Dry-type transformer temperature control method and system based on PID (Proportion Integration Differentiation) technology

Abstract

The invention relates to the technical field of control and regulation, in particular to a dry-type transformer temperature control method and system based on the PID technology, and the method comprises the steps: obtaining the temperature data and load data of a dry-type transformer in each target operation stage; determining the load stability degree of the target operation stage by using the load data; determining an abnormal operation stage by using the load stability degree and the temperature data; determining an abnormal coefficient of the transformer in the abnormal operation stage by utilizing the temperature data; and regulating and controlling the output parameters of the PID controller in real time by using the abnormal coefficient of the transformer. According to the method, the two data dimensions of the temperature data and the load data are analyzed and compared, PID control parameters are corrected in real time, more accurate control and adjustment of the temperature of the dry-type transformer are achieved, and normal, safe and stable operation of the transformer and production equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation, and particularly relates to a dry-type transformer temperature control method and system based on PID technology. Background Art

[0002] A dry-type transformer is a transformer in which the iron core and windings are not impregnated in insulating oil, and is widely used in places such as local lighting, high-rise buildings, commercial buildings, airports, etc. The safe operation and service life of a dry-type transformer largely depend on the safety and reliability of the insulation of the transformer windings. When the winding temperature exceeds the insulation tolerance temperature, the insulation will be damaged, which is one of the main reasons for the abnormal operation of the transformer.

[0003] Some existing transformer temperature control schemes have a slow response speed to temperature changes. Some simply start or stop the cooling equipment based on simple threshold judgments. These methods cannot make precise adjustments according to the subtle changes in temperature, and cannot suppress the temperature rise in a timely and effective manner. As a result, the transformer may be in a high-temperature state in a short period of time, affecting the stable operation of the transformer, accelerating the aging of the equipment, reducing its service life, and even posing a safety risk. Summary of the Invention

[0004] In order to solve the technical problem that the existing transformer temperature control scheme has a slow response speed to temperature changes and is likely to affect the stable operation of the transformer, the purpose of the present invention is to provide a dry-type transformer temperature control method and system based on PID technology, and the specific technical solutions adopted are as follows:

[0005] The present invention provides a dry-type transformer temperature control method based on PID technology, and the method includes:

[0006] Obtain the temperature data and load data of the dry-type transformer in each target operation stage;

[0007] Use the load data to determine the load stability degree in the target operation stage;

[0008] Use the load stability degree and the temperature data to determine the abnormal operation stage;

[0009] Use the temperature data to determine the transformer abnormal coefficient in the abnormal operation stage;

[0010] Use the transformer abnormal coefficient to adjust the output parameters of the PID controller in real time.

[0011] Further, using the load data to determine the load stability degree in the target operation stage includes:

[0012] Use the load data to determine the overall load change trend and load change amplitude in the target operation stage;

[0013] Determine the load stability degree of the target operation stage by using the overall change trend of the load and the load change amplitude.

[0014] Further, determining the load stability degree of the target operation stage by using the overall change trend of the load and the load change amplitude includes:

[0015] Determine the first-order difference sequence of the load data;

[0016] Determine the total amount of data in the first-order difference sequence and the number of changing data greater than or equal to the preset stability threshold;

[0017] Determine the load stability degree of the target operation stage by using the overall change trend of the load, the load change amplitude, the total amount of data, and the number of changing data.

[0018] Further, determining the abnormal operation stage by using the load stability degree and the temperature data includes:

[0019] Determine the similarity of the change trends between the load data and the temperature data;

[0020] Determine the following consistency of the temperature with respect to the load change in the target operation stage by using the similarity of the change trends;

[0021] Determine whether the target operation stage is an abnormal operation stage by using the load stability degree and the following consistency.

[0022] Further, determining the following consistency of the temperature with respect to the load change in the target operation stage by using the similarity of the change trends includes:

[0023] Determine the change synchronization degree between the extreme points corresponding to the load data and the temperature data respectively;

[0024] Determine the following consistency of the temperature with respect to the load change in the target operation stage by using the similarity of the change trends and the change synchronization degree.

[0025] Further, determining whether the target operation stage is an abnormal operation stage by using the load stability degree and the following consistency includes:

[0026] Determine the corresponding cluster to which the target operation stage belongs according to the load stability degree;

[0027] Determine the temperature change difference between the target operation stage and any other operation stage in the cluster to which it belongs;

[0028] Determine the abnormal degree of the transformer temperature in the target operation stage by using the load stability degree, the following consistency, and the temperature change difference.

