Power distribution network operation index evaluation method based on terminal data fusion

Abstract

The invention relates to a power distribution network operation index evaluation method based on terminal data fusion, and belongs to the field of power distribution network operation evaluation. According to the method, the reference comparison method is introduced and compared with the optimal practical value, the defects and improvement space in the operation of the power distribution network are effectively identified, and the power distribution network is guided to optimize resource configuration and management strategies. Meanwhile, an entropy evaluation method and a Delphi method are combined, the defects that a subjective weighting method depends on personal experience and judgment of experts and an objective weighting method performs analysis completely based on data are overcome, and objective basis driven by data and deep insight of the experts can be comprehensively utilized in evaluation.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network operation evaluation, and in particular relates to a distribution network operation index evaluation method based on terminal data fusion. Background Art

[0002] With the rapid development of the economy and the continuous increase in electricity demand, the safety and reliability assessment of the distribution network, as a key component of the power system, has become increasingly important. The distribution network is responsible for transmitting electricity from the transmission network to the end user, and the safety and reliability of its operation directly affects the power quality and power reliability of the user. Driven by modern smart grid technology, the operating environment of the distribution network has become more complex, showing a high degree of nonlinearity and uncertainty. In this environment, accurate safety and reliability assessment has become the key to ensuring the efficient operation of the distribution network. However, traditional safety and reliability assessment methods have problems such as excessive subjectivity, relatively static determination of single indicator scores, and analysis based entirely on data, which makes it difficult to fully cope with the variable characteristics of modern distribution networks. Therefore, the proposed new safety and reliability assessment method can provide more reliable data support in the quantitative assessment of the reliability and safety of the distribution network operation, and also provide strong technical support for power grid dispatching and decision-making.

[0003] At present, some progress has been made in the evaluation methods of distribution network safety and reliability at home and abroad. Commonly used methods include hierarchical analysis method, Delphi method, grey correlation analysis method and fuzzy evaluation method. Although these methods have achieved certain results in application, they still have some shortcomings. The traditional single-item score determination method is relatively static and difficult to cope with the variability of modern distribution networks. The determination of weights in the subjective weighting method depends on the personal experience and judgment of decision makers or experts. The experience of experts may be insufficient, resulting in a lack of scientific basis for weight allocation, and it is also easily affected by subjective factors. The objective weighting method is completely based on data analysis, which may ignore some important qualitative factors or deep insights of experts, especially when the data cannot fully cover the complexity of the problem. Therefore, it is urgent to propose a new single-item score determination method and weighting method for distribution network safety and reliability evaluation. Summary of the invention

[0004] The purpose of the present invention is to provide a distribution network operation index evaluation method based on terminal data fusion in view of the requirements of high security and high reliability of active distribution networks, and the shortcomings of traditional scoring evaluation methods, subjective weighting methods, and objective weighting methods. By introducing a benchmark comparison method and comparing with the best practice values, the shortcomings and room for improvement in the operation of the distribution network can be effectively identified, and the distribution network can be guided to optimize resource allocation and management strategies. At the same time, the entropy method and the Delphi method are combined, and the combination of the two improves the shortcomings of the subjective weighting method that relies on the personal experience and judgment of experts and the objective weighting method that is completely based on data analysis, and can comprehensively utilize the objective basis driven by data and the profound insights of experts in the evaluation.

[0005] To achieve the above object, the technical solution of the present invention is: a distribution network operation index evaluation method based on terminal data fusion, comprising:

[0006] Modeling of safety and reliability evaluation system;

[0007] Obtain the data of each smart terminal in the distribution network required for the modeling of the safety and reliability evaluation system, and use the benchmark comparison method to determine the score of each single indicator in the modeling of the safety and reliability evaluation system;

[0008] A combined weighting method combining the Delphi method and the entropy method is proposed to reflect the importance of each indicator in the modeling of the safety and reliability evaluation system.

[0009] In one embodiment of the present invention, the basic operating data of the indicators required in the modeling of the safety and reliability evaluation system are derived from various business systems of the distribution network, including the power supply reliability system, the dispatching management system, the distribution automation system, the power grid dispatching automation system, and the power supply service command system; the original data of the power supply reliability system, the dispatching management system, the distribution automation system, the power grid dispatching automation system, and the power supply service command system are derived from various intelligent terminals in the distribution network, including TTU, RTU, FTU, DTU, meter collection terminal, and GZ.

