How to Apply Natural Language Understanding Technology in the Power Grid Industry?
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This article shares the current development status and future trends of smart grids, and specifically explains the application of natural language understanding technology in the power grid industry.
The power grid is an efficient and rapid energy transmission channel and an optimization and allocation platform, serving as a critical link for sustainable energy and power development. In the modern energy supply system, the power grid plays a pivotal role, impacting national energy security. Since 2010, the scale of the national power grid has nearly doubled, meeting the energy and power demands of economic and social development.
With the rapid advancement of artificial intelligence technology, the advantages of machine intelligence are gradually penetrating various industries. This article will focus on the Chinese market, discussing the current applications and future prospects of natural language understanding technology in the power grid industry.
Natural Language Processing (NLP) technology aims to study the understanding, processing, and application of human language text information through computer devices. It is one of the most typical and challenging areas in artificial intelligence research.
Currently, the State Grid is attempting to apply NLP technology to power grid-related services, accelerating the development of the industry through technological advancements.
A smart grid broadly refers to an intelligent grid enabled by artificial intelligence technology. It is based on an integrated high-speed bidirectional communication network, utilizing advanced control methods and decision support system technologies to achieve more reliable, secure, and efficient grid services. This ensures high-quality power supply for users and promotes the development of the electricity market.
Through technologies such as text reading comprehension, text similarity calculation, and knowledge graphs, NLP is being implemented in various power grid business scenarios, including project bidding, detection and early warning, maintenance and repair, and customer service. These applications enhance the efficiency of power grid operations, benefiting the general public.
Since 2005, the attention on smart grids has been steadily increasing, reflecting the growing complexity of modern power systems. The concept of a smart grid encompasses scientific and technological research, solutions, and policy and regulatory mechanisms. In the coming years, the increasing share of new energy installations and generation will be an inevitable trend, with the power grid undergoing systematic investment and upgrades centered around clean energy. Additionally, the integration of numerous intermittent distributed power sources in central and eastern regions will require the support of intelligent distribution networks.
The demand side of China's smart grid market primarily includes the State Grid, Southern Power Grid, local power supply bureaus, and regional power companies. Currently, there are very few suppliers capable of providing comprehensive smart grid solutions. However, we observe that most companies have already begun strategic deployments in one or more specialized areas of smart grid technology.
Text information extraction primarily combines machine reading comprehension with Word2vec models to identify and extract key information from text data automatically. This technology can be applied to the structured storage of power grid bidding documents, facilitating clustering and organization. It is also suitable for tasks such as detecting and alerting issues in power grid enterprise documentation.
Document similarity analysis is a typical NLP task that relies on semantic similarity calculations of text content, commonly used for fuzzy matching in information retrieval and knowledge Q&A. This technology is being explored for use in the power grid maintenance industry, where repair personnel can quickly search vast documentation by asking questions or using keyword searches. The system can pinpoint relevant content and generate explanations for the repair personnel.
A knowledge graph is a knowledge base that includes information and semantic relationships between different entities, effectively organizing and linking knowledge within a system to enable interconnected and communicative data. Building graph databases for technical and knowledge-based text data allows for complex retrieval functions and intelligent decision-making support. Graph databases improve the quality of text information retrieval, making them useful for scenarios like power grid management and monitoring, as well as intelligent Q&A customer service for power grid knowledge.
Sentiment recognition technology involves clustering and understanding dialogue content in business operations to identify the emotional information expressed by users. This technology primarily relies on Long Short-Term Memory (LSTM) algorithms to deeply understand the context of business dialogues, combining contextual information to determine the emotional polarity of the content and infer the topic and intent of the conversation.
(Jiangsu Power Grid and Hohai University) Traditional power grid detection and alarm systems cannot accurately judge alarm events in a short time. Given the current inefficiency of monitoring and alarm information, artificial intelligence technology provides an effective solution for power grid operations.
First, NLP technology is used to analyze and organize the features of alarm information text, followed by preprocessing. Next, the Word2vec model is used to vectorize the monitoring alarm information. Finally, based on the characteristics of the alarm information, a monitoring and alarm event recognition model combining LSTM and CNN is established. This model can be compared with various recognition models to verify its feasibility and effectiveness.
(Southern Power Grid and Boning Technology) Machine reading comprehension technology is used to read and analyze power grid safety regulation documents, creating indexes for paragraphs in the documents. When a repair worker asks a question, the system first searches the index for relevant paragraphs and then identifies the paragraph most likely to answer the question using a BERT model (on academic reading comprehension datasets, humans achieve 86.8, while the best models reach 88.6). The system returns the paragraph, refining it into a concise answer by extracting part of the original text as the output for the repair personnel.
(State Grid Jiangsu Electric Power Research Institute) During the project bidding and procurement process, power grid companies typically conduct duplicate checks on bidding documents, searching historical project databases for similar projects to prevent redundant bidding and avoid financial waste. Using latent semantic indexing, the semantic content of documents is analyzed. NLP techniques such as Chinese word segmentation, word vector conversion, word weight calculation, and topic modeling are employed to build a document similarity analysis system. This system can quickly identify projects similar to the target document in a vast historical database and calculate the similarity percentage, assisting procurement staff in determining the compliance of bidding documents. The research and application of this system hold significant practical value for standardizing power grid company project bidding and procurement management.
The application of NLP technology in the power grid industry is still in its early stages, with most cases remaining experimental or exploratory. Currently, very few projects have been implemented in real-world scenarios, with progress mainly stemming from POC projects by research institutions and tech companies. The data in these projects are often experimental, lacking objectivity and generalizability. From the current development perspective, data acquisition and organization in the power grid industry will pose significant challenges.
Content in the power grid field is highly specialized, requiring extensive manual annotation to train effective models. Additionally, power grid companies are traditional energy enterprises with established work methods and systems. Compared to other industries, the cost and barriers to智能化 are higher in the power grid sector. In the short term, NLP technology in the power grid industry will primarily aim to assist human work, which also requires从业人员 to adapt to and master new working methods and models.
In fact, industry leaders such as China's State Grid and Southern Power Grid have been actively collaborating with multiple technology companies and university laboratories to explore the application of artificial intelligence technology in the power grid sector. This initiative aims to promote the strategic goal of grid intelligence and better serve the public. In the future, more power grid-related business data will be mined and recorded, and the primary application scenarios of NLP technology will no longer be limited to intelligent customer service centers supporting business operations. By then, intelligent technology will further penetrate into the specific management, inspection, and maintenance of power grid-related operations.