CN115600933B - Electric meter power quality detection method and system based on Internet of things - Google Patents
Electric meter power quality detection method and system based on Internet of things Download PDFInfo
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Abstract
The application discloses an electric energy quality detection method and system for an electric meter based on the Internet of things, which belong to the technical field of data processing, and comprise the following steps: the method comprises the steps of obtaining power quality evaluation dimensionality, matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index, traversing the evaluation indexes, constructing a power quality scoring table, carrying out weight distribution on the voltage deviation, the three-phase balance evaluation index and the power supply reliability according to a preset weight distribution rule, collecting an index characteristic value set in a preset area through the Internet of things, inputting a power quality evaluation dimensionality weight distribution result and the index characteristic value set into the power quality scoring table, generating a power quality comprehensive score, determining a negative scoring evaluation dimensionality and sending the negative scoring evaluation dimensionality to a power quality manager. The technical problems that the power quality detection efficiency is low and the detection result is unreliable in the prior art are solved, the technical effects that the power quality is comprehensively evaluated and the power detection accuracy is improved are achieved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an electric energy quality detection method and system for an electric meter based on the Internet of things.
Background
With the development of economy, the demand of people on living standard is increasing day by day, and the demand on electric energy is also increasing continuously. In order to improve the power supply level, the traditional power grid and the power utilization mode are reformed through a smart power grid, and the smart power meter, as the most important infrastructure, is widely applied to industry and life, can intuitively provide reference data in the aspect of power utilization for users, and fully plays the positive role of a smart power grid system on intelligent power utilization.
At present, the detection of the power quality mainly includes acquiring data through a special power quality acquisition device, such as a power quality monitoring instrument, evaluating the power quality level through monitoring power related parameters in real time, establishing a connection with an accident that has occurred, finding appropriate remedial measures, finding potential risks as soon as possible through monitoring the parameters, and performing adaptive treatment on the risks.
However, the existing power quality monitoring instrument has few parameters to monitor, cannot evaluate the power quality comprehensively and objectively after obtaining monitoring data, and has low monitoring efficiency and poor monitoring accuracy. The technical problems of low power quality detection efficiency and unreliable detection results exist in the prior art.
Disclosure of Invention
The application aims to provide an electric energy quality detection method and system for an electric meter based on the Internet of things, and the method and system are used for solving the technical problems that in the prior art, the electric energy quality detection efficiency is low, and the detection result is unreliable.
In view of the above problems, the application provides an electric energy quality detection method and system for an electric meter based on the internet of things.
In a first aspect, the application provides an electric energy quality detection method for an electric meter based on the internet of things, wherein the electric energy quality detection method is applied to an electric energy quality detection system for the electric meter, and the method comprises the following steps: acquiring a power quality evaluation dimension, wherein the power quality evaluation dimension comprises voltage deviation, three-phase balance, frequency and power supply reliability; traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index; traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to construct an electric energy quality scoring table; carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result; acquiring an index characteristic value set in a preset area through the Internet of things on the basis of the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index; inputting the power quality evaluation dimension weight distribution result and the index characteristic value set into the power quality scoring table to generate a power quality comprehensive score; and determining a negative score evaluation dimension according to the electric energy quality comprehensive score, and sending the negative score evaluation dimension to an electric energy quality manager.
On the other hand, this application still provides an ammeter electric energy quality detection system based on thing networking, wherein, includes: an evaluation dimension obtaining module, configured to obtain a power quality evaluation dimension, where the power quality evaluation dimension includes a voltage deviation, a three-phase balance, a frequency, and a power supply reliability; an index matching module for traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index; the evaluation table building module is used for traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to build an electric energy quality evaluation table; the weight distribution module is used for carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result; the index characteristic value acquisition module is used for acquiring an index characteristic value set in a preset area through the Internet of things on the basis of the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index; the score generation module is used for inputting the power quality evaluation dimension weight distribution result and the index characteristic value set into the power quality scoring table to generate a power quality comprehensive score; and the evaluation dimension sending module is used for determining a negative score evaluation dimension according to the electric energy quality comprehensive score and sending the negative score evaluation dimension to an electric energy quality manager.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method, multiple dimensions for evaluating the power quality are obtained, wherein the power quality evaluation dimensions comprise voltage deviation, three-phase balance, frequency and power supply reliability, further, the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index are obtained by performing index matching on the voltage deviation, the three-phase balance, the frequency evaluation index and the power supply reliability one by one, further, a power quality evaluation table is built according to index values and power abnormality conditions corresponding to each evaluation index, the voltage deviation, the three-phase balance, the frequency and the power supply reliability are subjected to weight distribution according to a preset weight distribution rule to obtain a power quality evaluation dimension weight distribution result, then according to the obtained voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index, index characteristic value sets in a preset area are collected through the Internet of things, the obtained power quality evaluation dimension weight distribution result and the index characteristic value sets are input into the power quality evaluation table to obtain a power quality comprehensive score considering the multiple dimensions, further, a negative evaluation dimension is determined according to the comprehensive score of the power quality, and the negative evaluation dimension is sent to a power quality manager. Therefore, the technical effects of scientifically and objectively detecting the electric energy quality of the electric meter and improving the accuracy and efficiency of detection are achieved.
Drawings
In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
Fig. 1 is a schematic flow chart of an electric energy quality detection method for an electric meter based on the internet of things according to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating matching evaluation indexes in the electric energy quality detection method for the electric meter based on the internet of things according to the embodiment of the application;
fig. 3 is a schematic flow chart of building a power quality score table in the method for detecting the power quality of an electric meter based on the internet of things according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of an electric energy quality detection system of an electric meter based on the internet of things.
