Relay protector fault identification method, device and equipment based on digital twin transformer

Abstract

The invention provides a relay protector fault identification method, device and equipment based on a digital twin transformer. The method comprises the steps of firstly obtaining an input signal of a power transformer and considering errors and noise; a nonlinear transformer model with lag is adopted for modeling, and parameters are updated according to input signals. Then, digital twin modeling is performed based on the model, a digital twin transformer is generated, and a calculation output value of the power transformer is calculated. Meanwhile, the actual measurement output value of the power transformer is obtained. And the difference between the two is accumulated through CUSUM to obtain a deviation value, and the deviation value is compared with a preset threshold value to judge whether the differential relay protector has a fault or not. According to the method, the difference between the calculated output value and the measured output value is accumulated through the cumulative summation control chart (CUSUM), CT saturation interference is avoided, the excitation surge current and the fault current can be correctly distinguished, and the accuracy and reliability of fault identification are improved.

Description

Technical Field

[0001] The present application belongs to the field of differential relay protectors, and in particular, relates to a relay protector fault identification method, device and equipment based on a digital twin transformer. Background Art

[0002] Power transformers play a vital role in the power system, and their operating status directly affects the stability and safety of the entire power system. However, power transformers may encounter various faults during operation. If these faults are not discovered and handled in time, they will cause serious damage to the power system and even cause large-scale power outages. Therefore, it is particularly important to effectively identify and protect power transformers.

[0003] Traditional differential relays identify internal faults by comparing the secondary currents of the current transformer (CT) when they differ. However, this approach faces some challenges in practical applications. On the one hand, some transient and steady-state conditions may result in incorrect baseline currents, which can cause false operations. To solve this problem, constrained dual-slope characteristic curves are widely used around the world, but in many cases, dual-slope relays still cannot accurately distinguish between fault currents and excitation inrush currents, so a conservative approach has to be adopted to select a higher slope, which in turn reduces the sensitivity of the relay to a certain extent.

[0004] On the other hand, most transformers currently use the second harmonic braking method to resist the interference of the excitation inrush current. However, when the fault is serious, the current flowing through the CT may be too large and accompanied by a DC component, which can easily lead to CT saturation. CT saturation will cause a large deviation between the measured current and the actual current, and the measured current contains a high second harmonic content. If the harmonic criterion is used, it is easy to be confused with the excitation inrush current, resulting in the failure to be discovered in time. Summary of the invention

[0005] The purpose of this application is to overcome the above-mentioned prior art and provide a method, device and equipment for identifying relay protection faults based on digital twin transformers.

[0006] The present application provides a relay protection device fault identification method based on a digital twin transformer, comprising:

[0007] The input signal of the power transformer is obtained, and the expression of the input signal is:

[0008]

[0009] Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Indicates all;

[0010] A nonlinear transformer model with hysteresis is used to model a power transformer, and parameters of the nonlinear transformer model are updated according to the input signal to obtain a power transformer model;

[0011] Performing digital twin modeling according to the power transformer model to generate a digital twin transformer;

[0012] The digital twin transformer calculates a calculated output value of the power transformer according to the input signal;

[0013] Obtaining an actual measured output value of the power transformer;

[0014] The difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value;

[0015] The deviation value is compared with a preset threshold value, and whether the differential relay protector is faulty is determined according to the comparison result.

[0016] Optionally, the input signal of the power transformer is obtained, and the measurement device used includes: a current transformer.

[0017] Optionally, the nonlinear transformer model is updated with parameters according to the input signal, and the expression is as follows:

[0018]

[0019] Among them, v Lm and i Lm is the inductor voltage and current, N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, A e is the effective core cross-sectional area, l e is the effective core length, μ0 is the air magnetic permeability, H and M are the magnetic field intensity and core magnetization intensity of the power transformer respectively.

[0020] Optionally, the calculation formula for the core magnetization intensity of the power transformer is as follows:

[0021]

[0022] Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. iis an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, when H[n]<0, δ=-1, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, L m [n] = N 2 μ0μ r [n]A e / l e ;

[0023] where μ r [n] = |(B[n]-B[n-1]) / (H[n]-H[n-1])|, (H[n]-H[n-1])| is greater than i Lm [n].

[0024] Optionally, digital twin modeling is performed according to the power transformer model to generate a digital twin transformer, and the expression is as follows:

[0025]

[0026] in, And ρ=(x[n]-x[n-1]) / T s , T s is the sampling period, k v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

[0027] Optionally, the calculation expression of the difference between the calculated output value and the measured output value is as follows:

[0028]

[0029] The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of where w is the window size and the third equation normalizes the residuals.

