Dry-type transformer temperature control method and system based on PID technology

Abstract

The present invention relates to the field of control and regulation technology, and more specifically to a dry-type transformer temperature control method and system based on PID technology. The method comprises: obtaining temperature data and load data of the dry-type transformer in each target operating stage; using the load data to determine the load stability of the target operating stage; using the load stability and temperature data to determine the abnormal operating stage; using the temperature data to determine the transformer abnormality coefficient in the abnormal operating stage; and using the transformer abnormality coefficient to adjust the output parameters of the PID controller in real time. The present invention analyzes and compares the two data dimensions of temperature data and load data, and makes real-time corrections to the PID control parameters, thereby achieving more precise control and regulation of the dry-type transformer temperature, thereby ensuring the normal, safe, and stable operation of the transformer and production equipment.

Description

Technical Field

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

[0002] Dry-type transformers, whose core and windings are not immersed in insulating oil, are widely used in locations such as local lighting, high-rise buildings, commercial buildings, and airports. The safe operation and service life of dry-type transformers depend heavily on the safety and reliability of the transformer winding insulation. Winding temperatures exceeding the insulation tolerance temperature can damage the insulation and are one of the main causes of transformer malfunction.

[0003] Some existing transformer temperature control solutions respond slowly to temperature changes, and some only start or stop cooling equipment based on simple threshold judgments. These methods cannot accurately adjust to subtle temperature changes and cannot effectively suppress temperature rise in a timely manner. As a result, the transformer may be in a high-temperature state for a short period of time, affecting the stable operation of the transformer, and may accelerate equipment aging, reduce its service life, and even pose safety risks. Summary of the Invention

[0004] In order to solve the technical problem that the existing transformer temperature control solution 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. The technical solution adopted is as follows:

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

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

[0007] Determining load stability during a target operating phase using the load data;

[0008] determining an abnormal operation stage using the load stability and the temperature data;

[0009] Determining the abnormal coefficient of the transformer in the abnormal operation stage using the temperature data;

[0010] The transformer abnormality coefficient is used to adjust the output parameters of the PID controller in real time.

[0011] Furthermore, the load data is used to determine the load stability of the target operation phase, including:

[0012] Determining the overall load change trend and load change amplitude during the target operation phase using the load data;

[0013] The load stability in the target operation phase is determined by utilizing the overall load change trend and the load change amplitude.

[0014] Furthermore, the load stability in the target operation phase is determined by utilizing the overall load change trend and the load change amplitude, including:

[0015] determining a 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 change data that is greater than or equal to a preset stability threshold;

[0017] The load stability in the target operation phase is determined by utilizing the overall load change trend, the load change amplitude, the total amount of data, and the amount of changed data.

[0018] Furthermore, determining the abnormal operation stage by utilizing the load stability and the temperature data includes:

[0019] Determining a similarity in change trends between the load data and the temperature data;

[0020] Determining the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend;

[0021] Whether the target operation phase is an abnormal operation phase is determined by using the load stability and the following consistency.

[0022] Furthermore, the similarity of the change trend is used to determine the consistency of the temperature change with the load in the target operation stage, including:

[0023] Determining the degree of synchronization between the extreme value points corresponding to the load data and the temperature data;

[0024] The degree of consistency of the temperature in the target operation phase following the load change is determined by using the change trend similarity and the change synchronization.

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

[0026] Determining the cluster corresponding to the target operation stage according to the load stability;

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

[0028] Determining the degree of abnormal temperature of the transformer in the target operation phase by using the load stability, the follow consistency, and the temperature change difference;

[0029] 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.

[0030] Furthermore, the temperature data is used to determine the abnormal coefficient of the transformer in the abnormal operation stage, including:

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

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

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

[0034] The abnormality coefficient of the transformer in the abnormal operation stage is calculated using the abnormality degree of the transformer temperature and the temperature drop.

[0035] Furthermore, the transformer abnormality coefficient in the abnormal operation stage is calculated using the abnormal degree of transformer temperature and the temperature drop, including:

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

[0037] The abnormality coefficient of the transformer in the abnormal operation stage is calculated using the abnormality degree of the transformer temperature, the temperature drop and the number of extreme value points.