Description of the reference numerals: the system comprises an evaluation dimension obtaining module 11, an index matching module 12, a score table building module 13, a weight distribution module 14, an index characteristic value collecting module 15, a score generating module 16 and an evaluation dimension sending module 17.
Detailed Description
The electric energy quality detection method and system for the electric meter based on the Internet of things solve the technical problems that in the prior art, the electric energy quality detection efficiency is low, and the detection result is unreliable, and achieve the technical effects of comprehensively evaluating the electric energy quality and improving the electric energy detection accuracy.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
In the following, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.
Example one
As shown in fig. 1, the application provides an electric energy quality detection method for an electric meter based on the internet of things, wherein the electric energy quality detection method is applied to an electric energy quality detection system for the electric meter, and the method comprises the following steps:
step S100: acquiring a power quality evaluation dimension, wherein the power quality evaluation dimension comprises voltage deviation, three-phase balance, frequency and power supply reliability;
specifically, when the electric energy quality of the electric meter is detected, the electric energy quality can be evaluated from four dimensions of voltage deviation, three-phase balance, frequency and power supply reliability, and the comprehensiveness and objectivity of evaluation are improved. The power quality evaluation dimension is an evaluation target when evaluating power quality, and can comprehensively reflect power quality conditions. The voltage deviation is the difference between the actual voltage value and the rated voltage value of the system, and reflects the deviation condition of the voltage. The three-phase balance refers to the unbalanced degree of three-phase voltage. The frequency is the number of times of completing the periodic change in unit time, and reflects the frequency degree of the change. The power supply reliability refers to the capability of a power supply system for continuously supplying power. Therefore, the technical effects of establishing a systematic analysis object for subsequent electric energy analysis and improving the quality detection accuracy are achieved.
Step S200: traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index;
further, as shown in fig. 2, traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index, step S200 in the embodiment of the present application further includes:
step S210: according to the voltage deviation, matching the qualification rate of the power supply voltage, and setting the qualification rate as the voltage deviation evaluation index;
step S220: matching three-phase voltage unbalance according to the three-phase balance, and setting the three-phase voltage unbalance as the three-phase balance evaluation index;
step S230: according to the frequency, matching frequency failure rate, and setting as the frequency evaluation index;
step S240: and matching the average power failure duration, the average power failure times and the system power failure equivalent hours according to the power supply reliability evaluation index, and setting the index as the power supply reliability evaluation index.
Specifically, after the power quality evaluation dimensions are obtained, corresponding evaluation indexes are matched for each dimension to reflect the power quality of each dimension. The power supply voltage qualification rate is used as an evaluation index for evaluating the voltage deviation degree, and the voltage deviation evaluation index is obtained; setting the three-phase voltage unbalance degree as an evaluation index for evaluating the three-phase balance degree to obtain the three-phase balance evaluation index; the frequency failure rate is used as an evaluation index for evaluating the power frequency condition to obtain the frequency evaluation index; and taking the average power failure duration, the average power failure times and the system power failure equivalent hours as evaluation indexes for evaluating the power supply reliability to obtain the power supply reliability evaluation indexes. The power supply voltage qualification rate is the percentage of the voltage quantity meeting the power supply requirement in the total power supply voltage quantity obtained by judging whether the actual voltage value of the voltage meets the rated voltage value of the system or not through collecting the actual voltage value of the voltage. The three-phase voltage unbalance is obtained by obtaining a three-phase sequence, further decomposing asymmetric components in the three phases to obtain positive sequence, negative sequence and zero sequence voltages, analyzing numerical values of the components to obtain the voltage unbalance, and further, preferably, calculating the three-phase voltage unbalance according to the following formula: three-phase voltage unbalance (= (negative sequence voltage/positive sequence voltage) × 100%.
Specifically, the calculation formula of the positive sequence voltage and the negative sequence voltage of the three-phase voltage is as follows:
wherein the content of the first and second substances,
is a rotation operator;
，
；
is the positive sequence component of the phase voltages,
is the negative sequence component of the phase voltages,
is the phasor of the a-phase voltage,
is a phasor of the phase of the B-phase voltage,
is the C-phase voltage phasor.
The above calculation method of the three-phase voltage unbalance is only a preferred embodiment, and no limitation is imposed on other conventional calculation methods which can realize the three-phase voltage unbalance.
Specifically, the frequency reject rate is a ratio of the number of frequency deviations from the fundamental frequency in the power supply process to the total power supply frequency. The average power failure duration refers to the average power failure time in the detection period, the average power failure times are the average power failure times of power supply clients in the detection period, the system power failure equivalent hours refer to the equivalent hours of power failure of all the clients due to the influence of the system on the power failure of the clients in the detection period, and the sum of the multiplication of each power failure capacity and each power failure time is divided by the total system power supply capacity. Therefore, the corresponding evaluation indexes are matched for all the dimensions, the power quality of each dimension is evaluated, and the technical effect of improving the comprehensiveness and accuracy of power quality evaluation is achieved.
