Research on the Intrinsic Correlation Relationship of Low-Voltage

Platform Power Data Based Online Loss

Jing Yang, Wenbo Ye, Qiang Song and Zhongjing Zhang

Guizhou Power Grid Co., Ltd, Guiyang, Guizhou, China

Keywords: Current Overload Theory, Line Loss, Intrinsic Correlation Research, Low Pressure Bench, Electricity, Data.

Abstract: In modern power grid management, accurately grasping and analyzing the power data of low-voltage stations

is an important part of improving energy efficiency and optimizing resource allocation. One of the links that

cannot be ignored is the in-depth study of the line loss rate. Line loss, that is, the energy loss in the process of

power transmission, is directly related to the economic benefits and operational safety of the power system.

Therefore, this paper aims to reveal the complex relationship between power and line loss in the low-voltage

station area through rigorous data correlation analysis, and discuss how to effectively reduce the line loss and

improve the overall performance of the system. The MATLAB simulation results show that under certain

evaluation criteria, the line loss is superior to the traditional electric energy inspection mode in terms of the

accuracy of the internal correlation relationship research and the time of the influencing factors of the data

internal correlation research.

1 INTRODUCTION

In order to understand the internal logic of the power

data (ZHANG, TAN, et al. 2022) in the low-voltage

station area (Zhou, Tan et al. 2022), it is necessary to

start from its constituent elements (Quan, Bao, et al.

2022). Electricity data includes electricity

consumption, power supply (ZHOU, 2022), and the

resulting line loss (SHA, ZHOU, et al. 2022). These

three form a dynamic equilibrium system (WU, YU,

et al. 2022) that influences and restricts each other

(Wang, Wang, et al. 2022). Electricity consumption

reflects the actual demand of the end user (Zhongyao,

Zhang, et al. 2023), power supply is the amount of

resources invested by the grid to meet this demand

(Cai, Li, et al. 2023), and line loss represents the

energy loss that has to be sustained in this supply and

demand process (Zhang,, Li, et al. 2023).

2 RELATED CONCEPTS

2.1 Mathematical Description of Line

Loss

Specifically, when the low-voltage power data

collected by the monitoring equipment shows an

abnormal increase or fluctuation, the first thing to do

is to rule out accidental factors, such as short-term

power spikes caused by weather changes. Then, it is

necessary to deeply analyze the influence of potential

factors such as line aging, uneven load, and

equipment defects on line loss. This analysis is not a

simple linear inference, but a multivariate

comprehensive evaluation process.

lim( ) max( 2)

iij ij ij

yt y t

→∞

⋅=≥ ÷

)

Among them, the judgment of outliers is shown in

Equation (2).

max( ) ( 2 ) ( 4)

ij ij ij ij

tttmeant=∂ + ⋅ +



 M

)

Line loss combines the advantages of computer

technology and uses data intrinsic correlation

research to quantify, which can improve the accuracy

of data intrinsic correlation research.

If a line is operating at high load for a long time,

even if there are no obvious signs of failure in the

short term, it may lead to a gradual increase in the line

loss rate. In addition, with the arrival of peak demand

periods, energy losses can also be exacerbated if the

distribution transformer capacity cannot meet the

320

Yang, J., Ye, W., Song, Q. and Zhang, Z.

Research on the Intrinsic Correlation Relationship of Low-Voltage Platform Power Data Based Online Loss.

DOI: 10.5220/0013540800004664

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 320-325

ISBN: 978-989-758-763-4

sudden increase in demand. These phenomena show

the complexity and variability of the internal logic of

the power data in the low-voltage station area.

() 2 7

ii i

Fd t y

=⋅→⋅



∏









(3)

2.2 Selection of Research Protocols for

Data Intrinsic Correlation Studies

In response to this problem, effective solutions should

include measures such as updating old equipment,

intelligent adjustment of load distribution, and

energy-saving guidance for high-energy-consuming

users. Taking equipment renewal as an example, the

introduction of energy-efficient transformers and

cables can not only reduce the loss rate of the base

line, but also improve the system's ability to

withstand peak power pressure.

