Transformer health state diagnosis method and system based on Internet of Things

Abstract

The invention discloses a transformer health state diagnosis method and system based on the Internet of Things, and belongs to the technical field of health state diagnosis. Acquiring load data of the transformer; constructing a data acquisition period, recording the load data as periodic load data, constructing a transformer load data-time coordinate system, sequentially connecting the coordinate points to construct a transformer load curve, calculating the fluctuation amplitude, and constructing a fluctuation amplitude-time coordinate system; calculating a slope mean value; acquiring the maximum fluctuation range in the fluctuation amplitude-time coordinate system in all the data acquisition periods; and based on the slope mean value and the maximum fluctuation range, calculating a comprehensive health index of the transformer, presetting a threshold value, and analyzing and outputting an early warning. Through fusion of load data analysis, dynamic feature extraction and comprehensive health assessment, stable operation of the transformer is guaranteed, and a scientific basis is provided for power consumption management of commercial buildings, so that the fault risk of the transformer is effectively reduced, and the operation reliability of a power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health status diagnosis, and in particular to a transformer health status diagnosis method and system based on the Internet of Things. Background Art

[0002] With the rapid growth of electricity demand in modern commercial buildings, the operating status of transformers, as the core equipment for power transmission and distribution, has a vital impact on the power supply safety and equipment efficiency in the building; traditional transformer monitoring methods mainly rely on regular manual inspections and offline analysis of equipment operating parameters, but their real-time and comprehensiveness are poor, and it is difficult to meet the complex and changeable power load requirements in commercial buildings; in recent years, the rapid development of Internet of Things technology has provided new solutions for transformer health monitoring. Through Internet of Things technology, transformer operating data can be collected in real time, and data analysis methods can be used for status assessment and fault warning, thereby realizing intelligent monitoring of equipment operation; however, existing transformer monitoring methods based on Internet of Things mostly focus on the monitoring of single indicators, such as temperature, voltage or current, lack of comprehensive analysis of dynamic changes in load, and it is difficult to fully reflect the operating stability of the transformer.

[0003] In the field of transformer health diagnosis, the existing technology's monitoring methods pay little attention to the dynamic characteristics of transformer load, which may lead to the failure to timely identify potential hidden dangers in operation. For example, in scenarios with drastic load changes, the transformer may accelerate aging or even fail due to long-term overload or unstable operation. The existing technology usually adopts static health assessment indicators and fails to effectively combine the time series characteristics of load changes for dynamic diagnosis, which has obvious limitations when dealing with complex power load environments. Summary of the invention

[0004] The purpose of the present invention is to provide a transformer health status diagnosis method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A transformer health status diagnosis method based on the Internet of Things, the method comprising the following steps: collecting load data of all transformers in a commercial building according to the floors of the commercial building, and constructing a load data set; constructing a data collection cycle, recording the load data as periodic load data; constructing a transformer load data-time coordinate system based on the periodic load data, and sequentially connecting coordinate points to construct a transformer load curve; calculating a load mean based on the transformer load curve; calculating the fluctuation amplitude of the transformer load curve based on the load mean, and constructing a fluctuation amplitude-time coordinate system; calculating the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system; calculating the slope mean within all data collection cycles; obtaining the maximum fluctuation range in the fluctuation amplitude-time coordinate system within all data collection cycles; calculating a comprehensive health index of the transformer based on the slope mean and the maximum fluctuation range, presetting a threshold, and analyzing and outputting an early warning.

[0007] As a preferred solution of the transformer health status diagnosis method based on the Internet of Things described in the present invention, all transformers in the commercial building are obtained, and the load data of all transformers in the commercial building are collected through a data acquisition terminal, and the load data of all transformers in the commercial building are summarized according to the floors of the commercial building.

[0008] According to the summary data, a load data set is constructed, denoted as FH = {LD i,a |i∈[1,I],a∈[1,A}, where LD i,a represents the load data of the ath transformer in the i-th floor, I represents the total number of floors in the commercial building, and A represents the total number of transformers in the i-th floor.

[0009] As a preferred solution of the transformer health status diagnosis method based on the Internet of Things described in the present invention, the time in a day is evenly divided into T hours, and the data collection cycle is constructed with the hour as a unit, and the load data LD of the a-th transformer in the i-th floor in the t-th data collection cycle is collected. i,a Recorded as periodic load data LD i,a (t).

[0010] Based on periodic load data LD i,a (t), construct a transformer load data-time coordinate system based on the i-th floor, the horizontal axis of the transformer load data-time coordinate system is the A transformers arranged in sequence on the i-th floor, and the vertical axis of the transformer load data-time coordinate system is the load data of the A transformers arranged in sequence on the i-th floor in the t-th data collection cycle.

[0011] Obtain the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtain all the coordinate points, and connect them in sequence to form a curve, and record the curve as the transformer load curve DCL of the i-th floor in the t-th data collection cycle i (t).

[0012] As a preferred solution of the transformer health status diagnosis method based on the Internet of Things described in the present invention, based on the transformer load curve DCL i (t), calculate the load mean of the i-th floor in the t-th data collection cycle, the calculation formula is as follows:

[0013]

[0014] in, It represents the load mean of the i-th floor in the t-th data collection cycle.

