[Retracted] Knowledge Graph Construction and Application of Power Grid Equipment

Huang, Haichao; Hong, Zhouzhenyan; Zhou, Huiming; Wu, Jiaxian; Jin, Ning

doi:https://doi.org/10.1155/2020/8269082

Mathematical Problems in Engineering

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Abstract Introduction Experimental Results Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

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Research Article | Open Access

Volume 2020 | Article ID 8269082 | https://doi.org/10.1155/2020/8269082

[Retracted] Knowledge Graph Construction and Application of Power Grid Equipment

Haichao Huang,¹Zhouzhenyan Hong,²Huiming Zhou,³Jiaxian Wu,⁴and Ning Jin⁴

Academic Editor: Sang-Bing Tsai

Received05 Jun 2020

Accepted26 Sept 2020

Published14 Oct 2020

Abstract

Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.

1. Introduction

In the era of big data and artificial intelligence (AI), a large number of data from various sources are constantly generated from different perspectives of human lives [1–3]. Various AI powered service technologies are proposed utilizing the existing big data to facilitate the current sustainable smart city design, e.g., the development of smart grid [4], smart building [5], smart communication systems [6], and many more [7–9]. Such kinds of AI powered service technologies include Internet of Things (IoT) [10], cloud computing [11], edge computing [12], the fifth-generation (5G) mobile communication network [13], sensing networks [14], social networks [15, 16], big data recommendation systems [17], etc. Research on data characteristics and correlations is demanded for deep analysis to make a more comprehensive and accurate judgment [16, 17].

The power grid system is usually a very complex and huge system, especially for large countries, such as China and the United States (USA). There are thousands of different types of basic devices existing in the current power grid system [18, 19]. In China, the state grid power system carried out data center construction since 2016, transforming the existing power grid towards the next generation smart grid system. As of January 2018, the total number of equipment devices in operation in a provincial power grid is as follows: 2.17 million main network, 25.68 million distribution network, and 11.53 million low-voltage equipment devices, a total of 29.83 million sets. The total data storage capacity is 560.48 TB, including 209.5 TB of structured data, 254.86 TB of unstructured data, 72.02 TB of real-time measurement data, and 24.1 TB of online application data. In the current condition with such a huge data volume, a development of data visualization using knowledge graph is highly demanded.

Based on the grid equipment database provided by the State Grid China, this paper uses the AI-enhanced labeling system to construct a knowledge graph model for facilitating the grid management and search functions. The model construction process can be generally divided into data collection, labeling, analysis, and application phases. The whole construction process is semi-automated with the help of Neo4j [20]. The proposed knowledge graph construction model has the following contributions to both fields of computer science and smart grid development. The developed knowledge graph system is greatly helpful enhancing the stability and reliability of the existing power grid system, the smart maintenance system, and sharing the grid equipment utilization information to a broad range of user groups. The knowledge construction process is semiautomatic using the emerging data management tool named Neo4j. The entire construction process therefore is more transparent and easier for implementation compared to the traditional knowledge graph construction approaches. A graphic processor unit (GPU) optimized breath-first searching algorithm is designed to output the internal connection between any two nodes existing in the knowledge graph. The proposed searching algorithm is optimized in terms of searching efficiency. According to the experimental results, the proposed search algorithm is two to three times faster than existing algorithms.

2. Labeling Technology and Knowledge Graph

A labeling system refers to a summary of existing features of a specific group of objects, where in the current context it is referring to the grid equipment devices. In general, business entities are labeled reflecting the business entities’ properties from multiple perspectives. Particularly, the description of the power grid equipment includes the perspectives of type, voltage level, area, line, daily operation status, etc. Since the description of a specific object from various perspectives is difficult, a multilabeling system is proposed for grouping the devices with similar properties.