[0030] Optionally, the difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value, and the calculation expression is as follows:

[0031]

[0032] where g k and g k ′(g0,g0′=0) are the outputs of CUSUM for rising and falling changes respectively, Th is the threshold selected under the premise of the expected confidence level, Fault represents the fault, Indicates all.

[0033] The present application also provides a relay protection device fault identification device based on a digital twin transformer, comprising:

[0034] The acquisition module acquires the input signal of the power transformer. The expression of the input signal when considering the error and noise is:

[0035]

[0036] Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Indicates all;

[0037] A modeling module, which uses a nonlinear transformer model with hysteresis to model a power transformer, updates parameters of the nonlinear transformer model according to the input signal, and obtains a power transformer model;

[0038] A digital twin module performs digital twin modeling according to the power transformer model to generate a digital twin transformer;

[0039] A calculation module, wherein the digital twin transformer calculates a calculated output value of the power transformer according to the input signal;

[0040] A measurement module, for obtaining an actual measured output value of the power transformer;

[0041] A deviation module accumulates the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value;

[0042] The judgment module compares the deviation value with a preset threshold value and judges whether the differential relay protector is faulty according to the comparison result.

[0043] Optionally, the acquisition module acquires the input signal of the power transformer, and the measurement device used includes: a current transformer.

[0044] Optionally, the modeling module updates the parameters of the nonlinear transformer model according to the input signal, and the expression is as follows:

[0045]

[0046] Among them, v Lm and i Lm is the inductor voltage and current, N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, Ae is the effective core cross-sectional area, l e is the effective core length, μ0 is the air magnetic permeability, H and M are the magnetic field intensity and core magnetization intensity respectively.

[0047] Optionally, M is calculated as follows:

[0048]

[0049] Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. i is an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, when H[n]<0, δ=-1, and finally, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, and L m [n] = N 2 μ0μ r [n]A e / l e , where μ r [n]=|(B[n]-B[n-1]) / (H[n]-H[n-1])|, denominator is greater than i Lm [n].

[0050] Optionally, the digital twin module performs digital twin modeling according to the power transformer model to generate a digital twin transformer, and the expression is as follows:

[0051]

[0052] in, And ρ=(x[n]-x[n-1]) / T s , T s is the sampling period, k v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

[0053] Optionally, the calculation expression of the difference between the calculated output value and the measured output value is as follows:

[0054]

[0055] The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of The mean of the residuals of , where w is the window size.

[0056] 14. A relay protection fault identification device based on a digital twin transformer according to claim 8, characterized in that the difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value, and the calculation expression is as follows:

[0057]

[0058] where g k and g k '(g0,g0′=0) are the outputs of CUSUM for rising and falling changes respectively, and Th is the threshold selected under the premise of the expected confidence level.

[0059] The present application also provides a relay protection device fault identification device based on a digital twin transformer, comprising:

[0060] Memory;

[0061] The processor is used to retrieve the computer executable program of the above-mentioned relay protection device fault identification method based on digital twin transformer from the memory, and execute: obtaining the input signal of the power transformer, the expression of the input signal when considering error and noise is:

[0062]

[0063] Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Represents all; adopts a nonlinear transformer model with hysteresis to model the power transformer, updates the parameters of the nonlinear transformer model according to the input signal, and obtains the power transformer model; performs digital twin modeling according to the power transformer model to generate a digital twin transformer; the digital twin transformer calculates the calculated output value of the power transformer according to the input signal; obtains the actual measured output value of the power transformer; accumulates the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value; compares the deviation value with a preset threshold, and determines whether the differential relay protector is faulty according to the comparison result.

[0064] The present application also provides a storage medium, including: a computer executable program stored therein, which is used to be called by a processor to execute the steps of the above-mentioned relay protection device fault identification method based on a digital twin transformer.

[0065] The beneficial effects of this application are:

[0066] The present application provides a relay protection device fault identification method based on a digital twin transformer, comprising: obtaining an input signal of a power transformer, wherein the expression of the input signal when considering error and noise is:

[0067]

[0068] Where n is the number of samples, k is the total number of VT or CT, To have Zero-mean Gaussian noise with variance, and is the true value of the measurement; a nonlinear transformer model with hysteresis is used to model the power transformer, and the parameters of the nonlinear transformer model are updated according to the input signal to obtain the power transformer model; digital twin modeling is performed according to the power transformer model to generate a digital twin transformer; the digital twin transformer calculates the calculated output value of the power transformer according to the input signal; the actual measured output value of the power transformer is obtained; the difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value; the deviation value is compared with a preset threshold value, and whether the differential relay protector is faulty is determined according to the comparison result. In this application, the difference between the calculated output value and the measured output value is accumulated through a cumulative sum control chart (CUSUM) to obtain a deviation value, and the deviation value is compared with a preset threshold value to determine whether the differential relay protector is faulty. This method can be free from CT saturation interference, correctly distinguish between excitation inrush current and fault current, thereby improving the accuracy and reliability of fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the fault identification process of the relay protection device based on the digital twin transformer in this application;

