Voltage temporary rise and fall rapid detection method and system based on intelligent fusion terminal

Abstract

The application relates to the field of power quality monitoring, in particular to a voltage temporary rise and drop rapid detection method and system based on an intelligent fusion terminal, wherein the method comprises the following steps: acquiring an actual voltage sequence and a fundamental voltage sequence of a power grid, constructing a high-frequency voltage component sequence according to the actual voltage sequence and the fundamental voltage sequence, and splitting the high-frequency voltage component sequence into multiple high-frequency component sub-sequences; determining an adaptive deviation threshold of the corresponding high-frequency component sub-sequence according to the waveform distortion degree of the high-frequency component sub-sequence; generating a symbol state sequence according to the adaptive deviation threshold, calculating a voltage temporary rise probability and a voltage drop probability of the power grid according to the symbol state sequence; and judging whether a voltage temporary rise event or a voltage drop event exists in the power grid according to the voltage temporary rise probability and the voltage drop probability. The technical scheme of the application can solve the problem that the prior art cannot effectively distinguish and inhibit the high-frequency component noise interference in the power grid voltage, and realizes rapid detection of voltage temporary rise and drop.

Description

Technical Field

[0001] This application relates to the field of power quality detection technology. In particular, it relates to a method and system for rapid detection of voltage sags and dips based on an intelligent fusion terminal. Background Technology

[0002] A voltage sag refers to a brief drop in the effective value of the power frequency voltage to below 90% of the rated voltage, while a voltage rise refers to a brief rise in the effective value of the power frequency voltage to above 110% of the rated voltage. Voltage sags and rises typically last from milliseconds to seconds and are the most frequent and widespread short-term disturbances in power system power quality. In industrial manufacturing production lines, data center servers, and other voltage-sensitive loads, voltage amplitude fluctuations are intolerable. Millisecond-level voltage rises or sags can lead to equipment interruptions, production line shutdowns, data loss, and other problems, causing economic losses and safety hazards. Therefore, rapidly detecting voltage rises and sags is a crucial issue that needs to be addressed to reduce equipment damage and minimize economic losses in production.

[0003] Existing voltage sag detection methods suffer from insufficient noise interference resistance. Specifically, the power grid operating environment is rife with noise interference such as harmonics caused by the switching actions of power electronic equipment. This noise interference is superimposed on the power frequency voltage waveform, causing instantaneous voltage fluctuations and resulting in misjudgments of voltage sag or voltage rise events. If the threshold used for judgment is artificially increased in order to suppress misjudgments, it will lead to the missed detection of real voltage sag or voltage rise events with short durations. Therefore, it is impossible to simultaneously ensure the sensitivity and anti-interference capability of the detection method. Summary of the Invention

[0004] This application provides a method and system for rapid detection of voltage sags and dips based on an intelligent fusion terminal, which is used to solve the technical problem that existing technologies are unable to effectively distinguish and suppress high-frequency noise interference in grid voltage, so as to achieve the purpose of accurate and rapid detection of voltage sags and dips.

[0005] In a first aspect, this application provides a method for rapid detection of voltage sags and dips based on a smart fusion terminal, comprising the following steps: The actual voltage sequence and fundamental voltage sequence of the power grid are obtained. The difference between the actual voltage sequence and the fundamental voltage sequence is calculated to obtain the high-frequency voltage component sequence of the power grid. The high-frequency voltage component sequence is then split into multiple consecutive high-frequency component sub-sequences. The waveform distortion degree of each of the high-frequency component subsequences is calculated, and the adaptive deviation threshold of the corresponding high-frequency component subsequence is determined by using the waveform distortion degree of each. The high-frequency voltage component sequence is quantized and distinguished according to the adaptive deviation threshold to obtain the symbol state sequence corresponding to the high-frequency voltage component sequence; The voltage rise probability of the power grid is calculated based on the symbol state distribution of the symbol state sequence and the voltage sag probability of the power grid is calculated based on the number of each rise chain and the preset rise order weight. The existence of a voltage sag or voltage rise event in the power grid is determined based on the voltage rise probability and the voltage fall probability.

[0006] Further, the calculation of the waveform distortion degree of each of the high-frequency component sub-sequences includes: Calculate the energy distribution dispersion and waveform variation randomness of each of the high-frequency component subsequences; The waveform distortion degree of each high-frequency component subsequence is obtained by weighting the corresponding energy distribution dispersion according to the randomness of each waveform change.

