A transformer area line loss intelligent diagnosis analysis system and analysis method

Abstract

The present application relates to the technical field of intelligent diagnosis analysis, and more particularly to a transformer area line loss intelligent diagnosis analysis method, the present application judges the cable insulation skin form by acquiring image data, and combines the real-time control area to execute different protection measures on the abnormal cable; in the compliance form, further judge the current period, and determine the power consumption based on the power cost and the power consumption matching degree, distinguish the load fluctuation and the load overload, and start the power consumption parameter analysis program under different conditions, through the detection of the actual control number, the real-time current fluctuation frequency and the line resistance, calculate the correction collection coefficient, and then select the corresponding line loss protection or abnormal alarm mode. The present application integrates visual detection, power consumption matching, current fluctuation and line resistance and other multi-source real-time parameters, realizes the accurate distinction of power failure, instability and electricity stealing and other various power supply abnormal states, and uses dynamic correction collection coefficient to improve the timeliness and accuracy of data collection.

Description

Technical Field

[0001] This invention relates to the field of intelligent diagnostic analysis technology, and in particular to an intelligent diagnostic analysis method for transformer substation line loss. Background Technology

[0002] A low-voltage distribution area refers to a region centered around a distribution transformer, responsible for reducing electricity from high-voltage transmission lines to low voltage suitable for residential and business use. Line loss is a crucial indicator of power system operating efficiency, directly impacting the effective utilization of energy resources and the economic benefits of power supply companies. The existence of line losses in low-voltage distribution areas severely affects power system operating efficiency, increases electricity costs for users, and may even lead to power grid safety incidents. However, by analyzing and processing large amounts of data in real time and with precision, a deeper understanding of the power loss situation in distribution areas can be achieved, helping operators and managers optimize distribution area operations, reduce line loss rates, and improve power supply quality.

[0003] Chinese patent document CN113267692A discloses a method and system for intelligent diagnosis and analysis of line loss in low-voltage distribution areas. This method collects line loss data by adding intelligent hardware, calculates line loss based on a distributed framework, then determines whether the line loss is abnormal based on low-voltage distribution area line loss indicators, further identifies the type of line loss anomaly, automatically determines the cause of the line loss anomaly based on anomaly judgment rules, and determines the number of anomalies. It further utilizes intelligent electricity consumption big data and data mining analysis technology to perform intelligent diagnosis and analysis of low-voltage distribution area line loss, generating a distribution area health check report to assist on-site personnel in handling the issue. It is evident that the existing technology adopts a modular and distributed architecture, achieving systematic and centralized line loss calculation and anomaly detection through anomaly judgment rules, facilitating large-scale data storage and distributed real-time computing. However, this system focuses on data acquisition and distributed computing, lacking comprehensive analysis of multi-source real-time parameters, and insufficient description of problem type identification methods, dynamic judgment of real-time parameters, and corresponding protection measures. Summary of the Invention

[0004] To address this, the present invention provides an intelligent diagnostic analysis method for transformer substation line losses, which overcomes the problems of poor timeliness and low accuracy in data acquisition in the prior art.

[0005] To achieve the above objectives, the present invention provides an intelligent diagnostic analysis method for transformer substation line losses, comprising:

[0006] Acquire image data within the visual inspection area and determine the morphology of the cable insulation sheath, including compliant and abnormal morphologies, and select the corresponding line loss protection mode based on the judgment result and the real-time control area area;

[0007] If the cable insulation sheath is in compliance with regulations, determine whether the current period is within the peak electricity consumption period, and determine the current electricity consumption situation based on the matching degree between electricity cost and real-time electricity consumption, including the occurrence of load fluctuations and load overloads;

[0008] Specifically, if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption matching degree is within the electricity consumption fluctuation range during the current peak electricity consumption period, it is determined that there is a load fluctuation; if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption is not within the electricity consumption matching range during the current peak electricity consumption period, it is determined that there is a load overload.

[0009] When the current power consumption situation is characterized by load fluctuations, execute the power consumption parameter analysis program to select the corresponding line loss protection mode or abnormal alarm mode.

[0010] The process of executing the electricity consumption parameter analysis program includes: obtaining the actual number of controlled households in the electricity consumption area and comparing it with the number of controlled households in the standard electricity consumption area; after meeting the comparison conditions, updating the current collection coefficient to the corrected collection coefficient based on real-time electricity consumption data; the corrected collection coefficient includes the upper limit of the corrected collection frequency and the upper limit of the corrected collection points, as well as selecting the corresponding line loss protection mode or abnormal alarm mode based on the real-time current fluctuation frequency and real-time internal resistance.

[0011] When the current power consumption situation indicates an overload, the suspected abnormal line loss is determined to be an actual abnormal line loss or the existence of electricity theft based on real-time temperature data.

[0012] Furthermore, acquiring image data within the visual inspection area and determining the condition of the cable insulation sheath includes,

[0013] Real-time images of the cable are acquired and the images are preprocessed into unit input data. The smoothness of the insulation layer is judged on the unit input data. Based on the judgment result, the shape of the cable is determined, including compliant shape and abnormal shape.

[0014] When obtaining the form compliance result, determine whether the current time period is within the peak electricity consumption period;

[0015] When obtaining the morphological abnormality results, determine the area of ​​the real-time control zone corresponding to the abnormal cable and compare it with the standard control zone range.

