A power grid harmonic detection method and device, electronic equipment and storage medium

Abstract

The application provides a power grid harmonic detection method and device, electronic equipment and storage medium, and relates to the technical field of power grid detection. In the application, an edge computing node acquires power grid data collected by a terminal node at the current time; the data transmission delay between the terminal node and the edge computing node is less than a set transmission delay threshold; active current signals and reactive current signals corresponding to the current time are obtained based on the power grid data, and at least one harmonic component is obtained based on the active current signals and the reactive current signals; harmonic anomaly detection is performed on the at least one harmonic component based on a harmonic detection threshold determined based on the at least one harmonic component, a harmonic detection result of the power grid is obtained, and the real-time performance of harmonic detection of the power grid is improved.

Description

Technical Field

[0001] This application relates to the field of power grid detection technology, and in particular to a method, apparatus, electronic device and storage medium for detecting harmonics in a power grid. Background Technology

[0002] With the rapid development of power systems, a large number of nonlinear power electronic devices are used in power grid loads, which exacerbates harmonic pollution in the power grid. This not only distorts the current waveform and reduces power supply quality, but also causes resonance, leading to equipment damage.

[0003] To effectively address the impact of harmonics, it is necessary to continuously monitor harmonics in the power grid online, record waveforms in real time during fault conditions, and identify the distribution of harmonics in the power grid through data acquisition and analysis. This will allow for the analysis of the resonance mechanism under harmonic conditions and provide a reliable basis for harmonic mitigation in the power grid.

[0004] Currently, when performing harmonic detection on the power grid, terminal nodes typically need to report the collected power grid data (such as current or voltage data) to a cloud computing center. Upon receiving the power grid data, the cloud computing center can then analyze the data using a pre-defined harmonic detection algorithm to determine whether harmonic problems exist in the power grid.

[0005] However, using the aforementioned harmonic detection method requires transmitting power grid data to a cloud computing center, as the pre-defined harmonic detection algorithm typically only runs on such a center. This can lead to poor real-time performance in harmonic detection due to long communication delays. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and storage medium for detecting harmonics in a power grid, thereby improving the real-time performance of harmonic detection in the power grid.

[0007] In a first aspect, embodiments of this application provide a harmonic detection method for a power grid, applied to an edge computing node, the method comprising:

[0008] Acquire the power grid data collected by the terminal node at the current moment; wherein the data transmission latency between the terminal node and the edge computing node is less than the set transmission latency threshold;

[0009] Based on the power grid data, the active current signal and reactive current signal corresponding to the current moment are obtained, and based on the active current signal and reactive current signal, at least one harmonic component is obtained.

[0010] Based on a harmonic detection threshold determined by at least one harmonic component, harmonic anomaly detection is performed on at least one harmonic component to obtain the harmonic detection results of the power grid.

[0011] In one optional embodiment, obtaining the active current signal and reactive current signal corresponding to the current moment based on grid data includes:

[0012] Multiple first current signals in the first reference coordinate system are obtained from the power grid data;

[0013] Based on a preset first coordinate system transformation matrix, multiple first current signals are transformed from the first reference coordinate system to the second reference coordinate system to obtain at least two second current signals corresponding to the multiple first current signals.

[0014] Based on a second current signal matrix composed of at least two second current signals and a preset first current signal adjustment matrix, active current signals and reactive current signals are obtained; wherein, the first current signal adjustment matrix is ​​determined based on sine and cosine signals.

[0015] In one optional embodiment, at least one harmonic component is obtained based on the active current signal and the reactive current signal, including:

[0016] The active current signal and the reactive current signal are filtered separately to obtain the first DC component corresponding to the active current signal and the second DC component corresponding to the reactive current signal.

[0017] Based on the second coordinate system transformation matrix, the second current signal adjustment matrix, and the DC component matrix composed of the first DC component and the second DC component, the fundamental components corresponding to the multiple first current signals are obtained respectively; wherein, the second coordinate system transformation matrix is ​​the transpose of the first coordinate system transformation matrix, and the second current signal adjustment matrix is ​​the inverse of the first current signal adjustment matrix;

[0018] At least one harmonic component is obtained based on multiple first current signals and multiple fundamental components.

[0019] In one optional embodiment, harmonic anomaly detection is performed on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component to obtain the harmonic detection result of the power grid, including:

[0020] Determine the total harmonic distortion rate of at least one harmonic component at the current time, and obtain the total harmonic distortion rate of the previous historical time adjacent to the current time;

[0021] The harmonic detection threshold is obtained based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0022] If at least one harmonic component contains a harmonic component that is greater than or equal to the harmonic detection threshold, then the harmonic detection result is determined to be that there are abnormal harmonics in the power grid.

[0023] In one alternative embodiment, determining the total harmonic distortion rate of at least one harmonic component at the current moment includes:

[0024] Based on at least one harmonic component and its corresponding harmonic impedance, at least one harmonic voltage is obtained;

[0025] The total harmonic distortion rate is determined based on at least one harmonic voltage and its corresponding fundamental component, as well as a preset standard voltage.

[0026] In one optional embodiment, a harmonic detection threshold is obtained based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment, including:

[0027] Send the first information to the central computing node; the first information is used to indicate the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment;

[0028] The second information received from the central computing node is used to indicate the harmonic detection threshold, which is determined by the central computing node through multiple iterative calculations based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0029] In one optional embodiment, after obtaining the harmonic detection result of the power grid by performing harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component, the method further includes:

[0030] If the harmonic detection result indicates the presence of abnormal harmonics in the power grid, an alarm message is sent to the terminal node; the alarm message is used to indicate the presence of abnormal harmonics in the power grid.

