CN113376469A - Analysis method of power quality disturbance data - Google Patents
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Abstract
The invention discloses an analysis method of power quality disturbance data, which comprises the following steps: s1: acquiring power quality disturbance data; s2: and carrying out availability analysis on the power quality disturbance data to obtain an analysis result. The analysis method of the power quality disturbance data provided by the invention can solve the problem of incomplete analysis data caused by single analysis of the existing power quality disturbance data.
Description
Technical Field
The invention relates to the technical field of power quality monitoring, in particular to an analysis method of power quality disturbance data.
Background
With the installation and use of a large number of monitoring devices, a modern power grid generates massive power quality disturbance monitoring data at every moment, and the data are important bases for supporting relevant analysis and decision making of a power company. However, the generation of data is always accompanied by poor quality data, which may greatly affect the result of data analysis and mislead decision making. Due to the rapid development of data cleaning technology and weak available data analysis in recent years, poor data is available to a certain extent, so that the evaluation of the availability of the data becomes the basis of subsequent data selection or data cleaning and is a necessary condition for ensuring the credibility of data analysis results. However, for the characteristics of the power quality monitoring data, the existing method generally adopts a single index to perform one-sided evaluation on the data quality, not only neglects the influence difference of different data quality problems on the data availability, but also lacks an effective means for quantifying the extent to which the monitoring data can be utilized by subsequent analysis, and further lacks visualization software for grasping the data availability from the whole situation. Therefore, on the premise of deeply analyzing the characteristics of the power quality data, the method for evaluating and visualizing the availability of the power quality disturbance data is provided, and the method has important significance.
Disclosure of Invention
The invention aims to provide an analysis method of power quality disturbance data, which aims to solve the problem of incomplete analysis data caused by single analysis of the existing power quality disturbance data.
The technical scheme for solving the technical problems is as follows:
the invention provides an analysis method of power quality disturbance data, which comprises the following steps:
s1: acquiring power quality disturbance data;
s2: and carrying out availability analysis on the power quality disturbance data to obtain an analysis result.
Optionally, in the step S2, the usability analysis includes a time efficiency analysis, an accuracy analysis and an integrity analysis.
Optionally, the time effectiveness analysis comprises:
a1: acquiring frequency data of monitoring points in a regional network;
a2: according to the frequency data, Euclidean distance measurement data of the frequency data is obtained;
a3: and obtaining a timeliness analysis result according to the frequency data and the Euclidean distance measurement data.
Optionally, in the step a2, the euclidean distance measurement data of the frequency data is obtained by:
wherein D isiIs the Euclidean distance between the frequency data of the ith group of monitoring points and the average value of the frequency data of all the monitoring points in the regional network, fitFrequency value of i-th group data at time t, ftI represents the ith group of monitoring points in the regional network, t represents the time t, and n represents the quantity of frequency data acquired from the monitoring points.
Optionally, the accuracy analysis comprises:
b1: carrying out accuracy analysis and error correction on a signal access channel of the monitoring device;
b2: acquiring monitoring data of monitoring points in a regional network according to the monitoring device;
b3: and obtaining the accuracy data of the power quality disturbance data according to the monitoring data of the monitoring points in the area network.
Optionally, the step B1 includes the following substeps:
b11: judging whether the power quality disturbance data are within the rated range of the sensor measurement, if so, entering step B12; otherwise, go to step B16;
b12: judging whether the power quality disturbance data has the phase of the abnormal area, if so, entering the step B13; otherwise, go to step B16;
b13: analyzing the abnormal reason of the phase of the abnormal area;
b14: according to the abnormal reasons, a correction scheme is made to obtain correct power quality disturbance data;
b15: updating derivative parameters according to the correct power quality disturbance data and outputting the derivative parameters, and entering step B2;
b16: and deleting the power quality disturbance data and/or marking the power quality disturbance data as unavailable, outputting a deletion result and/or a marking result, and entering the step B2.
Optionally, in the step B13, the abnormal cause of the phase of the abnormal region includes a phase error and/or a polarity error;
in step B14, the making a correction scenario includes:
if the abnormal reason is a phase error, redistributing the phase to an expected position and rechecking the phase;
and if the abnormal reason is a polarity error, correcting the phase by rotating a fundamental component.