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

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

[0040] The transformer abnormality coefficient is used 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 based on PID technology as described in any of the above items; the system includes:

[0042] A data acquisition module is used to obtain temperature data and load data of the dry-type transformer at each target operating stage;

[0043] an abnormality locating module, configured to determine the load stability of a target operation phase using the load data; and to determine an abnormal operation phase using the load stability and the temperature data;

[0044] The output control module is used to use the temperature data to determine the transformer abnormality coefficient in the abnormal operation stage; and use the transformer abnormality coefficient to adjust the output parameters of the PID controller in real time.

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

[0046] The present invention is based on the principle that the temperature data of the dry-type transformer in each operating stage has a high correlation with the load data of the connected equipment. The two data dimensions are analyzed and compared, and the abnormality and degree of the transformer temperature change are reflected and determined by the load operating status. The transformer temperature data of each transformer input into the PID are compensated in real time by the transformer abnormality coefficient, and the control parameters of the PID are corrected in real time. This realizes more accurate control and regulation of the dry-type transformer temperature, greatly avoids the safety hazards caused by excessive temperature and untimely response to temperature changes of the dry-type transformer, and ensures the normal, safe and stable operation of the transformer and production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A flow chart of the steps of a dry-type transformer temperature control method based on PID technology provided by one embodiment of the present invention;

[0049] Figure 2 A detailed flow chart of step S2 in a dry-type transformer temperature control method based on PID technology provided by one embodiment of the present invention;

[0050] Figure 3 A detailed flow chart of step S3 in a dry-type transformer temperature control method based on PID technology provided by one embodiment of the present invention;

[0051] Figure 4 A detailed flow chart of step S4 in a dry-type transformer temperature control method based on PID technology provided by one embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the hardware operating environment of a dry-type transformer temperature control device based on PID technology according to an embodiment of the present invention;

[0053] Figure 6This is a schematic diagram of the framework structure of a dry-type transformer temperature control system based on PID technology according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a dry-type transformer temperature control method based on PID technology proposed in the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0055] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0056] The following describes in detail a specific solution of a dry-type transformer temperature control method based on PID technology provided by the present invention with reference to the accompanying drawings.

[0057] Example 1:

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

[0059] The method comprises:

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

[0061] In this embodiment, a high-precision temperature sensor can be used to collect the temperature of the dry-type transformer in the production workshop. The temperature sensor is installed in 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 the higher temperature during the operation of the transformer). 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 temperature data collected by the sensor can be received by the data acquisition unit and transmitted to the data center for storage and analysis;

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

[0064] It is also necessary to obtain the corresponding load data: a smart meter can be used to measure and obtain the corresponding load data. The load here refers to the electrical equipment associated with the transformer, such as production equipment in a 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 corresponding load data of the dry-type transformer in each operating stage can be collected. The target operating stage here refers to the operating and use status of the transformer and electrical equipment.

[0067] Step S2, using the load data, determining the load stability of the target operation phase;

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

[0069] Step S21, using the load data, determining the overall load change trend and load change amplitude in the target operation phase;

[0070] Step S22: Determine the load stability in the target operation phase by using the overall load change trend and the load change amplitude.

[0071] Said step S22 specifically further comprises:

[0072] determining a 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 that is greater than or equal to a preset stability threshold;

[0074] The load stability in the target operation phase is determined by utilizing the overall load change trend, the load change amplitude, the total amount of data, and the amount of changed data.

[0075] Electrical equipment typically has a strict regular maintenance schedule. For example, pumps may require weekly, monthly, or quarterly maintenance, depending on their importance and workload. This includes checking seals, lubrication conditions, and motor operating parameters. During these maintenance sessions, the equipment may be temporarily shut down, and its related or ancillary equipment may also be shut down. This period of equipment downtime is referred to as the maintenance phase, which is interspersed with the operation phase. This means that the operation phase is followed by the maintenance phase, and the maintenance phase is followed by the operation phase again.

