A temperature control method for power transformer

Abstract

The present invention provides a method and system for controlling the temperature of a power transformer. The method includes monitoring the temperature of the power transformer in real time based on a temperature sensor and a control algorithm, and then adjusting the temperature using a PID control strategy to obtain a temperature control instruction; obtaining a temperature adjustment starting point in the temperature control instruction, setting adjustment parameters according to the adjustment starting point, adjusting the cooling system of the transformer respectively, and obtaining adjustment effect data; setting control parameters according to the adjustment effect data, and limiting the adjustment range of the PID control strategy; under the criterion of transformer temperature stability, based on the thermal balance principle and the transformer loss model, searching for the optimal cooling system adjustment parameters, and controlling the transformer temperature. The present invention can improve the operating efficiency and safety of the transformer, and reduce energy consumption and maintenance costs.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to a temperature control method for a power transformer. Background Art

[0002] During the operation of existing power transformers, the temperature control mainly relies on traditional temperature monitoring and regulation methods. These methods usually include installing temperature sensors to monitor the temperature of the transformer and using simple control logic to adjust the cooling system. However, these traditional methods have some limitations, such as insufficient response to temperature changes, inaccurate setting of regulation parameters, and lack of dynamic adaptability to transformer load changes and ambient temperature. In addition, traditional temperature control strategies often cannot make full use of the historical operation data and real-time data of the transformer, resulting in the inability to achieve optimal temperature control effects.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the traditional temperature control method cannot achieve accurate control of the transformer temperature, especially when the transformer load changes and the ambient temperature fluctuates greatly, it is difficult to maintain the transformer in the optimal operating temperature range. At the same time, the existing control strategy often ignores the transformer loss model and the thermal balance principle, resulting in the inability to achieve efficient and energy-saving operation of the transformer. In addition, the existing monitoring system lacks intelligent early warning and adjustment functions, and cannot take timely measures when the transformer temperature is abnormal, increasing the risk of transformer failure. Summary of the invention

[0004] The invention provides a method and system for controlling the temperature of a power transformer.

[0005] In a first aspect of the present invention, there is provided a method for controlling temperature of a power transformer, comprising:

[0006] Based on the temperature sensor and control algorithm, the temperature of the power transformer is monitored in real time, and then the PID control strategy is used to adjust it to obtain the temperature control instruction;

[0007] Obtain the temperature adjustment starting point in the temperature control instruction, set the adjustment parameters according to the adjustment starting point, adjust the cooling system of the transformer respectively, and obtain the adjustment effect data; set the control parameters according to the adjustment effect data to limit the adjustment range of the PID control strategy;

[0008] Under the criterion of transformer temperature stability, based on the thermal balance principle and transformer loss model, the optimal cooling system adjustment parameters are searched to control the transformer temperature.

[0009] Further, the control algorithm is a fuzzy control algorithm;

[0010] Furthermore, the real-time monitoring of the temperature of the power transformer includes:

[0011] Install temperature sensors at key locations of the transformer, estimate the current temperature based on historical temperature data, and select the optimal PID control parameters based on temperature and transformer load;

[0012] Obtain corresponding control instructions according to PID control parameters, and adjust the cooling system of the transformer according to the control instructions;

[0013] According to the regulation effect, the coefficient of the FIR filter is designed by using the frequency sampling method; the temperature signal is filtered by using the FIR filter, and then the temperature of the power transformer is regulated by using the control instructions corresponding to the PID control parameters.

[0014] Furthermore, the optimal PID control parameter is the PID control parameter corresponding to the minimum temperature fluctuation under different loads.

[0015] Further, obtaining the temperature adjustment starting point in the temperature control instruction includes:

[0016] The temperature signal of the transformer is processed to obtain the result in the temperature domain;

[0017] Search the position with the maximum temperature change in the temperature domain as the adjustment starting point, perform curve fitting on multiple adjacent temperature points in segments, and count the relative deviation of each point in the curve fitting result and the average deviation of the points in the segment;

[0018] The segment with the smallest average deviation is taken as the adjustment segment, and the adjustment starting point is selected according to the relative deviation threshold, the temperature information of the selected adjustment starting point is extracted, and the most stable temperature point is selected as the temperature adjustment starting point.

[0019] Furthermore, the temperature control of the transformer includes: dividing the temperature data of the transformer into multiple segments along the time axis according to the load of the transformer, first obtaining the initial temperature and target temperature of the transformer according to the load change and ambient temperature, and then obtaining the temperature and load changes in other time periods.

[0020] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the power transformer temperature control method of the present invention can realize accurate monitoring and adjustment of the transformer temperature. Through real-time monitoring based on temperature sensors and control algorithms, combined with PID control strategies, it is possible to quickly respond to temperature changes of the transformer and adjust the cooling system in time, thereby maintaining the transformer operating within a suitable temperature range. In addition, through the selection of fuzzy control algorithms and optimal PID control parameters, the accuracy and stability of temperature control can be further improved, the transformer loss caused by temperature fluctuations can be reduced, and the service life of the equipment can be extended.

