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CN105678463B - Method for simulating abnormal data of electric energy metering device for training - Google Patents

Method for simulating abnormal data of electric energy metering device for training Download PDF

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CN105678463B
CN105678463B CN201610016158.7A CN201610016158A CN105678463B CN 105678463 B CN105678463 B CN 105678463B CN 201610016158 A CN201610016158 A CN 201610016158A CN 105678463 B CN105678463 B CN 105678463B
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吴琦
李婷婷
赵伟
程小东
谭玉茹
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Anhui Nanrui Zhongtian Electric Power Electronics Co ltd
State Grid Corp of China SGCC
Training Center of State Grid Anhui Electric Power Co Ltd
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State Grid Corp of China SGCC
Training Center of State Grid Anhui Electric Power Co Ltd
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Abstract

本发明公开一种培训用的电能计量装置异常数据的模拟方法，通过收集现场的各类计量信息，通过各类算法模拟出标准、正确的数据；读取教学用异常点后，结合“计量异常专家库”判断需模拟哪些异常数据，接着修改对应数据，通过“计量异常专家库”的精确判断以及对标准数据的修改，得到需要的异常数据，本发明通过模拟出异常数据，方便教学和培训，保证教学和培训的质量。

Figure 201610016158

The invention discloses a method for simulating abnormal data of an electric energy metering device for training. By collecting various types of metering information on site, standard and correct data are simulated by various algorithms; "Expert database" determines which abnormal data needs to be simulated, and then modify the corresponding data, and obtain the required abnormal data through the accurate judgment of the "measurement abnormal expert database" and the modification of the standard data. The present invention simulates the abnormal data, which is convenient for teaching and training. , to ensure the quality of teaching and training.

Figure 201610016158

Description

Method for simulating abnormal data of electric energy metering device for training
Technical Field
The invention relates to the field of electric power training, in particular to a method for simulating abnormal data of an electric energy metering device for training.
Background
Today, enterprise competition increasingly represents talent competition, training is undoubtedly an important means for enterprises to improve employee qualifications and enhance enterprise core competitiveness. The method has the advantages that the deep training of the metering equipment is carried out on the electric power staff through the metering abnormity training, channels for staff training of power supply enterprises are further enriched, the power supply service level is improved, and the method is an urgent and beneficial matter.
The metering device in daily work has the characteristics of multiple classifications (including classification of metering abnormality, equipment abnormality, electricity utilization abnormality, communication channel abnormality and the like), complex conditions (including voltage loss, current loss, reverse phase sequence abnormality, wiring error, electric energy meter flying, voltage out-of-limit, current out-of-limit, electric energy meter uncovering and programming, metering door opening and closing, strong magnetic field interference, ferromagnetic resonance causing damage of a traditional voltage transformer, transformer saturation caused by magnetic biasing and the like, and a plurality of abnormalities have similar representations but different properties and severities), and the like.
The abnormal data is required to be displayed during training, the data source generally comes from the data of the existing power company, but only part of the data is available, or a teacher compiles some typical data, which is not in accordance with the actual situation and is not beneficial to the teaching achievement, the training content is as rich and diversified as possible, the teaching quality is improved, and therefore the abnormal data closer to the actual data is required.
Disclosure of Invention
The invention aims to provide a method for simulating abnormal data of an electric energy metering device for training, which simulates the abnormal data close to the reality for training staff and improves the training quality.
