Power system fault response methods, devices, equipment, media and procedures products

Abstract

This application provides a power system fault response method, apparatus, equipment, medium, and program product, relating to the field of power system technology. The method acquires first electrical quantity data of the target power system at the current moment, and based on this data, determines a target characteristic frequency corresponding to the first electrical quantity data and related to the volatility of renewable energy generation used to provide power to the target power system. Further, based on the target characteristic frequency and the rate of change of the electrical quantity, it determines the fault probability of the target power system at the current moment. Then, based on the fault probability and an adaptive threshold, it determines whether a fault has occurred in the target power system, and executes protection actions when a fault is determined. By comprehensively considering the impact of the volatility of renewable energy generation on the target power system, the method can improve the accuracy of fault detection in the target power system, avoid maloperation and failure to operate of relay protection devices, and thus ensure the safe and stable operation of the target power system.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a power system fault response method, apparatus, equipment, medium and program product. Background Technology

[0002] With the increasing global demand for clean energy, the proportion of new energy sources such as solar and wind power in the power system is rising. However, the volatility and intermittency of these new energy sources pose significant challenges to the stable operation of the power system. When new energy sources are connected to the grid on a large scale, electrical quantities such as voltage, current, and power in the power system may fluctuate rapidly. Traditional relay protection devices used to detect power system faults are unable to respond to these changes quickly and accurately, which can easily lead to malfunctions or failures to operate, thus seriously threatening the safe and reliable operation of the power system.

[0003] In related technologies, during the operation of a power system, relay protection devices monitor electrical quantities such as current, voltage, and power, and compare these quantities with corresponding preset thresholds to determine whether a fault has occurred in the power system. If a fault is detected, appropriate protective actions are executed, such as disconnecting the faulty circuit. Specifically, a fault is determined to have occurred in the power system when any one or more electrical quantities such as current, voltage, and power exceed their corresponding preset thresholds; conversely, no fault is determined to have occurred in the power system when all electrical quantities are below their corresponding preset thresholds.

[0004] However, in the scenario of new energy grid connection, the above-mentioned power system fault response methods have the problem of low fault detection accuracy. Summary of the Invention

[0005] This application provides a power system fault response method, apparatus, equipment, medium, and program product to solve the problem of low fault detection accuracy in related technologies for power system fault response methods.

[0006] In a first aspect, this application provides a power system fault response method, comprising: acquiring first electrical quantity data of a target power system at the current moment; determining a target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data, wherein the target characteristic frequency is related to the volatility of a target renewable energy generation, and the target renewable energy is a renewable energy source used to provide power to the target power system; determining the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities, wherein the rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data, wherein the second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment; determining whether a fault has occurred in the target power system based on the fault probability and an adaptive threshold, and performing a protection action when it is determined that a fault has occurred in the target power system.

[0007] In one possible implementation, determining the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data includes: filtering and normalizing the first electrical quantity data to obtain the target electrical quantity data; converting the target electrical quantity data into frequency domain data based on the Fast Fourier Transform algorithm; and extracting the characteristic frequency from the frequency domain data to obtain the target characteristic frequency.

[0008] In one possible implementation, the fault probability of the target power system at the current moment is determined based on the target characteristic frequency and the rate of change of electrical quantities, including: inputting the target characteristic frequency and the rate of change of electrical quantities into a fuzzy logic system to obtain the fault level output by the fuzzy logic system; and defuzzifying the fault level to obtain the fault probability corresponding to the fault level.

[0009] In one possible implementation, determining whether a fault has occurred in the target power system based on the fault probability and an adaptive threshold includes: determining whether the fault probability is greater than the adaptive threshold; if it is greater, determining that the target power system has failed; if it is less than or equal to the threshold, determining that the target power system has not failed.

[0010] In one possible implementation, the adaptive threshold is obtained by inputting the first electrical quantity data into the adaptive threshold calculation model to obtain the adaptive threshold output by the adaptive threshold calculation model; the adaptive threshold calculation model is trained using historical electrical quantity data of the target power system.

[0011] In one possible implementation, the sampling frequency for collecting electrical quantity data in the target power system is determined based on the volatility of the target renewable energy generation.

[0012] Secondly, this application provides a power system fault response device, comprising:

[0013] The acquisition module is used to acquire the first electrical quantity data of the target power system at the current moment;

[0014] The first determining module is used to determine the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data. The target characteristic frequency is related to the volatility of the target new energy power generation. The target new energy is a new energy source used to provide power to the target power system.