[0029] Compare the abnormal degree of the transformer temperature with a preset temperature abnormality threshold to determine whether the target operation stage is an abnormal operation stage.

[0030] Further, use the temperature data to determine the transformer abnormality coefficient in the abnormal operation stage, including:

[0031] Determine the target maintenance stage corresponding to the abnormal operation stage;

[0032] Determine the temperature drop between the initial temperature data and any intermediate temperature data in the target maintenance stage;

[0033] Determine the abnormal degree of the transformer temperature;

[0034] Use the abnormal degree of the transformer temperature and the temperature drop to calculate the transformer abnormality coefficient in the abnormal operation stage.

[0035] Further, use the abnormal degree of the transformer temperature and the temperature drop to calculate the transformer abnormality coefficient in the abnormal operation stage, including:

[0036] Determine the number of extreme points in the temperature data corresponding to the target maintenance stage;

[0037] Use the abnormal degree of the transformer temperature, the temperature drop, and the number of extreme points to calculate the transformer abnormality coefficient in the abnormal operation stage.

[0038] Further, use the transformer abnormality coefficient to adjust the output parameters of the PID controller in real time, including:

[0039] Determine the initial input parameters of the PID controller;

[0040] Use the transformer abnormality coefficient to correct the initial input parameters to adjust the output parameters of the PID controller in real time.

[0041] The present invention also provides a dry-type transformer temperature control system based on PID technology, which is used to implement the dry-type transformer temperature control method described in any one of the above; the system includes:

[0042] A data acquisition module for obtaining the temperature data and load data of the dry-type transformer in each target operation stage;

[0043] An abnormality location module for using the load data to determine the load stability degree of the target operation stage; using the load stability degree and the temperature data to determine the abnormal operation stage;

[0044] An output regulation module is used to determine the abnormal coefficient of the transformer in the abnormal operation stage by using the temperature data, and to regulate the output parameters of the PID controller in real time by using the abnormal coefficient of the transformer.

[0045] The present invention has the following beneficial effects:

[0046] Based on the principle that the temperature data of the dry-type transformer in each operation stage has a high correlation with the load data of the connected equipment, the present invention analyzes and compares these two data dimensions, and determines whether and to what extent the temperature change of the transformer is abnormal through the load operation condition. The abnormal coefficient of the transformer is used to compensate each transformer temperature data input to the PID in real time, and then the control parameters of the PID are corrected in real time, realizing more accurate control and regulation of the temperature of the dry-type transformer, greatly avoiding the safety hazards caused by the dry-type transformer due to excessive temperature and untimely response to temperature changes, and ensuring the normal, safe and stable operation of the transformer and production equipment. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a step flowchart of a dry-type transformer temperature control method based on PID technology provided by an embodiment of the present invention;

[0049] Figure 2 It is a refined flowchart of step S2 in a dry-type transformer temperature control method based on PID technology provided by an embodiment of the present invention;

[0050] Figure 3 It is a refined flowchart of step S3 in a dry-type transformer temperature control method based on PID technology provided by an embodiment of the present invention;

[0051] Figure 4 It is a refined flowchart of step S4 in a dry-type transformer temperature control method based on PID technology provided by an embodiment of the present invention;

[0052] Figure 5 It is a structural schematic diagram of the hardware operation environment of a dry-type transformer temperature control device related to the embodiment solution of the present invention;

[0053] Figure 6Schematic diagram of the framework structure of the dry-type transformer temperature control system based on PID technology involved in the embodiment solution of the present invention. Detailed implementation manners

[0054] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a dry-type transformer temperature control method based on PID technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0056] The following specifically describes the specific solution of a dry-type transformer temperature control method provided by the present invention with reference to the drawings.

[0057] Embodiment 1:

[0058] For a dry-type transformer temperature control method provided by the present invention, please refer to Figure 1 , which shows the flowchart of the steps of the dry-type transformer temperature control method based on PID technology provided by an embodiment of the present invention.

[0059] The method includes:

[0060] Step S1, obtaining the temperature data and load data of the dry-type transformer in each target operation stage;

[0061] In this embodiment, a temperature sensor with relatively high precision can be used to collect the temperature of the dry-type transformer in the production workshop. The temperature sensor can be installed at a position that can reflect the temperature of the transformer winding. The temperature sensor can be installed on the surface of the low-voltage winding (because the low-voltage winding is often the part with higher temperature during the operation of the transformer), and a material with good thermal conductivity (such as thermal grease) is used to fill the gap between the sensor and the winding to ensure good heat conduction, so that the temperature sensor can monitor more accurate transformer temperature data;

[0062] The data acquisition unit can receive the temperature data collected by the sensor and transmit it to the data center for storage and analysis;

[0063] The acquisition frequency of the temperature data can be once every 15 minutes, which can be adjusted specifically.