[0016] If the area of ​​the real-time control zone is larger than the standard control zone, the first protection measure shall be implemented.

[0017] If the area of ​​the real-time control zone is less than or equal to the standard control zone, a second protection measure shall be implemented.

[0018] Furthermore, it is necessary to determine whether the current time period falls within peak electricity consumption hours.

[0019] Determine the date category corresponding to the current time period, and based on the determination result, retrieve the corresponding peak electricity consumption time period, comparing the current time period with the peak electricity consumption time period.

[0020] If the current time period is within the peak electricity consumption period, execute the electricity cost determination process and the real-time electricity consumption matching degree determination process.

[0021] The process for determining the electricity cost is as follows:

[0022] Obtain the real-time electricity cost and compare it with the minimum electricity cost:

[0023] When the real-time electricity cost is less than or equal to the minimum electricity cost, the real-time electricity consumption matching degree judgment process is executed.

[0024] Furthermore, the real-time electricity consumption matching degree judgment process includes,

[0025] Read the electricity consumption area category and the corresponding user's historical electricity consumption data, and determine the electricity consumption matching interval based on the user's historical electricity consumption data. The electricity consumption matching interval includes the standard electricity consumption interval and the electricity consumption fluctuation interval.

[0026] Real-time electricity consumption is obtained, the real-time electricity consumption matching degree is calculated, and compared with the electricity consumption matching interval.

[0027] If the electricity consumption matching degree is within the standard range of electricity consumption, the current electricity consumption is considered normal.

[0028] If the electricity consumption matching degree is not within the standard range of electricity consumption, it is determined that there is a suspected abnormal line loss, or an overload occurs. Based on real-time temperature data, it is determined that the suspected abnormal line loss is an actual abnormal line loss or that there is electricity theft, and the first warning measure or the third protection measure is implemented.

[0029] If the real-time electricity consumption is within the fluctuation range, it is determined that there is a suspected abnormal line loss, resulting in load fluctuation, and the electricity consumption parameter analysis program is executed.

[0030] Furthermore, implementing first warning measures or third protective measures based on real-time temperature data includes,

[0031] Real-time temperature data of the environment corresponding to the load overload is obtained to get the regional average temperature, local maximum temperature, and real-time temperature difference. The real-time temperature difference is then compared with the fault heat threshold, wherein:

[0032] If the real-time temperature difference is greater than or equal to the fault heat threshold, the suspected line loss anomaly is determined to be an actual line loss anomaly, and the third protection measure is executed.

[0033] If the real-time temperature difference is less than the fault heat threshold, the suspected abnormal line loss is determined to be due to electricity theft, and the first warning measure is implemented.

[0034] Furthermore, the execution of the power consumption parameter analysis procedure includes,

[0035] Obtain the actual number of households controlled in the electricity consumption area and compare it with the number of households controlled in the standard electricity consumption area.

[0036] If the number of households controlled in the actual electricity consumption area is greater than or equal to the number of households controlled in the standard electricity consumption area, the current collection coefficient will be updated to the corrected collection coefficient based on the real-time electricity consumption data.

[0037] The current fluctuation frequency and real-time internal resistance of the line loss to be detected are collected by the corrected acquisition coefficient. Based on the current fluctuation frequency, it is determined whether to implement the first warning measure, and based on the real-time current fluctuation frequency and real-time internal resistance, the second warning measure is implemented, or the corresponding line loss protection mode is selected.

[0038] The corrected acquisition coefficients include the upper limit of the corrected acquisition frequency and the upper limit of the corrected acquisition points.

[0039] Furthermore, determining whether to update the current collection coefficients to the corrected collection coefficients based on real-time electricity consumption data includes:

[0040] The power consumed in collecting real-time current, voltage, and power data is obtained to determine the power consumption per sampling.

[0041] The upper limit of the current sampling frequency is calculated based on the power consumption of a single sampling.

[0042] Obtain the amount of real-time current, voltage, and power data collected within the current acquisition period to obtain a single...

[0043] The amount of data sampled per time;

[0044] Calculate and correct the upper limit of the number of sampling points based on the amount of data sampled in a single instance; select the sampling frequency control mode or the number of sampling points control mode according to the real-time data volume distribution difference index.

[0045] Among them, the sampling frequency control mode adjusts the current sampling frequency upper limit to the corrected sampling frequency upper limit; the sampling point control mode adjusts the current sampling point upper limit to the corrected sampling point upper limit.

[0046] Furthermore, the formula for calculating the upper limit of the sampling frequency is revised as follows:

[0047] ;

[0048] in,

[0049] Power consumption per sampling;

[0050] Total duration of peak hours;

[0051] The maximum amount of extra battery power allowed to be consumed;

[0052] The formula for calculating the upper limit of the number of collection points is as follows:

[0053] ;

[0054] in,

[0055] : Adjust the upper limit of the number of data collection points;

[0056] The maximum bandwidth allowed for additional processing;

[0057] : Data volume per single sampling.

[0058] Furthermore, determining whether to implement the first warning measure based on the real-time current fluctuation frequency includes:

[0059] Obtain the actual fluctuation frequency of the line corresponding to the suspected line loss, set the current signal change threshold and the standard range of current fluctuation frequency, and compare the actual fluctuation frequency with the standard current fluctuation frequency range.

[0060] If the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the first warning measure will not be implemented;

[0061] If the actual fluctuation frequency is lower than the minimum value of the standard current fluctuation frequency range, the first warning measure shall be implemented.

