A power grid equipment asset data fusion method and its application based on LCC theory

Abstract

A method for fusion of power grid equipment asset data based on LCC theory, which first obtains basic data information of various types of equipment in the power grid at various stages of the entire life cycle from multiple external systems, and then divides them into strongly associated data and weakly associated data based on the data granularity that needs to be associated and the characteristics of each basic data information. For strongly associated data, at least one of the physical "ID" and equipment / asset / project coding is used for strong association fusion, and for weakly associated data, at least one of text mining and fuzzy matching is used for weak association fusion. This method not only realizes the fusion of important information at various stages of the entire life cycle of power grid equipment in a simple and efficient manner, but can also be applied to evaluate the effectiveness of technical transformation and overhaul projects in different governance directions, and predict indicators such as the safety efficiency of different future technical transformation and overhaul projects.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid automation, and in particular relates to a power grid equipment asset data fusion method and application based on LCC theory. Background Art

[0002] With the development of smart grids, the power system has entered the era of big data. Since the data of power grid equipment is huge, complex and diverse, it is often stored in multiple data management systems. The merging of data from multiple systems will cause problems such as field mismatch and data redundancy, which requires further deletion and fusion, thereby increasing the difficulty of power grid companies in managing massive data and affecting the accuracy of comprehensive evaluation of power grid equipment. It can be seen that adopting an effective power grid asset data fusion evaluation method is of great practical significance for promoting the construction of a lean management system.

[0003] In recent years, relevant researchers have carried out a lot of research work on power grid asset data management. The document "Heterogeneous data hybrid acquisition system for power big data" (Sun Chao, Wang Yonggui, Chang Xiaqin, et al. [J]. Computer System Applications, 2018, 27(12): 62-68.) proposed a hybrid scheduling and management method for heterogeneous data source acquisition tasks based on hybrid data acquisition models and acquisition clusters. Through data confidence labeling technology, the original data is retained and the data quality is marked and stored in the big data platform. The document "Intelligent analysis method for power grid monitoring based on big data labeling technology" (Ye Kang, Leng Xiwu, Xiao Fei, et al. [J]. Electrical Measurement and Instrumentation, 2019, 56(4): 75-79.) introduces data labeling technology to sort out the data physical table, integrates the experience of business experts with the data physical table structure, and realizes highly refined data information, solving the defects of traditional storage data integration and integrated analysis methods. Existing research makes full use of the effective information of big data and has certain reference significance for the fusion evaluation of power grid asset data, but the theories and algorithms used are complex and difficult to operate. Therefore, the integrated evaluation of massive data on power grid equipment assets is a major problem that my country's power grid asset management industry needs to urgently solve. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of complex data fusion algorithms and difficult operations in the prior art, and to provide a simple and efficient power grid equipment asset data fusion method and application based on LCC theory.

[0005] To achieve the above objectives, the present invention provides the following technical solutions:

[0006] A power grid equipment asset data fusion method based on LCC theory includes the following steps in sequence:

[0007] Step A: acquiring basic data information of power grid equipment assets from multiple external systems, wherein the basic data information of power grid equipment assets includes basic data information of various types of equipment in the power grid at various stages of the entire life cycle;

[0008] Step B: Based on the granularity of the data to be associated, the data is divided into strongly associated data and weakly associated data in combination with the information of each basic data;

[0009] Step C: For strongly associated data, at least one of the physical "ID" and equipment / asset / project coding is used for strong association fusion; for weakly associated data, at least one of text mining and fuzzy matching is used for weak association fusion.

[0010] In step A, the basic data information of various types of equipment in the power grid at various stages of the entire life cycle includes project information in the planning and design stage, equipment quantity and technology scale, value scale, equipment defects, health status information, forced shutdowns, equipment failures, electricity sales and amount, load, operation and maintenance, and technical transformation and overhaul information.

[0011] The strongly associated data includes basic data information with project file number, physical "ID" or equipment / asset code, and the weakly associated data is basic data information lacking project file number, physical "ID" or equipment / asset code.

