Digital sampling method and system for mutual inductor

Abstract

The invention relates to the technical field of power system signal measurement, in particular to a mutual inductor digital sampling method and system. The method comprises the following steps: acquiring a signal output by a secondary side of a mutual inductor for a power system in real time; determining the load fluctuation degree at the current moment; determining a dynamic sensitivity coefficient at the current moment; obtaining a dynamic reference mean value, a dynamic relaxation parameter and a dynamic decision threshold value used in the CUSUM algorithm at the current moment; determining the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment; and determining the sampling frequency of the mutual inductor according to the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment and the size of the dynamic decision threshold so as to realize digital sampling of the mutual inductor. According to the invention, through adaptive adjustment of the reference mean value, the relaxation parameter and the decision threshold value, the rate of missing report and false report is reduced, and the identification capability of initial faults such as mutual inductor turn-to-turn short circuit and the like is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system signal measurement, and in particular to a mutual inductor digital sampling method and system. BACKGROUND

[0002] The mutual inductor is a key device in the power system for measuring high voltage and large current and transmitting the primary side electrical quantity to the secondary side measurement and protection device in proportion. With the rapid development of the smart power system and the digital substation, high-precision and high-efficiency digital sampling of the mutual inductor output signal has become a core technical link. In order to accurately capture the high-frequency transient information generated during the power system fault or disturbance while effectively saving network bandwidth and storage resources, the variable rate sampling method can be used for the mutual inductor. The variable rate sampling method uses a lower sampling rate during the steady operation of the power system, and then switches to a high sampling rate instantaneously when a transient event is detected.

[0003] The Cumulative Sum Change-Point Detection (CUSUM) algorithm is a common technical means for realizing the variable rate sampling method because of its small amount of calculation and sensitivity to weak and continuous changes in signal statistical characteristics. The basic principle of the CUSUM algorithm is that the deviation between the cumulative signal characteristics and the steady-state expected value is accumulated, and when the cumulative sum exceeds a preset threshold, it is judged that a transient event occurs and triggers high-speed sampling.

[0004] However, the traditional CUSUM algorithm has a core defect in the triggering method. The core control parameters, such as the reference mean value of the mutual inductor secondary side output signal, the relaxation coefficient for suppressing normal fluctuations, and the threshold for triggering decision, are all fixed values set once during the system debugging stage according to experience. However, the load level, operation mode and background noise of the power system as a complex dynamic system will directly reflect in the secondary side output signal of the mutual inductor. For example, there is a significant difference in the normal fluctuation range of the "steady-state" signal output by the mutual inductor between the daytime heavy load period and the night light load period. If a set of fixed parameters is used, the parameters set for the heavy load condition are relatively slow, which may not be able to capture the initial hidden fault characteristics such as inter-turn short circuit in the mutual inductor signal in the light load state, resulting in a false negative. Conversely, if the parameters are set to be too sensitive for the light load condition, the normal fluctuations in the mutual inductor output signal during normal load growth or switching operation may be misjudged as a transient event, causing the high-speed sampling to be triggered frequently and failing to achieve the purpose of saving resources. SUMMARY

[0005] In order to solve the problem that the core control parameters of the traditional CUSUM algorithm are fixed values, if the parameters are set to be relatively slow, the initial hidden fault features such as turn-to-turn short circuit in the potential signal of the transformer under light load state cannot be captured, resulting in a false negative, on the contrary, if the parameters are set to be too sensitive, the normal fluctuations of the transformer output signal in the normal load growth or switching operation may be misjudged as a transient event, resulting in that the high-speed sampling is frequently triggered, and the problem of resource waste is caused, the present application provides a kind of transformer digital sampling method and system.

[0006] In the first aspect, the present application provides a kind of transformer digital sampling method, adopt the following technical solutions: A kind of transformer digital sampling method, comprising: real-time acquisition is used for the signal of the secondary side output of the transformer of power system;According to the difference of the effective value of the signal of all adjacent two time points in the observation period of current time, and the mean of the effective value of the signal of all time points in the preset observation period of current time, determine the load fluctuation degree of current time;According to the load fluctuation degree, and the load fluctuation degree of the time corresponding to current time under ideal working condition, determine the dynamic sensitivity coefficient of current time;The reference mean of CUSUM algorithm is adjusted based on the load fluctuation degree, to obtain the dynamic reference mean of current time;The ratio of the relaxation parameter of CUSUM algorithm and the dynamic sensitivity coefficient is used as the dynamic relaxation parameter of current time;The ratio of the decision threshold of CUSUM algorithm and the dynamic sensitivity coefficient is used as the dynamic decision threshold of current time;Based on the cumulative sum of the deviation between the signal and the dynamic reference value in CUSUM algorithm of previous time, the first order difference value of current time and dynamic reference mean and dynamic relaxation parameter, the cumulative sum of the signal and the dynamic reference value in CUSUM algorithm of current time is obtained;According to the size of the cumulative sum of the signal and the dynamic reference value in CUSUM algorithm of current time and the dynamic decision threshold, the sampling frequency of transformer is determined, to realize the digital sampling of transformer.