[0070] Figure 2 This is the single-phase schematic diagram in the application;

[0071] Figure 3 This is the model diagram of the single-phase transformer in the application;

[0072] Figure 4 This is a single-phase schematic diagram of the test case in the application;

[0073] Figure 5 The transformer core parameters for the test case in the application;

[0074] Figure 6 This is the CT current waveform on the high-voltage side of the F1 fault in the application;

[0075] Figure 7 For application Figure 5Schematic diagram of normalized residual corresponding to the waveform shown;

[0076] Figure 8 This is the CT current waveform on the high-voltage side of the F2 fault in the application;

[0077] Fig. 9 For application Figure 7 Schematic diagram of normalized residual corresponding to the waveform shown;

[0078] Fig.10 This is a schematic diagram of a relay protection device fault identification device based on a digital twin transformer in this application. DETAILED DESCRIPTION

[0079] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementing the present disclosure should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0080] Please refer to Figure 1 As shown, the present application provides a relay protection device fault identification method based on a digital twin transformer, comprising:

[0081] S101. Obtain an input signal of a power transformer.

[0082] In order to ensure accurate measurement of voltage and current on both sides of the power transformer, the present application takes special consideration of the performance characteristics of the current transformer (CT) when selecting it. Specifically, the CT used in the present application is a Rogowski CT with a coreless design. This CT has significant advantages such as good linearity, wide-band response, high precision and high reliability, and strong anti-saturation capability. The coreless design avoids the core saturation problem that may exist in traditional CTs, thereby improving the accuracy and stability of the measurement.

[0083] The wideband response enables Rogowski CT to accurately measure various current waveforms, including transient currents, which is crucial for timely detection and handling of power transformer faults.

[0084] In the fault identification process of power transformers, the selection of sampling frequency is crucial to capture subtle changes in current and voltage. This application selects a relatively high sampling frequency of 1920 samples per second (the sampling frequency is 32 times 60, assuming the system frequency is 60Hz, and 32 points are sampled per cycle). Such a sampling frequency can ensure accurate measurement of current and voltage, and also help improve the accuracy and sensitivity of fault identification.

[0085] In addition to the sampling frequency, the synchronization of the sampling time is also an important factor in ensuring the accuracy of the measured data. This application requires that the sampling time must be strictly synchronized to avoid measurement errors caused by time asynchrony.

[0086] In addition, the impact of noise on the measurement results needs to be considered during the measurement process. In order to accurately evaluate the reliability of the measurement data, this application introduces the consideration of error and noise. Specifically, the input signal calculation formula when the sample considers error and noise is as follows:

[0087]

[0088] Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Indicates all.

[0089] By introducing this calculation formula, the present application can more accurately evaluate the reliability of the measurement data and provide strong support for subsequent fault identification.

[0090] S102: adopt a nonlinear transformer model with hysteresis to model a power transformer, and update parameters of the nonlinear transformer model according to the input signal to obtain a power transformer model.

[0091] A nonlinear transformer model with hysteresis is used for power transformer modeling.

[0092] In the power system, the power transformer is a key transmission equipment, and its operating state directly affects the stability and safety of the power system. Traditional transformer models are often based on linear assumptions, but in actual situations, transformers exhibit obvious nonlinear characteristics, especially in the case of core saturation, excitation inrush current, etc. Therefore, the present invention adopts a nonlinear transformer model with hysteresis to more accurately describe the actual operating state of the transformer.

[0093] The transformer model includes high-voltage side resistance R1, high-voltage side inductance L1, low-voltage side resistance R2, low-voltage side inductance L2, transformer core parameters Rc and Lm, etc.

[0094] Among them, Rc is the linear time-invariant resistance, and the value of Lm is updated after receiving each sample to reflect the real-time status of the transformer.

[0095] The core material of the transformer has hysteresis characteristics, that is, the change of magnetic flux depends not only on the current magnetic field strength, but also on the historical magnetic field strength.

[0096] In the model, the hysteresis effect is described by introducing a hysteresis loop, which represents the relationship between the magnetic flux density B and the magnetic field intensity H, and takes into account parameters such as the interdomain coupling magnetization factor, the reversible magnetization factor, and the irreversible factor.