[0021] The power transformer temperature control method of the present invention can realize the real-time collection, transmission and processing of transformer temperature data. By using intelligent algorithms and monitoring systems, not only can the temperature state of the transformer be monitored in real time, but also abnormal conditions can be warned and measures can be taken in time to prevent failures. At the same time, the monitoring method can dynamically adjust the temperature control strategy according to the load changes and ambient temperature of the transformer, and optimize the operating efficiency and energy-saving performance of the transformer. This intelligent temperature monitoring and adjustment mechanism can significantly improve the reliability and economy of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0023] Figure 1 A schematic diagram of a flow chart of a method for monitoring temperature of a power transformer provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a flow chart of a method for monitoring temperature of a power transformer provided by an embodiment of the present invention;

[0025] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0027] Reference below Figure 1 , Figure 1 The following is a flow chart of a method for monitoring the temperature of a power transformer provided by an embodiment of the present invention. Figure 1 As shown, a method 100 for monitoring temperature of a power transformer includes:

[0028] Step 101, based on the temperature sensor and the control algorithm, the temperature of the power transformer is monitored in real time, and then the PID control strategy is adopted to adjust it to obtain a temperature control instruction;

[0029] Step 102, obtaining the temperature adjustment starting point in the temperature control instruction, setting the adjustment parameters according to the adjustment starting point, adjusting the cooling system of the transformer respectively, and obtaining the adjustment effect data; setting the control parameters according to the adjustment effect data, and limiting the adjustment range of the PID control strategy;

[0030] Step 103, under the criterion of transformer temperature stability, based on the thermal balance principle and the transformer loss model, searching for the optimal cooling system adjustment parameters, and controlling the transformer temperature.

[0031] Specifically, the temperature sensors in this embodiment are installed in key parts of the transformer, such as windings, cores, etc., to ensure that the actual temperature of the transformer can be accurately monitored. The control algorithm adopts a fuzzy control algorithm, which can intelligently adjust the PID control parameters, namely the proportional P, integral I and differential D parameters, according to real-time temperature data and historical data to achieve accurate regulation of the transformer cooling system. For example, the proportional parameter can be set to 0.1 to 0.5, the integral parameter can be set to 0.01 to 0.1, and the differential parameter can be set to 0.01 to 0.05. The specific values ​​of these parameters can be adjusted according to the specific model and operating conditions of the transformer.

[0032] Preferably, the PID control strategy in this embodiment also includes determining the starting point of regulation. By processing the temperature signal of the transformer, the position with the largest temperature change can be found as the starting point of regulation. Then, curve fitting is performed on multiple temperature points near this position, and the relative deviation of each point and the average deviation of the points in the segment are calculated. The segment with the smallest average deviation is selected as the regulation segment, and the specific regulation starting point is determined according to the relative deviation threshold. In addition, the control range of the regulation parameters, such as the minimum and maximum values ​​of the temperature, can be set to further optimize the control effect and ensure that the transformer operates within a safe temperature range.

[0033] Furthermore, in this example, assuming that the current operating temperature of the transformer is 85°C, the set safety upper limit is 90°C, and the adjustment starting point is set to 85°C, the adjustment parameters of the cooling system are selected and set according to the adjustment starting point:

[0034] The coolant flow rate was initially set at 200 L / h and increased to 250 L / h after adjustment; the cooling fan speed was initially set at 1000 RPM and increased to 1200 RPM after adjustment;

[0035] Adjustment 1:

[0036] Increase the coolant flow rate from 200 L / h to 250 L / h, keep the fan speed at 1000 RPM, monitor continuously for 10 minutes, and record the temperature change of the transformer. At the end of the monitoring, the transformer temperature dropped to 82°C, a drop of 3°C.

[0037] Adjustment 2:

[0038] Keep the coolant flow rate at 250 L / h, increase the fan speed from 1000 RPM to 1200 RPM, and continue monitoring for 10 minutes. At the end of the monitoring, the transformer temperature further dropped to 80°C, a drop of 2°C.

[0039] According to the above adjustment effect data, the control parameters are set to limit the adjustment range of the PID control strategy: set to 79°C to 90°C to ensure that the transformer operates within a safe range, the proportional P is set to 1.5, the integral I is set to 0.2, the differential D is set to 0.1, the coolant flow rate is limited to between 200 L / h and 300 L / h, and the fan speed is limited to between 1000 RPM and 1500 RPM to ensure that the fan is within a safe and effective working range.