[0007] The beneficial effects are that: by real-time evaluating the load fluctuation degree of the secondary side output signal of the mutual inductor, the key parameters (reference mean value, relaxation parameter and decision threshold) of the CUSUM algorithm are dynamically adjusted, so that the sampling system can automatically adapt to different working conditions according to the load change of the power system, and the applicability and flexibility of the parameters are improved; the dynamic sensitivity adjustment enables the system to maintain a high sensitivity in a light load state, so as to more accurately capture the initial hidden fault characteristics such as inter-turn short circuit, and reduce the possibility of false reporting; in a heavy load or normal fluctuation, the system reduces the sensitivity to avoid misjudging the normal load change as a transient fault, reduces unnecessary high-speed sampling triggering, thereby saving data acquisition and processing resources, and effectively reducing the waste of resources caused by misjudgment; based on the dynamic updating mechanism of the CUSUM cumulative sum, the system can more agilely respond to signal changes, ensuring that digital sampling can accurately capture abnormalities while also adjusting the sampling frequency in time, enhancing real-time response capability and stability.

[0008] Further, the signal is a current signal.

[0009] Further, the signal is a voltage signal.

[0010] Further, the load fluctuation degree satisfies: ; in the formula, is the load fluctuation degree at the current moment, is the number of moments in the preset observation period at the current moment, is the effective value of the signal at the i th moment in the preset observation period at the current moment, is the effective value of the signal at the i th moment in the observation period at the current moment, is the effective value of the signal at the i th moment in the observation period at the current moment, is the absolute value symbol.

[0011] The beneficial effects are that: by calculating the average value of the absolute difference value of the signal effective values of adjacent moments, and dividing by the average value of the signal effective values in the observation period, the normalized representation of the load fluctuation size is realized, and the problem of amplification or reduction of the absolute fluctuation value with the load size is avoided; accurate load fluctuation degree quantization can help the system better distinguish between normal load fluctuation and fault signal, provide a more reliable dynamic reference for fault detection, and improve detection accuracy and reliability, especially in the early identification of hidden faults.

[0012] Further, the dynamic sensitivity coefficient satisfies: ; in the formula, is the dynamic sensitivity coefficient at the current moment, is the load fluctuation degree at the current moment, ​a load fluctuation degree at a time corresponding to the current time under an ideal working condition, a preset adjustment factor for adjusting the sensitivity according to the severity of the load fluctuation, a natural exponential function.

[0013] The beneficial effect is that, by constructing a natural exponential function, the sensitivity coefficient presents a nonlinear response to the ratio of the load fluctuation degree, which can more flexibly and accurately adjust the sensitivity of the CUSUM algorithm parameter, avoiding the shortcomings caused by simple linear adjustment; when the current load fluctuation is higher than the reference load fluctuation under the ideal working condition, the sensitivity is reduced to avoid misjudgment caused by large fluctuation; on the contrary, when the load fluctuation is small, the sensitivity is improved, which is more conducive to capturing early fault features; reasonable adjustment of the dynamic sensitivity coefficient enables the CUSUM algorithm to balance the sensitive capture of slight fault signals and the suppression of normal load fluctuations, thereby effectively improving the accuracy of fault detection and the stability of system operation.

[0014] Further, the ideal working condition is the actual operating state of the power system when no fault occurs.

[0015] Further, the dynamic reference mean value satisfies: ; in the formula, is the dynamic reference mean value at the current time, is the reference mean value of CUSUM, is the load fluctuation degree at the current time, is the load fluctuation degree at a time corresponding to the current time under an ideal working condition, is a preset limiting factor for avoiding deviation of the reference mean value from the actual reasonable range due to excessive load fluctuation.