[0012] The step C is specifically as follows:

[0013] For project information in the planning and design stage, strong association and integration are performed through the planned construction project file number to which the equipment belongs;

[0014] For basic data information with physical "ID" or equipment / asset code, strong association and fusion are performed through physical "ID" or equipment / asset code;

[0015] For forced outages, equipment failures, electricity sales and amounts, and load information that lack physical "IDs" or equipment / asset codes, weak association fusion is achieved through fuzzy matching or text mining of equipment names, stations and lines to which the equipment belongs, and organizational layers;

[0016] For operation and maintenance, technical transformation and overhaul information that lacks physical "ID" or equipment / asset codes, character fuzzy matching is used for weak correlation fusion.

[0017] Before step B, the basic data information is cleansed, and the data cleaning method includes at least one of filling in value gaps with default values, mapping and filling non-standard values ​​with standard values, filtering non-statistical range values, and replacing associated field values.

[0018] In step B, the data granularity that needs to be associated is the single device layer, voltage level layer, line / substation layer or organizational unit layer.

[0019] An application of a power grid equipment asset data fusion method based on LCC theory, wherein the fusion method is used to evaluate the effectiveness of technical transformation and overhaul projects in different governance directions.

[0020] The application comprises the following steps in sequence:

[0021] The first step is to associate and integrate the information of each technical transformation and overhaul project with the corresponding governance direction information;

[0022] The second step is to use the fusion method to correlate and fuse the information of each technical renovation and overhaul project after correlation and fusion based on the constructed evaluation dimensions and corresponding indicators;

[0023] The third step is to evaluate the effectiveness of different governance directions based on the associated and integrated information of each technical transformation and overhaul project, using the hierarchical analysis method.

[0024] In the second step,

[0025] The evaluation dimensions include equipment scale, utilization efficiency, production cost, safety quality, and benefit dimensions. The equipment scale dimension includes value scale, average age, old equipment data and technology scale, and overaged equipment scale indicators. The utilization efficiency dimension includes equipment operation rate and light and heavy load rate indicators. The production cost includes maintenance cost and operating cost indicators. The safety quality includes equipment availability coefficient, serious abnormality rate, defect incidence rate, failure incidence rate, and forced shutdown rate indicators. The benefit dimension includes electricity sales and line loss rate indicators.

[0026] The first step is to perform strong and weak correlation data fusion based on the integrity of various aspects of basic business data throughout the life cycle of the power grid equipment.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present invention is a method for fusion of power grid equipment asset data based on LCC theory. The method first obtains basic data information of various types of equipment in the power grid at various stages of the entire life cycle from multiple external systems, and then divides it into strongly associated data and weakly associated data based on the data granularity that needs to be associated and in combination with the basic data information. For strongly associated data, at least one of the physical "ID" and equipment / asset / project coding is used for strong association fusion. For weakly associated data, at least one of text mining and fuzzy matching is used for weak association fusion. The method uses a variety of association technologies to associate the basic data of the power grid from multiple source business systems, and realizes the effective fusion of important information at various stages in the entire life cycle of the power grid equipment. Not only is the processing method simple, but it also has a high processing efficiency. Therefore, the present invention simply and efficiently realizes the fusion of important information at various stages in the entire life cycle of the power grid equipment.

[0029] 2. The power grid equipment asset data fusion method based on LCC theory of the present invention is based on the effective correlation and fusion of data at various stages in the entire life cycle of power grid equipment, so that it can not only be used to effectively evaluate the effectiveness of technical transformation and overhaul projects in different governance directions, but also to predict the safety, efficiency, and benefits of different future technical transformation and overhaul projects. Therefore, the present invention can not only effectively evaluate the effectiveness of technical transformation and overhaul projects in different governance directions, but also predict the safety, efficiency, and benefits of different future technical transformation and overhaul projects. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with specific implementation modes.

