Device and method for evaluating state of box transformer substation

Abstract

The invention discloses a device and a method for evaluating the state of a box transformer, relates to the technical field of transformers, and obtains a magnetic leakage loss index Ploss through quantification by associating the dynamic influence of a load rate with an environmental condition through a magnetic leakage loss model. And further combining the input power fluctuation value Pinput, dynamically calculating the electromagnetic efficiency Rt of the box transformer substation, and forming a comprehensive evaluation result of the operation state. According to the method, single dependence on static operation parameters is broken through, comprehensive evaluation from a single index to a multi-dimensional dynamic index is realized by introducing magnetic leakage characteristics and dynamic power fluctuation characteristics, the problem that the magnetic leakage change rate Qt and the magnetic field energy density Et cannot be fully utilized is effectively solved, and the magnetic leakage change rate Qt and the magnetic field energy density Et are converted into quantifiable magnetic leakage loss index Ploss, so that the reliability of the magnetic leakage loss index Ploss is improved. And the dynamic influence of the load rate and the environmental condition is further associated, so that the dynamic sensing capability of the operation state of the box transformer is improved, and rapid diagnosis and fault early warning of the operation state of the transformer are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer evaluation, and in particular to a box-type transformer state evaluation device and evaluation method. Background Art

[0002] Box transformers generally refer to box-type transformers, which concentrate traditional transformers in a box-type housing. They are small in size, light in weight, low in noise, low in loss, and high in reliability. They are widely used in residential areas, commercial centers, light stations, airports, factories, mines, enterprises, hospitals, schools and other places. As a key node in the power distribution network, the main function of box transformers is to reduce the high-voltage electric energy through transformers into electric energy suitable for low-voltage users.

[0003] When the box-type transformer is in operation, it usually faces complex power load conditions, which leads to changes in the electromagnetic environment and fluctuations in electrical performance. Among the components of the box-type transformer, the existence of leakage magnetic field is inevitable. It is the distribution of magnetic flux around the transformer that is not captured by the magnetic circuit. Although leakage magnetic field is a normal phenomenon in transformer design, it directly affects the energy efficiency and operating stability of the transformer. For the box-type transformer in operation, monitoring and evaluating the impact of leakage magnetic field loss on electromagnetic efficiency can not only optimize the system energy consumption, but also improve the reliability of equipment operation.

[0004] At present, the monitoring and evaluation of magnetic flux leakage loss mostly rely on calculations in the theoretical design stage and partial experimental verification. These methods are usually based on the assumption of ideal operating conditions and ignore the complex load characteristics and electromagnetic environment in actual operation. For example, traditional methods mostly estimate magnetic flux leakage loss through fixed formulas, which fail to reflect the changing trend under dynamic operating conditions. In addition, most evaluation methods require additional hardware equipment, such as high-precision magnetic field detection instruments, which increases costs and equipment complexity, making it difficult to directly apply them in existing operating systems. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a box-type transformer status assessment device and assessment method, which solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides a method for evaluating the state of a box-type transformer, comprising the following steps: S1, obtaining the signal of the box-type transformer in operation, and preprocessing the signal to obtain an initial feature vector set F1, and uniquely marking the box-type transformer, and transmitting the initial feature vector set F1 to the cloud processing center; S2, the cloud processing center extracts the leakage magnetic feature parameters of the initial feature vector set F1 to obtain the magnetic flux change rate △Qt and the magnetic field energy density Et; the magnetic flux change rate △Qt and the magnetic field energy density Et are integrated to obtain the leakage magnetic feature set F2; S3, establishing a magnetic flux leakage loss model according to the magnetic flux leakage feature set F2, and correlating the dynamic influence of the load rate and the environmental conditions to determine the magnetic flux leakage loss index Ploss; S4, performing fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the box-type transformer operating state at time t; S5. Determine the operating state of the box-type transformer by comparing the electromagnetic efficiency Rt with a preset electromagnetic efficiency operating state warning threshold Rz, and output an operating evaluation result.

[0008] Preferably, in S1, a signal of the box transformer in operation is obtained through a PT sensor, a CT sensor and a temperature sensor, and the time information of time t is marked on the collected signal to obtain a voltage Ut at time t, a current It at time t, a load rate LRt at time t and an operating temperature Tt at time t; The voltage Ut, the current It, the load rate LRt and the operating temperature Tt are respectively uniquely identified with ID, and form an initial data set R1={Ut, It, LRt, Tt, ID}.