[0062] Furthermore, a second warning measure is implemented based on the real-time current fluctuation frequency and real-time internal resistance, or the corresponding line loss protection mode is selected, including...

[0063] Set the internal resistance value of the cable under normal operating conditions to the standard line internal resistance range;

[0064] When the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the real-time line internal resistance is calculated and compared with the standard line internal resistance.

[0065] When the real-time line resistance is greater than or equal to the maximum value of the standard line resistance, the fourth protection measure shall be taken.

[0066] When the real-time line resistance is within the standard line resistance, the fifth protection measure shall be taken;

[0067] When the real-time line resistance is less than the minimum standard line resistance, a second warning measure is taken.

[0068] Compared with existing technologies, the advantages of this invention lie in its ability to accurately distinguish abnormal power supply states without affecting normal user power consumption by integrating multiple key parameters such as visual inspection, temperature monitoring, power consumption matching, current fluctuation frequency, and line internal resistance. Specifically, by introducing real-time visual inspection and temperature difference analysis of insulation layer status, the appearance condition and fault heat of cables can be effectively determined, allowing for timely implementation of targeted protection measures. Simultaneously, by constructing a real-time power consumption matching judgment process, comparing real-time power consumption data with standard and fluctuation ranges constructed from historical data, different abnormal phenomena such as power outages, instability, and electricity theft can be accurately identified. Furthermore, this invention utilizes real-time current fluctuation frequency and dynamic line internal resistance assessment technology to quantitatively analyze the degree of internal line damage, further distinguishing between circuit faults and electricity theft. In addition, the use of acquisition coefficient evaluation to optimize the acquisition frequency and the number of connected measuring instruments improves the accuracy of data acquisition and ensures the high efficiency and stability of system operation. In summary, this invention has significant advantages in real-time performance, refined anomaly identification, and intelligent response, providing an efficient and feasible solution for transformer substation line loss monitoring and fault early warning. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating the intelligent diagnostic analysis method for line loss in transformer substations according to the present invention.

[0070] Figure 2 This is a logic diagram for determining whether the current time period is within the peak electricity consumption period, as shown in an embodiment of the present invention.

[0071] Figure 3 This is a logic diagram illustrating the execution of a first warning measure or a third protection measure based on real-time temperature data in an embodiment of the present invention.

[0072] Figure 4 This is a logic decision diagram for executing the power consumption parameter analysis program in an embodiment of the present invention. Detailed Implementation

[0073] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0074] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0075] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0076] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0077] Please see Figure 1 As shown, this is a flowchart illustrating the intelligent diagnostic analysis method for transformer substation line losses according to the present invention. The present invention provides an intelligent diagnostic analysis method for transformer substation line losses, comprising:

[0078] Step S1: Acquire image data within the visual inspection area and determine the morphology of the cable insulation sheath, including compliant and abnormal morphologies, and select the corresponding line loss protection mode based on the determination result and the real-time control area area.

[0079] Step S2: If the cable insulation sheath is in compliance with regulations, determine whether the current time period is within the peak electricity consumption period, and determine the current electricity consumption situation based on the electricity cost and the real-time electricity consumption matching degree, including the occurrence of load fluctuations and load overloads.

[0080] Specifically, if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption matching degree is within the electricity consumption fluctuation range during the current peak electricity consumption period, it is determined that there is a load fluctuation; if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption is not within the electricity consumption matching range during the current peak electricity consumption period, it is determined that there is a load overload.

[0081] Step S3: When the current power consumption situation is characterized by load fluctuations, execute the power consumption parameter analysis program to select the corresponding line loss protection mode or abnormal alarm mode.

[0082] The process of executing the electricity consumption parameter analysis program includes: obtaining the actual number of controlled households in the electricity consumption area and comparing it with the number of controlled households in the standard electricity consumption area; after meeting the comparison conditions, updating the current collection coefficient to the corrected collection coefficient based on real-time electricity consumption data; the corrected collection coefficient includes the upper limit of the corrected collection frequency and the upper limit of the corrected collection points, as well as selecting the corresponding line loss protection mode or abnormal alarm mode based on the real-time current fluctuation frequency and real-time internal resistance.

[0083] Step S4: When the current power consumption situation is that there is an overload, determine the suspected abnormal line loss based on real-time temperature data as an actual abnormal line loss or the existence of electricity theft.

[0084] By integrating multiple key parameters such as visual inspection, temperature monitoring, power consumption matching, current fluctuation frequency, and line internal resistance, this invention can accurately distinguish abnormal power supply states without affecting users' normal power consumption. Specifically, by introducing real-time visual inspection and temperature difference analysis of the insulation layer condition, the appearance condition of cables and fault heat can be effectively determined, allowing for timely and targeted protective measures. Simultaneously, by constructing a real-time power consumption matching judgment process, comparing real-time power consumption data with standard and fluctuation ranges constructed from historical data, different abnormal phenomena such as power outages, instability, and electricity theft can be accurately identified. Furthermore, this invention utilizes real-time current fluctuation frequency and dynamic line internal resistance assessment technology to quantitatively analyze the degree of internal line damage, further distinguishing between circuit faults and electricity theft. In addition, the use of acquisition coefficient evaluation to optimize the acquisition frequency and the number of connected measuring instruments improves the accuracy of data acquisition and ensures the high efficiency and stability of system operation. In summary, this invention has significant advantages in real-time performance, refined anomaly identification, and intelligent response, providing an efficient and feasible solution for transformer substation line loss monitoring and fault early warning.