[0015] The second determining module is used to determine the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities. The rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data. The second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment.

[0016] The response module is used to determine whether a fault has occurred in the target power system based on the fault probability and an adaptive threshold, and to perform protection actions when a fault is determined to have occurred in the target power system.

[0017] In one possible implementation, the first determining module is specifically used to: filter and normalize the first electrical quantity data to obtain the target electrical quantity data; convert the target electrical quantity data into frequency domain data based on the fast Fourier transform algorithm; and extract the characteristic frequency from the frequency domain data to obtain the target characteristic frequency.

[0018] In one possible implementation, the second determining module is specifically used to: input the target characteristic frequency and the rate of change of electrical quantity into the fuzzy logic system to obtain the fault level output by the fuzzy logic system; and perform defuzzification processing on the fault level to obtain the fault probability corresponding to the fault level.

[0019] In one possible implementation, the response module is specifically used to: determine whether the fault probability is greater than an adaptive threshold; if it is greater, then determine that the target power system has a fault; if it is less than or equal to, then determine that the target power system has not a fault.

[0020] In one possible implementation, the adaptive threshold is obtained by inputting the first electrical quantity data into the adaptive threshold calculation model to obtain the adaptive threshold output by the adaptive threshold calculation model; the adaptive threshold calculation model is trained using historical electrical quantity data of the target power system.

[0021] In one possible implementation, the sampling frequency for collecting electrical quantity data in the target power system is determined based on the volatility of the target renewable energy generation.

[0022] Thirdly, this application provides an electronic device, including a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the first aspect above.

[0023] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method provided in the first aspect above.

[0024] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method provided in the first aspect above.

[0025] The power system fault response method, apparatus, equipment, medium, and program product provided in this application acquires the first electrical quantity data of the target power system at the current moment, and determines the target characteristic frequency corresponding to the first electrical quantity data and related to the volatility of the new energy power generation used to provide power to the target power system based on the first electrical quantity data. Further, based on the target characteristic frequency and the electrical quantity change rate, the fault probability of the target power system at the current moment is determined. Then, based on the fault probability and an adaptive threshold, it is determined whether the target power system has experienced a fault, and when it is determined that the target power system has experienced a fault, a protection action is executed. The electrical quantity change rate is determined based on the first electrical quantity data and the second electrical quantity data, and the second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment. This application obtains a target characteristic frequency related to the volatility of new energy power generation used to provide power to the target power system based on first electrical quantity data, and obtains the fault probability based on the target characteristic frequency and the rate of change of electrical quantity. Furthermore, it detects faults in the target power system through adaptive threshold and fault probability. By comprehensively considering the impact of the volatility of new energy power generation on the target power system, it can improve the accuracy of fault detection in the target power system, avoid maloperation and failure to operate of relay protection devices, and thus ensure the safe and stable operation of the target power system. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0027] Figure 1 A flowchart illustrating the power system fault response method provided in this application embodiment. Figure 1 ;

[0028] Figure 2 A flowchart illustrating the power system fault response method provided in this application embodiment. Figure 2 ;

[0029] Figure 3 A schematic flowchart of the power system fault response method provided in the embodiments of this application. Figure 3 ;

[0030] Figure 4 This is a schematic diagram of the structure of the power system fault response device provided in the embodiments of this application;

[0031] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0032] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0034] The terminology used in the embodiments of this application will be explained below.

[0035] New energy: mainly refers to renewable energy sources such as wind and solar power. New energy power generation is characterized by fluctuations and intermittency.

[0036] Relay protection devices: Devices used to detect faults in power systems and perform protective actions such as quickly disconnecting faulty circuits when a fault occurs, thereby protecting the power system and the safe operation of electrical equipment within the power system.

[0037] Fast Fourier Transform (FFT): An efficient algorithm for computing the Discrete Fourier Transform, used to convert time-domain signals into frequency-domain signals in order to analyze the frequency components of the frequency-domain signals.

[0038] Fuzzy logic: a mathematical tool for handling uncertainty and fuzziness, simulating human thinking through fuzzy rules and fuzzy reasoning. In this application, it is used to handle the complex operating conditions of new energy grid connection.

[0039] Adaptive threshold: A threshold that is dynamically adjusted based on the real-time operating status of the power system. It is used to determine the occurrence and severity of power system faults. Compared with a fixed threshold, it can better adapt to the fluctuations in grid connection of new energy power generation.

