Responsibility subject power quality trend identification method under typical interference scenario

Abstract

The application discloses a responsibility subject power quality trend identification method under a typical interference scene and belongs to the field of power quality analysis, and comprises the following steps: acquiring power quality time sequence data, decomposing the power quality time sequence data to obtain a steady-state time sequence trend component, testing the steady-state time sequence trend component through a trend test method to obtain a trend type, and calculating the steady-state time sequence trend component according to the trend type to obtain a trend span index to quantitatively identify the power quality trend. Through the technical solution, the power quality trend can be effectively identified quantitatively.

Description

Technical Field

[0001] The present invention belongs to the technical field of power quality trend analysis, and in particular relates to a method for identifying power quality trends of responsible entities in typical interference scenarios. Background Art

[0002] With the accelerating pace of industrialization and the continuous improvement of electrification, power load fluctuations and power quality issues are becoming increasingly prominent. In urban planning and power system design, increasing attention is being paid to power quality issues. Furthermore, with the widespread application of electronic equipment and information technology, the requirements for power quality are becoming increasingly stringent, and power quality issues are gradually becoming a major issue affecting socioeconomic development and human health. Therefore, the study of the evolution of power quality trends is becoming increasingly important.

[0003] The study of the evolution of power quality trends requires the use of a variety of methods and techniques, including: (1) Measurement and analysis: By monitoring various indicators of power in real time, the power quality is analyzed and diagnosed, and the trend evolution of power quality is understood through the analysis results. (2) Data mining and processing: By mining and processing a large amount of historical data, the key indicators and characteristics of power quality are extracted, and the trend evolution law is analyzed. (3) Mathematical modeling: A mathematical model is established to fit and predict power quality data, thereby revealing its trend evolution law.

[0004] Research on this topic focuses primarily on the measurement, analysis, and modeling of grid voltage, current, and harmonics; while also focusing on the study of power quality factors such as flicker, intermittent interference, and nonlinear loads. These research methods employ a wide range of modern approaches, including statistical analysis, time series analysis, and neural networks. However, these existing technologies are insufficiently accurate in identifying the evolution of power quality trends. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes a method for identifying power quality trends of responsible entities under typical interference scenarios to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention provides a method for identifying power quality trends of responsible entities in typical interference scenarios, including:

[0007] Acquiring power quality time series data, and decomposing the power quality time series data to obtain a steady-state time series trend component;

[0008] The trend component of steady-state time series is tested by trend test method to obtain the trend type;

[0009] The steady-state time series trend component is calculated according to the trend type to obtain a trend span index to quantitatively identify the power quality trend.

[0010] Optionally, the power quality time series data is decomposed by a seasonal extraction decomposition method to obtain a steady-state time series trend component.

[0011] Optionally, the process of decomposing the power quality time series data includes:

[0012] Build an ARIMA model based on power quality time series data and obtain seasonal components through the ARIMA model;

[0013] Decomposing the power quality time series data by using a Prescott filter to obtain a trend component and a disturbance component;

[0014] Remove the trend component and seasonal component from the power quality time series data to obtain the residual component;

[0015] The ARIMA model, seasonal component and trend component are tested. When they meet the test requirements, the trend component is regarded as the steady-state time series trend component.

[0016] Optionally, the steady-state time series trend component can be tested using the Mann-Kendall trend test.

[0017] Optionally, the process of testing the steady-state time series trend component includes:

[0018] According to the steady-state time series trend component, a statistic is calculated, where:

[0019]

[0020] Among them, S represents the statistic, n represents the number of data in the power quality index time series, T j and T k Respectively represent any two time point data in the trend component, where j>k, and the subscripts j and k represent the corresponding data point numbers;

[0021] calculating the variance of the statistic;

[0022] V ar (S) = n(n-1)(2n-5) / 18;

[0023] Among them, V ar (S) represents the variance of the statistic;

[0024] A trend test statistic is obtained by calculating the variance of the statistic;

[0025]

[0026] Where Z represents the trend test statistic;

[0027] The trend test statistic value is judged to obtain the trend type.