[0007] Further, the step of determining the adaptive deviation threshold of the corresponding high-frequency component subsequence using each waveform distortion degree includes: The deviation weight value of the corresponding high-frequency component subsequence is determined according to the degree of distortion of each waveform, and the preset fixed deviation threshold is weighted and corrected according to each deviation weight value to obtain the adaptive deviation threshold of each high-frequency component subsequence.

[0008] Further, the step of determining whether a voltage sag or voltage rise event exists in the power grid based on the voltage rise probability and the voltage fall probability includes: Obtain a preset probability judgment threshold, and determine whether the voltage sag probability and the voltage rise probability are greater than the probability judgment threshold respectively; If the voltage spurt probability is greater than the probability judgment threshold, then it is determined that there is a voltage spur event in the power grid; If the voltage sag probability is greater than the probability judgment threshold, then the power grid is determined to have a voltage sag event.

[0009] Further, the step of splitting the high-frequency voltage component sequence into multiple consecutive high-frequency component sub-sequences includes: A sliding window of a preset length is obtained, and the high-frequency voltage component sequence is processed using the sliding window to obtain multiple continuous and non-overlapping high-frequency component sub-sequences.

[0010] Secondly, this application also provides a rapid voltage sag detection system based on an intelligent fusion terminal, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the steps of the rapid voltage sag detection method based on an intelligent fusion terminal described above.

[0011] The voltage sag and slump rapid detection method based on intelligent fusion terminal proposed in this application employs a method that calculates the degree of high-frequency waveform distortion of each high-frequency component subsequence and generates an adaptive deviation threshold accordingly. Since the degree of high-frequency waveform distortion represents the intensity distribution of high-frequency energy, dynamically adjusting the adaptive deviation threshold based on the degree of high-frequency waveform distortion can adaptively track the time-varying characteristics of background noise and waveform distortion, overcoming the defect of easy misjudgment or missed judgment under non-stationary conditions with a fixed threshold. Therefore, it achieves adaptive matching of detection sensitivity under different disturbance intensities. The method uses a method that calculates the probability of sag and sag by performing probability calculations on the sag and sag transfer chains respectively. Since the length and state transition frequency of the sag and sag transfer chains can reflect the persistence and morphological integrity of disturbance events, the sag and sag probabilities integrate the statistical characteristics of local voltage fluctuations and global events into a unified confidence level for measurement. Therefore, it realizes statistical decision-making on real voltage events under voltage fluctuation interference, and ultimately achieves rapid detection of voltage sag and sag.

[0012] The above and other objects, advantages and features of this application will become more apparent to those skilled in the art from the following detailed description of specific embodiments of this application in conjunction with the accompanying drawings. Attached Figure Description

[0013] The following sections will describe some specific embodiments of this application in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or components. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a schematic flowchart of a method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to an embodiment of this application; Figure 2 This is a schematic flowchart illustrating the calculation of the high-frequency waveform distortion degree of a target high-frequency component subsequence according to an embodiment of this application; Figure 3 This is a schematic diagram of a voltage sag and dip rapid detection system based on an intelligent fusion terminal according to an embodiment of this application. Detailed Implementation

[0014] The following reference Figures 1 to 3This application describes a method and system for rapid detection of voltage sags and dips based on an intelligent fusion terminal. In this description, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature, that is, include one or more of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. When a feature "includes or contains" one or more of the features it encompasses, unless otherwise specifically described, this indicates that other features are not excluded and may be further included.

[0015] In the description of this embodiment, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0016] This embodiment provides a method for rapid detection of voltage sags and dips based on a smart fusion terminal. This method operates on a smart fusion terminal and can achieve rapid detection of voltage sags and dips even in the presence of high-frequency noise interference in the power grid operating environment.

[0017] Please see Figure 1 , Figure 1 This is a schematic flowchart of a method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to an embodiment of this application. The method generally includes: Step S101: Obtain the actual voltage sequence and fundamental voltage sequence of the power grid, and subtract the actual voltage sequence and fundamental voltage sequence to construct the high-frequency voltage component sequence of the power grid; Step S102: The high-frequency voltage component sequence of the power grid is split into multiple continuous high-frequency component subsequences with equal duration. Step S103: Calculate the waveform distortion degree of each high-frequency component subsequence, and determine the adaptive deviation threshold of the corresponding high-frequency component subsequence using each waveform distortion degree. Step S104: The high-frequency voltage component sequence is ternarily quantized and distinguished according to the adaptive deviation threshold of each high-frequency component subsequence to obtain the symbol state sequence corresponding to the high-frequency voltage component sequence, and the temporary rising chain and temporary falling chain are identified according to the symbol state distribution of the symbol state sequence. Step S105: Calculate the voltage sag probability of the power grid based on the number of sag chains of each order in the symbol state sequence and the preset sag order weight; calculate the voltage sag probability of the power grid based on the number of sag chains of each order in the symbol state sequence and the preset sag order weight. Step S106: Determine whether a voltage sag or voltage rise event exists in the power grid based on the voltage rise probability and voltage fall probability of the power grid.