[0016] The beneficial effect is that, by linearly adjusting the reference mean value of CUSUM according to the relative change of the load fluctuation degree, the reference mean value can dynamically change with the load fluctuation, fully reflecting the true operating state of the current system; the limiting factor prevents the reference mean value from deviating from the reasonable range due to excessive load fluctuation, ensures that the reference mean value changes moderately, maintains the stability and reliability of the system, and avoids misjudgment or missed judgment caused by excessive reference adjustment; when the load is small, fine-tuning the reference mean value makes the algorithm more likely to detect weak abnormal signals, enhancing the early fault recognition capability; when the load is large, appropriate improvement of the reference mean value can reduce false alarms caused by normal fluctuations, better distinguishing normal fluctuations from fault features; the dynamic reference mean value adjustment method can combine the background characteristics of different monitoring points and different time periods to realize personalized and real-time parameter optimization, improving the overall sensitivity and applicability of power system monitoring.

[0017] Further, the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment satisfies: ; wherein, is the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment, is the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the previous moment of the current moment, is the first-order difference value at the current moment, is the dynamic reference mean value at the current moment, is the dynamic relaxation coefficient at the current moment.

[0018] The beneficial effects are that: by combining the first-order difference of the signal at the current moment, the dynamic reference mean value and the dynamic relaxation coefficient, the update of the cumulative sum is dynamically adjusted, which ensures that the CUSUM algorithm can sensitively capture the cumulative trend of abnormal deviations in the signal, improves the accuracy and timeliness of fault detection; the maximum function is used to limit the cumulative sum in the non-negative range, which effectively avoids the negative value accumulation affecting the detection effect, ensures the focus of the algorithm on the cumulative abnormal change, and strengthens the response to the positive abnormal signal; the dynamic reference mean value and the dynamic relaxation parameter are used, so that the update process of the cumulative sum can adapt to the current load fluctuation, avoid false negatives and false positives caused by invalid fixed parameter setting, and enhance the robustness of CUSUM; the cumulative mechanism enhances the detection capability of weak but continuous abnormal signals, so that early features of hidden faults such as inter-turn short circuit can be effectively captured, helping early warning and timely processing of faults.

[0019] Further, the determination of the sampling frequency of the mutual inductor comprises: in response to the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment being less than the dynamic decision threshold, it is determined that the power system is in a steady state, and the sampling frequency of the mutual inductor is maintained at a preset low sampling rate; otherwise, it is determined that the power system is in a transient event, and the sampling frequency of the mutual inductor is adjusted to a preset high sampling rate.

[0020] In a second aspect, the present application provides a mutual inductor digital sampling system, which adopts the following technical scheme: A mutual inductor digital sampling system, comprising: a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, the above-mentioned mutual inductor digital sampling method is realized.

[0021] By adopting the above technical scheme, the above-mentioned mutual inductor digital sampling method is generated into a computer program and stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0022] The present application has the following technical effects: (1) Break through the limitations of fixed parameters in traditional CUSUM algorithm, generate dynamic sensitivity coefficient by real-time calculation of load fluctuation degree, realize adaptive adjustment of benchmark mean, relaxation parameter and decision threshold. Automatically reduce sensitivity during heavy load period to avoid false negatives, increase sensitivity during light load period to capture hidden fault characteristics, effectively solve the adaptability contradiction of fixed parameters under different working conditions, significantly reduce the false negative and false positive rates, and improve the identification ability of initial faults such as transformer interturn short circuit.

[0023] (2) Reasonably trigger high-speed sampling through dynamic decision threshold to avoid false triggering caused by too sensitive parameters, reduce invalid sampling operation, and reduce waste of data storage and processing resources. At the same time, ensure accurate start of high-speed sampling when real fault occurs, realize on-demand allocation of sampling resources, and improve system operation efficiency.

[0024] (3) Based on real-time signal characteristics, dynamically adjust algorithm parameters, so that the sampling method can follow the changes of power system load level, operation mode and background noise. No need for manual parameter adjustment. Whether it is the alternation of daytime heavy load and night light load, or sudden load switching operation, it can maintain stable detection performance, and greatly improve the robustness of transformer digital sampling in complex power environment.