[0010] In one embodiment of the present invention, the safety and reliability evaluation is divided into reliability evaluation and safety evaluation, both of which belong to the criterion layer; reliability is evaluated by power supply reliability rate, fault outage rate, and self-healing success rate, and safety is evaluated by low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate; the details are as follows:

[0011] 1) Reliability indicators

[0012]

[0013]

[0014]

[0015] In the formula, K e Represents the power supply reliability rate, K g Represents the failure downtime rate, K r represents the self-healing success rate, L represents the total kilometers of lines within the distribution network, and N r 、N g They represent the number of successful self-recovery and outage times after line faults within the statistical period, t represents the power outage time of the electricity user, n represents the number of users, and T represents the total time of the statistical period;

[0016] 2) Safety indicators

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Where P ov Represents the overvoltage limit rate within a statistical period, p ov Represents the real-time overvoltage limit rate, P lv Represents the low voltage limit rate in a statistical period, p lv Represents the real-time low voltage over-limit rate, P lo Represents the heavy overload rate of the line in a statistical period, p lo Represents the real-time line overload rate, P to Represents the heavy overload rate of the distribution transformer in a statistical period, p to Represents the real-time distribution transformer overload rate, N l 、N t Represents the number of lines and distribution transformers within the distribution network, T d Represents the total number of days in the statistical period, K lol Represents the number of severe overloads that occurred on the line during the statistical period, k lol Represents the number of overloaded lines at the current moment, K tol Represents the number of heavy overloads that occurred in the distribution transformer during the statistical period, k tol Represents the number of heavy overloads of the distribution transformer at the current moment, K lov and Ktov Respectively represent the number of overvoltage limit occurrences of the line and distribution transformer in the statistical period, k lov and k tov They represent the number of overvoltage limit of the line and distribution transformer at the current moment, K llv and K tlv They represent the number of low voltage crossings of the line and distribution transformer during the statistical period, respectively. llv and k tlv They respectively represent the number of low voltage limit violations in the lines and distribution transformers at the current moment.

[0026] In one embodiment of the present invention, the specific method of using the benchmark comparison method to determine the score of a single indicator in the modeling of the safety and reliability evaluation system is as follows:

[0027] The benchmark comparison method needs to calculate the scores of various indicators based on the benchmark values ​​and actual values ​​of the reliability indicators and safety indicators obtained in the distribution network. The specific process of the benchmark comparison method is as follows:

[0028] 1) Relative gap percentage

[0029]

[0030] In the formula, r represents the relative difference between the benchmark value and the actual value, X a , X b They represent the size of the benchmark values ​​and actual values ​​of the reliability index and the safety index respectively; the positive or negative value of r represents the excess or deficiency of the benchmark value over the target value;

[0031] 2) Scoring of individual indicators

[0032]

[0033] In the formula, S represents the score value of one of the safety index and reliability index;

[0034] In order to convert the relative gap percentage into a score, the relative gap percentage is allocated to a predetermined score range; through the predetermined score range, the gaps of different degrees are converted into single scores.

[0035] In one embodiment of the present invention, the preset range of the scoring interval is adjusted according to the actual situation of the power distribution network.

[0036] In one embodiment of the present invention, the Delphi method performs weighted calculation on the scores of each single indicator of the safety index and reliability index obtained by the benchmark comparison method to obtain the reliability and safety criterion layer scores; the specific steps are as follows:

[0037] 1) First calculate the concentration of expert opinions

[0038]

[0039] In the formula, represents the weighted average of expert opinions on the jth indicator, m ij represents the weighted value given by the ith expert for the jth indicator, and n represents the total number of experts;

[0040] 2) Calculate the standard deviation

[0041]

[0042] Where, X j is the weighted value standard deviation of the jth indicator;

[0043] 3) Calculate the coefficient of variation

[0044]

[0045] In the formula, CV j is the weighted value variation coefficient of the jth indicator;

[0046] The smaller the coefficient of variation, the lower the dispersion of expert opinions, that is, the more consistent the expert opinions are; conversely, the higher the coefficient of variation, the greater the divergence of opinions; in the Delphi method, through multiple rounds of expert opinion collection and adjustment, the final coefficient of variation is significantly reduced;

[0047] 4) Experts assign weights

[0048] The first three steps are repeated until the coefficient of variation is less than 20%, and the expert opinions are considered to have reached a high consistency. At this time, the weighted average of the expert opinions is used as the given weight for the criterion layer score calculation:

[0049]

[0050] In the formula, m j represents the expert-given weight of the jth indicator in the criterion layer calculation;

[0051] 5) Calculate the score of the criterion layer

[0052]

[0053] Wherein, L1 is the reliability criterion layer score, L2 is the safety criterion layer score, x1, x2, x3, x4, x5, x6, and x7 respectively represent the power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate scores obtained through the benchmark comparison method, and m1, m2, m3, m4, m5, m6, and m7 respectively represent the power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate obtained by the Delphi method.