[0018] In step S101 above, the intelligent fusion terminal is a terminal device in the power system with data acquisition and data analysis functions. It is connected to a voltage transformer, which can acquire the power frequency voltage of the power grid according to a preset sampling frequency to obtain multiple actual voltage values ​​of the power grid. The intelligent fusion terminal sorts the obtained actual voltage values ​​according to the acquisition time to obtain the actual voltage sequence of the power grid. The preset sampling frequency can be an integer multiple of the power frequency voltage of the power grid to ensure the reliability of the actual voltage acquisition. In this embodiment, the preset sampling frequency is 100 Hz.

[0019] In this embodiment, a Discrete Fourier Transform can be performed on the acquired actual voltage sequence to obtain the fundamental phase of the power grid. Assume the actual voltage sequence has n sampling points, where the actual voltage value of the i-th sampling point is... The frequency classification of the kth harmonic is as follows: Let the fundamental component of the power grid be... Where A is the real part of the fundamental component. Let j be the imaginary part of the fundamental component, and j be the imaginary unit. Then, according to the formula... Calculate the values ​​of A and B, and calculate the fundamental phase. .

[0020] Then, the intelligent fusion terminal aligns the preset standard fundamental voltage waveform with the actual voltage waveform of the power grid to obtain the fundamental voltage waveform of the power grid, wherein the amplitude of the standard fundamental voltage is 220V and the frequency is 50Hz; finally, the fundamental voltage waveform of the power grid is sampled using a preset sampling frequency to obtain multiple fundamental voltage values, and the obtained fundamental voltage values ​​are sorted according to time to obtain the fundamental voltage sequence of the power grid.

[0021] The intelligent fusion terminal obtains the actual voltage sequence and the fundamental voltage sequence of the power grid. It subtracts the fundamental voltage value at the corresponding sampling time from each actual voltage value in the actual voltage sequence to obtain the high-frequency voltage value at each sampling time. The high-frequency voltage values ​​are then sorted according to the sampling time to obtain the high-frequency voltage component sequence of the power grid.

[0022] In step S102 above, the high-frequency voltage component sequence of the power grid can be split into multiple consecutive high-frequency component sub-sequences with the same duration by using a preset duration. The multiple high-frequency component sub-sequences are connected to each other in sequence without overlapping. The preset duration is multiple sampling periods. The longer the preset duration, the more accurate the fluctuation analysis of the high-frequency component sub-sequences and the more accurate the adaptive deviation threshold of each high-frequency component sub-sequence. This improves the accuracy of identifying spurious chain and spurious chain, as well as the accuracy of identifying spurious events and spurious events.

[0023] For example, in this embodiment, a sliding window of a preset duration can be obtained first, wherein the preset duration is 20 sampling periods of the grid voltage. The first high-frequency voltage value of the high-frequency voltage component sequence of the grid is used as the first high-frequency voltage value in the first sliding window, and a first sampling window of a preset duration is obtained. Then, the sliding window is moved in the high-frequency voltage component sequence with a preset duration as the sliding step size to obtain a second sampling window. Then, the sliding window is moved in the high-frequency voltage component sequence with a preset duration as the sliding step size to obtain a third sampling window. This process is repeated to obtain multiple sampling windows. A high-frequency component subsequence is constructed based on the high-frequency voltage value in each sampling window. That is, each sampling window corresponds to a high-frequency component subsequence, and the high-frequency voltage value in each sampling window is used as the high-frequency voltage value in its corresponding high-frequency component subsequence.

[0024] In step S103 above, the waveform distortion degree corresponding to each high-frequency component subsequence can be obtained according to the change of high-frequency voltage value in each high-frequency component subsequence, and the preset deviation threshold can be corrected according to each waveform distortion degree to obtain the adaptive deviation threshold of each high-frequency component subsequence.