[0040] Currently, commonly used relay protection devices in power systems have many shortcomings in dealing with the fluctuating grid connection of renewable energy generation. On the one hand, during power system operation, relay protection devices monitor electrical quantities such as current, voltage, and power, and compare these quantities with corresponding preset thresholds to determine whether a fault has occurred in the power system. If a fault is detected, appropriate protective actions, such as disconnecting the faulty circuit, are executed. However, the fixed preset thresholds in this method cannot adapt to the frequent fluctuations in electrical quantities caused by the fluctuating grid connection of renewable energy generation. For example, when the power output of renewable energy generation changes, the electrical quantities may briefly exceed the preset thresholds, but this does not necessarily indicate a real fault. This can lead to false tripping of the relay protection devices, affecting the normal power supply of the power system. Furthermore, for some slowly developing faults, because the initial changes in electrical quantities are small and have not reached the fixed preset thresholds, the relay protection devices may not be able to detect the fault in time, causing the fault to escalate.

[0041] On the other hand, the volatility of renewable energy grid connection can be addressed by enhancing the detection capabilities of relay protection devices, such as by adding voltage harmonic detection. Specifically, a harmonic detection module is added to the power system to monitor the voltage and current harmonic content in real time. When either the voltage or current harmonic content exceeds a set standard, a harmonic anomaly alarm signal is sent to the relay protection device. The relay protection device then combines the electrical quantity data with preset thresholds and the harmonic anomaly alarm signal to determine if a power system fault has occurred. While this method adds harmonic detection, it does not fully consider the comprehensive impact of the volatility of renewable energy generation on the overall electrical quantities of the power system. Harmonic detection and traditional electrical quantity detection based on fixed preset thresholds are independent of each other, lacking effective fusion analysis and failing to accurately determine complex fault conditions. Furthermore, this method increases the hardware cost and computational burden of the device, reduces the response speed of the relay protection device, and cannot meet the requirements for rapid response from the volatile grid connection of renewable energy generation.

[0042] Based on the problems existing in related technologies, the embodiments of this application obtain characteristic frequencies related to the volatility of new energy power generation used to provide electricity to the power system based on electrical quantity data in the power system, and obtain the fault probability of the power system based on the characteristic frequency and the rate of change of electrical quantities. Furthermore, the faults of the power system are detected by adaptive threshold and fault probability. By comprehensively considering the impact of the volatility of new energy power generation on the power system, the accuracy of power system fault detection can be improved, the maloperation and failure to operate of relay protection devices can be avoided, and the safe and stable operation of the power system can be guaranteed.

[0043] The application scenarios applicable to the embodiments of this application will be described in detail below.

[0044] The power system fault response method provided in this application is applicable to power systems that include new energy grid connection, i.e., power systems that provide power through new energy generation.

[0045] The following describes in detail the power system fault response method provided in this application, taking the relay protection device in the power system as the main implementer and combining specific embodiments.

[0046] Figure 1 A flowchart illustrating the power system fault response method provided in this application embodiment. Figure 1 .like Figure 1 As shown, the specific implementation of this power system fault response method may include the following steps:

[0047] S101, Obtain the first electrical quantity data of the target power system at the current moment.

[0048] For example, the target power system can be a power system that includes grid-connected renewable energy sources. The types of renewable energy sources included in the target power system can be one or more; this application embodiment does not limit this, and the specific type can be determined according to actual application requirements.

[0049] For example, the first electrical quantity data may include voltage data, current data, and power data, etc.

[0050] For example, the voltage, current, and power data at the current moment can each contain multiple data points. The specific number of data points is determined based on the data acquisition frequency, and this application does not limit this number.

[0051] For example, the first electrical quantity data can be acquired using a high-precision sensor. The acquisition frequency of the high-precision sensor can be determined based on the fluctuation characteristics of the target renewable energy generation used to provide power to the target power system at the current moment.

[0052] It is understandable that by determining the acquisition frequency of high-precision sensors based on the fluctuation characteristics of the target new energy power generation used to provide power to the target power system at the current moment, the rapid changes of the first electrical quantity data at the current moment can be accurately captured, thereby improving the accuracy of the acquisition of the first electrical quantity data.

[0053] For example, when the target power system contains multiple different types of new energy sources, and the new energy sources currently used to provide power to the target power system include multiple different types of new energy sources, the first electrical quantity data can be obtained by collecting different high-precision sensors. The number of different high-precision sensors can be consistent with the number of different types of new energy sources; that is, one high-precision sensor corresponds to one type of new energy source, and the sampling frequency of the high-precision sensor is determined based on the fluctuation characteristics of the corresponding type of new energy generation.