[0054] In one embodiment of the present invention, in step 1), the concentration of expert opinions is calculated, and the concentration of expert opinions can also be determined by the median and the mode.

[0055] In one embodiment of the present invention, the entropy method first performs data processing on the reliability and safety criterion layer scores of distribution networks in different regions according to the reliability and safety criterion layer scores calculated by the Delphi method; specifically, as follows:

[0056] 1) Data processing

[0057] Assuming there are m regional distribution networks, the original matrix is ​​written in matrix form as follows:

[0058]

[0059] Where, L i1 is the score of the reliability criterion layer in the distribution network in the ith region calculated by the Delphi method, L i2 is the score of the security criterion layer in the distribution network of the ith region calculated by the Delphi method;

[0060] 2) Calculate the data weight:

[0061]

[0062] In the formula, p ij represents the ratio of the jth index of the distribution network in the i-th region to the sum of the jth indexes in all regions, L ij represents the score of the jth indicator in the distribution network of the i-th region;

[0063] 3) Calculate entropy

[0064]

[0065] In the formula, is a constant, ensuring that the entropy value is between 0 and 1. E1 represents the entropy value of the reliability criterion layer, and E2 represents the entropy value of the security criterion layer.

[0066] 4) Calculate the weight of each indicator

[0067]

[0068] In the formula, w1 represents the weight of the reliability criterion layer, and w2 represents the weight of the security criterion layer;

[0069] 5) Calculate the comprehensive evaluation score

[0070]

[0071] Where Y i Represents the comprehensive evaluation score of the ith distribution network.

[0072] The present invention also provides a distribution network operation index evaluation system based on terminal data fusion, which is characterized in that it includes a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0073] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0074] Compared with the prior art, the present invention has the following beneficial effects: by introducing a benchmark comparison method and comparing with the best practice values, the present invention effectively identifies deficiencies and room for improvement in the operation of the distribution network, and guides the distribution network to optimize resource allocation and management strategies. At the same time, the present invention combines the entropy method and the Delphi method. The entropy method provides an objective weighting mechanism based on data, while the Delphi method introduces the subjective experience and judgment of experts. The combination of the two improves the shortcomings of the subjective weighting method that relies on the personal experience and judgment of experts and the objective weighting method that is completely based on data analysis. The objective basis driven by data and the profound insights of experts can be comprehensively utilized in the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 The figure is a flow chart of the method of the present invention.

[0076] Figure 2 This is a safety and reliability evaluation model for the present invention. DETAILED DESCRIPTION

[0077] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0078] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0079] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0080] The present invention provides a distribution network operation index evaluation method based on terminal data fusion, comprising:

[0081] Modeling of safety and reliability evaluation system;

[0082] Obtain the data of each smart terminal in the distribution network required for the modeling of the safety and reliability evaluation system, and use the benchmark comparison method to determine the score of each single indicator in the modeling of the safety and reliability evaluation system;

[0083] A combined weighting method combining the Delphi method and the entropy method is proposed to reflect the importance of each indicator in the modeling of the safety and reliability evaluation system.

[0084] The following is the specific implementation process of the present invention.