[0040] Furthermore, in this example, it is assumed that the iron loss is proportional to the frequency and the square of the magnetic flux density, and the copper loss is proportional to the square of the load current. Therefore, the total transformer loss can be expressed as:

[0041] ,

[0042] Among them, K1 and K2 are empirical coefficients, f is the frequency, B is the magnetic flux density, and I is the load current;

[0043] Use the particle swarm optimization algorithm to search for the optimal cooling system adjustment parameters:

[0044] The number of particles is set to 20, the position of each particle is randomly initialized, and the objective function is defined as the temperature deviation, that is, the difference between the actual temperature and the set temperature. In each iteration, the fitness (objective function value) of each particle is calculated, and the speed and position of the particle are updated, and the individual optimal position and the global optimal position are updated to guide the particle towards a better solution; multiple iterations are performed until the fitness reaches the preset threshold or the maximum number of iterations is reached;

[0045] Assume that after algorithm iteration, the following optimal adjustment parameters are obtained:

[0046] Coolant flow rate 250 L / h, fan speed 1200 RPM;

[0047] Adjust the cooling system to the above parameters, monitor the transformer temperature in real time, record the temperature changes for 20 minutes, and observe whether a stable state is reached; after adjustment, the stable temperature is maintained at 88°C (not exceeding the set upper limit of 90°C), and the temperature fluctuation does not exceed ±1°C during the monitoring time.

[0048] In some embodiments, the control algorithm is a fuzzy control algorithm;

[0049] The mathematical model of the PID control strategy is:

[0050] ,

[0051] in, To control the amount, is the temperature deviation, , , They are proportional, integral and differential control parameters respectively.

[0052] It should be noted that this embodiment describes in detail a method for controlling the temperature of a power transformer using a fuzzy control algorithm and a PID control strategy. The fuzzy control algorithm can handle uncertainty and ambiguity, while the PID control strategy provides a standardized method for dynamic system control.

[0053] Specifically, the fuzzy control algorithm processes temperature data by defining a series of fuzzy sets and rules. These sets and rules can convert precise temperature values ​​into membership in fuzzy logic, allowing the system to make more flexible decisions in the face of uncertainty. The mathematical model of the PID control strategy includes three main parameters: proportional P, integral I, and differential D. In this embodiment, these parameters can be adjusted according to the specific requirements and operating conditions of the transformer. For example, the proportional parameter can be set to 0.6 to quickly respond to temperature changes; the integral parameter can be set to 0.05 to eliminate steady-state errors; and the differential parameter can be set to 0.02 to predict future temperature change trends.

[0054] Preferably, the PID control strategy in this embodiment also includes dynamic adjustment of control parameters. By real-time monitoring of the temperature and load changes of the transformer, the system can automatically adjust the PID parameters to adapt to different operating conditions. In addition, an adaptive control algorithm can be introduced to enable the system to automatically optimize the PID parameters based on historical data and real-time feedback to further improve the control effect. In some cases, advanced control algorithms such as model predictive control MPC or neural network control can also be considered to further improve the accuracy and stability of transformer temperature control. These alternatives can be selected and implemented based on the specific needs of the transformer and the available technical resources.

[0055] Furthermore, a temperature sensor and a load sensor are provided to record the temperature (e.g., 85°C) and load current (e.g., 200 A) of the transformer in real time, and the safety temperature range is set to 80°C to 90°C.

[0056] According to the changes in temperature and load, the following strategies are used to dynamically adjust the PID parameters:

[0057] If the temperature deviation is large (e.g. >5°C), increase the proportional gain to enhance the response to the temperature deviation;

[0058] If the temperature is close to the set value, the proportional gain is reduced to reduce system oscillation;

[0059] If the load increases (e.g. the load current increases by more than 30%), the integral gain is increased to overcome the steady-state error caused by the load change;

[0060] If the temperature fluctuates within the set range, the integral gain is reduced to prevent overshoot;

[0061] If the load changes dramatically or the temperature rises rapidly, increase the differential gain to slow down the temperature change and enhance system stability.

[0062] After the system reaches a stable state, reduce the derivative gain to reduce unnecessary control signals.

[0063] The monitored status is: current temperature 85°C, current load 220A;

[0064] Calculate the temperature deviation:

[0065] ,

[0066] Adjust PID parameters according to monitoring status:

[0067] Proportional gain adjustment:

[0068] The current deviation is 5°C, and the proportional gain is increased to 2.0;

[0069] Integral gain adjustment:

[0070] As the load increases, the integral gain increases to 0.15 to overcome the steady-state error caused by the load change;

[0071] Differential gain adjustment:

[0072] Due to the high load and rapid temperature change, the derivative gain is increased to 0.1 to enhance the stability of the system.

[0073] In some embodiments, the real-time monitoring of the temperature of the power transformer includes:

[0074] Install temperature sensors at key locations of the transformer, estimate the current temperature based on historical temperature data, and select the optimal PID control parameters based on temperature and transformer load;

[0075] Obtain corresponding control instructions according to PID control parameters, and adjust the cooling system of the transformer according to the control instructions;

[0076] According to the adjustment data, the coefficient of the FIR filter is designed by using the frequency sampling method; the temperature signal is filtered by using the FIR filter, and then the temperature of the power transformer is adjusted by using the control instruction corresponding to the PID control parameter.