[0076] In industrial production, electrical equipment such as production equipment is often the main load of dry-type transformers. If 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 primarily affected by the load and ambient temperature. However, in some factories and workshops, such as chemical plants, dry-type transformers are typically installed in separate transformer rooms for safety reasons, isolating the transformer from the chemical production area. Reasonable air-conditioning system design and operation can effectively control the indoor temperature, ensuring a stable ambient temperature and preventing fires or explosions caused by transformer failures. This also facilitates maintenance and management by technicians. Therefore, this embodiment only considers the impact of the load on the temperature of the dry-type transformer, ignoring the impact of the ambient temperature.

[0078] When the electrical equipment is in a normal and stable operating state, the temperature change of the dry-type transformer is relatively gentle. When the equipment stops running, the transformer temperature is also stable, but it will drop to a certain extent compared with the temperature during operation.

[0079] Because the maintenance cycle of the equipment is fixed, the corresponding detected transformer temperature also has a certain cycle. Therefore, according to the maintenance time of the equipment, the operation time of the equipment is divided into the operation stage and the maintenance stage, and the operation stage and the maintenance stage are interspersed;

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

[0081] According to the load data of each target operation stage, a load sequence can be obtained. A straight line is fitted based on the load data to represent the overall change trend of the load during the operation stage. The absolute value of the slope of the straight line is obtained and recorded as k.

[0082] The flatter the straight line is and the smaller the slope k is, the more stable the load is and the less it changes during the operating period;

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

[0084] Perform first-order difference on the load sequence to obtain a first-order difference sequence. Normalize the absolute values ​​of all data in the first-order difference sequence to [0, 1]. The stability threshold can be preset to 0.1 (specific adjustment is possible). Because the load is relatively stable during stable operation, the difference between two adjacent loads is small. Therefore, each data in the first-order difference sequence is close to 0. When the load changes, the difference will be larger. Therefore, when the data in the first-order difference sequence is greater than or equal to the preset stability threshold, it can be considered that the load has changed significantly at that location.

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

[0086]

[0087] Where A a Indicates the load stability (or load stability) of any a-th operating stage; k a Indicates the overall load change trend in the a-th operating stage, k a The smaller the value, the more stable the load; a Indicates the load change amplitude within the ath operation stage. When there is a load change, z a The smaller the value, the smaller the load variation and the more stable the load; a Indicates the number of all data in the first-order difference sequence of the a-th running stage (total amount of data); s1 a represents the number of data (number of changing data) greater than or equal to the preset stability threshold in the first-order difference sequence of the a-th operating stage; exp is an exponential function with a natural constant as the base; Indicates the proportion of data greater than or equal to the preset stability threshold in the first-order difference sequence to the total data. The smaller the value, the fewer times the load changes significantly, and the higher the load stability. a The larger the value of .

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

[0089] Step S3, determining an abnormal operation stage using the load stability and the temperature data;

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

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

[0092] Step S32, using the similarity of the change trend to determine the consistency of the temperature change with the load in the target operation stage;

[0093] Said step S32 specifically further comprises:

[0094] Determining the degree of synchronization between the extreme value points corresponding to the load data and the temperature data;

[0095] The degree of consistency of the temperature in the target operation phase following the load change is determined by using the change trend similarity and the change synchronization.

[0096] Step S33 : using the load stability and the following consistency, determining whether the target operation phase is an abnormal operation phase.

[0097] The step S33 specifically further includes:

[0098] Determining the cluster corresponding to the target operation stage according to the load stability;

[0099] Determine the temperature change difference between the target operating stage and any other operating stage in the cluster to which it belongs;

[0100] Determining the degree of abnormal temperature of the transformer in the target operation phase by using the load stability, the follow consistency, and the temperature change difference;

[0101] 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.

[0102] When the transformer operates normally and stably, and the load in each operating stage is also stable, the corresponding monitored transformer temperature is also relatively stable and consistent. Therefore, firstly, based on the characteristics of the transformer temperature under the same load, preliminarily determine the operating stage where the transformer temperature changes abnormally:

[0103] Clustering is performed based on the load stability of each operating stage. All operating stages are divided into several clusters. The load stability of all operating stages in each cluster is relatively consistent, but the load stability between clusters may vary greatly.