[0031] If the harmonic detection result indicates that the harmonics in the power grid are normal, then at least one harmonic component is sent to the central computing node so that the central computing node can perform harmonic anomaly detection on at least one harmonic component.

[0032] Secondly, embodiments of this application also provide a harmonic detection device for a power grid, applied to an edge computing node, the device comprising:

[0033] The data acquisition module is used to acquire the power grid data collected by the terminal node at the current moment; wherein the data transmission latency between the terminal node and the edge computing node is less than the set transmission latency threshold.

[0034] The data processing module is used to obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data, and to obtain at least one harmonic component based on the active current signal and reactive current signal.

[0035] The anomaly detection module is used to perform harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component, and obtain the harmonic detection results of the power grid.

[0036] In one optional embodiment, when the active current signal and reactive current signal corresponding to the current moment are obtained based on grid data, the data processing module is specifically used for:

[0037] Multiple first current signals in the first reference coordinate system are obtained from the power grid data;

[0038] Based on a preset first coordinate system transformation matrix, multiple first current signals are transformed from the first reference coordinate system to the second reference coordinate system to obtain at least two second current signals corresponding to the multiple first current signals.

[0039] Based on a second current signal matrix composed of at least two second current signals and a preset first current signal adjustment matrix, active current signals and reactive current signals are obtained; wherein, the first current signal adjustment matrix is ​​determined based on sine and cosine signals.

[0040] In one optional embodiment, when obtaining at least one harmonic component based on the active current signal and the reactive current signal, the data processing module is specifically used for:

[0041] The active current signal and the reactive current signal are filtered separately to obtain the first DC component corresponding to the active current signal and the second DC component corresponding to the reactive current signal.

[0042] Based on the second coordinate system transformation matrix, the second current signal adjustment matrix, and the DC component matrix composed of the first DC component and the second DC component, the fundamental components corresponding to the multiple first current signals are obtained respectively; wherein, the second coordinate system transformation matrix is ​​the transpose of the first coordinate system transformation matrix, and the second current signal adjustment matrix is ​​the inverse of the first current signal adjustment matrix;

[0043] At least one harmonic component is obtained based on multiple first current signals and multiple fundamental components.

[0044] In one optional embodiment, when performing harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component to obtain the harmonic detection result of the power grid, the anomaly detection module is specifically used for:

[0045] Determine the total harmonic distortion rate of at least one harmonic component at the current time, and obtain the total harmonic distortion rate of the previous historical time adjacent to the current time;

[0046] The harmonic detection threshold is obtained based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0047] If at least one harmonic component contains a harmonic component that is greater than or equal to the harmonic detection threshold, then the harmonic detection result is determined to be that there are abnormal harmonics in the power grid.

[0048] In one alternative embodiment, when determining the total harmonic distortion rate of at least one harmonic component at the current moment, the anomaly detection module is specifically used to:

[0049] Based on at least one harmonic component and its corresponding harmonic impedance, at least one harmonic voltage is obtained;

[0050] The total harmonic distortion rate is determined based on at least one harmonic voltage and its corresponding fundamental component, as well as a preset standard voltage.

[0051] In one optional embodiment, when obtaining the harmonic detection threshold based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment, the anomaly detection module is specifically used for:

[0052] Send the first information to the central computing node; the first information is used to indicate the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment;

[0053] The second information received from the central computing node is used to indicate the harmonic detection threshold, which is determined by the central computing node through multiple iterative calculations based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0054] In an optional embodiment, after performing harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component to obtain the harmonic detection result of the power grid, the anomaly detection module is further configured to:

[0055] If the harmonic detection result indicates the presence of abnormal harmonics in the power grid, an alarm message is sent to the terminal node; the alarm message is used to indicate the presence of abnormal harmonics in the power grid.

[0056] If the harmonic detection result indicates that the harmonics in the power grid are normal, then at least one harmonic component is sent to the central computing node so that the central computing node can perform harmonic anomaly detection on at least one harmonic component.

[0057] Thirdly, embodiments of this application also provide an electronic device, including:

[0058] Processor; and

[0059] Stored program memory,

[0060] The program includes instructions that, when executed by a processor, cause the processor to perform the harmonic detection method for the power grid as described in the first aspect.

[0061] Fourthly, embodiments of this application also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the power grid harmonic detection method as described in the first aspect.

[0062] Fifthly, this application provides a computer program product that, when invoked by a computer, causes the computer to execute the steps of the power grid harmonic detection method as described in the first aspect.

[0063] The beneficial effects of this application are as follows:

[0064] In the power grid harmonic detection method provided in this application embodiment, edge computing nodes can acquire power grid data collected by terminal nodes with data transmission delays less than a set transmission delay threshold. Furthermore, they can obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data. Based on the active current signal and reactive current signal, at least one harmonic component is obtained. Then, based on the harmonic detection threshold determined by the at least one harmonic component, harmonic anomaly detection is performed on the at least one harmonic component to obtain the power grid harmonic detection result. Therefore, it is evident that harmonic detection of the power grid can be achieved without reporting the power grid data collected by the terminal nodes to the central computing node (e.g., a cloud computing center). This enables local processing of power grid data, reduces data transmission delay, and improves the real-time performance of power grid harmonic detection.