The purpose of the invention is realized by the following technical scheme:
the method for simulating the abnormal data of the electric energy metering device for training comprises the following steps:
the method comprises the following steps: collecting various metering information of a site, and extracting representative data from the metering information as sample information to serve as the basis and reference of abnormal data;
step two: referring to the sample information, the data of 'forward active total electric quantity', 'forward active peak electric quantity', 'forward active average electric quantity' and 'forward active valley electric quantity' are simulated according to the normal distribution rule, and according to the central limit theorem, the data of 'normal total N (mu, sigma-delta electric quantity')2) In the method, samples with the sample number of n are randomly drawn, the average number of the samples also follows normal distribution, even if the samples are sampled from a biased populationWhen n is large enough, the distribution of the mean number of samples still follows a normal distribution
Figure BDA0000902001490000021
The normal distribution rule algorithm is as follows:
1) obtaining upper month sample data X1,X2....XnCalculating the sample mean sample standard deviation
Figure BDA0000902001490000023
2) Estimating an ensemble mean
Figure BDA0000902001490000024
Estimate the overall standard deviation as
Figure BDA0000902001490000025
3) According to estimated normal distribution parameters
Figure BDA0000902001490000026
Generation of simulated data sample X '(X'1,X′2....X′Y) Wherein Y is the total number of data to be simulated;
4) according to linear transformation XiN (0,1), then Yi＝σXi+μ～N(μ,σ2) Calculating to obtain Yi，YiY strips are taken as normalized simulation data;
step three: referring to sample information, simulating data of a communication mode, a user type, an electric energy meter rate, an electric energy meter wiring mode, transformer capacity and a power factor according to a uniform distribution rule, wherein the uniform distribution rule algorithm is as follows:
generating N [0, 1] intervals corresponding to each other according to a random number function, and converting the intervals into required interval random numbers according to requirements;
step four: the sum of the results of the second step and the third step and the sample information generates standard data, and abnormal points for teaching and training are read;
step five: the correlation measurement abnormity expert database judges which abnormal data need to be simulated in the standard data, and the measurement abnormity expert database is a data warehouse which records the measurement abnormity judgment basis and the performance characteristics;
step six: modifying data by combining with the metering exception expert database, and verifying the correctness of the data by combining with the metering exception expert database after modifying the data;
step seven: judging the correctness of the data, and if the data is correct, obtaining abnormal data of the electric energy metering device for training; if the error is found, judging whether the abnormal measurement expert library is correct, if the abnormal measurement expert library is correct, returning to the step six, and if the abnormal measurement expert library is wrong, returning to the step five.
Preferably, the sample information includes user information, measurement point information, power amount information, a time period in which measurement information is generated, and a measurement information generation area.
Further, the total number Y of data to be simulated is obtained by the following method:
(1) the generation principle is as follows: using the Lideberger-Levy central limit theorem if the random variable sequence X1,X2....XnIndependent co-distribution with limited mathematical expectation and variance, then for all x ∈ R
Figure BDA0000902001490000031
Then for random variable X obeying uniform distributioniRandom variable provided n is sufficiently large
Figure BDA0000902001490000032
(2) Generating N from a random function0A (0,1)]Random number x of intervali，
Figure BDA0000902001490000033
Generating Y, S as requiredYObey N (N)0μ,N0σ2) In which N is0＝200,μ＝0.5,σ2＝1/12；
(3) Using formulas
Figure BDA0000902001490000041
Calculating to obtain Xi；
(4) According to linear transformation Xi～N(0,1)，Yi＝σXi+μ～N(μ,σ2) Calculating to obtain Yi。
Has the advantages that:
(1) the invention judges and screens the disordered, disordered and fuzzy metering events of each power utilization site, simulates typical abnormal metering events, equipment abnormality, power utilization abnormality, communication channel abnormality and other abnormal events by a scientific algorithm, and simulates abnormal data for training, so that the various abnormal events can be displayed more systematically and visually.