Knowledge graph is a huge knowledge system built on the semantic network. The knowledge graph itself refers to an emerging technology for large-scale knowledge management and intelligent services in the era of big data [21]. The knowledge graph captures and presents the intricate relationship between domain concepts and connects the fragmented knowledge, which plays a vital role in applications such as information retrieval, question answering, and visualization [22, 23]. Ji et al. [24] introduced an adaptive sparse transfer matrix for knowledge graph entity relationship linkages. The proposed “TranSparse” knowledge graph outperforms most existing knowledge graph approaches. Song et al. [25] studied a graph summarization framework to accelerate the knowledge graph information search. Zheng et al. [26] proposed a meta path-based knowledge graph, which extends entities using entity set expansions (ESEs). Another famous example of knowledge graph technique raised by Google is knowledge expressing in documents [27]. The knowledge graph is constructed based on wiki-data and freebase databases as well as public databases [28]. Various sources of semantic search information are utilized to enhance the effectiveness of search engines [29].

The equipment devices existing in the current power grid lie in forms of network structures, which are easily interpreted using the knowledge graph. As a result, the knowledge graph is constantly evolving and it has become an efficient management tool for grid data. The visualized knowledge graph helps people understand massive information much easier. In the knowledge graph, knowledge exists in the form of entity-relationship-entity triplets, and the relationship between entities and entities is presented in the form of nodes and edges. The knowledge graph provides an ideal technical means for solving the problem of knowledge islands in the power grid and improving the service quality of the grid data center.

3. Constructing Power Grid Equipment Portrait System

3.1. Constructing the Labeling System

The labeling system of the power grid equipment devices is constructed based on the main business system of the power grid in China. Corresponding to the profile of each power grid equipment device, the labeling system is designed based on the historical and current operating status of the equipment, the possible future position of each device, the inspection, management and maintenance status, and the operational quality of various manufacturers. The hierarchical relationship of the grid equipment labeling system is shown in Figure 1.

From Figure 1, for each data piece collected from the power grid, three levels of the labels can be assigned, namely, the fact label, the model label, and the decision label. The fact label is the lowest level label, which most of the data pieces should have. The fact label is a fundamental fact that can be easily extracted from the data. The model label indicates the most appropriate decision model for the data piece generating the decision label. Not all data pieces have the model labels and decision labels. The basic rules of generating the labeling system include the following: Standard rule: The standard for generating labels for each level must be consistent between different data pieces. Connection rule: The total number of the children is equivalent to the total number of parents; otherwise, the division is incomplete or there are more children. Division rule: The divided concepts cannot be compatible, and the genus concepts cannot be parallel.

Based on the above three basic rules of the labeling system, the ultimate labels are determined based on the extraction sources, data association relationships, and extraction logics. The difficulty and complexity of generating rules increase gradually with the labeling level increment.

There are four updating strategies for the labeling system:(1)Updating strategy: The updating cycles for different labels are different. In general, an updating cycle for a particular label can be real-time, monthly, or three-monthly depending on the label type.(2)Updating conditions: This strategy establishes the label updating trigger mechanisms based on the properties of data pieces. For each label, the label update is triggered under various situations.(3)Updating authority strategy: The authority strategy determines the label updating authorization priority sequence based on the classification levels of the original data.(4)Recycling strategy: There is also a label elimination mechanism to delete useless labels to avoid wasting resources.

The knowledge graph construction process labels each piece of power grid data following the above four strategies. For data pieces that have multiple labels or conflicting labels, the above four rules are re-visited to determine the highest priority label for that particular data piece.

3.2. Data Preprocessing

The construction of power grid equipment portraits involves connectivity information among the huge number of equipment devices. A robust and efficient data processing framework/technique is demanded to support data storage, analysis, and knowledge graph construction. In this study, a three-layer data preprocessing framework is proposed consisting of the data layer, the preprocessing layer, and the analysis layer, as shown in Figure 2.

3.2.1. The Data Layer

The basic data required for the power grid equipment portrait consists of two parts, namely, the power grid system data and the third-party data, according to the types of sources. Among them, the grid data mainly includes equipment account data, equipment operation data, and equipment management data. The equipment account data consists of the type, voltage level, name, information of the storage grid equipment, etc. Device operating data is the voltage, current, active power, reactive power, and events of the storage device during the operations. The equipment management data stores work operation tickets, inspection reports, and maintenance reports related to equipment operation and maintenance. In order to further expand and label the power grid equipment data, the relationships between grid energy production, consumption, and environment data, as well as data from the third-party entities, e.g., the national economic data or the national meteorological environment data, are considered externally wherever necessary. In this study, both grid data and third-party data consist of structured data, semi-structured data, and unstructured data.