[0097] According to the law of electromagnetic induction and Ohm's law of magnetic circuit, nonlinear equations describing the relationship between transformer inductance voltage and current are established. These equations take into account not only the current voltage and current values, but also the historical voltage and current values ​​and the hysteresis effect of the iron core.

[0098] At each sampling moment, the inductance value Lm is updated according to the received voltage and current samples, as well as the current magnetic flux and hysteresis loop equation. This enables the model to dynamically reflect the real-time status of the transformer.

[0099] In this application, the voltage and current values ​​are updated after receiving the sample according to the following formula:

[0100]

[0101] Among them, v Lm and i Lm is the inductor voltage and current. N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, A e is the effective core cross-sectional area, l e is the effective core length, μ0 is the air permeability, H and M are the magnetic field strength and core magnetization strength respectively. The value of M at any given time depends not only on the current H, but also on the historical value of H. The calculation formula for M is as follows:

[0102]

[0103] Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. i is an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, and when H[n]<0, δ=-1. Finally, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, and L m [n] = N 2 μ0μ r [n]A e / l e , where μ r [n]=|(B[n]-B[n-1]) / (H[n]-H[n-1])|, denominator is greater than i Lm [n].

[0104] S103. Perform digital twin modeling according to the power transformer model to generate a digital twin transformer.

[0105] The power transformer model is a single-phase transformer model in matrix form, and the single-phase transformer model in matrix form is implemented in the digital twin as follows:

[0106]

[0107] in And ρ=(x[n]-x[n-1]) / T s , T s k is the sampling period. v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

[0108] S104. The digital twin transformer calculates a calculated output value of the power transformer according to the input signal.

[0109] The difference (residual) calculation formula of sample data is:

[0110]

[0111] The residual normalization calculation formula of the sample is:

[0112]

[0113] The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of The third equation normalizes the residuals.

[0114] S105: Obtaining an actual measured output value of the power transformer.

[0115] In this application, in order to ensure the accuracy and reliability of the data, a transformer with high precision, wide-band response and strong anti-saturation capability, such as Rogowski CT, is usually selected as a measuring device.

[0116] like Figure 4 As shown, in this application, the CT adopts the Rogowski CT with coreless design, good linearity, wide-band response, high precision and high reliability, and strong anti-saturation ability to ensure the accuracy of the measurement.

[0117] When obtaining the measured value, it is necessary to set a suitable sampling frequency and ensure the synchronization of the sampling time. This application selects a sampling frequency of 1920Hz (i.e., 32 sampling points per cycle, assuming the system frequency is 60Hz) to ensure that the rapid changes in voltage and current can be captured. At the same time, by synchronizing the sampling time, it is ensured that the measured values ​​obtained from both ends of the transformer are consistent in time.

[0118] In the actual measurement process, due to the influence of various factors, the measured value may contain a certain amount of noise. In order to reduce the impact of noise on subsequent calculations and analysis, it is usually necessary to filter or denoise the measured value. In the application, the noise component is estimated and removed by statistical analysis and preprocessing of the measured value.

[0119] The acquired measurement values ​​need to be recorded in real time and stored in a designated storage medium for subsequent fault identification and analysis. These data usually include the measurement values ​​of key parameters such as voltage and current, as well as information such as sampling timestamps.

[0120] S106. Accumulate the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value.

[0121] The normalized residual is fed into CUSUM, and the output of CUSUM is calculated as follows:

[0122]

[0123] where g k and g k '(g0,g0′=0) are the outputs of CUSUM for rising and falling changes respectively, Th is the threshold selected under the premise of the expected confidence level, Fault represents a fault, Indicates all.

[0124] S107: Compare the deviation value with a preset threshold value, and determine whether the differential relay protector is faulty according to the comparison result.

[0125] In the method for fault identification and malfunction prevention of differential relay protectors based on digital twins, after the previous steps (such as S105 obtaining the actual measured output value of the power transformer and the predicted value calculated by the digital twin model, etc.), the deviation value (or residual) between the actual measured value and the model predicted value has been obtained. This deviation value reflects the difference between the current state of the power transformer and the ideal or normal state.

[0126] In the previous steps, the voltage and current of the power transformer have been predicted by the digital twin model and compared with the actual measured values. This comparison process will produce one or more deviations, which are expressed in the form of residuals, reflecting the inconsistency between the actual state and the predicted state.

[0127] In order to accurately determine whether the differential relay protection device has a fault, one or more preset thresholds need to be set. These thresholds are determined based on the normal operating status of the power transformer, historical data, and the sensitivity of the fault identification algorithm. The setting of the preset threshold is crucial for accurately determining the fault. It must be able to capture the real fault signal while avoiding false alarms and missed alarms.