[0064] It is also necessary to obtain the corresponding load data: The corresponding load data can be measured using a smart meter. Here, the load refers to the electrical equipment associated with the transformer, such as the production equipment in the workshop.

[0065] Since the temperature of the transformer is also affected by the ambient temperature, the temperature of the working environment can also be measured as needed.

[0066] According to the above process, the temperature data and the corresponding load data of the dry-type transformer in each operation stage can be collected. Here, the target operation stage refers to the operating conditions of the transformer and the electrical equipment during operation and use.

[0067] Step S2, using the load data, determine the load stability degree of the target operation stage;

[0068] Please refer to Figure 2 , specifically, the step S2 includes:

[0069] Step S21, using the load data, determine the overall load change trend and the load change amplitude of the target operation stage;

[0070] Step S22, using the overall load change trend and the load change amplitude, determine the load stability degree of the target operation stage.

[0071] The step S22 specifically further includes:

[0072] Determine the first-order difference sequence of the load data;

[0073] Determine the total amount of data in the first-order difference sequence and the number of change data greater than or equal to the preset stability threshold;

[0074] Using the overall load change trend, the load change amplitude, the total amount of data, and the number of change data, determine the load stability degree of the target operation stage.

[0075] Electrical equipment usually has a strict regular maintenance plan. For example, for pump equipment, according to its importance and working intensity, there may be minor maintenance every week, month, or quarter, including checking seals, lubrication conditions, motor operating parameters, etc. When performing these minor maintenance, the equipment may stop running briefly, and its related equipment or auxiliary equipment will also stop running. The stage where the equipment stops running here can be called the maintenance stage, which is adjacent and interspersed with the operation stage, that is, the operation stage is followed by the maintenance stage, and the maintenance stage is followed by the operation stage again;

[0076] In industrial production, electrical equipment such as production equipment is often the main load of the dry-type transformer. When the equipment powered by the dry-type transformer stops running, the load of the transformer will also decrease accordingly. When the equipment resumes normal operation, the load will increase again;

[0077] The temperature of a dry-type transformer is mainly affected by the load and the ambient temperature. However, in some factories and workshops, such as chemical plants, for safety considerations, dry-type transformers are usually installed in independent transformer rooms to isolate the transformer from the chemical production area. Through reasonable design and operation of the air-conditioning system, the indoor temperature can be effectively controlled, ensuring a stable ambient temperature, preventing fires or explosion accidents caused by transformer failures, and at the same time facilitating maintenance and management by technicians. Therefore, in this embodiment, only the influence of the load on the temperature of the dry-type transformer needs to be considered, that is, the influence of the ambient temperature is ignored.

[0078] When the electrical equipment is in a normal and stable operating state, the temperature change of the dry-type transformer is relatively gentle. During the period when the equipment stops running, the transformer temperature is also stable, but compared with the temperature during operation, there will be a certain decrease.

[0079] Since the maintenance cycle of the equipment is fixed, the detected transformer temperature also has a certain period. Therefore, according to the maintenance time of the equipment, the operating time of the equipment is divided into an operating stage and a maintenance stage, and the operating stage and the maintenance stage are interspersed.

[0080] Under the condition that the equipment operates normally and stably and the production conditions remain unchanged, the load in each operating stage should be the same. However, during the operation of the equipment, there may also be load changes due to production process adjustments. The change in the load will cause a change in the temperature of the transformer. For example, starting additional auxiliary equipment will increase the load on the transformer, or when some equipment stops, the load will decrease accordingly. Therefore, first, it is necessary to determine the load change situation in each operating stage.

[0081] According to the load data of each target operating stage, a load sequence can be obtained. By performing linear fitting on the load data, it is used to represent the overall change trend of the load during the operating stage, and the absolute value of the slope of this line is obtained and denoted as k.

[0082] The flatter the line, the smaller the slope k, indicating that the load is more stable and changes less during the operating time period.

[0083] Obtain the difference between the maximum value and the minimum value in the load sequence, denoted as z, which is used to represent the change range of the load (load change range) during the operating time period. The smaller the value of z, the smaller the change range and the more stable the load.