Step S300: traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to construct a power quality evaluation table;
further, as shown in fig. 3, the step S300 of traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index, and the power supply reliability evaluation index to construct an electric energy quality score table further includes:
step S310: traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index, and acquiring voltage deviation electric energy abnormity recorded data, three-phase unbalance electric energy abnormity recorded data, frequency deviation electric energy abnormity recorded data and power supply reliability deviation electric energy abnormity recorded data in the preset area;
step S320: constructing a first grading calibration curve according to the voltage deviation electric energy abnormity recorded data;
step S330: constructing a second grading calibration curve according to the three-phase unbalanced electric energy abnormal record data;
step S340: constructing a third grading calibration curve according to the frequency deviation electric energy abnormity recorded data;
step S350: constructing a fourth grading calibration curve according to the power supply reliability deviation electric energy abnormity recorded data;
step S360: traversing the first scoring calibration curve, the second scoring calibration curve, the third scoring calibration curve and the fourth scoring calibration curve according to a preset slope to carry out segmentation so as to generate a plurality of groups of curve segmentation results;
step S370: and traversing the multiple groups of curve segmentation results according to the preset total score of the power quality to perform scoring interval division, and generating the power quality scoring table.
Further, step S360 in the embodiment of the present application further includes:
step S361: extracting a voltage qualified rate record list and a first electric energy abnormal frequency record list from the voltage deviation electric energy abnormal record data;
step S362: constructing the first grading calibration curve by taking the voltage qualification rate recording list as first coordinate data and taking the first electric energy abnormal frequency recording list as second coordinate data;
step S363: extracting an unbalance degree record list and a second electric energy abnormal frequency record list from the three-phase unbalance electric energy abnormal record data;
step S364: establishing a second grading calibration curve by taking the unbalance degree recording list as first coordinate data and taking the second electric energy abnormal frequency recording list as second coordinate data;
step S365: extracting a non-fundamental frequency proportion record list and a third electric energy abnormal frequency record list from the frequency deviation electric energy abnormal record data;
step S366: establishing a third grading calibration curve by taking the non-fundamental frequency proportion record list as first coordinate data and the third electric energy abnormal frequency record list as second coordinate data;
step S367: extracting reliability deviation record data and a fourth electric energy abnormal frequency record list from the power supply reliability deviation electric energy abnormal record data;
step S368: and constructing the fourth grading calibration curve by using the reliability deviation record data as first coordinate data and the fourth electric energy abnormal frequency record list as second coordinate data.
Specifically, after the evaluation indexes of each dimension are obtained, data acquired according to the indexes needs to be processed, and the electric energy quality is evaluated after comprehensive analysis. The electric energy quality scoring table is a calculation table for obtaining the electric energy quality comprehensive score by quantitatively scoring the data input into the scoring table. The electric energy quality scoring table is a quality scoring table which scores the electric energy quality of four dimensions according to the scoring results by respectively dividing scoring intervals of a plurality of groups of curve segmentation results according to the preset total score of the electric energy quality, scoring and assigning values to each curve segmentation section.
Specifically, a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index are used as indexes, and historical abnormal record data of relevant indexes are collected in a preset area. The preset area is any area needing to be subjected to electric energy quality detection of the electric meter. The voltage deviation electric energy abnormal recording data refers to an electric energy abnormal condition and a voltage deviation value condition caused by voltage deviation. The three-phase unbalance electric energy abnormal recording data refer to an electric energy abnormal condition and a three-phase unbalance degree value caused by three-phase unbalance. The frequency deviation electric energy abnormal recording data refers to an electric energy abnormal condition caused by frequency deviation and a proportional condition with frequency being non-fundamental wave frequency. The power supply reliability deviation electric energy abnormal recording data are data of average power failure duration, average power failure times and system power failure equivalent hours, and abnormal frequency data in a detection period.
Specifically, the first scoring calibration curve takes a voltage qualification rate recording list as first coordinate data, and takes a first electric energy abnormal frequency recording list as second coordinate data, preferably, the first coordinate is an abscissa, and the second coordinate is a ordinate. The values in the voltage qualification rate recording list and the first electric energy abnormal frequency recording list correspond to each other one by one, so that the coordinate value in the first grading calibration curve can be obtained. And connecting all the coordinate values in sequence to obtain the first grading calibration curve. According to the same method, an unbalance degree recording list and a second electric energy abnormal frequency recording list are extracted from three-phase unbalance electric energy abnormal recording data, and the data in the unbalance degree recording list and the data in the second electric energy abnormal frequency recording list are in one-to-one correspondence. And the data in the second electric energy abnormal frequency recording list are obtained according to the electric energy abnormal frequency value in the detection interval corresponding to the unbalance degree value in the unbalance degree recording list. Therefore, the data corresponding to one in the unbalance degree recording list and the second electric energy abnormal frequency recording list are used as coordinates in the curve, and all coordinate points are connected in sequence to obtain the second grading calibration curve. The third scoring calibration curve is obtained by constructing data in a non-fundamental frequency proportion record list obtained according to proportion values of non-fundamental frequencies in the total frequency in a preset region and a third electric energy abnormal frequency record list obtained by summarizing third electric energy abnormal frequencies corresponding to the non-fundamental frequency proportion record values, the non-fundamental frequency proportion record list is used as first coordinate data, and the third electric energy abnormal frequency record list is used as second coordinate data. The fourth grading calibration curve comprises three grading calibration curves, namely an average power failure duration grading calibration curve, an average power failure frequency grading calibration curve and a system power failure equivalent hour grading calibration curve. The average power failure duration scoring calibration curve is a calibration curve obtained by taking the average power failure duration as first coordinate data and taking the frequency of power quality abnormity caused by the power failure duration as second coordinate data. The average power failure frequency scoring calibration curve is a calibration curve obtained by taking the average power failure frequency as first coordinate data and taking the frequency of power quality abnormity caused by the power failure frequency as second coordinate data. The system power failure equivalent hour scoring calibration curve is a calibration curve obtained by taking the system power failure equivalent hours as first coordinate data and taking the frequency of power quality abnormity caused by system power failure as second coordinate data.
