Electric energy quality data monitoring method

Abstract

The invention discloses an electric energy quality data monitoring method, and relates to the technical field of electric power system monitoring and analysis, and the method comprises the following steps: S1, obtaining an original voltage and current waveform set; s2, performing time-frequency domain decomposition by using the waveform set; s3, performing feature fusion by using the feature component set; s4, performing time serialization by using the electric energy quality index set; and S5, performing multi-dimensional structured integration by using the time sequence index data. By setting a voltage and current characteristic component coupling mechanism, unified expression of voltage and current characteristics can be ensured on an original signal level, and fine-grained description of electric energy quality characteristics is realized in an index calculation link, so that the problems of data dimension separation and insufficient index precision in the prior art are effectively solved. The method not only improves the comprehensiveness and reliability of the electric energy quality monitoring result, but also lays a solid data foundation for subsequent time serialization and structuralization integration.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring and analysis technology, specifically a method for monitoring power quality data. Background Technology

[0002] With the continuous expansion of the power system and the continuous increase in the proportion of new energy access, power quality problems in power grid operation are becoming increasingly prominent, such as voltage fluctuations, current distortion, harmonic interference and frequency deviation. These problems directly affect the stability and lifespan of electrical equipment, and may even lead to the instability of power system operation.

[0003] In existing technologies, voltage and current signals are typically acquired and processed independently, lacking targeted data fusion methods. This makes it difficult to reflect the coupling relationships between waveform features, thereby reducing the accuracy of monitoring results. Furthermore, in acquiring power quality indicators, existing methods often rely on a single calculation process, failing to improve the precision of the indicators through detailed quantification, making it difficult for the final generated indicators to meet the monitoring needs of complex operating scenarios. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a power quality data monitoring method to solve the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for monitoring power quality data, comprising the following steps: S1. Obtain the original voltage and current waveform set; S2. Use the waveform set to perform time-frequency domain decomposition to obtain the feature component set; S3. Use the feature component set to perform feature fusion to obtain the power quality index set; S4. Use the power quality index set to perform time series generation to obtain index sequence data; S5. Use time series index data to perform multi-dimensional structured integration to obtain the power quality monitoring dataset.

[0006] To further optimize this technical solution, the waveform set expression obtained in step S1 is as follows: ; in, This represents the set of phase indices for a three-phase power grid. ; This is the phase index, and its value range is the set of three phases. ; For window indexing, .

[0007] To further optimize this technical solution, in step S1, For the first Phase within a time window The voltage waveform vector represents an ordered vector composed of the voltage data from all sampling points within the window: ; In the formula, Indicates phase In the global sampling sequence, the first Voltage values ​​at each sampling point; Sampling point index Belongs to the A set of indices for a time window ; The number of sampling points contained in a single time window, representing the vector. and Dimension size; Representing the real number field 3D vector space.

[0008] To further optimize this technical solution, in step S1, For the first Phase within a time window Current waveform vector: ; In the formula, Indicates phase In the global sampling sequence, the first The current value at each sampling point.

[0009] To further optimize this technical solution, step S2 first performs a time-frequency transformation operation, then extracts feature components, and finally constructs a feature component set. In step S2, during the time-frequency transformation operation, the time-frequency transformation operator is: Its expression is: ; in, This represents the input waveform, which can be a voltage vector. or current vector ; Indicates frequency index; Indicates the time location index; This represents the local energy distribution of the signal in the time-frequency domain.

[0010] To further optimize this technical solution, in step S2, when extracting feature components, the time-frequency distribution results are considered. The target frequency component and transient component are extracted, and the following expression is given: ; in, This represents a feature component extraction operator used to select specific components in the time-frequency plane; Indicates from the first window, phase The characteristic component vector obtained from the waveform.

[0011] To further optimize this technical solution, in step S2, when constructing the feature component set, all phases With window The components are integrated into the final output, and its expression is: ; In the formula, It is a feature component set, containing information about voltage and current components; : indicates the first Time window, phase The voltage characteristic component vector; : indicates the first Time window, phase The current characteristic component vector.

[0012] To further optimize this technical solution, step S3 first couples the voltage and current components, then calculates the indices, and finally constructs a power quality index set. In step S3, during voltage and current component coupling, in each window and phase The following establishes a correspondence between voltage and current components: ; in: This represents a fusion operator used to establish the relationship between voltage and current components. It maps voltage and current components one by one and extracts quantitative features that can simultaneously reflect the correlation between the two by comparing their amplitude, phase and frequency characteristics. Display window Phase The fusion results are as follows.