[0065] Furthermore, other features and advantages of this application will be set forth in the following description and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described herein are used to provide a further understanding of this application, constitute a part of this application, and do not constitute an improper limitation of this application. In the accompanying drawings:

[0067] Figure 1 This is a schematic diagram of the system architecture of an optional harmonic detection system applicable to embodiments of this application;

[0068] Figure 2 A schematic diagram illustrating the implementation process of a harmonic detection method for a power grid provided in this application embodiment;

[0069] Figure 3A logical diagram illustrating a coordinate system corresponding to a current signal, provided in an embodiment of this application;

[0070] Figure 4 A schematic diagram illustrating the implementation process of a harmonic anomaly detection method provided in this application embodiment;

[0071] Figure 5 A logic diagram illustrating the determination of a harmonic detection threshold provided in an embodiment of this application;

[0072] Figure 6 A schematic diagram of the structure of a power grid harmonic detection device provided in an embodiment of this application;

[0073] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0074] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0075] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0076] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0077] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0078] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0079] The following explanations of some terms used in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0080] (1) Active current: refers to the current generated by the load absorbing electrical energy, representing the part of the circuit that actually does work. It is related to the resistance in the circuit, represents the rate at which electrical energy is converted into other forms of energy, and is directly related to power.

[0081] (2) Reactive current: refers to the current generated in a circuit to establish a magnetic field or for the charging and discharging of capacitors. It does not directly do work but participates in energy exchange. It is related to inductance or capacitance, represents the rate of energy exchange in the circuit, and is related to the power factor.

[0082] (3) Two-phase stationary coordinate system: It is one of the commonly used coordinate systems in motor control, used to simplify the mathematical model and control algorithm of three-phase AC motor.

[0083] (4) Three-phase stationary coordinate system: This is a coordinate system used to describe a three-phase AC power system. It provides important support for the safe and stable operation and sustainable development of the power system by simplifying circuit analysis, improving power calculation and enhancing control performance.

[0084] (5) Total harmonic distortion (THD) is an indicator that measures the degree of distortion of voltage or current waveforms. It represents the ratio of the harmonic content to the fundamental component in a periodic AC quantity, and is usually expressed as a percentage. Specifically, the harmonic distortion rate is the ratio of the root mean square value of all harmonic content to the root mean square value of the fundamental component.

[0085] First, the design concept of the embodiments of this application will be briefly introduced below:

[0086] In the development of new power systems, the proportion of electricity generated primarily from green energy sources is increasing. However, the extensive use of power electronic components in power conversion systems inevitably leads to harmonic pollution, resulting in issues such as the broken window effect. Excessive harmonic components in the power conversion system generate additional energy consumption, reducing the grid's energy utilization rate. Furthermore, during DC transmission, continuous harmonic interference can cause instability, affecting the safe and stable operation of the grid and thus reducing transmission quality.

[0087] To effectively address the impact of harmonics, it is necessary to continuously monitor harmonics in the power grid online, record waveforms in real time during fault conditions, and identify the distribution of harmonics in the power grid through data acquisition and analysis. This will allow for the analysis of the resonance mechanism under harmonic conditions and provide a reliable basis for harmonic mitigation in the power grid.

[0088] Traditional harmonic detection methods typically suffer from issues such as real-time performance and spectrum leakage, and are generally limited to power grid harmonic detection in cloud computing centers, making them suitable for scenarios with low real-time requirements. However, harmonic components generated by different circuits in the power grid are data with high real-time requirements. Therefore, using the aforementioned harmonic detection methods, since the preset harmonic detection algorithms can usually only run on cloud computing centers, it is necessary to transmit power grid data to the cloud computing center. This may result in poor real-time performance for power grid harmonic detection due to long communication delays.

[0089] In view of this, in order to solve or improve the above problems, this application proposes a harmonic detection method for power grids, applied to the edge computing node of a harmonic detection system. Specifically, it includes: acquiring power grid data collected by the terminal node at the current moment; wherein the data transmission delay between the terminal node and the edge computing node is less than a set transmission delay threshold; then, obtaining the active current signal and reactive current signal corresponding to the current moment based on the power grid data, and obtaining at least one harmonic component based on the active current signal and reactive current signal; finally, performing harmonic anomaly detection on the at least one harmonic component based on the harmonic detection threshold determined by the at least one harmonic component, and obtaining the harmonic detection result of the power grid. Therefore, it can be seen that harmonic detection of the power grid can be achieved without reporting the power grid data collected by the terminal node to the central computing node, realizing local processing of power grid data, reducing data transmission delay, and improving the real-time performance of harmonic detection of the power grid.

[0090] In particular, the preferred embodiments of this application will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments of this application and the features in the embodiments can be combined with each other without conflict.

[0091] See Figure 1 The diagram shown illustrates the system architecture of a harmonic detection system applicable to an embodiment of this application. The harmonic detection system may include: a terminal node cluster 101, an edge computing node cluster 102, and a central computing node 103. Edge computing nodes in the edge computing node cluster 102 can interact with terminal nodes in the terminal node cluster 101 and the central computing node 103 via a communication network. The communication network may employ wireless communication or wired communication methods.

[0092] For example, the edge computing nodes in the edge computing node cluster 102 can access the network through cellular mobile communication technology and communicate with the terminal nodes in the terminal node cluster 101 and the central computing node 103, respectively.

[0093] The cellular mobile communication technology mentioned above includes, for example, 5G technology or next-generation mobile communication technology.

[0094] Optionally, the edge computing nodes in the edge computing node cluster 102 can access the network via short-range wireless communication to communicate with the terminal nodes in the terminal node cluster 101 and the central computing node 103, respectively. The short-range wireless communication method may include, for example, wireless fidelity (Wi-Fi) technology.