[0084] Take the first-order difference of the load sequence to obtain the first-order difference sequence. Normalize the absolute values of all data in the first-order difference sequence to [0, 1]. A stable threshold can be preset to 0.1 (which can be adjusted specifically). Because during stable operation, the load is also relatively stable and the difference between adjacent loads is small, so each data in the first-order difference sequence is close to 0. When there is a change in the load, it will be relatively large. Therefore, when the data in the first-order difference sequence is greater than or equal to the preset stable threshold, it can be considered that a large change in the load has occurred at that point;

[0085] The load stability of each target operation stage can be expressed as:

[0086]

[0087] In the formula, A a represents the load stability (or load stability degree) of any a-th operation stage; k a represents the overall load change trend within the a-th operation stage. The smaller the value of k a , the more stable the load; z a represents the load change amplitude within the a-th operation stage. When there is a load change, the smaller the value of z a , the smaller the load change amplitude and the more stable the load; s a represents the number of all data (total data volume) in the first-order difference sequence within the a-th operation stage; s1 a represents the number of data greater than or equal to the preset stable threshold (number of changed data) in the first-order difference sequence within the a-th operation stage; exp is the exponential function with the natural constant as the base; represents the proportion of data greater than or equal to the preset stable threshold in the total data volume in the first-order difference sequence, The smaller the value of which, the fewer the number of times of large load changes, the higher the load stability, and the larger the corresponding value of A a .

[0088] Through the above implementation process, the load stability of each target operation stage is calculated accordingly.

[0089] Step S3, use the load stability and the temperature data to determine the abnormal operation stage;

[0090] Please refer to Figure 3 , specifically, the step S3 includes:

[0091] Step S31, determine the similarity of the change trends between the load data and the temperature data;

[0092] Step S32, use the similarity of the change trends to determine the follow-up consistency of the temperature with the load change in the target operation stage;

[0093] Step S32 specifically further includes:

[0094] Determine the change synchronization degree between the extreme points corresponding to the load data and the temperature data respectively;

[0095] Use the change trend similarity and the change synchronization degree to determine the following consistency of the temperature with respect to the load change in the target operation stage.

[0096] In step S33, use the load stability degree and the following consistency to determine whether the target operation stage is an abnormal operation stage.

[0097] Step S33 specifically further includes:

[0098] According to the load stability degree, determine the corresponding cluster to which the target operation stage belongs;

[0099] Determine the temperature change difference between the target operation stage and any other operation stage in the corresponding cluster;

[0100] Use the load stability degree, the following consistency, and the temperature change difference to determine the abnormal degree of the transformer temperature in the target operation stage;

[0101] Compare the abnormal degree of the transformer temperature with a preset temperature abnormal threshold to determine whether the target operation stage is an abnormal operation stage.

[0102] When the transformer operates normally and stably, and the load in each operation stage is also stable, then the monitored transformer temperature should also be relatively stable and consistent. Therefore, first, based on the characteristics of the transformer temperature under the same load, preliminarily determine the operation stages with abnormal transformer temperature changes:

[0103] According to the load stability of each operation stage, perform clustering to divide all operation stages into several clusters. In each cluster, the load stabilities of all included operation stages are relatively consistent, but there are significant differences in load stability between each cluster;

[0104] For all operation stages in each cluster, their load stabilities are close. Under normal circumstances, when the loads change consistently, the corresponding transformer temperature changes should also be consistent. Therefore, first determine the consistency of the transformer temperature with respect to the load change for each operation stage in each cluster:

[0105] In the same coordinate system, based on the load data and the temperature data, draw the load change curve and the temperature change curve for each operation stage, with the horizontal axis being time and the vertical axis being load and temperature;

[0106] Obtain the dtw (Dynamic Time Warping) distance between the load change curve and the temperature change curve, denoted as d. It can characterize the similarity of the change trends between the load data and the temperature data. The smaller the value of the dtw distance d between the two curves, the more similar the change trends of the two curves, indicating that the change of temperature is more consistent with the change of load.

[0107] Obtain the extreme points of each curve, that is, the points corresponding to the peaks and valleys. When the temperature and the load change synchronously, the times corresponding to all the extreme points of the two curves should be the same; when the temperature does not change with the load, it may be a transformer fault (local short circuit or decreased heat dissipation performance) or a temperature sensor fault.

[0108] Taking the load change curve as the reference, the absolute value of the time interval between each peak in the load change curve and the peak in the temperature change curve with the closest time interval (similarly for valleys) is denoted as the temperature-load change interval, represented by Δt. It characterizes the change synchronization degree between the extreme points corresponding to the load data and the temperature data. The smaller the value of Δt, the more synchronous the changes of temperature and load.

