Information Operation and Maintenance Optimization Strategy for

Power Industry Based on Artificial Intelligence

Hao Wang, Xiangcong Zhang, Bingjie Wang, Lei Wang and Feifei Zhang

State Grid Henan Information & Telecommunication Company, State Grid Henan Electric Power Company Zhengzhou

Henan, 450000, China

Keywords: Power Industry Information, Artificial Intelligence, Big Data Technology, O&M Optimization Strategy.

Abstract: In this study, an AI-based information operation and maintenance management system for the power industry

was designed to improve the efficiency and reliability of information operation and maintenance in the power

industry. The system mainly includes multiple layers, such as data acquisition layer, data processing and

analysis layer, decision support layer, execution and control layer, user interface and reporting layer, etc. In

this system, it uses integrated smart sensors and technologies such as RTUs and PMUs to collect high-quality

real-time data. In addition, it also uses big data platforms and machine learning algorithms to carry out data

processing and analysis, and at the same time, optimizes power generation dispatch and grid load

management. Based on the multiple strategies provided by the system, fault isolation and system recovery

operations can be automatically controlled, improving the overall response speed and accuracy of the power

grid. With this system, the equipment failure rate can be greatly reduced, and the power resource allocation

can be optimized, and at the same time, the power operation and maintenance efficiency can be improved.

1 INTRODUCTION

With the advancement of technology, especially the

development of artificial intelligence and big data

technology, the power industry is ushering in a huge

change in the way of operation and maintenance (Biro

and Jakovac. 2022). In other words, traditional power

system O&M relies too much on manual monitoring

and regular maintenance, resulting in inefficiency and

inability to handle complex and emergency situations

(Fang and Qin , et al. 2024). Based on this, this study

is based on the design of a set of information

operation and maintenance management system for

the power industry, to demonstrate a powerful

operation and maintenance management optimization

strategy, and to use artificial intelligence technology

to achieve real-time data collection and analysis

(Hunter and Albert, et al. 2024), intelligent decision

support, and automatic controlIn this way, the

efficiency and reliability of the system operation in

the power industry can be greatly improved, and the

current power supply and its system information

operation and maintenance management will

maintain a high level of security (Jakubik and

Vössing, et al. 2024).

2 RESEARCH METHODS

2.1 Theoretical Analysis

In the early stage of the design of this system, this

paper determines the basic framework and functional

requirements of the system based on the specific

analysis of relevant theories, literatures and prior

technologies.

2.2 Data Analysis Method

In this study, in the data processing and analysis layer

of the system, the researchers carried out in-depth

analysis of the collected power system data based on

the effective use of data analysis methods, so as to

propose specific optimization strategies and

effectively predict faults (Kieseberg and Tjoa, et al.

2024).

Wang, H., Zhang, X., Wang, B., Wang, L. and Zhang, F.

Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artiﬁcial Intelligence.

DOI: 10.5220/0013548600004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 537-541

ISBN: 978-989-758-763-4

537

2.3 Empirical Research Method

In this paper, the important power system operation

data is collected through the deployment of the

system in related systems, and the effectiveness and

practicability of the system design are verified

(Mercado and Mercado. 2022).

2.4 Simulation Method

In this paper, the simulation method is used to carry

out specific research and test a variety of power

dispatching and fault response strategies when

studying the decision-making level, so as to ensure

the scientific and effective strategies of the strategies.

2.5 Algorithm Testing Method

In this study, for the execution and control layer, the

researchers deliberately develop and test algorithms

that can be applied to the automation control, so as to

ensure the execution accuracy and response speed of

the system.

3 RESEARCH PROCESS

3.1 Overview of the Design of Each

Layer

The design of this power industry information

operation and maintenance management system

needs to involve many different dimensions, and at

the same time, it needs to include a multi-layer

structure. Based on this, the following article will

elaborate on these specific designs:

First, the data acquisition layer. At this layer,

smart sensors and smart meters need to be deployed,

while also collecting data such as load and generation,

voltage, line load and frequency (Müller, 2022). At

the same time, it is necessary to be able to collect

high-quality and real-time grid operation data based

on the two units of RTU and PUMU; Second, the

data processing and analysis layer (Spring and

Faulconbridge, et al. 2022). At this layer, a big data

platform is needed to complete the integration,

storage and collection of data, so as to maintain the

integrity and security of its data. In addition, it is also

necessary to process and analyze data based on

machine learning algorithms, including load

forecasting and fault detection, equipment

maintenance demand forecasting, etc. Third, the

decision support layer (Thaler and Williams, et al.