[0015] Based on the load mean of the i-th floor in the t-th data collection cycle Calculate the transformer load curve DCL i The fluctuation range of (t) is calculated as follows:

[0016]

[0017] Among them, BF[DCL i (t)] represents the transformer load curve DCL of the ith floor in the tth data collection cycle i The fluctuation range of (t).

[0018] It should be noted that the main function of the load mean calculation formula in the present invention is to calculate the average load of each floor at a specific time point, so as to evaluate the overall working intensity of the transformer system on the floor; for example, when planning the power capacity of a commercial building, by calculating the average load of each floor at different times, it is possible to understand which floors have greater power demand in which time periods; the calculation result of this average load can help determine whether it is necessary to adjust the transformer capacity of the floor, or whether it is necessary to optimize the layout of the electrical equipment on the floor, so as to ensure that the transformer can operate at an appropriate working intensity and avoid long-term overload or underload; the main function of the fluctuation amplitude calculation formula is to measure the fluctuation of the transformer load on each floor, and to provide a quantitative indicator for judging the operating stability of the transformer; for example, on a floor, if the startup of some electrical equipment is random, by calculating the fluctuation amplitude of the transformer load dynamic curve, the degree of influence of this randomness on the operation of the transformer can be accurately evaluated; a larger fluctuation amplitude means that the load change of the transformer is more drastic, which may lead to increased loss and shortened life of the transformer. By monitoring this indicator, timely measures can be taken, such as adding voltage stabilizing equipment or adjusting the use strategy of electrical equipment, to ensure the stable operation of the transformer.

[0019] Based on the fluctuation range BF[DCL i (t)], construct a fluctuation amplitude-time coordinate system, the abscissa of the fluctuation amplitude-time coordinate system is the data acquisition period, and the ordinate of the fluctuation amplitude-time coordinate system is the transformer load curve DCL corresponding to each data acquisition period i The fluctuation range of (t) BF[DCL i (t)].

[0020] Based on the fluctuation amplitude-time coordinate system, the slope between adjacent coordinate points is calculated using the following formula:

[0021]

[0022] Among them, k i (t→t+1) represents the slope between adjacent coordinate points, BF[DCL i (t+1)] represents the fluctuation amplitude in the t+1th data collection cycle.

[0023] It should be noted that the main function of this formula is to reflect the speed of change of the transformer load fluctuation amplitude between adjacent time points, and is used to monitor short-term changes in the transformer operating status; for example, when a large-scale electrical equipment in a commercial building suddenly starts or stops, by calculating the slope of the fluctuation amplitude between adjacent time points, the immediate impact of such an event on the transformer operation can be quickly detected; this rapid monitoring of short-term changes can help operation and maintenance personnel respond in a timely manner, such as determining whether a temporary load adjustment of the transformer is required, or checking whether there are faults in related electrical equipment, thereby ensuring the stable operation of the transformer in a short period of time.

[0024] As a preferred solution of the transformer health status diagnosis method based on the Internet of Things described in the present invention, based on the slope k between adjacent coordinate points i (t→t+1), calculate the slope mean value within all data collection periods, the calculation formula is as follows:

[0025]

[0026] Among them, k i It represents the mean slope value during all data collection periods.

[0027] Based on the fluctuation amplitude-time coordinate system, the maximum fluctuation range of the fluctuation amplitude in the entire data collection period is obtained by the following formula:

[0028] ΔBF[DCL i (t)] = BF[DCL i (t)] max -BF[DCL i (t)] min ;

[0029] Among them, ΔBF[DCL i (t)] represents the maximum fluctuation range, BF[DCL i (t)] max Indicates the maximum fluctuation range within the entire data collection period, BF[DCL i (t)] min Indicates the minimum fluctuation amplitude within the entire data collection period.

[0030] Based on the slope mean k i and the maximum fluctuation range ΔBF[DCL i (t)], calculate the comprehensive health index of the transformer on the i-th floor, and the calculation formula is as follows:

[0031] F i =α×k i ×β×ΔBF[DCL i (t)];

[0032] Among them, F i represents the comprehensive health index of the transformer on the i-th floor, and α represents the preset slope mean k i The influence factor of β represents the preset maximum fluctuation range ΔBF[DCL i (t)].

[0033] Preset the transformer comprehensive health index threshold τ, if the transformer comprehensive health index F of the i-th floor i >τ, the health status of the transformer on the ith floor is judged to be abnormal, and an early warning is output.

[0034] Let i=i+1, and perform comprehensive transformer health index analysis on all floors in the commercial building.