[0009] Preferably, it also includes: Preprocessing the initial data set R1 to generate a standardized initial feature vector set F1, and transmitting it to a cloud processing center via the Internet, including using Modbus, CAN bus and industrial Ethernet protocols; The initial feature vector set F1 is transmitted to the Internet of Things gateway, and the Internet of Things gateway encrypts the initial feature vector set F1 by using 4G, 5G network, optical fiber and NB-IoT communication mode and transmits it to the cloud processing center; Wherein, the preprocessing includes denoising preprocessing and normalization preprocessing; The denoising preprocessing is to remove the noise interference in the signal of the initial data set R1 by using the Kalman filter algorithm to obtain the denoised denoising feature vector set ZF; The normalization preprocessing is to standardize the denoising feature vector set ZF by using the min-max normalization method, unify the range, determine the voltage Ut, the current It, the load rate LRt and the operating temperature Tt after normalization, and redefine them as the initial feature vector set F1 after substituting them into the denoising feature vector set ZF.

[0010] Preferably, in S2, the magnetic flux change rate ΔQt is obtained by the following calculation formula: In the formula, exp represents an exponential function, sin represents a sine function, k1, k2, k3 and k4 represent fitting coefficients, k1 is specifically used to adjust the direct influence of the current It, the load rate LRt and the operating temperature Tt on the leakage flux, k2 is specifically used to adjust the suppression effect of the operating temperature Tt on the leakage flux, k3 is specifically used to adjust the modulation intensity of the voltage Ut on the leakage flux, k4 is specifically used to adjust the influence frequency of the voltage Ut on the periodic fluctuation of the leakage flux, and k1+k2+k3+k4=1, and the specific value is set by the user.

[0011] Preferably, in S2, the magnetic field energy density Et is obtained by the following calculation formula: Wherein, k5 and k6 represent fitting coefficients, k5 is specifically used to adjust the influence intensity of the magnetic flux change rate △Qt on the magnetic field energy density, k6 is specifically used to adjust the influence coefficient of the operating temperature Tt on the magnetic field energy density, and k5+k6=1, and the specific value is set by the user.

[0012] Preferably, in S3, the magnetic leakage loss index Ploss is obtained by the following calculation formula: In the formula, log represents a logarithmic function, n represents a load nonlinear index, p1, p2, p3, p4 and L represent the fitting coefficients of the model, p1 is specifically used to adjust the overall influence of the load rate LRt on the loss, p2 is specifically used to adjust the sublinear contribution of the magnetic field energy density Et to the leakage loss, p3 is specifically used to adjust the periodic modulation intensity factor of the magnetic flux change rate △Qt, so as to capture the dynamic fluctuation effect of the magnetic flux change rate △Qt on the loss, and p4 is specifically used to adjust the intensity factor of the operating temperature Tt's inhibitory effect on the leakage loss, so as to reflect the regulating effect of the operating temperature Tt on the leakage loss.

[0013] Preferably, in S4, performing fitting calculation based on the magnetic leakage loss index Ploss and the characteristic vector set F1 includes: Calculate the input power fluctuation value △Pinput, evaluate the transient characteristics of the power grid, and determine the results of the power grid stability assessment; The input power fluctuation value ΔPinput is obtained by the following calculation formula: ; In the formula, represents the historical mean value of the voltage Ut, Pmax represents the rated upper limit transformer input power, Represents the power factor; the electromagnetic efficiency Rt is expressed by Obtain the calculation formula.

[0014] Preferably, in S5, the electromagnetic efficiency Rt is compared with a preset electromagnetic efficiency operation state warning threshold Rz, including: When the electromagnetic efficiency Rt≥the electromagnetic efficiency operation state warning threshold Rz, the operation evaluation result is determined to be qualified, indicating that the operation state of the current box-type transformer is normal, and the operation state is determined as status=0; When the electromagnetic efficiency Rt is less than the electromagnetic efficiency operating status warning threshold Rz, the operating evaluation result is determined to be unqualified, indicating that the operating status of the current box-type transformer is abnormal, triggering the iterative optimization mechanism, and marking the determined operating status status=1.

[0015] Preferably, the triggering iterative optimization mechanism includes: optimizing the magnetic flux leakage loss model and the model fitting coefficient; The triggering iterative optimization mechanism is triggered in the following ways: When the running status status=0, the total number of running abnormal records YZS=0 is initialized, the iterative optimization mechanism is not triggered, and the leakage magnetic loss model and model fitting coefficient are not optimized; When the operating status status=1, the total number of operating abnormal records YZS is processed with +status, which triggers the iterative optimization mechanism to optimize the leakage magnetic loss model and the model fitting coefficient; among them, when the total number of operating abnormal records YZS≥3, the early warning notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the inspection and maintenance list to be notified to perform maintenance and inspection on the transformer; when the total number of operating abnormal records YZS≥6, the early warning power-off notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the emergency processing list to wait for the relevant personnel to confirm the execution of the power-off command.