[0128] After obtaining the deviation value, it needs to be compared with the preset threshold. This comparison process can be completed automatically by a computer program or manually by an operator. The result of the comparison will determine the next step of fault judgment.

[0129] If the deviation exceeds the preset threshold, it can be considered that the differential relay protection may have failed. At this time, the system can issue an alarm to prompt the operator to perform further inspection and maintenance. If the deviation does not exceed the preset threshold, the system can continue to operate normally without additional operation.

[0130] Once a fault is identified, the system can also perform follow-up processing according to the preset fault handling process. This may include automatically cutting off the power supply, starting the backup power supply, notifying maintenance personnel, etc. These follow-up processing measures are aimed at reducing the impact of the fault on the power system and ensuring the safe and stable operation of the power system.

[0131] Test description:

[0132] Depend on Figure 2 It can be seen that a particular computer receives a pair of voltage and current measurement samples in real time in the form of [v'1(t),i'1(t)] T and [v'2(t),i'2(t)] T , and after calculation, the calculated sample can be obtained. The calculated sample is compared with the measured sample and the difference (residual) of each data can be obtained. The residual calculated previously is normalized and fed into CUSUM. The cumulative sum control chart (CUSUM) is used to detect any changes in the residual pattern.

[0133] By setting the threshold Th at the desired confidence level, we can obtain the g output by CUSUM. k and g k '(g0,g0′=0), which is used to indicate the change of rising and falling.

[0134] The focus of the present invention is a three-phase transformer group, and its single-phase model is as follows Figure 3 As shown, this example simulates single-phase turn-to-turn faults, power-on and power-on process faults, and external faults. The schematic diagram is shown in Figure 4 shown.

[0135] In this example, the sampling rate is 32 samples per cycle, the high voltage side voltage is 132∠0kV, the low voltage side is 31.35∠-5kV, Z1=2.44+8.1jΩ, Z2=3.8+10.98jΩ, and the system frequency is 60Hz.

[0136] First, there is an inter-turn fault F1. In this example, the inter-turn fault is simulated by short-circuiting 1% of the winding in the middle of the high-voltage winding through a 1Ω resistor at t=0.75s.

[0137] Figure 6 The high-voltage side CT current for a 3% turn-to-turn fault is shown, where the additional voltage noise is N(0,1.7 2 ), the additional current noise is N(0,0.07 2 ), since both sides of the transformer are connected to an infinite bus, the voltage drop is negligible.

[0138] from Figure 6 It can be seen from the figure that the current on the high voltage side rises immediately after the inter-turn fault occurs.

[0139] Figure 7 Depicted Figure 6 The normalized residual corresponding to the wave shown in the figure can be found to be Figure 6 The current changes shown correspond to, Figure 7 The waveform also changed dramatically at t=0.75s, thus identifying the internal fault.

[0140] The second is the power-on and power-off process fault F2.

[0141] exist Figure 8 In the example, the transformer is powered off at the beginning, and the high-voltage side is powered on at t=0.126s, resulting in a large inrush current.

[0142] Fig. 9 Schematic diagram of the normalized residual of the high-voltage side current. At this time, the difference between the calculated sample and the measured sample is only caused by the additional noise. Therefore, the proposed method can keep the relay protection device stationary under the surge current generated during power-on.

[0143] In addition, the model does not need to be aware of the switch action and only uses the zero voltage and zero current on the low voltage side to calculate the current on the high voltage side. At t = 0.25s, a turn-to-turn fault occurs, and the resulting associated residual changes are as follows: Fig. 9As shown, the proposed relay protection method is able to maintain constraints during magnetizing inrush current as well as detect faults during energization.

[0144] Finally, there is the external fault F3, which is simulated by connecting parallel resistors of different values ​​at t = 0.1s. By observing the normalized residual diagram, it can be found that the residual follows the noise form and the residual value is basically unchanged.

[0145] The new method for fault identification and prevention of false operation of relay protectors based on digital twins and digital twin transformers proposed in this application can detect internal faults and prevent false operation when external faults occur. The method can receive voltage and current measurement values ​​from either end of the transformer, obtain the electrical parameters of the other end by calculation, and compare the measured values ​​with the calculated values. If the generated residual is not caused by noise, the internal fault can be identified.

[0146] like Fig.10 As shown, the present application provides a relay protection device fault identification device based on a digital twin transformer, including: an acquisition module 111, a modeling module 112, a digital twin module 113, a calculation module 114, a measurement module 115, a deviation module 116 and a judgment module 117.

