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CN109389280B - Evaluation Method of Energy Meter Manufacturers Based on Multi-attribute Decision-Making Model - Google Patents

Evaluation Method of Energy Meter Manufacturers Based on Multi-attribute Decision-Making Model Download PDF

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CN109389280B
CN109389280B CN201810943549.2A CN201810943549A CN109389280B CN 109389280 B CN109389280 B CN 109389280B CN 201810943549 A CN201810943549 A CN 201810943549A CN 109389280 B CN109389280 B CN 109389280B
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CN109389280A (en
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杨佳
杨光盛
贺民
刘晟源
林振智
陈康
周歆
季德伟
李熊
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Zhejiang University ZJU
State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses an electric energy meter manufacturer evaluation method based on a multi-attribute decision model, and relates to an evaluation method. The method comprises the following steps: acquiring original data of an electric energy meter, and determining an evaluation index; calculating each index value of the electric energy meter, constructing an index matrix, and forming a decision matrix; calculating index value information entropy and spearman grade correlation coefficients of each item of the electric energy meter, determining objective weights of quality evaluation indexes according to a CRITIC method, and carrying out weighting treatment on the decision matrix to obtain an electric energy meter provider evaluation matrix; calculating positive ideal solutions and negative ideal solutions of the electric energy meter suppliers, and calculating Euclidean distances from each electric energy meter supplier to positive ideal point vectors and negative ideal point vectors; and calculating the relative approximation degree of each electric energy meter supplier and the ideal solution, and determining the quality grade of each electric energy meter supplier according to the high-low degree. The technical scheme provides objective and scientific basis for the electric energy meter quality evaluation and supervision of the power grid company.

Description

基于多属性决策模型的电能表生产厂商评价方法Evaluation Method of Energy Meter Manufacturers Based on Multi-attribute Decision-Making Model

技术领域technical field

本发明涉及电力系统领域，特别是涉及基于多属性决策模型的电能表生产厂商评价方法。The invention relates to the field of power systems, in particular to an evaluation method for electric energy meter manufacturers based on a multi-attribute decision-making model.

背景技术Background technique

用电信息采集系统在电力交易结算、电量分析、需求侧管理等业务应用中发挥着重要的技术支持作用，电能表作为用电信息采集系统的重要组成部分，其质量直接影响用户用电信息的数据质量和应用水平，电力用户用电计量的准确性以及该系统的安全、稳定和经济运行，甚至也直接关系到供电的可靠性。电能表的质量评估是保证和提高电能表质量的重要手段之一，是电能表全寿命周期管理的关键环节。构建电能表的质量评估模型对电能计量设备质量进行综合评价，可协助电网公司对于供应商进行进一步的管理，能为电网公司开展电能表质量评估与监督提供客观和科学的依据，对于智能电网的建设具有非常重要的实际意义和工程价值。The power consumption information collection system plays an important technical support role in business applications such as power transaction settlement, power analysis, and demand side management. As an important part of the power consumption information collection system, the energy meter directly affects the quality of the user's power consumption information. The data quality and application level, the accuracy of electricity metering by power users, and the safe, stable and economical operation of the system are even directly related to the reliability of power supply. The quality assessment of electric energy meters is one of the important means to ensure and improve the quality of electric energy meters, and it is a key link in the life cycle management of electric energy meters. Constructing a quality evaluation model of electric energy meters to comprehensively evaluate the quality of electric energy metering equipment can assist power grid companies to further manage suppliers, and can provide objective and scientific basis for power grid companies to carry out quality evaluation and supervision of electric energy meters. Construction has very important practical significance and engineering value.

现有的方法一般对电能表的运行质量评估目前停留在运维人员或专家的主观评价上，缺乏实际运行数据支撑。因此，电能表的质量评估指标体系不够全面，评估模型与方法不够完善，缺乏对电能计量设备质量作出科学、合理的定量评价，未能较好地为营销计量、物资管理提供科学合理的评价支持。Existing methods generally evaluate the operation quality of electric energy meters, and currently remain on the subjective evaluation of operation and maintenance personnel or experts, lacking the support of actual operation data. Therefore, the quality evaluation index system of electric energy meters is not comprehensive enough, the evaluation models and methods are not perfect enough, lack of scientific and reasonable quantitative evaluation of the quality of electric energy metering equipment, and fail to provide scientific and reasonable evaluation support for marketing measurement and material management. .

发明内容Contents of the invention

本发明要解决的技术问题和提出的技术任务是对现有技术方案进行完善与改进，提供基于多属性决策模型的电能表生产厂商评价方法，以达到对电能表质量作出科学、合理的定量评价的目的。为此，本发明采取以下技术方案。The technical problem to be solved and the technical task proposed by the present invention are to perfect and improve the existing technical solutions, and to provide an evaluation method for electric energy meter manufacturers based on a multi-attribute decision-making model, so as to achieve a scientific and reasonable quantitative evaluation of the quality of electric energy meters the goal of. For this reason, the present invention takes the following technical solutions.

基于多属性决策模型的电能表生产厂商评价方法，包括如下步骤：1)获取每个批次和区域中电能表的原始数据，根据采集终端设备的运行监测大数据，选择5个衡量电能表质量的评估指标，评估指标包括负荷采集可用率、数据采集完整率、平均无故障工作时间、计量异常报警次数、运行故障率；The evaluation method of electric energy meter manufacturers based on the multi-attribute decision-making model includes the following steps: 1) Obtain the original data of electric energy meters in each batch and area, and select five quality measurement electric energy meters according to the operation monitoring big data of the collected terminal equipment The evaluation indicators include the availability rate of load collection, the complete rate of data collection, the average trouble-free working time, the number of abnormal measurement alarms, and the operation failure rate;

负荷采集可用率计算公式为：The formula for calculating the availability of load collection is:

Figure BDA0001769646160000021
Figure BDA0001769646160000021

式中：Q表示该供应商生产的电能表总批次数，L表示某一批次设备安装的总区域数，Nij、Eij和eij分别表示第i个批次设备在第j个安装区域中的数量、总负荷采集量和经剔除坏数据后评估系统可用的负荷采集量；ωSAMP,j是表征第j个区域中非质量因素对负荷采集可用率产生影响的修正因子，0≤ωSAMP,j≤1且

Figure BDA0001769646160000022
In the formula: Q indicates the total number of batches of electric energy meters produced by the supplier, L indicates the total number of areas where a certain batch of equipment is installed, N ij , E ij and e ij respectively indicate that the i-th batch of equipment is installed in the j-th The quantity in the region, the total load collection amount and the available load collection amount of the evaluation system after removing bad data; ω SAMP,j is a correction factor that characterizes the impact of non-quality factors in the jth region on the load collection availability rate, 0≤ ω SAMP,j ≤1 and
Figure BDA0001769646160000022

数据采集完整率计算公式为：The formula for calculating the completeness rate of data collection is:

Figure BDA0001769646160000023
Figure BDA0001769646160000023

式中：ψij和

Figure BDA0001769646160000024
分别表示第i个批次的电能表在第j个安装区域中的理论应采集数据量和实际采集数据量，ωINT,j是表征第j个区域中非质量因素对数据采集完整产生影响的修正因子，0≤ωINT,j≤1且/>
Figure BDA0001769646160000025
In the formula: ψ ij and
Figure BDA0001769646160000024
Respectively represent the amount of theoretical data collected and the actual amount of data collected by the electric energy meter of the i-th batch in the j-th installation area. Correction factor, 0≤ω INT, j ≤1 and />
Figure BDA0001769646160000025

平均无故障工作时间计算公式为：The formula for calculating the mean working time between failures is:

Figure BDA0001769646160000026
Figure BDA0001769646160000026

式中：Nj是第j个安装区域的电能表数，TF,jk是第j个安装区域中第k个设备发生首次故障时距离初始运行的时间，ωMTBF,j是表征第j个区域非质量因素对无故障工作时间产生影响的修正因子，0≤ωMTBF,j≤1且

Figure BDA0001769646160000027
In the formula: N j is the number of watt-hour meters in the jth installation area, T F,jk is the time from the initial operation when the kth equipment in the jth installation area fails for the first time, ω MTBF,j is the characteristic of the jth The correction factor for the impact of regional non-quality factors on the working time between failures, 0≤ω MTBF,j ≤1 and
Figure BDA0001769646160000027

异常报警信息的次数之和计算公式为：The calculation formula for the sum of the times of abnormal alarm information is:

Figure BDA0001769646160000028
Figure BDA0001769646160000028

式中：

Figure BDA0001769646160000029
和/>
Figure BDA00017696461600000210
分别表示第i个批次的产品在第j个安装区域的电量异常报警次数、电压电流异常报警次数和时钟异常报警次数；In the formula:
Figure BDA0001769646160000029
and />
Figure BDA00017696461600000210
Respectively represent the number of abnormal power alarms, voltage and current abnormal alarm times, and clock abnormal alarm times of the i-th batch of products in the j-th installation area;

运行故障率计算公式为：The formula for calculating the operating failure rate is:

Figure BDA0001769646160000031
Figure BDA0001769646160000031

式中：Trate,jk和Tstop,jk分别是第j个安装区域中第k台电能表标称运行时间和故障停机时间，ωFAULT,j是表征第j个区域非质量因素对故障停机产生影响的修正因子，0≤ωFAULT,j≤1且

Figure BDA0001769646160000032
In the formula: T rate,jk and T stop,jk are the nominal running time and downtime of the k-th electric energy meter in the j-th installation area, respectively, and ω FAULT,j is the non-quality factor that represents the j-th area. A correction factor that affects, 0≤ω FAULT,j ≤1 and
Figure BDA0001769646160000032

2)计算电能表的各个评估指标值，构建指标矩阵；对指标矩阵进行标准化处理形成决策矩阵；2) Calculate each evaluation index value of the electric energy meter and construct an index matrix; standardize the index matrix to form a decision matrix;

3)计算电能表各项的评估指标值信息熵和斯皮尔曼等级相关系数，根据CRITIC方法确定质量评估指标的客观权重，根据客观权重对所决策矩阵进行加权处理，得到电能表供应商评估矩阵；3) Calculate the evaluation index value information entropy and Spearman rank correlation coefficient of each item of the electric energy meter, determine the objective weight of the quality evaluation index according to the CRITIC method, and weight the decision matrix according to the objective weight to obtain the electric energy meter supplier evaluation matrix ;

4)根据评估矩阵，计算电能表供应商的理想解，理想解包括正理想解和负理想解，计算各个电能表供应商至正理想点向量和负理想点向量的欧式距离；4) According to the evaluation matrix, calculate the ideal solution of the energy meter supplier, the ideal solution includes the positive ideal solution and the negative ideal solution, and calculate the Euclidean distance from each energy meter supplier to the positive ideal point vector and the negative ideal point vector;

5)计算各个电能表供应商与理想解的相对逼近度，按照从高至低确定各个电能表供应商的质量等级。5) Calculate the relative approximation between each energy meter supplier and the ideal solution, and determine the quality level of each energy meter supplier from high to low.

作为优选技术手段：在步骤3)计算采集终端设备各项的评估指标值信息熵时，熵权计算公式为：As a preferred technical means: in step 3) when calculating the evaluation index value information entropy of collecting terminal equipment items, the entropy weight calculation formula is:

Figure BDA0001769646160000033
Figure BDA0001769646160000033

式中：

Figure BDA0001769646160000034
rij为评估问题的决策矩阵元素，/>
Figure BDA0001769646160000035
并且假定，当fij＝0时，fijln fij＝0；M为评价指标个数，N为待评价方案个数；wi表示第i个指标的熵权，0≤wi≤1，/>
Figure BDA0001769646160000036
In the formula:
Figure BDA0001769646160000034
r ij is the decision matrix element of the evaluation problem, />
Figure BDA0001769646160000035
And suppose, when f ij =0, f ij ln f ij =0; M is the number of evaluation indicators, N is the number of schemes to be evaluated; w i represents the entropy weight of the i-th index, 0≤w i ≤1 , />
Figure BDA0001769646160000036

计算采集终端设备各项的斯皮尔曼等级相关系数时，斯皮尔曼等级相关系数计算公式为：When calculating the Spearman rank correlation coefficient of each item of the acquisition terminal equipment, the calculation formula of the Spearman rank correlation coefficient is:

Figure BDA0001769646160000041
Figure BDA0001769646160000041

Figure BDA0001769646160000042
Figure BDA0001769646160000042

式中：

Figure BDA0001769646160000043
为排序值向量/>
Figure BDA0001769646160000044
和/>
Figure BDA0001769646160000045
的协方差；/>
Figure BDA0001769646160000046
和/>
Figure BDA0001769646160000047
分别为排序值向量
Figure BDA0001769646160000048
和/>
Figure BDA0001769646160000049
的标准差；/>
Figure BDA00017696461600000410
和/>
Figure BDA00017696461600000411
分别为排序值向量/>
Figure BDA00017696461600000412
和/>
Figure BDA00017696461600000413
的均值；/>
Figure BDA00017696461600000414
和
Figure BDA00017696461600000415
是具有N个元素的两列变量，其第i个变量值分别为zij和zik(1≤i≤N),其中j∈{1,2,...,M}，k∈{1,2,...,M}；/>
Figure BDA00017696461600000416
和/>
Figure BDA00017696461600000417
分别是/>
Figure BDA00017696461600000418
和/>
Figure BDA00017696461600000419
的排序值向量，其中/>
Figure BDA00017696461600000420
和/>
Figure BDA00017696461600000421
分别为zij和zik在/>
Figure BDA00017696461600000422
和/>
Figure BDA00017696461600000423
的排序值；ρik表示第j个和第k个指标之间的斯皮尔曼等级相关系数，ρj表示第j个指标与其他指标的整体肯德尔相关系数；In the formula:
Figure BDA0001769646160000043
is a vector of sorted values />
Figure BDA0001769646160000044
and />
Figure BDA0001769646160000045
covariance of ;/>
Figure BDA0001769646160000046
and />
Figure BDA0001769646160000047
vector of sorted values, respectively
Figure BDA0001769646160000048
and />
Figure BDA0001769646160000049
standard deviation of ;/>
Figure BDA00017696461600000410
and />
Figure BDA00017696461600000411
vector of sorted values respectively />
Figure BDA00017696461600000412
and />
Figure BDA00017696461600000413
mean value; />
Figure BDA00017696461600000414
and
Figure BDA00017696461600000415
is a two-column variable with N elements, and its i-th variable values are z ij and z ik (1≤i≤N), where j∈{1,2,...,M}, k∈{1 ,2,...,M};/>
Figure BDA00017696461600000416
and />
Figure BDA00017696461600000417
respectively />
Figure BDA00017696461600000418
and />
Figure BDA00017696461600000419
A vector of sorted values where />
Figure BDA00017696461600000420
and />
Figure BDA00017696461600000421
z ij and z ik respectively in />
Figure BDA00017696461600000422
and />
Figure BDA00017696461600000423
ρ ik represents the Spearman rank correlation coefficient between the j-th and k-th indicators, and ρ j represents the overall Kendall correlation coefficient between the j-th indicator and other indicators;

客观权重计算公式为：The objective weight calculation formula is:

Figure BDA00017696461600000424
Figure BDA00017696461600000424

式中：Cj表示第j个指标的客观权重。In the formula: C j represents the objective weight of the jth indicator.