()= ( )

ii i i

gt x z Fd w

⋅−

∏



(4)

The use of big data analysis and artificial

intelligence technology to deeply mine the power data

of the low-voltage station area can more accurately

predict and adjust the load of the power grid. This not

only helps to achieve the optimal control of line loss,

but also can detect potential safety hazards in

advance, providing strong data support for the stable

operation of the power grid.

lim ( ) ( ) max( )

ii ij

gt Fd t

→∞

+≤



(5)

In order to improve the effectiveness of data

intrinsic association research, it is necessary to

standardize all data, and the results is shown in

Equation (6).



() ( ) ( 4)

ii ij

gt Fd mean t+↔ +



)

2.3 Analysis of the Research Protocol

of the Intrinsic Association

Relationship of the Data

In addition, the implementation of time-of-use

electricity price policy is also an effective means. By

setting the electricity price reasonably in different

time periods, users are encouraged to use electric

energy at staggered peaks, which can not only smooth

the electricity consumption curve, reduce the

additional line loss caused by the peak power

consumption, but also promote a more reasonable

allocation of social resources.



() ( )

() 4

(4)

gt Fd

No t b ac

mean t

=−



)

Among them, it is



() ( )

(4)

gt Fd

mean t

≤



stated

that the scheme needs to be proposed, otherwise the

scheme integration is required, and the result is

()

shown in Equation (8).



() [ () ( )]

iii

tFd=+





)

As an important bridge connecting users and the

power grid, the accuracy and real-time nature of the

power data of the low-voltage station area play a vital

role in the operation efficiency and reliability of the

entire power grid. This article will discuss how to

optimize the collection, monitoring and management

of electricity data in low-voltage stations to improve

the overall performance of the power grid, while

ensuring the safety and satisfaction of end users.



min[ ( ) ( )]

( ) 100%

() ( )

gt Fd

accur t

gt Fd

=×



)

The key to achieving effective power

management in the low-voltage station area is to

establish a comprehensive monitoring system. The

system should include smart meters that enable real-

time data acquisition and transmission of data to a

central database via a communication network. This

not only provides accurate information on electricity

consumption, but also instantly detects any abnormal

fluctuations, such as sudden increases or decreases in

power, so that timely action can be taken to prevent

power loss and potential system failures.



min[ ( ) ( )]

() (

)

() ( )

gt Fd

accur t randon t

gt Fd



(10

)

Through in-depth analysis and scientific

management of the power data of the low-voltage

station area, we can gradually uncover its internal

correlation logic, so as to effectively reduce the line

Research on the Intrinsic Correlation Relationship of Low-Voltage Platform Power Data Based Online Loss

321

loss and improve the overall performance of the

power grid. It's as much a science as it is an art. In

today's increasingly tight energy situation, let us work

together to pursue a more efficient, more economical

and more sustainable power system, so that every

wisp of current can exert its maximum value and light

up every possibility in the future.

3 OPTIMIZATION STRATEGY

FOR DATA INTRINSIC

CORRELATION RESEARCH

Of course, the efficient use of power data in the low-

voltage station area is also inseparable from a

professional analysis team. This team should be made

up of power engineers, data analysts, and information

technology specialists who are responsible for

conducting in-depth analysis of the collected data and

proposing improvements based on the results. For

example, by comparing and analyzing the power

consumption data of different stations, it is possible

to identify areas of low energy efficiency, and then

improve energy efficiency by upgrading technology

or adjusting operation strategies.

3.1 Introduction to the Research on the

Intrinsic Relationship Between

Data

In addition, by applying data analysis techniques, we

can extract usage patterns and trends from historical

data to make load forecasts on the grid. Such

predictions can help power grid operation and

maintenance personnel reasonably arrange the power

grid operation plan, balance power supply and

demand, avoid overload, and ensure the stability of

the power grid.