[0147] The acquisition module 111 acquires the input signal of the power transformer. The expression of the input signal when considering the error and noise is:

[0148]

[0149] Where n is the number of samples, k is the total number of VT or CT, To have Zero-mean Gaussian noise with variance, and is the true value of the measurement;

[0150] The acquisition module collects the voltage and current data of the power transformer in real time through high-precision measurement equipment (such as Rogowski CT), and passes these data as input signals to subsequent modules for processing.

[0151] A modeling module 112, which uses a nonlinear transformer model with hysteresis to model a power transformer, and updates parameters of the nonlinear transformer model according to the input signal to obtain a power transformer model;

[0152] The modeling module uses a nonlinear transformer model with hysteresis to model the power transformer. This model can accurately reflect the electrical characteristics and dynamic behavior of the power transformer. The modeling module updates the parameters of the nonlinear transformer model according to the input signal to obtain the power transformer model.

[0153] The parameter update process involves multiple complex calculation steps, including the calculation of inductor voltage and current, the calculation of magnetic flux and magnetic flux density, and the calculation of magnetization intensity and magnetic field intensity, etc. These calculation steps are based on precise physical formulas and mathematical models to ensure the accuracy and reliability of the power transformer model.

[0154] The digital twin module 113 performs digital twin modeling according to the power transformer model to generate a digital twin transformer;

[0155] The digital twin module performs digital twin modeling based on the power transformer model to generate a digital twin transformer. The digital twin transformer is a mapping of the power transformer in virtual space, which can reflect the operating status and changes of the power transformer in real time.

[0156] In the digital twin modeling process, the digital twin module imports the parameters and data of the power transformer model into the virtual environment, and generates a digital twin transformer through simulation and emulation. In this way, the digital twin transformer can keep pace with and be consistent with the actual power transformer.

[0157] A calculation module 114, wherein the digital twin transformer calculates a calculated output value of the power transformer according to the input signal;

[0158] The calculation module is responsible for using the digital twin transformer to calculate the calculated output values ​​of the power transformer according to the input signal. These calculated output values ​​include electrical parameters such as voltage and current. They are the simulation and prediction of the actual power transformer operating status by the digital twin transformer in a virtual environment.

[0159] During the calculation process, the calculation module uses the mathematical model and parameters of the digital twin transformer to perform calculations to ensure the accuracy and reliability of the calculated output value.

[0160] The measuring module 115 obtains the actual measured output value of the power transformer;

[0161] The measurement module 116 is responsible for obtaining the actual measured output values ​​of the power transformer. These measured output values ​​are collected in real time by high-precision measuring equipment, and they reflect the actual operating status and electrical parameters of the power transformer.

[0162] In actual operation, the measurement module is similar to the acquisition module, and also collects the voltage and current data of the power transformer in real time through high-precision measurement equipment (such as Rogowski CT), and passes these data as measurement output values ​​to subsequent modules for processing.

[0163] The deviation module 117 accumulates the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value;

[0164] The deviation module is responsible for accumulating the difference between the calculated output value and the measured output value through CUSUM (Cumulative Sum Control Chart) to obtain the deviation value. CUSUM is a commonly used statistical control method that can effectively detect small changes in data and accumulate these changes to form obvious deviation values.

[0165] In the deviation calculation process, the deviation module first compares the calculated output value with the measured output value and calculates the difference between them. Then, the difference is accumulated using the CUSUM algorithm to form a deviation value. These deviation values ​​can reflect the changes in the operating status of the power transformer and are used for subsequent fault judgment.

[0166] The judgment module 118 compares the deviation value with a preset threshold value, and judges whether the differential relay protection device is faulty according to the comparison result.

[0167] The judgment module is responsible for comparing the deviation value with the preset threshold value, and judging whether the differential relay protector is faulty according to the comparison result. The preset threshold value is set according to the difference between the normal operation state and the fault state of the power transformer, which can reflect the characteristic changes when the power transformer fails.

[0168] During the judgment process, the judgment module compares the deviation value with the preset threshold. If the deviation value exceeds the preset threshold, it is judged that the differential relay protector has a fault; if the deviation value does not exceed the preset threshold, it is judged that the differential relay protector is operating normally. According to the judgment result, the device can issue a corresponding alarm or control signal to realize fault identification and protection of the power transformer.

[0169] Furthermore, the acquisition module acquires the input signal of the power transformer, and the measurement device used includes: a current transformer.

[0170] Furthermore, the modeling module updates the parameters of the nonlinear transformer model according to the input signal, and the expression is as follows:

[0171]

[0172] Among them, v Lm and i Lm is the inductor voltage and current, N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, A e is the effective core cross-sectional area, l e is the effective core length, μ0 is the air magnetic permeability, H and M are the magnetic field intensity and core magnetization intensity respectively.