[0025] (4) Precise sampling triggering and fault feature capturing ensure that the obtained transformer secondary side output signal can truly reflect the device running state. Based on dynamic parameter optimization of sampling data, it can provide high-quality data basis for transformer health state evaluation, life prediction and power system fault diagnosis, and help to realize intelligent operation and maintenance of power equipment. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a method flowchart of a transformer digital sampling method according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] An embodiment of the present application discloses a transformer digital sampling method, referring to Figure 1 , comprising steps S1-S6: S1: Real-time acquisition of signals output from the secondary side of the transformer for the power system.

[0029] It should be noted that the system continuously samples the analog voltage or current signal output from the secondary side of the transformer (transformers are divided into voltage transformers and current transformers, and the voltage or current signal is obtained according to the different types of transformers) at a preset low sampling rate (e.g., 1kHz).

[0030] Specifically, the signal is a current signal.

[0031] Specifically, the signal is a voltage signal.

[0032] S2: Determine the degree of load fluctuation at the current moment.

[0033] It is important to note that the "steady state" of a power system is not absolutely static, but rather exhibits normal fluctuations related to load levels. For example, the effective value of the signal during periods of heavy load may vary slowly over a wide range, while the fluctuation range of the effective value of the signal during periods of light load is relatively small. Using only a fixed threshold to determine whether fluctuations are normal is insufficient to adapt to actual operating conditions under different loads. Therefore, to quantify the current level of calm or activity in the power grid, this step constructs a load fluctuation index. This index obtains the dynamic stability state of the power grid at the current moment by analyzing the relationship between the magnitude of changes in the effective value of the signal and its average value during the observation period.

[0034] The load fluctuation level at the current moment is determined based on the difference in the effective values ​​of the signals at all two adjacent moments within the observation period at the current moment, and the average of the effective values ​​of the signals at all moments within the preset observation period at the current moment.

[0035] Specifically, the load fluctuation level satisfies: ; In the formula, The degree of load fluctuation at the current moment. This represents the number of times within the preset observation period at the current time. The first observation within the preset observation period at the current time. The effective value of a signal at a given moment (the effective value of a signal is the square root of the average of the squares of the signal values ​​of a periodically changing signal within one power frequency cycle, which is equivalent to the value of a DC signal that generates the same energy in the same time period; where the power frequency cycle can be calculated theoretically, such as 20ms at a standard frequency of 50Hz, or converted after measuring the real-time frequency with tools such as an oscilloscope and frequency counter, or obtained by directly measuring the waveform period). The first observation within the current observation period The effective value of the signal at each moment. It is the absolute value symbol.

[0036] The implementation personnel can set the length of the observation period according to the specific implementation, and it is only required that the length of the observation period is greater than the length of the power frequency cycle. The effective value of the signal at each time in the observation period is not limited to the power frequency cycle in the observation period. For example, the power frequency cycle of the first time in the observation period is the power frequency cycle formed by a plurality of times before the observation period.

[0037] wherein, represents the cumulative change amount of the signal effective value in the observation period. The greater the value, the more frequent and larger the fluctuation of the signal effective value in the observation period, and the more active the power grid operation, and then is greater; on the contrary, if the value is smaller, it indicates that the signal effective value changes smoothly, and the power grid operation is more calm, and then is smaller. represents the average value of the signal effective value in the observation period. The greater the value, the higher the overall load level in the observation period, and at this time the normal fluctuation of the signal itself is larger, and then is smaller under the condition that the cumulative change amount is unchanged; on the contrary, the smaller the value, the lower the overall load level in the observation period, and at this time the slight signal anomaly may be an early feature of a hidden fault, and then is greater under the condition that the cumulative change amount is unchanged. The purpose of multiplying and is to adjust the average value of the signal effective value in the observation period in scale to match the calculation dimension of the cumulative change amount (the cumulative change amount is the sum of adjacent differences), so that the numerator (cumulative change amount) and the denominator (adjusted average value) form a reasonable proportional relationship in the mathematical sense, and finally the relative size of the fluctuation degree relative to the load level is objectively reflected through the ratio.

[0038] S3: Determine the dynamic sensitivity coefficient of the current time.