[0025] Taking one of the high-frequency component subsequences as an example, this high-frequency component subsequence is used as the target high-frequency component subsequence, and the method for calculating the waveform distortion degree of the target high-frequency component subsequence is as follows: Figure 2 As shown, it includes the following steps: Step S111: Calculate the energy distribution dispersion and waveform variation randomness of the target high-frequency component subsequence; Step S112: The energy distribution dispersion is weighted according to the randomness of the waveform change of the target high-frequency component subsequence to obtain the waveform distortion degree of the target high-frequency component subsequence.

[0026] In step S111, the method for calculating the energy distribution dispersion of the target high-frequency component subsequence includes: First, the instantaneous energy metric is calculated for each sampling moment in the target high-frequency component subsequence. This instantaneous energy metric is proportional to the instantaneous energy. Since the high-frequency voltage values ​​in the target high-frequency component subsequence can be both positive and negative, in order to prevent the cancellation of positive and negative values ​​and the smoothing of fluctuation characteristics, the instantaneous energy metric is used to calculate the energy distribution dispersion of the target high-frequency component subsequence in this embodiment. Then, a target energy subsequence corresponding to the target high-frequency component subsequence is constructed based on the instantaneous energy metric at each sampling time. Finally, the coefficient of variation of the target energy subsequence is calculated, and this coefficient of variation is used as the energy distribution dispersion of the target high-frequency component subsequence to reflect the degree of dispersion of high-frequency energy within the target high-frequency component subsequence.

[0027] In this embodiment, the target high-frequency component subsequence has high-frequency voltage values ​​at n sampling times, where the high-frequency voltage value at the i-th sampling time is... Instantaneous energy measurement is ,but Let the discreteness of the target energy subsequence be CV, then: in, Let be the average instantaneous energy measure of the target high-frequency component subsequence, and s be the sample standard deviation of the target high-frequency component subsequence, i.e.: The method for calculating the randomness of waveform changes in the target high-frequency component subsequence in this embodiment includes: First, calculate the first-order forward difference sequence of the target high-frequency component subsequence. This first-order forward difference sequence has n-1 difference values. Let the i-th difference value be... ,but: Then, calculate the product between every two adjacent difference values ​​in the first-order forward difference sequence, and count the number of times the product is negative, let the number of times be m; Finally, the randomness of the waveform variation of the target high-frequency component subsequence is calculated. Let the randomness of this waveform variation be Q, then: In step S112, let the waveform distortion degree of the target high-frequency component subsequence be Z, then: in, It is the hyperbolic tangent function. The preset adjustment coefficient has a value range of (0,1). In this embodiment, the preferred value of the adjustment coefficient is 0.6. In other embodiments, the value of the adjustment coefficient can be set according to the sensitivity requirement of the waveform distortion degree to random noise interference in the target high-frequency component subsequence. The higher the sensitivity requirement of the waveform distortion degree to random noise interference in the target high-frequency component subsequence, the larger the value of the adjustment coefficient.

[0028] The distribution of high-frequency noise in the power grid is random and disordered, exhibiting overall energy dispersion and fluctuation with irregular positive and negative changes. The greater the energy distribution dispersion of the target high-frequency component subsequence, the more drastic the fluctuations in the high-frequency signal energy of the target high-frequency component subsequence; the greater the randomness of the waveform changes of the target high-frequency component subsequence, the more frequently the waveform changes in a disordered manner. Therefore, in this embodiment, the waveform distortion degree is calculated based on the energy distribution dispersion and the randomness of the waveform changes of the target high-frequency component subsequence. This waveform distortion degree can characterize the intensity of random noise interference in the power grid, and the greater the waveform distortion degree, the stronger the random noise interference in the power grid.

[0029] In this embodiment, a high-frequency component subsequence is used as an example. Let this high-frequency component subsequence be the r-th high-frequency component subsequence in the high-frequency voltage component sequence, and let the waveform distortion degree of this high-frequency component subsequence be... The adaptive bias threshold for each sampling point in this high-frequency component subsequence is ,but: in, This refers to a preset fixed deviation threshold. In this embodiment, the fixed deviation threshold is 10% of the rated voltage of the power grid. In other embodiments, the fixed deviation threshold can be determined according to the accuracy requirements for identifying swell and droop events. The higher the accuracy of identifying swell and droop events, the smaller the fixed deviation threshold.