[0028] Optionally, the process of judging the trend test statistic includes:

[0029] When the trend test statistic is greater than 0, the power quality time series data is in an upward trend; and when the trend test statistic is less than 0, the power quality time series data is in a downward trend.

[0030] Optionally, when the trend type is an upward trend, the trend span index is calculated according to the steady-state time series trend component:

[0031]

[0032] Among them, P c Indicates the trend span index, max(T vc ) and min(T vc ) are the maximum and minimum values ​​of the trend component of the cth group of power quality time series data, T vc is the steady-state time series trend component of the cth group of power quality time series data, u c This is the national standard limit range corresponding to this indicator.

[0033] On the other hand, the present application provides a responsible entity power quality trend identification system in a typical interference scenario, which is used to execute the above method.

[0034] Compared with the prior art, the present invention has the following advantages and technical effects:

[0035] 1. The SEATS decomposition method proposed in the present invention is a time series decomposition method that decomposes the time series into three parts: trend, seasonality, and residual. The parameters of the ARIMA model can be adaptively adjusted according to different time series to best capture the trend and seasonal changes in the time series. The results output by SEATS are very clear and can clearly indicate the changes in the trend, seasonality, and residual parts of the time series, making it convenient for analysts to conduct further analysis and interpretation. Since SEATS uses the ARIMA model to decompose time series, it can be implemented using existing ARIMA model tools, and the processing speed is faster. The SEATS method has high reliability in time series decomposition, can provide more accurate decomposition results, and can also test the ARIMA model, seasonality, and trend model to ensure the reliability of the decomposition.

[0036] 2. The MK trend test method proposed in the present invention is a commonly used non-parametric statistical method that does not require sample data to follow a certain distribution and has a good processing effect on non-normally distributed data sets.

[0037] 3. The trend span index proposed in the present invention has a clear meaning, a simple and fast calculation method, is applicable to different types of power quality indicator time series, and is convenient for analyzing massive power quality monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0039] Figure 1 This is a basic principle diagram of a power quality trend identification method based on the SEATS decomposition method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] The present invention discloses a method for identifying power quality trends based on the seasonal extraction of differential integrated moving average (ARIMA) time series (SEATS) decomposition method. The method comprises the following steps: decomposing power quality time series data using the SEATS decomposition method to extract characteristic components reflecting the overall trend change of the time series; testing the trend components of the steady-state power quality indicator time series obtained from the SEATS decomposition using the Mann-Kendall (MK) trend test method to determine the significance of the trend change of the power quality indicator and determine the trend type of the time series data; and calculating the trend span index to quantitatively describe the degree of trend change of different power quality indicators, thereby realizing the study of the trend evolution law of power quality. Compared with conventional trend identification methods, the SEATS decomposition method proposed in the present invention is a time series decomposition method that decomposes the time series into three parts: trend, seasonality, and residual. The parameters of the ARIMA model can be adaptively adjusted according to different time series to optimally capture the trend and seasonal changes in the time series. Since SEATS uses the ARIMA model to decompose the time series, it can be implemented using existing ARIMA model tools, and the processing speed is relatively fast.

[0043] In response to the shortcomings of the existing technology, the present invention proposes a power quality trend identification method based on the seasonal extraction of differential integrated moving average autoregressive model (ARIMA) time series (SEATS) decomposition method. The method uses the SEATS method to decompose power quality time series data, extracts characteristic components that reflect the overall trend changes of the time series, uses the MK trend test method to test the trend components of the steady-state power quality indicator time series, calculates the trend span index, and quantitatively describes the trend change degree of different power quality indicators, ultimately completing the study of the evolution law of power quality trends.