[0016] In a second aspect, the present application provides a box-type transformer state assessment device, which is used to execute a box-type transformer state assessment method as described above, including: a box-type transformer signal acquisition module, a signal feature extraction module, a modeling and analysis module, a fitting assessment module and a decision module; The signal acquisition module collects the signals of the box-type transformer in operation, performs preprocessing to obtain the initial feature vector set F1, marks the box-type transformer with a unique mark, and transmits it to the cloud processing center via the Internet; The signal feature extraction module extracts leakage magnetic feature parameters from the initial feature vector set F1, obtains the magnetic flux change rate △Qt and the magnetic field energy density Et, and integrates them to obtain the leakage magnetic feature set F2; The modeling and analysis module establishes a magnetic leakage loss model according to the magnetic leakage feature set F2, and associates the dynamic influence of the load rate with environmental conditions to obtain a magnetic leakage loss index Ploss; The fitting evaluation module performs fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the operating state of the box-type transformer at time t; The decision module compares the electromagnetic efficiency Rt with a preset electromagnetic efficiency operating state warning threshold Rz to determine the operating state of the box-type transformer and obtain an operating evaluation result.

[0017] The present invention has the following beneficial effects: (1) The leakage loss model is used to associate the dynamic impact of the load rate with environmental conditions, and the leakage loss index Ploss is quantified. The electromagnetic efficiency Rt of the box-type transformer is further calculated in combination with the input power fluctuation value △Pinput, and compared with the preset electromagnetic efficiency operation status warning threshold Rz to form a comprehensive evaluation result of the operation status. Based on the traditional transformer status monitoring, this method breaks through the single reliance on static operation parameters. By introducing leakage characteristics and dynamic power fluctuation characteristics, it realizes a comprehensive evaluation from a single indicator to a multi-dimensional dynamic indicator, effectively solves the problem that the leakage change rate △Qt and the magnetic field energy density Et are not fully utilized, and converts them into a quantifiable leakage loss index Ploss. It further associates the dynamic impact of the load rate and environmental conditions, improves the dynamic perception ability of the box-type transformer operation status, realizes the rapid diagnosis of the transformer operation status and fault warning, and significantly improves the operation stability and energy efficiency management level of the box-type transformer.

[0018] (2) By constructing a leakage loss model based on the leakage feature set F2 and comprehensively considering the dynamic influence of load rate LRT and operating temperature Tt, the leakage loss index Ploss is accurately quantified, which carefully captures the influence mechanism of various factors in the leakage characteristics on energy consumption. By correlating and dynamically fitting the multi-dimensional factors of leakage loss, it can not only capture the influence trend of load rate LRT and operating temperature Tt on loss change, but also accurately quantify the dynamic modulation effect of periodic fluctuation of magnetic flux change rate △Qt on loss, as well as the reflection of magnetic field energy density Et on loss sensitivity under different magnetic circuit designs. Compared with traditional methods, the calculation of leakage loss index Ploss is more in line with the actual operating conditions of transformers, significantly improving the quantitative evaluation ability of leakage loss, and providing a scientific basis for further analyzing the source of loss, optimizing operating conditions and improving equipment efficiency.

[0019] (3) By combining the leakage loss index Ploss and the feature vector set F1 for fitting calculation, the input power fluctuation value △Pinput is dynamically obtained to accurately reflect the transient characteristics of the power grid. Based on the input power fluctuation value △Pinput and the leakage loss index Ploss, the electromagnetic efficiency Rt at time t is calculated to generate the operation evaluation result, and the iterative optimization mechanism is triggered according to the operation status. When the number of abnormal operation statuses reaches different thresholds, the inspection notification or power failure warning notification mechanism is triggered to provide clear guidance for fault handling, and the real-time monitoring and hierarchical response mechanism of the operation status based on the electromagnetic efficiency Rt is realized. It can not only dynamically evaluate the operation efficiency of the box-type transformer, but also accurately monitor the long-term health trend of the transformer operation status through the quantitative management of the total number of accumulated abnormal records YZS. Compared with the traditional passive monitoring method that only relies on a single state parameter, the multi-dimensional comprehensive evaluation capability of the transformer operation status is enhanced; at the same time, through the iterative optimization mechanism and the warning notification mechanism, a closed-loop management from state evaluation to fault handling is established, which significantly improves the timeliness and accuracy of fault warning and maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a schematic diagram of the steps of a method for evaluating the state of a box-type transformer.

[0021] Figure 2 The present invention is a schematic diagram of a block diagram of a device for evaluating the state of a box-type transformer. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] Example 1 The present invention provides a method for evaluating the state of a box-type transformer. Figure 1 , including the following steps: S1, collect the signals of the box-type transformer in operation, perform preprocessing to obtain the initial feature vector set F1, mark the box-type transformer with a unique mark, and transmit it to the cloud processing center via the Internet; S2, extracting magnetic flux leakage characteristic parameters from the initial feature vector set F1, obtaining the magnetic flux change rate △Qt and the magnetic field energy density Et, and integrating them to obtain the magnetic flux leakage characteristic set F2; S3, establishing a magnetic flux leakage loss model according to the magnetic flux leakage feature set F2, and correlating the dynamic influence of the load rate and the environmental conditions to obtain a magnetic flux leakage loss index Ploss; S4, performing fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the box-type transformer operating state at time t; S5. By comparing the electromagnetic efficiency Rt with the preset electromagnetic efficiency operation status warning threshold Rz, the operation status of the box-type transformer is judged to obtain the operation evaluation result.