[0085] like Figure 1 As shown, this embodiment provides a distribution network operation index evaluation method based on terminal data fusion. First, the safety and reliability indicators are modeled. The basic operation data of the indicators required in the safety and reliability analysis are derived from the various business systems of the distribution network, mainly concentrated in the power supply reliability system, the dispatching management system, the distribution automation system, the power grid dispatching automation system, and the power supply service command system. The original data of each system comes from the intelligent terminals of the system, including TTU, RTU, FTU, DTU, meter acquisition terminal, and GZ. Through the above terminal data fusion, the index values ​​required in the safety and reliability analysis of the distribution network are obtained. In view of the problems that the traditional evaluation method is too subjective, completely based on data analysis, and the single score determination is relatively static, the present invention is based on the data of each terminal in the distribution network, and uses the benchmark comparison method to determine the single indicator score. By comparing with the best practice standards in the distribution network, the State Grid Distribution Company can objectively evaluate its own operation quality and identify the gaps in key indicators such as power supply reliability rate, fault outage rate, overvoltage limit rate, and low voltage limit rate. The best practice standards change with the development of the distribution network and keep pace, improving the traditional single indicator score evaluation method, which is relatively static and may be difficult to reflect the latest development trends and changes of the distribution network in a timely manner. At the same time, a combined weighting method combining the Delphi method and the entropy method is proposed, which can objectively reflect the importance of each indicator and make up for the problem of insufficient data or data deviation.

[0086] 1. Modeling of safety and reliability evaluation system

[0087] See also Figure 2 ,The safety and reliability evaluation of the distribution network can be divided into reliability evaluation and safety evaluation, both of which belong to the criteria layer. Reliability is evaluated by the power supply reliability rate, fault outage rate, and self-healing success rate, while safety is evaluated by the low voltage over-limit rate, over-voltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate.

[0088] 1) Reliability indicators

[0089]

[0090]

[0091]

[0092] In the formula, K e Represents the power supply reliability rate, K g Represents the failure downtime rate, K r Represents the success rate of self-healing. L represents the total kilometers of lines within the distribution network. N r 、N g They represent the number of successful self-recovery after line faults and the number of line fault outages during the statistical period. t represents the power outage time of the power user, n represents the number of users, and T represents the total time of the statistical period.

[0093] 2) Safety indicators

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Where P ov Represents the overvoltage limit rate (within a statistical period), p ov Represents the real-time overvoltage limit rate. lv Represents the low voltage over-limit rate (within a statistical period), p lv Represents the real-time low voltage limit rate.lo Represents the heavy overload rate of the line (within a statistical period), p lo Represents the real-time line overload rate. to Represents the heavy overload rate of the distribution transformer (within a statistical period), p to Represents the real-time distribution transformer overload rate. N l 、N t Represents the number of lines and distribution transformers within the distribution network, T d Represents the total number of days in the statistical period. K lol Represents the number of severe overloads that occurred on the line during the statistical period, k lol Represents the number of overloads on the line at the current moment. K tol Represents the number of heavy overloads that occurred in the distribution transformer during the statistical period, k tol Represents the number of heavy overloads of the distribution transformer at the current moment. K lov and K tov Respectively represent the number of overvoltage limit occurrences of the line and distribution transformer during the statistical period. lov and k tov Respectively represent the number of overvoltage limit of the line and distribution transformer at the current moment. K llv and K tlv Respectively represent the number of low voltage limit occurrences of the line and distribution transformer during the statistical period. llv and k tlv They respectively represent the number of low voltage limit violations in the lines and distribution transformers at the current moment.

[0103] 2. Method for determining the score of individual indicators based on the benchmark comparison method

[0104] Benchmarking is a strategic management tool that uses comparison to understand one's own strengths and weaknesses, so as to find ways to improve and innovate. The benchmarking method usually does not directly generate scores through a standard formula, but determines the gap and relative performance by comparing target data and benchmark data. The benchmarking method needs to calculate the scores of various indicators based on the reliability and safety indicator values ​​and benchmark values ​​obtained in the distribution network, including power supply reliability rate, fault outage rate, self-healing success rate, low voltage limit rate, overvoltage limit rate, line heavy overload rate, and distribution transformer heavy overload rate. The specific process of the benchmarking method is as follows:

[0105] 1) Relative gap percentage

[0106]

[0107] In the formula, r represents the relative difference percentage between the benchmark value and the actual value. a , X bThey respectively represent the reliability and safety indicators obtained from the integrated terminal data, including power supply reliability rate, fault outage rate, self-healing success rate, low voltage limit rate, overvoltage limit rate, line overload rate, and distribution transformer overload rate, and the size of the benchmark value and the actual value.

[0108] The positive or negative value of r can represent the excess or deficiency of the benchmark value over the target value. When using the above formula for calculation, it is necessary to ensure that the dimensions of the benchmark value and the actual value are the same.

[0109] 2) Scoring of individual indicators

[0110]

[0111] In the formula, S represents the score value of one of the indicators of security and reliability.