[0077] It should be noted that this embodiment focuses on the temperature monitoring and control of power transformers, and particularly emphasizes the importance of real-time monitoring and how to optimize PID control parameters based on monitoring data. This method can obtain temperature data in real time by installing temperature sensors at key locations of the transformer, and adjust the cooling system through fuzzy control algorithms and PID control strategies to achieve precise temperature control.

[0078] Specifically, temperature sensors are installed in hot spots of the transformer, such as windings, cores, and oil tanks, to ensure that the temperature changes of the transformer can be accurately captured. The data from these sensors is transmitted to the central control unit in real time through the data acquisition system. The fuzzy control algorithm is used here to process the sensor data. It converts the precise temperature value into the membership degree in fuzzy logic by defining a series of fuzzy sets and rules, thereby achieving a rapid response to temperature changes. PID control parameters, including proportional P, integral I, and differential D parameters, are dynamically adjusted according to the load and environmental conditions of the transformer. For example, the PID parameters can be adjusted in real time according to the load changes of the transformer to ensure optimal temperature control under different load conditions.

[0079] Preferably, the present embodiment also includes further processing of the temperature signal to improve the control accuracy. This includes filtering the temperature signal using an FIR filter to reduce noise and interference and improve the stability of the signal. The coefficients of the FIR filter can be designed by a frequency sampling method, which allows the coefficients of the filter to be determined according to specific frequency response requirements. In addition, other types of filters, such as IIR filters, or more advanced signal processing techniques, such as wavelet transforms, can also be considered to further improve the processing effect of the temperature signal. These alternatives can be selected and implemented according to the needs of the actual application and the available resources.

[0080] Furthermore, collect the temperature data for the past 10 minutes. Assume that the sampling frequency is 1 Hz and the collected temperature signal is T[n] (n is the sampling point). Select a cutoff frequency of 0.1 Hz to filter out high-frequency noise. Use the window function method to calculate the filter coefficients. Assume that the calculated FIR filter coefficients are:

[0081] h[n]=[0.2,0.4,0.4,0.2]

[0082] The temperature signal is filtered using a convolution operation:

[0083] Assume that the five most recent temperature sampling values ​​are [84.5, 85.0, 85.5, 86.0, 86.5], and use the filter coefficients to convolve these values ​​to obtain the filtered temperature value;

[0084] Assume the set temperature is 90°C and the filtered temperature is 86°C.

[0085] Temperature deviation calculation ;

[0086] Set the proportional gain Kp=1.5, the integral gain Ki=0.1, and the differential gain Kd=0.05;

[0087] Calculate the control volume:

[0088]

[0089] According to the calculated control command (6.45), the coolant flow rate and the fan speed are adjusted;

[0090] The actual temperature of the transformer is continuously monitored to ensure it is within the set range.

[0091] In some embodiments, the optimal PID control parameter is a PID control parameter corresponding to the minimum temperature fluctuation under different loads.

[0092] Specifically, the optimal PID control parameters are determined through experiments and simulations. First, it is necessary to collect operating data of the transformer under different load conditions, including temperature, load, and environmental conditions. Then, use this data to simulate the impact of different PID parameter settings on transformer temperature control. In this process, the proportional P, integral I, and differential D parameters are adjusted to observe their effects on temperature fluctuations. For example, the proportional parameter can be increased to respond quickly to temperature changes, the integral parameter can be adjusted to eliminate steady-state errors, and the differential parameter can be used to predict and suppress rapid changes in temperature.

[0093] Preferably, the present embodiment also includes an adaptive algorithm that can automatically adjust the PID parameters according to the actual operation of the transformer. This adaptive algorithm can be based on machine learning technology to predict the optimal PID parameters by analyzing historical data and real-time data. In addition, advanced control technologies such as fuzzy logic or neural networks can also be considered to further optimize the selection of PID parameters. These technologies can handle complex nonlinear relationships and provide more accurate and flexible control strategies. Through these methods, it can be ensured that the transformer can maintain a stable temperature under various operating conditions, thereby improving its performance and reliability.

[0094] Furthermore, multiple temperature sensors are installed at key locations of the transformer to monitor the temperature change of the transformer in real time and the load current of the transformer in order to obtain data on load changes;

[0095] Under different load conditions (such as light load, medium load, heavy load), record the temperature fluctuation data of the transformer:

[0096] Light load (100 A): Temperature fluctuation ±1°C

[0097] Medium load (200 A): Temperature fluctuation ±2°C

[0098] Heavy load (300 A): Temperature fluctuation ±3°C

[0099] According to historical data and experience, the PID parameters are preliminarily set:

[0100] Light load:

[0101] Kp=1.0, Ki=0.05, Kd=0.1

[0102] Medium load:

[0103] Kp=1.5, Ki=0.1, Kd=0.2

[0104] Overload:

[0105] Kp=2.0, Ki=0.15, Kd=0.3

[0106] Light load condition (100 A):

[0107] The observed temperature fluctuations were small, so lower PID parameters were used to reduce the sensitivity of the control system. Based on real-time data feedback, temperature fluctuations were continuously monitored and parameters were adjusted appropriately.