[0013] To further optimize this technical solution, in step S3, when calculating the indicators, the following is obtained: Then, further power quality indicators were constructed, including: Harmonic index calculation: From Extract the amplitudes of the fundamental component and each harmonic component, and calculate the total harmonic distortion or the harmonic content of each order. Calculation of flash-type indicators: by tracking The change in amplitude of low- and mid-frequency components over time yields the flicker intensity value, which reflects the degree of voltage fluctuation and is used to describe the flicker effect of the light source. Transient index calculation: detection The amplitude and duration of transient components appearing in the time-frequency domain are converted into transient event amplitude indices to reflect the degree of disturbance in power quality. By using the above method, the indicator calculation operator is set. , coupling features Convert to indicator value Its expression is: ; in: This indicates the power quality index value for the corresponding window and phase.

[0014] To further optimize this technical solution, in step S3, when constructing the power quality index set, the index results under all windows and phases are organized in a unified manner: ; This is the final set of power quality indicators.

[0015] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a power quality data monitoring method as described in the first aspect of the present invention.

[0016] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a power quality data monitoring method as described in the first aspect of the present invention.

[0017] Compared with the prior art, the present invention provides a method for monitoring power quality data, which has the following beneficial effects: This power quality data monitoring method, by establishing a coupling mechanism between voltage and current characteristic components, ensures a unified expression of voltage and current characteristics at the raw signal level and achieves fine-grained characterization of power quality features during the index calculation stage. This effectively solves the problems of data dimension separation and insufficient index accuracy in existing technologies. This method not only improves the comprehensiveness and reliability of power quality monitoring results but also lays a solid data foundation for subsequent time-series processing and structured integration. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of 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 below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a power quality data monitoring method proposed in this invention. Figure 2 This is a schematic diagram of the time-frequency domain decomposition process of a power quality data monitoring method proposed in this invention. Figure 3 This is a schematic diagram of the feature fusion process of a power quality data monitoring method proposed in this invention; Figure 4 This is a schematic diagram of the multi-dimensional structured integration process of a power quality data monitoring method proposed in this invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments. Example 1

[0023] Reference Figures 1-4This is the first embodiment of the present invention, which provides a method for monitoring power quality data, including the following steps: S1. Obtain the original voltage and current waveform set; Step S1 obtains the original voltage and current waveform set from the grid side, including the following sub-processes: Sampling input: By using existing mature high-precision synchronous sampling technology, the grid voltage and current signals are sampled to obtain continuous waveform data.

[0024] Data preprocessing: Mature anti-aliasing filtering technology is used to filter the sampled signal and remove interference components that are higher than the sampling bandwidth. Amplitude normalization is used to unify the voltage and current waveforms to the standard amplitude range, so as to avoid the deviation caused by the inconsistency of dimensions in subsequent decomposition.

[0025] Time window division and sequence construction: Using the existing mature sliding time window division method, the continuous sampled waveform is divided into short time sequences according to a fixed time window (such as one or more power frequency cycles); The time window should be set to an integer number of power frequency cycles (e.g., 1, 2 or more power frequency cycles) to ensure that the sampled data completely covers one or more cycle characteristics of voltage and current in time. For the Each time window has a sample index range that is a continuous interval from the start point to the end point in the sampling point sequence.

[0026] Results output and connection: After sampling, filtering, normalization, and time window division, a set of voltage and current waveforms is finally formed. This set uses mature preprocessing techniques to ensure the accuracy and usability of the input data.

[0027] In step S1, the duration of the fixed time window is... , is the duration of an integer number of power frequency cycles, the first... The sample index range for each window is ; The final waveform set expression obtained in step S1 is: ; in, This represents the set of phase indices for a three-phase power grid. ; This is the phase index, and its value range is the set of three phases. Used to identify the specific phase of a power system; For window indexing, , indicating the nth time window in the division; For the first Phase within a time window The voltage waveform vector represents an ordered vector composed of the voltage data from all sampling points within the window: ; In the formula, Indicates phase In the global sampling sequence, the first The voltage values ​​at each sampling point are discrete sampled voltage values ​​after filtering and normalization preprocessing. Sampling point index Belongs to the A set of indices for a time window , which represents the range of sampling points contained in the constraint vector; For the first Phase within a time window The current waveform vector represents an ordered vector composed of the current data from all sampling points within this window: ; In the formula, Indicates phase In the global sampling sequence, the first The current values ​​at each sampling point are discrete sampled current values ​​after filtering and normalization preprocessing. The number of sampling points contained in a single time window is equal to the product of the sampling frequency and the window duration, representing a vector. and Dimension size; Representing the real number field The 3D vector space indicates that the voltage and current window vectors are in real number form. Dimensional vector.