[0035] It should be noted that when the slope and the fluctuation range are multiplied, the result obtained is a comprehensive consideration of the transformer's operating stability. When the slope is large and the fluctuation range is also large, the result of the multiplication of the two will be large, which means that the transformer not only has a more obvious load fluctuation trend in the long term, but also has a significant load fluctuation degree in extreme cases. This situation indicates that the transformer has poor operating stability and may face a higher risk of failure. For example, in an industrial production environment, if the result of the multiplication of the slope and the fluctuation range of a transformer is large, it means that the transformer may often be in an unstable operating state, and may cause increased stress in the internal windings and accelerated aging of insulation materials due to frequent load changes. In addition, when evaluating the comprehensive health indicators of the transformer, the result of the multiplication of the slope and the fluctuation range can be used as an important weight factor to help operation and maintenance personnel more accurately judge the health status of the transformer. For example, when setting the threshold of the comprehensive health indicator, considering the result of the multiplication of the slope and the fluctuation range, the health level of the transformer can be more reasonably divided. If the multiplication result is large, even if other indicators are relatively normal, it may indicate that the transformer is in a potential unhealthy state and needs to be monitored more closely or maintained in advance.

[0036] A transformer health status diagnosis system based on the Internet of Things, the system comprises: a data acquisition module, a coordinate system and curve construction module, a load mean and slope calculation module and a transformer health early warning analysis module.

[0037] The data collection module collects the load data of all transformers in the commercial building according to the floors of the commercial building and constructs a load data set.

[0038] The coordinate system and curve construction module: constructs a data collection cycle and records the load data as periodic load data; constructs a transformer load data-time coordinate system based on the periodic load data, and sequentially connects the coordinate points to construct a transformer load curve.

[0039] The load mean and slope calculation module: calculates the load mean based on the transformer load curve; calculates the fluctuation amplitude of the transformer load curve based on the load mean, and constructs a fluctuation amplitude-time coordinate system; calculates the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system.

[0040] The transformer health warning analysis module: calculates the slope mean value within the entire data collection cycle; obtains the maximum fluctuation range in the fluctuation amplitude-time coordinate system within the entire data collection cycle; based on the slope mean value and the maximum fluctuation range, calculates the comprehensive health index of the transformer, presets the threshold, analyzes and outputs the warning.

[0041] Furthermore, the data acquisition module includes a data acquisition unit.

[0042] The data acquisition unit is configured to acquire all transformers in the commercial building, collect load data of all transformers in the commercial building through a data acquisition terminal, summarize the load data of all transformers in the commercial building according to the floors of the commercial building, and construct a load data set according to the summarized data.

[0043] Furthermore, the coordinate system and curve construction module includes a coordinate system construction unit and a curve construction unit.

[0044] The coordinate system construction unit: divides the time in a day evenly into multiple hours, constructs a data collection cycle with the hour as a unit, and records the load data of a single transformer in a single floor within a single data collection cycle as periodic load data; based on the periodic load data, constructs a transformer load data-time coordinate system based on a single floor, the horizontal axis of the transformer load data-time coordinate system is the transformers arranged in sequence on a single floor, and the vertical axis of the transformer load data-time coordinate system is the load data of the transformers arranged in sequence on a single floor in a single data collection cycle.

[0045] The curve construction unit: obtains the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtains all the coordinate points and connects them in sequence to form a curve, and records the curve as the transformer load curve of a single floor in a single data collection cycle.

[0046] Furthermore, the load mean and slope calculation module includes a load mean calculation unit and a slope calculation unit.

[0047] The load mean value calculation unit calculates the load mean value of a single floor in a single data collection cycle based on the transformer load curve; and calculates the fluctuation amplitude of the transformer load curve based on the load mean value of a single floor in a single data collection cycle.

[0048] The slope calculation unit: constructs a fluctuation amplitude-time coordinate system based on the fluctuation amplitude, wherein the horizontal coordinate of the fluctuation amplitude-time coordinate system is the data acquisition period, and the vertical coordinate of the fluctuation amplitude-time coordinate system is the fluctuation amplitude of the transformer load curve corresponding to each data acquisition period; and calculates the slope between adjacent coordinate points based on the fluctuation amplitude-time coordinate system.

[0049] Furthermore, the transformer health early warning analysis module includes a slope mean and maximum fluctuation range construction unit, a transformer comprehensive health index construction unit and an early warning analysis unit.

[0050] The slope mean and maximum fluctuation range construction unit calculates the slope mean within the entire data collection cycle based on the slope between adjacent coordinate points; and obtains the maximum fluctuation range of the fluctuation amplitude within the entire data collection cycle based on the fluctuation amplitude-time coordinate system.

[0051] The transformer comprehensive health index construction unit calculates the transformer comprehensive health index of a single floor based on the slope mean and the maximum fluctuation range.

[0052] The early warning analysis unit: presets a transformer comprehensive health index threshold value. If the transformer comprehensive health index of a single floor is greater than the transformer comprehensive health index threshold value, it is determined that the health status of the transformer on the single floor is abnormal, and an early warning is output; and the transformer comprehensive health index analysis is performed on all floors in the commercial building.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a transformer health status diagnosis method and system based on the Internet of Things provided by the present invention, by collecting and summarizing the load data of all transformers according to the floors of commercial buildings, a load data set is constructed, and orderly data management is achieved, laying a foundation for subsequent analysis; a load data-time coordinate system is constructed based on the data collection cycle and a transformer load curve is generated, and the load change law is clearly displayed through graphical data presentation; by calculating the load mean, fluctuation amplitude and slope of its time coordinate system, the dynamic characteristics of the transformer operation state are further extracted; this process can not only identify the overall working intensity of the load, but also measure the stability and short-term changes of the load fluctuation, providing an important basis for the operation optimization of the transformer; the slope mean and the maximum fluctuation range are comprehensively used to calculate the comprehensive health index of the transformer, and the transformer health state is analyzed and warned by a preset threshold, realizing accurate identification and rapid response to operation abnormalities; this method not only ensures the stable operation of the transformer by integrating load data analysis, dynamic feature extraction and comprehensive health assessment, but also provides a scientific basis for the power management of commercial buildings, thereby effectively reducing the risk of transformer failure and improving the operation reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0055] Figure 1 It is a schematic diagram of the steps of a transformer health status diagnosis method based on the Internet of Things of the present invention;