Specifically, after the first scoring calibration curve, the second scoring calibration curve, the third scoring calibration curve and the fourth scoring calibration curve are obtained, each scoring calibration curve is divided one by one according to a preset slope, and each calibration curve is divided into different curve segments by taking an intersection point of a straight line with the preset slope as the slope and the scoring calibration curve as a dividing point. And when the preset slope is larger than the preset slope, the difference between the power quality condition of the point and the normal power quality condition is larger. The preset total score of the power quality is a preset total score when the power quality is evaluated, and is set by a worker, and is not limited herein. And dividing the scoring intervals of the multiple groups of curve segmentation results one by one according to the preset total score of the power quality, wherein the specific dividing mode is that the preset total score of the power quality is divided into score intervals with the same number as the number of the segments according to the number of the segments segmented by the curve in the curve segmentation results, and the score intervals given by the closer distance are lower according to the distance from the origin of the coordinate, so that the scoring intervals of the segmentation areas in the curve are divided. And summarizing the grading interval division results of the multiple groups of curve division results to obtain the electric energy quality grading table. Therefore, the technical effects of quantitatively scoring the electric energy quality with different dimensions and improving the scoring accuracy are achieved.
Illustratively, according to the voltage deviation electric energy abnormal recording data, first electric energy abnormal frequencies corresponding to different voltage qualified rates are obtained, and because the electric energy abnormal frequency is not necessarily caused when the voltage is unqualified in the electric energy quality detection process, the abnormal frequency needs to be collected. For example: and performing 10 groups of data acquisition on the voltage in the preset area, wherein the deviation between 5 groups of voltages and the rated voltage value of the system meets the requirement, the voltage qualification rate is 50%, wherein 3 groups of electric energy are abnormal, the first electric energy abnormal frequency is 3 times, and the corresponding coordinates on the first scoring calibration curve are [50%,3]. And obtaining a plurality of groups of coordinates to form a first scoring calibration curve through multiple detections. And dividing the first scoring calibration curve into 4 parts by a preset slope, and correspondingly dividing the preset total power quality score into 4 parts. For example: the preset total power quality is divided into 100 points, and 4 groups of scoring intervals are respectively [0, 25], [25, 50], [50, 75], [75, 100]. Therefore, the power quality is graded from the dimension of the voltage deviation to provide a grading basis.
Step S400: carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result;
further, the performing weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result, where step S400 in the embodiment of the present application further includes:
step S410: informing a first scoring party, a second scoring party and an Nth scoring party, wherein the first scoring party, the second scoring party and the Nth scoring party are participants for scoring the importance of the dimension of the power quality assessment, and any two scoring parties are in an information isolation state during scoring;
step S420: inputting the voltage deviation, the three-phase balance, the frequency and the power supply reliability into the first scoring party, the second scoring party and the Nth scoring party, and acquiring a first scoring result, a second scoring result and the Nth scoring result;
step S430: and according to the first grading result, the second grading result and the Nth grading result, carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and generating a power quality evaluation dimension weight distribution result.
Further, according to the first scoring result, the second scoring result, and up to the nth scoring result, performing weight distribution on the voltage deviation, the three-phase balance, the frequency, and the power supply reliability to generate a power quality assessment dimension weight distribution result, where step S430 in this embodiment of the present application further includes:
step S431: summing the first scoring result, the second scoring result and the Nth scoring result to generate a scoring sum;
step S432: traversing the first scoring result and the second scoring result until the Nth scoring result is summed according to the voltage deviation to generate a voltage deviation scoring sum;
step S433: traversing the first scoring result and the second scoring result until the Nth scoring result is summed according to the three-phase balance to generate a three-phase balance scoring sum;
step S434: according to the frequency, traversing the first scoring result and the second scoring result until the Nth scoring result is added to generate a frequency scoring sum;
step S435: traversing the first scoring result and the second scoring result until the Nth scoring result is added according to the power supply reliability to generate a power supply reliability scoring sum;
step S436: and respectively comparing the total scores according to the total score of the voltage deviation, the total score of the three-phase balance, the total score of the frequency and the total score of the power supply reliability, and generating a dimension weight distribution result of the power quality evaluation.
Further, according to the power supply reliability, traversing the first scoring result and the second scoring result until the nth scoring result is summed to generate a power supply reliability scoring sum, where step S435 in the embodiment of the present application further includes:
step S4351: traversing the first grading result and the second grading result until the Nth grading result is added according to the power supply reliability to generate a total average power failure duration grading sum, a total average power failure frequency grading sum and a total system power failure equivalent hour grading sum;
step S4352: and carrying out weighted summation according to the average power failure duration score sum, the average power failure times score sum and the system power failure equivalent hour score sum, and determining the power supply reliability score sum.
Specifically, the first scoring party, the second scoring party and the nth scoring party are all members of expert group for power quality management, and when the importance degree of each dimension of power quality evaluation on power quality is scored, any two scoring parties cannot communicate with each other during scoring, information is transmitted, and the two scoring parties are in an information isolation state. Thus, fairness and accuracy of importance determination for each evaluation dimension are ensured.