[0024] In this embodiment, the dynamic influence of the load rate and the environmental conditions are associated with the leakage loss model to quantify the leakage loss index Ploss. Further combined with the input power fluctuation value △Pinput, the electromagnetic efficiency Rt of the box transformer is dynamically calculated, and compared with the preset electromagnetic efficiency operation status warning threshold Rz to form a comprehensive evaluation result of the operation status. Based on the traditional transformer status monitoring, this method breaks through the single dependence on static operation parameters, and realizes the comprehensive evaluation from a single indicator to a multi-dimensional dynamic indicator by introducing leakage characteristics and dynamic power fluctuation characteristics, effectively solving the problem that the leakage change rate △Qt and the magnetic field energy density Et are not fully utilized, converting them into a quantifiable leakage loss index Ploss, and further associating the dynamic influence of the load rate and environmental conditions, thereby improving the dynamic perception of the operation status of the box transformer. In addition, by constructing a calculation model of the input power fluctuation value △Pinput, it is possible to capture the real-time changes of voltage, current and load fluctuations, enhance the real-time evaluation capability of the transient characteristics of the power grid and the operation status of the transformer, and thus realize the accurate quantification of the transformer operation efficiency. It also realizes rapid diagnosis of transformer operating status and fault warning, provides a scientific basis for subsequent optimization decisions, and significantly improves the operating stability and energy efficiency management level of the box-type transformer.

[0025] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 , specifically: step S1 includes S11 and S12; S11. Collect signals from the box-type transformer during operation through installed electrical sensors, including PT sensors, CT sensors, and temperature sensors, and mark the collected signals with time information of time t to obtain the voltage Ut at time t, the current It at time t, the load rate LRt at time t, and the operating temperature Tt at time t; Then add the unique identification ID of the box transformer to the voltage Ut, current It, load rate LRT and operating temperature Tt to form the initial data set R1={Ut, It, LRT, Tt, ID}; Load factor LRt by The calculation formula is obtained, where Irated represents the rated current value of the transformer.

[0026] S12, preprocessing the initial data set R1 to generate a standardized initial feature vector set F1, and transmitting it to the cloud processing center via the Internet, including using Modbus, CAN bus and industrial Ethernet protocols to transmit the initial feature vector set F1 to the Internet of Things gateway, and the Internet of Things gateway encrypts the initial feature vector set F1 and transmits it to the cloud processing center by using 4G, 5G network, optical fiber and NB-IoT communication methods; Among them, preprocessing includes denoising preprocessing and normalization preprocessing; De-noising preprocessing uses the Kalman filter algorithm to remove noise interference in the signal of the initial data set R1, and obtains the denoised feature vector set ZF after denoising; Normalization preprocessing is performed by using the min-max normalization method to standardize the denoised feature vector set ZF, unify the range, obtain the normalized voltage Ut, current It, load rate LRt and operating temperature Tt, and redefine them as the initial feature vector set F1 after substituting them into the denoised feature vector set ZF.

[0027] In this embodiment, the voltage Ut, current It, load rate LR and operating temperature Tt of the box transformer are collected in real time through the installed electrical sensors, and the initial data set R1 is pre-processed for denoising, which significantly reduces the noise interference in the signal and improves the accuracy of the data. On this basis, by converting the denoising feature vector set ZF into a standardized initial feature vector set F1, the consistency of the data range is achieved, which is convenient for the comprehensive analysis and processing of multi-dimensional signals. At the same time, through the industrial Ethernet protocol and 4G and 5G network communication methods, the encrypted initial feature vector set F1 is safely and reliably transmitted to the cloud processing center to ensure the integrity and confidentiality of the data in remote processing. Compared with the traditional data acquisition and transmission method, this method not only realizes the high-precision acquisition of multi-dimensional signals, but also optimizes the data quality through the Kalman filter algorithm and normalization processing, and provides more accurate input for subsequent feature extraction and state evaluation. In particular, the initial data set R1 combined with the unique identification ID can support accurate data tracing and personalized state evaluation of multiple devices, making the monitoring range of the box transformer wider and the data processing more efficient, significantly improving the operation management capability and remote maintenance efficiency of the equipment.