[0030] In step S104 above, taking a sampling point in the high-frequency voltage component sequence as an example, let the high-frequency voltage value at that sampling point be... And if the sampling point is located in the r-th high-frequency component subsequence, then the method for distinguishing the sampling point using ternary quantization includes: assuming the symbol state of the sampling point is... ,like Greater than ,but The value is +1; if Less than ,but The value is -1; if In the interval [ , ],but The value is 0.

[0031] The symbol state of each sampling point in the high-frequency voltage component sequence is obtained sequentially according to the above method. Then, all the symbol states are sorted according to the sampling time of the sampling points to obtain the symbol state sequence corresponding to the high-frequency voltage component sequence. Then, based on the symbol state distribution of the symbol state sequence, the epochal ascending chain and epochal descending chain in the symbol state sequence are identified.

[0032] Specifically, in this embodiment, if two adjacent symbol states in the symbol state sequence are both +1, then these two adjacent symbol states are treated as a temporary ascending chain; if two adjacent symbol states are both -1, then these two adjacent symbol states are treated as a temporary descending chain. Then, adjacent temporary ascending chains are merged into a multi-order temporary ascending chain, and adjacent temporary descending chains are merged into a multi-order temporary descending chain.

[0033] In this embodiment, a spurious chain that is not adjacent to other spurious chains is designated as a first-order spurious chain, a multi-order spurious chain formed by merging two adjacent spurious chains is designated as a second-order spurious chain, and a multi-order spurious chain formed by merging three adjacent spurious chains is designated as a third-order spurious chain; similarly, a spurious chain that is not adjacent to other spurious chains is designated as a first-order spurious chain, a multi-order spurious chain formed by merging two adjacent spurious chains is designated as a second-order spurious chain, and a multi-order spurious chain formed by merging three adjacent spurious chains is designated as a third-order spurious chain.

[0034] In step S105 above, let the length of the symbol state sequence be... That is, the symbol state sequence contains The symbol state of each sampling point. In this embodiment, the method for calculating the voltage sag probability of the power grid based on the number of sag chains of each order in the symbol state sequence and a preset sag order weight includes: Let the number of first-order ascending chains in the symbol state sequence be . The number of second-order temporary ascending chains is The number of third-order temporary ascending chains is And the voltage sag probability of the power grid is ,but: in, The weights of the first-order temporary ascending chain, The weights of the second-order temporary ascending chain, The weights of the third-order temporary ascending chain and . , and The value of can be determined based on the influence of the corresponding order of the slew rate chain on the voltage slew rate probability, and the larger the weight of the slew rate chain on the voltage slew rate probability, the greater its weight value. Since the influence of the third-order slew rate chain on the voltage slew rate probability is greater than that of the second-order slew rate chain, and the influence of the second-order slew rate chain on the voltage slew rate probability is greater than that of the first-order slew rate chain, therefore, in this embodiment... ,For example , , .

[0035] This embodiment uses a method to calculate the voltage sag probability of the power grid based on the number of sag chains of each order in the symbol state sequence and a preset sag order weight. Let the number of first-order temporary descending chains in the symbol state sequence be . The number of second-order temporary descent chains is The number of third-order temporary descent chains is And the voltage sag probability of the power grid is ,but: in, The weights of the first-order temporary descent chain, The weights of the second-order temporary descent chain, The weights of the third-order temporary descent chain and . , and The value can be determined based on the influence of the corresponding order of the sag chain on the voltage sag probability, and the larger the weight of the sag chain of a certain order on the voltage sag probability, the greater its weight. Since the third-order sag chain has a greater influence on the voltage sag probability than the second-order sag chain, and the second-order sag chain has a greater influence on the voltage sag probability than the first-order sag chain, therefore in this embodiment... ,For example , , .

[0036] In step S106 above, the method for determining whether a voltage sag or voltage rise event exists in the power grid based on the voltage rise probability and voltage sag probability includes: Obtain a preset probability judgment threshold and determine whether the voltage sag probability of the power grid is greater than the probability judgment threshold; If so, it is determined that there is a voltage spurt event in the power grid; If not, then determine whether the probability of voltage sag in the power grid is greater than the probability judgment threshold. If the voltage is greater than the specified value, it is determined that there is a voltage sag event in the power grid; otherwise, it is determined that there is neither a voltage rise event nor a voltage sag event in the power grid.