[0109] Taking any Jth clustering cluster as an example, in any bth target operation stage, the abnormal degree of the transformer temperature can be expressed as:

[0110]

[0111]

[0112] In the formula, Y b represents the abnormal degree of the transformer temperature in the bth target operation stage; A b represents the load stability in the bth target operation stage; when the load is stable, the smaller the dtw distance d b (similarity of change trends) between the temperature change curve and the load change curve, the more stable the change of temperature, the more similar their change trends, and the more normal the temperature; j b represents the number of extreme points in the temperature change curve in the bth target operation stage; Δt i represents the synchronization (change synchronization degree) of temperature and load changes at the ith extreme point. The larger the value of Δt i , the more asynchronous their changes; represents the consistency (following consistency) of temperature change with load in the bth target operation stage, the larger the value, the more inconsistent, reflecting that the temperature is more abnormal.

[0113] C bIndicates the difference in temperature between the b-th target operation stage and the temperature changes in other operation stages within the same cluster; n J Indicates the number of operation stages included in the J-th clustering cluster; Δe b Indicates the absolute value of the first difference (Δe) between the temperature change amplitude (maximum value minus minimum value) of the b-th target operation stage and the temperature change amplitude of any l-th other operation stage within the cluster b = |e b - e l |, where e b Indicates the temperature change amplitude of the b-th stage, and e l Indicates the temperature change amplitude of the l-th stage); Δf b Indicates the absolute value of the second difference between the number of temperature changes (i.e., the number of extreme points in the temperature change curve) of the b-th target operation stage and the number of temperature changes of the l-th operation stage; Δe b ×Δf b Indicates the difference in temperature change between the b-th target operation stage and the temperature changes in other operation stages, Δe b ×Δf b The larger the value of b , the more abnormal the temperature of the b-th operation stage, corresponding to the greater degree of abnormal transformer temperature Y

[0114] Calculate the degree of abnormal temperature of the transformer in all operation stages within each clustering cluster according to the above implementation process; and normalize the value of Y b to the range of [0, 1]. A temperature abnormality threshold of 0.8 (which can be adjusted specifically) can be preset. When the normalized value of Y b is greater than or equal to 0.8, it can be considered that the temperature in the target operation stage is abnormally high, and it is recorded as an abnormal operation stage. Otherwise, it is not recorded as an abnormal operation stage. The abnormal operation stages need to be focused on for analysis.

[0115] Thus, all operation stages with abnormal transformer temperature are determined.

[0116] Step S4, using the temperature data, determine the abnormal coefficient of the transformer in the abnormal operation stage;

[0117] Please refer to Figure 4 , specifically, the step S4 includes:

[0118] Step S41, determine the target maintenance stage corresponding to the abnormal operation stage;

[0119] Step S42, determine the temperature drop between the initial temperature data and any intermediate temperature data in the target maintenance stage;

[0120] Step S43, determine the degree of abnormal transformer temperature;

[0121] Step S44: Calculate the transformer anomaly coefficient in the abnormal operation stage by using the abnormal degree of the transformer temperature and the temperature drop.

[0122] The step S44 specifically further includes:

[0123] Determine the number of extreme points in the temperature data corresponding to the target maintenance stage;

[0124] Calculate the transformer anomaly coefficient in the abnormal operation stage by using the abnormal degree of the transformer temperature, the temperature drop, and the number of extreme points.

[0125] When the transformer temperature is abnormal, it may be caused by a transformer fault or a temperature sensor fault. Under normal circumstances, when the equipment is shut down for maintenance, the load will drop sharply, and the transformer temperature will also drop. Moreover, the temperature will show the characteristics of a relatively fast drop rate at the beginning, then a gradually decreasing drop rate, and finally stabilizing. When the transformer has abnormal heat dissipation, the temperature drop of the transformer after the equipment stops running will be extremely slow. If the temperature sensor fails, during the process of the equipment running from operation to stop, the temperature data may have sudden jumps or random rises and falls;

[0126] Obtain the last temperature data of each abnormal operation stage (which is also the initial temperature data of the target maintenance stage), and the temperature data of the target maintenance stage after this abnormal operation stage;

[0127] Obtain the number of extreme points in the temperature data of the target maintenance stage, denoted as m. The smaller the value of m, the fewer the number of data mutations in the maintenance stage;

[0128] For each operation stage with abnormal temperature, the possibility that the temperature anomaly is caused by a transformer anomaly, that is, the transformer anomaly coefficient, can be expressed as:

[0129] B c =Y c ×exp[-((q1 c -q2 c )×m c )]