[0054] S102, Based on the first electrical quantity data, determine the target characteristic frequency corresponding to the first electrical quantity data. The target characteristic frequency is related to the volatility of the target new energy power generation. The target new energy is a new energy source used to provide power to the target power system.

[0055] For example, the target characteristic frequency can be the characteristic frequency caused by the volatility of the target new energy power generation.

[0056] For example, when the target new energy source is wind energy, the target characteristic frequency can be the characteristic frequency such as the blade rotation frequency and its harmonic frequency caused by the fluctuation of wind power generation; when the target new energy source is solar energy, the target characteristic frequency can be the characteristic frequency such as the DC component fluctuation frequency caused by the fluctuation of solar power generation.

[0057] It is understandable that the first power data is time-domain data. In this step, one possible implementation is as follows: first, convert the first power data into frequency-domain data, and then, based on the characteristic frequency caused by the volatility of the target new energy power generation, analyze the frequency components contained in the frequency-domain data and the amplitude information corresponding to different frequency components to identify the target characteristic frequency.

[0058] S103, determine the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities. The rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data. The second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment.

[0059] For example, the second electrical quantity data may include current data, voltage data, and power data of the target power system at the previous time adjacent to the current time. The acquisition method for the second electrical quantity data is similar to that for the first electrical quantity data, and will not be repeated here.

[0060] For example, the rate of change of electrical quantities may include the rate of change of current, the rate of change of voltage, and the rate of change of power.

[0061] For example, the rate of change of current can be the average rate of change of current between the previous moment and the current moment, which is adjacent to the current moment. Accordingly, the rate of change of current can be expressed by the following formula:

[0062]

[0063] Where I represents the rate of change of current, This represents the average value of the current data included in the first electrical quantity data set. This represents the average value of the current data included in the second electrical quantity data. Indicates the current moment. It represents the previous time that is adjacent to the current time.

[0064] For example, the rate of change of current can also be the instantaneous rate of change of current between the previous moment and the current moment, which is adjacent to the current moment. Accordingly, the rate of change of current can be expressed by the following formula:

[0065]

[0066] For example, the rate of change of voltage can be the average rate of change of voltage between the previous time point adjacent to the current time point and the current time point. Accordingly, the rate of change of voltage can be expressed by the following formula:

[0067]

[0068] Where V represents the rate of change of voltage, This represents the average value of the voltage data included in the first electrical quantity data set. This represents the average value of the voltage data included in the second electrical quantity data.

[0069] For example, the rate of change of voltage can also be the instantaneous rate of change of voltage between the previous moment and the current moment, which is adjacent to the current moment. Accordingly, the rate of change of voltage can be expressed by the following formula:

[0070]

[0071] For example, the rate of change of power can be the average rate of change of power between the previous time point adjacent to the current time point and the current time point. Accordingly, the rate of change of power can be expressed by the following formula:

[0072]

[0073] Where P represents the rate of change of power, This represents the average value of the power data included in the first electrical quantity data. This represents the average value of the power data included in the second electrical quantity data.

[0074] For example, the rate of change of power can also be the instantaneous rate of change of power between the previous moment and the current moment, which is adjacent to the current moment. Accordingly, the rate of change of power can be expressed by the following formula:

[0075]

[0076] One possible implementation of this step is to input the target characteristic frequency and the rate of change of electrical quantities into the fault probability model to obtain the fault probability output by the fault probability model. The fault probability model can be constructed based on the characteristics of the power system using methods such as statistical analysis, machine learning, and physical modeling, and can be trained based on historical fault data of the target power system.

[0077] S104 determines whether a fault has occurred in the target power system based on the fault probability and adaptive threshold, and performs protection actions when a fault is determined to have occurred in the target power system.

[0078] For example, the adaptive threshold is determined based on the first electrical quantity data.

[0079] For example, the protective actions performed by the relay protection device can include quickly disconnecting the faulty line, adjusting the operation mode of the power grid system, and issuing tripping commands.

[0080] In one possible implementation, when a fault is determined to occur in the target power system, the relay protection device can further determine the fault type and fault severity, and perform corresponding protection actions based on the fault type and fault severity.

[0081] For example, when the fault type is a short circuit fault, a large current will appear at the fault point, which may quickly damage the equipment. Therefore, the protection action performed by the relay protection device can be to quickly disconnect the faulty line to prevent the fault range from expanding, so as to ensure the normal operation of other parts of the target power system. When the fault type is an abnormal power fluctuation fault, the fault may be caused by the instability of new energy power generation or sudden changes in grid load. Therefore, the protection action performed by the relay protection device can be to adjust the grid operation mode, such as changing the power distribution, so as to maintain the stable operation of the target power system.