[0056] Figure 2 It is a structural schematic diagram of a transformer health status diagnosis system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] See also Figure 1 In the first embodiment of the present invention, a transformer health status diagnosis method based on the Internet of Things is provided, and the method comprises the following steps:

[0059] Step S1: Collect load data of all transformers in the commercial building according to the floors of the commercial building, and construct a load data set.

[0060] Specifically, all transformers in the commercial building are acquired, and load data of all transformers in the commercial building are collected through a data collection terminal, and the load data of all transformers in the commercial building are summarized according to the floors of the commercial building.

[0061] Furthermore, according to the summary data, a load data set is constructed, which is denoted as FH = {LD i,a |i∈[1,I],a∈[1,A}, where LD i,a represents the load data of the ath transformer in the i-th floor, I represents the total number of floors in the commercial building, and A represents the total number of transformers in the i-th floor.

[0062] Step S2: construct a data collection cycle, record the load data as periodic load data; construct a transformer load data-time coordinate system based on the periodic load data, and connect the coordinate points in sequence to construct a transformer load curve.

[0063] Specifically, the time in a day is evenly divided into T hours, and the data collection cycle is constructed with the hour as the unit, and the load data LD of the a-th transformer in the i-th floor in the t-th data collection cycle is i,a Recorded as periodic load data LD i,a (t).

[0064] Furthermore, based on the periodic load data LD i,a (t), construct a transformer load data-time coordinate system based on the i-th floor, the horizontal axis of the transformer load data-time coordinate system is the A transformers arranged in sequence on the i-th floor, and the vertical axis of the transformer load data-time coordinate system is the load data of the A transformers arranged in sequence on the i-th floor in the t-th data collection cycle.

[0065] Obtain the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtain all the coordinate points, and connect them in sequence to form a curve, and record the curve as the transformer load curve DCL of the i-th floor in the t-th data collection cycle i (t).

[0066] Step S3: Based on the transformer load curve, calculate the load mean; based on the load mean, calculate the fluctuation amplitude of the transformer load curve, and construct a fluctuation amplitude-time coordinate system; calculate the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system.

[0067] Specifically, based on the transformer load curve DCL i (t), calculate the load mean of the i-th floor in the t-th data collection cycle, the calculation formula is as follows:

[0068]

[0069] in, It represents the load mean of the i-th floor in the t-th data collection cycle.

[0070] Furthermore, based on the load average of the i-th floor in the t-th data collection cycle Calculate the transformer load curve DCL i The fluctuation range of (t) is calculated as follows:

[0071]

[0072] Among them, BF[DCL i (t)] represents the transformer load curve DCL of the ith floor in the tth data collection cycle i The fluctuation range of (t).

[0073] It should be noted that the main function of the load mean calculation formula in the present invention is to calculate the average load of each floor at a specific time point, so as to evaluate the overall working intensity of the transformer system on the floor; for example, when planning the power capacity of a commercial building, by calculating the average load of each floor at different times, it is possible to understand which floors have greater power demand in which time periods; the calculation result of this average load can help determine whether it is necessary to adjust the transformer capacity of the floor, or whether it is necessary to optimize the layout of the electrical equipment on the floor, so as to ensure that the transformer can operate at an appropriate working intensity and avoid long-term overload or underload; the main function of the fluctuation amplitude calculation formula is to measure the fluctuation of the transformer load on each floor, and to provide a quantitative indicator for judging the operating stability of the transformer; for example, on a floor, if the startup of some electrical equipment is random, by calculating the fluctuation amplitude of the transformer load dynamic curve, the degree of influence of this randomness on the operation of the transformer can be accurately evaluated; a larger fluctuation amplitude means that the load change of the transformer is more drastic, which may lead to increased loss and shortened life of the transformer. By monitoring this indicator, timely measures can be taken, such as adding voltage stabilizing equipment or adjusting the use strategy of electrical equipment, to ensure the stable operation of the transformer.

[0074] Furthermore, based on the fluctuation range BF[DCL i (t)], construct a fluctuation amplitude-time coordinate system, the abscissa of the fluctuation amplitude-time coordinate system is the data acquisition period, and the ordinate of the fluctuation amplitude-time coordinate system is the transformer load curve DCL corresponding to each data acquisition period i The fluctuation range of (t) BF[DCL i (t)].