2024). At this layer, the system generates specific

optimization recommendations and decision-making

plans based on the analysis results, including power

generation dispatching, grid load management, fault

response, and other aspects. At the same time, it will

also use the expert system and rule engine to provide

people with decision-making support in fault

handling, emergency response, etc.; Fourth, the

execution and control layer. At this level, the goal

of establishing an interface with the grid automation

system will be achieved, such as the use of SCADA

systems to implement optimized generation and

distribution plans (Yan and Yan, 2022). At the same

time, this layer will also automatically control fault

isolation and realize system recovery operations,

thereby reducing manual intervention and improving

response speed and accuracy. Fifth, the user

interface and reporting layer. At this layer, a user-

friendly interface is provided where operators can

monitor system status and view alerts and decision

support information. At the same time, it also

generates regular and demand-driven reports so that

management can get the decision-making they need.

3.2 Data Acquisition Layer

There are many technologies that need to be used in

the data collection layer, and their key technologies

and applications include:

First, smart sensors and smart meters. First, smart

sensors. Smart sensors are installed on power

equipment, including transformers and generators, so

that the system can monitor the specific operating

status of the equipment, including temperature and

vibration, voltage, current, etc. Generally speaking, it

has a certain data preprocessing ability, which can

carry out preliminary analysis and screening and

compress data before data transmission, so as to

reduce the communication load and improve the data

transmission efficiency. Secondly, smart meters.

Smart meters will be installed at the consumer end to

record the user's electricity consumption, and can

support remote meter reading and real-time data

transmission. The technical characteristics of smart

meters are that they can not only measure power

consumption, but also provide data such as load

curves and voltage quality, and support remote

control functions, such as remotely turning power on

and off； Second, RTU. RTUs are an important part

of power automation systems, as they capture data

from all field devices and transmit them directly to

the central control room. The technical characteristics

of RTUs are that they usually have strong real-time

data processing capabilities, can process signals

INCOFT 2025 - International Conference on Futuristic Technology

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transmitted from various sensors, and have control

functions, such as switch control; Thirdly, the PMU.

A PMU is a device that can provide high-precision

measurements, providing the system with data such

as voltage, current, frequency, and phase of the grid

operation, and it is synchronized with high temporal

accuracy. The technical characteristics of PMU are

that its data can reflect the dynamic state of the entire

grid in real time, and it can be said to be an important

tool in modern grid management, especially when it

can be applied to highly dynamic and interconnected

power systems.

There are a number of different key elements in

the data processing and analytics layer, including:

First, the effective application of big data

platforms. Data integration and storage can be

completed first. For the integration function, the big

data platform will carry out specific analysis of data

from different devices or units, such as data from

smart sensors and smart meters, RTUs, PMUs, etc.,

and provide them with a unified view. This includes

the fusion of data in different formats and structures,

based on which data consistency and integrity can be

maintained. At the same time, in terms of storage, it

adopts more efficient and reliable data storage

solutions, such as Hadoop distributed file system or

cloud storage services, so that massive data can be

stored and accessed effectively and quickly. In

addition, these technologies guarantee redundant

storage of data, so that it can be recovered in the event

of a hardware failure. As for data security,

specifically, it will use strict data encryption

technology to protect data in storage and in transit

from unauthorized access. In terms of access control,

its performance is strict, and fine-grained data access

control is adopted, so that only authorized users can

access the sensitive information in it, based on this, to

prevent data leakage; Second, the application of

machine learning algorithms in it. As far as load

forecasting is concerned. To select a model first.

Time series forecasting models such as ARIMA and

seasonal ARIMA, or LSTM neural networks, can be

used to predict grid load trends. Then, optimize the

goal. In this regard, power companies can optimize

power generation and distribution plans through more

accurate and reliable load forecasting, and reduce

energy waste, which can greatly improve energy

efficiency. In addition, for fault detection. For

example, pattern recognition. It can detect abnormal

behaviors and potential faults in the power system

through classification algorithms and anomaly

detection technologies, such as deep learning-based

autoencoders, random forests, and support vector

machines (SVMs). In addition, real-time monitoring.

It can complete real-time data stream processing,

quickly identify the abnormalities in the system,

shorten the fault response time, and at the same time,

reduce the impact of the fault. Third, equipment

maintenance demand forecasting. In this regard,

the design of this layer is to predict the possible

maintenance needs of equipment through the

application of machine learning models, such as

regression analysis and decision trees, and neural

networks, with the support of historical maintenance

data and real-time operating data. In terms of

maintenance optimization, this predictive

maintenance will enable companies to better develop

effective maintenance strategies that will reduce

unplanned equipment downtime and reduce

maintenance costs.

3.3 Decision Support Layer

At the decision support level, the main technologies

or contents include:

First, the expert system. Its definition and function

are mainly reflected in the fact that, as an effective

computer program that simulates the decision-

making process of human experts, the expert system

contains a huge amount of domain-specific

knowledge and reasoning rules. Expert systems can

help non-experts make complex decisions by

leveraging the knowledge and experience of

integrated power industry experts in the power

system. Its application advantage is that in the process

of power grid operation and maintenance, the expert

system can effectively analyze the overall stability of

the power system, assess the potential risks for it, and

provide more comprehensive fault prevention

suggestions. Second, the rules engine. For example,

definitions and functions. A rules engine is a reliable

system that can execute user-defined rules, and it is

mainly used in complex automated decision-making

processes. Rules can be built on the basis of logical

expressions, and when certain conditions are met,

actions can be triggered. For example, in terms of

applications. The rule engine can automatically

identify the fault indication information obtained

from the data analysis layer in the fault response, and

at the same time, based on the preset O&M rules,

quickly decide whether to cut off a certain power

supply line or use a reroute strategy to avoid power

supply interruption.