[0082] This embodiment of the application acquires first electrical quantity data of the target power system at the current moment, and determines, based on the first electrical quantity data, a target characteristic frequency related to the volatility of new energy power generation used to provide power to the target power system. Further, based on the target characteristic frequency and the rate of change of electrical quantities, the fault probability of the target power system at the current moment is determined. Then, based on the fault probability and an adaptive threshold, it is determined whether a fault has occurred in the target power system. If a fault is determined in the target power system, a protection action is executed. The rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data, where the second electrical quantity data is the electrical quantity data from the previous moment adjacent to the current moment. This embodiment of the application, by obtaining the target characteristic frequency related to the volatility of new energy power generation used to provide power to the target power system based on the first electrical quantity data, and obtaining the fault probability based on the target characteristic frequency and the rate of change of electrical quantities, further detects faults in the target power system through an adaptive threshold and fault probability. This comprehensively considers the impact of the volatility of new energy power generation on the target power system, which can improve the accuracy of fault detection in the target power system, avoid maloperation and failure to operate of relay protection devices, and thus ensure the safe and stable operation of the target power system.

[0083] Optionally, in the power system fault response method provided in this application embodiment, after the relay protection device performs the protection action, it further includes: the relay protection device sending the execution result of the protection action to the monitoring center of the target power system, so that the operation and maintenance personnel responsible for the target power system can understand the operating status of the target power system in a timely manner, and carry out subsequent maintenance and optimization of the relay protection device, etc.

[0084] The following is combined Figure 2 A detailed explanation is provided on a specific implementation method of step S102, which determines the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data.

[0085] Figure 2 A flowchart illustrating the power system fault response method provided in this application embodiment. Figure 2 .like Figure 2 As shown, a specific implementation of this method, which determines the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data, may include the following steps:

[0086] S201, the first electrical quantity data is filtered and normalized to obtain the target electrical quantity data.

[0087] In one possible implementation, based on an adaptive filtering algorithm, the first electrical quantity data is first filtered to remove noise and interference signals. Then, the filtered first electrical quantity data is normalized, mapping the first electrical quantity data of different magnitudes and ranges to a preset interval to obtain the target electrical quantity data. By normalizing the filtered first electrical quantity data, the influence of data dimensions on subsequent analysis can be eliminated, facilitating unified processing and comparison by the algorithm.

[0088] For example, the adaptive filtering algorithm can be the Kalman filter algorithm.

[0089] Understandably, the Kalman filter algorithm is a recursive algorithm that can estimate the true values ​​of electrical quantities in real time based on the state equations and observation equations of the power system, thereby improving the accuracy and stability of the electrical quantity data.

[0090] For example, the preset interval can be [0, 1]. This application embodiment does not limit the size of the preset interval; it can be determined according to actual application requirements.

[0091] S202, based on the Fast Fourier Transform algorithm, converts target electrical quantity data into frequency domain data.

[0092] For example, frequency domain data includes different frequency components corresponding to the target electrical quantity data and amplitude information corresponding to each frequency component.

[0093] For example, amplitude information can be used to reflect the degree of impact of the volatility of the target renewable energy generation on the target power system.

[0094] For example, the amplitude of the frequency component is positively correlated with the degree of impact of the volatility of the target renewable energy generation on the target power system. That is, the larger the amplitude of the frequency component, the greater the impact of the volatility of the target renewable energy generation on the target power system, and vice versa.

[0095] S203, extract the characteristic frequencies from the frequency domain data to obtain the target characteristic frequencies.

[0096] One possible implementation is to extract the characteristic frequency caused by the volatility of the target new energy power generation from the frequency domain data based on the volatility characteristics of the target new energy power generation, and obtain the target characteristic frequency.

[0097] It is understandable that the target characteristic frequency can contain different frequency components and the amplitude corresponding to each frequency component.

[0098] Understandably, analyzing frequency domain data makes it easier to understand the changes in frequency signals in the target power system, and thus identify the characteristic frequencies caused by the volatility of the target new energy power generation.

[0099] In this embodiment, target electrical quantity data is obtained by filtering and normalizing the first electrical quantity data. Based on the fast Fourier transform algorithm, the target electrical quantity data is converted into frequency domain data. The characteristic frequency of the frequency domain data is further extracted to obtain the target characteristic frequency. This takes into account the impact of the volatility of the target new energy power generation on the target power system and improves the accuracy of detecting faults in the target power system.