(2) The abnormal metering information is simulated and provided for the provincial level electricity utilization information acquisition platform abnormal data clustering analysis and training platform, the simulated data is more in line with the actual situation, compared with the selected partial data and the compiled data, the method can be used for carrying out deeper training on the metering equipment for electric power workers, further starting from the root cause of the problem of the metering equipment, and deepening the impression of the students aiming at the abnormal performance so that the students can obtain substantial learning.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flow chart of a normal distribution rule algorithm of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
As shown in fig. 1, the invention discloses a method for simulating abnormal data of an electric energy metering device for training, which comprises the following steps:
the method comprises the following steps: collecting various kinds of metering information on site, extracting representative data from the metering information as sample information, wherein the sample information comprises user information, metering point information, electric quantity information, time period for metering information generation and metering information generation area, and the sample information is used as the basis and reference of abnormal data;
step two: and (3) referring to the sample information, simulating data such as forward active total electric quantity, forward active peak electric quantity, forward active average electric quantity, forward active valley electric quantity and the like according to a normal distribution rule. As shown in fig. 2, data is simulated according to a normal distribution rule for sample information, samples with an example number of N are randomly extracted from a normal population N (μ, σ 2) according to the central limit theorem, the sample mean follows the normal distribution, and even if the samples are extracted from a biased population, when N is sufficiently large, the distribution of the sample mean follows the normal distribution
Figure BDA0000902001490000051
The normal distribution rule algorithm is as follows:
1) obtaining upper month sample data X1,X2....XnCalculating the sample mean sample standard deviation
Figure BDA0000902001490000056
Figure BDA0000902001490000053
2) Estimating an ensemble mean
Figure BDA0000902001490000054
Estimate the overall standard deviation as
Figure BDA0000902001490000055
3) Generating simulation data sample X ' (X ') according to data obeying normal overall N (mu, sigma 2) distribution '1,X′2....X′Y) Wherein Y is the total number of data to be simulated;
4) according to linear transformation XiN (0,1), then Yi＝σXi+μ～N(μ,σ2) Calculating to obtain Yi(Y bars in total), namely normalized simulation data;
step three: simulating data according to a uniform distribution rule for the sample information, wherein the algorithm of the uniform distribution rule is as follows:
generating N [0, 1] intervals corresponding to each other according to a random number function, and converting the intervals into required interval random numbers according to requirements;
step four: generating standard data according to the results of the second step and the third step and reading abnormal points for teaching and training;
step five: the correlation measurement abnormity expert base judges which abnormal data need to be simulated in the standard data;
step six: modifying data by combining with the metering exception expert database, and verifying the correctness of the data by combining with the metering exception expert database after modifying the data;
step seven: judging the correctness of the data, and if the data is correct, obtaining abnormal data of the electric energy metering device for training; if the error is found, judging whether the abnormal measurement expert library is correct, if the abnormal measurement expert library is correct, returning to the step six, and if the abnormal measurement expert library is wrong, returning to the step five.
The total number Y of data to be simulated is obtained by the following method:
(1) the generation principle is as follows: using the Lideberger-Levy central limit theorem if the random variable sequence X1,X2....XnIndependent co-distribution with limited mathematical expectation and variance, then for all x ∈ R
Figure BDA0000902001490000061
Then for random variable X obeying uniform distributioniRandom variable provided n is sufficiently large
Figure BDA0000902001490000062
Obey N (0, 1);
(2) generating N from a random function0A (0,1)]Random number x of intervali，
Figure BDA0000902001490000063
Generating Y, S as requiredYObey N (N)0μ,N0σ2) In which N is0＝200,μ＝0.5,σ2＝1/12；
(3) Using formulas
Figure BDA0000902001490000064
Calculating to obtain Xi；
(4) According to linear transformation Xi～N(0,1)，Yi＝σXi+μ～N(μ,σ2) Calculating to obtain Yi。
Example (b):
taking the electric energy meter flying and sudden change as an example, the abnormal record of the electric energy meter flying in 2015 12 months of Hefei city is generated as follows:
1. according to records of the electric energy meter flying and sudden change abnormity of 11 months in 2015 of Anhui province, 79 records of the combined fertilizer electric energy meter flying and sudden change abnormity are checked, and according to the following diagram, the simulation number in the month is 81 after 0 to 5 percent of random up-and-down floating according to the uniform distribution rule.