[0044] The purpose of the present invention is achieved through the following technical solutions:

[0045] A method for identifying power quality trends based on the seasonal extraction of differential integrated moving average autoregressive model (ARIMA) time series (SEATS) decomposition method includes the following steps:

[0046] S1: Use SEATS decomposition method to decompose power quality time series data and extract characteristic components that reflect the overall trend changes of the time series;

[0047] The power quality time series (time series) data is time series data of different power quality indicators, including voltage quality, frequency quality, waveform quality, etc.

[0048] S2: The Mann-Kendall (MK) trend test method is used to test the trend component of the steady-state power quality index time series obtained from the SEATS decomposition, to determine the significance of the trend change of the power quality index and to determine the trend type of the time series data;

[0049] S3: Calculate the trend span index to quantitatively describe the degree of trend change of different power quality indicators.

[0050] Furthermore, the SEATS method is used in S1 to decompose the original power quality time series data into three additive components, namely, trend component, seasonal component and irregular residual component: Y v =T v +S v +R v , where Y v is the original value of the time series at time v, T v 、S v 、R v They correspond to the trend component value, seasonal component value and residual component value at time v respectively. It includes the following steps:

[0051] (1) Establishing ARIMA model: First, the time series is converted into an ARIMA model, which can well capture the trend and seasonal changes in the time series.

[0052] (2) Estimation of seasonality: Use the ARIMA model to estimate the seasonal part in the time series, i.e., the seasonal component value, and remove this part from the original power quality time series data.

[0053] (3) Estimating Trend: Use the Hodrick-Prescott (HP) filter to estimate the trend component in the time series. The HP filter is a very popular filter that can decompose the time series into two parts: trend and disturbance.

[0054] (4) Estimated residual: Subtract the trend and seasonal parts, i.e., the trend component value and the seasonal component value, from the original power quality time series to obtain the residual part, i.e., the residual component value.

[0055] (5) Perform diagnostic tests: Test the ARIMA model, estimated seasonality, and trend models to ensure the reliability of the decomposition.

[0056] Furthermore, the MK method is used in S2 to test the trend component of the steady-state power quality index time series obtained from the SEATS decomposition, that is, the trend component value T v , which includes the following working steps:

[0057] (1) Assuming that the power quality index time series data Tv = (T1, T2,..., Tn) are random independent, construct the statistic S: n Where n is the number of data in the power quality index time series, S represents the statistic, T j and T k respectively represent any two data points in the trend component (j>k), the subscripts j and k represent the corresponding data point label, for the above n total data points, (n-1) (n) / 2 comparisons need to be calculated to determine the MK test, traverse all data pairs (not only adjacent points) to construct the statistic S, to ensure the comprehensiveness of the trend judgment.

[0058] (2) When n>8, the variance Var(S) of the statistical variable S: ar (S) = n(n-1)(2n-5) / 18;

[0059] (3) Trend test statistic Z: When Z>0, the power quality index time series has an upward trend, when Z<0, the power quality index time series has a downward trend.

[0060] Further, the S3 proposes a trend span index to quantitatively describe the degree of trend change of different power quality indicators. Assuming that there are C groups of power quality indicator time series determined by the MK trend test in S2, where the cth group of sequence is T vc , the trend span index P c of the cth group of time series is: Where max(T vc ) and min(T vc ) are the maximum and minimum values of the trend component of the cth group of power quality indicator time series, and u c is the corresponding national standard limit interval of the index.

[0061] Wherein, the MK method can identify significant upward trend (Z>0) and downward trend (Z<0) at the same time, but this method focuses on significant upward trend (because the increase of power quality indicators such as harmonic distortion rate usually indicates deterioration, which needs to be warned first). If you need to analyze the downward trend (such as frequency stability improvement), you can define a negative span index, which is mainly related to the comment on the risk of deterioration in this invention, such as the one-way control demand of the national standard for "exceeding the standard risk".