[0039] It should be noted that in the state monitoring and fault detection of the power system, the sensitivity (i.e. sensitivity) of the detection system to abnormal signals needs to be adapted to the actual operation state of the power grid. If a fixed sensitivity is used, in the case of severe load fluctuation (active power grid), normal large fluctuations are easy to misjudge as fault signals, leading to false positives; while in the case of smooth load fluctuation (calm power grid), the sensitivity may not be sufficient to capture weak early fault signals in time, resulting in false negatives. Therefore, the purpose of this step is to build a sensitivity coefficient that can be dynamically adjusted according to the current load fluctuation degree, so as to realize adaptive adjustment of the detection sensitivity, reduce the sensitivity when the power grid fluctuation is more severe than the ideal working condition, and reduce the misjudgment; when the power grid fluctuation is more smooth than the ideal working condition, increase the sensitivity, and enhance the capture ability of weak fault signals.

[0040] According to the load fluctuation degree and the load fluctuation degree of the time corresponding to the current time under the ideal working condition, the dynamic sensitivity coefficient of the current time is determined.

[0041] Specifically, the dynamic sensitivity coefficient satisfies: ; In the formula, is the dynamic sensitivity coefficient of the current time, is the load fluctuation degree of the current time, is the load fluctuation degree of the time corresponding to the current time under the ideal working condition (for example, the current time is 14 o'clock on a weekday, and is the load fluctuation degree of 14 o'clock on a weekday under the ideal working condition, is a preset adjustment factor for adjusting the severity of the sensitivity change with the load, is a natural exponential function.

[0042] Specifically, the ideal working condition is the actual operating state of each time when the power system has no fault.

[0043] The adjustment factor can be set by the implementer according to the specific implementation, for example, the adjustment factor .

[0044] wherein, the greater the value is, the more severe the actual fluctuation of the current power grid is than the ideal working condition, at this time, the detection sensitivity needs to be reduced to avoid false triggering, and the smaller the value is; on the contrary, if the value is smaller, it indicates that the current fluctuation is more gentle than the ideal working condition, and the detection sensitivity needs to be improved to capture weak fault signals, and the greater the value is. indicates that the fluctuation ratio is nonlinearly converted by the exponential function to obtain the dynamic sensitivity coefficient, and by performing the exponential operation on , the change of the sensitivity coefficient presents a smooth nonlinear characteristic, avoiding the influence of parameter mutation on the stability of the system, when is equal to , the exponential term is 0, equal to 1, that is, the sensitivity remains at the reference level; when is greater than , the exponential term is negative, less than 1, the sensitivity is reduced; when is less than , the exponential term is positive, greater than 1, the sensitivity is improved.

[0045] S4: Obtain the dynamic reference mean, the dynamic relaxation parameter and the dynamic decision threshold in the CUSUM algorithm at the current time.

[0046] It should be noted that in the CUSUM algorithm, the reference mean value is a reference for judging whether the signal deviates from the steady state. Due to the difference in the mean value of the first-order difference value of the signal (i.e. the reference mean value) under different load levels (the mean value of the difference value is larger under heavy load), if a fixed reference mean value is used, systematic errors will occur in the deviation calculation. Therefore, the reference mean value is dynamically adjusted by introducing the load fluctuation degree in this step, so that it matches the actual mean value of the difference value under the current working condition.

[0047] The reference mean value of the CUSUM algorithm is adjusted based on the load fluctuation degree to obtain a dynamic reference mean value at the current time.

[0048] Specifically, the dynamic reference mean value satisfies: ; In the formula, is the dynamic reference mean value at the current time, is the reference mean value of the CUSUM, is the load fluctuation degree at the current time, is the load fluctuation degree at the time corresponding to the current time under ideal working conditions, is a preset limiting factor for avoiding deviation of the reference mean value from the actual reasonable range due to excessive load fluctuation.

[0049] The limiting factor can be set by the implementer according to the specific implementation, for example, the limiting factor .

[0050] Wherein, indicates the average level of the initial reference mean value. The larger the value, the more intense the signal fluctuation of the reference mean value, and the larger the value; on the contrary, the smaller the value, the lower the reference level of . indicates the adjustment factor of the reference mean value based on the load fluctuation. The larger the value, the more intense the current load fluctuation than the ideal working condition, and the larger the value; on the contrary, if the value is smaller, it indicates that the current load fluctuation is relatively gentle, and the smaller the value.

[0051] It should be noted that in the CUSUM algorithm, the role of the relaxation parameter is to filter normal fluctuations in the signal, and only when the deviation exceeds the relaxation parameter is the cumulative sum counted. Under different load conditions, the amplitude of normal fluctuations is different: normal fluctuations are large under heavy load, and a larger relaxation parameter is needed to tolerate these fluctuations; normal fluctuations are small under light load, and a smaller relaxation parameter is needed to capture small deviations. Therefore, the relaxation parameter under the reference working condition is adjusted by the dynamic sensitivity coefficient.