[0112] In order to convert the above gaps into scores, the gaps are assigned to a predetermined scoring interval. Through the preset intervals, the gaps of different degrees are converted into single scores. The preset range of the interval can be adjusted according to the actual situation of the distribution network. The benchmark comparison method obtains the score of a single indicator by comparing the benchmark value and the actual value of each indicator. The benchmark value can be updated and scored according to different benchmark values, which solves the problem of relatively static scoring in traditional methods and makes the scores of each indicator better reflect the current operation status.

[0113] 3. Weight determination method based on combined weighting of entropy method and Delphi method

[0114] The present invention is based on the entropy method and the Delphi method, combining the information objectivity of the entropy method with the subjective wisdom of experts in the Delphi method, and accurately measuring the safety and reliability indicators of the distribution network through the deep integration of data-driven and expert opinions.

[0115] (1) Delphi method

[0116] The Delphi Method is a systematic forecasting and decision support method that aims to reach a consensus by organizing experts to conduct multiple rounds of anonymous consultation. The Delphi Method performs weighted calculations on the scores of each safety and reliability index obtained by the benchmark comparison method to obtain the criterion-level scores for reliability and safety. The specific steps are as follows:

[0117] 1) First calculate the concentration of expert opinions

[0118]

[0119] In the formula, represents the weighted average of expert opinions on the jth indicator, m ij represents the weighted value given by the ith expert for the jth indicator. n represents the total number of experts.

[0120] In addition to the weighted average, the median and mode can also be used to determine the concentration of expert opinions.

[0121] 2) Calculate the standard deviation

[0122]

[0123] Where, X j is the weighted standard deviation of the j-th indicator.

[0124] 3) Calculate the coefficient of variation

[0125]

[0126] In the formula, CV j is the weighted value variation coefficient of the j-th indicator.

[0127] The smaller the coefficient of variation, the lower the dispersion of expert opinions, that is, the more consistent the expert opinions are; conversely, the higher the coefficient of variation, the greater the divergence of opinions. In the Delphi method, through multiple rounds of expert opinion collection and adjustment, the final coefficient of variation can be significantly reduced.

[0128] 4) Experts assign weights

[0129] The first three steps are repeated until the coefficient of variation is less than 20%, which indicates that the expert opinions have reached a high consistency.

[0130] At this time, the weighted average of the expert opinions is used as the given weight for the criterion layer score calculation.

[0131]

[0132] In the formula, m j Represents the expert-given weight of the jth indicator in the criterion layer calculation.

[0133] 5) Calculate the score of the criterion layer

[0134]

[0135] In the formula, L1 is the reliability criterion layer score, and L2 is the safety criterion layer score. x1, x2, x3, x4, x5, x6, and x7 respectively represent the power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line overload rate, and distribution transformer overload rate scores obtained through the benchmark comparison method. m1, m2, m3, m4, m5, m6, and m7 respectively represent the expert-given weights of power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line overload rate, and distribution transformer overload rate obtained through the Delphi method.

[0136] (2) Entropy method

[0137] The entropy method is an objective weighting method based on information entropy. It is often used in multi-index decision-making problems. It determines the weight of the index by calculating its information entropy. The entropy method is used to compare the reliability and safety criterion layer scores of distribution networks in different regions to obtain the comprehensive evaluation results of distribution network safety and reliability. The entropy method first processes the reliability and safety criterion layer scores of distribution networks in different regions according to the reliability and safety criterion layer scores calculated by the Delphi method.

[0138] 1) Data processing

[0139] Assuming there are m regional distribution networks, the original matrix can be written in the form of a matrix as follows:

[0140]

[0141] Where, L i1 L is the score of the reliability criterion layer in the distribution network in the ith region calculated by the Delphi method. i2 is the score of the security criterion layer in the distribution network of the ith region calculated by the Delphi method.

[0142] 2) Calculate the data weight.

[0143]

[0144] In the formula, p ij Represents the ratio of the jth index of the distribution network in the i-th region to the sum of the jth indexes of all regions. ij Represents the score of the jth indicator in the distribution network of the i-th region.

[0145] 3) Calculate entropy

[0146]

[0147] In the formula, It is a constant, ensuring that the entropy value is between 0 and 1. E1 represents the entropy value of the reliability criterion layer, and E2 represents the entropy value of the security criterion layer.

[0148] 4) Calculate the weight of each indicator

[0149]

[0150] Where w1 represents the weight of the reliability criterion layer, and w2 represents the weight of the safety criterion layer.