[0085] Specifically, acquiring image data within the visual inspection area and determining the condition of the cable insulation sheath includes,

[0086] Real-time images of the cable are acquired and the images are preprocessed into unit input data. The smoothness of the insulation layer is judged on the unit input data. Based on the judgment result, the shape of the cable is determined, including compliant shape and abnormal shape.

[0087] When obtaining the form compliance result, determine whether the current time period is within the peak electricity consumption period;

[0088] When obtaining the morphological abnormality results, determine the area of ​​the real-time control zone corresponding to the abnormal cable and compare it with the standard control zone range.

[0089] If the area of ​​the real-time control zone is larger than the standard control zone, the first protection measure shall be implemented.

[0090] If the area of ​​the real-time control zone is less than or equal to the standard control zone, implement the second protection measure.

[0091] In this embodiment, the step for determining the smoothness of the insulating layer is as follows:

[0092] Real-time images of the cable are captured by a high-resolution camera, and the images are preprocessed into standardized unit input data through grayscale conversion, noise filtering and normalization. The smoothness of the cable is then determined based on edge detection and texture analysis algorithms.

[0093] The Canny edge detection operator is used to extract edge information of the cable surface in the image, and the gray-level co-occurrence matrix (GLCM) is used to calculate the surface texture parameters. The calculation formula is as follows:

[0094] ;

[0095] in,

[0096] S: Smoothness index;

[0097] : Average edge intensity within a unit area;

[0098] Texture variance;

[0099] A preset threshold of 1.2 is set, and the smoothness index is compared with the preset threshold.

[0100] When the smoothness index is greater than or equal to the preset threshold, a compliant shape result is obtained;

[0101] When the smoothness index is less than the preset threshold, abnormal morphology results are obtained;

[0102] When obtaining the morphological abnormality result, determine the area of ​​the real-time control region corresponding to the abnormal cable. The step of determining the area of ​​the real-time control region corresponding to the abnormal cable is as follows:

[0103] The area of ​​the control zone corresponding to the abnormal cable is read in real time by integrating a geographic information system (GIS) and compared with the standard control zone range.

[0104] The standard control area is the area within the control radius of the low-voltage distribution area line. In this embodiment, the control radius of the low-voltage distribution area line is taken as 150 meters, and the standard control area is the area enclosed by the cable as the center and a radius of 150 meters.

[0105] When the area of ​​the real-time control area is within the standard control area, the faulty cable is the main bus of the power supply area. The first protection measure is to activate the backup power equipment to carry out power outage maintenance and issue a fault notification in a timely manner.

[0106] When the area of ​​the real-time control zone is smaller than the standard control zone, and the faulty cable is a single household or a small area of ​​users, the second protection measure is implemented, namely, power outage and timely on-site repair.

[0107] This step not only accurately assesses the fault status of the cable, but also formulates targeted protection strategies based on the dynamic changes in the real-time control area, ensuring that the power supply system can respond quickly when an anomaly occurs, thereby improving power supply safety and reliability.

[0108] See Figure 2 As shown, it is a logic diagram for determining whether the current time period is within the peak electricity consumption period according to an embodiment of the present invention;

[0109] Specifically, determining whether the current time period is within the peak electricity consumption period includes...

[0110] Determine the date category corresponding to the current time period, and based on the determination result, retrieve the corresponding peak electricity consumption time period, comparing the current time period with the peak electricity consumption time period.

[0111] If the current time period is within the peak electricity consumption period, execute the electricity cost determination process and the real-time electricity consumption matching degree determination process.

[0112] The process for determining the electricity cost is as follows:

[0113] Obtain the actual electricity cost and compare it with the minimum electricity cost:

[0114] When the real-time electricity cost is less than or equal to the minimum electricity cost, the real-time electricity consumption matching degree judgment process is executed.

[0115] In this embodiment, the step of determining the corresponding peak electricity consumption period is as follows:

[0116] Determine whether the time period belongs to a workday based on the current date, and retrieve the high-frequency time period from the peak electricity consumption period database for workdays or holidays based on the result;

[0117] Taking weekdays as an example, the high-frequency electricity consumption periods are from 8:00 to 10:00 in the morning, from 16:00 to 18:00 in the morning, and from 19:00 to 21:00 in the evening; and determine whether the current period is within the peak period;

[0118] If the current time period falls within the selected peak electricity consumption period, the system will initiate the electricity cost determination process. This process first collects the actual electricity cost in real time and compares the collected data with the preset minimum electricity cost, where the preset minimum electricity cost is defined as zero, i.e., no outstanding payment.

[0119] When the actual electricity cost is less than or equal to the minimum electricity cost, the user has no outstanding fees, and the real-time electricity consumption matching process is further executed.

[0120] This step accurately identifies abnormal electricity consumption behavior during peak periods, enabling early warning of power supply anomalies, electricity theft, and other situations, thereby improving the safety and stability of power grid operation and providing reliable judgment results for power grid regulation and maintenance decisions.

[0121] Specifically, the real-time electricity consumption matching degree judgment process includes,

[0122] Read the electricity consumption area category and the corresponding user's historical electricity consumption data, and determine the electricity consumption matching interval based on the user's historical electricity consumption data. The electricity consumption matching interval includes the standard electricity consumption interval and the electricity consumption fluctuation interval.