[0104] For all operating stages in each cluster, the load stability is similar. Under normal circumstances, when the load changes are consistent, the corresponding transformer temperature changes should also be consistent. Therefore, we first determine the consistency of the transformer temperature change with load in each operating stage in each cluster:

[0105] In the same coordinate system, the load change curve and temperature change curve of each operation stage are drawn based on the load data and temperature data, 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. This distance can represent the similarity of the change trends between the load data and the temperature data. The smaller the dtw distance d between the two curves, the more similar the change trends of the two curves are, indicating that the temperature change is more consistent with the load change.

[0107] Obtain the extreme points of each curve, that is, the points corresponding to the peaks and troughs. When the temperature and load change synchronously, the time corresponding to all the extreme points of the two curves should be consistent. If the temperature does not change with the load, it may be a transformer failure (partial short circuit or degraded heat dissipation performance) or a temperature sensor failure.

[0108] The load change curve can be used as a standard, and the absolute value of the time interval between each peak in the load change curve and the peak with the closest time interval in the temperature change curve (the same applies to the trough) can be recorded as the temperature-load change interval, which is represented by Δt. It represents the synchronization degree of changes between the extreme points corresponding to the load data and the temperature data. The smaller the value of Δt, the more synchronized the changes in temperature and load.

[0109] Taking any J-th cluster as an example, in any b-th target operation stage, the abnormal degree of transformer temperature can be expressed as:

[0110]

[0111]

[0112] Where Y b Indicates the abnormal degree of transformer temperature in the bth target operation stage; A b Indicates the load stability of the bth target operation stage; when the load is stable, the dtw distance d between the temperature change curve and the load change curve b The smaller the (change trend similarity), the more stable the temperature change is. The more similar the change trends of the two are, the more normal the temperature is. b Indicates the number of extreme points in the temperature change curve during the bth target operation phase; Δt i Indicates the synchronization of temperature and load changes at the i-th extreme point (change synchronization), Δt i The larger the value of , the more out of sync the two changes; Indicates the consistency of temperature change with load in the bth target operation stage (following consistency), The larger the value, the more inconsistent it is, which reflects that the temperature is more abnormal.

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

[0114] According to the above implementation process, the temperature anomaly degree of the transformer in all operation stages in each cluster is calculated; and Y b The value of Y is normalized to the range of [0,1]. The temperature anomaly threshold can be preset to 0.8 (specific adjustment is possible). b When the value is greater than or equal to 0.8, it can be considered that the temperature in the target operation stage is abnormally large and it is recorded as an abnormal operation stage. Otherwise, it is not recorded as an abnormal operation stage, and the abnormal operation stage requires special attention and analysis.

[0115] This identifies all operating stages where the transformer temperature becomes abnormal.

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

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

[0118] Step S41, determining a target maintenance phase corresponding to the abnormal operation phase;

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

[0120] Step S43, determining the abnormality degree of the transformer temperature of the transformer;

[0121] Step S44 , using the abnormal degree of transformer temperature and the temperature drop, calculate the abnormal coefficient of the transformer in the abnormal operation stage.

[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] The abnormality coefficient of the transformer in the abnormal operation stage is calculated using the abnormality degree of the transformer temperature, the temperature drop and the number of extreme value points.

[0125] Abnormal transformer temperature may be caused by a transformer failure or a temperature sensor failure. Under normal circumstances, when the equipment is shut down for maintenance, the load will drop sharply and the transformer temperature will also drop. The temperature will show a rapid drop rate at the beginning, then gradually decrease, and finally stabilize. When the transformer has abnormal heat dissipation, the transformer temperature will drop abnormally slowly after the equipment stops running. If the temperature sensor fails, the temperature data may suddenly jump or rise and fall randomly during the process of the equipment switching from running to stopping.