Specifically, the voltage deviation, the three-phase balance, the frequency and the power supply reliability are input into a first scoring party, a second scoring party and an Nth scoring party, each scoring party scores the importance of four dimensions, and the total scoring value is set to be consistent. Illustratively, the standard score for scoring each dimension is 100 points, each scoring party scores the dimension separately, and the score may be 80 points, 90 points, etc., and cannot exceed the standard score by 100 points. And then, respectively scoring each dimensionality according to a scoring party to obtain the first scoring result, the second scoring result and the Nth scoring result.
Specifically, in the process of performing weight distribution according to the scoring result, the scoring sum is obtained, the scores of the dimensions in different scoring results are added and compared with the scoring sum, the scoring result of each scoring party can be obtained comprehensively, the importance degree of each dimension is quantified, and therefore weight distribution is performed according to the quantification result. And the total score is obtained by adding the score values of the first score result, the second score result and the nth score result. The total voltage deviation score is obtained by extracting the first score result, the second score result and the score values of all scoring parties in the Nth score result to the voltage deviation and summing the score values. The frequency score sum is obtained by extracting the first score result, the second score result and the score value of each scoring party to the frequency in the Nth score result and adding the score values. The three-phase balance score sum is obtained by extracting the score values of each scoring party to the three-phase unbalance degree in the first score result, the second score result and the Nth score result and adding the score values.
Specifically, the power supply reliability comprises three aspects of average power failure duration, average power failure times and system power failure equivalent hours, and further the scoring values of the three aspects are respectively added from the scoring result to obtain the average power failure duration scoring sum, the average power failure times scoring sum and the system power failure equivalent hours scoring sum, and further the weighted calculation sum is carried out according to a power supply reliability calculation formula to obtain the power supply reliability scoring sum. The power supply reliability calculation formula is as follows:
. The technical effect of accurately calculating the total power supply reliability score is achieved.
Specifically, after obtaining a voltage deviation score sum, a three-phase balance score sum, a frequency score sum and a power supply reliability score sum, respectively comparing the score sums, and obtaining a corresponding ratio which is a corresponding weight when performing power quality evaluation. The technical effects of scientific calculation of power quality detection and evaluation and improvement of detection accuracy are achieved.
Step S500: acquiring an index characteristic value set in a preset area through the Internet of things on the basis of the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index;
step S600: inputting the power quality evaluation dimension weight distribution result and the index characteristic value set into the power quality scoring table to generate a power quality comprehensive score;
step S700: and determining a negative score evaluation dimension according to the electric energy quality comprehensive score, and sending the negative score evaluation dimension to an electric energy quality manager.
Specifically, according to each evaluation index, the characteristic values of each index in a preset area are collected through the Internet of things, and the index characteristic value set is obtained. The index characteristic value corresponding to the voltage deviation evaluation index is a voltage effective value, the index characteristic value corresponding to the three-phase balance evaluation index is a voltage negative sequence, a voltage positive sequence and a voltage zero sequence, the index characteristic value corresponding to the frequency evaluation index is a fundamental wave frequency, and the index characteristic value corresponding to the power supply reliability is power failure time, power failure times and power failure capacity. And summarizing the index characteristic values to obtain the index characteristic value set.
Specifically, the index characteristic value set and the power quality assessment dimension weight distribution result are input into the power quality scoring table, each dimension is scored according to a scoring interval where the index characteristic value is located according to the power quality scoring table to obtain power quality scores of each dimension, and then weighting calculation is performed according to the weight distribution condition in the power quality assessment dimension weight distribution result to obtain the power quality comprehensive score. The comprehensive power quality score is a score obtained by evaluating the power quality after comprehensively considering each influence degree after performing multi-dimensional quality detection on the power.
Specifically, a power quality score threshold value is preset, a score lower than the power quality score threshold value in the power quality comprehensive score is determined as a negative score evaluation dimension, that is, the power quality cannot meet the requirement, and the negative score evaluation dimension is sent to a power quality manager, so that the power quality manager can deal with the situation that the requirement is not met in a targeted manner. And the electric energy quality score threshold is set by a worker according to the requirement on the electric energy quality. The negative score evaluation dimension is a score condition that fails to meet a power quality score threshold. Therefore, the technical effects of improving the accuracy of power quality detection and improving the quality of power management are achieved.
In summary, the electric energy quality detection method for the electric meter based on the internet of things has the following technical effects:
1. according to the embodiment of the application, the electric energy quality is subjected to multi-dimensional evaluation, the electric energy quality is comprehensively detected from four dimensions of voltage deviation, three-phase balance, frequency and power supply reliability, a foundation is laid for subsequent quantitative evaluation, the electric energy quality evaluation table is constructed according to traversing voltage deviation evaluation indexes, three-phase balance evaluation indexes, frequency evaluation indexes and power supply reliability evaluation indexes, the electric energy quality is quantitatively evaluated, the voltage deviation, three-phase balance, frequency and power supply reliability are subjected to weight distribution according to preset weight distribution rules, the importance degree of each index on the electric energy quality is comprehensively considered, scientific electric energy quality evaluation dimension weight distribution results are obtained, the characteristic values of each preset area are collected according to each evaluation index, and then an index characteristic value set and the electric energy quality evaluation dimension distribution results are input into the electric energy quality weight table to obtain electric energy quality comprehensive scores, further the electric energy quality comprehensive scores are determined, and the electric energy quality evaluation scores are sent to an electric energy management person. Therefore, the technical effect of improving the power quality detection efficiency and the detection quality is achieved.