[0130] In the formula, B c represents any c-th operation stage with abnormal temperature, and is the possibility of transformer anomaly (transformer anomaly coefficient); Y c represents the abnormal degree of the transformer temperature in any c-th operation stage with abnormal temperature; q1 c represents the earliest temperature data (initial temperature data) in the target maintenance stage corresponding to the c-th operation stage, that is, the last temperature data of the c-th operation stage; q2c It represents the temperature data at any intermediate moment during the maintenance stage of the c-th operation stage. It can be the exact middle moment or the temperature data at any moment between the beginning and the end of the maintenance stage; q1 c -q2 c It can represent the amplitude of temperature drop (temperature decrease rate) during the first half of the maintenance stage, q1 c -q2 c The smaller the value of, the less the temperature drops during the maintenance stage, and the more abnormal it is. It may be caused by a transformer fault or a temperature sensor abnormality. It still needs to be adjusted according to the mutation situation of the temperature data during the entire maintenance process; m c It represents the number of extreme points in the temperature data of the maintenance stage corresponding to the c-th temperature abnormal operation stage, m c The smaller the value, the fewer the mutation times of the data during the maintenance stage, and the more likely it is caused by a transformer abnormality, corresponding to B c The larger the value.

[0131] Calculate the possibility of transformer abnormality for each abnormal operation stage according to the above implementation process; and normalize the value of B c to the range of [1, 2]. Furthermore, it can be compensated to the temperature at each moment of each target operation stage. Among them, the possibility of transformer abnormality at the moment of normal temperature is 1, that is, there is no transformer fault when the transformer abnormality coefficient is equal to 1. When it is greater than 1 and less than or equal to 2, the transformer has different degrees of faults.

[0132] Step S5, use the transformer abnormality coefficient to adjust the output parameters of the PID controller in real time.

[0133] Specifically, the step S5 includes:

[0134] Determine the initial input parameters of the PID controller;

[0135] Use the transformer abnormality coefficient to correct the initial input parameters to adjust the output parameters of the PID controller in real time.

[0136] The transformer abnormality coefficient at each moment can be used to compensate and correct the temperature data at that moment (which belongs to the initial input parameters here), generating a new temperature data set as the input of the temperature control PID (Proportional Integral Derivative, PID control system) controller of the dry-type transformer. The PID outputs the control instructions for the air cooler or other refrigerating machines of the dry-type transformer, such as increasing or decreasing the power, so as to perform different degrees of cooling and heat exchange on the transformer based on different input temperature data, realizing the precise control of the transformer temperature.

[0137] Based on the principle that the temperature data of the dry-type transformer in each operation stage has a high correlation with the load data of the connected equipment, this invention analyzes and compares these two data dimensions, and determines whether and to what extent the temperature change of the transformer is abnormal through the load operation status. Each transformer temperature data input into the PID is compensated in real time through the transformer anomaly coefficient, and then the control parameters of the PID are corrected in real time, realizing more accurate control and adjustment of the temperature of the dry-type transformer, greatly avoiding the safety hazards caused by the over-high temperature of the dry-type transformer and the untimely response to temperature changes, and ensuring the normal, safe and stable operation of the transformer and production equipment.

[0138] Embodiment 2:

[0139] The embodiment of this invention also proposes a dry-type transformer temperature control device based on PID technology. The dry-type transformer temperature control device based on PID technology can be a data calculation and processing device such as a programmable logic controller, a computer, a server, or a combination of multiple devices.

[0140] As Figure 5 shown, Figure 5 is a schematic structural diagram of the hardware operation environment of the dry-type transformer temperature control device based on PID technology involved in the embodiment of this invention.

[0141] As Figure 5 shown, the dry-type transformer temperature control device based on PID technology can include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 can include a display (Display) and an input unit such as a control panel. Optionally, the user interface 1003 can also include a standard wired interface and a wireless interface. The network interface 1004 can optionally include a standard wired interface and a wireless interface (such as a WIFI interface). The memory 1005 can be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 can also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, can include a dry-type transformer temperature control program based on PID technology.

[0142] Those skilled in the art can understand that Figure 5 the hardware structure shown in

[0143] does not constitute a limitation to the device, and it can include more or fewer components than shown in the figure, or combine some components, or arrange different components. Figure 5 ,Figure 5 In [the device], the memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a dry-type transformer temperature control program based on the PID technology.

[0144] In Figure 5 [the device], the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the dry-type transformer temperature control program stored in the memory 1005 and execute the steps in each of the above embodiments.