Optionally, the step B2 includes the following substeps:
b21: acquiring the current day monitoring data and historical monitoring data of the current monitoring point;
b22: according to the historical monitoring data, a nuclear density estimation method is adopted to preliminarily judge whether the monitoring data of the current day is within a preset range, if so, the step B23 is carried out; otherwise, entering the next monitoring point and returning to the step B21;
b23: acquiring the monitoring data of the current monitoring point and the current moment of the adjacent monitoring points;
b24: judging whether the monitoring data of the current moment of the current monitoring point is accurate or not according to the monitoring data of the current moment of the adjacent monitoring point, and if so, outputting the monitoring data of the current moment; otherwise, the next monitoring point is entered and the process returns to step B21.
Optionally, the integrity analysis comprises:
the integrity of the monitored data is measured according to the following formula:
wherein, F represents the data integrity rate, including the voltage data integrity rate, the current data integrity rate and the frequency data integrity rate, S represents the attribute number recorded by the data set, C represents the data number in each attribute column, and Q represents the missing data number.
Alternatively, the usability analysis is performed by:
Qit＝w1×ρi+w2×Fit+w3×Ait
wherein Q isit、Fit、AitData availability, integrity and accuracy, w, respectively, of the ith monitoring device at time txIs the corresponding weight, x is a natural number, rhoiThe timeliness of the ith monitoring device.
The invention has the following beneficial effects:
1. the method combines the characteristics of the power quality disturbance data, carries out data quality evaluation from three dimensions of data timeliness, integrity and accuracy, quantifies an effective means for the subsequent analysis and utilization of the data to the extent, and avoids one-sided conclusion brought by single-dimension evaluation.
2. According to the invention, the timeliness of data is evaluated through the frequency correlation of multiple monitoring points, and the problem that the prior art ignores the error of disturbance time caused by clock drift of old equipment is solved.
3. The invention provides an accuracy evaluation method based on hierarchical rule inspection, which solves the problem that the prior art cannot detect wiring errors.
Drawings
FIG. 1 is a flow chart of a method for analyzing power quality disturbance data according to the present invention;
FIG. 2 is a flow chart of a time efficiency analysis of the method for analyzing power quality disturbance data provided by the present invention;
FIG. 3 is a flow chart of an accuracy analysis of the method for analyzing power quality disturbance data provided by the present invention;
FIG. 4 is a flow chart illustrating the substeps of step B1 of FIG. 3;
FIG. 5 is a flow chart illustrating the substeps of step B2 in FIG. 3;
fig. 6 is a display interface diagram of the visualization software of the analysis method based on the power quality disturbance data provided by the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Examples
The technical scheme for solving the technical problems is as follows:
the invention provides an analysis method of power quality disturbance data, which comprises the following steps:
s1: acquiring power quality disturbance data;
s2: and carrying out availability analysis on the power quality disturbance data to obtain an analysis result.
Specifically, in the present invention, the usability analysis includes time efficiency analysis, accuracy analysis, and integrity analysis.
Therefore, the method combines the characteristics of the power quality disturbance data, carries out data analysis from three dimensions of data timeliness, integrity and accuracy, quantifies an effective means for the subsequent analysis and utilization of the data to the extent, avoids one-sided conclusion brought by single-dimension evaluation, and can effectively utilize the analysis result to guide the power company to make a correct decision.
The following describes in detail the substep analysis of the usability analysis employed in the present invention:
alternatively, referring to fig. 2, the timeliness analysis includes:
a1: acquiring frequency data of monitoring points in a regional network;
a2: according to the frequency data, Euclidean distance measurement data of the frequency data is obtained;
a3: and obtaining a timeliness analysis result according to the frequency data and the Euclidean distance measurement data.
Optionally, in the step a2, the euclidean distance measurement data of the frequency data is obtained by:
di is the Euclidean distance between the frequency data of the ith group of monitoring points and the average value of the frequency data of all the monitoring points in the regional network, fitFrequency value of i-th group data at time t, ftI represents the ith group of monitoring points in the regional network, t represents the time t, and n represents the quantity of frequency data acquired from the monitoring points.
