indicates the number of operating transformers in the target substation; The previous monitoring period corresponding to the current monitoring period is recorded as the reference monitoring period. The oil level data of the reference monitoring period is extracted from the historical oil level change data stored in the cloud storage library. Then, the oil level change curve of each operating transformer is drawn with time as the horizontal coordinate and oil level as the vertical coordinate. Several monitoring points are evenly arranged for the oil level change curve of each operating transformer. The oil level corresponding to each monitoring point of the oil level change curve of each operating transformer is obtained, and then the oil level change evaluation index of each operating transformer of the target substation is obtained by analysis. ; The noise anomaly index and vibration frequency anomaly index of each operating transformer in the target substation are extracted and recorded as , ; Extract the defect locations and defect areas corresponding to each defect of each operating transformer in the target substation, and analyze and obtain the appearance quality evaluation index of each transformer in the target substation ; Using the formula The mechanical performance abnormality index of each operating transformer in the target substation is obtained by analysis ,in They represent the influence weight factors corresponding to the noise anomaly index and vibration frequency anomaly index of the transformer in operation of the target substation, They respectively represent the influencing weight factors corresponding to the oil level change evaluation index and the appearance quality evaluation index of the operating transformer of the target substation.

4. The intelligent management system for power supply facilities based on data analysis according to claim 3, characterized in that: The analysis obtains the oil level change evaluation index of each operating transformer in the target substation, and the specific analysis is as follows: Extract the oil level change curve of each operating transformer corresponding to the oil level of each monitoring point, and record it as ,in Indicates the number of the monitoring point. ,in Indicates the number of monitoring points; Using the formula The oil level change evaluation index of each operating transformer in the target substation is obtained by analysis ,in represents a natural constant, Indicates the preset reference oil level, Indicates the allowable difference between the oil level at the preset monitoring point and the reference oil level.

5. The intelligent management system for power supply facilities based on data analysis according to claim 3, characterized in that: The analysis obtains the appearance quality evaluation index of each transformer in the target substation, and the specific analysis is as follows: The defect positions and defect areas corresponding to the defects of each operating transformer of the target substation are extracted, and the defects of each operating transformer are classified according to the structural components where the defect positions are located to obtain the defects corresponding to the structural components of each operating transformer. The number of defects corresponding to the structural components of each operating transformer is obtained by counting, which is recorded as ,in Indicates the number of the structural component, ,in Indicates the number of structural components; The defect areas corresponding to the defects of each structural component of each operating transformer are accumulated and calculated to obtain the defect areas corresponding to each structural component of each operating transformer, which are recorded as ; Using the formula The appearance quality evaluation index of each transformer in the target substation is obtained by analysis ,in represents the reference defect area of ​​each structural component, Indicates the preset reference defect quantity of each structural component.

6. The intelligent management system for power supply facilities based on data analysis according to claim 3, characterized in that: The analysis of the abnormal electrical performance of each operating transformer in the target substation requires the construction of an abnormal electrical performance index of each operating transformer. The specific analysis method is as follows: Extract the input voltage and input frequency of each operating transformer in the current monitoring period, and then analyze and obtain the input voltage fluctuation and input frequency fluctuation of each operating transformer in the current monitoring period, which are recorded as , ; The output voltage of each operating transformer in the current monitoring period is extracted, and then the output voltage fluctuation curve of each operating transformer in the current monitoring period is drawn with time as the horizontal axis and output voltage as the vertical axis. Then, a number of output voltage monitoring points are arranged at equal intervals on the output voltage fluctuation curve of each operating transformer, and the output voltage corresponding to each output voltage monitoring point on the output voltage fluctuation curve of each operating transformer is obtained, which is recorded as ,in Indicates the number of the output voltage monitoring point, ,in Indicates the number of output voltage monitoring points; Using the formula Analyze and obtain the output voltage fluctuation index of each operating transformer during the current monitoring period ,in Indicates the preset reference output voltage, Indicates the permissible difference between the preset output voltage of the operating transformer and the reference output voltage; Similarly, the output current fluctuation index of each operating transformer during the current monitoring period can be obtained: ; Using the formula Analyze and obtain the electrical performance abnormality index of each operating transformer in the target substation ,in They respectively represent the influence weight factors corresponding to the output voltage fluctuation index and the output current fluctuation index of the preset operating transformer.

7. The intelligent management system for power supply facilities based on data analysis according to claim 6, characterized in that: The specific analysis of determining whether there is abnormal transformer operation in the target substation is as follows: Extract the mechanical performance abnormality index and electrical performance abnormality index of each operating transformer in the target substation, and then sum them up according to the weights to obtain the operation abnormality index of each operating transformer in the target substation; The operation abnormality index of each operating transformer of the target substation is compared with the preset operation abnormality index threshold. If the operation abnormality index of a certain operating transformer is greater than the operation abnormality index threshold, the operating transformer is judged to be abnormal; otherwise, the operating transformer is judged to be normal. When there is an abnormal operation of the transformer in the target substation, it is determined that there is an abnormal operation of the transformer in the target substation; otherwise, it is determined that there is no abnormal operation of the transformer in the target substation.

8. The intelligent management system for power supply facilities based on data analysis according to claim 1, characterized in that: The specific method for determining whether a transformer switching operation is required is as follows: The target value of the regulating load power is extracted and then compared with the preset regulating load power threshold range. If the target value of the regulating load power is within the regulating load power threshold range, it is determined that the transformer switching operation is not required. Otherwise, it is determined that the transformer switching operation is required.

9. The intelligent management system for power supply facilities based on data analysis according to claim 8, characterized in that: The analysis of identifying the specific switching direction is as follows: Extract the target value of the regulated load power, and then compare it with the upper and lower limits corresponding to the preset regulated load power threshold range. If the target value of the regulated load power is greater than the upper limit corresponding to the regulated load power threshold range, it is determined that a cutting operation is required; if the target value of the regulated load power is less than the lower limit corresponding to the regulated load power threshold range, it is determined that a throwing operation is required.

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