[0028] Example 3 This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: step S2 includes S21 and S22; S21, extracting leakage magnetic characteristic parameters from the characteristic vector set F1, obtaining voltage Ut, current It, load rate LRt and operating temperature Tt, and substituting them into the leakage magnetic flux change rate calculation formula to obtain the magnetic flux change rate △Qt at time t; The flux change rate △Qt is obtained by the following calculation formula: In the formula, exp represents an exponential function, which is specifically used to reflect the relationship of exponential growth or attenuation; sin represents a sine function, which is specifically used to describe periodic fluctuations or nonlinear relationships; k1, k2, k3 and k4 represent fitting coefficients; k1 is specifically used to adjust the direct effects of current It, load rate LRt and operating temperature Tt on magnetic leakage; k2 is specifically used to adjust the suppression effect of operating temperature Tt on magnetic leakage; k3 is specifically used to adjust the modulation intensity of voltage Ut on magnetic leakage; k4 is specifically used to adjust the frequency of influence of voltage Ut on periodic fluctuations of magnetic leakage; and k1+k2+k3+k4=1. The specific value is set by the user, including that the value of k1 depends on the design characteristics of the transformer, such as winding structure and magnetic circuit material; the value of k2 is related to the temperature sensitivity of the transformer material, such as the magnetic properties of silicon steel sheets will weaken at high temperatures; the value of k3 is related to the rated voltage range of the transformer and the steady-state characteristics of the system; the value of k4 determines the scope of action of the sine function in the magnetic leakage characteristics, such as small fluctuations or violent fluctuations; S22, by substituting the magnetic flux change rate △Qt and the operating temperature Tt into the magnetic field energy density calculation formula to calculate, obtain the magnetic field energy density Et at time t, and then integrate it with the magnetic flux change rate △Qt to form the leakage magnetic feature set F2={△Qt, Et}; The magnetic field energy density Et is obtained by the following calculation formula: Wherein, k5 and k6 represent fitting coefficients, k5 is used to adjust the influence of the flux change rate △Qt on the magnetic field energy density, emphasizing the contribution of the change rate to energy consumption, k6 is used to adjust the influence coefficient of the operating temperature Tt on the magnetic field energy density, reflecting the modulation effect of environmental conditions on magnetic properties, and k5+k6=1. The specific value is set by the user, including the value of k5 is related to the magnetic circuit characteristics of the transformer, such as the loss characteristics of the winding design and the core material, and the value of k6 is related to the temperature dependence of the transformer material, such as the decrease in material permeability or the increase in hysteresis loss in a high temperature environment.

[0029] In this embodiment, the key parameters extraction and integration from the feature vector set F1 to the leakage magnetic feature set F2 are realized, laying a solid foundation for the evaluation of the operating status of the box transformer. This method first effectively captures the nonlinear dynamic law of the leakage magnetic characteristics by obtaining the flux change rate △Qt at time t. Secondly, the interaction characteristics of the flux change rate △Qt and the operating temperature Tt are further quantified as the magnetic field energy density Et through the magnetic field energy density calculation formula, and integrated into the leakage magnetic feature set F2, realizing the multi-dimensional dynamic characteristic modeling of the leakage magnetic parameters. This method breaks through the limitation of being based only on a single leakage magnetic characteristic, and deeply captures the leakage magnetic dynamic law and energy consumption characteristics during the operation of the box transformer by introducing the comprehensive characteristic construction of the flux change rate △Qt and the magnetic field energy density Et. Thereby, the generation of the leakage magnetic feature set F2 is more universal and accurate. The implementation of this method significantly improves the quantification and dynamic description capabilities of the magnetic performance state of the box transformer, provides more comprehensive data support for leakage magnetic loss modeling and electromagnetic efficiency evaluation, and also provides a scientific basis for efficient maintenance and optimization of transformer operation.

[0030] Example 4 This embodiment is explained in Example 3, please refer to Figure 1 , specifically: step S3 includes S31; S31, establishing a magnetic leakage loss model according to the magnetic leakage feature set F2, and associating the dynamic influence of the load rate and the environmental conditions, including associating the load rate LRt and the operating temperature Tt, to obtain a magnetic leakage loss index Ploss; The leakage loss index Ploss is obtained by the following calculation formula: In the formula, log represents the logarithmic function, which is specifically used to process nonlinear relationships and compress faster growing data into slow growth. n represents the load nonlinear index. The higher the load rate, the more significant the leakage loss. p1, p2, p3, p4 and L represent the fitting coefficients of the model. p1 is specifically used to adjust the overall influence of the load rate LRt on the loss. The larger its value, the more significant the effect of the load rate on the leakage loss. p2 is specifically used to adjust the sublinear contribution of the magnetic field energy density Et to the leakage loss. It is suitable for describing the sensitivity of different magnetic circuit designs and materials to the leakage loss. p3 is specifically used to adjust the periodic modulation intensity factor of the flux change rate △Qt to capture the dynamic fluctuation effect of the flux change rate △Qt on the loss. The larger its value, the more significant the modulation effect of the leakage flux fluctuation on the loss. p4 is specifically used to adjust the intensity factor of the operating temperature Tt on the leakage loss suppression effect to reflect the regulating effect of the operating temperature Tt on the leakage loss. The larger its value, the more significant the weakening effect of temperature on the loss.