[0123] Real-time electricity consumption is obtained, the real-time electricity consumption matching degree is calculated, and compared with the electricity consumption matching interval.

[0124] If the electricity consumption matching degree is within the standard range of electricity consumption, the current electricity consumption is considered normal.

[0125] If the electricity consumption matching degree is not within the standard range of electricity consumption, it is determined that there is a suspected abnormal line loss, or an overload occurs. Based on real-time temperature data, it is determined that the suspected abnormal line loss is an actual abnormal line loss or that there is electricity theft, and the first warning measure or the third protection measure is implemented.

[0126] If the real-time electricity consumption is within the electricity consumption fluctuation range, it is determined that there is a suspected abnormal line loss, load fluctuation occurs, and the electricity consumption parameter analysis program is executed.

[0127] The specific steps of the real-time power consumption matching degree judgment process in this embodiment are as follows:

[0128] Real-time current data within a predetermined time period is read. In this embodiment, a fixed detection period of five minutes is used to read the data and calculate the historical average current for that period. and historical current standard deviation Define the real-time current fluctuation matching degree. for:

[0129] ;

[0130] in,

[0131] Real-time power consumption matching degree;

[0132] The average current value obtained by sampling within a fixed detection period;

[0133] In this embodiment, when 0 ≤ M < 1, it represents the standard range for electricity consumption. When, it represents the range of electricity consumption fluctuations; when At that time, outside the range of electricity consumption fluctuations;

[0134] The real-time electricity consumption matching degree is compared with the electricity consumption fluctuation range.

[0135] when At that time, the current power consumption was normal;

[0136] when If the current fluctuation is within an acceptable range and a load fluctuation occurs, it is determined that there is a suspected abnormal line loss, and the power consumption parameter analysis program is executed for further judgment.

[0137] when At that time, it is believed that there is an abnormally high fluctuation, the load is overloaded, and there is a suspected abnormal line loss. Based on real-time temperature data, it is determined that the suspected abnormal line loss is an actual abnormal line loss or there is electricity theft. The first warning measure or the third protection measure is implemented. The current fluctuation is within the acceptable fluctuation range. Further judgment is required by combining other parameters.

[0138] This step quantitatively compares real-time fluctuations with historical load characteristics to ensure accurate differentiation between normal and abnormal fluctuations during peak hours. This provides a scientific basis for subsequent power consumption parameter analysis, temperature difference comparison, and the implementation of protection measures. It improves monitoring accuracy while ensuring dynamic early warning and timely fault response, thus guaranteeing the stable operation of the power supply system.

[0139] See Figure 3 As shown, it is a logic decision diagram for executing the first warning measure or the third protection measure based on real-time temperature data in an embodiment of the present invention;

[0140] Specifically, implementing first warning measures or third protective measures based on real-time temperature data includes:

[0141] Real-time temperature data of the environment corresponding to the load overload is obtained to get the regional average temperature, local maximum temperature, and real-time temperature difference. The real-time temperature difference is then compared with the fault heat threshold, wherein:

[0142] If the real-time temperature difference is greater than or equal to the fault heat threshold, the suspected line loss anomaly is determined to be an actual line loss anomaly, and the third protection measure is executed.

[0143] If the real-time temperature difference is less than the fault heat threshold, the suspected abnormal line loss is determined to be due to electricity theft, and the first warning measure is implemented.

[0144] The real-time temperature difference is the difference between the regional average temperature and the local maximum temperature.

[0145] In this embodiment, the fault heat threshold is set to 60°C. The real-time temperature difference is compared with the fault heat threshold, wherein:

[0146] If the real-time temperature difference is greater than or equal to the fault heat threshold, the suspected line loss abnormality is determined to be an actual line loss abnormality, and the third protection measure is implemented, namely, immediately cutting off the fault power supply and starting the backup power equipment, replacing the damaged power supply cable, and at the same time testing the function of relevant protection devices such as circuit breakers and fuses.

[0147] If the real-time temperature difference is less than the fault heat threshold, it is determined that the suspected abnormal line loss is due to electricity theft. The first warning measure is to promptly report the electricity theft information to the smallest affected upstream power supply end.

[0148] This measure ensures that electricity theft can be identified and dealt with in the shortest possible time, thereby minimizing the risks to the power supply system and economic losses.

[0149] See Figure 4 As shown, it is a logic decision diagram for executing the power consumption parameter analysis program in an embodiment of the present invention;

[0150] Specifically, performing the electricity consumption parameter analysis procedure includes,

[0151] Obtain the actual number of households controlled in the electricity consumption area and compare it with the number of households controlled in the standard electricity consumption area.

[0152] If the number of households controlled in the actual electricity consumption area is greater than or equal to the number of households controlled in the standard electricity consumption area, the current collection coefficient will be updated to the corrected collection coefficient based on the real-time electricity consumption data.

[0153] The current fluctuation frequency and real-time internal resistance of the line loss to be detected are collected by the corrected acquisition coefficient. Based on the current fluctuation frequency, it is determined whether to implement the first warning measure, and based on the real-time current fluctuation frequency and real-time internal resistance, the second warning measure is implemented, or the corresponding line loss protection mode is selected.

[0154] The corrected acquisition coefficients include the upper limit of the corrected acquisition frequency and the upper limit of the corrected acquisition points.

[0155] The average power consumption per sampling is taken as 0.0005 kWh, which is obtained through actual sampling statistics of the transformer substation.