[0173] Furthermore, the calculation formula of M is as follows:

[0174]

[0175] Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. i is an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, and when H[n]<0, δ=-1. Finally, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, and L m [n] = N 2 μ0μ r [n]A e / l e , where μ r [n]=|(B[n]-B[n-1]) / (H[n]-H[n-1])|, denominator is greater than i Lm [n].

[0176] Furthermore, the digital twin module performs digital twin modeling according to the power transformer model to generate a digital twin transformer, and the expression is as follows:

[0177]

[0178] in, And ρ=(x[n]-x[n-1]) / T s , T s k is the sampling period. v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

[0179] Furthermore, the calculation expression of the difference between the calculated output value and the measured output value is as follows:

[0180]

[0181] The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of The mean of the residuals of , where w is the window size.

[0182] Furthermore, the deviation module accumulates the difference between the calculated output value and the measured output value through CU SUM to obtain a deviation value, and the calculation expression is as follows:

[0183]

[0184] where g k and g k '(g0,g0'=0) are the outputs of CUSUM for rising and falling changes respectively, and Th is the threshold selected under the premise of the desired confidence level.

[0185] The present application provides a relay protection device fault identification device based on a digital twin transformer, comprising:

[0186] Memory;

[0187] The processor is used to retrieve the computer executable program of the above-mentioned relay protection device fault identification method based on digital twin transformer from the memory, and execute: obtaining the input signal of the power transformer, the expression of the input signal when considering error and noise is:

[0188]

[0189] Where n is the number of samples, k is the total number of VT or CT, To have Zero-mean Gaussian noise with variance, and is the true value of the measurement; a nonlinear transformer model with hysteresis is used to model the power transformer, and the parameters of the nonlinear transformer model are updated according to the input signal to obtain the power transformer model; digital twin modeling is performed according to the power transformer model to generate a digital twin transformer; the digital twin transformer calculates the calculated output value of the power transformer according to the input signal; the actual measured output value of the power transformer is obtained; the difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value; the deviation value is compared with a preset threshold value, and whether the differential relay protector is faulty is determined according to the comparison result.

[0190] The present application provides a storage medium, comprising: storing a computer executable program, which is used to be called by a processor to execute the steps of the above-mentioned relay protection device fault identification method based on a digital twin transformer.

[0191] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A relay protection device fault identification method based on digital twin transformer, characterized in that: include: The input signal of the power transformer is obtained, and the expression of the input signal is: Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Indicates all; A nonlinear transformer model with hysteresis is used to model a power transformer, and parameters of the nonlinear transformer model are updated according to the input signal to obtain a power transformer model; Performing digital twin modeling according to the power transformer model to generate a digital twin transformer; The digital twin transformer calculates a calculated output value of the power transformer according to the input signal; Obtaining an actual measured output value of the power transformer; The difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain a deviation value; The deviation value is compared with a preset threshold value, and whether the differential relay protector is faulty is determined according to the comparison result.

2. According to the method for fault identification of relay protection device based on digital twin transformer according to claim 1, it is characterized in that: To obtain the input signal of the power transformer, the measuring devices used include: current transformer.

3. According to the method for fault identification of relay protection device based on digital twin transformer of claim 1, it is characterized in that: The parameters of the nonlinear transformer model are updated according to the input signal, and the expression is as follows: Among them, v Lm and i Lm is the inductor voltage and current, N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, A e is the effective core cross-sectional area, l e is the effective core length, μ0 is the air magnetic permeability, H and M are the magnetic field intensity and core magnetization intensity of the power transformer respectively.

4. According to claim 3, a relay protection device fault identification method based on digital twin transformer is characterized in that: The calculation formula of the core magnetization intensity of the power transformer is as follows: Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. i is an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, when H[n]<0, δ=-1, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, L m [n] = N 2 μ0μ r [n]A e / l e ; among them r [n]=|(B[n]-B[n-1]) / (H[n]-H[n-1])|,(H[n]-H[n-1])| greater than i Lm [n]。 5. According to the method for fault identification of relay protection device based on digital twin transformer of claim 1, it is characterized in that: Digital twin modeling is performed according to the power transformer model to generate a digital twin transformer. The expression is as follows: in, And ρ=(x[n]-x[n-1]) / T s , T s is the sampling period, k v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

6. According to the method for fault identification of relay protection device based on digital twin transformer of claim 1, it is characterized in that: The calculation expression of the difference between the calculated output value and the measured output value is as follows: The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of where w is the window size and the third equation normalizes the residuals.

7. The method for identifying relay protection faults based on digital twin transformer according to claim 1, characterized in that: The difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain the deviation value, and the calculation expression is as follows: where g k and g k '(g0,g0′=0) are the outputs of CUSUM for rising and falling changes respectively, Th is the threshold selected under the premise of the expected confidence level, Fault represents a fault, Indicates all.