[0075] Based on the fluctuation amplitude-time coordinate system, the slope between adjacent coordinate points is calculated using the following formula:

[0076]

[0077] Among them, k i (t→t+1) represents the slope between adjacent coordinate points, BF[DCL i (t+1)] represents the fluctuation amplitude in the t+1th data collection cycle.

[0078] It should be noted that the main function of this formula is to reflect the speed of change of the transformer load fluctuation amplitude between adjacent time points, and is used to monitor short-term changes in the transformer operating status; for example, when a large-scale electrical equipment in a commercial building suddenly starts or stops, by calculating the slope of the fluctuation amplitude between adjacent time points, the immediate impact of such an event on the transformer operation can be quickly detected; this rapid monitoring of short-term changes can help operation and maintenance personnel respond in a timely manner, such as determining whether a temporary load adjustment of the transformer is required, or checking whether there are faults in related electrical equipment, thereby ensuring the stable operation of the transformer in a short period of time.

[0079] Step S4: Calculate the slope mean value within all data collection cycles; obtain the maximum fluctuation range in the fluctuation amplitude-time coordinate system within all data collection cycles; calculate the comprehensive health index of the transformer based on the slope mean value and the maximum fluctuation range, preset the threshold, analyze and output the warning.

[0080] Specifically, based on the slope k between adjacent coordinate points i (t→t+1), calculate the slope mean value within all data collection periods, the calculation formula is as follows:

[0081]

[0082] Among them, k i It represents the mean slope value during all data collection periods.

[0083] Furthermore, based on the fluctuation amplitude-time coordinate system, the maximum fluctuation range of the fluctuation amplitude in the entire data collection period is obtained by the following formula:

[0084] ΔBF[DCL i (t)] = BF[DCL i (t)] max -BF[DCL i (t)] min ;

[0085] Among them, ΔBF[DCL i (t)] represents the maximum fluctuation range, BF[DCL i (t)] max Indicates the maximum fluctuation range within the entire data collection period, BF[DCL i (t)] min Indicates the minimum fluctuation amplitude within the entire data collection period.

[0086] Furthermore, based on the slope mean k i and the maximum fluctuation range ΔBF[DCL i (t)], calculate the comprehensive health index of the transformer on the i-th floor, and the calculation formula is as follows:

[0087] F i =α×k i ×β×ΔBF[DCL i (t)];

[0088] Among them, F i represents the comprehensive health index of the transformer on the i-th floor, and α represents the preset slope mean k i The influence factor of β represents the preset maximum fluctuation range ΔBF[DCL i (t)].

[0089] Furthermore, a threshold value τ of the transformer comprehensive health index is preset. If the transformer comprehensive health index F i >τ, the health status of the transformer on the ith floor is judged to be abnormal, and an early warning is output.

[0090] Let i=i+1, and perform comprehensive transformer health index analysis on all floors in the commercial building.

[0091] It should be noted that when the slope and the fluctuation range are multiplied, the result obtained is a comprehensive consideration of the transformer's operating stability. When the slope is large and the fluctuation range is also large, the result of the multiplication of the two will be large, which means that the transformer not only has a more obvious load fluctuation trend in the long term, but also has a significant load fluctuation degree in extreme cases. This situation indicates that the transformer has poor operating stability and may face a higher risk of failure. For example, in an industrial production environment, if the result of the multiplication of the slope and the fluctuation range of a transformer is large, it means that the transformer may often be in an unstable operating state, and may cause increased stress in the internal windings and accelerated aging of insulation materials due to frequent load changes. In addition, when evaluating the comprehensive health indicators of the transformer, the result of the multiplication of the slope and the fluctuation range can be used as an important weight factor to help operation and maintenance personnel more accurately judge the health status of the transformer. For example, when setting the threshold of the comprehensive health indicator, considering the result of the multiplication of the slope and the fluctuation range, the health level of the transformer can be more reasonably divided. If the multiplication result is large, even if other indicators are relatively normal, it may indicate that the transformer is in a potential unhealthy state and needs to be monitored more closely or maintained in advance.

[0092] See also Figure 2 In the second embodiment of the present invention, a transformer health status diagnosis system based on the Internet of Things is provided, which includes a data acquisition module, a coordinate system and curve construction module, a load mean and slope calculation module, and a transformer health early warning analysis module.

[0093] The data collection module collects the load data of all transformers in the commercial building according to the floors of the commercial building and constructs a load data set.

[0094] The coordinate system and curve construction module: constructs a data collection cycle and records the load data as periodic load data; constructs a transformer load data-time coordinate system based on the periodic load data, and sequentially connects the coordinate points to construct a transformer load curve.

[0095] The load mean and slope calculation module: calculates the load mean based on the transformer load curve; calculates the fluctuation amplitude of the transformer load curve based on the load mean, and constructs a fluctuation amplitude-time coordinate system; calculates the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system.

[0096] The transformer health warning analysis module: calculates the slope mean value within the entire data collection cycle; obtains the maximum fluctuation range in the fluctuation amplitude-time coordinate system within the entire data collection cycle; based on the slope mean value and the maximum fluctuation range, calculates the comprehensive health index of the transformer, presets the threshold, analyzes and outputs the warning.

[0097] Furthermore, the data acquisition module includes a data acquisition unit.