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

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

[0128] At each operating stage of temperature anomaly, the possibility that the temperature anomaly is caused by 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] Where B c Indicates the probability of transformer abnormality (transformer abnormality coefficient) at any c-th temperature abnormal operation stage; Y c Indicates the degree of transformer temperature abnormality in any c-th temperature abnormal operation stage; q1 c Indicates the initial temperature data (initial temperature data) in the target maintenance phase corresponding to the c-th operating phase, that is, the last temperature data of the c-th operating phase; q2c Indicates the temperature data at any time during the maintenance phase of the cth operation phase. It can be the middle moment or any time between the beginning and the end of the maintenance phase. c -q2 c It can represent the temperature drop in the first half of the maintenance phase (temperature drop), q1 c -q2 c The smaller the value, the less the temperature drops during the maintenance phase, and the more abnormal it is. This may be caused by a transformer failure or an abnormal temperature sensor. Adjustments are also required based on the sudden change in temperature data during the entire maintenance process. c represents the number of extreme points in the temperature data of the maintenance phase corresponding to the cth temperature abnormal operation phase, m c The smaller the value, the fewer the mutation times of data in the maintenance phase, and the more likely it is caused by transformer abnormality, corresponding to B c The larger the value of .

[0131] According to the above implementation process, the possibility of each abnormal operation stage being a transformer abnormality is calculated; and B c The value of is normalized to the range of [1,2], and then the temperature at each moment of each target operation stage can be compensated. The abnormal possibility of the transformer at the normal temperature moment is 1, that is, when the transformer abnormality coefficient is equal to 1, there is no transformer fault. When it is greater than 1 and less than or equal to 2, the transformer has different degrees of faults.

[0132] Step S5: Using the transformer abnormality coefficient, the output parameters of the PID controller are adjusted in real time.

[0133] Specifically, step S5 includes:

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

[0135] The transformer abnormality coefficient is used 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 (here it belongs to the initial input parameter) to generate a new temperature data set as the input of the dry-type transformer temperature control PID (Proportional Integral Derivative, PID control system) controller. The PID output corresponds to the dry-type transformer's air cooler or other refrigerator control instructions, such as increasing or decreasing the power. Based on the different input temperature data, the transformer is cooled and heat-exchanged to different degrees, achieving precise control of the transformer temperature.

[0137] The present invention is based on the principle that the temperature data of the dry-type transformer in each operating stage has a high correlation with the load data of the connected equipment. The two data dimensions are analyzed and compared, and the abnormality and degree of the transformer temperature change are reflected and determined by the load operating status. The transformer temperature data of each transformer input into the PID are compensated in real time by the transformer abnormality coefficient, and the control parameters of the PID are corrected in real time. This realizes more accurate control and regulation of the dry-type transformer temperature, greatly avoids the safety hazards caused by excessive temperature and untimely response to temperature changes of the dry-type transformer, and ensures the normal, safe and stable operation of the transformer and production equipment.

[0138] Example 2:

[0139] The embodiment of the present invention further provides 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 computing and processing device such as a programmable logic controller, a computer, a server, or a combination of multiple devices.

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

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

[0142] Those skilled in the art will understand that Figure 5 The hardware structure shown in the figure does not constitute a limitation of the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0143] Continue to refer to Figure 5 , Figure 5 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 PID technology.

[0144] exist Figure 5 In the embodiment, the network communication module is mainly used to connect to the server and can communicate data with the server; and the processor 1001 can call the dry-type transformer temperature control program based on PID technology stored in the memory 1005 and execute the steps in the above embodiments.

[0145] The hardware structure of the dry-type transformer temperature control device based on the PID technology is used to implement various embodiments of the dry-type transformer temperature control method based on the PID technology of the present invention.

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

[0147] The data acquisition module A10 is used to obtain the temperature data and load data of the dry-type transformer at each target operation stage;

[0148] The abnormality locating module A20 is configured to determine the load stability of the target operation phase using the load data; and to determine the abnormal operation phase using the load stability and the temperature data;

[0149] The output control module A30 is used to use the temperature data to determine the transformer abnormality coefficient in the abnormal operation stage; and use the transformer abnormality coefficient to adjust the output parameters of the PID controller in real time.

[0150] Furthermore, the abnormality locating module A20 is further configured to:

[0151] Determining the overall load change trend and load change amplitude during the target operation phase using the load data;

[0152] The load stability in the target operation phase is determined by utilizing the overall load change trend and the load change amplitude.

[0153] Furthermore, the abnormality locating module A20 is further configured to:

[0154] determining a 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 that is greater than or equal to a preset stability threshold;

[0156] The load stability in the target operation phase is determined by utilizing the overall load change trend, the load change amplitude, the total amount of data, and the amount of changed data.

