Research on Verification of 10kv High Voltage Digital Electric Energy
Measurement based on RF Synchronization Technology
Haining Chen
1,
*, Yan Li
1
, Yao Chen
1
and Jun Yang
2
1
State Grid QingHai Electric Power Company Marketing Service Center, Qinghai, China
2
NARI-TECH Nanjing Control Systems Ltd., Nanjing, China
Keywords: RF Synchronization Technology, 10KV High Voltage, Digitization, Electric Energy Metering, Check.
Abstract: With the deepening of power system reform, great attention has been paid to the economic benefits of power,
among which the accurate measurement of electric energy is the key. In order to improve the economic benefit
and stability of power grid operation, the verification of electric energy measurement is studied. The
traditional high-voltage digital power metering calibration method has not been optimized, which leads to
large error. Therefore, a 10kv High-voltage digital power metering calibration method based on RF
synchronization technology is proposed. The analysis model of high-voltage digital power parameters is
constructed. The RF synchronous technology is used to collect high-voltage digital power parameters. The
offset compensation method of average current period is used to adjust the compensation and feedback of
high-voltage digital power parameters. The steady-state gain regulation model of high-voltage digital power
transmission is constructed, and the RF synchronous control method is used to complete the model prediction
and parameter estimation in the process of high-voltage digital power measurement. Combined with the deep
learning method, the optimization control in the verification process of high-voltage digital power
measurement is realized. The parameters such as electric power, working voltage and output power gain are
taken as constraint variables, and the power consumption is calculated by the power synchronization method
The method of upper limit compensation and error factor adjustment is used to realize 10kv High-voltage
digital energy metering verification. The simulation results show that the improved piezoelectric digital
energy metering method has higher accuracy and better application performance.
1 INTRODUCTION
10KV High-voltage power transmission has become
the main operation mode of power transmission in the
future. In the process of high-voltage power
transmission, it is disturbed by the environmental
disturbance and oscillation factors of the line,
resulting in the low accuracy of electric energy
measurement of high-voltage power transmission. It
is necessary to build an optimized 10KV High-
voltage digital electric energy measurement
verification method and control the verification
results of 10KV High-voltage digital electric energy
measurement in combination with RF synchronous
control technology so as to improve the output
stability and reliability of electric energy
measurement (Jing, 2019, Chen, 2019, Tan, 2019,
Wang, 2019). The related research on 10KV High-
voltage digital electric energy measurement
verification method has a wide application value in
high-voltage transmission and digital electric energy
measurement.
At present, in the existing research, the more
typical power metering verification method is the
implementation scheme of remote online verification
of digital metering secondary equipment proposed in
document (Yu, 2019, Bai, 2019, Zhou, 2019, et al).
In this study, the device communication modeling is
carried out based on the electric energy metering
model of IEC 61850 project. The special logic node
MSCN for metering verification is used to
accumulate the measured electric energy value,
transmit the verification data to the station control
layer through MMS service, and complete the non-
invasive verification based on the clock
synchronization of the metering equipment in the
station. There are two verification implementation
modes of "local verification" and "background
verification". The design route of this method is
648
Chen, H., Li, Y., Chen, Y. and Yang, J.
Research on Verification of 10kV High Voltage Digital Electric Energy Measurement based on RF Synchronization Technology.
DOI: 10.5220/0011203400003440
In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 648-653
ISBN: 978-989-758-593-7
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
feasible, but in practical application, its timeliness
and accuracy need to be further improved.
Aiming at the disadvantages of traditional
methods, a 10KV High voltage digital electric energy
measurement verification method based on RF
synchronization technology is proposed in this paper.
The simulation test results show that this method has
superior performance in improving the verification
accuracy of 10KV High voltage digital electric
energy metering.