[0156] The average reference value for a single sample is 20 kilobytes, obtained through actual sampling measurements.

[0157] Specifically, determining whether to update the current collection coefficient to the corrected collection coefficient based on real-time electricity consumption data includes:

[0158] The power consumed in collecting real-time current, voltage, and power data is obtained to determine the power consumption per sampling.

[0159] The upper limit of the current sampling frequency is calculated based on the power consumption of a single sampling.

[0160] Obtain the amount of real-time current, voltage, and power data collected within the current acquisition period to obtain a single...

[0161] The amount of data sampled per time;

[0162] The upper limit of the number of sampling points is calculated and corrected based on the amount of data collected in a single sampling.

[0163] Obtain real-time data volume distribution difference indicators and compare them with standard data volume distribution difference indicators.

[0164] When the real-time data volume distribution difference index is less than or equal to the standard data volume distribution difference index, select the acquisition frequency control mode.

[0165] When the real-time data volume distribution difference index is greater than the standard data volume distribution difference index, select the data collection point control mode.

[0166] Among them, the sampling frequency control mode adjusts the current sampling frequency upper limit to the corrected sampling frequency upper limit; the sampling point control mode adjusts the current sampling point upper limit to the corrected sampling point upper limit.

[0167] In this embodiment, the formula for calculating the standard data volume distribution difference index (CV) is as follows:

[0168] ;

[0169] in,

[0170] Historical average current;

[0171] Historical current standard deviation;

[0172] The standard data volume distribution difference index is set to 0.1. The real-time data volume distribution difference index is obtained and compared with the standard data volume distribution difference index.

[0173] When the real-time data volume distribution difference index is less than or equal to 0.1, the data volume distribution of each channel is uniform within the acquisition period. At this time, the system selects the acquisition frequency control mode, that is, adjusts the current acquisition frequency upper limit to the corrected acquisition frequency upper limit value.

[0174] When the real-time data volume distribution difference index is greater than 0.1, there is a large difference in the data volume distribution of each channel. At this time, the system selects the collection point control mode, that is, adjusts the current collection point limit to the corrected collection point limit value.

[0175] Specifically, the formula for calculating the upper limit of the sampling frequency is as follows:

[0176] ;

[0177] in,

[0178] Power consumption per sampling;

[0179] Total duration of peak hours;

[0180] The maximum amount of extra battery power allowed to be consumed;

[0181] The formula for calculating the upper limit of the number of collection points is as follows:

[0182] ;

[0183] in,

[0184] : Adjust the upper limit of the number of data collection points;

[0185] The maximum bandwidth allowed for additional processing;

[0186] : Data volume per single sampling;

[0187] In this embodiment, the current peak period is 07:00–09:00; the average power consumption for each electrical parameter acquisition is 0.0005kWh; the system's energy consumption limit is 0.5kWh; the data per sampling is 20KB; and the network bandwidth is 1000KB.

[0188] Based on the above parameters, the system calculates that the upper limit of frequency is 8.33 times / min and the upper limit of points is 50.

[0189] If the difference index of the real-time data volume distribution of current data at each point during the monitoring process is 0.065, the sampling frequency adjustment mode is adopted to increase the sampling frequency to 8 times / min in order to improve the time accuracy of the data.

[0190] If the real-time data volume distribution difference index is 0.152, the number of collection points will be adjusted to 50 to cover more locations.

[0191] This step utilizes a mechanism that dynamically adjusts the sampling coefficient based on actual sampled power and data distribution. This allows for intelligent control of the sampling frequency and number of sampling points according to load status and regional electrical parameter differences, effectively improving the timeliness and coverage of system monitoring. While ensuring data quality, this strategy controls additional power consumption and data transmission pressure, enhancing the system's adaptability and energy efficiency in large-scale low-voltage distribution substation environments.

[0192] Specifically, determining whether to implement the first warning measure based on the real-time current fluctuation frequency includes:

[0193] Obtain the actual fluctuation frequency of the line corresponding to the suspected line loss, set the current signal change threshold and the standard range of current fluctuation frequency, and compare the actual fluctuation frequency with the standard current fluctuation frequency range.

[0194] If the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the first warning measure will not be implemented;

[0195] If the actual fluctuation frequency is lower than the minimum value of the standard current fluctuation frequency range, the first warning measure shall be implemented.

[0196] In this embodiment, the specific steps for determining whether to execute the first warning measure based on the real-time current fluctuation frequency are as follows:

[0197] The fluctuation frequency of the real-time current is extracted using signal processing algorithms, and the standard range of current fluctuation frequency is set to 0.8Hz to 1.2Hz based on historical data.

[0198] The standard range of current fluctuation frequency is the actual fluctuation frequency of the line under normal operating conditions.

[0199] The actual fluctuation frequency is compared with the standard current fluctuation frequency range.

[0200] If the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, and the line current change is in line with normal load fluctuation, the first warning measure will not be implemented.

[0201] If the actual fluctuation frequency is lower than the minimum value of the standard current fluctuation frequency range, and the current change does not meet the normal conditions of peak electricity consumption, the load is artificially reduced due to the electricity theft, thereby triggering the first warning measure, that is, issuing an electricity theft alarm information and quickly reporting to the relevant monitoring center to implement the first warning measure.