3.2.2. The Preprocessing Layer

Above the data layer is the data preprocessing layer. The preprocessing steps for power grid equipment data include collection, cleaning, integration, reduction, and feature extraction.

Data collection refers to the unified accesses of grid equipment and operation, operation and maintenance data of the supervisory control and data acquisition (SCADA) center, energy management system, user acquisition system, distribution automation system, property management system (PMS), etc.

Data cleaning performs tasks such as omission filling, anomaly elimination, noise smoothing, and correction of inconsistent data in the aggregated data.

Data integration carries out pattern integration, data entity identification, and splicing processing on data from multiple systems and summarizes, aggregates, generalizes, and normalizes data.

Data reduction balances the efficiency and value of data processing in the case of large-scale grid data analysis of complex content data that requires a lot of time and computer resources. The specific data analysis tools include cubic aggregation, dimensionality reduction, data compression, data block reduction, and other processing.

The data feature extraction process utilizes two basic AI techniques, i.e., the principal component analysis (PCA) method [30] and the linear discriminant analysis (LDA) method [31]. The PCA method projects the original data into higher dimension to reduce the data dimension using matrix multiplication. The reduced datasets are further processed using LDA with the label information. LDA is a supervised data reduction method and can be greatly helpful for data retrieval and data management for the constructed knowledge graph. The ultimate purposed of data reduction is to improve the data retrieval efficiency in the data management level.

3.2.3. The Analysis Layer

The analysis layer is the core layer for realizing the knowledge graph of the power grid equipment. It can be divided into two major blocks, namely, the strategy models block and the data analysis block. The strategy models include behavior model, funnel model, survival model, and distribution model. The data analysis block includes classification analysis, comparative analysis, association analysis, and comprehensive analysis. A database management system called Neo4j is employed to build the analysis layer for the power grid equipment devices. The Neo4j graphic platform is originally introduced by Webber in 2012 [32]. We extend the current Neo4j platform implementing both strategy models’ block and the data analysis block for the power grid equipment management system.

3.3. Visualization of the Power Grid Equipment Connections Using the Knowledge Graph

Considering the current database has a large amount of unstructured data, this study employs the Data-Driven Documents (D³) to visualize the knowledge graph for the power grid equipment devices. D³ is a function library written in JavaScript, which was proposed by Bostock et al. in 2011 [33]. The D³ technique is nowadays widely adopted handling unstructured data for data visualization.

Since the number of power grid equipment devices is huge, and the scale of the corresponding power grid equipment knowledge graph is large, we only show part of the documented knowledge graph in Figure 3. The nodes and edges represent the equipment and the relationship between power grid equipment devices, respectively. Each node contains detailed information about the equipment, such as equipment type, equipment status, equipment name, voltage level, and commissioning time. The knowledge graph of power grid equipment displays the connection between the equipment devices in the form of a graphical network and provides equipment specific information. Users can browse the knowledge graph interactively and select one of the devices to further explore the information or construct queries. The relationship between equipment and equipment in the knowledge graph is intricate. These relationships are difficult to discover by observing database tables. It helps staff solve the knowledge island problem of the relationship between equipment devices and enhance the connectivity of knowledge resources of power grid equipment. At the same time, it can also help staff browse the knowledge of power grid equipment at the conceptual level and discover the potential connections between different types of equipment, so as to better understand the complexity of the power network. The graphic user interface of Neo4j allows us to visualize the devices and connections with a connectivity graph. Several examples of the proposed knowledge graph construction are shown in Figures 3 and 4.

The knowledge graph supports querying the details, which can be viewed by selecting the device you want to know. This paper takes selecting a substation type node as an example. The knowledge graph can also be clicked on the device node to continuously extend the display outwards, as shown in Figure 4. Due to data confidentiality requirements, some details in the figure are treated anonymously. For example, “Substation X” is a substation type node. The enlarged part of Figure 4 shows some equipment nodes related to “ Substation X”, including transmission lines, line switches, high-voltage fuses, capacitors, and capacitor grounding blades device.