[0151] 5) Calculate the comprehensive evaluation score

[0152]

[0153] Where Yi Represents the comprehensive evaluation score of the ith distribution network.

[0154] The Delphi method can obtain the reliability and safety criterion layer evaluation scores of different distribution networks, and can determine the reliability and safety of each distribution network. The entropy method obtains the comprehensive safety and reliability evaluation scores of each distribution network based on the reliability and safety criterion layer scores obtained by the Delphi method, and realizes the comprehensive safety and reliability evaluation of the distribution network. A distribution network area can also be divided into different sub-areas for corresponding analysis, and the reliability, safety and safety and comprehensive safety and reliability evaluation scores of different sub-areas can be obtained by using the Delphi method and the entropy method. The combination of the two makes comprehensive use of subjective experience and objective data, and improves the shortcomings of traditional safety and reliability evaluation methods that rely on personal experience of experts and are completely based on data for analysis.

[0155] The present invention also provides a distribution network operation index evaluation system based on terminal data fusion, which is characterized in that it includes a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0156] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0157] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0161] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A distribution network operation index evaluation method based on terminal data fusion, characterized in that: include: Modeling of safety and reliability evaluation system; Obtain the data of each smart terminal in the distribution network required for the modeling of the safety and reliability evaluation system, and use the benchmark comparison method to determine the score of each single indicator in the modeling of the safety and reliability evaluation system; A combined weighting method combining the Delphi method and the entropy method is proposed to reflect the importance of each indicator in the modeling of the safety and reliability evaluation system.

2. A distribution network operation index evaluation method based on terminal data fusion according to claim 1, characterized in that: The basic operating data of the indicators required in the modeling of the safety and reliability evaluation system are derived from various business systems of the distribution network, including the power supply reliability system, dispatching management system, distribution automation system, power grid dispatching automation system, and power supply service command system; the original data of the power supply reliability system, dispatching management system, distribution automation system, power grid dispatching automation system, and power supply service command system are derived from various intelligent terminals in the distribution network, including TTU, RTU, FTU, DTU, meter collection terminal, and GZ.

3. A distribution network operation index evaluation method based on terminal data fusion according to claim 1, characterized in that: Safety and reliability evaluation is divided into reliability evaluation and safety evaluation, both of which belong to the criteria layer; reliability is evaluated by power supply reliability rate, fault outage rate, and self-healing success rate, and safety is evaluated by low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate; the details are as follows: 1) Reliability indicators In the formula, K e Represents the power supply reliability rate, K g Represents the failure downtime rate, K r represents the self-healing success rate, L represents the total kilometers of lines within the distribution network, and N r 、N g They represent the number of successful self-recovery and outage times after line faults within the statistical period, t represents the power outage time of the electricity user, n represents the number of users, and T represents the total time of the statistical period; 2) Safety indicators Where P ov Represents the overvoltage limit rate within a statistical period, p ov Represents the real-time overvoltage limit rate, P lv Represents the low voltage limit rate in a statistical period, p lv Represents the real-time low voltage over-limit rate, P lo Represents the heavy overload rate of the line in a statistical period, p lo Represents the real-time line overload rate, P to Represents the heavy overload rate of the distribution transformer in a statistical period, p to Represents the real-time distribution transformer overload rate, N l 、N t Represents the number of lines and distribution transformers within the distribution network, T d Represents the total number of days in the statistical period, K lol Represents the number of severe overloads that occurred on the line during the statistical period, k lol Represents the number of overloaded lines at the current moment, K tol Represents the number of heavy overloads that occurred in the distribution transformer during the statistical period, k tol Represents the number of heavy overloads of the distribution transformer at the current moment, K lov and K tov Respectively represent the number of overvoltage limit occurrences of the line and distribution transformer in the statistical period, k lov and k tov They represent the number of overvoltage limit of the line and distribution transformer at the current moment, K llv and K tlv They represent the number of low voltage crossings of the line and distribution transformer during the statistical period, respectively. llv and k tlv They respectively represent the number of low voltage limit violations in the lines and distribution transformers at the current moment.