[0031] In this embodiment, by constructing a leakage loss model based on the leakage feature set F2, and comprehensively considering the dynamic influence of the load rate LRT and the operating temperature Tt, the leakage loss index Ploss is accurately quantified, and the influence mechanism of various factors in the leakage characteristics on energy consumption is carefully captured. Through the multi-dimensional factor correlation and dynamic fitting of the leakage loss, it is possible to not only capture the influence trend of the load rate LRT and the operating temperature Tt on the loss change, but also to accurately quantify the dynamic modulation effect of the periodic fluctuation of the flux change rate △Qt on the loss, and the reflection of the magnetic field energy density Et on the loss sensitivity under different magnetic circuit designs. Compared with the traditional method, the calculation of the leakage loss index Ploss is more in line with the actual operating conditions of the transformer, significantly improves the quantitative evaluation capability of the leakage loss, and provides a scientific basis for further analyzing the source of loss, optimizing operating conditions and improving equipment efficiency.

[0032] Example 5 This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: step S4 includes S41 and S42; S41, based on the magnetic leakage loss index Ploss and the characteristic vector set F1, fitting is performed to dynamically reflect the operating state of the box-type transformer, obtain the input power fluctuation value △Pinput, evaluate the transient characteristics of the power grid, and obtain the power grid stability evaluation result; The input power fluctuation value △Pinput is obtained by the following calculation formula: In the formula, represents the historical mean value of voltage Ut, Pmax represents the rated upper limit transformer input power, Indicates the power factor, which is obtained by the ratio of active power to apparent power. Active power represents the actual power consumed, and apparent power represents the total power of the system.

[0033] S42, based on the input power fluctuation value △Pinput and the leakage magnetic loss index Ploss, dynamically evaluate the electromagnetic efficiency Rt of the box-type transformer in the operating state at time t; Electromagnetic efficiency Rt is Obtain the calculation formula.

[0034] Step S5 includes S51 and S52; S51, by comparing the electromagnetic efficiency Rt with the preset electromagnetic efficiency operation state warning threshold Rz, the operation state of the box-type transformer is judged to obtain the operation evaluation result; The evaluation results are obtained by comparing: When the electromagnetic efficiency Rt≥the electromagnetic efficiency operation status warning threshold Rz, the operation evaluation result is qualified, indicating that the operation status of the current box-type transformer is normal, and the operation status status=0 is marked; When the electromagnetic efficiency Rt is less than the electromagnetic efficiency operation status warning threshold Rz, the operation evaluation result is obtained as unqualified, indicating that the operation status of the current box-type transformer is abnormal, triggering the iterative optimization mechanism, and marking the operation status status=1.

[0035] S52. triggering an iterative optimization mechanism according to the operation evaluation results, including optimizing the magnetic flux leakage loss model and the model fitting coefficient; The iterative optimization mechanism is triggered in the following ways: When the running status status=0, the total number of running abnormal records YZS=0 is initialized, the iterative optimization mechanism is not triggered, and the leakage magnetic loss model and model fitting coefficient are not optimized; When the operation status status=1, the total number of operation abnormality records YZS is processed with +status, triggering the iterative optimization mechanism to optimize the leakage magnetic loss model and the model fitting coefficient. When the total number of operation abnormality records YZS≥3, the early warning notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the inspection and maintenance list for waiting for processing, and then the relevant personnel are notified to perform maintenance and inspection on the transformer. When the total number of operation abnormality records YZS≥6, the early warning power-off notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the emergency processing list for waiting for the relevant personnel to confirm the execution of the power-off command.

[0036] In this embodiment, by combining the leakage loss index Ploss and the characteristic vector set F1 for fitting calculation, the input power fluctuation value △Pinput is dynamically obtained to accurately reflect the transient characteristics of the power grid, and based on the input power fluctuation value △Pinput and the leakage loss index Ploss, the electromagnetic efficiency Rt at time t is calculated to generate the operation evaluation result, and the iterative optimization mechanism is triggered according to the operation state. When the number of abnormal operation states reaches different thresholds, the inspection notification or power failure warning notification mechanism is triggered to provide clear guidance for fault handling, and the real-time monitoring and hierarchical response mechanism of the operation state based on the electromagnetic efficiency Rt is realized. It can not only dynamically evaluate the operation efficiency of the box transformer, but also accurately monitor the long-term health trend of the transformer operation state through the quantitative management of the total number of accumulated abnormal records YZS. Compared with the traditional passive monitoring method that only relies on a single state parameter, this method enhances the multi-dimensional comprehensive evaluation capability of the transformer operation state through the dynamic association of the input power fluctuation value △Pinput and the leakage loss index Ploss; at the same time, through the iterative optimization mechanism and the early warning notification mechanism, a closed-loop management from state evaluation to fault handling is established, which significantly improves the timeliness and accuracy of fault warning and maintenance decision-making. This mechanism effectively reduces the operational risks caused by equipment abnormalities and provides an innovative solution for ensuring the overall stability of the power grid and improving operation and maintenance efficiency.