[0031] A power grid equipment asset data fusion method based on LCC theory includes the following steps in sequence:

[0032] Step A: acquiring basic data information of power grid equipment assets from multiple external systems, wherein the basic data information of power grid equipment assets includes basic data information of various types of equipment in the power grid at various stages of the entire life cycle;

[0033] Step B: Based on the granularity of the data to be associated, the data is divided into strongly associated data and weakly associated data in combination with the information of each basic data;

[0034] Step C: For strongly associated data, at least one of the physical "ID" and equipment / asset / project coding is used for strong association fusion; for weakly associated data, at least one of text mining and fuzzy matching is used for weak association fusion.

[0035] In step A, the basic data information of various types of equipment in the power grid at various stages of the entire life cycle includes project information in the planning and design stage, equipment quantity and technology scale, value scale, equipment defects, health status information, forced shutdowns, equipment failures, electricity sales and amount, load, operation and maintenance, and technical transformation and overhaul information.

[0036] The strongly associated data includes basic data information with project file number, physical "ID" or equipment / asset code, and the weakly associated data is basic data information lacking project file number, physical "ID" or equipment / asset code.

[0037] The step C is specifically as follows:

[0038] For project information in the planning and design stage, strong association and integration are performed through the planned construction project file number to which the equipment belongs;

[0039] For basic data information with physical "ID" or equipment / asset code, strong association and fusion are performed through physical "ID" or equipment / asset code;

[0040] For forced outages, equipment failures, electricity sales and amounts, and load information that lack physical "IDs" or equipment / asset codes, weak association fusion is achieved through fuzzy matching or text mining of equipment names, stations and lines to which the equipment belongs, and organizational layers;

[0041] For operation and maintenance, technical transformation and overhaul information that lacks physical "ID" or equipment / asset codes, character fuzzy matching is used for weak correlation fusion.

[0042] Before step B, the basic data information is cleansed, and the data cleaning method includes at least one of filling in value gaps with default values, mapping and filling non-standard values ​​with standard values, filtering non-statistical range values, and replacing associated field values.

[0043] In step B, the data granularity that needs to be associated is the single device layer, voltage level layer, line / substation layer or organizational unit layer.

[0044] An application of a power grid equipment asset data fusion method based on LCC theory, wherein the fusion method is used to evaluate the effectiveness of technical transformation and overhaul projects in different governance directions.

[0045] The application comprises the following steps in sequence:

[0046] The first step is to associate and integrate the information of each technical transformation and overhaul project with the corresponding governance direction information;

[0047] The second step is to use the fusion method to correlate and fuse the information of each technical renovation and overhaul project after correlation and fusion based on the constructed evaluation dimensions and corresponding indicators;

[0048] The third step is to evaluate the effectiveness of different governance directions based on the associated and integrated information of each technical transformation and overhaul project, using the hierarchical analysis method.

[0049] In the second step,

[0050] The evaluation dimensions include equipment scale, utilization efficiency, production cost, safety quality, and benefit dimensions. The equipment scale dimension includes value scale, average age, old equipment data and technology scale, and overaged equipment scale indicators. The utilization efficiency dimension includes equipment operation rate and light and heavy load rate indicators. The production cost includes maintenance cost and operating cost indicators. The safety quality includes equipment availability coefficient, serious abnormality rate, defect incidence rate, failure incidence rate, and forced shutdown rate indicators. The benefit dimension includes electricity sales and line loss rate indicators.

[0051] The first step is to perform strong and weak correlation data fusion based on the integrity of various aspects of basic business data throughout the life cycle of the power grid equipment.

[0052] The principle of the present invention is described as follows:

[0053] Based on the physical "ID" or equipment / asset code, the present invention can effectively integrate the equipment ledger information (including quantity and technical scale indicators, etc.) and equipment value scale information (asset original value, net value and depreciation information, etc.) from the PMS system and the ERP system with accurate and strong association, specifically:

[0054] Based on the physical "ID" or equipment / asset code, the equipment's ledger information and equipment value scale information records are queried, and then the two pieces of information are merged and integrated.

[0055] The present invention can realize weak correlation fusion based on the information such as the equipment name and the name of the affiliated station line (substation and line) from the PMS system equipment ledger and the project business work order from the technical transformation and overhaul project management module, so as to obtain the scale of operation and maintenance funds invested in the equipment at different business stages, specifically:

[0056] Based on the name of a single device and the name of the station line to which it belongs, fuzzy matching is performed with the project content of the technical transformation and overhaul project work order. The content containing the device name or the station line name of the single asset is the technical transformation and overhaul project work order information that needs to be integrated. Then, the basic information of this single asset is merged and integrated with the queried project record work order.