[0095] This application embodiment does not impose any limitation on the number of communication devices involved in the above system architecture. For example, the above system architecture may include more central computing nodes, or fewer edge computing nodes, or other network devices. Figure 1 As shown, only the terminal node cluster 101, edge computing node cluster 102 and central computing node 103 are described as examples. The following is a brief introduction to each of the above communication devices and their respective functions.

[0096] The terminal node cluster 101 may include multiple terminal nodes. Each terminal node is typically a sensor of various types deployed in the power grid, used to collect and report power grid data (e.g., power conversion systems). For example, the aforementioned terminal nodes may include, but are not limited to, current sensors, voltage sensors, and power sensors. Therefore, the terminal node cluster 101 can collect power parameter data (i.e., power grid data) from various parts of the power grid in real time.

[0097] Terminal nodes can utilize common data acquisition systems and real-time monitoring systems. The data acquisition system can collect and store power grid data from various parts of the power grid in real time. The real-time monitoring system can monitor the power grid data acquisition process in real time, removing outliers and erroneous data. Furthermore, after acquiring power grid data, the terminal node can upload the collected data to nearby edge computing nodes.

[0098] The edge computing node cluster 102 may include multiple edge computing nodes. Each edge computing node is a node that utilizes the computing and storage capabilities of the network edge through reasonable deployment and allocation. It can store the power grid data reported by the corresponding terminal node, calculate at least one harmonic component corresponding to the terminal node, and perform harmonic anomaly detection.

[0099] Edge computing node cluster 102 monitors different locations on the power grid, forming a distributed protection mechanism. Terminal nodes in terminal node cluster 101 can directly connect to the edge computing nodes. Each edge computing node can be defined as a node edge cluster head, simultaneously responsible for multiple distributed terminal nodes, enabling the transmission and processing of collected data, thus improving data processing efficiency and accuracy.

[0100] It is worth noting that the edge computing node in this embodiment can obtain the power grid data collected by the terminal node at the current moment; then, based on the power grid data, it obtains the active current signal and reactive current signal corresponding to the current moment, and based on the active current signal and reactive current signal, it obtains at least one harmonic component; finally, based on the harmonic detection threshold determined by at least one harmonic component, it performs harmonic anomaly detection on at least one harmonic component to obtain the harmonic detection result of the power grid.

[0101] The central computing node 103, also known as a cloud computing center or cloud computing node, or other names, is not specifically limited in this embodiment. The central computing node 103 can receive data reported by edge computing nodes and perform harmonic anomaly detection based on the aforementioned data, thereby further ensuring the accuracy of harmonic anomaly detection.

[0102] As can be seen, each edge computing node in the edge computing node cluster 102 and the central computing node 103 can perform harmonic anomaly detection on the power grid based on power grid data. The edge computing nodes in the edge computing node cluster 102 can improve the efficiency of harmonic anomaly detection, while the central computing node 103 can avoid the problem of inaccurate harmonic anomaly detection results due to the limited harmonic anomaly detection capability of the edge computing nodes, thus ensuring the accuracy of harmonic anomaly detection.

[0103] The following describes the harmonic detection method for power grids provided by exemplary embodiments of this application in conjunction with the above-described system architecture and with reference to the accompanying drawings. It should be noted that the above-described system architecture is only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way in this respect.

[0104] See Figure 2The diagram shown illustrates the implementation flow of a harmonic detection method for a power grid according to an embodiment of this application. Taking any edge computing node in the harmonic detection system as an example, the specific implementation flow of this method is as follows:

[0105] S201: Obtain the power grid data collected by the terminal node at the current moment.

[0106] Specifically, the data transmission latency between the terminal node and the edge computing node is less than a set transmission latency threshold. That is, the terminal node is a node adjacent to the edge computing node, rather than a remote (or longer-latent) central computing node. This enables efficient and low-latency subsequent harmonic detection of the power grid.

[0107] It should be understood that the power grid data collected by the terminal node at the current moment can also be called current power grid data, or it can have other names, such as first power grid data. This application embodiment does not specifically limit this. For example, the power grid data can be the data obtained by the terminal node from the sinusoidal alternating current with symmetrical phase difference generated by the winding of the generator.

[0108] Optionally, after acquiring the aforementioned power grid data, the edge computing node can perform a series of data preprocessing operations such as data filtering, standardization, and interpolation. For example, the edge computing node can use the network width cut of a digital filter to process the power grid data (i.e., the raw data) to remove high-frequency noise and interference. As another example, the edge computing node can also standardize the acquired power grid data to ensure a consistent amplitude range, which facilitates comparison and analysis between different devices and allows for unified processing in subsequent steps. Furthermore, for cases where the power grid data may contain missing or drifting data, the edge computing node can use mean-field forward inference to fill in the missing data or perform data compensation and other interpolation operations to supplement or improve the power grid data.

[0109] In this way, by preprocessing the power grid data, the data quality and accuracy can be improved.

[0110] S202: Obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data, and obtain at least one harmonic component based on the active current signal and reactive current signal.

[0111] To improve the computational efficiency of harmonic components, in one optional implementation, during step S202, the edge computing node can obtain multiple first current signals in a first reference coordinate system from the power grid data; then, based on a preset first coordinate system transformation matrix, the multiple first current signals are transformed from the first reference coordinate system to a second reference coordinate system to obtain at least two second current signals corresponding to the multiple first current signals; finally, based on a second current signal matrix composed of at least two second current signals and a preset first current signal adjustment matrix, active current signals and reactive current signals are obtained. The first current signal adjustment matrix can be determined based on sine and cosine signals.