[0037] Example 6 A box transformer status assessment device, please refer to Figure 2 Specifically: it includes box-type transformer signal acquisition module, signal feature extraction module, modeling and analysis module, fitting evaluation module and decision-making module.

[0038] The signal acquisition module collects the signals of the box-type transformer in operation, performs preprocessing to obtain the initial feature vector set F1, marks the box-type transformer with a unique mark, and transmits it to the cloud processing center via the Internet.

[0039] The signal feature extraction module extracts the leakage magnetic feature parameters of the initial feature vector set F1, obtains the magnetic flux change rate △Qt and the magnetic field energy density Et, and integrates them to obtain the leakage magnetic feature set F2.

[0040] The modeling and analysis module establishes a leakage magnetic loss model according to the leakage magnetic feature set F2, and associates the dynamic influence of the load rate with environmental conditions to obtain the leakage magnetic loss index Ploss.

[0041] The fitting evaluation module performs fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the box-type transformer operating state at time t.

[0042] The decision module compares the electromagnetic efficiency Rt with the preset electromagnetic efficiency operation status warning threshold Rz to judge the operation status of the box-type transformer and obtain the operation evaluation result.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the state of a box-type transformer, characterized in that: The following steps are involved: S1, obtaining the signal of the box-type transformer in operation, and preprocessing the signal to obtain an initial feature vector set F1, and uniquely marking the box-type transformer, and transmitting the initial feature vector set F1 to the cloud processing center; S2, the cloud processing center extracts the leakage magnetic feature parameters of the initial feature vector set F1 to obtain the magnetic flux change rate △Qt and the magnetic field energy density Et; the magnetic flux change rate △Qt and the magnetic field energy density Et are integrated to obtain the leakage magnetic feature set F2; S3, establishing a magnetic flux leakage loss model according to the magnetic flux leakage feature set F2, and correlating the dynamic influence of the load rate and the environmental conditions to determine the magnetic flux leakage loss index Ploss; S4, performing fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the box-type transformer operating state at time t; S5. Determine the operating state of the box-type transformer by comparing the electromagnetic efficiency Rt with a preset electromagnetic efficiency operating state warning threshold Rz, and output an operating evaluation result.

2. A method for evaluating the state of a box-type transformer according to claim 1, characterized in that: In S1, the signals of the box transformer in operation are obtained through the PT sensor, the CT sensor and the temperature sensor, and the time information of the collected signals is marked at time t to obtain the voltage Ut at time t, the current It at time t, the load rate LRt at time t and the operating temperature Tt at time t; The voltage Ut, the current It, the load rate LRt and the operating temperature Tt are respectively uniquely identified with ID, and form an initial data set R1={Ut, It, LRt, Tt, ID}.

3. A method for evaluating the state of a box-type transformer according to claim 2, characterized in that: Also includes: Preprocessing the initial data set R1 to generate a standardized initial feature vector set F1, and transmitting it to a cloud processing center via the Internet, including using Modbus, CAN bus and industrial Ethernet protocols; The initial feature vector set F1 is transmitted to the Internet of Things gateway, and the Internet of Things gateway encrypts the initial feature vector set F1 by using 4G, 5G network, optical fiber and NB-IoT communication mode and transmits it to the cloud processing center; Wherein, the preprocessing includes denoising preprocessing and normalization preprocessing; The denoising preprocessing is to remove the noise interference in the signal of the initial data set R1 by using the Kalman filter algorithm to obtain the denoised denoising feature vector set ZF; The normalization preprocessing is to standardize the denoising feature vector set ZF by using the min-max normalization method, unify the range, determine the voltage Ut, the current It, the load rate LRt and the operating temperature Tt after normalization, and redefine them as the initial feature vector set F1 after substituting them into the denoising feature vector set ZF.

4. A method for evaluating the state of a box-type transformer according to claim 3, characterized in that: In S2, the magnetic flux change rate ΔQt is obtained by the following calculation formula: In the formula, exp represents an exponential function, sin represents a sine function, k1, k2, k3 and k4 represent fitting coefficients, k1 is specifically used to adjust the direct influence of the current It, the load rate LRt and the operating temperature Tt on the leakage flux, k2 is specifically used to adjust the suppression effect of the operating temperature Tt on the leakage flux, k3 is specifically used to adjust the modulation intensity of the voltage Ut on the leakage flux, k4 is specifically used to adjust the influence frequency of the voltage Ut on the periodic fluctuation of the leakage flux, and k1+k2+k3+k4=1, and the specific value is set by the user.