City of land Month of the year Type of exception Number of
1 Mixed fertilizer Year 2015, 11 months Electric energy meter flies and changes suddenly 79
2 Anqing Year 2015, 11 months Electric energy meter flies and changes suddenly 63
3 Huaibei Year 2015, 11 months Electric energy meter flies and changes suddenly 34
4 Huainan Year 2015, 11 months Electric energy meter flies and changes suddenly 59
5 Turnip lake Year 2015, 11 months Electric energy meter flies and changes suddenly 83
2. According to the occurrence time of the abnormity of Anhui province and the statistical records of the local cities, checking the time region of the occurrence of the daily flying event, counting the occurrence number according to the region from 8 to 12 points, from 12 to 16 points, from 16 to 20 points, from 20 to 24 points and from 24 to 8 points on the next day, and randomly distributing the occurrence time set in the time domain.
3. And according to the user information of the joint fertilization market, randomly acquiring 81 meters, associating corresponding electric meter information, terminal information and random time set, writing the electric meter information, the terminal information and the random time set into the abnormal occurrence time field, and integrating the coherence of related data.
4. And according to a normal distribution rule, simulating and generating a daily positive active total of one household to generate standard data of the electric energy meter flying event.
(1) Firstly, acquiring the maximum positive active total electric quantity of basic historical data of a user and the daily electric quantity delta X of the daily electric quantity of a common month according to X1,X2...XnRepresents Δ x daily, where n is the number of days of the month.
(2) According to the normal distribution rule described above, according to X1,X2...XnBy calculating
Figure BDA0000902001490000072
(3) Generating 360 pieces of random data according to a normal distribution rule, wherein the randomly generated data are as follows:
93，103，119，97，109，93，99，111，95，89，115，92，107，105，102，95，99，98，98，89，98，101，105，102，105，91，107，89，100，100，96，87，95，109，88，74，117，99，113，112，96，90，99，101，105，100，108，111，103，108，94，106，100，99，104，103，102，104，91，89，101，105，107，101，78，102，107，94，109，85，98，105，101，94，108，89，100，110，103，104，98，99，91，97，105，105，99，112，101，109，103，96，84，96，101，110，95，95，102，98，94，107，98，101，108，114，98，99，102，103，99，99，101，101，103，85，93，92，106，105，103，111，104，94，103，110，103，89，103，101，88，93，96，99，101，100，107，100，90，93，103，98，103，102，108，117，85，93，98，100，98，101，106，96，97，93，110，103，100，96，114，111，95，92，110，104，102，93，112，98，88，104，102，98，83，94，84，103，100，98，92，89，92，105，93，99，99，108，101，98，101，112，98，115，104，100，94，95，91，99，108，90，98，103，90，100，91，97，95，109，95，104，112，95，98，99，99，116，105，99，110，94，99，113，114，95，93，96，100，105，99，85，114，109，107，98，110，95，92，101，113，109，109，105，109，94，97，105，96，112，102，103，97，103，97，105，108，109，106，105，97，110，110，90，100，101，91，97，108，103，88，108，112，119，101，100，100，107，102，99，111，115，102，97，100，98，106，106，94，100，96，98，109，107，105，99，98，92，105，102，102，110，111，91，105，97，107，107，102，91，91，104，108，96，119，92，95，88，114，104，97，101，100，95，112，102，106，108，106，99，102，103，94，100，110，97，102，102，88，104，103，106，113，93，108，97，105，97，108，104，100，99，87，92，99，105，115，91，100
(4) according to
Figure BDA0000902001490000081
360 strips were obtained, obeying N (0,1), where N is 200, μ is 0.5, σ2＝1/12。
(5) Mixing XiAccording to Yi＝σXi+μ～N(μ，σ2) The principle linearly transforms data into data corresponding to the requirements,
wherein Y isi μ＝μ0＝15，σ＝σ0＝4.2
(1) Will YiAnd accumulating the positive active power data to the daily positive active power sum in sequence to obtain the electric quantity data which accords with the practical situation and meets the corresponding normal distribution. The power consumption peak period in summer of 6, 7, 8 and 8 is multiplied by a coefficient of 2.6, and the power consumption peak period in winter of 11, 12, 1 and 1 is multiplied by a coefficient of 1.9, so that the power consumption peak period is closer to reality.