[0108] Medium load condition (200 A):

[0109] Temperature fluctuations increase, and PID parameters are fine-tuned according to monitoring data;

[0110] For example, increase the proportional gain Kp to 1.7 and the integral gain Ki to 0.12 to enhance the control strength;

[0111] Heavy load condition (300 A):

[0112] The temperature fluctuation further increased. In order to quickly respond to load changes, all PID parameters were appropriately increased, Kp was adjusted to 2.5, Ki to 0.18, and Kd to 0.4 to enhance the system's responsiveness;

[0113] During transformer operation, the corresponding PID parameters are selected according to the current load conditions, the temperature deviation is calculated in real time and the PID control strategy is applied, the temperature changes are monitored, and the PID parameters are adjusted according to the feedback to ensure that the temperature fluctuation is as small as possible under different loads. The effectiveness of the PID parameter adjustment is verified by reducing the temperature fluctuation.

[0114] In some embodiments, obtaining the temperature adjustment starting point in the temperature control instruction includes:

[0115] The temperature signal of the transformer is processed to obtain the result in the temperature domain;

[0116] Search the position with the maximum temperature change in the temperature domain as the adjustment starting point, perform curve fitting on multiple adjacent temperature points in segments, and count the relative deviation of each point in the curve fitting result and the average deviation of the points in the segment;

[0117] The segment with the smallest average deviation is taken as the adjustment segment, and the adjustment starting point is selected according to the relative deviation threshold, the temperature information of the selected adjustment starting point is extracted, and the most stable temperature point is selected as the temperature adjustment starting point.

[0118] Specifically, the determination of the starting point of temperature regulation involves the detailed processing of the transformer temperature signal. First, the temperature data collected from the temperature sensor needs to be preprocessed to eliminate noise and outliers. Then, by analyzing these data, the location with the largest temperature change is found, which is considered to be the best candidate for the regulation starting point. Next, curve fitting is performed on the temperature points near these candidate locations to determine the relative deviation of each point and the average deviation of the points within the segment. For example, polynomial fitting or linear regression methods can be used to fit the temperature curve and calculate the deviation.

[0119] Furthermore, the temperature data is collected once every second, and the temperature signal is continuously collected within 10 minutes. The FIR filter is applied to the collected temperature signal to remove high-frequency noise and ensure data smoothness;

[0120] The processed temperature signal is recorded as T filtered[n] ,Convert the processed temperature signal into the temperature domain, generate a series of temperature value distribution graphs, calculate the rate of change of the temperature signal, and find the location with the largest temperature change;

[0121] The temperature change rate can be obtained by taking the first-order difference of the temperature signal:

[0122] ,

[0123] By analyzing ΔT[n], find the point with the largest absolute value, record it as nmax, select several adjacent points before and after nmax (such as 5), establish a curve fitting model; use polynomial regression to fit the selected adjacent temperature points, and get the fitting function:

[0124] ,

[0125] Among them, a0, a1, a2 are fitting coefficients;

[0126] Calculate the relative deviation between the fitting curve and the actual temperature point and the average deviation of the points within the segment, record the average deviation of each fitting segment, and select the segment with the smallest average deviation;

[0127] Set a relative deviation threshold (e.g. 5%) according to the requirements, and find the temperature point with a relative deviation less than the threshold in the selected segment with the smallest average deviation as a candidate adjustment starting point;

[0128] Among the candidate adjustment starting points, select the point with the smallest temperature change (highest stability) and record it as the adjustment starting point T set , record this temperature point as the starting point for subsequent temperature control to ensure the accuracy of temperature control.

[0129] Preferably, in order to further improve the accuracy of determining the regulation starting point, more advanced data processing techniques can be used. For example, machine learning algorithms, such as support vector machines (SVMs) or random forests, can be used to identify temperature variation patterns and predict the best regulation starting point. In addition, time series analysis methods, such as autoregressive moving average (ARMA) models, can also be considered to analyze the time dependency of temperature data to more accurately determine the regulation starting point. These methods can provide deeper insights to help optimize the temperature control strategy of the transformer.

[0130] Furthermore, temperature sensors are installed at key locations of the transformer to collect temperature data, and the data collection frequency is set to once per second;

[0131] Define a time window (e.g. 10 minutes) and continuously record temperature data within this window;

[0132] Perform moving average filtering on the collected temperature data to reduce the impact of noise and obtain a smooth temperature curve;

[0133] At the end of each time window, the standard deviation of the temperature change within that window is calculated, indicating the degree of temperature fluctuation;

[0134] According to the standard deviation of historical data, set the relative deviation threshold. The formula can be expressed as:

[0135] ,

[0136] Where k is an empirical coefficient (e.g., 2), and σ is the standard deviation of temperature variation within the past window;

[0137] At the location with the largest temperature change found, multiple adjacent temperature points are selected for curve fitting, and the relative deviation of each fitting segment is recorded; the relative deviation of each fitting segment is compared with the dynamic threshold, and the segment with a relative deviation less than the dynamic threshold is selected as a candidate adjustment segment;

[0138] In the candidate adjustment section, the standard deviation of each temperature point is analyzed, and the point with the smallest standard deviation is selected as the final adjustment starting point.