作为优选技术手段：在步骤2)中，指标矩阵为：As an optimal technical means: in step 2), the index matrix is:

Figure BDA00017696461600000425
Figure BDA00017696461600000425

式中：rij表示电能表i对应的指标j的指标值，N为电能表供应商个数，NPM为电能表个数，M为衡量供应商的电能表质量的评价指标个数，i∈{1,2,...,NPM},j∈{1,2,...,M}；在本发明中M等于5；In the formula: r ij represents the index value of index j corresponding to electric energy meter i, N is the number of electric energy meter suppliers, N PM is the number of electric energy meters, M is the number of evaluation indexes to measure the quality of electric energy meters of suppliers, i ∈{1,2,...,N PM }, j∈{1,2,...,M}; M is equal to 5 in the present invention;

成本型指标标准化处理计算公式为：The calculation formula for the standardization treatment of cost-type indicators is:

Figure BDA00017696461600000426
Figure BDA00017696461600000426

效益型指标标准化处理计算公式为：The calculation formula for the standardization treatment of benefit-type indicators is:

Figure BDA0001769646160000051
Figure BDA0001769646160000051

各供应商对应的各个指标的决策矩阵计算公式为：The formula for calculating the decision matrix of each indicator corresponding to each supplier is:

y”＝(y”kj)N×M y”=(y” kj ) N×M

式中：

Figure BDA0001769646160000052
Ωk表示属于供应商k生产的电能表的集合。In the formula:
Figure BDA0001769646160000052
Ω k represents the set of energy meters belonging to supplier k.

作为优选技术手段：确定电能表供应商评估问题的理想解

Figure BDA0001769646160000053
和反理想解/>
Figure BDA0001769646160000054
其中/>
Figure BDA00017696461600000512
j∈{1,2,...,M}。As a preferred technical means: Determining the ideal solution to the energy meter supplier evaluation problem
Figure BDA0001769646160000053
and anti-ideal solution/>
Figure BDA0001769646160000054
where />
Figure BDA00017696461600000512
j∈{1,2,...,M}.

作为优选技术手段：在步骤4)中，计算电能表供应商评估分别与理想解和反理想解的欧氏距离

Figure BDA0001769646160000056
和/>
Figure BDA0001769646160000057
的计算公式为：As an optimal technical means: in step 4), calculate the Euclidean distance between the evaluation of the electric energy meter supplier and the ideal solution and the anti-ideal solution respectively
Figure BDA0001769646160000056
and />
Figure BDA0001769646160000057
The calculation formula is:

Figure BDA0001769646160000058
Figure BDA0001769646160000058

Figure BDA0001769646160000059
Figure BDA0001769646160000059

式中：

Figure BDA00017696461600000510
为加权后的电能表供应商评估矩阵Z的第i行。In the formula:
Figure BDA00017696461600000510
Row i of matrix Z is evaluated for weighted energy meter suppliers.

作为优选技术手段：在步骤5)中，计算各个电能表供应商与理想解的相对逼近度，并依次从高至低确定各个电能表供应商的质量等级，对应计算公式为：As an optimal technical means: in step 5), calculate the relative approximation degree between each electric energy meter supplier and the ideal solution, and determine the quality grade of each electric energy meter supplier in turn from high to low, and the corresponding calculation formula is:

Figure BDA00017696461600000511
Figure BDA00017696461600000511

式中：Ci是各个电能表供应商与理想解的相对逼近度，可以将其依次从高至低确定各个电能表供应商的质量等级。In the formula: C i is the relative approximation degree between each energy meter supplier and the ideal solution, which can be used to determine the quality level of each energy meter supplier in turn from high to low.

有益效果：本技术方案能够计及指标之间的相关性，能够较为合理地对电能表的运行质量进行评估，能够为物资管理、营销计量专业部门提供科学合理的评价支撑。采用多个评估指标，电能表的质量评估指标体系全面，评估模型与方法完善，可对电能计量设备质量作出科学、合理的定量评价，能较好地为营销计量、物资管理提供科学合理的评价支持。能为电网公司开展电能表质量评估与监督提供客观和科学的依据。Beneficial effects: the technical scheme can take into account the correlation between indicators, can reasonably evaluate the operation quality of electric energy meters, and can provide scientific and reasonable evaluation support for professional departments of material management and marketing measurement. Using multiple evaluation indicators, the quality evaluation index system of the electric energy meter is comprehensive, and the evaluation model and method are perfect. It can make scientific and reasonable quantitative evaluation of the quality of electric energy measurement equipment, and can better provide scientific and reasonable evaluation for marketing measurement and material management. support. It can provide an objective and scientific basis for the power grid company to carry out the quality assessment and supervision of electric energy meters.

附图说明Description of drawings

图1为本发明流程图。Fig. 1 is the flow chart of the present invention.

具体实施方式Detailed ways

为了更好地理解本发明的目的、技术方案以及技术效果，以下结合附图对本发明进行进一步的讲解说明。In order to better understand the purpose, technical solution and technical effect of the present invention, the present invention will be further explained below in conjunction with the accompanying drawings.

参考图1，图1所示为本技术方案的多属性决策模型的电能表生产厂商评价方法流程图，包括如下步骤：With reference to Fig. 1, Fig. 1 shows the flow chart of the electric energy meter manufacturer's evaluation method of the multi-attribute decision-making model of the technical solution, including the following steps:

S10，获取每个批次和区域中电能表的原始数据，综合考虑电能表的运行监测大数据，提出5个衡量电能表质量的评估指标：负荷采集可用率、数据采集完整率、平均无故障工作时间、计量异常报警次数、运行故障率。S10, obtain the original data of the electric energy meter in each batch and area, comprehensively consider the operation monitoring big data of the electric energy meter, and propose five evaluation indicators for measuring the quality of the electric energy meter: load collection availability rate, data collection complete rate, and average fault-free Working hours, measurement abnormality alarm times, operation failure rate.

在本技术方案中：In this technical solution:

电能表在用户侧采集与负荷的相关数据时因为电流、电压突变以及电磁干扰等因素会产生坏数据，因此在负荷采集数据进入评估系统前首先要剔除不可用数据，留下可用的采集数据进行质量评估。可用数据越多说明电能表采集越稳定、质量越好。上述负荷采集可用率可以为：When the energy meter collects data related to the load on the user side, bad data will be generated due to factors such as current, voltage mutation, and electromagnetic interference. quality assessment. The more available data, the more stable and the better the quality of energy meter collection. The availability rate of the above load collection can be:

Figure BDA0001769646160000061
Figure BDA0001769646160000061

式中：Q表示该供应商生产的电能表总批次数，L表示某一批次设备安装的总区域数，Nij、Eij和eij分别表示第i个批次设备在第j个安装区域中的数量、总负荷采集量和经剔除坏数据后评估系统可用的负荷采集量。ωSAMP,j是表征第j个区域中非质量因素对负荷采集可用率产生影响的修正因子，0≤ωSAMP,j≤1且

Figure BDA0001769646160000062
In the formula: Q indicates the total number of batches of electric energy meters produced by the supplier, L indicates the total number of areas where a certain batch of equipment is installed, N ij , E ij and e ij respectively indicate that the i-th batch of equipment is installed in the j-th The quantity in the area, the total load collection and the load collection available to the evaluation system after removing bad data. ω SAMP,j is a correction factor that characterizes the impact of non-quality factors in the jth region on the availability of load collection, 0≤ω SAMP,j ≤1 and
Figure BDA0001769646160000062