2. According to the method and the device, abnormal data corresponding to each evaluation index are collected, a first grading calibration curve is constructed according to voltage deviation electric energy abnormal recording data, a second grading calibration curve is constructed according to three-phase unbalance electric energy abnormal recording data, a third grading calibration curve is constructed according to frequency deviation electric energy abnormal recording data, a fourth grading calibration curve is constructed according to power supply reliability deviation electric energy abnormal recording data, the first grading calibration curve, the second grading calibration curve, the third grading calibration curve and the fourth grading calibration curve are traversed according to a preset slope to be divided, a multi-group curve dividing result is generated, grading interval division is performed according to electric energy quality preset total score traversal multi-group curve dividing results, and an electric energy quality grading table is generated. Therefore, the technical effects of constructing the electric energy quality scoring table capable of quantitatively evaluating the electric energy quality and improving the scoring accuracy are achieved.
Example two
Based on the same inventive concept as the internet of things-based electric energy quality detection method for the electric energy meter in the foregoing embodiment, as shown in fig. 4, the present application further provides an internet of things-based electric energy quality detection system for the electric energy meter, wherein the system includes:
an evaluation dimension obtaining module 11, wherein the evaluation dimension obtaining module 11 is configured to obtain a power quality evaluation dimension, and the power quality evaluation dimension includes a voltage deviation, a three-phase balance, a frequency and a power supply reliability;
an index matching module 12, wherein the index matching module 12 is configured to traverse the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and match a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index;
the scoring table building module 13 is configured to traverse the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to build an electric energy quality scoring table;
the weight distribution module 14, the weight distribution module 141 is configured to perform weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule, and generate a power quality evaluation dimension weight distribution result;
an index characteristic value acquisition module 15, where the index characteristic value acquisition module 15 is configured to acquire an index characteristic value set in a preset area through the internet of things based on the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index, and the power supply reliability evaluation index;
a score generation module 16, wherein the score generation module 16 is configured to input the power quality evaluation dimension weight distribution result and the index feature value set into the power quality scoring table, and generate a power quality comprehensive score;
and the evaluation dimension sending module 17 is configured to determine a negative score evaluation dimension according to the power quality comprehensive score, and send the negative score evaluation dimension to a power quality manager.
Further, the system further comprises:
the voltage deviation evaluation index setting unit is used for matching the qualification rate of the power supply voltage according to the voltage deviation and setting the qualification rate as the voltage deviation evaluation index;
the three-phase balance evaluation index setting unit is used for matching three-phase voltage unbalance according to the three-phase balance and setting the three-phase balance evaluation index;
a frequency evaluation index setting unit configured to set a frequency failure rate according to the frequency as the frequency evaluation index;
and the reliability evaluation index setting unit is used for matching the average power failure duration, the average power failure times and the system power failure equivalent hours according to the power supply reliability evaluation index and setting the index as the power supply reliability evaluation index.
Further, the system further comprises:
the abnormal recording data acquisition unit is used for traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index, and acquiring voltage deviation electric energy abnormal recording data, three-phase unbalance electric energy abnormal recording data, frequency deviation electric energy abnormal recording data and power supply reliability deviation electric energy abnormal recording data in the preset area;
the first grading calibration curve construction unit is used for constructing a first grading calibration curve according to the voltage deviation electric energy abnormity recorded data;
the second grading calibration curve construction unit is used for constructing a second grading calibration curve according to the three-phase unbalanced electric energy abnormal record data;
the third grading calibration curve construction unit is used for constructing a third grading calibration curve according to the frequency deviation electric energy abnormity record data;
the fourth grading calibration curve construction unit is used for constructing a fourth grading calibration curve according to the power supply reliability deviation electric energy abnormity record data;
the multi-group curve segmentation result generation unit is used for traversing the first scoring calibration curve, the second scoring calibration curve, the third scoring calibration curve and the fourth scoring calibration curve according to a preset slope to generate a plurality of groups of curve segmentation results;
and the electric energy quality scoring table generating unit is used for traversing the multiple groups of curve segmentation results according to the preset total score of the electric energy quality to divide scoring intervals and generate the electric energy quality scoring table.
Further, the system further comprises:
the recording list extraction unit is used for extracting a voltage qualified rate recording list and a first electric energy abnormal frequency recording list from the voltage deviation electric energy abnormal recording data;
the first grading construction unit is used for constructing a first grading calibration curve by taking the voltage qualified rate recording list as first coordinate data and taking the first electric energy abnormal frequency recording list as second coordinate data;
the electric energy abnormal record data extraction unit is used for extracting an unbalance degree record list and a second electric energy abnormal frequency record list from the three-phase unbalanced electric energy abnormal record data;
the second grading construction unit is used for constructing a second grading calibration curve by taking the unbalance degree recording list as first coordinate data and taking the second electric energy abnormal frequency recording list as second coordinate data;
the electric energy abnormal frequency data extraction unit is used for extracting a non-fundamental wave frequency proportion record list and a third electric energy abnormal frequency record list from the frequency deviation electric energy abnormal record data;
the third grading construction unit is used for constructing a third grading calibration curve by taking the non-fundamental frequency proportion record list as first coordinate data and the third electric energy abnormal frequency record list as second coordinate data;
the reliability abnormal data extraction unit is used for extracting reliability deviation record data and a fourth electric energy abnormal frequency record list from the power supply reliability deviation electric energy abnormal record data;
and the fourth score construction unit is used for constructing a fourth score calibration curve by taking the reliability deviation record data as first coordinate data and taking the fourth electric energy abnormal frequency record list as second coordinate data.