[0100] The following is combined Figure 3 A detailed explanation is provided of a specific implementation method for determining the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities in step S103.

[0101] Figure 3 A schematic flowchart of the power system fault response method provided in the embodiments of this application. Figure 3 .like Figure 3 As shown, a specific implementation of this method, which determines the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities, may include the following steps:

[0102] S301: Input the target characteristic frequency and the rate of change of electrical quantity into the fuzzy logic system to obtain the fault level output by the fuzzy logic system.

[0103] For example, fault levels are used to reflect the risk level of a fault occurring in the target power system.

[0104] For example, the fault level can be low, medium, or high.

[0105] For example, the fault level is determined based on fuzzy rules in a fuzzy logic system.

[0106] For example, the fuzzy rules in a fuzzy logic system are formulated based on the operating experience and theoretical analysis of the target power system.

[0107] For example, the fuzzy rules can be as follows: when any one of the rates of change of current, voltage, and power in the electrical quantity change rate is greater than the corresponding preset threshold, and the target characteristic frequency is greater than the corresponding preset threshold, the fault level is determined to be high; when any one of the rates of change of current, voltage, and power in the electrical quantity change rate is greater than the corresponding preset threshold, or the target characteristic frequency is greater than the corresponding preset threshold, the fault level is determined to be medium; when the rates of change of current, voltage, and power in the electrical quantity change rate are all less than or equal to the corresponding preset threshold, and the target characteristic frequency is less than or equal to the corresponding preset threshold, the fault level is determined to be low.

[0108] One possible implementation of this step is to use the rate of change of electrical quantities and the target characteristic frequency as fuzzy input variables of the fuzzy logic system, and calculate the fault level output by the fuzzy logic system through fuzzy inference based on fuzzy rules. The target characteristic frequency includes frequency components and their corresponding amplitudes.

[0109] For example, fuzzy reasoning can employ methods such as Mamdani reasoning or Sugeno reasoning.

[0110] S302, Defuzzify the fault level to obtain the fault probability corresponding to the fault level.

[0111] One possible implementation is to use a deblurring method to deblur the fault level and obtain the fault probability corresponding to the fault level.

[0112] For example, deblurring methods could include the centroid method, etc.

[0113] For example, there is a certain correspondence between fault level and fault probability. For instance, when the fault level is high, the corresponding fault probability can be greater than 0.5; when the fault level is medium, the corresponding fault probability can be 0.5; and when the fault level is low, the corresponding fault probability can be less than 0.5.

[0114] In this embodiment, the target characteristic frequency and the rate of change of electrical quantities are input into a fuzzy logic system to obtain the fault level output by the fuzzy logic system. The fault level is then defuzzified to obtain the fault probability corresponding to the fault level. This embodiment uses a fuzzy logic system to handle the uncertainty of the target power system at the current moment, thereby improving the accuracy of the fault probability and ultimately enhancing the accuracy of fault detection in the target power system.

[0115] Optionally, one possible implementation of determining whether the target power system has a fault based on the fault probability and the adaptive threshold in step S104 is as follows: determine whether the fault probability is greater than the adaptive threshold; if it is greater, determine that the target power system has a fault; if it is less than or equal to the threshold, determine that the target power system has not a fault.

[0116] In this embodiment of the application, by using an adaptive threshold as the threshold for judging faults in the target power system, the accuracy of fault detection in the target power system can be improved.

[0117] Optionally, the adaptive threshold provided in this application embodiment is obtained by: inputting the first electrical quantity data into the adaptive threshold calculation model to obtain the adaptive threshold output by the adaptive threshold calculation model; the adaptive threshold calculation model is trained by training the historical electrical quantity data of the target power system.

[0118] For example, the adaptive threshold calculation model can be a Support Vector Regression (SVR) model.

[0119] One possible implementation involves dynamically calculating an adaptive threshold based on the real-time operating status of the target power system, including factors such as the target renewable energy generation capacity and grid load (e.g., current, voltage). For example, a mathematical model is established between the adaptive threshold and the power system operating parameters, and the model is trained using a large amount of historical power system data to accurately predict reasonable thresholds for the power system under different operating conditions.

[0120] In this embodiment, by inputting the first electrical quantity data into the adaptive threshold calculation model, the adaptive threshold output by the adaptive threshold calculation model is obtained, and the adaptive threshold is applied to the fault judgment of the target power system. This enables the target power system to better adapt to the changes in electrical quantity data caused by the fluctuating grid connection of the target new energy power generation, thereby improving the accuracy of fault detection of the target power system and the operational stability of the target power system.