Combining a 'metering exception expert library' to simulate exception data:
(1) the characteristic of the 'electric energy meter flying and sudden change' event is as follows: aiming at a resident table, obtaining the maximum value (Imax) of current in months 12, 1, 2, 7, 8 and 9, calculating the ratio of electric quantity to the electric quantity in the day according to 8 hours and 12 hours, if the ratio is more than or equal to 1, setting the ratio as to be observed, and if the current is established for continuous 7 days, converting the ratio into new abnormity; during the period, if recovery lasts for 3 days, the history is abnormal; and 3 Ib (rated current) is taken in 3, 4, 5, 6, 10, 11 and 12 months, and the ratio of the calculated electric quantity to the daily electric quantity is more than or equal to 1 in 8 hours and 12 hours, and the current is set to be observed.
Simulating daily electric quantity data according to abnormal characteristics of 'electric energy meter flying and sudden change': and reversely deducing the daily current according to the actual voltage, and forwards deducing for 7 days according to the abnormal occurrence date, randomly amplifying the daily current to ensure that the ratio of daily current/3 (8 hours) to the daily electric quantity is more than 1, and completing the generation of the electric energy meter flying and mutation abnormal data after verifying that the abnormal requirement is met.

Claims (2)

1.培训用的电能计量装置异常数据的模拟方法，其特征在于，包括以下步骤：1. the simulation method of the abnormal data of the electric energy metering device used for training, is characterized in that, comprises the following steps: 步骤一：收集现场的各类计量信息，并从中抽取具有代表性的数据作为样本信息，作为异常数据的基础及参照；Step 1: Collect all kinds of measurement information on the site, and extract representative data from it as sample information, as the basis and reference for abnormal data; 步骤二：参考样本信息，按正态分布规则模拟“正向有功总电量”、“正向有功尖电量”、“正向有功峰电量”、“正向有功平电量”、“正向有功谷电量”数据，根据中心极限定理，从正态总体N(μ,σ2)中，随机抽取例数为n的样本，样本均数也服从正态分布，若从偏态总体中抽样，当n足够大时，样本均数的分布服从正态分布
Figure FDA0002825469640000011
正态分布规则算法如下：Step 2: Referring to the sample information, simulate "positive total active power", "forward active peak power", "forward active peak power", "forward active flat power" and "forward active valley" according to the normal distribution rule. According to the central limit theorem, randomly select n samples from the normal population N(μ,σ 2 ), and the sample mean also obeys the normal distribution. If sampling from the skewed population, when n When large enough, the distribution of the sample mean follows a normal distribution
Figure FDA0002825469640000011
The normal distribution rule algorithm is as follows: 1)获取上月样本数据X1,X2....Xn，计算样本均值
Figure FDA0002825469640000012
样本标准差
Figure FDA0002825469640000013
1) Obtain the sample data X 1 , X 2 ....X n of the previous month, and calculate the sample mean
Figure FDA0002825469640000012
sample standard deviation
Figure FDA0002825469640000013
 2)估算整体均值
Figure FDA0002825469640000014
估算整体标准差为
Figure FDA0002825469640000015
2) Estimate the overall mean
Figure FDA0002825469640000014
The estimated overall standard deviation is
Figure FDA0002825469640000015
 3)根据估算正态分布参数μ0,
Figure FDA0002825469640000016
生成模拟数据样本X'(X'1,X'2....X'Y)，其中Y为需要模拟的数据总数；3) According to the estimated normal distribution parameter μ 0 ,
Figure FDA0002825469640000016