[0057] The various stages of the entire life cycle described in the present invention include planning and design, installation and construction, operation and maintenance, technical transformation and overhaul, and scrapping and decommissioning.

[0058] In addition to being used in the effectiveness evaluation of technical transformation and overhaul projects, the fusion method of the present invention can also be applied to the study of the distribution law of the correlation between the operation and maintenance cost of equipment and the number of equipment defect occurrences during the entire life cycle.

[0059] Embodiment 1:

[0060] This embodiment evaluates the effectiveness of a number of technical renovation and overhaul projects for different power grid equipment in different management directions, and proceeds in the following steps:

[0061] 1. Obtain basic data information of all stages of the life cycle of power grid equipment involved before and after the implementation of each technical renovation and overhaul project from ERP, PMS, reliability system, and marketing system. Based on the determined six governance directions of A, B, C, D, E, and F, determine which governance directions each technical renovation and overhaul project belongs to. For technical renovation and overhaul projects involving only one governance direction, use fuzzy matching to weakly associate and fuse the project with the governance direction. For technical renovation and overhaul projects involving multiple governance directions at the same time, first use fuzzy matching to weakly associate and fuse the project with the governance direction to form a technical renovation and overhaul project data set in which a single project belongs to multiple governance directions. Then decompose the technical renovation and overhaul project data to form multiple technical renovation and overhaul projects belonging to different governance directions.

[0062] 2. Construct evaluation dimensions and corresponding indicators. The evaluation dimensions include equipment scale, utilization efficiency, production cost, safety quality, and benefit dimensions. The equipment scale dimension includes value scale, average age, old equipment data and technology scale, and overage equipment scale indicators. The utilization efficiency dimension includes equipment in-operation rate and light and heavy load rate indicators. The production cost includes maintenance cost and operating cost indicators. The safety quality includes equipment availability factor, serious abnormality rate, defect incidence rate, failure incidence rate, and forced outage rate indicators. The benefit dimension includes electricity sales and line loss rate indicators.

[0063] 3. Perform data cleaning on the acquired basic data information of power grid equipment, wherein the data cleaning method includes filling in value gaps with default values, filtering values ​​outside the statistical range, and replacing values ​​of associated fields;

[0064] 4. Based on the data granularity organization layer that needs to be associated, combined with the basic data information, it is divided into:

[0065] Strongly correlated data: value scale, average age, old equipment data and technology scale, overage equipment scale, equipment operation rate, light and heavy load rate, serious abnormality rate, and defect incidence rate;

[0066] Weakly correlated data: maintenance cost, operating cost, equipment availability factor, failure rate, forced outage rate, power sales, line loss rate;

[0067] For indicators with physical "ID" or equipment / asset code, such as value scale, average age, old equipment data and technology scale, overage asset scale, equipment operation rate, serious abnormality rate, and defect incidence rate, strong correlation and fusion are performed at the single device level through physical "ID" or equipment / asset code;

[0068] For the light and heavy load rate indicators with equipment codes, strong correlation and fusion are performed through equipment codes;

[0069] For the maintenance cost and operation cost indicators, weak correlation fusion is performed through fuzzy matching between maintenance work orders and operation units.

[0070] For the indicators of equipment availability coefficient, failure rate, forced outage rate, power sales, and line loss rate, weak correlation fusion is performed through fuzzy matching with the affiliated units;

[0071] 5. Based on the associated and integrated information of various technical transformation and overhaul projects, the hierarchical analysis method is used to evaluate the effectiveness of different governance directions.

[0072] The evaluation results of the effectiveness of different governance directions at the organizational data granularity obtained in this embodiment are shown in Table 1:

[0073]

[0074] From the results shown in Table 1, it can be seen that organizational unit 3 has the best overall effectiveness and balance in each governance direction, followed by organizational units 5 and 8. Organizational unit 3 has the lowest overall effectiveness, and the effectiveness in each governance direction is at the lowest level. This result is consistent with the actual situation.