[0098] The data acquisition unit is configured to acquire all transformers in the commercial building, collect load data of all transformers in the commercial building through a data acquisition terminal, summarize the load data of all transformers in the commercial building according to the floors of the commercial building, and construct a load data set according to the summarized data.

[0099] Furthermore, the coordinate system and curve construction module includes a coordinate system construction unit and a curve construction unit.

[0100] The coordinate system construction unit: divides the time in a day evenly into multiple hours, constructs a data collection cycle with the hour as a unit, and records the load data of a single transformer in a single floor within a single data collection cycle as periodic load data; based on the periodic load data, constructs a transformer load data-time coordinate system based on a single floor, the horizontal axis of the transformer load data-time coordinate system is the transformers arranged in sequence on a single floor, and the vertical axis of the transformer load data-time coordinate system is the load data of the transformers arranged in sequence on a single floor in a single data collection cycle.

[0101] The curve construction unit: obtains the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtains all the coordinate points and connects them in sequence to form a curve, and records the curve as the transformer load curve of a single floor in a single data collection cycle.

[0102] Furthermore, the load mean and slope calculation module includes a load mean calculation unit and a slope calculation unit.

[0103] The load mean value calculation unit calculates the load mean value of a single floor in a single data collection cycle based on the transformer load curve; and calculates the fluctuation amplitude of the transformer load curve based on the load mean value of a single floor in a single data collection cycle.

[0104] The slope calculation unit: constructs a fluctuation amplitude-time coordinate system based on the fluctuation amplitude, wherein the horizontal coordinate of the fluctuation amplitude-time coordinate system is the data acquisition period, and the vertical coordinate of the fluctuation amplitude-time coordinate system is the fluctuation amplitude of the transformer load curve corresponding to each data acquisition period; and calculates the slope between adjacent coordinate points based on the fluctuation amplitude-time coordinate system.

[0105] Furthermore, the transformer health early warning analysis module includes a slope mean and maximum fluctuation range construction unit, a transformer comprehensive health index construction unit and an early warning analysis unit.

[0106] The slope mean and maximum fluctuation range construction unit calculates the slope mean within the entire data collection cycle based on the slope between adjacent coordinate points; and obtains the maximum fluctuation range of the fluctuation amplitude within the entire data collection cycle based on the fluctuation amplitude-time coordinate system.

[0107] The transformer comprehensive health index construction unit calculates the transformer comprehensive health index of a single floor based on the slope mean and the maximum fluctuation range.

[0108] The early warning analysis unit: presets a transformer comprehensive health index threshold value. If the transformer comprehensive health index of a single floor is greater than the transformer comprehensive health index threshold value, it is determined that the health status of the transformer on the single floor is abnormal, and an early warning is output; and the transformer comprehensive health index analysis is performed on all floors in the commercial building.

[0109] In the third embodiment: since the load reflects the overall situation of power consumption in the power system to some extent, and active power and reactive power are different components thereof, they are interrelated and affect each other through the power triangle and power factor;

[0110] Therefore, in order to verify the load data of the present invention, this third embodiment is demonstrated through data modeling.

[0111] Taking phase A as an example, the voltage on the high-voltage side of the transformer is calculated using the following formula:

[0112] U 1A =U 2A ×K+Z×I2A ∠(90-θ 2A );

[0113] Among them, U 1A Indicates the voltage of phase A on the high voltage side of the transformer, U 2A represents the A-phase voltage on the low-voltage side, K represents the rated transformation ratio of the transformer, Z represents the equivalent impedance, I 2A Indicates the current of phase A on the low voltage side, θ 2A represents the power factor angle of phase A on the low voltage side, 1 represents the high voltage side, and 2 represents the low voltage side; similarly, solve for U 1B ,U 1C .

[0114] Taking phase A as an example, the single-phase reactive power of the transformer is calculated using the following formula:

[0115]

[0116] Among them, Q A Indicates the reactive power of transformer phase A, I 1A Indicates the A-phase current on the high-voltage side of the transformer, I0 indicates the percentage of no-load current, U k represents the percentage of the transformer short-circuit impedance voltage during the kth operation, and S represents the rated capacity of the transformer; similarly, Q B ,Q C .

[0117] Taking phase A as an example, the single-phase active power loss of the transformer is calculated using the following formula:

[0118]

[0119] Among them, P SA Indicates the active power loss of phase A of the transformer, U 2A Indicates the voltage of phase A on the low voltage side, I 2A Indicates the current of phase A on the low voltage side, θ 2A represents the power factor angle of phase A on the low voltage side; similarly, solve for P SB ,P SC .

[0120] Assuming that a total of k data packets are uploaded in this cycle, where one data packet is generated in one operation, the average power loss in this cycle is calculated by the following formula:

[0121]

[0122] Among them, P S Represents the average power loss of the transformer, P SA (i) represents the active power loss value of phase A calculated for the i-th time in a calculation cycle, P SB(i) represents the active power loss value of phase B calculated for the i-th time in a calculation cycle, P SC (i) represents the active power loss value of phase C calculated for the i-th time in a calculation cycle, Δt(i) represents the time deviation between the i-th uploaded data and the i-1-th uploaded data in a calculation cycle, and the unit is s, and k represents the total number of uploaded data packets.