Further, the system further comprises:
the evaluation dimension importance degree scoring unit is used for informing a first scoring party, a second scoring party and an Nth scoring party, wherein the first scoring party, the second scoring party and the Nth scoring party are participants for scoring the electric energy quality evaluation dimension importance degree, and any two scoring parties are in an information isolation state during scoring;
a scoring result obtaining unit, configured to input the voltage deviation, the three-phase balance, the frequency, and the power supply reliability to the first scoring party, the second scoring party, and up to the nth scoring party, and obtain a first scoring result, a second scoring result, and up to the nth scoring result;
and the evaluation weight distribution unit is used for carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to the first grading result, the second grading result and the Nth grading result to generate the power quality evaluation dimension weight distribution result.
Further, the system further comprises:
the score summing unit is used for summing the first score result, the second score result and the Nth score result to generate a score sum;
the voltage deviation summing unit is used for traversing the first scoring result and the second scoring result until the Nth scoring result is summed according to the voltage deviation to generate a voltage deviation scoring sum;
the three-phase balance adding unit is used for traversing the first scoring result and the second scoring result until the Nth scoring result is added according to the three-phase balance to generate a three-phase balance scoring sum;
the frequency score summing unit is used for traversing the first score result and the second score result according to the frequency until the Nth score result is summed to generate a frequency score sum;
the reliability score adding unit is used for traversing the first score result and the second score result until the Nth score result is added according to the power supply reliability to generate a power supply reliability score sum;
and the total score comparison unit is used for respectively comparing the total scores according to the total voltage deviation score, the total three-phase balance score, the total frequency score and the total power supply reliability score to generate the power quality evaluation dimension weight distribution result.
Further, the system further comprises:
the score sum obtaining unit is used for traversing the first score result and the second score result until the Nth score result is added according to the power supply reliability, and generating an average power failure duration score sum, an average power failure frequency score sum and a system power failure equivalent hour score sum;
and the reliability score sum determining unit is used for carrying out weighted summation according to the average power failure duration score sum, the average power failure times score sum and the system power failure equivalent hour score sum to determine the power supply reliability score sum.
In the present description, each embodiment is described in a progressive manner, and the focus of the description of each embodiment is on the difference from other embodiments, and the foregoing method for detecting the electric energy quality of the electric meter based on the internet of things in the first embodiment of fig. 1 and the specific example are also applicable to the system for detecting the electric energy quality of the electric meter based on the internet of things in the present embodiment. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. An electric meter power quality detection method based on the Internet of things is characterized by being applied to an electric meter power quality detection system and comprising the following steps:
acquiring a power quality evaluation dimension, wherein the power quality evaluation dimension comprises voltage deviation, three-phase balance, frequency and power supply reliability;
traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index;
traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to construct an electric energy quality scoring table;
carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result;
acquiring an index characteristic value set in a preset area through the Internet of things on the basis of the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index;
inputting the power quality evaluation dimension weight distribution result and the index characteristic value set into the power quality scoring table to generate a power quality comprehensive score;
determining a negative score evaluation dimension according to the electric energy quality comprehensive score, and sending the negative score evaluation dimension to an electric energy quality manager;
wherein, traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to construct an electric energy quality scoring table, comprises:
traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index, and acquiring voltage deviation electric energy abnormity recorded data, three-phase unbalance electric energy abnormity recorded data, frequency deviation electric energy abnormity recorded data and power supply reliability deviation electric energy abnormity recorded data in the preset area;
constructing a first grading calibration curve according to the voltage deviation electric energy abnormity recorded data;
constructing a second grading calibration curve according to the three-phase unbalanced electric energy abnormal record data;
constructing a third grading calibration curve according to the frequency deviation electric energy abnormity recorded data;
constructing a fourth grading calibration curve according to the power supply reliability deviation electric energy abnormity recorded data;
traversing the first scoring calibration curve, the second scoring calibration curve, the third scoring calibration curve and the fourth scoring calibration curve according to a preset slope to perform segmentation, and generating a plurality of groups of curve segmentation results;
and traversing the multiple groups of curve segmentation results according to the preset total score of the power quality to perform scoring interval division, and generating the power quality scoring table.
2. The method of claim 1, wherein said traversing said voltage deviation, said three-phase balance, said frequency, and said power reliability, matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index, and a power reliability evaluation index comprises:
according to the voltage deviation, matching the qualification rate of the power supply voltage, and setting the qualification rate as the voltage deviation evaluation index;
matching three-phase voltage unbalance according to the three-phase balance, and setting the three-phase voltage unbalance as the three-phase balance evaluation index;
according to the frequency, matching the frequency failure rate, and setting the frequency failure rate as the frequency evaluation index;
and matching the average power failure duration, the average power failure times and the system power failure equivalent hours according to the power supply reliability evaluation index, and setting the index as the power supply reliability evaluation index.
3. The method of claim 1, comprising:
extracting a voltage qualified rate record list and a first electric energy abnormal frequency record list from the voltage deviation electric energy abnormal record data;
constructing the first grading calibration curve by taking the voltage qualification rate recording list as first coordinate data and taking the first electric energy abnormal frequency recording list as second coordinate data;
extracting an unbalance degree record list and a second electric energy abnormal frequency record list from the three-phase unbalance electric energy abnormal record data;
establishing a second grading calibration curve by taking the unbalance degree recording list as first coordinate data and taking the second electric energy abnormal frequency recording list as second coordinate data;
extracting a non-fundamental frequency proportion record list and a third electric energy abnormal frequency record list from the frequency deviation electric energy abnormal record data;
establishing a third grading calibration curve by taking the non-fundamental frequency proportion record list as first coordinate data and the third electric energy abnormal frequency record list as second coordinate data;
extracting reliability deviation record data and a fourth electric energy abnormal frequency record list from the power supply reliability deviation electric energy abnormal record data;
and constructing the fourth scoring calibration curve by using the reliability deviation record data as first coordinate data and using the fourth electric energy abnormal frequency record list as second coordinate data.