实际的电能表在采集各项数据时可能会出现某几个时刻或某一时间段内采集不到数据的情况，即数据采集不完整，这会导致计量系统产生相应的计量误差。因此，数据采集越完整，电能表的计量结果越精确。上述数据采集完整率可以为：When the actual energy meter collects various data, there may be situations where data cannot be collected at certain moments or within a certain period of time, that is, the data collection is incomplete, which will cause corresponding measurement errors in the measurement system. Therefore, the more complete the data collection, the more accurate the measurement results of the energy meter. The completeness rate of the above data collection can be:

Figure BDA0001769646160000063
Figure BDA0001769646160000063

式中：ψij和

Figure BDA0001769646160000071
分别表示第i个批次的电能表在第j个安装区域中的理论应采集数据量和实际采集数据量，ωINT,j是表征第j个区域中非质量因素对数据采集完整产生影响的修正因子，0≤ωINT,j≤1且/>
Figure BDA0001769646160000072
In the formula: ψ ij and
Figure BDA0001769646160000071
Respectively represent the amount of theoretical data collected and the actual amount of data collected by the electric energy meter of the i-th batch in the j-th installation area. Correction factor, 0≤ω INT, j ≤1 and />
Figure BDA0001769646160000072

平均无故障工作时间是指电能表发生第一次故障前能够正常运行的平均时间。这是衡量电能表可靠性的重要参数，平均无故障时间越长，电能表的可靠性越高。考虑其安装区域等因素，上述平均无故障工作时间可以为：The mean time between failures refers to the average time that the energy meter can operate normally before the first failure occurs. This is an important parameter to measure the reliability of the energy meter. The longer the mean time between failures, the higher the reliability of the energy meter. Considering its installation area and other factors, the above average trouble-free working time can be:

Figure BDA0001769646160000073
Figure BDA0001769646160000073

式中：Nj是第j个安装区域的电能表数，TF,jk是第j个安装区域中第k个设备发生首次故障时距离初始运行的时间，ωMTBF,j是表征第j个区域非质量因素对无故障工作时间产生影响的修正因子，0≤ωMTBF,j≤1且

Figure BDA0001769646160000074
In the formula: N j is the number of watt-hour meters in the jth installation area, T F,jk is the time from the initial operation when the kth equipment in the jth installation area fails for the first time, ω MTBF,j is the characteristic of the jth The correction factor for the impact of regional non-quality factors on the working time between failures, 0≤ω MTBF,j ≤1 and
Figure BDA0001769646160000074

电能表在运行时，往往会出现较多异常报警信息，其中与计量设备故障相关的异常报警信息主要分为如下3大类：电量异常(包括电能表示值不平、电能表飞走、电能表倒走、电能表停走、需量异常、自动核抄异常等)、电压电流异常(包括电压失压、电压断相、电压越限、电压不平衡、电流失流、电流不平衡等)和时钟异常。上述异常报警信息的次数之和可以为：When the electric energy meter is running, there will often be many abnormal alarm information. Among them, the abnormal alarm information related to the failure of the metering equipment is mainly divided into the following three categories: abnormal electric quantity (including uneven electric energy indication value, flying away electric energy meter, downturned electric energy meter) running, power meter stop, abnormal demand, abnormal automatic reading, etc.), voltage and current abnormalities (including voltage loss, voltage phase failure, voltage limit, voltage unbalance, current loss, current unbalance, etc.) and clock abnormal. The sum of the times of the above abnormal alarm information can be:

Figure BDA0001769646160000075
Figure BDA0001769646160000075

式中：

Figure BDA0001769646160000076
和/>
Figure BDA0001769646160000077
分别表示第i个批次的产品在第j个安装区域的电量异常报警次数、电压电流异常报警次数和时钟异常报警次数。In the formula:
Figure BDA0001769646160000076
and />
Figure BDA0001769646160000077
Respectively represent the number of abnormal power alarms, voltage and current abnormal alarm times, and clock abnormal alarm times of the i-th batch of products in the j-th installation area.

电能表在出厂时厂家提供了其标称的使用寿命小时数，在实际运行时电能表可能会出现发生故障后经自复位或检修后继续运行的情况。故障时，电能表停止工作，停机时间越长，其造成计量偏差越大，在综合评估中质量越差。上述运行故障率可以为：When the energy meter leaves the factory, the manufacturer provides its nominal service life hours. In actual operation, the energy meter may continue to run after self-resetting or maintenance after failure. When a fault occurs, the energy meter stops working, and the longer the downtime, the greater the measurement deviation caused by it, and the worse the quality in the comprehensive evaluation. The above operating failure rate can be:

Figure BDA0001769646160000081
Figure BDA0001769646160000081

式中：Trate,jk和Tstop,jk分别是第j个安装区域中第k台电能表标称运行时间和故障停机时间，ωFAULT,j是表征第j个区域非质量因素对故障停机产生影响的修正因子，0≤ωFAULT,j≤1且

Figure BDA0001769646160000082
In the formula: T rate,jk and T stop,jk are the nominal running time and downtime of the k-th electric energy meter in the j-th installation area, respectively, and ω FAULT,j is the non-quality factor that represents the j-th area. A correction factor that affects, 0≤ω FAULT,j ≤1 and
Figure BDA0001769646160000082

S20，计算电能表的各个指标值，构建指标矩阵；对指标矩阵进行标准化处理形成决策矩阵；S20, calculating each index value of the electric energy meter, constructing an index matrix; standardizing the index matrix to form a decision matrix;

在本技术方案中，上述指标矩阵可以为：In this technical solution, the above index matrix can be:

Figure BDA0001769646160000083
Figure BDA0001769646160000083

式中：rij表示电能表i对应的指标j的指标值，N为电能表供应商个数，NPM为电能表个数，M为衡量供应商的电能表质量的评价指标个数，i∈{1,2,...,NPM},j∈{1,2,...,M}。在本发明中M等于5。In the formula: r ij represents the index value of index j corresponding to electric energy meter i, N is the number of electric energy meter suppliers, N PM is the number of electric energy meters, M is the number of evaluation indexes to measure the quality of electric energy meters of suppliers, i ∈{1,2,...,N PM },j∈{1,2,...,M}. M is equal to 5 in the present invention.

因为电能表评估指标的量纲不一样，指标值之间没有可比性，且不同供应商的正在运行的电能表数目不一样。为了使得指标值之间具有一定的可比性以及更为合理地对供应商进行评估，这里需要对所有电能表下的各个指标原始值进行无量纲化处理或标准化处理，然后对标准化处理后同一个供应商的电能表的指标值取平均值来代表该供应商的电能表的质量指标。此外，指标又可分为成本型和效益型2种类型，其中成本型的指标取值越大越差，而效益型的指标取值越小越差。故需要分别对成本型和效益型这2种类型的指标分别采用如下的无量纲化或标准化处理。Because the dimensions of the evaluation indicators of electric energy meters are different, there is no comparability between index values, and the number of operating electric energy meters of different suppliers is not the same. In order to make the index values comparable to a certain extent and to evaluate suppliers more reasonably, it is necessary to perform dimensionless processing or standardization processing on the original values of each index under all electric energy meters, and then standardize the same The index value of the supplier's energy meter is averaged to represent the quality index of the supplier's energy meter. In addition, indicators can be divided into two types: cost-type and benefit-type. The larger the value of the cost-type index is, the worse it will be, while the smaller the value of the benefit-type index will be, the worse it will be. Therefore, it is necessary to adopt the following dimensionless or standardized treatment for the two types of indicators of cost and benefit.