[0139] In some embodiments, the control range of the setting adjustment parameter is:

[0140] ,

[0141] in, is the transformer temperature, , are the minimum and maximum values ​​of temperature, respectively. is the base temperature of the transformer, is the allowable temperature adjustment range.

[0142] Specifically, the control range of the adjustment parameter is defined by setting the minimum and maximum values ​​of the transformer temperature. Here, the base temperature of the transformer is a key reference point, which represents the typical temperature of the transformer under normal operating conditions. The allowable temperature adjustment range is a preset value that defines the temperature range that can be adjusted above and below the base temperature. For example, if the base temperature of the transformer is set to 50°C and the allowable temperature adjustment range is ±5°C, the minimum and maximum values ​​of the control parameter can be set to 45°C and 55°C respectively.

[0143] Preferably, in order to make the setting of control parameters more flexible and adaptable to different operating conditions, an adaptive mechanism can be introduced to dynamically adjust the control range. For example, the base temperature and the allowable temperature adjustment range can be automatically adjusted according to the actual load and ambient temperature changes of the transformer. In addition, the use of predictive control algorithms can also be considered to predict future temperature changes based on historical data and real-time data, so as to adjust the control parameters in advance to maintain the stability of the transformer temperature. These methods can improve the accuracy and response speed of transformer temperature control and ensure that the transformer can operate safely and effectively under various conditions.

[0144] Furthermore, based on historical temperature fluctuations, the temperature change threshold is dynamically set to distinguish between normal fluctuations and abnormal fluctuations, and the initial PID control parameters (proportional, integral and differential) are set and applied to the temperature control strategy;

[0145] According to the real-time monitored temperature changes and fluctuations, the PID parameters are automatically adjusted. The adjustment logic is as follows:

[0146] Proportional parameter (Kp):

[0147] When the temperature change rate is higher than the set threshold, the proportional parameter is increased to enhance the system response;

[0148] When the temperature fluctuations stabilize, reduce the proportional parameter to reduce over-regulation;

[0149] Integration parameter (Ki):

[0150] When the temperature deviation persists, gradually increase the integral parameter to eliminate the steady-state error;

[0151] When the temperature is close to the set value, reduce the integral parameter to prevent over-adjustment;

[0152] Differentiation parameter (Kd):

[0153] When rapid temperature changes are detected, the derivative parameter is increased to improve the predictive power of the system.

[0154] When the temperature is stable, reduce the differential parameter to reduce the effect of noise.

[0155] In some embodiments, the temperature control of the transformer includes: dividing the temperature data of the transformer into multiple segments along the time axis according to the load of the transformer, first obtaining the initial temperature and target temperature of the transformer according to the load change and ambient temperature, and then obtaining the temperature and load changes in other time periods.

[0156] Specifically, the temperature data segmentation of the transformer is based on the changes in the operating load and ambient temperature of the transformer. The initial temperature and target temperature are obtained by analyzing the load and environmental conditions of the transformer in a specific time period. For example, the maximum and minimum temperatures that the transformer may reach in different time periods can be predicted through historical data and real-time monitoring data. Temperature and load changes in other time periods are tracked through continuous monitoring and data analysis to ensure that the temperature control strategy can adapt to these changes.

[0157] Preferably, in order to further improve the accuracy and adaptability of temperature control, advanced data analysis and prediction techniques can be used. For example, machine learning algorithms can be used to analyze historical data and predict the temperature change trend of the transformer under different load and environmental conditions. In addition, adaptive control strategies can be considered to dynamically adjust PID control parameters based on real-time data to achieve more accurate temperature control. These methods can help the system respond more effectively to changes in the operating conditions of the transformer, ensuring that the transformer can remain within a safe and efficient operating temperature range under various conditions.

[0158] The above-mentioned embodiments of the present invention have the following beneficial effects: by implementing the power transformer temperature control method, the operating efficiency and safety of the transformer can be improved. By real-time monitoring of the temperature of the transformer and adjusting it using the PID control strategy, it can be ensured that the transformer can be maintained within the optimal operating temperature range under various load conditions. This method can reduce transformer losses caused by temperature fluctuations and extend the service life of the equipment by accurately controlling the cooling system, and can prevent over-adjustment by limiting the adjustment range of the PID control strategy, thereby improving the stability and reliability of the system.

[0159] In addition, this method can further optimize the temperature control effect of the transformer through the fuzzy control algorithm and the selection of the optimal PID control parameters. By installing temperature sensors at key parts of the transformer and estimating the current temperature based on historical temperature data, the optimal PID control parameters can be selected more accurately. This method also filters the temperature signal by designing the coefficients of the FIR filter using the frequency sampling method to improve the accuracy of the control instructions. Through these measures, the risk of transformer failure caused by abnormal temperature can be effectively reduced, and the stability and economy of the power system can be improved.