[0112] See Figure 3 As shown, taking the first reference coordinate system as a three-phase stationary coordinate system (abc) and the second reference coordinate system as a two-phase stationary coordinate system (αβ) as an example, the edge computing node can use the Clack transformation to transform the voltage and current signals in the power grid data collected by the terminal node from the three-phase stationary coordinate system to the two-phase stationary coordinate system. For example, the edge computing node can use the Clack transformation to transform the instantaneous three-phase voltages v1, v2, and v3 into the instantaneous two-phase voltage v α and v β And transforming instantaneous three-phase currents i1, i2, and i3 into instantaneous two-phase currents i α and i β Specifically, it can be expressed as follows:

[0113]

[0114] Where r1 and r2 are the standard coefficients of the Clack transformation, whose values ​​can be predefined, and C1 is the Clack transformation matrix, i.e., the preset first coordinate system transformation matrix. Optionally, C1 is a 2x3 matrix, which can be specifically the following matrix:

[0115]

[0116] The edge computing node obtains two second current signals (i.e., instantaneous two-phase currents i1, i2, and i3) corresponding to the three first current signals (i.e., instantaneous three-phase currents i1, i2, and i3) in a two-phase stationary coordinate system. α and i β After that, the instantaneous two-phase current i can be used as a basis. α and i β And a preset first current signal adjustment matrix, to obtain the (instantaneous) active current signal i p and (instantaneous) reactive current signal i q For example, the active current signal i p and reactive current signal i qThe calculation formula can be expressed as follows:

[0117]

[0118] Where C' represents the preset first current signal adjustment matrix. Optionally, C' can be specifically the following matrix:

[0119]

[0120] The sine signal sinwt and the cosine signal -coswt in C′ can be obtained using a phase-locked loop and a sine and cosine signal generation circuit. Furthermore, the sine signal sinwt and the cosine signal -coswt can be in phase with a in the three-phase stationary coordinate system.

[0121] Therefore, the above-mentioned active current signal i p and reactive current signal i q The calculation formula can also be expressed as follows:

[0122] i p (t)=r1i α sinwt-r2i β coswt

[0123] i q (t)=-r1i α coswt-r2i β sinwt

[0124] Among them, i p (t) represents the active current signal i p i q (t) represents the reactive current signal i q r1 and r2 represent the standard coefficients of the Clack transform, sinwt represents the sine signal, -coswt represents the cosine signal, and i α and i β This represents the instantaneous two-phase current.

[0125] Furthermore, after obtaining the active and reactive current signals, the edge computing node can determine at least one harmonic component based on these signals. In this way, at least one harmonic component can be quickly identified using the active and reactive current signals, thereby improving the efficiency of subsequent harmonic detection.

[0126] Optionally, the edge computing nodes filter the active current signal and the reactive current signal respectively to obtain the first DC component corresponding to the active current signal and the second DC component corresponding to the reactive current signal. Then, based on the second coordinate system transformation matrix, the second current signal adjustment matrix, and the DC component matrix composed of the first and second DC components, the fundamental components corresponding to multiple first current signals are obtained. Furthermore, based on the multiple first current signals and the multiple fundamental components, at least one harmonic component is obtained. The second coordinate system transformation matrix can be the transpose of the first coordinate system transformation matrix, and the second current signal adjustment matrix can be the inverse of the first current signal adjustment matrix.

[0127] Taking the instantaneous three-phase currents i1, i2, and i3 mentioned above as an example, the edge computing node uses a low-pass filter to filter the active current signal i p and reactive current signal i q By performing filtering, the active current signal i can be obtained. p The corresponding first DC component (can be denoted as: The second DC component corresponding to the reactive current signal (which can be denoted as:) Next, based on the transpose of the first coordinate system transformation matrix C1 (i.e., the second coordinate system transformation matrix, which can be denoted as C2), the inverse of the first current signal adjustment matrix C' (i.e., the second current signal adjustment matrix, which can be denoted as C″), and the first DC component, Second DC component The DC component matrix is ​​formed to obtain the fundamental components corresponding to the instantaneous three-phase currents i1, i2 and i3 respectively.

[0128] Therefore, the calculation formulas for the fundamental components corresponding to the instantaneous three-phase currents i1, i2, and i3 mentioned above can be expressed as follows:

[0129]

[0130] Among them, i 1f This represents the fundamental component corresponding to the instantaneous three-phase current i1, i 2f i represents the fundamental component corresponding to the instantaneous three-phase current i2. 3f This represents the fundamental component corresponding to the instantaneous three-phase current i3. It should be understood that the calculation process of the fundamental component also realizes the transformation from the second reference coordinate system to the second reference coordinate system.

[0131] Since power grids typically contain some interference (such as conducted interference and radiated interference), in order to accurately calculate the fundamental components corresponding to the instantaneous three-phase currents i1, i2, and i3, it is necessary to consider the interference present in the power grid. Optionally, a first disturbance coefficient σ1 can be introduced to compensate for the aforementioned interference in the power grid. Therefore, the specific formulas for calculating the fundamental components corresponding to the instantaneous three-phase currents i1, i2, and i3 can be expressed as follows:

[0132]

[0133] Based on the above method, the edge computing node obtains the fundamental components (i.e., i1, i2, and i3) corresponding to the instantaneous three-phase currents i1, i2, and i3, respectively. 1f i 2f and i 3f After that, the fundamental component i can be obtained by comparing the instantaneous three-phase currents i1, i2, and i3 with the fundamental component i. 1f i 2f and i 3f Subtracting them, we obtain the harmonic components corresponding to the instantaneous three-phase currents i1, i2, and i3, respectively, i. 1h i 2h and i 3h .