上述成本型指标标准化处理方法可以为：The standardization processing method of the above-mentioned cost-type indicators can be as follows:

Figure BDA0001769646160000084
Figure BDA0001769646160000084

上述效益型指标标准化处理方法可以为：The standardization processing method of the above-mentioned benefit-type indicators can be as follows:

Figure BDA0001769646160000091
Figure BDA0001769646160000091

上述各供应商对应的各个指标的决策矩阵可以为：The decision matrix of each indicator corresponding to the above suppliers can be:

y”＝(y”kj)N×M y”=(y” kj ) N×M

式中：

Figure BDA0001769646160000092
Ωk表示属于供应商k生产的电能表的集合。In the formula:
Figure BDA0001769646160000092
Ω k represents the set of energy meters belonging to supplier k.

S30，计算电能表各项的指标值信息熵和斯皮尔曼等级相关系数，根据CRITIC方法确定质量评估指标的客观权重，根据客观权重对所决策矩阵进行加权处理，得到电能表供应商评估矩阵；S30, calculating the index value information entropy and the Spearman rank correlation coefficient of each item of the electric energy meter, determining the objective weight of the quality evaluation index according to the CRITIC method, performing weighting processing on the decision matrix according to the objective weight, and obtaining the electric energy meter supplier evaluation matrix;

CRITIC法是多属性决策问题中一种指标权重的客观赋权法，该方法是基于评估指标的差异程度和评估指标间的相关性来确定评估指标的客观权重。这里采用熵和斯皮尔曼等级相关系数分别来衡量指标在不同评价对象的取值差异性(即评价指标的对比强度)和评价指标之间的冲突性。在本技术方案中上述熵权可以为：The CRITIC method is an objective weighting method for index weights in multi-attribute decision-making problems. This method determines the objective weights of evaluation indicators based on the degree of difference in evaluation indicators and the correlation between evaluation indicators. Here, entropy and Spearman's rank correlation coefficient are used to measure the value difference of indicators in different evaluation objects (that is, the contrast strength of evaluation indicators) and the conflict between evaluation indicators. In this technical solution, the above-mentioned entropy weight can be:

Figure BDA0001769646160000093
Figure BDA0001769646160000093

式中：

Figure BDA0001769646160000094
rij为评估问题的决策矩阵元素，/>
Figure BDA0001769646160000095
并且假定，当fij＝0时，fijln fij＝0；M为评价指标个数，N为待评价方案个数；wi表示第i个指标的熵权，0≤wi≤1，/>
Figure BDA0001769646160000096
In the formula:
Figure BDA0001769646160000094
r ij is the decision matrix element of the evaluation problem, />
Figure BDA0001769646160000095
And suppose, when f ij =0, f ij ln f ij =0; M is the number of evaluation indicators, N is the number of schemes to be evaluated; w i represents the entropy weight of the i-th index, 0≤w i ≤1 , />
Figure BDA0001769646160000096

斯皮尔曼等级相关系数是统计学中反映两组等级变量相关程度的一种相关系数。斯皮尔曼等级相关系数对数据条件的要求没有积差相关系数严格，只要两个变量的观测值是成对的等级评定资料，或者是由连续变量观测资料转化得到的等级资料，不论两个变量的总体分布形态、样本容量的大小如何，都可以用斯皮尔曼等级相关来进行研究。上述斯皮尔曼等级相关系数可以包括：Spearman's rank correlation coefficient is a correlation coefficient in statistics that reflects the degree of correlation between two groups of rank variables. Spearman's rank correlation coefficient is not as strict as the product difference correlation coefficient for the data conditions, as long as the observed values of the two variables are paired rank evaluation data, or rank data converted from continuous variable observation data, regardless of the two variables The overall distribution shape of , and the size of the sample size can all be studied using Spearman rank correlation. The Spearman rank correlation coefficients above can include:

Figure BDA0001769646160000097
Figure BDA0001769646160000097

Figure BDA0001769646160000101
Figure BDA0001769646160000101

式中：

Figure BDA0001769646160000102
为排序值向量/>
Figure BDA0001769646160000103
和/>
Figure BDA0001769646160000104
的协方差；/>
Figure BDA0001769646160000105
和/>
Figure BDA0001769646160000106
分别为排序值向量
Figure BDA0001769646160000107
和/>
Figure BDA0001769646160000108
的标准差；/>
Figure BDA0001769646160000109
和/>
Figure BDA00017696461600001010
分别为排序值向量/>
Figure BDA00017696461600001011
和/>
Figure BDA00017696461600001012
的均值。/>
Figure BDA00017696461600001013
和
Figure BDA00017696461600001014
是具有N个元素的两列变量，其第i个变量值分别为zij和zik(1≤i≤N),其中j∈{1,2,...,M}，k∈{1,2,...,M}；/>
Figure BDA00017696461600001015
和/>
Figure BDA00017696461600001016
分别是/>
Figure BDA00017696461600001017
和/>
Figure BDA00017696461600001018
的排序值向量，其中/>
Figure BDA00017696461600001019
和/>
Figure BDA00017696461600001020
分别为zij和zik在/>
Figure BDA00017696461600001021
和/>
Figure BDA00017696461600001022
的排序值。ρik表示第j个和第k个指标之间的斯皮尔曼等级相关系数，ρj表示第j个指标与其他指标的整体肯德尔相关系数。In the formula:
Figure BDA0001769646160000102
is a vector of sorted values />
Figure BDA0001769646160000103
and />
Figure BDA0001769646160000104
covariance of ;/>
Figure BDA0001769646160000105
and />
Figure BDA0001769646160000106
vector of sorted values, respectively
Figure BDA0001769646160000107
and />
Figure BDA0001769646160000108
standard deviation of ;/>
Figure BDA0001769646160000109
and />
Figure BDA00017696461600001010
vector of sorted values respectively />
Figure BDA00017696461600001011
and />
Figure BDA00017696461600001012
mean value. />
Figure BDA00017696461600001013
and
Figure BDA00017696461600001014
is a two-column variable with N elements, and its i-th variable values are z ij and z ik (1≤i≤N), where j∈{1,2,...,M}, k∈{1 ,2,...,M};/>
Figure BDA00017696461600001015
and />
Figure BDA00017696461600001016
respectively />
Figure BDA00017696461600001017
and />
Figure BDA00017696461600001018
A vector of sorted values where />
Figure BDA00017696461600001019
and />
Figure BDA00017696461600001020
z ij and z ik respectively in />
Figure BDA00017696461600001021
and />
Figure BDA00017696461600001022
sort value. ρik represents the Spearman rank correlation coefficient between the jth and kth indicators, and ρj represents the overall Kendall correlation coefficient between the jth indicator and other indicators.

当指标j的斯皮尔曼等级相关系数为1时，表明该指标与其他指标具有一致的等级相关性；而斯皮尔曼等级相关系数为0时，则表明该指标与其他指标是相互独立的。When the Spearman rank correlation coefficient of index j is 1, it indicates that the index has a consistent rank correlation with other indices; and when the Spearman rank correlation coefficient is 0, it indicates that the index is independent of other indices.

从上述可以看出，熵和斯皮尔曼等级相关系数分别可以用于衡量评价指标的对比强度和评价指标之间的冲突性，因此综合熵和斯皮尔曼等级相关系数可以用于确定各个指标的客观权重上述客观权重可以为：It can be seen from the above that the entropy and the Spearman rank correlation coefficient can be used to measure the contrast strength of the evaluation index and the conflict between the evaluation indexes, so the comprehensive entropy and the Spearman rank correlation coefficient can be used to determine the value of each index. Objective Weights The above objective weights can be:

Figure BDA00017696461600001023
Figure BDA00017696461600001023

式中：Cj表示第j个指标的客观权重。In the formula: C j represents the objective weight of the jth index.