[0160] like Figure 2 As shown, a method 200 for monitoring temperature of a power transformer in some embodiments includes:

[0161] Step 201, obtaining temperature data obtained by a temperature control method for a power transformer;

[0162] Step 202, based on the data acquisition system, transmit the temperature data to the monitoring center in real time;

[0163] Step 203, according to the instruction of the monitoring center, the temperature data is processed in real time by the CPU, and the temperature data to be displayed is loaded into the display interface;

[0164] Step 204: Based on the intelligent algorithm, the monitoring system is used to perform real-time monitoring and early warning of the temperature of the transformer.

[0165] It is understandable that the steps described in the power transformer temperature control system 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the power transformer temperature control method are also applicable to the power transformer temperature control system 200 and the steps contained therein, and will not be repeated here.

[0166] In some embodiments, the real-time transmission of temperature data to a monitoring center based on a data acquisition system includes:

[0167] According to the preset data transmission frequency, wireless or wired communication is used to collect and transmit temperature data in real time to obtain temperature data for each time period;

[0168] According to the temperature data in adjacent time periods, the temperature change trend is analyzed until the accuracy requirement of temperature monitoring is met to obtain temperature monitoring data.

[0169] Specifically, the data acquisition system is responsible for collecting the temperature data of the transformer in real time. This usually involves installing multiple temperature sensors at key locations of the transformer and transmitting the data to the monitoring center via wired or wireless means. The preset data transmission frequency refers to the time interval set by the system as needed to control the data transmission. For example, the data transmission frequency can be set to once per minute or once per second according to the operating conditions and monitoring requirements of the transformer. In addition, the collection and transmission of temperature data can be achieved by adopting different communication protocols and standards, such as Modbus, DNP3 or IEC61850.

[0170] Preferably, in order to improve the efficiency and accuracy of data collection and transmission, some advanced technologies and methods can be used. For example, wireless sensor networks (WSN) can be used to achieve wireless data collection and transmission, which can reduce the complexity and cost of wiring. At the same time, data compression and encryption technologies can be used to improve the security and efficiency of data transmission. In addition, edge computing technology can be introduced to perform preliminary data processing and analysis near the data collection point to reduce the amount of data transmitted to the monitoring center and improve the response speed. These alternatives can be selected and implemented according to actual monitoring needs and resource conditions.

[0171] In some embodiments, the method of processing the temperature data in real time using the CPU according to the instruction of the monitoring center and loading the temperature data to be displayed into the display interface includes:

[0172] Calculate the address of the temperature data file to be processed and displayed according to the current needs of the monitoring center; use the database management system to construct the temperature data, and initialize the processing and display of the temperature data;

[0173] When the demand of the monitoring center changes, the type of demand change is determined, and the temperature data processed and displayed is updated; the update threshold of the display interface is calculated, it is determined whether the data in the display interface needs to be updated, and the required update operation is performed.

[0174] Specifically, the instructions of the monitoring center are used to direct the CPU to process the collected temperature data. This includes calculating the address of the temperature data file that needs to be processed and displayed, and using the database management system to build and initialize the temperature data. For example, parameters such as the query frequency of the database, the data update interval, and the data retention period can be set to optimize the efficiency and accuracy of data processing. When the needs of the monitoring center change, the system needs to be able to identify these changes and update the processed and displayed temperature data accordingly. This may involve adjusting the parameters of the database query, reinitializing the data set, or updating the layout and content of the display interface.

[0175] Preferably, in order to improve the flexibility and response speed of data processing and display, some advanced technologies and methods can be adopted. For example, a real-time database system can be introduced, which can provide faster data reading and writing capabilities and better concurrent processing capabilities.

[0176] In addition, dynamic data visualization techniques, such as real-time charts and dashboards, can be used to intuitively display the changing trends and abnormalities of temperature data. You can also consider using predictive analysis tools to predict future temperature changes based on historical data and real-time data, so as to adjust monitoring strategies and warning thresholds in advance. These alternatives can be selected and implemented based on the specific needs and resource conditions of the monitoring system.