Specifically, the timeliness of the ith monitoring device is expressed as:
where ρ isiAnd (d) representing the timeliness of the ith monitoring device, wherein Di is the Euclidean distance between the frequency data of the ith group of monitoring points and the average value of the frequency data of all the monitoring points in the regional network.
Alternatively, as shown with reference to fig. 3, the accuracy analysis includes:
b1: carrying out accuracy analysis and error correction on a signal access channel of the monitoring device;
b2: acquiring monitoring data of monitoring points in a regional network according to the monitoring device;
b3: and obtaining the accuracy data of the power quality disturbance data according to the monitoring data of the monitoring points in the area network.
Specifically, referring to fig. 4, the step B1 includes the following sub-steps:
b11: judging whether the power quality disturbance data are within the rated range of the sensor measurement, if so, entering step B12; otherwise, go to step B16;
b2: judging whether the power quality disturbance data has the phase of the abnormal area, if so, entering the step B13; otherwise, go to step B16;
here, the judgment of whether the power quality disturbance data has the phase of the abnormal region is to judge whether the negative sequence current amplitude is greater than 0.5p.u or whether any phase is outside the expected region (the phase a is-30 to 30 degrees, the phase B is 210 to 270 degrees, and the phase C is 90 to 150 degrees), and the phase abnormality of the monitoring device can be preliminarily judged.
B13: judging an abnormal reason of the phase of the abnormal area, wherein the abnormal reason comprises phase errors and/or polarity errors;
b14: according to the abnormal reasons, a correction scheme is made to obtain correct power quality disturbance data;
b15: updating derivative parameters according to the correct power quality disturbance data and outputting the derivative parameters, and entering step B2;
and for the phase positioned in the abnormal area, normalizing all phasors relative to the phase A, identifying all phases based on expected positions, judging the phases, updating the derived parameters if the phases are at the expected positions and have expected polarities, and otherwise, reallocating the phases, rotating the phase angle of the fundamental wave component by 180 degrees and then updating the derived parameters. Here, the derived parameters include phase, fundamental component phase angle, voltage, current, and the like.
B16: and deleting the power quality disturbance data and/or marking the power quality disturbance data as unavailable, outputting a deletion result and/or a marking result, and entering the step B2.
Optionally, in the step B13, the abnormal cause of the phase of the abnormal region includes a phase error and/or a polarity error;
in step B14, the making a correction scenario includes:
if the abnormal reason is a phase error, redistributing the phase to an expected position and rechecking the phase;
and if the abnormal reason is a polarity error, correcting the phase by rotating a fundamental component.
Alternatively, referring to fig. 5, the step B2 includes the following sub-steps:
b21: acquiring the current day monitoring data and historical monitoring data of the current monitoring point;
b22: according to the historical monitoring data, a nuclear density estimation method is adopted to preliminarily judge whether the monitoring data of the current day is within a preset range, if so, the step B23 is carried out; otherwise, entering the next monitoring point and returning to the step B21;
it should be noted that, the identification of the abnormal data of the power quality based on the kernel density estimation is to determine whether the value of the statistical day is abnormal according to the probability distribution rule of the historical data. The method does not need to assume the distribution of parameter obedience in advance, eliminates subjective factors, and further objectively estimates the probability distribution rule of the parameters
Suppose { V1,V2,V3,…,VnIs n random samples, { v }1,v2,v3,…,viI observations for each sample.
Since there is an optimal solution for the bandwidth, i.e. selecting too large or too small results in large fluctuation of the density curve, mean-integral mean-square error (MISE) is used to measure the kernel density estimate
The estimated effect of the unknown function f (x) is shown as follows:
where MISE (h) represents the mean integrated mean square error,
representing the infinitesimal number associated with n, h being the bandwidth; n is the number of samples; f (x) is an unknown function, AMISE (h) is an asymptotically integrated mean square error, which is calculated as follows
Wherein R (f ″) - (f ″ (x))2dx，m2(K)＝∫x2K (x) dx, K (·) is a kernel function.
in the formula: h is the bandwidth; n is the number of samples; k (-) is the kernel function.