S40，根据评估矩阵，计算电能表供应商的正理想解和负理想解，计算各个电能表供应商至正理想点向量和负理想点向量的欧式距离；S40, according to the evaluation matrix, calculate the positive ideal solution and the negative ideal solution of the electric energy meter supplier, and calculate the Euclidean distance from each electric energy meter supplier to the positive ideal point vector and the negative ideal point vector;

在本技术方案中，上述电能表供应商评估问题的理想解可以确定为

Figure BDA00017696461600001024
反理想解可以确定为/>
Figure BDA00017696461600001025
其中/>
Figure BDA00017696461600001032
j∈{1,2,...,M}。In this technical solution, the ideal solution to the above-mentioned energy meter supplier evaluation problem can be determined as
Figure BDA00017696461600001024
The anti-ideal solution can be determined as />
Figure BDA00017696461600001025
where />
Figure BDA00017696461600001032
j∈{1,2,...,M}.

上述电能表供应商与理想解和反理想解的欧氏距离

Figure BDA00017696461600001027
和/>
Figure BDA00017696461600001028
分别可以为：The Euclidean distance between the above energy meter suppliers and the ideal solution and the inverse ideal solution
Figure BDA00017696461600001027
and />
Figure BDA00017696461600001028
Can be respectively:

Figure BDA00017696461600001029
Figure BDA00017696461600001029

Figure BDA00017696461600001030
Figure BDA00017696461600001030

式中：

Figure BDA00017696461600001031
为加权后的电能表供应商评估矩阵Z的第i行。In the formula:
Figure BDA00017696461600001031
Row i of matrix Z is evaluated for weighted energy meter suppliers.

S50，计算各个电能表供应商与理想解的相对逼近度，按照从高至低确定各个电能表供应商的质量等级S50, calculate the relative approximation between each energy meter supplier and the ideal solution, and determine the quality level of each energy meter supplier from high to low

在本技术方案中，各个电能表供应商与理想解的相对逼近度可以为：In this technical solution, the relative approximation between each energy meter supplier and the ideal solution can be:

Figure BDA0001769646160000111
Figure BDA0001769646160000111

式中，Ci是各个电能表供应商与理想解的相对逼近度，可以将其依次从高至低确定各个电能表供应商的质量等级。In the formula, C i is the relative approximation degree between each energy meter supplier and the ideal solution, which can be used to determine the quality level of each energy meter supplier in turn from high to low.

为了进一步理解本发明，以下以国网浙江省电力公司宁波供电公司管辖的某地区电能表数据为例，来解释本发明的实际应用。该原始数据集共有11565条数据，经过数据清洗之后可用的数据为11312条，共有17个待评价的电能表供应商，每个供应商下有若干设备批次，所有的设备批次为58个批次。表1给出了标准化后各个供应商的电能表质量评估矩阵。In order to further understand the present invention, the practical application of the present invention will be explained below by taking the electric energy meter data of a certain area under the jurisdiction of Ningbo Power Supply Company of State Grid Zhejiang Electric Power Company as an example. The original data set has a total of 11565 pieces of data. After data cleaning, the available data is 11312 pieces. There are 17 electric energy meter suppliers to be evaluated. Each supplier has several equipment batches, and all equipment batches are 58. batch. Table 1 shows the quality evaluation matrix of energy meters of various suppliers after standardization.

表1标准化后的电能表质量评价指标值Table 1 Standardized energy meter quality evaluation index value

Figure BDA0001769646160000112
Figure BDA0001769646160000112

首先，根据各个指标的定义计算电能表供应商各个指标的数值，从而形成电能表综合评估问题的的指标矩阵，然后对其标准化处理；接着，根据熵、熵权和斯皮尔曼等级相关系数的定义，分别计算各个指标的熵、熵权、斯皮尔曼等级相关系数以及客观综合权重，其结果如表2所示。Firstly, according to the definition of each index, the value of each index of the electric energy meter supplier is calculated, so as to form the index matrix of the comprehensive evaluation problem of electric energy meter, and then standardize it; then, according to the entropy, entropy weight and Spearman rank correlation coefficient Define, respectively calculate the entropy, entropy weight, Spearman rank correlation coefficient and objective comprehensive weight of each index, and the results are shown in Table 2.

表2各个指标的熵、熵权、相关系数和综合权重Table 2 Entropy, entropy weight, correlation coefficient and comprehensive weight of each index

Figure BDA0001769646160000121
Figure BDA0001769646160000121

从表2可以看出：平均无故障工作时间指标具有最小的熵，其值为0.8944，这表明各个供应商在该指标上的取值差别最大，故其熵权最大(取值为0.4476)，即该指标给供应商的综合质量评估提供了较多有用的信息，故该指标在供应商的综合质量评估中的比重应较大；数据采集完整率指标具有最大的熵，其值为0.9763，这表明各个供应商在该指标上的取值差别最小，故其熵权最小(取值为0.1004)，即该指标给供应商的综合质量评估提供了较少有用的信息，故该指标在供应商的综合质量评估中的比重应较小。此外，从表2还可以看出：计量异常报警次数指标具有最小的斯皮尔曼等级相关系数，其值为0.1521，这表明该指标和其他指标的相关性最小，即该指标提供的有用信息和其他指标重合度不大，因此该指标在节点的综合重要度评估中的比重应较大；平均无故障工作时间指标具有最大的斯皮尔曼等级相关系数，其值为0.2482，这表明该指标和其他指标的相关性较大，提供的有用信息重合度较大，因此该指标在节点的综合重要度评估中的比重应较小。从5个指标的定义可以看出，计量异常报警次数指标与平均无故障工作时间、运行故障率这2个指标基本上不相关，而与负荷采集可用率、数据采集完整率这2个指标有一定的关系，但相关性不强，经计算得到的该指标的斯皮尔曼等级相关系数最小，因此这与该指标的实际相关性是吻合的；平均无故障工作时间与负荷采集可用率、数据采集完整率和运行故障率这3个指标非常相关，经计算得到该指标的斯皮尔曼等级相关系数取值最大，因此这也与该指标的实际相关性相吻合。综合熵权和斯皮尔曼等级相关系数后，可以得到5个指标的综合客观权重分别为：0.1607、0.0968、0.4275、0.1103和0.2048。从中可以看出：经过综合后，平均无故障工作时间指标具有最大的权重，而计量异常报警次数指标具有最小的权重；考虑了指标的相关性后，平均无故障工作时间指标的客观综合权重比未考虑相关性的熵权小了一些，而计量异常报警次数指标比未考虑相关性的熵权大了一些。It can be seen from Table 2 that the average trouble-free working time index has the smallest entropy, and its value is 0.8944, which shows that the value difference of each supplier on this index is the largest, so its entropy weight is the largest (the value is 0.4476), That is to say, this index provides more useful information for the supplier's comprehensive quality assessment, so the proportion of this indicator in the supplier's comprehensive quality assessment should be larger; the data collection completeness rate index has the largest entropy, and its value is 0.9763, This shows that the value difference of each supplier on this indicator is the smallest, so its entropy weight is the smallest (value is 0.1004), that is, this indicator provides less useful information for the comprehensive quality evaluation of suppliers, so this indicator is in the supply chain. The proportion in the comprehensive quality assessment of quotient should be small. In addition, it can also be seen from Table 2 that the indicator of the number of abnormal measurement alarms has the smallest Spearman rank correlation coefficient, and its value is 0.1521, which indicates that the correlation between this indicator and other indicators is the smallest, that is, the useful information provided by this indicator and The coincidence degree of other indicators is not large, so the proportion of this indicator in the comprehensive importance evaluation of nodes should be larger; the average trouble-free working time indicator has the largest Spearman rank correlation coefficient, and its value is 0.2482, which shows that this indicator and The correlation of other indicators is relatively high, and the overlap of useful information provided is relatively large, so the proportion of this indicator in the comprehensive importance evaluation of nodes should be small. From the definitions of the five indicators, it can be seen that the number of abnormal measurement alarms is basically irrelevant to the two indicators of the average trouble-free working time and the operation failure rate, but is related to the two indicators of the load collection availability rate and the data collection integrity rate. There is a certain relationship, but the correlation is not strong. The calculated Spearman rank correlation coefficient of this index is the smallest, so this is consistent with the actual correlation of this index; The three indicators of acquisition integrity rate and operation failure rate are very correlated, and the Spearman rank correlation coefficient of this indicator is calculated to be the largest, so this is also consistent with the actual correlation of this indicator. After integrating the entropy weight and Spearman's rank correlation coefficient, the comprehensive objective weights of the five indicators can be obtained as follows: 0.1607, 0.0968, 0.4275, 0.1103 and 0.2048. It can be seen from it that after synthesis, the average working time without failure index has the largest weight, while the abnormal measurement alarm times index has the smallest weight; after considering the correlation of the indicators, the objective comprehensive weight ratio of the average working time without failure index is The entropy weight without considering the correlation is a little smaller, and the index of the number of abnormal measurement alarms is larger than the entropy weight without considering the correlation.