3.4. Search and Recommendation System Design for Power Grid Knowledge Graph

The knowledge graph enables users by entering search conditions according to their needs. When a device failure occurs, the search page can automatically bring out the relevant fault information of the current device. Furthermore, decision recommendations are sent to the users for possible actions to solve the device failure instance.

The power network is huge and complex in structure, and the speed of query operation using the traditional database technology is extremely slow and poor. Knowledge graph can significantly improve the efficiency of knowledge retrieval and make the search results more comprehensive and accurate. It can systematically understand the user’s query intent and directly return accurate answers instead of a large number of search results. In this paper, a grid knowledge intelligent retrieval system is developed based on the grid knowledge map. For example, in the power grid system, if you want to know whether a device failure will affect a certain key device, the traditional relational database searches for the relationship path between the two devices in advance, making the whole query process slow and difficult to edit.

In the proposed knowledge graph construction framework, an optimized breadth-first search strategy based on graphic processor unit (GPU) programming is proposed to search through the Neo4j database. The time complexity of the data network traversal is only O (n). The proposed breadth-first search algorithm returns the shortest path from the starting vertex to the target vertex . The detailed algorithm is listed in Algorithm 1; and the flowchart of Algorithm 1 is depicted in Figure 5.

Input: Database D; Input vertex ; Target vertex
Output: The path between and
(1)	From the , look for the adjacent vertices ;
(2)	For each :
(3)	A separated GPU unit and a distributed memory unit are assigned to
(4)	if ( is )
(5)	return path;
(6)	end if
(7)	Look for the adjacent vertices of
(8)	end for

This paper takes the query of two substation nodes that are adequately separated as an example. The returned path is shown in Figure 6. The knowledge graph retrieval system can quickly and accurately return the relationship path between two devices. The blue nodes in the path represent transmission lines, the green nodes represent substations, and the orange nodes represent distribution lines.

4. Experimental Results

For the purposes of reflecting the efficiency and effectiveness of the proposed knowledge graph construction technique for the knowledge retrieval tasks in the power grid system, a series of experiments were carried out in this section. We implemented the proposed knowledge graph technology on the grid system and performed knowledge retrieval tasks with relational databases. It is noted that it is a completely different scenario for knowledge retrieval tasks to be handled using the knowledge graph compared to the relational database. More complex data routines are stored in the relational networks in Neo4j with much more connectivity information compared to the traditional relational database management system. The searching engine is also optimized using GPU, which retrieves data relational paths more efficiently and accurately. In Table 1, we show the performance comparison using a set of the same knowledge retrieval tasks using the knowledge graph and relational database. The total time consumed by both methods and the numbers of returned paths are listed. The column of “Performance improvement” shows the percentage of time/output advances of the proposed knowledge gra ph data management method over the traditional relational database management method.

From Table 1, it is evident that, for all knowledge retrieval tasks, the time required of the knowledge graph is always shorter than that of the traditional relational database. In some tasks, the number of searching records (calculated outputs) of knowledge graph is more than that of relational database. For more complex tasks, which cannot be accomplished by relational database, it is still possible for the knowledge graph to find out the paths, since the underlying data structures of the knowledge graph are more advanced using Neo4j. The underline implementation of the knowledge graph stored in Neo4j is a high-performance graphic engine with GPU. It stores structured data using the relational networks instead of using simple tables. It overcomes the fact that traditional relational databases are not efficient at dealing with relational networks. For those relationships between the searched device nodes, which are too complex or where the searched path is too long, searching failure messages are returned from the relational database management system. The results listed in Table 1 show that, for the same searching result, the proposed knowledge graph database management system is more efficient. And for the more complex searching problems, which the traditional relational database management system cannot handle, the knowledge graph system returns more accurate (exact) paths. The averaged performance improvement is around 56%.