[0145] Based on the above hardware structure of the dry-type transformer temperature control device based on the PID technology, each embodiment of the dry-type transformer temperature control method based on the PID technology of the present invention is implemented.

[0146] In addition, the present invention also provides a dry-type transformer temperature control system based on the PID technology. Please refer to Figure 6 , the dry-type transformer temperature control system based on the PID technology includes:

[0147] A data acquisition module A10, configured to obtain temperature data and load data of the dry-type transformer in each target operation stage;

[0148] An anomaly location module A20, configured to use the load data to determine the load stability degree of the target operation stage; use the load stability degree and the temperature data to determine the abnormal operation stage;

[0149] An output regulation module A30, configured to use the temperature data to determine the transformer anomaly coefficient in the abnormal operation stage; use the transformer anomaly coefficient to adjust the output parameters of the PID controller in real time.

[0150] Further, the anomaly location module A20 is further configured to:

[0151] Use the load data to determine the overall load change trend and load change amplitude of the target operation stage;

[0152] Use the overall load change trend and the load change amplitude to determine the load stability degree of the target operation stage.

[0153] Further, the anomaly location module A20 is further configured to:

[0154] Determine the first-order difference sequence of the load data;

[0155] Determine the total amount of data in the first-order difference sequence and the number of change data greater than or equal to a preset stability threshold;

[0156] Determine the load stability degree of the target operation stage by using the overall change trend of the load, the load change amplitude, the total amount of data, and the number of changed data.

[0157] Further, the anomaly location module A20 is further configured to:

[0158] Determine the similarity of the change trends between the load data and the temperature data;

[0159] Use the similarity of the change trends to determine the consistency of the temperature following the load change in the target operation stage;

[0160] Use the load stability degree and the consistency of the temperature following the load change to determine whether the target operation stage is an abnormal operation stage.

[0161] Further, the anomaly location module A20 is further configured to:

[0162] Determine the change synchronization degree between the extreme value points corresponding to the load data and the temperature data respectively;

[0163] Use the similarity of the change trends and the change synchronization degree to determine the consistency of the temperature following the load change in the target operation stage.

[0164] Further, the anomaly location module A20 is further configured to:

[0165] Determine the corresponding cluster to which the target operation stage belongs according to the load stability degree;

[0166] Determine the temperature change difference between the target operation stage and any other operation stage in the corresponding cluster;

[0167] Use the load stability degree, the consistency of the temperature following the load change, and the temperature change difference to determine the abnormal degree of the transformer temperature in the target operation stage;

[0168] Compare the abnormal degree of the transformer temperature with a preset temperature anomaly threshold to determine whether the target operation stage is an abnormal operation stage.

[0169] Further, the output regulation module A30 is further configured to:

[0170] Determine the target maintenance stage corresponding to the abnormal operation stage;

[0171] Determine the temperature drop between the initial temperature data and any intermediate temperature data in the target maintenance stage;

[0172] Determine the abnormal degree of the transformer temperature of the transformer;

[0173] Calculate the abnormal coefficient of the transformer in the abnormal operation stage by using the abnormal degree of the transformer temperature and the temperature drop.

[0174] Furthermore, the output regulation module A30 is further configured to:[[]]

[0175] Determine the number of extreme points in the temperature data corresponding to the target maintenance stage;

[0176] Calculate the abnormal coefficient of the transformer in the abnormal operation stage by using the abnormal degree of the transformer temperature, the temperature drop and the number of extreme points.

[0177] Furthermore, the output regulation module A30 is further configured to:[[]]

[0178] Determine the initial input parameters of the PID controller;

[0179] Modify the initial input parameters by using the abnormal coefficient of the transformer to adjust the output parameters of the PID controller in real time.

[0180] The specific implementation manner of the dry-type transformer temperature control system based on the PID technology of the present invention is basically the same as that of the above-mentioned embodiments of the dry-type transformer temperature control method based on the PID technology, and will not be elaborated here.

[0181] In addition, the present invention also provides a computer-readable storage medium. A dry-type transformer temperature control program based on the PID technology is stored on the computer-readable storage medium of the present invention. When the dry-type transformer temperature control program based on the PID technology is executed by a processor, the steps of the dry-type transformer temperature control method as described above are implemented.

[0182] Among them, the method implemented when the dry-type transformer temperature control program based on the PID technology is executed can refer to the various embodiments of the dry-type transformer temperature control method based on the PID technology of the present invention, and will not be elaborated here.