Information Operation and Maintenance Optimization Strategy for Power Industry Based on Artiﬁcial Intelligence

539

3.4 Execution and Control Layer

There are many aspects that need to be included in the

design of the execution and control layer, and the

following are the relevant points:

First, system integration and automation control.

In terms of system integration, it is necessary to

perform integration with the control level, SCADA,

and other automation systems (e.g. EMS and DMS

systems). At the same time, APIs and special

protocols are used to achieve effective

communication with the above systems, and real-time

monitoring and control of the power grid. In terms of

automated control, automation technologies such as

rapid circuit breakers and reconfiguration

technologies can be used to isolate faults and restore

systems. These include quickly isolating the affected

area when a fault is detected, and automatically

reconfiguring the network when the fault is resolved,

so that normal power can be restored as quickly as

possible. Second, dynamic scheduling and

optimization. It works by adapting power generation

and distribution strategies based on real-time data

analysis and load forecasting. Through the effective

use of dynamic scheduling software and optimization

algorithms, it can automatically adjust its power

generation and distribution network configuration, so

as to better respond to real-time demand changes and

market dynamics. The benefits that can be

demonstrated are that such a dynamic dispatching

department can effectively optimize the cost-

effectiveness and energy efficiency of the power grid,

so that its power supply has a certain stability and

economy; Third, enhance responsiveness and

accuracy. The execution and control layer mainly

chooses more advanced and reliable control

algorithms and machine learning technology, which

not only ensures that it can automatically perform

routine operations, but also ensures that it can learn

and adapt to the actual operating environment of the

power grid, so as to reduce human operation errors

and improve operation accuracy. In terms of response

optimization, the automation system has a strong

ability to respond quickly, which is significantly

higher than the traditional manual operation,

especially in some emergency situations, it can

respond quickly to ensure that the system loss and

impact are greatly reduced.

3.5 User Interface Design

In terms of user interface design, the contents that

need to be involved are: first, the operation

interface. Functionality for the user interface.

Designers need to design a user-friendly interface that

allows operators to easily implement real-time

monitoring of power system status, such as

monitoring its real-time power flow, equipment

status, system load, etc. At the same time, it is

necessary to ensure the interactivity of the operation

interface. For example, it supports friendly interactive

operation, can set alarm thresholds, adjust load

distribution, and respond to fault handling at the same

time, so that O&M personnel can respond quickly.

Second, visualization tools. In this regard, in the

process of designing the user interface, the designer

should be able to ensure that there are charts,

dashboards, GIS and other parts in it, so as to display

key data such as grid load maps, historical data trends,

and prediction results, so that operation and

maintenance personnel can have an intuitive

understanding of the operation status of the system.

At the same time, the interface should be able to

ensure that the data can be updated in real time, and

provide the latest status and alarm information of the

system for the operation and maintenance personnel,

so that the operation and maintenance personnel can

respond to potential changes or failures in a timely

manner. Third, the report generation function. At the

user level, it should be able to provide regular

reporting functions, such as system operation

summary, equipment operation and maintenance

records, energy efficiency analysis, fault logs, etc.

The availability of these reports allows management

to monitor the performance and operational

efficiency of the system in a comprehensive manner.

In addition, it is necessary to allow users to set the

report generation cycle as daily, weekly, or monthly,

so that management can customize it freely.

4 STUDY RESULTS

Through the detailed research in this paper, the

strategies for information operation and maintenance

optimization in the power industry based on artificial

intelligence can be derived, which include:

First, enhance data-driven, predictive

maintenance. In this study, the researchers used

machine learning algorithms in the data processing

and analysis layers to analyze the device's historical

operating data and real-time performance data to

predict possible equipment failures and maintenance

needs. This predictive maintenance strategy can

reduce unplanned downtime of equipment to a certain

extent, and make the equipment service as long as

possible, thereby reducing the operation and

maintenance costs of the power industry. Second,

INCOFT 2025 - International Conference on Futuristic Technology

540

effective dynamic load management and adaptive

control. From this study, it can be seen that based on

the close collaboration between the decision support

layer and the executive and control layers of the

system, effective dynamic load management can be

achieved (mainly through real-time data). Through

the application of advanced control algorithms,

people can ensure the automatic adjustment of power

generation and distribution strategies based on the

real-time load and energy demand of the grid. Third,

enhance the intelligence of the decision support

system and its automatic response ability. The focus

of this strategy is that it can learn historical data and

operation results based on the development and

integration of decision support systems with a high

level of intelligence, so as to automatically propose

better and intelligent processing strategies and

response measures.

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