While the number of provincial power grid equipment devices reaches 100 million, the efficiency and timeliness of data migration is another important indicator of the evaluation model. In the process of implementing the knowledge map of the power grid, we recorded the time consumptions of data analysis using the traditional LOAD-CSV method and the Neo4j-Import method proposed in this paper with randomized orders of nodes. LOAD-CSV and Neo4j-Import are two data analysis methods provided by Neo4j, suitable for different application scenarios. The comparison results of the two methods are shown in Figure 7, where Figure 7(a) shows the time comparisons between the traditional LOAD-CSV method and the Neo4j method and Figure 7(b) shows the actual differences.

(a)

(b)

From Figure 7(a), it is evident that when the number of nodes increases, the required data analysis time of the LOAD-CSV method increases from 1.579 s to 534.505 s, while the time requirements of the Neo4j-Import method only increase from 1.582 s to 15.463 s. The efficiency of Neo4j-Import method is significantly higher than that of LOAD-CSV in the data import and analysis stage. From Figure 7(b), the data analysis between the two methods is positively correlated with the amount of data increments. The time requirement differences increased from the initial −0.003 s to 5519.042 s, where −0.003 s is considered the program testing error. It is noted that, for actual power grid equipment data, the size of power grid equipment data is exponentially larger than that adopted in the experiment, with much more complex relationships between the nodes. Hence, the data analysis efficiency improvement using the Neo4j-Import is extremely important. The whole power grid knowledge graph construction process can be realized as a semiautomatic process, which saves tremendous amount of human resources, time, and financial costs.

5. Conclusion

In power grid management, the number of power grid equipment devices can be huge in appliance level, with enormous amount of information generated every day. The traditional data management systems and approaches are not only inefficient but also inaccurate, causing serious flaws in knowledge retrieval and data analysis for the next-generation smart grid implementation. The storage, query, and management of power grid equipment information became an emerging issue for the smart grid system development, especially for developing countries. This paper proposed to realize the functions of power grid equipment devices and power grid equipment information by constructing a next-generation power grid knowledge graph integrating AI technologies and GPU programming.

The proposed knowledge graph construction process is generally divided into three steps. First, the raw grid equipment information is preprocessed using data analysis tools, generating multiple relationship tables. Next, a data migration model is proposed to transfer the grid equipment information from the relational table to the Neo4j graph database in a semiautomatic way. Finally, based on the Neo4j database, the functions of power grid equipment information visualization and power grid equipment information search are revealed using the constructed knowledge graph. In the process of data migration, this article uses the Neo4j-Import method, which is significantly faster than the LOAD-CSV method when the amount of data is large. In the field of data visualization, this method facilitates the grid staff to view the equipment information more clearly. The parameters and operation status of each equipment in the substation are also displayed, which is beneficial for the data management.

The experimental results show that the proposed knowledge graph searches more records in a shorter time than the traditional relational database. In addition, the search path can be visually displayed, which enhances the stability and reliability of the power system, which can be greatly useful in sharing, utilizing, and analyzing the power grid equipment information.

The main limitation of the proposed work is that the current study (including the experimental simulation) is only restricted in the area of power grid knowledge graph construction. The usage of the proposed algorithm in other knowledge/data management areas is not justified. As one of the future works, the proposed knowledge graph construction algorithm will be extended to the research field, such as molecular modeling [34, 35], healthcare engineering [36, 37], and business applications [38]. In addition, the topology analysis function development of the power grid subtasks is another future task for power grid appliances flow calculation, state estimation, line loss calculation, etc., targeting more efficient analysis tools for the operating states and faults of the power grid. The topology analysis function improves the safety performance of the power grid system and brings higher economic benefits of the power grid.

Data Availability

The data are confidential.

Conflicts of Interest

The authors declare no conflicts of interest.

Authors’ Contributions

H.H. and N.J. contributed to conceptualization; H.H. and H.Z. contributed to methodology; J.W. contributed to software, data curation, and visualization; Z.H. contributed to validation; H.Z. contributed to formal analysis; N.J. contributed to investigation, writing, review, and editing, project administration, and funding acquisition; H.H. contributed to resources; J.W. and N.J. contributed to original draft preparation.

Acknowledgments

This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant no. LY19F020016.

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Copyright © 2020 Haichao Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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