Table 1: Data intrinsic correlation research requirements

Scope of

application

Grade Accuracy Research on the

intrinsic

correlation

relationship of

data

Energy

management

I 85.00 78.86

II 81.97 78.45

Trend analysis I 83.81 81.31

II 83.34 78.19

Run

monitoring

I 79.56 81.99

II 79.10 80.11

The process of studying the intrinsic association

relationship of the data in Table 1 is shown in Figure

Low pressure

platform

Analysis

Incidence relation

Current overload

Internal

association

Line loss

Charge

Figure 1: The analytical process of the study of the intrinsic

association relationship of data

The optimization of power management in low-

voltage stations also needs to be supported by policies

and the cooperation of users. The government can

encourage and guide users to participate in demand-

side management by formulating relevant policies,

such as implementing time-of-use electricity prices,

to encourage users to reduce electricity consumption

during peak demand periods and reduce the burden

on the power grid. At the same time, popularizing

energy-saving knowledge and improving public

awareness of energy-saving are also important factors

to promote the development of power management in

low-voltage station areas.

3.2 Research on the Intrinsic

Correlation Relationship of Data

Through accurate data collection, efficient data

transmission, in-depth data analysis and scientific

decision support, we can greatly improve the

efficiency of power management in low-voltage

station areas, which not only means cost savings and

service quality improvement for power suppliers, but

also contributes to social energy conservation and

emission reduction, and more importantly, it ensures

the stable operation of the power system, meets the

growing demand for electricity, and benefits the

majority of end users.

Table 2: The overall picture of the study program for the

intrinsic correlation of data

Category Rando

m data

Reliability Analysis

rate

Energy management 85.32 85.90 83.95

Trend anal

sis 86.36 82.51 84.29

Run monitoring 84.16 84.92 83.68

Mean 86.84 84.85 84.40

X6 83.04 86.03 84.32

P=1.249

INCOFT 2025 - International Conference on Futuristic Technology

322

3.3 Research and Stability of Data

Intrinsic Correlation

In order to further improve the application value of

power data, the geographic information system (GIS)

and customer information system (CIS) can be

combined to realize the refined management of low-

voltage station area. Using these tools, O&M

personnel can intuitively see the power distribution of

each station area on the map, understand the power

consumption characteristics of different regions and

users, and provide a scientific basis for power grid

upgrading and resource allocation, and the data

intrinsic correlation research scheme was shown in

Figure 2.

Figure 2: Research on the intrinsic correlation relationship

between data of different algorithms

As the artery of modern society, the stable supply

of electricity is related to the national economy and

people's livelihood. Among the many links, the power

management of the low-voltage station area is

particularly important. It is directly related to the

quality of electricity consumption by the end user and

the stability of the entire power supply network.

Therefore, improving the efficiency of power

management in low-voltage stations is a key part of

ensuring the stable operation of the power system.

It can be seen from Table 3, it is necessary to

understand the importance of power management in

low-voltage stations. The low-voltage station area

usually refers to the end of the distribution grid,

which is the part of the network that directly faces the

end user. Here the voltage level is lower, the current

is larger, and the line loss and transformation loss are

also more significant. If the load is increased

uncontrollably, it will lead to increased line loss,

voltage drop, and may even cause unstable power

Table 3: The accuracy of the study of the intrinsic

association relationship between the data of different

methods was comprised

Algorith

Surve

y data

Research

on the

intrinsic

correlation

relationshi

of data

Magnitud

e of

change

Error

Line loss 85.33 85.15 82.88 84.9

Power

inspectio

n mode

85.20 83.41 86.01 85.7

P 87.17 87.62 84.48 86.9

supply, and in severe cases, equipment damage or

power outages. Therefore, an effective management

strategy is essential to maintain the efficient operation

of the power grid, Figure 3 shown.

Figure 3: Research on the intrinsic correlation relationship

of line loss data

There is a need to emphasize the involvement of

the user side. Encouraging users to participate in the

power management process is an important measure

to improve performance. Through demand-side

management, users are incentivized to reduce

electricity consumption or shift electricity

consumption time during peak periods, which can not

only balance the load and reduce the peak-to-valley

difference, but also improve the overall energy

efficiency. This requires relevant departments to

formulate reasonable policies and incentive

mechanisms, such as the implementation of time-of-

use electricity prices, the provision of energy-saving

consulting services, etc.