[0183] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0184] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0185] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0186] The above are only the preferred embodiments of the present invention, and do not limit the protection scope of the present invention. Any equivalent structure / method transformation made by using the content of the specification and drawings of the present invention under the inventive concept of the present invention, or any direct / indirect application in other related technical fields is included in the protection scope of the present invention.

Claims

1. A dry-type transformer temperature control method based on PID technology, characterized in that: The method comprises: Obtain temperature data and load data of the dry-type transformer at each target operation stage; Determining load stability during a target operation phase using the load data; Determining an abnormal operation stage using the load stability and the temperature data; Determine the transformer abnormality coefficient in the abnormal operation stage by using the temperature data; The transformer abnormality coefficient is used to adjust the output parameters of the PID controller in real time.

2. The dry-type transformer temperature control method based on PID technology according to claim 1 is characterized in that: Using the load data, determining the load stability of the target operation phase includes: Using the load data, determining the overall load change trend and load change amplitude in the target operation phase; The load stability in the target operation phase is determined by using the overall load change trend and the load change amplitude.

3. The dry-type transformer temperature control method based on PID technology according to claim 2 is characterized in that: Determining the load stability in the target operation phase by using the overall load change trend and the load change amplitude includes: Determining a first-order difference sequence of the load data; Determine the total amount of data in the first-order difference sequence and the amount of change data that is greater than or equal to a preset stability threshold; The load stability in the target operation phase is determined by using the overall load change trend and the load change amplitude as well as the total amount of data and the amount of changed data.

4. The dry-type transformer temperature control method based on PID technology according to claim 1 is characterized in that: Determining an abnormal operation stage by using the load stability and the temperature data includes: Determining a similarity of a change trend between the load data and the temperature data; Determine the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend; By using the load stability and the following consistency, it is determined whether the target operation phase is an abnormal operation phase.

5. The dry-type transformer temperature control method based on PID technology according to claim 4 is characterized in that: Determining the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend includes: Determine the degree of synchronization of changes between the extreme value points corresponding to the load data and the temperature data; The change trend similarity and the change synchronization are used to determine the consistency of the temperature in the target operation stage following the load change.

6. The dry-type transformer temperature control method based on PID technology according to claim 4 is characterized in that: Determining whether the target operation phase is an abnormal operation phase by using the load stability and the following consistency includes: According to the load stability, determining the cluster corresponding to the target operation stage; Determine the temperature change difference between the target operation stage and any other operation stage in the cluster; Determine the abnormality of transformer temperature in the target operation phase by using the load stability, the follow consistency and the temperature change difference; The transformer temperature abnormality degree is compared with a preset temperature abnormality threshold to determine whether the target operation stage is an abnormal operation stage.

7. The dry-type transformer temperature control method based on PID technology according to claim 1 is characterized in that: Using the temperature data, determining the transformer abnormality coefficient in the abnormal operation stage includes: Determine the target maintenance phase corresponding to the abnormal operation phase; Determine the temperature drop between the initial temperature data and any intermediate temperature data in the target maintenance stage; Determine the degree of transformer temperature abnormality of the transformer; The transformer abnormality coefficient in the abnormal operation stage is calculated using the abnormal degree of transformer temperature and the temperature drop.

8. The dry-type transformer temperature control method based on PID technology according to claim 7 is characterized in that: The transformer abnormality coefficient in the abnormal operation stage is calculated by using the abnormal degree of transformer temperature and the temperature drop, including: Determine the number of extreme points in the temperature data corresponding to the target maintenance stage; The transformer abnormality coefficient in the abnormal operation stage is calculated using the abnormal degree of transformer temperature, the temperature drop and the number of extreme value points.

9. The dry-type transformer temperature control method based on PID technology according to claim 1 is characterized in that: The transformer abnormality coefficient is used to adjust the output parameters of the PID controller in real time, including: Determine the initial input parameters of the PID controller; The transformer abnormality coefficient is used to correct the initial input parameter to adjust the output parameter of the PID controller in real time.

10. A dry-type transformer temperature control system based on PID technology, characterized in that: The system is used to implement the dry-type transformer temperature control method based on PID technology as described in any one of claims 1 to 9; the system includes: A data acquisition module is used to obtain temperature data and load data of the dry-type transformer at each target operation stage; an abnormality locating module, used to determine the load stability of the target operation stage by using the load data; and to determine the abnormal operation stage by using the load stability and the temperature data; The output control module is used to determine the abnormal coefficient of the transformer in the abnormal operation stage by using the temperature data; and to control the output parameters of the PID controller in real time by using the abnormal coefficient of the transformer.

Images