[0121] Optionally, in the power system fault response method provided in this application embodiment, the acquisition frequency for collecting electrical quantity data in the target power system is determined based on the volatility of the target new energy power generation.

[0122] In this embodiment of the application, by determining the collection frequency of electrical quantity data based on the fluctuation characteristics of the target new energy power generation that provides power to the target power system, the rapid changes in electrical quantity data can be accurately captured, thereby improving the accuracy of electrical quantity data collection.

[0123] In summary, a specific implementation of the power system fault response method provided in this application embodiment may include the following steps:

[0124] S1, obtain the first electrical quantity data of the target power system at the current moment.

[0125] S2, the first electrical quantity data is filtered and normalized to obtain the target electrical quantity data.

[0126] S3, based on the Fast Fourier Transform algorithm, converts the target electrical quantity data into frequency domain data.

[0127] S4 extracts the characteristic frequencies from the frequency domain data to obtain the target characteristic frequencies.

[0128] S5 inputs the target characteristic frequency and the rate of change of electrical quantity into the fuzzy logic system to obtain the fault level output by the fuzzy logic system.

[0129] The rate of change of electrical quantity is determined based on the first electrical quantity data and the second electrical quantity data, where the second electrical quantity data is the electrical quantity data of the previous time adjacent to the current time.

[0130] S6. Defuzzify the fault level to obtain the fault probability corresponding to the fault level.

[0131] S7 determines whether the probability of a fault is greater than the adaptive threshold.

[0132] In this step, it is determined whether the fault probability is greater than the adaptive threshold. If it is greater, step S8 is executed; if it is less than or equal to the threshold, step S9 is executed.

[0133] S8 determines that a fault has occurred in the target power system and executes protection actions.

[0134] S9, Real-time monitoring and collection of electrical quantity data in the target power system, and repeating steps S1 to S8.

[0135] In summary, the power system fault response method provided in this application has the following beneficial effects:

[0136] 1) By performing frequency domain analysis, fuzzy logic judgment, and adaptive threshold adjustment on the collected first electrical quantity data, the impact of the volatility of the target new energy source on the target power system is comprehensively considered. This enables more accurate fault detection, improves the accuracy of fault detection in the target power system, and reduces the occurrence of maloperation and failure to operate of relay protection devices in the target power system.

[0137] 2) By determining the acquisition frequency of electrical quantity data in the target power system based on the volatility of the target new energy power generation, and by normalizing the acquired first electrical quantity data, the first electrical quantity data can be processed quickly, and the target power system faults can be judged and responded to in a timely manner. This shortens the time from the occurrence of the fault to the relay protection device executing the protection action, improves the fault response speed, and thus effectively ensures the safe operation of the target power system under fault conditions and reduces the damage of the fault to the power equipment in the target power system.

[0138] 3) By adopting an adaptive threshold adjustment mechanism, the relay protection device can automatically adjust the fault judgment threshold according to the real-time operating status of the target power system, better adapting to the changes in electrical quantities caused by the fluctuating grid connection of the target renewable energy generation. This ensures stable operation of the relay protection device even under significant fluctuations in renewable energy generation power and changes in grid load, improving the adaptability of the target power system to the target renewable energy and the overall operational stability of the target power system.

[0139] 4) Accurate fault detection and rapid response reduce damage to electrical equipment and power outages in the target power system caused by the escalation of faults, thereby lowering the operation and maintenance costs of the target power system. Simultaneously, by reducing malfunctions of relay protection devices, unnecessary power outages and maintenance are avoided, further improving the economic efficiency of the target power system.

[0140] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0141] Figure 4 This is a schematic diagram of the structure of a power system fault response device provided in an embodiment of this application. Figure 4 As shown, the power system fault response device 40 includes: an acquisition module 410, a first determination module 420, a second determination module 430, and a response module 440.

[0142] The acquisition module 410 is used to acquire the first electrical quantity data of the target power system at the current moment;

[0143] The first determining module 420 is used to determine the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data. The target characteristic frequency is related to the volatility of the target new energy power generation. The target new energy is a new energy source used to provide power to the target power system.

[0144] The second determining module 430 is used to determine the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities. The rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data. The second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment.

[0145] The response module 440 is used to determine whether a fault has occurred in the target power system based on the fault probability and an adaptive threshold, and to perform protection actions when a fault is determined to have occurred in the target power system.