[0028] S2. Use the waveform set to perform time-frequency domain decomposition to obtain the feature component set; Step S2 is based on the output of step S1 For each window voltage vector and current vector The frequency distribution and time characteristics are obtained through time-frequency domain transformation, and the results are mapped to a unified set of components.

[0029] Step S2 first performs time-frequency transformation, then extracts feature components, and finally constructs a set of feature components. In step S2, during the time-frequency transformation operation, the time-frequency transformation operator is: This is used to map a one-dimensional discrete signal to the time-frequency domain, and its expression is: ; in, This represents the input waveform, which can be a voltage vector. or current vector ; Indicates frequency index; Indicates the time location index; This represents the local energy distribution of the signal in the time-frequency domain.

[0030] According to this formula, the voltage or current waveform within a single window is expanded to the time-frequency plane, so that the energy characteristics of the signal at different frequencies and time locations can be represented.

[0031] In step S2, when extracting feature components, the time-frequency distribution results are used as a basis. The target frequency component and transient component are extracted, and the following expression is given: ; in, This represents a feature component extraction operator used to select specific components in the time-frequency plane; Indicates from the first window, phase The feature component vector obtained from the waveform; According to this formula, the original complex time-frequency distribution result is transformed into a discrete set of components that can be used for subsequent calculations.

[0032] In step S2, when constructing the feature component set, all phases are... With window The components are integrated into the final output, and its expression is: ; In the formula, It is a feature component set, containing information about voltage and current components; : indicates the first Time window, phase The voltage characteristic component vector; : indicates the first Time window, phase The current characteristic component vector.

[0033] In existing mature technologies, time-frequency decomposition is usually only used as a spectrum estimation method for a single signal (such as harmonic analysis of voltage or current alone), and the organization of components depends on conventional spectrum output.

[0034] The difference in the analysis logic of step S2 is that it not only performs time-frequency expansion on voltage and current separately, but also organizes them into a unified set of characteristic components. This provides a direct input interface for subsequent cross-signal coupling analysis.

[0035] S3. Use the feature component set to perform feature fusion to obtain the power quality index set; Step S3 is based on the output of step S2 The characteristic components of voltage and current are integrated within a unified calculation framework; a set of indicators reflecting the characteristics of power quality is obtained through mathematical relationships.

[0036] Step S3 first couples the voltage and current components, then calculates the indices, and finally constructs a set of power quality indices. In step S3, during voltage and current component coupling, in each window and phase The following establishes a correspondence between voltage and current components: ; in: This represents a fusion operator used to establish the relationship between voltage and current components. It maps voltage and current components one by one and extracts quantitative features that can simultaneously reflect the correlation between the two by comparing their amplitude, phase and frequency characteristics. Display window Phase The fusion results are as follows.

[0037] This subprocess pairs voltage and current characteristics to obtain coupling characteristics that reflect phase and window properties.

[0038] In step S3, during the index calculation, the following is obtained: Then, further power quality indicators were constructed, including: Harmonic index calculation: From Extract the amplitudes of the fundamental component and each harmonic component, and calculate the total harmonic distortion (THD) or the harmonic content of each order. Calculation of flash-type indicators: by tracking The change in amplitude of low- and mid-frequency components over time yields the flicker intensity value, which reflects the degree of voltage fluctuation and is used to describe the flicker effect of the light source. Transient index calculation: detection The amplitude and duration of transient components appearing in the time-frequency domain are converted into transient event amplitude indices to reflect the degree of disturbance in power quality. By using the above method, the indicator calculation operator is set. , coupling features Convert to indicator value Its expression is: ; in: This indicates the power quality index value for the corresponding window and phase.

[0039] In step S3, when constructing the power quality index set, the index results for all windows and phases are organized in a unified manner: ; This is the final set of power quality indicators.