[0134] It should be understood that the number of harmonic components is usually the same as the number of the first current signal. However, if all the calculated harmonic components are harmonic components with a value of 0, it can be considered that the number of harmonic components is less than the number of the first current signal.

[0135] S203: Based on a harmonic detection threshold determined by at least one harmonic component, perform harmonic anomaly detection on at least one harmonic component to obtain the harmonic detection result of the power grid.

[0136] In one optional implementation, during step S203, after obtaining the at least one harmonic component, the edge computing node can combine historical data of the power grid with a harmonic detection threshold set for the at least one harmonic component to determine whether abnormal harmonics exist in the power grid, and perform harmonic anomaly detection on the at least one harmonic component. For example, see [link to relevant documentation]. Figure 4 The diagram shown illustrates the implementation flow of a harmonic anomaly detection method provided in this application. The execution entity is still an edge computing node, and the specific implementation flow of this method is as follows:

[0137] S401: Determine the total harmonic distortion rate of at least one harmonic component at the current time, and obtain the total harmonic distortion rate of the previous historical time adjacent to the current time.

[0138] For example, when executing step S401, the edge computing node can obtain at least one harmonic voltage based on at least one harmonic component and its corresponding harmonic impedance, and then determine the total harmonic distortion rate at the current moment based on the at least one harmonic voltage, its corresponding fundamental component, and a preset standard voltage. Optionally, the formula for calculating the total harmonic distortion rate at the current moment can be specifically expressed as follows:

[0139]

[0140] Among them, TD h1 (t) represents the total harmonic distortion rate at the current moment, i nh Let r represent the nth harmonic component. nh This represents the harmonic impedance corresponding to the nth harmonic component, i.e., r. nh i nh U represents the nth harmonic voltage. N Indicates the nominal voltage of the power grid (e.g., 220V), i nf This represents the nth fundamental component.

[0141] Similarly, the method for calculating the total harmonic distortion (THD) of the previous historical moment adjacent to the current moment is the same as the method for calculating the THD of the current moment. h1 The calculation method for (t) is the same, and will not be repeated here in the embodiments of this application. The total harmonic distortion rate of the previous historical moment adjacent to the current moment can be expressed as TD. h2 (t).

[0142] S402: Based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment, obtain the harmonic detection threshold. For example, the formula for calculating the aforementioned harmonic detection threshold can be specifically expressed as follows:

[0143] TD h =αTD h1 +βTD h2

[0144] Among them, TD h TD represents the harmonic detection threshold. h1 TD represents the total harmonic distortion rate at the current moment. h2 α represents the total harmonic distortion at the previous historical moment, where α is the TD. h1 The corresponding weighting factor, β, is TD. h2 The corresponding weighting factors. If the harmonic detection threshold is calculated based on the initial harmonic detection threshold, then β > α; as the actual situation of the power grid changes, then α > β.

[0145] To further improve the accuracy of harmonic detection threshold calculation, a second disturbance coefficient σ2 can be introduced to compensate for errors caused by interference in the power grid or other influences, thus correcting the aforementioned potential errors. Therefore, the calculation formula for the harmonic detection threshold can be specifically expressed as follows:

[0146] TD h =αTD h1 +βTD h2 +σ2

[0147] Based on the above method, edge computing nodes can obtain more accurate harmonic detection thresholds, thereby improving the accuracy of subsequent determination of the presence of abnormal harmonics in the power grid based on at least one harmonic component and the harmonic detection threshold. Furthermore, based on the harmonic components at the current moment and historical power grid data, the harmonic detection threshold for each location in the power grid can be determined.

[0148] Because central computing nodes possess greater computing, storage, and communication resources than edge computing nodes, they offer higher accuracy in calculating harmonic detection thresholds. Therefore, in one optional implementation method, see [reference needed]. Figure 5 As shown, the edge computing node can send first information to the central computing node; the first information is used to indicate the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment; and receive second information from the central computing node; the second information is used to indicate the harmonic detection threshold, which is determined by the central computing node through multiple iterative calculations based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0149] For example, after receiving the first information indicating the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment, the central computing node can use the tunnel catfish (TC) method with the execution flow of "input population size → initialize population → calculate fitness value → initialize parameters → iterate in a loop → output optimal solution" to obtain a more accurate harmonic detection threshold, and then send the second information indicating the harmonic detection threshold to the edge computing node.

[0150] S403: If at least one harmonic component contains a harmonic component that is greater than or equal to the harmonic detection threshold, then the harmonic detection result is determined to be that there are abnormal harmonics in the power grid.

[0151] If the harmonic detection result indicates the presence of abnormal harmonics in the power grid, the edge computing node can send an alarm message to the terminal node. This alarm message can be used to indicate the presence of abnormal harmonics in the power grid. At this time, the harmonic detection system can also automatically take measures to quickly adjust the abnormal harmonic components in the power grid.

[0152] Optionally, in order to respond promptly to harmonic anomalies, edge computing nodes can also use a real-time monitoring system to quickly detect harmonic anomalies.

[0153] If at least one harmonic component is less than the preset harmonic detection threshold, the edge computing node can preliminarily determine that the harmonic detection result is that there are no abnormal harmonics in the power grid.