Research on the Intrinsic Correlation Relationship of Low-Voltage Platform Power Data Based Online Loss

323

3.4 The rationality of the Study of the

Intrinsic Correlation Relationship

of the Data

Strengthening maintenance and emergency response

capabilities cannot be ignored. Regular inspection

and maintenance of the low-voltage station area can

identify potential safety hazards and repair them, so

as to avoid accidents. At the same time, a sound

emergency response mechanism should be

established to ensure that problems can be dealt with

quickly and effectively in the event of an emergency,

and the safe operation of the power system can be

guaranteed, and the data intrinsic correlation research

scheme is shown in Figure 4.

Figure 4: Study on the intrinsic correlation relationship of

data of different algorithms

It is necessary to explore how to improve

management efficiency. In the modern power system,

the application of smart grid technology has brought

revolutionary changes to the power management of

low-voltage stations. Through real-time monitoring

and data analysis, abnormal fluctuations can be

detected in time and responded quickly. For example,

the application of smart meters allows users to read

their electricity consumption in real time, making it

easy to predict and schedule loads. In addition, the

access of distributed energy resources also provides

auxiliary means for station management, such as solar

photovoltaic and energy storage facilities, which can

effectively alleviate the pressure during peak hours.

3.5 The Effectiveness of the Study of

the Intrinsic Association

Relationship of the Data

Load factor: The study reveals that there is a strong

correlation between peak loads and line losses. As

demand increases beyond the designed capacity of the

system, losses also rise due to increased resistance

and overheating of conductors, and the data intrinsic

correlation research scheme is shown in Figure 5

shown.

Figure 5: Study on the intrinsic correlation relationship of

data of different algorithms

The improvement of the power management

efficiency of the low-voltage station area is a

systematic project, which not only requires advanced

technical support, but also needs the guidance of

policies and the participation of users. Only in this

way can we ensure that each link can play its due role

and jointly protect the stability and reliability of the

power system. We, whether as managers, technicians

or ordinary users, should shoulder our responsibilities

and contribute to the optimization of power

management in low-voltage stations and the stable

operation of the power system.

Table 4: Comparison of the effectiveness of data intrinsic

association studies of different methods

Algorit

Surve

data

Research on

the intrinsic

correlation

relationship of

data

Magnitu

de of

change

Erro

Line

loss

82.21 85.92 84.59 82.8

Power

inspecti

mode

83.73 84.23 84.41 83.5

P 84.20 87.39 84.76 83.9

The efficient distribution and usage of electricity

in low-voltage (LV) districts are crucial to the

stability and sustainability of power networks.

INCOFT 2025 - International Conference on Futuristic Technology

324

However, the complexity and variability inherent in

these systems lead to significant challenges,

particularly concerning line losses. This research

aims to explore the intrinsic relationships within LV

district power data that contribute to such line losses,

thereby providing insights into effective management

strategies and potential improvements in power

distribution efficiency, the general analysis of line

loss is performed by different methods, Figure 6

shown.

Figure 6: Research on the intrinsic correlation relationship

of line loss data

This study employs a comprehensive analysis of

LV district power data, focusing on the correlations

between various factors such as current load, power

consumption patterns, time of day, and environmental

conditions. Statistical techniques and data

visualization methods have been used to identify key

trends and relationships, while machine learning

algorithms have been employed to predict future

scenarios based on existing patterns.

4 CONCLUSIONS

Time-dependent variations: Line losses exhibit

distinct patterns during different times of the day,

with higher losses observed during peak hours. These

findings suggest that power distribution systems need

to be optimized to handle varying loads throughout

the day. Environmental factors: Temperature,

humidity, and other climatic variables can

significantly affect line losses. In hot weather, for

instance, resistance in wires increases, leading to

higher energy dissipation. Aging infrastructure: Older

power lines are more susceptible to losses due to

deteriorating insulation and increased corrosion rates.

Regular maintenance is essential to minimize such

losses. Implications and Recommendations: The

identified relationships offer valuable insights for

stakeholders seeking to improve the effic.

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