[0177] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0178] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0179] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0180] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0181] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for controlling the temperature of a power transformer, characterized in that: The steps include: Step 101, based on the temperature sensor and the control algorithm, the temperature of the power transformer is monitored in real time, and then the PID control strategy is adopted to adjust it to obtain a temperature control instruction; Step 102, obtaining the temperature adjustment starting point in the temperature control instruction, setting the adjustment parameters according to the adjustment starting point, adjusting the cooling system of the transformer respectively, and obtaining the adjustment effect data; setting the control parameters according to the adjustment effect data, and limiting the adjustment range of the PID control strategy; Step 103, under the criterion of transformer temperature stability, based on the thermal balance principle and transformer loss model, searching for the optimal cooling system adjustment parameters to control the transformer temperature; In step 101, the PID control strategy is used to adjust and obtain the temperature control instruction, including: The system reads the real-time temperature value of the temperature sensor and compares it with the set target temperature, calculates the temperature deviation, and uses the calculated control output as the temperature control instruction to indicate the adjustment degree of the cooling system. According to the control instruction, the operating state of the cooling system is adjusted to make the transformer temperature gradually approach the set value. The system continuously monitors the temperature changes and repeats the above steps to dynamically adjust the control instruction; In step 102, obtaining the temperature adjustment starting point in the temperature control instruction includes: The temperature signal of the transformer is processed to obtain the result in the temperature domain; Search the position with the maximum temperature change in the temperature domain as the adjustment starting point, perform curve fitting on multiple adjacent temperature points in segments, and count the relative deviation of each point in the curve fitting result and the average deviation of the points in the segment; The segment with the smallest average deviation is taken as the adjustment segment, and the adjustment starting point is selected according to the relative deviation threshold, the temperature information of the selected adjustment starting point is extracted, and the most stable temperature point is selected as the temperature adjustment starting point; These include: collecting temperature data once a second, continuously collecting temperature signals within 10 minutes, applying FIR filters to the collected temperature signals to remove high-frequency noise and ensure data smoothness; The processed temperature signal is recorded as T filtered[n] ,Convert the processed temperature signal into the temperature domain, generate a series of temperature value distribution graphs, calculate the rate of change of the temperature signal, and find the location with the largest temperature change; The temperature change rate is obtained by taking the first-order difference of the temperature signal: ， By analyzing ΔT[n], find the point of change with the largest absolute value, record it as nmax, select several adjacent points before and after nmax, and establish a curve fitting model; use polynomial regression to fit the selected adjacent temperature points to obtain the fitting function; Calculate the relative deviation between the fitting curve and the actual temperature point and the average deviation of the points within the segment, record the average deviation of each fitting segment, and select the segment with the smallest average deviation; Set the relative deviation threshold according to the requirements, and find the temperature point with the relative deviation less than the threshold in the selected segment with the smallest average deviation as the candidate adjustment starting point; Among the candidate adjustment starting points, select the point with the smallest temperature change and record it as the adjustment starting point T set ; In step 103, the optimal cooling system adjustment parameters are searched based on the thermal balance principle and the transformer loss model, including: Select the cooling system parameters that need to be optimized, set the initial value for each adjustable parameter, and set the learning rate; In each iteration, the gradient of the objective function with respect to each parameter is calculated; the gradient represents the rate of change of the objective function in the parameter space and indicates the direction of parameter adjustment; Based on the calculated gradient and learning rate, the value of each parameter is updated. By subtracting the result of the gradient multiplied by the learning rate, the parameter will be adjusted in the direction of reducing the objective function. The steps of calculating the gradient and updating the parameters are repeated until a specific stopping condition is met.

2. The power transformer temperature control method according to claim 1, characterized in that: The control algorithm is a fuzzy control algorithm.

3. The power transformer temperature control method according to claim 1, characterized in that: The real-time monitoring of the temperature of the power transformer comprises: Install temperature sensors at key locations of the transformer, estimate the current temperature based on historical temperature data, and select the optimal PID control parameters based on temperature and transformer load; Obtain corresponding control instructions according to PID control parameters, and adjust the cooling system of the transformer according to the control instructions; According to the adjustment data, the coefficient of the FIR filter is designed by using the frequency sampling method; the temperature signal is filtered by using the FIR filter, and then the temperature of the power transformer is adjusted by using the control instruction corresponding to the PID control parameter.

4. The power transformer temperature control method according to claim 3, characterized in that: The optimal PID control parameters are the PID control parameters corresponding to the minimum temperature fluctuation under different loads.

5. The power transformer temperature control method according to claim 1, characterized in that: The PID control strategy is then used to adjust and obtain the temperature control instruction, including: The system reads the real-time temperature value of the temperature sensor and compares it with the set target temperature, calculates the temperature deviation, and uses the calculated control output as the temperature control instruction to indicate the adjustment degree of the cooling system. According to the control instruction, the operating status of the cooling system is adjusted to make the transformer temperature gradually approach the set value. The system continuously monitors temperature changes and repeats the above steps to dynamically adjust the control instruction.

6. The power transformer temperature control method according to claim 1, characterized in that: The control parameters are set according to the adjustment effect data to limit the adjustment range of the PID control strategy, including: After implementing PID control, record the temperature change, response time and stabilization time data for each adjustment, analyze the collected data, and evaluate the performance of the current PID control; Based on the data analysis results, adjust the parameters of the PID controller and set the limit range of the control output according to the adjustment effect; Define the upper and lower limits of PID output; update the control range in real time according to temperature changes and regulation effects.

7. The power transformer temperature control method according to claim 1, characterized in that: The temperature control of the transformer includes: dividing the temperature data of the transformer into multiple segments along the time axis according to the load of the transformer, first obtaining the initial temperature and target temperature of the transformer according to the load change and the ambient temperature, and then obtaining the temperature and load changes in other time periods.

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