[0202] This method enables quantitative analysis of real-time current fluctuations by setting specific threshold values ​​for current signal changes and standard fluctuation frequency ranges. This allows for accurate differentiation between normal fluctuations and abnormal fluctuations caused by electricity theft, providing a scientific basis for electricity theft early warning.

[0203] Specifically, a second warning measure is implemented based on the real-time current fluctuation frequency and real-time internal resistance, or the corresponding line loss protection mode is selected, including...

[0204] Set the internal resistance value of the cable under normal operating conditions to the standard line internal resistance range;

[0205] When the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the real-time line internal resistance is calculated and compared with the standard line internal resistance.

[0206] When the real-time line resistance is greater than or equal to the maximum value of the standard line resistance, the fourth protection measure shall be taken.

[0207] When the real-time line resistance is within the range of the standard line resistance, the fifth protection measure shall be taken;

[0208] When the real-time line resistance is less than the minimum standard line resistance, a second warning measure shall be taken.

[0209] In this embodiment, the formula for calculating the real-time internal resistance is:

[0210] ;

[0211] in,

[0212] :Voltage; : represents electric current; : refers to electromagnetic inductance; : This refers to the system's power frequency; under healthy conditions, the standard internal resistance range of the power supply cable is set to 30Ω to 50Ω to determine whether there are any internal defects such as fuses or poor connections in the cable. The real-time line internal resistance is calculated and compared with the standard line internal resistance.

[0213] When the real-time line internal resistance is greater than or equal to the maximum value of the standard line internal resistance, there are phenomena such as poor contact, oxidation or local breakage in the line, which leads to an increase in additional impedance, thereby affecting the normal operation and thermal balance of the circuit. The fourth protection measure is to shorten the cable replacement cycle and replace the cable in time during the off-peak electricity consumption period.

[0214] When the real-time line resistance is within the standard line resistance, the fifth protection measure is taken, which is considered a normal working state, and the line status is monitored continuously.

[0215] When the real-time line internal resistance is less than the minimum standard line internal resistance, it is due to an abnormal current distribution caused by partial short circuit or internal insulation damage, which causes the line impedance to drop abnormally. The second warning measure is to promptly trigger the alarm and replace the faulty cable.

[0216] This step can effectively distinguish abnormal situations caused by aging lines, physical damage, or electricity theft, thereby providing accurate fault warnings and protection responses for the power supply system of the transformer area.

[0217] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0218] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent diagnosis and analysis of line loss in transformer substations, characterized in that, include, Acquire image data within the visual inspection area and determine the morphology of the cable insulation sheath, including compliant and abnormal morphologies, and select the corresponding line loss protection mode based on the judgment result and the real-time control area area. If the cable insulation sheath is in compliance with regulations, determine whether the current period is within the peak electricity consumption period, and determine the current electricity consumption situation based on the matching degree between electricity cost and real-time electricity consumption, including the occurrence of load fluctuations and load overloads; Specifically, if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption matching degree is within the electricity consumption fluctuation range during the current peak electricity consumption period, it is determined that a load fluctuation has occurred; if the real-time electricity cost is greater than the minimum electricity cost and the real-time electricity consumption is not within the electricity consumption matching range during the current peak electricity consumption period, it is determined that a load overload has occurred. When the current power consumption situation is characterized by load fluctuations, execute the power consumption parameter analysis program to select the corresponding line loss protection mode or abnormal alarm mode. The process of executing the electricity consumption parameter analysis program includes: obtaining the actual number of controlled households in the electricity consumption area and comparing it with the number of controlled households in the standard electricity consumption area; after meeting the comparison conditions, updating the current collection coefficient to the corrected collection coefficient based on real-time electricity consumption data; the corrected collection coefficient includes the upper limit of the corrected collection frequency and the upper limit of the corrected collection points, as well as selecting the corresponding line loss protection mode or abnormal alarm mode based on the real-time current fluctuation frequency and real-time internal resistance. When the current power consumption situation indicates an overload, the suspected abnormal line loss is determined to be an actual abnormal line loss or the existence of electricity theft based on real-time temperature data.

2. The intelligent diagnostic analysis method for transformer substation line loss according to claim 1, characterized in that, Get Visual inspection of image data within the inspection area and determination of the cable insulation condition includes, Real-time images of the cable are acquired and the images are preprocessed into unit input data. The smoothness of the insulation layer is judged on the unit input data. Based on the judgment result, the shape of the cable is determined, including compliant shape and abnormal shape. When obtaining the form compliance result, determine whether the current time period is within the peak electricity consumption period; When obtaining the morphological abnormality results, determine the area of ​​the real-time control zone corresponding to the abnormal cable and compare it with the standard control zone range. If the area of ​​the real-time control zone is larger than the standard control zone, the first protection measure shall be implemented. If the area of ​​the real-time control zone is less than or equal to the standard control zone, a second protection measure shall be implemented.

3. The intelligent diagnostic analysis method for transformer substation line loss according to claim 2, characterized in that, judge Does the current time period include peak electricity consumption hours? Determine the date category corresponding to the current time period, and based on the determination result, retrieve the corresponding peak electricity consumption time period, comparing the current time period with the peak electricity consumption time period. If the current time period is within the peak electricity consumption period, execute the electricity cost determination process and the real-time electricity consumption matching degree determination process. The process for determining the electricity cost is as follows: Obtain the actual electricity cost and compare it with the minimum electricity cost: When the real-time electricity cost is less than or equal to the minimum electricity cost, the real-time electricity consumption matching degree judgment process is executed.