[0040] Existing technologies mostly use a single signal component (such as voltage harmonic amplitude) to directly calculate power quality indicators, lacking a systematic integration between voltage and current components.

[0041] The logical difference in step S3 lies in the introduction of a fusion operator at the time-frequency component level. This involves establishing a correlation between the voltage and current components before converting them into an index set. This allows the voltage-current coupling characteristics to be reflected in the indicator generation process.

[0042] S4. Use the power quality index set to perform time series generation to obtain index sequence data; Step S4 is based on the output of step S3 Discrete power quality indicators Arrange the data in an orderly manner according to the time window sequence; establish a sequence of indicators changing over time using mature time series methods; and output time series data that can directly reflect the dynamic changes of power quality indicators.

[0043] Step S4 first arranges the windows in order, then performs indicator merging and mapping, and finally performs time serialization; In step S4, when arranging the windows in order, the windows from step S3 are... Indicator results Arranged in chronological order: ; Each component Indicates the first Window, phase Next The power quality index value is determined by Expanding on this, This represents the number of indicator items.

[0044] In step S4, during the index merging mapping, the phase dimension is maintained. Without changing the context, map the metric values ​​under each window to a unified time index: using a mature timestamp mapping method, each... By mapping the actual sampling time points, we can obtain indicator data with clear time references. Use each window in step S1 Recorded timestamps (Based on mature timestamp mapping methods) Establish a time reference, then the phase Next The time series of the indicators is an ordered set of pairs: .

[0045] In step S4, during time series conversion, mature sequence construction methods (e.g., sliding window concatenation, sequence index mapping) are used to connect the indicator data from each window and each phase into a complete time series, resulting in indicator sequence data. Each phase Corresponding to one indicator sequence: ; Among them, the first A row is a vector , No. The column is the window vector. ; All phase sequence sets form a complete indicator sequence dataset, including all phases. matrix These are collected together to form a complete dataset of indicator sequences, denoted as . : .

[0046] S5. Use time series index data to perform multi-dimensional structured integration to obtain a power quality monitoring dataset; Step S5 is based on the output of step S4 This method integrates serialization results from different phases, indicators, and time windows in multiple dimensions; maintains consistency in time, phase, and indicator dimensions within a unified data framework; and ultimately generates a dataset that can be directly used for power quality monitoring and analysis.

[0047] Step S5 first performs a unified dimensional mapping, then performs structured organization, and finally performs full sequence integration. Step S5, during the dimensional unification mapping, preserves the time series index. If the output of step S4 remains unchanged, Perform dimensional mapping: Phase Corresponding to the three-phase or multi-phase structure of the power system; Indicator Dimensions Corresponding to various power quality characteristics; Time dimension Keep it consistent with the actual monitoring timestamp.

[0048] In step S5, during the structured organization, the mapped data is uniformly stored as matrix blocks: ; Each of them Indicates within the time window The overall structure of all phases and indicators.

[0049] Step S5 involves integrating all sequences during the whole sequence integration process. Stacking them in chronological order yields the final monitoring dataset: ; This set retains information from the time dimension, phase dimension, and indicator dimension simultaneously.

[0050] Existing technologies typically store and analyze data for only a single indicator or a single time dimension, lacking a holistic integration of multiple dimensions including "time-phase-indicator". This step, however, emphasizes generating a comprehensive dataset that simultaneously reflects temporal dynamics, phase differences, and indicator characteristics through multi-dimensional structured organization based on the serialized results, thus distinguishing it from traditional methods in terms of data organization.

[0051] Step S5 first performs a unified dimensional mapping, then performs structured organization, and finally performs full sequence integration. Step S5, during the dimensional unification mapping, preserves the time series index. If the output of step S4 remains unchanged, Perform dimensional mapping: Phase Corresponding to the three-phase or multi-phase structure of the power system; Indicator Dimensions Corresponding to various power quality characteristics; Time dimension Keep it consistent with the actual monitoring timestamp.

[0052] In step S5, during the structured organization, the mapped data is uniformly stored as matrix blocks: ; Each of them Indicates within the time window The overall structure of all phases and indicators.

[0053] Step S5 involves integrating all sequences during the whole sequence integration process. Stacking them in chronological order yields the final monitoring dataset, whose expression is: ; Existing technologies typically store and analyze data for only a single indicator or a single time dimension, lacking a holistic integration of multiple dimensions including "time-phase-indicator". This step, however, emphasizes generating a comprehensive dataset that simultaneously reflects temporal dynamics, phase differences, and indicator characteristics through multi-dimensional structured organization based on the serialized results, thus distinguishing it from traditional methods in terms of data organization. Example 2

[0054] This embodiment also provides a computer device applicable to a power quality data monitoring method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the power quality data monitoring method proposed in the above embodiment.