[0154] To avoid inaccurate harmonic detection results in the power grid due to the limited harmonic anomaly detection capabilities of edge computing nodes, edge computing nodes can, after initially determining that there are no abnormal harmonics in the power grid (i.e., the harmonic detection result indicates that the harmonics in the power grid are normal), send at least one harmonic component to the central computing node. This allows the central computing node to perform harmonic anomaly detection on at least one harmonic component. In this way, because the central computing node has a better harmonic anomaly detection capability, the accuracy of the harmonic detection results is ensured.

[0155] In summary, in the power grid harmonic detection method provided in this application embodiment, the edge computing node can acquire power grid data collected by the terminal node with a data transmission delay less than a set transmission delay threshold. Furthermore, it can obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data. Based on the active current signal and reactive current signal, at least one harmonic component is obtained. Then, based on the harmonic detection threshold determined by the at least one harmonic component, harmonic anomaly detection is performed on the at least one harmonic component to obtain the power grid harmonic detection result. Therefore, it is evident that harmonic detection of the power grid can be achieved without reporting the power grid data collected by the terminal node to the central computing node, realizing local processing of power grid data, reducing data transmission delay, and improving the real-time performance of power grid harmonic detection.

[0156] Furthermore, based on the same technical concept, embodiments of this application provide a harmonic detection device for a power grid, which can be applied to any edge computing node in a harmonic detection system. This user data processing device is used to implement the above-described method flow of embodiments of this application. For example, see [link to relevant documentation]. Figure 6 As shown, the user data processing device 600 may include: a data acquisition module 601, a data processing module 602, and an anomaly detection module 603, wherein:

[0157] The data acquisition module 601 is used to acquire the power grid data collected by the terminal node at the current moment; wherein the data transmission delay between the terminal node and the edge computing node is less than the set transmission delay threshold.

[0158] The data processing module 602 is used to obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data, and to obtain at least one harmonic component based on the active current signal and reactive current signal.

[0159] The anomaly detection module 603 is used to perform harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component, and obtain the harmonic detection result of the power grid.

[0160] In an optional embodiment, when the active current signal and reactive current signal corresponding to the current moment are obtained based on the power grid data, the data processing module 602 is specifically used for:

[0161] Multiple first current signals in the first reference coordinate system are obtained from the power grid data;

[0162] Based on a preset first coordinate system transformation matrix, multiple first current signals are transformed from the first reference coordinate system to the second reference coordinate system to obtain at least two second current signals corresponding to the multiple first current signals.

[0163] Based on a second current signal matrix composed of at least two second current signals and a preset first current signal adjustment matrix, active current signals and reactive current signals are obtained; wherein, the first current signal adjustment matrix is ​​determined based on sine and cosine signals.

[0164] In an optional embodiment, when at least one harmonic component is obtained based on the active current signal and the reactive current signal, the data processing module 602 is specifically used for:

[0165] The active current signal and the reactive current signal are filtered separately to obtain the first DC component corresponding to the active current signal and the second DC component corresponding to the reactive current signal.

[0166] Based on the second coordinate system transformation matrix, the second current signal adjustment matrix, and the DC component matrix composed of the first DC component and the second DC component, the fundamental components corresponding to the multiple first current signals are obtained respectively; wherein, the second coordinate system transformation matrix is ​​the transpose of the first coordinate system transformation matrix, and the second current signal adjustment matrix is ​​the inverse of the first current signal adjustment matrix;

[0167] At least one harmonic component is obtained based on multiple first current signals and multiple fundamental components.

[0168] In an optional embodiment, when performing harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component to obtain the harmonic detection result of the power grid, the anomaly detection module 603 is specifically used for:

[0169] Determine the total harmonic distortion rate of at least one harmonic component at the current time, and obtain the total harmonic distortion rate of the previous historical time adjacent to the current time;

[0170] The harmonic detection threshold is obtained based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0171] If at least one harmonic component contains a harmonic component that is greater than or equal to the harmonic detection threshold, then the harmonic detection result is determined to be that there are abnormal harmonics in the power grid.

[0172] In an optional embodiment, when determining the total harmonic distortion rate of at least one harmonic component at the current moment, the anomaly detection module 603 is specifically used for:

[0173] Based on at least one harmonic component and its corresponding harmonic impedance, at least one harmonic voltage is obtained;

[0174] The total harmonic distortion rate is determined based on at least one harmonic voltage and a preset standard voltage.

[0175] In an optional embodiment, when obtaining the harmonic detection threshold based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment, the anomaly detection module 603 is specifically used for:

[0176] Send the first information to the central computing node; the first information is used to indicate the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment;

[0177] The second information received from the central computing node is used to indicate the harmonic detection threshold, which is determined by the central computing node through multiple iterative calculations based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

[0178] In an optional embodiment, after performing harmonic anomaly detection on at least one harmonic component based on a harmonic detection threshold determined by at least one harmonic component to obtain the harmonic detection result of the power grid, the anomaly detection module 603 is further configured to:

[0179] If the harmonic detection result indicates the presence of abnormal harmonics in the power grid, an alarm message is sent to the terminal node; the alarm message is used to indicate the presence of abnormal harmonics in the power grid.

[0180] If the harmonic detection result indicates that the harmonics in the power grid are normal, then at least one harmonic component is sent to the central computing node so that the central computing node can perform harmonic anomaly detection on at least one harmonic component.

[0181] Based on the description of the method and apparatus embodiments above, an exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method according to an embodiment of the present invention.