4. A distribution network operation index evaluation method based on terminal data fusion according to claim 3, characterized in that: The specific method of using the benchmark comparison method to determine the score of a single indicator in the modeling of the safety and reliability evaluation system is as follows: The benchmark comparison method needs to calculate the scores of various indicators based on the benchmark values ​​and actual values ​​of the reliability indicators and safety indicators obtained in the distribution network. The specific process of the benchmark comparison method is as follows: 1) Relative gap percentage In the formula, r represents the relative difference between the benchmark value and the actual value, X a , X b They represent the size of the benchmark value and the actual value of the reliability index and the safety index respectively; the positive or negative value of r represents the excess or deficiency of the benchmark value over the target value; 2) Scoring of individual indicators In the formula, S represents the score value of one of the safety index and reliability index; In order to convert the relative gap percentage into a score, the relative gap percentage is allocated to a predetermined score range; through the predetermined score range, the gaps of different degrees are converted into single scores.

5. A distribution network operation index evaluation method based on terminal data fusion according to claim 4, characterized in that: The preset range of the scoring interval is adjusted according to the actual situation of the distribution network.

6. A distribution network operation index evaluation method based on terminal data fusion according to claim 1, characterized in that: The Delphi method performs weighted calculation on the scores of each individual indicator of safety index and reliability index obtained by the benchmark comparison method to obtain the reliability and safety criterion layer scores; The specific steps are as follows: 1) First calculate the concentration of expert opinions In the formula, represents the weighted average of expert opinions on the jth indicator, m ij represents the weighted value given by the ith expert for the jth indicator, and n represents the total number of experts; 2) Calculate the standard deviation Where, X j is the weighted value standard deviation of the jth indicator; 3) Calculate the coefficient of variation In the formula, CV j is the weighted value variation coefficient of the jth indicator; The smaller the coefficient of variation, the lower the dispersion of expert opinions, that is, the more consistent the expert opinions are; conversely, the higher the coefficient of variation, the greater the divergence of opinions; in the Delphi method, through multiple rounds of expert opinion collection and adjustment, the final coefficient of variation is significantly reduced; 4) Experts assign weights The first three steps are repeated until the coefficient of variation is less than 20%, and the expert opinions are considered to have reached a high consistency. At this time, the weighted average of the expert opinions is used as the given weight for the criterion layer score calculation: In the formula, m j represents the expert-given weight of the jth indicator in the criterion layer calculation; 5) Calculate the score of the criterion layer Wherein, L1 is the reliability criterion layer score, L2 is the safety criterion layer score, x1, x2, x3, x4, x5, x6, and x7 respectively represent the power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate scores obtained through the benchmark comparison method, and m1, m2, m3, m4, m5, m6, and m7 respectively represent the power supply reliability rate, fault outage rate, self-healing success rate, low voltage over-limit rate, overvoltage over-limit rate, line heavy overload rate, and distribution transformer heavy overload rate obtained by the Delphi method.

7. A distribution network operation index evaluation method based on terminal data fusion according to claim 6, characterized in that: In step 1), the concentration of expert opinions is calculated, and the concentration of expert opinions can also be judged by the median and mode.

8. The method for evaluating distribution network operation index based on terminal data fusion according to claim 1 is characterized in that: The entropy method first processes the reliability and safety criterion layer scores of distribution networks in different regions according to the reliability and safety criterion layer scores calculated by the Delphi method; the details are as follows: 1) Data processing Assuming there are m regional distribution networks, the original matrix is ​​written in matrix form as follows: Where, L i1 is the score of the reliability criterion layer in the distribution network in the ith region calculated by the Delphi method, L i2 is the score of the security criterion layer in the distribution network of the ith region calculated by the Delphi method; 2) Calculate the data weight: In the formula, p ij represents the ratio of the jth index of the distribution network in the i-th region to the sum of the jth indexes in all regions, L ij represents the score of the jth indicator in the distribution network of the i-th region; 3) Calculate entropy In the formula, is a constant, ensuring that the entropy value is between 0 and 1. E1 represents the entropy value of the reliability criterion layer, and E2 represents the entropy value of the security criterion layer. 4) Calculate the weight of each indicator In the formula, w1 represents the weight of the reliability criterion layer, and w2 represents the weight of the security criterion layer; 5) Calculate the comprehensive evaluation score Where Y i Represents the comprehensive evaluation score of the ith distribution network.

9. A distribution network operation index evaluation system based on terminal data fusion, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, the method steps as claimed in any one of claims 1 to 8 can be implemented.

10. A computer-readable storage medium storing computer program instructions that can be executed by a processor. When the processor executes the computer program instructions, the method steps described in any one of claims 1 to X can be implemented.

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