Claims

1. A method for fusion of power grid equipment asset data based on LCC theory, characterized by: The fusion method comprises the following steps in sequence: Step A: acquiring basic data information of power grid equipment assets from multiple external systems, wherein the basic data information of power grid equipment assets includes basic data information of various types of equipment in the power grid at various stages of the entire life cycle; Step B: Based on the granularity of data to be associated, the data is divided into strongly associated data and weakly associated data in combination with the information of each basic data, wherein the strongly associated data includes basic data information with project file number, physical "ID" or equipment / asset code, and the weakly associated data is basic data information without project file number, physical "ID" or equipment / asset code; Step C: For strongly related data, at least one of the following methods, namely, physical "ID" and equipment / asset / project coding, is used for strong related fusion. For weakly related data, at least one of text mining and fuzzy matching is used for weakly related fusion. Specifically, For project information in the planning and design stage, strong association and integration are performed through the planned construction project file number to which the equipment belongs; For basic data information with physical "ID" or equipment / asset code, strong association and fusion are performed through physical "ID" or equipment / asset code; For forced outages, equipment failures, electricity sales and amounts, and load information that lack physical "IDs" or equipment / asset codes, fuzzy matching or text mining is performed through the equipment name, the station line to which the equipment belongs, and the organizational layer to achieve weak correlation fusion; For operation and maintenance, technical transformation and overhaul information that lacks physical "ID" or equipment / asset codes, character fuzzy matching is used for weak correlation fusion.

2. The method for fusion of power grid equipment asset data based on LCC theory according to claim 1 is characterized by: In step A, the basic data information of various types of equipment in the power grid at various stages of the entire life cycle includes project information in the planning and design stage, equipment quantity and technology scale, value scale, equipment defects, health status information, forced shutdowns, equipment failures, electricity sales and amount, load, operation and maintenance, and technical transformation and overhaul information.

3. A method for fusion of power grid equipment asset data based on LCC theory according to claim 1 or 2, characterized in that: Before step B, the basic data information is cleansed, and the data cleaning method includes at least one of filling in value gaps with default values, mapping and filling non-standard values ​​with standard values, filtering non-statistical range values, and replacing associated field values.

4. A method for fusion of power grid equipment asset data based on LCC theory according to claim 1 or 2, characterized in that: In step B, the data granularity that needs to be associated is the single device layer, voltage level layer, line / substation layer or organizational unit layer.

5. An application of a power grid equipment asset data fusion method based on LCC theory, characterized in that: The fusion method is used to evaluate the effectiveness of technical renovation and overhaul projects in different governance directions, and includes the following steps in sequence: The first step is to associate and integrate the information of each technical transformation and overhaul project with the corresponding governance direction information for the first time; Step 2: Based on the constructed evaluation dimensions and corresponding indicators, the fusion method described in claim 1 is used to perform a second correlation fusion on the information of each technical renovation and overhaul project after the first correlation fusion; The third step is to evaluate the effectiveness of different governance directions based on the information of each technical transformation and overhaul project after the second correlation fusion using the hierarchical analysis method.

6. The application of the power grid equipment asset data fusion method based on LCC theory according to claim 5 is characterized by: In the second step, The evaluation dimensions include equipment scale, utilization efficiency, production cost, safety quality, and benefit dimensions. The equipment scale dimension includes value scale, average age, old equipment data and technology scale, and overaged equipment scale indicators. The utilization efficiency dimension includes equipment operation rate and light and heavy load rate indicators. The production cost includes maintenance cost and operating cost indicators. The safety quality includes equipment availability coefficient, serious abnormality rate, defect incidence rate, failure incidence rate, and forced shutdown rate indicators. The benefit dimension includes electricity sales and line loss rate indicators.

7. The application of the power grid equipment asset data fusion method based on LCC theory according to claim 5 is characterized by: The first step is to perform strong and weak correlation data fusion based on the integrity of various aspects of basic business data throughout the life cycle of the power grid equipment.

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