[0182] This application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0183] This application also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this application.

[0184] See Figure 7 The diagram shown below illustrates the structure of an electronic device 700 that can serve as a server or client in this application, and is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0185] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0186] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, output unit 707, storage unit 708, and communication unit 709. Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 708 may include, but is not limited to, disk and optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth devices, WiFi devices, worldwide interoperability for microwave access (WiMax) devices, cellular communication devices, and / or the like.

[0187] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the above-described harmonic detection method for the power grid can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. In some embodiments, the computing unit 701 can be configured to perform the above-described harmonic detection method for the power grid by any other suitable means (e.g., by means of firmware).

[0188] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0189] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM) or flash memory, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0190] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device, PLD) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0191] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0192] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0193] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0194] Furthermore, it should be understood that the above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of this invention are still within the scope of this application.

Claims

1. A method for detecting harmonics in a power grid, characterized in that, Applied to edge computing nodes, including: The system acquires the power grid data collected by the terminal node at the current moment; wherein the data transmission delay between the terminal node and the edge computing node is less than a set transmission delay threshold. Based on the power grid data, the active current signal and reactive current signal corresponding to the current moment are obtained, and based on the active current signal and the reactive current signal, at least one harmonic component is obtained. Based on the harmonic detection threshold determined by the at least one harmonic component, harmonic anomaly detection is performed on the at least one harmonic component to obtain the harmonic detection result of the power grid.

2. The method as described in claim 1, characterized in that, The process of obtaining the active current signal and reactive current signal corresponding to the current moment based on the power grid data includes: From the power grid data, obtain multiple first current signals in the first reference coordinate system; Based on a preset first coordinate system transformation matrix, the plurality of first current signals are transformed from the first reference coordinate system to the second reference coordinate system to obtain at least two second current signals corresponding to the plurality of first current signals; The active current signal and the reactive current signal are obtained based on a second current signal matrix composed of at least two second current signals and a preset first current signal adjustment matrix; wherein the first current signal adjustment matrix is ​​determined based on a sine signal and a cosine signal.

3. The method as described in claim 2, characterized in that, The step of obtaining at least one harmonic component based on the active current signal and the reactive current signal includes: The active current signal and the reactive current signal are filtered respectively to obtain the first DC component corresponding to the active current signal and the second DC component corresponding to the reactive current signal. Based on the second coordinate system transformation matrix, the second current signal adjustment matrix, and the DC component matrix composed of the first DC component and the second DC component, the fundamental components corresponding to the plurality of first current signals are obtained respectively; wherein, the second coordinate system transformation matrix is ​​the transpose of the first coordinate system transformation matrix, and the second current signal adjustment matrix is ​​the inverse of the first current signal adjustment matrix. Based on the plurality of first current signals and the plurality of fundamental components, the at least one harmonic component is obtained.

4. The method according to any one of claims 1-3, characterized in that, The process of detecting harmonic anomalies in the at least one harmonic component based on a harmonic detection threshold determined by the at least one harmonic component, and obtaining the harmonic detection result of the power grid, includes: Determine the total harmonic distortion rate of the at least one harmonic component at the current time, and obtain the total harmonic distortion rate of the previous historical time adjacent to the current time; The harmonic detection threshold is obtained based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment. If, among the at least one harmonic component, there exists a harmonic component that is greater than or equal to the harmonic detection threshold, then the harmonic detection result is determined to indicate that there are abnormal harmonics in the power grid.

5. The method as described in claim 4, characterized in that, Determining the total harmonic distortion rate of the at least one harmonic component at the current time includes: Based on the at least one harmonic component and its corresponding harmonic impedance, at least one harmonic voltage is obtained; The total harmonic distortion rate is determined based on the at least one harmonic voltage and its corresponding fundamental component, as well as a preset standard voltage.

6. The method as described in claim 4, characterized in that, The process of obtaining the harmonic detection threshold based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment includes: Send first information to the central computing node; the first information is used to indicate the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment; The central computing node receives second information; the second information is used to indicate the harmonic detection threshold, which is determined by the central computing node through multiple iterative calculations based on the total harmonic distortion rate at the current moment and the total harmonic distortion rate at the previous historical moment.

7. The method according to any one of claims 1-3, characterized in that, After obtaining the harmonic detection result of the power grid by performing harmonic anomaly detection on the at least one harmonic component based on the harmonic detection threshold determined by the at least one harmonic component, the method further includes: If the harmonic detection result indicates that there are abnormal harmonics in the power grid, an alarm message is sent to the terminal node; the alarm message is used to indicate that there are abnormal harmonics in the power grid. If the harmonic detection result indicates that the harmonics in the power grid are normal, then the at least one harmonic component is sent to the central computing node so that the central computing node can perform harmonic anomaly detection on the at least one harmonic component.

8. A harmonic detection device for a power grid, characterized in that, Applied to edge computing nodes, including: The data acquisition module is used to acquire the power grid data collected by the terminal node at the current moment; wherein the data transmission delay between the terminal node and the edge computing node is less than a set transmission delay threshold. The data processing module is used to obtain the active current signal and reactive current signal corresponding to the current moment based on the power grid data, and to obtain at least one harmonic component based on the active current signal and the reactive current signal. An anomaly detection module is used to perform harmonic anomaly detection on the at least one harmonic component based on a harmonic detection threshold determined by the at least one harmonic component, and obtain the harmonic detection result of the power grid.

9. An electronic device, comprising: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method as described in any one of claims 1-7.

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