[0062] Based on the trend span index, the degree of change of the trend component of the power quality index time series in the corresponding national standard limit interval is studied. The higher the index, the greater the upward amplitude of the cth group of power quality indicator time series, and the more serious the deterioration trend of the power quality indicator. Thus, the research on the evolution law of power quality trend is completed. ​

[0063] Among them, "Power Quality Public Grid Harmonics" (GB / T 14549-93) clearly stipulates the harmonic voltage limit. When the trend span index calculation is based on this limit range, the severity level can be directly defined by the provisions of the standard such as "exceeding the standard by 10% requires warning, and exceeding the standard by 30% requires mandatory treatment".

[0064] In some examples of the present invention, a method for identifying power quality trends based on the seasonal extraction of differential integrated moving average autoregressive model (ARIMA) time series (SEATS) decomposition method is disclosed, such as Figure 1 As shown, the following steps are included:

[0065] S1: Use SEATS decomposition method to decompose power quality time series data and extract characteristic components that reflect the overall trend changes of the time series;

[0066] The SEATS method is used to decompose the original power quality time series data into three additive components, namely trend component, seasonal component and irregular residual component: Y v =T v +S v +R v , where Y v is the original value of the time series at time v, T v 、S v 、R v They correspond to the trend component value, seasonal component value and residual component value at time v respectively. It includes the following steps:

[0067] (1) Establishing ARIMA model: First, the time series is converted into an ARIMA model, which can well capture the trend and seasonal changes in the time series.

[0068] (2) Estimating seasonality: Use the ARIMA model to estimate the seasonal part in the time series and remove this part from the original series.

[0069] (3) Estimating Trend: Use the Hodrick-Prescott (HP) filter to estimate the trend portion of the time series. The HP filter is a very popular filter that can decompose the time series into two parts: trend and disturbance.

[0070] (4) Estimated residuals: Subtract the trend and seasonal components from the original series to obtain the residual component.

[0071] Although the trend component is the primary factor in subsequent calculations, the extraction of the trend component must be verified based on the residual. The residual component is primarily used to verify the reliability of the ARIMA model: the Ljung-Box test verifies whether it is white noise (if it fails, it indicates that the ARIMA model has not fully extracted regularities). Furthermore, its mutation component can be used to locate abnormal events such as lightning strikes (for example, if the peak of the residual matches the fault time recorded by the SCADA system). Even if the trend component is subsequently used, the residual still provides "traceability" for the decomposition process, meeting the data integrity requirements of IEC 61000-4-30.

[0072] (5) Perform diagnostic tests: Test the ARIMA model and the estimated seasonal and trend components to ensure the reliability of the decomposition.

[0073] The above-mentioned tests are conducted through the following methods: The ARIMA model is tested through residual white noise tests (e.g., Ljung-Box test p-value > 0.05) and normality verification (QQ plot linear distribution). The seasonal component and trend component are tested to assess the rationality of the decomposition results. For the seasonal component, spectral analysis is used to verify the absence of significant residual periodic energy in the residuals (e.g., a 90% decrease in the main frequency amplitude after Fourier transformation). For the trend component, HP filter parameter sensitivity tests (e.g., comparing the trend smoothness difference between λ = 14400 and λ = 129600) and ADF stationarity tests are performed to ensure that the trend component series has no unit roots. Finally, the model reliability is comprehensively assessed through the decomposition-reconstruction error rate (e.g., RMSE < 5%). If the reliability test is passed, the trend component is used for subsequent data processing. If it fails, the ARIMA model and filter are re-adjusted or redesigned for component extraction and estimation.

[0074] S2: The Mann-Kendall (MK) trend test method is used to test the trend component of the steady-state power quality index time series obtained from the SEATS decomposition, to determine the significance of the trend change of the power quality index and to determine the trend type of the time series data;

[0075] The MK method is used to test the trend component T of the time series of steady-state power quality indicators obtained from SEATS decomposition. v , which includes the following working steps:

[0076] (1) Assuming that the power quality index time series data T v =(T1,T2,…,T n ) are then independent and the constructed statistic S is: Where n is the number of data in the power quality indicator time series;

[0077] (2) When n>8, the variance of the statistical variable S Var(S): V ar (S) = n(n-1)(2n-5) / 18;

[0078] (3) Trend test statistic Z: When Z>0, the time series of power quality indicators has an upward trend, and when Z<0, the time series of power quality indicators has a downward trend.