Generate simulated data samples X'(X' 1 ,X' 2 ....X' Y ), where Y is the total number of data to be simulated; 4)根据线性转换Xi～N(0,1)，则Yi＝σXi+μ～N(μ,σ2),计算获得Yi，Yi共Y条，即为正态化模拟数据；4) According to the linear transformation X i ～N(0,1), then Y i =σX i +μ～N(μ,σ 2 ), calculate and obtain Y i , Y i has a total of Y pieces, that is, the normalized simulation data ; 步骤三：参考样本信息，按均匀分布规则模拟“通讯方式”、“用户类别”、“电能表费率”、“电能表接线方式”、“变压器容量”、“功率因数”数据，均匀分布规则算法为：Step 3: Referring to the sample information, simulate the data of "communication method", "user category", "electric energy meter rate", "electric energy meter wiring method", "transformer capacity", and "power factor" according to the uniform distribution rules. The algorithm is: 根据随机数函数生成N个[0，1]区间对应，按要求转换成需求的区间随机数；Generate N corresponding [0, 1] intervals according to the random number function, and convert them into required interval random numbers as required; 步骤四：步骤二和步骤三的结果与样本信息总和产生标准数据，读取教学培训用的异常点；Step 4: The sum of the results of Steps 2 and 3 and the sample information generates standard data, and reads the abnormal points for teaching and training; 步骤五：关联计量异常专家库判断标准数据中需模拟哪些异常数据，计量异常专家库是记录了计量异常判断依据及表现特点的数据仓库；Step 5: Associate the measurement anomaly expert database to determine which abnormal data needs to be simulated in the standard data. The measurement anomaly expert database is a data warehouse that records the measurement anomaly judgment basis and performance characteristics; 步骤六：结合计量异常专家库修改数据，修改数据后结合计量异常专家库验证数据正确性；Step 6: Modify the data in combination with the measurement abnormality expert database, and verify the correctness of the data in combination with the measurement abnormality expert database after modifying the data; 步骤七：判断数据的正确性，若正确，则得到培训用的电能计量装置异常数据；若错误，判断计量异常专家库是否正确，若计量异常专家库正确，返回步骤六，若计量异常专家库错误，返回步骤五；Step 7: Judging the correctness of the data, if it is correct, get the abnormal data of the electric energy metering device for training; if it is wrong, judge whether the abnormal measurement expert database is correct, if the abnormal measurement expert database is correct, return to step 6, if the abnormal measurement expert database error, go back to step 5; 需要模拟的数据总数Y通过以下方法得到：The total number of data Y to be simulated is obtained by: (1)生成原理：利用李德伯格-莱维中心极限定理，如果随机变量序列X1,X2....Xn独立同分布，并具有有限的数学期望和方差，则对一切x∈R有
Figure FDA0002825469640000021
(1) Generating principle: Using the Liedberg-Levy central limit theorem, if the random variable sequence X 1 , X 2 ....X n is independent and identically distributed and has a finite mathematical expectation and variance, then for all x∈R Have
Figure FDA0002825469640000021
 则对服从均匀分布的随机变量Xi，只要n足够大，则随机变量
Figure FDA0002825469640000022
Then for a random variable X i that obeys a uniform distribution, as long as n is large enough, the random variable
Figure FDA0002825469640000022
 (2)根据随机函数生成N0个[0，1]区间的随机数xi，
Figure FDA0002825469640000023
按要求生成Y个，SY服从N(N0μ,N0σ2)，其中N0＝200,μ＝0.5,σ2＝1/12；(2) Generate N 0 random numbers x i in the [0, 1] interval according to the random function,
Figure FDA0002825469640000023
Generate Y as required, S Y obeys N(N 0 μ, N 0 σ 2 ), where N 0 =200, μ=0.5, σ 2 =1/12; (3)利用公式
Figure FDA0002825469640000024
计算获得Xi；(3) Using the formula
Figure FDA0002825469640000024
Calculate to obtain X i ; (4)根据线性转换Xi～N(0,1)，Yi＝σXi+μ～N(μ,σ2)，计算获得Yi。(4) According to the linear transformation X i ˜N(0,1), Y i =σX i +μ˜N(μ,σ 2 ), Y i is obtained by calculation. 2.根据权利要求1所述培训用的电能计量装置异常数据的模拟方法，其特征在于，样本信息包含用户信息、计量点信息、电量信息、计量信息发生的时间段、计量信息发生区域。2 . The method for simulating abnormal data of an electric energy metering device for training according to claim 1 , wherein the sample information includes user information, metering point information, electric quantity information, time period when metering information occurs, and metering information generating area. 3 .
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