8. A relay protection device fault identification device based on digital twin transformer, characterized in that: include: The acquisition module acquires the input signal of the power transformer. The expression of the input signal when considering the error and noise is: Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Indicates all; A modeling module, which uses a nonlinear transformer model with hysteresis to model a power transformer, updates parameters of the nonlinear transformer model according to the input signal, and obtains a power transformer model; A digital twin module performs digital twin modeling according to the power transformer model to generate a digital twin transformer; A calculation module, wherein the digital twin transformer calculates a calculated output value of the power transformer according to the input signal; A measurement module, for obtaining an actual measured output value of the power transformer; A deviation module accumulates the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value; The judgment module compares the deviation value with a preset threshold value and judges whether the differential relay protector is faulty according to the comparison result.

9. A relay protection device fault identification device based on a digital twin transformer according to claim 8, characterized in that: The acquisition module acquires the input signal of the power transformer, and the measurement device used includes: a current transformer.

10. A relay protection device fault identification device based on a digital twin transformer according to claim 8, characterized in that: The modeling module updates the parameters of the nonlinear transformer model according to the input signal, and the expression is as follows: Among them, v Lm and i Lm is the inductor voltage and current, N is the number of winding turns on the Lm side, φ is the magnetic flux, B is the magnetic flux density, A e is the effective core cross-sectional area, l e is the effective core length, μ0 is the air magnetic permeability, H and M are the magnetic field intensity and core magnetization intensity of the power transformer respectively.

11. A relay protection device fault identification device based on digital twin transformer according to claim 10, characterized in that: The calculation formula of the core magnetization intensity of the power transformer is as follows: Among them, M an is the non-hysteresis susceptibility, M s is the saturation point, α<10-3 and c are called the interdomain coupling susceptibility factor and the reversible susceptibility factor, respectively. i is an irreversible factor, δ is the sign function of H, that is, when H[n]≥0, δ=1, when H[n]<0, δ=-1, and finally, H e [n] = H[n] + αM[n], k is the volume coupling coefficient, and L m [n] = N 2 μ0μ r [n]A e / l e , where μ r [n]=|(B[n]-B[n-1]) / (H[n]-H[n-1])|, denominator is greater than i Lm [n].

12. A relay protection device fault identification device based on a digital twin transformer according to claim 8, characterized in that: The digital twin module performs digital twin modeling according to the power transformer model to generate a digital twin transformer, and the expression is as follows: in, And ρ=(x[n]-x[n-1]) / T s , T s is the sampling period, k v1 , k v2 , k i1 and k i2 is the correction factor, RC is a constant.

13. A relay protection device fault identification device based on a digital twin transformer according to claim 8, characterized in that: The calculation expression of the difference between the calculated output value and the measured output value is as follows: The first equation updates the variance of the residual by using the correction of the last w samples, with an initial value of The second equation is found to have an initial value of The mean of the residuals of , where w is the window size.

14. The relay protection device fault identification device based on digital twin transformer according to claim 8, characterized in that: The difference between the calculated output value and the measured output value is accumulated through CUSUM to obtain the deviation value, and the calculation expression is as follows: where g k and g k '(g0,g0'=0) are the outputs of CUSUM for rising and falling changes respectively, and Th is the threshold selected under the premise of the desired confidence level.

15. A relay protection fault identification device based on a digital twin transformer, characterized in that: include: Memory; A processor, configured to retrieve from the memory a computer executable program of a method for fault identification of a relay protection device based on a digital twin transformer according to any one of claims 1 to 7, and execute: obtaining an input signal of a power transformer, wherein the expression of the input signal when considering error and noise is: Where n is the number of samples, k is the total number of voltage measurement devices or current measurement devices, To have Zero-mean Gaussian noise with variance, and are the true values ​​of voltage and current measured in the input signal, Represents all; adopts a nonlinear transformer model with hysteresis to model the power transformer, updates the parameters of the nonlinear transformer model according to the input signal, and obtains the power transformer model; performs digital twin modeling according to the power transformer model to generate a digital twin transformer; the digital twin transformer calculates the calculated output value of the power transformer according to the input signal; obtains the actual measured output value of the power transformer; accumulates the difference between the calculated output value and the measured output value through CUSUM to obtain a deviation value; compares the deviation value with a preset threshold, and determines whether the differential relay protector is faulty according to the comparison result.

16. A storage medium, characterized in that: include: A computer executable program is stored, which is used to be called by a processor to execute the steps of a relay protection device fault identification method based on a digital twin transformer as described in any one of claims 1 to 7.

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