[0146] In one possible implementation, the first determining module 420 is specifically used to: filter and normalize the first electrical quantity data to obtain target electrical quantity data; convert the target electrical quantity data into frequency domain data based on the fast Fourier transform algorithm; and extract the characteristic frequency from the frequency domain data to obtain the target characteristic frequency.

[0147] In one possible implementation, the second determining module 430 is specifically used to: input the target characteristic frequency and the rate of change of electrical quantity into the fuzzy logic system to obtain the fault level output by the fuzzy logic system; and perform defuzzification processing on the fault level to obtain the fault probability corresponding to the fault level.

[0148] In one possible implementation, the response module 440 is specifically used to: determine whether the fault probability is greater than an adaptive threshold; if it is greater, determine that the target power system has a fault; if it is less than or equal to, determine that the target power system has not a fault.

[0149] In one possible implementation, the adaptive threshold is obtained by inputting the first electrical quantity data into the adaptive threshold calculation model to obtain the adaptive threshold output by the adaptive threshold calculation model; the adaptive threshold calculation model is trained using historical electrical quantity data of the target power system.

[0150] In one possible implementation, the sampling frequency for collecting electrical quantity data in the target power system is determined based on the volatility of the target renewable energy generation.

[0151] The power system fault response device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0152] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0153] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0154] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0155] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0156] The memory may include random access memory (RAM) and non-volatile memory (NVM), such as at least one disk storage device.

[0157] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0158] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0159] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0160] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0161] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0162] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0165] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0167] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of power system fault response, characterized by, include: Obtain the first electrical quantity data of the target power system at the current moment; Based on the first electrical quantity data, a target characteristic frequency corresponding to the first electrical quantity data is determined. The target characteristic frequency is related to the volatility of the target new energy power generation. The target new energy is a new energy source used to provide electricity to the target power system. Based on the target characteristic frequency and the rate of change of electrical quantities, the failure probability of the target power system at the current moment is determined. The rate of change of electrical quantities is determined based on the first electrical quantity data and the second electrical quantity data, where the second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment. Based on the fault probability and the adaptive threshold, it is determined whether the target power system has experienced a fault, and when it is determined that the target power system has experienced a fault, a protection action is performed.

2. The electric power system failure response method of claim 1, wherein, The step of determining the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data includes: The first electrical quantity data is filtered and normalized to obtain the target electrical quantity data; Based on the Fast Fourier Transform algorithm, the target electrical quantity data is converted into frequency domain data; The target characteristic frequency is obtained by extracting the characteristic frequency from the frequency domain data.

3. The electric power system failure response method of claim 1, wherein, Determining the fault probability of the target power system at the current moment based on the target characteristic frequency and the rate of change of electrical quantities includes: The target characteristic frequency and the rate of change of electrical quantity are input into the fuzzy logic system to obtain the fault level output by the fuzzy logic system. The fault level is defuzzified to obtain the fault probability corresponding to the fault level.

4. The electric power system failure response method of claim 1, wherein, The step of determining whether the target power system has experienced a fault based on the fault probability and the adaptive threshold includes: Determine whether the fault probability is greater than the adaptive threshold; If the value is greater than 1, then the target power system is determined to have a fault. If it is less than or equal to, then the target power system is determined to be without fault.

5. The power system fault response method of any one of claims 1 to 4, wherein, The adaptive threshold is obtained in the following way: The first electrical quantity data is input into the adaptive threshold calculation model to obtain the adaptive threshold output by the adaptive threshold calculation model; the adaptive threshold calculation model is trained using historical electrical quantity data of the target power system.

6. The electric power system failure response method according to any one of claims 1 to 4, characterized by, In the target power system, the sampling frequency for collecting electrical quantity data is determined based on the volatility of the target new energy power generation.

7. A power system fault response apparatus characterized by, include: The acquisition module is used to acquire the first electrical quantity data of the target power system at the current moment; The first determining module is used to determine the target characteristic frequency corresponding to the first electrical quantity data based on the first electrical quantity data. The target characteristic frequency is related to the volatility of the target new energy power generation. The target new energy is a new energy source used to provide power to the target power system. The second determining module is used to determine the failure probability of the target power system at the current moment based on the target characteristic frequency and the electrical quantity change rate, wherein the electrical quantity change rate is determined based on the first electrical quantity data and the second electrical quantity data, and the second electrical quantity data is the electrical quantity data of the previous moment adjacent to the current moment. The response module is used to determine whether the target power system has experienced a fault based on the fault probability and an adaptive threshold, and to perform protection actions when the target power system is determined to have experienced a fault.

8. An electronic device, comprising: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterised in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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