[0079] S3: Calculate the trend span index to quantitatively describe the degree of trend change of different power quality indicators.

[0080] The trend span index is proposed to quantitatively describe the degree of trend change of different power quality indicators. Assume that there are C groups of power quality indicator time series with a significant upward trend after the MK trend test in S2, of which the cth group is T vc , then the trend span index P of the c-th group of time series c for: Where max(T vc ) and min(T vc ) are the maximum and minimum values ​​of the trend components of the time series of power quality indicators in group c, u c The trend span index (TSI) is used to study the variation of the trend component of the power quality indicator time series within the corresponding national standard limit range. The higher the index, the greater the increase in the power quality indicator time series in group c, and the more severe the deterioration of the power quality indicator. This completes the study of the evolution of power quality trends.

[0081] The present invention proposes a power quality trend identification method based on the SEATS decomposition method. The SEATS decomposition method is used to decompose the power quality time series data, extract the characteristic components that reflect the overall trend changes of the time series, and can adaptively adjust the parameters of the ARIMA model according to different time series to best capture the trend and seasonal changes in the time series. The MK method is used to test the trend component T of the steady-state power quality index time series obtained from the SEATS decomposition. v , judge the significance of the trend change of power quality indicators, determine the trend type of time series data, do not require sample data to follow a certain distribution, have a good processing effect on non-normally distributed data sets, propose a trend span index, which is applicable to different types of power quality indicator time series, and is convenient for analyzing massive power quality monitoring data, and is used to quantitatively describe the degree of trend change of different power quality indicators.

[0082] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for identifying power quality trends of responsible entities in typical interference scenarios, characterized by: include: Acquiring power quality time series data, and decomposing the power quality time series data to obtain a steady-state time series trend component; The trend component of steady-state time series is tested by trend test method to obtain the trend type; Calculating the steady-state time series trend component according to the trend type to obtain a trend span index to quantitatively identify the power quality trend; The process of decomposing the power quality time series data includes: Build an ARIMA model based on power quality time series data and obtain seasonal components through the ARIMA model; Decomposing the power quality time series data by using a Prescott filter to obtain a trend component and a disturbance component; Remove the trend component and seasonal component from the power quality time series data to obtain the residual component; The ARIMA model, seasonal component and trend component are tested. If they meet the test requirements, the trend component is regarded as the steady-state time series trend component. The process of testing the steady-state time series trend component includes: According to the steady-state time series trend component, a statistic is calculated, where: ； Among them, S represents the statistic, n represents the number of data in the power quality index time series, and Respectively represent any two time point data in the trend component, where j>k, and the subscripts j and k represent the corresponding data point numbers; calculating the variance of the statistic; ； in, represents the variance of the statistic; A trend test statistic is obtained by calculating the variance of the statistic; ； Where Z represents the trend test statistic; Judging the trend test statistic value to obtain a trend type; When the trend type is an upward trend, the trend span index is calculated according to the steady-state time series trend component: ； Among them, P c represents the Trend Span Index, and are the maximum and minimum values ​​of the trend component of the cth group of power quality time series data, T vc is the steady-state time series trend component of the cth group of power quality time series data, u c is the corresponding national standard limit range.

2. The method according to claim 1, characterized in that The power quality time series data is decomposed by a seasonal extraction decomposition method to obtain a steady-state time series trend component.

3. The method according to claim 1, characterized in that The steady-state time series trend component was tested using the Mann-Kendall trend test.

4. The method according to claim 1, wherein The process of judging the trend test statistic value includes: When the trend test statistic is greater than 0, the power quality time series data is in an upward trend; and when the trend test statistic is less than 0, the power quality time series data is in a downward trend.

5. The power quality trend identification system for responsible entities in typical interference scenarios is characterized by: Used to execute the method according to any one of claims 1 to 4 above.

Images