[0052] The ratio of the relaxation parameter of the CUSUM algorithm and the dynamic sensitivity coefficient is taken as a dynamic relaxation parameter at the current moment. When the dynamic sensitivity coefficient is greater than 1, that is, in the requirement of light load and high sensitivity, the dynamic relaxation parameter is reduced, and the micro deviation is more sensitive. When the dynamic sensitivity coefficient is less than 1, that is, in the requirement of heavy load and low sensitivity, the dynamic relaxation parameter is increased, so that a larger normal fluctuation can be tolerated.

[0053] It should be noted that in the CUSUM algorithm, the decision threshold is a critical value for judging whether the cumulative deviation reaches the event occurrence standard. In the high sensitivity scenario, the threshold needs to be reduced to ensure that the weak deviation can be accumulated to the trigger condition; in the low sensitivity scenario, the threshold needs to be increased to avoid the normal fluctuation from accumulating to the false trigger. Therefore, by associating the decision threshold under the reference working condition with the dynamic sensitivity coefficient, the threshold can be dynamically adjusted according to the sensitivity requirement.

[0054] The ratio of the decision threshold of the CUSUM algorithm and the dynamic sensitivity coefficient is taken as a dynamic decision threshold at the current moment. When the dynamic sensitivity coefficient is greater than 1, that is, in the requirement of light load and high sensitivity, the dynamic decision threshold is reduced, so that the anomaly is more easily accumulated and triggered; when the dynamic sensitivity coefficient is less than 1, that is, in the requirement of heavy load and low sensitivity, the dynamic decision threshold is increased, enhancing the anti-interference ability of the CUSUM algorithm and reducing the false trigger caused by normal fluctuation.

[0055] S5: determining a cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment.

[0056] It should be noted that the cumulative sum is the core of the CUSUM algorithm, which continuously accumulates the deviation between the observation value and the reference value, and triggers the event response when the cumulative deviation exceeds the threshold. Since the power grid working condition is dynamically changing, the calculation of the cumulative deviation must be based on the real-time updated dynamic reference mean value, dynamic relaxation parameter, etc., so as to accurately reflect the signal deviation degree under the current working condition. Therefore, the cumulative sum of this step introduces dynamic parameters, so that the deviation accumulation process can adapt to the power grid operating state in real time.

[0057] Based on the cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm at the previous moment, the first order difference value at the current moment and the dynamic reference mean value, and the dynamic relaxation parameter, the cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm at the current moment is obtained.

[0058] Specifically, the cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm at the current moment satisfies: ; In the formula, a cumulative sum of the signal and the dynamic reference value in the CUSUM algorithm at the current moment, a cumulative sum of the signal and the dynamic reference value in the CUSUM algorithm at the previous moment of the current moment, a first-order difference value at the current moment, a dynamic reference mean value at the current moment, a dynamic relaxation parameter at the current moment.

[0059] wherein, represents that, on the basis of the deviation of the first-order difference value of the signal at the current moment and the dynamic reference mean value, the dynamic relaxation parameter, the cumulative sum of the previous moment (the initial cumulative sum is 0) is superimposed, the greater the value, the higher the cumulative degree of the current and historical deviation, and the greater the possibility that the signal deviates from the steady state, and therefore when the value is greater than 0, the greater, the higher the probability of the event; if the value is less than 0, it means that the deviation does not exceed the relaxation parameter, and the cumulative sum is reset to 0, that is, it is considered that there is no significant deviation at present.

[0060] S6: determining the sampling frequency of the transformer according to the size of the cumulative sum of the signal and the dynamic reference value in the CUSUM algorithm at the current moment and the dynamic decision threshold, to realize digital sampling of the transformer.

[0061] Specifically, the determination of the sampling frequency of the transformer comprises: in response to the cumulative sum of the signal and the dynamic reference value in the CUSUM algorithm at the current moment being less than the dynamic decision threshold, it is determined that the power system is in a steady state, and the sampling frequency of the transformer is maintained at a preset low sampling rate; otherwise, it is determined that the power system is in a transient event, and the sampling frequency of the transformer is adjusted to a preset high sampling rate.

[0062] The implementation personnel can set the high sampling rate according to the specific implementation, for example, the high sampling rate is 100 kHz.