[0055] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a power quality data monitoring method as described in the above embodiments.

[0056] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0057] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0059] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0060] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0061] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power quality data monitoring method, characterized by, The method comprises the following steps: S1, obtaining a set of original voltage and current waveforms; S2, performing time-frequency domain decomposition using the set of waveforms to obtain a set of characteristic components; S3, performing feature fusion using the set of characteristic components to obtain a set of power quality indicators; S4, performing time sequencing using the set of power quality indicators to obtain indicator sequence data; S5, performing multi-dimensional structured integration using the time sequence indicator data to obtain a power quality monitoring data set.

2. The power quality data monitoring method of claim 1, wherein, The waveform set expression obtained in the step S1 is: ； wherein denotes a set of phase indices of the three-phase power grid, ; is a phase index, taking a value from a set of three phases ; to window index, .

3. A power quality data monitoring method according to claim 2, characterized in that, In the step S1, For the first time window, the voltage waveform vector of phase is an ordered vector consisting of voltage data of all sampling points within the window: ； wherein denotes the phase the voltage value at the th sample point in the global sampling sequence; index of the sampling point belongs to the index set of the time window ; number of sampling points contained for a single time window, dimension size of with dimension size; represents a real-valued vector space of dimension dimension d.

4. The power quality data monitoring method of claim 2, wherein, The step S1, is the first time window, the phase current waveform vector: ； wherein denotes the phase the current value at the th sample point in the global sampling sequence.

5. The method of claim 1, wherein, The step S2 first performs time-frequency transformation operation, then performs characteristic component extraction, and finally performs characteristic component set construction; Step S2, when the time-frequency transform operation is performed, the time-frequency transform operator is whose expression is: ； Wherein, represents an input waveform, which can be a voltage vector or a current vector ; denotes a frequency index; represents a time position index; represents the local energy distribution of the signal in the time-frequency domain.

6. A power quality data monitoring method according to claim 5, characterized in that, The step S2, when performing feature component extraction, extracts target frequency components and transient components based on the time-frequency distribution result , and has the following expression: ； Wherein, representing a feature component extraction operator for selecting a particular component on a time-frequency plane; represents a feature component vector obtained from the waveforms of the first window, phase of the second window.

7. The power quality data monitoring method of claim 5, wherein, In step S2, when constructing the feature component set, all phases are... With window The components are integrated into the final output, and its expression is: ； In the formula, a set of characteristic components, comprising component information of the voltage and the current; : indicates the first Time window, phase The voltage characteristic component vector; : represents the current characteristic component vector of the th time window, phase​ 8. The method of claim 1, wherein, The step S3 first performs voltage and current component coupling, then performs indicator calculation, and finally performs power quality indicator set construction; Step S3 establishes a correspondence between the voltage and current components at each window and phase when the voltage and current components are coupled. ； Wherein: represents a fusion operator, which is used to establish the relationship between the voltage component and the current component, and correspond the voltage component and the current component one by one, and extract the quantified features which can reflect the correlation of both by comparing their amplitude, phase and frequency characteristics; representative window , phase of the fusion result.

9. The power quality data monitoring method of claim 8, wherein, The step S3 obtains After that, the power quality index is further constructed, including: Harmonic index calculation: extract fundamental and harmonic components from and calculate total harmonic distortion or harmonic content of each order; Flicker class indicator calculation: by tracking The amplitude of the medium and low frequency components changes over time, and the flicker intensity value reflecting the voltage fluctuation degree is obtained, which is used to describe the flickering effect of the light source. Transient class indicator calculation: detection The amplitude and duration of the transient component appearing in the time-frequency domain is converted into a transient event amplitude indicator, which is used to reflect the disturbance degree of power quality; By the above manner, the index calculation operator is arranged The coupling feature is converted into an index value The expression is: ； Wherein: represents the power quality indicator value under the corresponding window and phase.

10. The power quality data monitoring method of claim 8, wherein, The step S3, when performing power quality indicator set construction, uniformly organizes the indicator results under all windows and phases: ； is the final set of power quality indices.

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