接着，计算供应商的电能表质量评估问题的理想解和反理想解，进而得到各供应商分别与理想解和反理想解的欧氏距离，在此基础上分别计算各个供应商与理想解的相对逼近度，其结果如表3所示。从表3可以看出：电能表的运行质量最好的前10家供应商分别为：10、9、6、5、11、7、4、13、14和16，其中供应商10生产的电能表的运行质量最优，供应商9生产的电能表的运行质量最优。Then, calculate the ideal solution and anti-ideal solution of the supplier's electric energy meter quality evaluation problem, and then obtain the Euclidean distance between each supplier and the ideal solution and the anti-ideal solution, and then calculate the distance between each supplier and the ideal solution Relative approximation, the results are shown in Table 3. It can be seen from Table 3 that the top 10 suppliers with the best operating quality of electric energy meters are: 10, 9, 6, 5, 11, 7, 4, 13, 14 and 16, and the electric energy produced by supplier 10 The running quality of the meter is the best, and the running quality of the energy meter produced by Supplier 9 is the best.

表3基于本发明方法和熵权法的供应商电能表质量评估结果与比较Table 3 is based on the method of the present invention and the supplier's energy meter quality evaluation result and comparison of entropy weight method

Figure BDA0001769646160000131
Figure BDA0001769646160000131

此外，为了验证所提出方法的有效性，表3给出了采用熵权法评估得到的供应商计量设备质量结果。从表3可以看出：电能表的运行质量最好的前10家供应商分别为：10、9、6、5、7、11、13、4、3和14，其中供应商10生产的电能表的运行质量最优，供应商9生产的电能表的运行质量最优。对比基于本发明方法和基于熵权法的电能表的运行质量评估结果可以看出：采用本发明方法和采用熵权法得到的电能表质量评估结果中排名前4的供应商是一致的；且有9个供应商都在排名前10的集合中，只是排序稍微有所区别。因此，这在一定程度上表明本发明所提的方法可以较好地从电能表的运行质量方面对供应商进行评估，具有一定的有效性。此外，从表3的对比结果还可以看出：采用本发明方法和采用熵权法得到的电能表质量评估结果中排名前10的供应商集合中，不一致的供应商分别为供应商3和16，其中本发明方法评估的结果是供应商16优于供应商3。结合表1和表2可以看出：供应商16和3的负荷采集可用率、数据采集完整率和计量异常报警次数这3个指标的取值差别不大，而平均无故障工作时间和运行故障率指标值差别较大；平均无故障工作时间指标与其他4个指标的相关性较强，而运行故障率指标与其他4个指标的相关性较弱。平均无故障工作时间指标在其他4个指标的数值中已部分体现，故对其权重应适当降低；反之，运行故障率指标的权重应适当增大。因此，由于本发明方法考虑了指标之间的相关性，使得平均无故障工作时间指标对综合质量评估结果影响过大，正是由于适当减少了该指标的权重使得本发明方法对供应商16的评估结果排在前10内，而把供应商3的评估结果排在前10外。综上，与熵权法相比，本发明提出的方法能够计及指标之间的相关性，在一定程度上能够较为合理地对供应商的电能表的质量进行评估。In addition, in order to verify the effectiveness of the proposed method, Table 3 presents the quality results of the supplier's metering equipment evaluated by the entropy weight method. It can be seen from Table 3 that the top 10 suppliers with the best operating quality of electric energy meters are: 10, 9, 6, 5, 7, 11, 13, 4, 3 and 14, and the electric energy produced by supplier 10 The running quality of the meter is the best, and the running quality of the energy meter produced by Supplier 9 is the best. Comparing the operation quality evaluation results of electric energy meters based on the method of the present invention and the entropy weight method, it can be seen that the top 4 suppliers in the quality evaluation results of electric energy meters obtained by the method of the present invention and the entropy weight method are consistent; and There are 9 suppliers in the top 10 set, but the order is slightly different. Therefore, this shows to a certain extent that the method proposed in the present invention can better evaluate the supplier from the aspect of the operation quality of the electric energy meter, and has certain validity. In addition, it can also be seen from the comparison results in Table 3 that among the top 10 supplier sets in the quality evaluation results of electric energy meters obtained by using the method of the present invention and the entropy weight method, the inconsistent suppliers are respectively supplier 3 and 16 , wherein the evaluation result of the method of the present invention is that supplier 16 is better than supplier 3. Combining Table 1 and Table 2, it can be seen that there is little difference in the values of the three indicators of the load collection availability rate, data collection completeness rate and metering abnormality alarm times of suppliers 16 and 3, while the average trouble-free working time and operation failure There is a large difference in the value of the rate index; the correlation between the average trouble-free working time index and the other four indexes is strong, while the correlation between the operation failure rate index and the other four indexes is weak. The average trouble-free working time index has been partially reflected in the values of the other four indexes, so its weight should be appropriately reduced; on the contrary, the weight of the operational failure rate index should be appropriately increased. Therefore, because the method of the present invention considers the correlation between indexes, the mean working time without failure index has too much influence on the comprehensive quality evaluation result, and it is precisely because the weight of the index is appropriately reduced that the method of the present invention has a great influence on the supplier 16 The evaluation results are ranked in the top 10, while the evaluation results of supplier 3 are ranked outside the top 10. To sum up, compared with the entropy weight method, the method proposed by the present invention can take into account the correlation between indicators, and can reasonably evaluate the quality of the supplier's electric energy meters to a certain extent.

结果表明本发明所提出的方法能够计及指标之间的相关性，能够较为合理地对电能表的运行质量进行评估，能够为物资管理、营销计量专业部门提供科学合理的评价支撑。The results show that the method proposed by the present invention can take into account the correlation between indicators, can reasonably evaluate the operation quality of electric energy meters, and can provide scientific and reasonable evaluation support for professional departments of material management and marketing measurement.