[0123] The average load during this period is calculated using the following formula:

[0124]

[0125] Among them, S represents the average load, U 2A (i) represents the voltage on the low-voltage side of phase A corresponding to the i-th uploaded data in a calculation cycle, I 2A (i) represents the current on the low-voltage side of phase A corresponding to the i-th uploaded data in a calculation cycle, U 2B (i) represents the voltage on the low-voltage side of phase B corresponding to the i-th uploaded data in a calculation cycle, I 2B (i) represents the current on the low-voltage side of phase B corresponding to the i-th uploaded data in a calculation cycle, U 2C (i) represents the voltage on the low-voltage side of phase C corresponding to the i-th uploaded data in a calculation cycle, I 2C (i) represents the current on the low-voltage side of phase C corresponding to the i-th uploaded data in a calculation cycle.

[0126] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0127] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A transformer health status diagnosis method based on the Internet of Things, characterized in that: The method comprises the following steps: Step S1: collecting load data of all transformers in the commercial building according to the floors of the commercial building, and constructing a load data set; Step S2: constructing a data collection cycle, recording the load data as periodic load data; constructing a transformer load data-time coordinate system based on the periodic load data, and sequentially connecting the coordinate points to construct a transformer load curve; Step S3: Based on the transformer load curve, calculate the load mean; based on the load mean, calculate the fluctuation amplitude of the transformer load curve, and construct a fluctuation amplitude-time coordinate system; calculate the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system; Step S4: Calculate the slope mean value within all data collection cycles; obtain the maximum fluctuation range in the fluctuation amplitude-time coordinate system within all data collection cycles; calculate the comprehensive health index of the transformer based on the slope mean value and the maximum fluctuation range, preset the threshold, analyze and output the warning.

2. The method for diagnosing transformer health status based on the Internet of Things according to claim 1 is characterized in that: The specific implementation process of step S1 includes: Acquire all transformers in the commercial building, collect load data of all transformers in the commercial building through a data acquisition terminal, and summarize the load data of all transformers in the commercial building according to the floors of the commercial building; According to the summary data, a load data set is constructed, denoted as FH = {LD i,a |i∈[1,I],a∈[1,A}, where LD i,a represents the load data of the ath transformer in the i-th floor, I represents the total number of floors in the commercial building, and A represents the total number of transformers in the i-th floor.

3. The method for diagnosing transformer health status based on the Internet of Things according to claim 2 is characterized in that: The specific implementation process of step S2 includes: The time in a day is evenly divided into T hours, and the data collection cycle is constructed with the hour as the unit. The load data LD of the ath transformer in the i-th floor in the t-th data collection cycle is i,a Recorded as periodic load data LD i,a (t); Based on periodic load data LD i,a (t), constructing a transformer load data-time coordinate system based on the i-th floor, wherein the abscissa of the transformer load data-time coordinate system is A transformers arranged sequentially on the i-th floor, and the ordinate of the transformer load data-time coordinate system is the load data of A transformers arranged sequentially on the i-th floor in the t-th data collection cycle; Obtain the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtain all the coordinate points, and connect them in sequence to form a curve, and record the curve as the transformer load curve DCL of the i-th floor in the t-th data collection cycle i (t).

4. The method for diagnosing transformer health status based on the Internet of Things according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Based on transformer load curve DCL i (t), calculate the load mean of the i-th floor in the t-th data collection cycle, the calculation formula is as follows: in, represents the load mean of the i-th floor in the t-th data collection cycle; Based on the load mean of the i-th floor in the t-th data collection cycle Calculate the transformer load curve DCL i The fluctuation range of (t) is calculated as follows: Among them, BF[DCL i (t)] represents the transformer load curve DCL of the ith floor in the tth data collection cycle i (t) fluctuation range; Based on the fluctuation range BF[DCL i (t)], construct a fluctuation amplitude-time coordinate system, the abscissa of the fluctuation amplitude-time coordinate system is the data acquisition period, and the ordinate of the fluctuation amplitude-time coordinate system is the transformer load curve DCL corresponding to each data acquisition period i The fluctuation range of (t) BF[DCL i (t)]; Based on the fluctuation amplitude-time coordinate system, the slope between adjacent coordinate points is calculated using the following formula: Among them, k i (t→t+1) represents the slope between adjacent coordinate points, BF[DCL i (t+1)] represents the fluctuation amplitude in the t+1th data collection cycle.