4. The method of claim 1, wherein the step of performing weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality assessment dimension weight distribution result comprises the steps of:
informing a first scoring party, a second scoring party and an Nth scoring party, wherein the first scoring party, the second scoring party and the Nth scoring party are participants for scoring the importance of the electric energy quality assessment dimension, and any two scoring parties are in an information isolation state during scoring;
inputting the voltage deviation, the three-phase balance, the frequency and the power supply reliability into the first scoring party, the second scoring party and the Nth scoring party, and acquiring a first scoring result, a second scoring result and the Nth scoring result;
and according to the first grading result, the second grading result and the Nth grading result, carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability, and generating a power quality evaluation dimension weight distribution result.
5. The method of claim 4, wherein the weighting the voltage deviation, the three-phase balance, the frequency, and the power supply reliability according to the first scoring result, the second scoring result, and up to the Nth scoring result to generate the power quality assessment dimensional weighting distribution result comprises:
summing the first scoring result, the second scoring result and the Nth scoring result to generate a scoring sum;
traversing the first scoring result and the second scoring result until the Nth scoring result is summed according to the voltage deviation to generate a voltage deviation scoring sum;
traversing the first scoring result and the second scoring result until the Nth scoring result is summed according to the three-phase balance to generate a three-phase balance scoring sum;
according to the frequency, traversing the first scoring result and the second scoring result until the Nth scoring result is added to generate a frequency scoring sum;
traversing the first scoring result and the second scoring result until the Nth scoring result is added according to the power supply reliability to generate a power supply reliability scoring sum;
and respectively comparing the total scores according to the total score of the voltage deviation, the total score of the three-phase balance, the total score of the frequency and the total score of the power supply reliability, and generating a dimension weight distribution result of the power quality evaluation.
6. The method of claim 5, wherein traversing the first scoring result and the second scoring result until the Nth scoring result is summed to generate a power reliability score sum according to the power reliability, comprising:
traversing the first grading result and the second grading result until the Nth grading result is added according to the power supply reliability to generate a total average power failure duration grading sum, a total average power failure frequency grading sum and a total system power failure equivalent hour grading sum;
and carrying out weighted summation according to the average power failure duration score sum, the average power failure times score sum and the system power failure equivalent hour score sum, and determining the power supply reliability score sum.
7. The utility model provides an ammeter power quality detecting system based on thing networking which characterized in that includes:
an evaluation dimension obtaining module, configured to obtain a power quality evaluation dimension, where the power quality evaluation dimension includes a voltage deviation, a three-phase balance, a frequency, and a power supply reliability;
an index matching module for traversing the voltage deviation, the three-phase balance, the frequency and the power supply reliability, matching a voltage deviation evaluation index, a three-phase balance evaluation index, a frequency evaluation index and a power supply reliability evaluation index;
the evaluation table building module is used for traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index to build an electric energy quality evaluation table;
the weight distribution module is used for carrying out weight distribution on the voltage deviation, the three-phase balance, the frequency and the power supply reliability according to a preset weight distribution rule to generate a power quality evaluation dimension weight distribution result;
the index characteristic value acquisition module is used for acquiring an index characteristic value set in a preset area through the Internet of things on the basis of the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index;
the score generation module is used for inputting the power quality evaluation dimensional weight distribution result and the index characteristic value set into the power quality scoring table to generate a power quality comprehensive score;
the evaluation dimension sending module is used for determining a negative score evaluation dimension according to the electric energy quality comprehensive score and sending the negative score evaluation dimension to an electric energy quality manager;
the abnormal recording data acquisition unit is used for traversing the voltage deviation evaluation index, the three-phase balance evaluation index, the frequency evaluation index and the power supply reliability evaluation index, and acquiring voltage deviation electric energy abnormal recording data, three-phase unbalance electric energy abnormal recording data, frequency deviation electric energy abnormal recording data and power supply reliability deviation electric energy abnormal recording data in the preset area;
the first grading calibration curve construction unit is used for constructing a first grading calibration curve according to the voltage deviation electric energy abnormity recorded data;
the second grading calibration curve construction unit is used for constructing a second grading calibration curve according to the three-phase unbalanced electric energy abnormal record data;
the third grading calibration curve construction unit is used for constructing a third grading calibration curve according to the frequency deviation electric energy abnormity record data;
the fourth grading calibration curve construction unit is used for constructing a fourth grading calibration curve according to the power supply reliability deviation electric energy abnormity record data;
the multi-group curve segmentation result generation unit is used for traversing the first scoring calibration curve, the second scoring calibration curve, the third scoring calibration curve and the fourth scoring calibration curve according to a preset slope to generate a plurality of groups of curve segmentation results;
and the electric energy quality scoring table generating unit is used for traversing the multiple groups of curve segmentation results according to the preset total score of the electric energy quality to divide scoring intervals and generate the electric energy quality scoring table.
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| CN114037220A (en) * | 2021-10-19 | 2022-02-11 | 华北电力大学（保定） | Distribution network reliability grade division standard and evaluation method |
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