5. A method for evaluating the state of a box-type transformer according to claim 3, characterized in that: In S2, the magnetic field energy density Et is obtained by the following calculation formula: Wherein, k5 and k6 represent fitting coefficients, k5 is specifically used to adjust the influence intensity of the magnetic flux change rate △Qt on the magnetic field energy density, k6 is specifically used to adjust the influence coefficient of the operating temperature Tt on the magnetic field energy density, and k5+k6=1, and the specific value is set by the user.

6. A method for evaluating the state of a box-type transformer according to claim 4, characterized in that: In S3, the magnetic leakage loss index Ploss is obtained by the following calculation formula: In the formula, log represents a logarithmic function, n represents a load nonlinear index, p1, p2, p3, p4 and L represent the fitting coefficients of the model, p1 is specifically used to adjust the overall influence of the load rate LRt on the loss, p2 is specifically used to adjust the sublinear contribution of the magnetic field energy density Et to the leakage loss, p3 is specifically used to adjust the periodic modulation intensity factor of the magnetic flux change rate △Qt, so as to capture the dynamic fluctuation effect of the magnetic flux change rate △Qt on the loss, and p4 is specifically used to adjust the intensity factor of the operating temperature Tt's inhibitory effect on the leakage loss, so as to reflect the regulating effect of the operating temperature Tt on the leakage loss.

7. A method for evaluating the state of a box-type transformer according to claim 6, characterized in that: In S4, fitting calculation is performed based on the magnetic leakage loss index Ploss and the characteristic vector set F1, including: Calculate the input power fluctuation value △Pinput, evaluate the transient characteristics of the power grid, and determine the results of the power grid stability assessment; The input power fluctuation value ΔPinput is obtained by the following calculation formula: In the formula, represents the historical mean value of the voltage Ut, Pmax represents the rated upper limit transformer input power, Represents the power factor; the electromagnetic efficiency Rt is expressed by Obtain the calculation formula.

8. A method for evaluating the state of a box-type transformer according to claim 2, characterized in that: In S5, the electromagnetic efficiency Rt is compared with a preset electromagnetic efficiency operation state warning threshold Rz, including: When the electromagnetic efficiency Rt≥the electromagnetic efficiency operation state warning threshold Rz, the operation evaluation result is determined to be qualified, indicating that the operation state of the current box-type transformer is normal, and the operation state is determined as status=0; When the electromagnetic efficiency Rt is less than the electromagnetic efficiency operating status warning threshold Rz, the operating evaluation result is determined to be unqualified, indicating that the operating status of the current box-type transformer is abnormal, triggering the iterative optimization mechanism, and marking the determined operating status status=1.

9. A method for evaluating the state of a box-type transformer according to claim 8, characterized in that: The triggering iterative optimization mechanism includes: optimizing the magnetic flux leakage loss model and the model fitting coefficient; The triggering iterative optimization mechanism is triggered in the following ways: When the running status status=0, the total number of running abnormal records YZS=0 is initialized, the iterative optimization mechanism is not triggered, and the leakage magnetic loss model and model fitting coefficient are not optimized; When the operating status status=1, the total number of operating abnormal records YZS is processed with +status, which triggers the iterative optimization mechanism to optimize the leakage magnetic loss model and the model fitting coefficient; among them, when the total number of operating abnormal records YZS≥3, the early warning notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the inspection and maintenance list to be notified to perform maintenance and inspection on the transformer; when the total number of operating abnormal records YZS≥6, the early warning power-off notification mechanism is triggered, and the unique identification ID of the current box-type transformer is sent to the relevant personnel in the emergency processing list to wait for the relevant personnel to confirm the execution of the power-off command.

10. A box transformer state assessment device, used to execute a box transformer state assessment method according to any one of claims 1 to 9, characterized in that: It includes the transformer signal acquisition module, signal feature extraction module, modeling analysis module, fitting evaluation module and decision-making module; The signal acquisition module collects the signals of the box-type transformer in operation, performs preprocessing to obtain the initial feature vector set F1, marks the box-type transformer with a unique mark, and transmits it to the cloud processing center via the Internet; The signal feature extraction module extracts leakage magnetic feature parameters from the initial feature vector set F1, obtains the magnetic flux change rate △Qt and the magnetic field energy density Et, and integrates them to obtain the leakage magnetic feature set F2; The modeling and analysis module establishes a magnetic leakage loss model according to the magnetic leakage feature set F2, and associates the dynamic influence of the load rate with environmental conditions to obtain a magnetic leakage loss index Ploss; The fitting evaluation module performs fitting calculation based on the leakage magnetic loss index Ploss and the characteristic vector set F1 to obtain the electromagnetic efficiency Rt of the operating state of the box-type transformer at time t; The decision module compares the electromagnetic efficiency Rt with a preset electromagnetic efficiency operating state warning threshold Rz to determine the operating state of the box-type transformer and obtain an operating evaluation result.

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