4. The intelligent diagnostic analysis method for transformer substation line loss according to claim 3, characterized in that, Electricity The quantitative matching degree judgment process includes, Read the electricity consumption area category and the corresponding user's historical electricity consumption data, and determine the electricity consumption matching interval based on the user's historical electricity consumption data. The electricity consumption matching interval includes the standard electricity consumption interval and the electricity consumption fluctuation interval. Real-time electricity consumption is obtained, the real-time electricity consumption matching degree is calculated, and compared with the electricity consumption matching interval. If the electricity consumption matching degree is within the standard range of electricity consumption, the current electricity consumption is considered normal. If the electricity consumption matching degree is not within the standard range of electricity consumption, it is determined that there is a suspected abnormal line loss, or an overload occurs. Based on real-time temperature data, it is determined that the suspected abnormal line loss is an actual abnormal line loss or that there is electricity theft, and the first warning measure or the third protection measure is implemented. If the real-time electricity consumption is within the fluctuation range, it is determined that there is a suspected abnormal line loss, resulting in load fluctuation, and the electricity consumption parameter analysis program is executed.

5. The intelligent diagnostic analysis method for transformer substation line loss according to claim 4, characterized in that, based on Real-time temperature data triggers first or third warning measures, including: Real-time temperature data of the environment corresponding to the load overload is obtained to obtain the regional average temperature, local maximum temperature, and real-time temperature difference. The real-time temperature difference is then compared with the fault heat threshold, wherein: If the real-time temperature difference is greater than or equal to the fault heat threshold, the suspected line loss anomaly is determined to be an actual line loss anomaly, and the third protection measure is executed. If the real-time temperature difference is less than the fault heat threshold, the suspected abnormal line loss is determined to be due to electricity theft, and the first warning measure is implemented.

6. The intelligent diagnostic analysis method for transformer substation line loss according to claim 4, characterized in that, implement The power consumption parameter analysis program includes, Obtain the actual number of households controlled in the electricity consumption area and compare it with the number of households controlled in the standard electricity consumption area. If the number of households controlled in the actual electricity consumption area is greater than or equal to the number of households controlled in the standard electricity consumption area, the current collection coefficient will be updated to the corrected collection coefficient based on the real-time electricity consumption data. The current fluctuation frequency and real-time internal resistance of the line loss to be detected are collected by the corrected acquisition coefficient. Based on the current fluctuation frequency, it is determined whether to implement the first warning measure, and based on the real-time current fluctuation frequency and real-time internal resistance, the second warning measure is implemented, or the corresponding line loss protection mode is selected. The corrected acquisition coefficients include the upper limit of the corrected acquisition frequency and the upper limit of the corrected acquisition points.

7. The intelligent diagnostic analysis method for transformer substation line loss according to claim 6, characterized in that, based on Determining whether to update the current collection coefficient to the corrected collection coefficient based on real-time electricity consumption data includes: The power consumed in acquiring real-time current, voltage, and power data is used to obtain the power consumption per sampling. quantity; The upper limit of the current sampling frequency is calculated based on the power consumption of a single sampling. Obtain the amount of real-time current, voltage, and power data collected within the current acquisition period to obtain a single... The amount of data sampled per time; The upper limit of the number of sampling points is calculated and corrected based on the amount of data collected in a single sampling. Select the acquisition frequency control mode or the acquisition point control mode based on the real-time data volume distribution difference index. Among them, the sampling frequency control mode is to adjust the current upper limit of the sampling frequency to the corrected upper limit of the sampling frequency; The data collection point control mode adjusts the current upper limit of data collection points to the corrected upper limit of data collection points.

8. The intelligent diagnostic analysis method for transformer substation line loss according to claim 7, characterized in that, The formula for calculating the upper limit of the sampling frequency is as follows: ； in, Power consumption per sampling; Total duration of peak hours; The maximum amount of extra battery power allowed to be consumed; The formula for calculating the upper limit of the number of collection points is as follows: ； in, : Adjust the upper limit of the number of data collection points; The maximum bandwidth allowed for additional processing; : Data volume per single sampling.

9. The intelligent diagnostic analysis method for transformer substation line loss according to claim 6, characterized in that, Determining whether to implement the first warning measure based on the real-time current fluctuation frequency includes: The actual fluctuation frequency of the line corresponding to the suspected line loss is obtained. A threshold for current signal change and a standard range for current fluctuation frequency are set. The actual fluctuation frequency is then compared with the standard current fluctuation frequency range. If the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the first warning measure will not be implemented; If the actual fluctuation frequency is lower than the minimum value of the standard current fluctuation frequency range, the first warning measure shall be implemented.

10. The intelligent diagnostic analysis method for transformer substation line loss according to claim 7, characterized in that, A second warning measure is implemented based on the real-time current fluctuation frequency and real-time internal resistance, or the corresponding line loss protection mode is selected, including... Set the internal resistance value of the cable under normal operating conditions to the standard line internal resistance range; When the actual fluctuation frequency is higher than the maximum value of the standard current fluctuation frequency range, the real-time line internal resistance is calculated and compared with the standard line internal resistance. When the real-time line resistance is greater than or equal to the maximum value of the standard line resistance, the fourth protection measure shall be taken. When the real-time line resistance is within the standard line resistance, the fifth protection measure shall be taken; When the real-time line resistance is less than the minimum standard line resistance, a second warning measure is taken.

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