To minimize MISE (h), pole AMISE (h) is calculated, and the pole is obtained as:
wherein: σ is the standard deviation and n is the number of samples.
Specifically, the power quality data accuracy evaluation based on the kernel density estimation is to judge whether the numerical value of the statistical day is accurate according to the probability distribution rule of the historical data. The method does not need to assume the distribution obeyed by the parameters in advance, eliminates subjective factors, and further objectively estimates the probability distribution rule of the parameters. Since the normal distribution has a3 σ criterion, i.e., the area in the μ +3 σ range under the normal distribution probability density function curve is 99.73%, only a value of about 0.27% is likely to fall outside the range. Based on the method, a confidence interval can be set as [ v-3 sigma, v +3 sigma ], so that when the monitoring data of the current day falls into the confidence interval, the accuracy of the monitoring data of the current day of the monitoring point exists, if the distance from the confidence interval is large, the accuracy of the monitoring data of the current day of the monitoring point does not exist, the data of the monitoring point can be removed and is not used, and then the monitoring point enters the next monitoring point until all the monitoring points in the area are finished.
However, for the monitoring data without accuracy problem, the above method may cause the partially accurate data to be misjudged. To solve this problem, a second layer rule check method is introduced as a complement: because in the distribution network, each node has two adjacent nodes except the end node, can calculate respectively according to adjacent node monitoring value and obtain the voltage calculation value of a plurality of this nodes, specifically as follows:
b23: acquiring the monitoring data of the current monitoring point and the current moment of the adjacent monitoring points;
b24: judging whether the monitoring data of the current moment of the current monitoring point is accurate or not according to the monitoring data of the current moment of the adjacent monitoring point, and if so, outputting the monitoring data of the current moment; (ii) a Otherwise, the next monitoring point is entered and the process returns to step B21. All output monitoring data are finally gathered, and the total number of data with accuracy is determined for subsequent data processing.
The relationship between the node voltages at two ends of a line can be expressed as:
in the formula of UsAnd U1The voltage of the initial node and the final node of the line, P and Q are respectively the active power and the reactive power flowing from the initial node to the final node.
Considering that in practice, the power quality monitoring equipment has measurement errors, and a voltage amplitude measured value of a node i at the time t is assumed to be uit＝ui+uiξitWherein u isiFor the voltage amplitude, xi, which is accurate at that momentitThe maximum allowable error of the device is monitored. When u isitWhen the measurement accuracy is within the range, the error satisfies | (u) for the adjacent node i and the node sit-ui′t)/uit|≈|ξit-ξst|≤||||ξit|+|ξstL, where ui′tIs the voltage amplitude value xi obtained by the calculation of the relation of the node voltages at two ends of a linestThe maximum allowable error of the monitoring equipment on the node S (the standard of grade A is +/-0.1%, and the standard of grade S is +/-0.5%).
In summary, the result of the accuracy evaluation can be expressed as:
in the formula AitIs a number ofAccording to the accuracy, NitTotal number of data at time t for the ith monitoring device, C1The number of data with accuracy.
Optionally, the integrity analysis comprises:
the integrity of the monitored data is measured according to the following formula:
wherein, F represents the data integrity including the voltage data integrity, the current data integrity, the frequency data integrity, etc., S represents the number of attributes recorded by the data set, C represents the number of data in each attribute column, and Q represents the number of missing data.
Optionally, the weight of each parameter is obtained by an analytic hierarchy process, and usability analysis is performed:
an Analytic Hierarchy Process (AHP) is a qualitative and quantitative combined decision analysis method for solving the complex problem of multiple targets.