5. The method for diagnosing transformer health status based on the Internet of Things according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Based on the slope k between adjacent coordinate points i (t→t+1), calculate the slope mean value within all data collection periods, the calculation formula is as follows: Among them, k i It represents the slope mean value in all data collection cycles; Based on the fluctuation amplitude-time coordinate system, the maximum fluctuation range of the fluctuation amplitude in the entire data collection period is obtained by the following formula: ΔBF[DCL i (t)]=BF[DCL i (t)] max -BF[DCL i (t)] min ; Among them, ΔBF[DCL i (t)] represents the maximum fluctuation range, BF[DCL i (t)] max Indicates the maximum fluctuation range within the entire data collection period, BF[DCL i (t)] min Indicates the minimum fluctuation amplitude within the entire data collection cycle; Based on the slope mean k i and the maximum fluctuation range ΔBF[DCL i (t)], calculate the comprehensive health index of the transformer on the i-th floor, and the calculation formula is as follows: F i =α×k i ×β×ΔBF[DCL i (t)]; Among them, F i represents the comprehensive health index of the transformer on the i-th floor, and α represents the preset slope mean k i The influence factor of β represents the preset maximum fluctuation range ΔBF[DCL i (t)] impact factor; Preset the transformer comprehensive health index threshold τ, if the transformer comprehensive health index F of the i-th floor i >τ, the health status of the transformer on the ith floor is determined to be abnormal, and an early warning is output; Let i=i+1, and perform comprehensive transformer health index analysis on all floors in the commercial building.

6. A transformer health status diagnosis system based on the Internet of Things, executing a transformer health status diagnosis method based on the Internet of Things as described in any one of claims 1-5, characterized in that: The system includes: a data acquisition module, a coordinate system and curve construction module, a load mean and slope calculation module and a transformer health warning analysis module: The data collection module collects load data of all transformers in the commercial building according to the floors of the commercial building and constructs a load data set; The coordinate system and curve construction module: constructs a data collection cycle, records the load data as periodic load data; constructs a transformer load data-time coordinate system based on the periodic load data, and sequentially connects the coordinate points to construct a transformer load curve; The load mean and slope calculation module: calculates the load mean based on the transformer load curve; calculates the fluctuation amplitude of the transformer load curve based on the load mean, and constructs a fluctuation amplitude-time coordinate system; calculates the slope between adjacent coordinate points in the fluctuation amplitude-time coordinate system; The transformer health warning analysis module: calculates the slope mean value within the entire data collection cycle; obtains the maximum fluctuation range in the fluctuation amplitude-time coordinate system within the entire data collection cycle; based on the slope mean value and the maximum fluctuation range, calculates the comprehensive health index of the transformer, presets the threshold, analyzes and outputs the warning.

7. The transformer health status diagnosis system based on the Internet of Things according to claim 6 is characterized in that: The data acquisition module includes a data acquisition unit; The data acquisition unit: acquires all transformers in the commercial building, collects load data of all transformers in the commercial building through the data acquisition terminal, and summarizes the load data of all transformers in the commercial building according to the floors of the commercial building; Based on the summary data, a load data set is constructed.

8. The transformer health status diagnosis system based on the Internet of Things according to claim 7 is characterized in that: The coordinate system and curve construction module includes a coordinate system construction unit and a curve construction unit; The coordinate system construction unit: divides the time in a day evenly into multiple hours, constructs a data collection cycle with the hour as a unit, and records the load data of a single transformer in a single floor within a single data collection cycle as periodic load data; based on the periodic load data, constructs a transformer load data-time coordinate system based on a single floor, the abscissa of the transformer load data-time coordinate system is the transformers arranged in sequence on a single floor, and the ordinate of the transformer load data-time coordinate system is the load data of the transformers arranged in sequence on a single floor in a single data collection cycle; The curve construction unit: obtains the coordinate points of the transformer and the load data corresponding to the transformer in the transformer load data-time coordinate system; obtains all the coordinate points and connects them in sequence to form a curve, and records the curve as the transformer load curve of a single floor in a single data collection cycle.

9. The transformer health status diagnosis system based on the Internet of Things according to claim 8 is characterized in that: The load mean and slope calculation module includes a load mean calculation unit and a slope calculation unit; The load average calculation unit calculates the load average of a single floor in a single data collection cycle based on the transformer load curve; Calculate the fluctuation range of transformer load curve based on the load average of a single floor in a single data collection cycle; The slope calculation unit: constructs a fluctuation amplitude-time coordinate system based on the fluctuation amplitude, wherein the abscissa of the fluctuation amplitude-time coordinate system is the data acquisition period, and the ordinate of the fluctuation amplitude-time coordinate system is the fluctuation amplitude of the transformer load curve corresponding to each data acquisition period; Based on the fluctuation amplitude-time coordinate system, the slope between adjacent coordinate points is calculated.

10. The transformer health status diagnosis system based on the Internet of Things according to claim 9, characterized in that: The transformer health early warning analysis module includes a slope mean and maximum fluctuation range construction unit, a transformer comprehensive health index construction unit and an early warning analysis unit; The slope mean and maximum fluctuation range construction unit calculates the slope mean within the entire data collection cycle based on the slopes between adjacent coordinate points; Based on the fluctuation amplitude-time coordinate system, the maximum fluctuation range of the fluctuation amplitude in all data collection cycles is obtained; The transformer comprehensive health index construction unit calculates the transformer comprehensive health index of a single floor based on the slope mean and the maximum fluctuation range; The early warning analysis unit: presets a transformer comprehensive health index threshold value, and if the transformer comprehensive health index of a single floor is greater than the transformer comprehensive health index threshold value, determines that the health state of the transformer on the single floor is abnormal, and outputs an early warning; A comprehensive transformer health index analysis is performed on all floors in the commercial building.

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