Claims (2)

1. The electric energy meter manufacturer evaluation method based on the multi-attribute decision model is characterized by comprising the following steps of:
1) Acquiring the original data of the electric energy meters in each batch and area, and selecting a plurality of evaluation indexes for measuring the quality of the electric energy meters according to the operation monitoring big data of the acquisition terminal equipment, wherein the evaluation indexes comprise load acquisition availability, data acquisition integrity rate, average fault-free working time, metering abnormal alarm times and operation fault rate;
2) Calculating each evaluation index value of the electric energy meter, and constructing an index matrix; performing standardization processing on the index matrix to form a decision matrix; the index matrix is:
Figure FDA0004124928610000011
wherein: r is (r) ij Index value representing index j corresponding to electric energy meter i, N being the number of suppliers of electric energy meter, N PM For the number of electric energy meters, M is the number of evaluation indexes for measuring the quality of the electric energy meters of suppliers, i epsilon {1,2,. PM E {1,2,. }, M }; in the present invention M is equal to 5;
the calculation formula of the standardized treatment of the cost index is as follows:
Figure FDA0004124928610000012
the calculation formula of the benefit index standardization processing is as follows:
Figure FDA0004124928610000013
the decision matrix calculation formula of each index corresponding to each provider is as follows:
y”＝(y″ kj ) N×M
wherein:
Figure FDA0004124928610000014
Ω k representing a set of electric energy meters belonging to the production of supplier k;
3) Calculating the information entropy of each evaluation index value of the electric energy meter and the correlation coefficient of the Speermann grade, determining the objective weight of the quality evaluation index according to the CRITIC method, and carrying out weighting treatment on the decision matrix according to the objective weight to obtain an electric energy meter supplier evaluation matrix; when the entropy of the evaluation index value information of each item of the acquisition terminal equipment is calculated, the entropy weight calculation formula is as follows:
Figure FDA0004124928610000021
wherein:
Figure FDA0004124928610000022
r ij decision matrix element for evaluating a problem, +.>
Figure FDA0004124928610000023
And assume that when f ij When=0, f ij ln f ij =0; m is the number of evaluation indexes, and N is the number of schemes to be evaluated; w (w) i Entropy weight of i index is represented, w is more than or equal to 0 i ≤1，
Figure FDA0004124928610000024
When calculating the spearman class correlation coefficient of each item of the acquisition terminal equipment, the spearman class correlation coefficient
The calculation formula is as follows:
Figure FDA0004124928610000025
Figure FDA0004124928610000026
wherein:
Figure FDA0004124928610000027
for sorting value vector +.>
Figure FDA0004124928610000028
And->
Figure FDA0004124928610000029
Is a covariance of (2); />
Figure FDA00041249286100000210
And->
Figure FDA00041249286100000211
Respectively are sorting value vectors->
Figure FDA00041249286100000212
And
Figure FDA00041249286100000213
standard deviation of (2); />
Figure FDA00041249286100000214
And->
Figure FDA00041249286100000215
Respectively are sorting value vectors->
Figure FDA00041249286100000216
And->
Figure FDA00041249286100000217
Is the average value of (2); />
Figure FDA00041249286100000218
And
Figure FDA00041249286100000219
is a two-column variable with N elements, and the ith variable value is z respectively ij And z ik (1.ltoreq.i.ltoreq.N), where j.epsilon. {1,2, M, k e {1,2,., M; />
Figure FDA00041249286100000220
And->
Figure FDA00041249286100000221
Are respectively->
Figure FDA00041249286100000222
And->
Figure FDA00041249286100000223
Is a vector of ordered values, wherein->
Figure FDA00041249286100000224
And->
Figure FDA00041249286100000225
Z respectively ij And z ik At->
Figure FDA00041249286100000226
And->
Figure FDA00041249286100000227
Is a ranking value of (2); ρ ik Representing the spearman rank correlation coefficient, ρ, between the jth and kth indices j An overall kendel correlation coefficient representing the jth index and other indexes;
the objective weight calculation formula is:
Figure FDA00041249286100000228
wherein: c (C) j Objective weight representing the j-th index;
4) According to the evaluation matrix, calculating ideal solutions of the electric energy meter suppliers, wherein the ideal solutions comprise positive ideal solutions and negative ideal solutions, and calculating Euclidean distances from each electric energy meter supplier to positive ideal point vectors and negative ideal point vectors; determining an ideal solution to a power meter vendor assessment problem
Figure FDA0004124928610000031
And anti-ideal solution->
Figure FDA0004124928610000032
Wherein->
Figure FDA0004124928610000033
Calculating Euclidean distance between electric energy meter supplier and ideal solution and anti-ideal solution respectively
Figure FDA0004124928610000034
And->
Figure FDA0004124928610000035
The calculation formula of (2) is as follows:
Figure FDA0004124928610000036
Figure FDA0004124928610000037
wherein:
Figure FDA0004124928610000038
evaluating the ith row of the matrix Z for the weighted electric energy meter provider;
5) Calculating the relative approximation degree of each electric energy meter supplier and the ideal solution, and determining the quality grade of each electric energy meter supplier according to the high-low degree;
calculating the relative approximation degree of each electric energy meter supplier and the ideal solution, and determining the quality grade of each electric energy meter supplier from high to low in sequence, wherein the corresponding calculation formula is as follows:
Figure FDA0004124928610000039
wherein: c (C) i The relative approximation degree of each electric energy meter supplier and the ideal solution can be determined from high to low in sequence.
2. The method for evaluating a manufacturer of an electric energy meter based on a multi-attribute decision model according to claim 1, wherein in step 1), a load acquisition availability calculation formula is:
Figure FDA00041249286100000310
wherein: q represents the total batch number of the electric energy meter produced by the supplier, L represents the total area number of equipment installation of a certain batch, and N ij 、E ij And e ij Respectively representing the number of the ith batch of equipment in the jth installation area, the total load acquisition amount and the load acquisition amount available to the evaluation system after bad data are removed; omega SAMP,j Is a correction factor for representing the influence of non-quality factors in the jth region on the load acquisition availability, wherein omega is not less than 0 SAMP,j Is less than or equal to 1
Figure FDA0004124928610000041
The calculation formula of the data acquisition integrity rate is as follows:
Figure FDA0004124928610000042
wherein: psi phi type ij And
Figure FDA0004124928610000043
respectively representing theoretical data quantity to be collected and actual data quantity to be collected of electric energy meters in ith batch in jth installation area, omega INT,j Is a correction factor for representing that the non-quality factors in the jth region influence the data acquisition integrity, and is more than or equal to 0 and less than or equal to omega INT,j Is less than or equal to 1 and is%>
Figure FDA0004124928610000044
The average fault-free working time calculation formula is as follows:
Figure FDA0004124928610000045
wherein: n (N) j The number of electric energy meters in the j-th installation area T F,jk Is the time, omega, from the initial operation of the kth device in the jth installation area when the kth device fails for the first time MTBF,j Is a correction factor for representing the influence of non-quality factors of the jth region on the fault-free working time, wherein omega is not less than 0 MTBF,j Is less than or equal to 1
Figure FDA0004124928610000046
The sum of the times of the abnormal alarm information is calculated as follows:
Figure FDA0004124928610000047
wherein:
Figure FDA0004124928610000048
and->
Figure FDA0004124928610000049
Respectively indicating that the ith batch of products is in the jth installation areaThe number of abnormal electric quantity alarms, the number of abnormal voltage and current alarms and the number of abnormal clock alarms;
the operation failure rate calculation formula is:
Figure FDA00041249286100000410
wherein: t (T) rate,jk And T stop,jk The nominal operation time and the fault shutdown time of the kth electric energy meter in the jth installation area are respectively omega FAULT,j Is a correction factor for representing the influence of non-quality factors of the jth region on the fault shutdown, wherein omega is not less than 0 FAULT,j Is less than or equal to 1
Figure FDA0004124928610000051
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