Thus, in the present invention, the following conclusions can be drawn using the analytic hierarchy process:
(1) when the timestamp of the monitoring device is inaccurate, due to the time-varying property of the power quality monitoring data, the accurate data at the moment can be misjudged as the inaccurate data at other moments, namely the timeliness of the data can further influence the accuracy of the data;
(2) due to lack of timeliness, the calculation result of the integrity is recorded to the next time period along with the time offset delta t, and an error result is obtained no matter the calculation of the integrity or the filling and repairing of subsequent data;
(3) since the timeliness problem mostly exists in old equipment, the occurrence frequency is related to the number of the old equipment, and once the timeliness problem occurs, all data recorded by a monitoring device in a period of time can be influenced, so that the influence of the timeliness on the data availability is maximum;
(4) when data integrity is missing, the calculated standard deviation deviates from the actual value, resulting in a deviation of the confidence interval calculated based on the 3 σ criterion, affecting the evaluation of data accuracy. In summary, the influence degree of each availability index on the subsequent analysis result is ranked as: timeliness > integrity > accuracy.
Therefore, under the conclusion, the timeliness, the integrity and the accuracy are compared in importance, and the importance degree is assigned according to the table 1 to form a judgment matrix A.
TABLE 1 significance Scale of values
Where the corner labels 1, 2, 3 represent availability, integrity and accuracy, respectively, then a12A comparison of availability and completeness is indicated, a11Indicating availability and a comparison of availability.
In addition, when importance comparison is performed on a plurality of characteristics, in order to prevent occurrence of a logical error, a consistency check should be performed on the determination matrix. Firstly, calculating a consistency index CI:
wherein λmaxFor the maximum eigenvalue, n represents the order of matrix a. Then, look-up table is performed to determine the corresponding average random consistency index ri (random index), as shown in table 2:
TABLE 2 average random consistency index
| Order of the scale | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.36 |
The consistency ratio CR (consistency ratio) is calculated and judged.
When CR is reached<At 0.1, the consistency of the decision matrix is considered acceptable, CR>When the time is 0.1, the judgment matrix is considered not to meet the consistency requirement, and the judgment matrix needs to be revised again. When the matrix is judged to meet the consistency requirement, the method is used for lambdamaxCorresponding feature vector W*Performing normalization to obtainTo W ═ W1,w2,…,wn]TIs the weight of each characteristic.
Thus, the usability analysis can be performed by:
Qit＝w1×ρi+w2×Fit+w3×Ait
wherein Q isit、Fit、AitData availability, integrity and accuracy, w, respectively, of the ith monitoring device at time txIs the corresponding weight, x is a natural number, rhoiThe timeliness of the ith monitoring device.
In addition, the invention also provides a visualization software based on the analysis method of the power quality disturbance data, referring to fig. 6, the method provided by the invention is input to show the final analysis result, specifically, the visualization software developed based on the power quality monitoring system has the main functions of: inputting voltage monitoring data, evaluating the accuracy, timeliness and integrity of the data, presenting an availability evaluation result and correspondingly coloring, and further visually presenting the availability level of the data. The software interface comprises three parts of a menu bar, a query bar and an availability presentation: the user can select the concerned node and time scale in the menu bar; historical month data is provided in the query bar, and the interface body presents results of the multiple monitoring points at different time scales.
In conclusion, the invention has the following beneficial effects:
1. the method combines the characteristics of the power quality disturbance data, carries out data quality evaluation from three dimensions of data timeliness, integrity and accuracy, quantifies an effective means for the subsequent analysis and utilization of the data to the extent, and avoids one-sided conclusion brought by single-dimension evaluation.
2. According to the invention, the timeliness of data is evaluated through the frequency correlation of multiple monitoring points, and the problem that the prior art ignores the error of disturbance time caused by clock drift of old equipment is solved.
3. The invention provides an accuracy evaluation method based on hierarchical rule inspection, which solves the problem that the prior art cannot detect wiring errors.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method for analyzing power quality disturbance data, the method comprising:
s1: acquiring power quality disturbance data;
s2: and carrying out availability analysis on the power quality disturbance data to obtain an analysis result.
2. The method for analyzing power quality disturbance data according to claim 1, wherein in the step S2, the usability analysis includes a time efficiency analysis, an accuracy analysis and an integrity analysis.
3. The method of analyzing power quality disturbance data according to claim 2, wherein the time-based analysis comprises:
a1: acquiring frequency data of monitoring points in a regional network;
a2: according to the frequency data, Euclidean distance measurement data of the frequency data is obtained;
a3: and obtaining a timeliness analysis result according to the frequency data and the Euclidean distance measurement data.