[0157] Furthermore, the abnormality locating module A20 is further configured to:

[0158] Determining a similarity in change trends between the load data and the temperature data;

[0159] Determining the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend;

[0160] Whether the target operation phase is an abnormal operation phase is determined by using the load stability and the following consistency.

[0161] Furthermore, the abnormality locating module A20 is further configured to:

[0162] Determining the degree of synchronization between the extreme value points corresponding to the load data and the temperature data;

[0163] The degree of consistency of the temperature in the target operation phase following the load change is determined by using the change trend similarity and the change synchronization.

[0164] Furthermore, the abnormality locating module A20 is further configured to:

[0165] Determining the cluster corresponding to the target operation stage according to the load stability;

[0166] Determine the temperature change difference between the target operating stage and any other operating stage in the cluster to which it belongs;

[0167] Determining the degree of abnormal temperature of the transformer in the target operation phase by using the load stability, the follow consistency, and the temperature change difference;

[0168] 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.

[0169] Furthermore, the output control module A30 is further configured to:

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

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

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

[0173] The abnormality coefficient of the transformer in the abnormal operation stage is calculated using the abnormality degree of the transformer temperature and the temperature drop.

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

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

[0176] The abnormality coefficient of the transformer in the abnormal operation stage is calculated using the abnormality degree of the transformer temperature, the temperature drop and the number of extreme value points.

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

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

[0179] The transformer abnormality coefficient is used to correct the initial input parameters to adjust the output parameters of the PID controller in real time.

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

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

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

[0183] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0184] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0185] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0186] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the scope of protection 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 and load data of the dry-type transformer at each target operating stage; Determining load stability during a target operating phase using the load data; Determining an abnormal operation stage by utilizing the load stability and the temperature data includes: Determining a similarity in change trends between the load data and the temperature data; Determining the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend; Determining whether the target operation phase is an abnormal operation phase by using the load stability and the following consistency; Determining the consistency of the temperature change with the load during the target operation phase by using the similarity of the change trend includes: Determining the degree of synchronization between the extreme value points corresponding to the load data and the temperature data; Determining the consistency of the temperature change during the target operation phase with the load change using the change trend similarity and the change synchronization; Determining whether the target operation phase is an abnormal operation phase by using the load stability and the following consistency includes: Determining the cluster corresponding to the target operation stage according to the load stability; Determine the temperature change difference between the target operating stage and any other operating stage in the cluster to which it belongs; Determining the degree of abnormal temperature of the transformer in the target operation phase by using the load stability, the follow consistency, and the temperature change difference; Comparing the abnormal temperature degree of the transformer with a preset abnormal temperature threshold to determine whether the target operation stage is an abnormal operation stage; Determining the abnormal coefficient of the transformer in the abnormal operation stage using the temperature data; 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 phase; Determine the degree of transformer temperature abnormality of the transformer; Calculate the transformer abnormality coefficient during the abnormal operation phase using the abnormal temperature degree of the transformer and the temperature drop; The transformer abnormality coefficient in the abnormal operation stage is calculated 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; Calculate the transformer abnormality coefficient in the abnormal operation stage using the abnormal temperature degree of the transformer, the temperature drop, and the number of extreme value points; 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: Determining load stability during a target operation phase using the load data includes: Using the load data, determining an overall load change trend and a load change amplitude during a target operation phase; The load stability in the target operation phase is determined by utilizing 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 utilizing 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 number 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 utilizing the overall load change trend, the load change amplitude, 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: 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 parameters to adjust the output parameters of the PID controller in real time.

5. 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 according to any one of claims 1 to 4; the system includes: A data acquisition module is used to obtain temperature data and load data of the dry-type transformer at each target operating stage; an abnormality locating module, configured to determine the load stability of a target operation phase using the load data; and to determine an abnormal operation phase using the load stability and the temperature data; The output control module is used to use the temperature data to determine the transformer abnormality coefficient in the abnormal operation stage; and use the transformer abnormality coefficient to regulate the output parameters of the PID controller in real time.

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