[0063] The embodiment of the application also discloses a transformer digital sampling system comprising a processor and a memory, and the memory stores computer program instructions which realize the transformer digital sampling method according to the application when executed by the processor.

[0064] The above system also comprises a communication bus and a communication interface and other components familiar to those skilled in the art, and the settings and functions thereof are known in the art, and therefore will not be described here.

[0065] The above are the preferred embodiments of the application, and are not intended to limit the protection scope of the application, so that: any equivalent changes made on the basis of the structure, shape, principle of the application should be covered within the protection scope of the application.

Claims

1. A method of digital sampling of a transformer, characterized by, The method comprises: obtaining, in real time, a signal of a secondary side output of a mutual inductor for a power system; determining a load fluctuation degree of a current time according to a difference between effective values of signals of two adjacent times within an observation period of the current time and a mean value of effective values of signals of all times within a preset observation period of the current time; determining a dynamic sensitivity coefficient of the current time according to the load fluctuation degree and a load fluctuation degree of a time corresponding to the current time under an ideal working condition; adjusting a reference mean value of a CUSUM algorithm based on the load fluctuation degree to obtain a dynamic reference mean value of the current time; taking a ratio of a relaxation parameter of the CUSUM algorithm to the dynamic sensitivity coefficient as a dynamic relaxation parameter of the current time; and taking a ratio of a decision threshold of the CUSUM algorithm to the dynamic sensitivity coefficient as a dynamic decision threshold of the current time; obtaining a cumulative sum of a deviation between the signal and the dynamic reference value in the CUSUM algorithm of a previous time, a first-order difference value of the current time and the dynamic reference mean value and the dynamic relaxation parameter, to obtain a cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm of the current time; determining a sampling frequency of the mutual inductor according to a size of the cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm of the current time and the dynamic decision threshold, to realize digital sampling of the mutual inductor.

2. A method of digital sampling of a transformer according to claim 1, characterized in that, The signal is a current signal.

3. The method of claim 1, wherein, The signal is a voltage signal.

4. The method of claim 1, wherein, The load fluctuation degree satisfies: ； In the formula, is the load fluctuation degree at the current time, is the number of times in the preset observation period at the current time, is the effective value of the signal at the i-th time in the preset observation period at the current time, is the effective value of the signal at the i-th time in the preset observation period at the current time, is the effective value of the signal at the i-th time in the preset observation period at the current time, is the effective value of the signal at the i-th time in the preset observation period at the current time, is the absolute value symbol.

5. The method of digital sampling of a transformer of claim 1, wherein, The dynamic sensitivity coefficient satisfies: ； In the formula, is a dynamic sensitivity coefficient at the current time, is a load fluctuation degree at the current time, is a load fluctuation degree at a time corresponding to the current time under an ideal working condition, is a preset adjustment factor for adjusting the degree of change of the sensitivity with the load, is a natural exponential function.

6. The digital sampling method of a mutual inductor according to claim 1 or 5, characterized by, The ideal working condition is an actual operating state of each time when the power system is free from faults.

7. The method of digital sampling of a transformer of claim 1, wherein, The dynamic reference mean value satisfies: ； In the formula, is a dynamic reference average value at the current moment, is a reference average value of the CUSUM, is a load fluctuation degree at the current moment, is a load fluctuation degree at a moment corresponding to the current moment under an ideal working condition, is a preset limiting factor for avoiding deviation of the reference average value from an actual reasonable range due to excessively large load fluctuation.

8. The method of digital sampling of a transformer of claim 1, wherein, The cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm of the current time satisfies: ； wherein is the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current time instant, is the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the previous time instant of the current time instant, is the first order difference value at the current time instant, is the dynamic reference mean value at the current time instant, is the dynamic relaxation coefficient at the current time instant.

9. The method of digital sampling of a transformer sensor of claim 1, wherein, The determination of the sampling frequency of the mutual inductor comprises: in response to the cumulative sum of the deviation between the signal and the dynamic reference value in the CUSUM algorithm of the current time being less than the dynamic decision threshold, it is determined that the power system is in a steady state, and the sampling frequency of the mutual inductor is maintained at a preset low sampling rate; otherwise, it is determined that the power system is in a transient event, and the sampling frequency of the mutual inductor is adjusted to a preset high sampling rate.

10. A mutual inductor digital sampling system characterized by, The method comprises: a processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement a mutual inductor digital sampling method according to any one of claims 1-9.

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