4. The method for analyzing power quality disturbance data according to claim 3, wherein in the step A2, Euclidean distance measurement data of the frequency data is obtained by:
wherein D isiIs as followsEuclidean distance f between frequency data of i groups of monitoring points and average value of frequency data of all monitoring points in regional networkitFrequency value of i-th group data at time t, ftI represents the ith group of monitoring points in the regional network, t represents the time t, and n represents the quantity of frequency data acquired from the monitoring points.
5. The method of analyzing power quality disturbance data according to claim 2, wherein the accuracy analysis comprises:
b1: carrying out accuracy analysis and error correction on a signal access channel of the monitoring device;
b2: acquiring monitoring data of monitoring points in a regional network according to the monitoring device;
b3: and obtaining the accuracy data of the power quality disturbance data according to the monitoring data of the monitoring points in the area network.
6. The method for analyzing power quality disturbance data according to claim 5, wherein the step B1 comprises the following sub-steps:
b11: judging whether the power quality disturbance data are within the rated range of the sensor measurement, if so, entering step B12; otherwise, go to step B16;
b12: judging whether the power quality disturbance data has the phase of the abnormal area, if so, entering the step B13; otherwise, go to step B16;
b13: analyzing the abnormal reason of the phase of the abnormal area;
b14: according to the abnormal reasons, a correction scheme is made to obtain correct power quality disturbance data;
b15: updating derivative parameters according to the correct power quality disturbance data and outputting the derivative parameters, and entering step B2;
b16: and deleting the power quality disturbance data and/or marking the power quality disturbance data as unavailable, outputting a deletion result and/or a marking result, and entering the step B2.
7. The method for analyzing electric energy quality disturbance data according to claim 5, wherein in the step B13, the abnormal cause of the phase of the abnormal region comprises a phase error and/or a polarity error;
in step B14, the making a correction scenario includes:
if the abnormal reason is a phase error, redistributing the phase to an expected position and rechecking the phase;
and if the abnormal reason is a polarity error, correcting the phase by rotating a fundamental component.
8. The method for analyzing power quality disturbance data according to claim 5, wherein the step B2 comprises the following sub-steps:
b21: acquiring the current day monitoring data and historical monitoring data of the current monitoring point;
b22: according to the historical monitoring data, a nuclear density estimation method is adopted to preliminarily judge whether the monitoring data of the current day is within a preset range, if so, the step B23 is carried out; otherwise, entering the next monitoring point and returning to the step B21;
b23: acquiring the monitoring data of the current monitoring point and the current moment of the adjacent monitoring points;
b24: judging whether the monitoring data of the current moment of the current monitoring point is accurate or not according to the monitoring data of the current moment of the adjacent monitoring point, and if so, outputting the monitoring data of the current moment; otherwise, the next monitoring point is entered and the process returns to step B21.
9. The method for analyzing power quality disturbance data according to claim 2, wherein the integrity analysis comprises:
the integrity of the monitored data is measured according to the following formula:
wherein, F represents the data integrity rate, including the voltage data integrity rate, the current data integrity rate and the frequency data integrity rate, S represents the attribute number recorded by the data set, C represents the data number in each attribute column, and Q represents the missing data number.
10. The method for analyzing power quality disturbance data according to any one of claims 1 to 9, characterized in that the usability analysis is performed by:
Qit＝w1×ρi+w2×Fit+w3×Ait
wherein Q isit、Fit、AitData availability, integrity and accuracy, w, respectively, of the ith monitoring device at time txIs the corresponding weight, x is a natural number, rhoiThe timeliness of the ith monitoring device.
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| CN116316586A (en) * | 2023-03-15 | 2023-06-23 | 国网湖北省电力有限公司随州供电公司 | Method for tracing power jump in power system by adopting jump analysis method |
| CN116316586B (en) * | 2023-03-15 | 2023-10-10 | 国网湖北省电力有限公司随州供电公司 | Method for tracing power jump in power system by adopting jump analysis method |
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