[0037] The voltage sag and droop detection method based on an intelligent fusion terminal provided in this application employs a method that calculates the degree of high-frequency waveform distortion for each high-frequency component subsequence and generates an adaptive deviation threshold accordingly. Since the degree of high-frequency waveform distortion represents the intensity distribution of high-frequency energy, dynamically adjusting the adaptive deviation threshold based on the degree of high-frequency waveform distortion can adaptively track the time-varying characteristics of background noise and waveform distortion, overcoming the defect of fixed thresholds being prone to misjudgment or missed judgment under non-stationary operating conditions. Therefore, it achieves adaptive matching of detection sensitivity under different disturbance intensities. Furthermore, it employs a method that calculates the sag and droop probabilities separately, because the length and state transition frequency of the sag and droop transfer chains can reflect the persistence and morphological integrity of disturbance events. The sag and droop probabilities integrate local voltage fluctuations and global event statistical characteristics into a unified confidence level for measurement, thus achieving statistical decision-making on real voltage events under voltage fluctuation interference, ultimately realizing rapid detection of voltage sags and droops.

[0038] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0039] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0040] This embodiment also provides a rapid voltage sag and dip detection system based on an intelligent fusion terminal, such as... Figure 3 As shown, it includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of the voltage sag and dip fast detection method based on the intelligent fusion terminal of any of the above embodiments.

[0041] The computer program used to perform the operations of this application may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a Local Area Network (LAN) or Wide Area Network (WAN)) or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this application, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions using status information from computer-readable program instructions to personalize the electronic circuits.

[0042] Therefore, those skilled in the art should recognize that although many exemplary embodiments of this application have been shown and described in detail herein, many other variations or modifications conforming to the principles of this application can be directly determined or derived from the disclosure of this application without departing from the spirit and scope of this application. Thus, the scope of this application should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for rapid detection of voltage sags and dips based on an intelligent fusion terminal, characterized in that, Includes the following steps: The actual voltage sequence and fundamental voltage sequence of the power grid are obtained. The difference between the actual voltage sequence and the fundamental voltage sequence is calculated to obtain the high-frequency voltage component sequence of the power grid. The high-frequency voltage component sequence is then split into multiple consecutive high-frequency component sub-sequences. The waveform distortion degree of each of the high-frequency component subsequences is calculated, and the adaptive deviation threshold of the corresponding high-frequency component subsequence is determined by using the waveform distortion degree of each. The high-frequency voltage component sequence is quantized and distinguished according to the adaptive deviation threshold to obtain the symbol state sequence corresponding to the high-frequency voltage component sequence; The voltage rise probability of the power grid is calculated based on the symbol state distribution of the symbol state sequence and the voltage sag probability of the power grid is calculated based on the number of each rise chain and the preset rise order weight. The existence of a voltage sag or voltage rise event in the power grid is determined based on the voltage rise probability and the voltage fall probability.

2. The method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to claim 1, characterized in that, The calculation of the waveform distortion degree of each of the high-frequency component sub-sequences includes: Calculate the energy distribution dispersion and waveform variation randomness of each of the high-frequency component subsequences; The energy distribution dispersion of each waveform change is weighted according to its randomness to obtain the waveform distortion degree of each high-frequency component subsequence.

3. The method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to claim 1, characterized in that, The step of determining the adaptive deviation threshold of the corresponding high-frequency component subsequence based on the distortion degree of each waveform includes: The deviation weight value of the corresponding high-frequency component subsequence is determined according to the degree of distortion of each waveform, and the preset fixed deviation threshold is weighted and corrected according to each deviation weight value to obtain the adaptive deviation threshold of each high-frequency component subsequence.

4. The method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to claim 1, characterized in that, The step of determining whether a voltage sag or voltage rise event exists in the power grid based on the voltage rise probability and the voltage fall probability includes: Obtain a preset probability judgment threshold, and determine whether the voltage sag probability and the voltage rise probability are greater than the probability judgment threshold respectively; If the voltage spurt probability is greater than the probability judgment threshold, then it is determined that there is a voltage spur event in the power grid; If the voltage sag probability is greater than the probability judgment threshold, then the power grid is determined to have a voltage sag event.

5. The method for rapid detection of voltage sags and dips based on an intelligent fusion terminal according to claim 1, characterized in that, The step of splitting the high-frequency voltage component sequence into multiple consecutive high-frequency component sub-sequences includes: A sliding window of a preset length is obtained, and the high-frequency voltage component sequence is processed using the sliding window to obtain multiple continuous and non-overlapping high-frequency component sub-sequences.

6. A rapid voltage sag / slip detection system based on an intelligent fusion terminal, characterized in that, It includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the steps of the rapid voltage sag and dip detection method based on an intelligent fusion terminal according to any one of claims 1-5.

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