2 10KV HIGH VOLTAGE
DIGITAL ELECTRIC ENERGY
METERING COEFFICIENT
COMPENSATION
2.1 Electric Energy Measurement
Information Acquisition Based on
RF Synchronization Technology
The RF synchronization technology (Newsham,
2019, Li, 2016, Du, 2016, Zhu, 2016) is introduced to
build the RF information acquisition model of 10KV
High-voltage digital electric energy metering. Based
on the planning and measured data of power grid, it
is obtained that the correlation fusion time
T
of
10KV High-voltage digital electric energy is:
()
'
ii
i
i
Noo
Ty
t
∗+
=+
（1）
In it,
N
is the sequence length of 10KV High
voltage digital electric energy coefficient (Jin, 2018,
Song, 2018, Gong, 2018, et al), i.e. 32; The 10KV
High voltage digital electric energy coefficient
component
i
t
collected at the sampling time point
i
represents the distance from the origin of the time
axis
1
N
i
i
ttN
=
=
; 10KV High voltage digital
electric energy coefficient distribution value
i
y
collected for the
i
RF tag; And
i
o
and
'
i
o
are the
adjacent samples of 10KV High-voltage digital
electric energy coefficient measurement, and their
mean values can be expressed as
y
( Dong, 2015, Li,
2015). The least square estimation value of RF
information of 10KV High-voltage digital electric
energy measurement is obtained by using the method
of optimal decision-making of comprehensive
benefits (Wu, 2017, Peng, 2017, Wang, 2017, Zhang,
2019, Zhao, 2019, Zhang, 2019). The symbolic value
of RF information of 10KV high-voltage digital
electric energy measurement is obtained by scalar
time series analysis:
()
[
]
cos 'bm T y N y=+++
（2）
Through the grouping detection method, the RF
synchronous control model of 10KV High-voltage
digital electric energy metering is constructed. And
the expression of the electric energy consumption
model of the electrolytic cell is obtained as follows:
() ()
1
'
vjk
m
p
abmsy
=
=++
（3）
Among them,
j
k
s
is the RF identification
information of 10KV High-voltage digital electric
energy metering collected in section
j
and section
k
. According to the increase amplitude of electric
heating power, the RF information acquisition model
of 10KV High-voltage digital electric energy
metering is constructed by using the constraint
method of heat storage and heat release power, as
shown in Figure 1.
RF synchronous tag identification
Feature fusion
Figure 1: RF information acquisition model for 10kV High voltage digital electric energy metering.
Research on Verification of 10kV High Voltage Digital Electric Energy Measurement based on RF Synchronization Technology
649
2.2 Realization of Electric Energy
Metering Coefficient Compensation
The RF synchronization technology is used to collect
10KV High-voltage digital electric energy
coefficient, and the offset compensation method of
current cycle average value is used for 10KV High-
voltage digital electric energy coefficient
compensation and feedback adjustment (Guo, 2020,
Li, 2020, Zhou, 2020, Wang, 2020). The spatial
distribution coordinate of 10KV High-voltage digital
electric energy coefficient compensation is defined as
11
11
77
8, 8
nn
ii
in in
ty
++
==
. The value of 10KV
High-voltage digital electric energy coefficient
collected in each section is
1
n
, and the values are 1,
4, 14 and 24 respectively;
M
is the mean square
error value of the least square estimation of 10KV
High voltage digital electric energy coefficient
compensation; the covariance error
j
k
M
of 10KV
High voltage digital electric energy coefficient fusion
from the sampling points of section
j
and section
k
is obtained. Using the least square programming
design method, the equivalent constraint coefficient
of 10KV High-voltage digital electric energy
coefficient compensation meets the minimum
ω
.
Using the polynomial fitting method, the regression
analysis term of electric energy coefficient
0
ii
ξξ
>（
）
is obtained. And the three-phase stator
current waveform is constructed to obtain the
constraint coefficient
C
. The quadratic
programming problem of 10KV High-voltage digital
electric energy metering is as follows:
() ()
()
()
v
Uw bm pa
ω
=+⋅
（4）
Lagrange multiplier
( , 1, 2,..., ; 0)
ii
ai la=≥
is
introduced to construct the Lagrange function
solution model for 10KV High voltage digital electric
energy measurement and verification. The dual
problem of 10KV High voltage digital electric energy
measurement and verification is as follows:
() ( )
1
v
w
da Uwdw
ω
=
=+
（5）
The steady-state gain regulation model of 10KV
High-voltage digital electric energy transmission is
constructed. The RF synchronous control method is
used for model prediction and coefficient estimation
in the process of 10KV High-voltage digital electric
energy metering (Zhan, 2018, Hu, 2018, Wei, 2018).
Combined with nonlinear transformation and relevant
theories of functional theory, the optimization model
of 10KV High-voltage digital electric energy
metering process is obtained, which is expressed as:
() ()
()
s
s
se al
M
M
g
φ
+
=⋅
（6）
Among them,
g
is the RF synchronous control
coefficient and the new quadratic programming
objective function for 10KV High voltage digital
electric energy metering verification is
()se . The
bias of current is processed (Zhu, 2020, Zhang, 2020,
Cao, 2020) to solve the classification decision
function
()al of dual inverter winding control.
The kernel function fuzzy decision method is
adopted to obtain the support vector machine (SVM-
RBF) control chart model for 10KV High voltage
digital electric energy metering verification, which is
shown as follows:
()
()
()
gs
De M se
γ
=∗ +
（7）
Where,
y
is the coefficient of radial basis
kernel function. Therefore, a 10KV High voltage
digital electric energy metering coefficient
compensation model is constructed (Yin, 2020, Guo,
2020, Wang, 2020, Wang, 2020, Pan, 2020).
3 10KV HIGH VOLTAGE
DIGITAL ELECTRIC ENERGY
METERING VERIFICATION
3.1 Power Metering Verification
Weight Acquisition
Based on the compensation results of 10KV High
voltage digital electric energy metering coefficient,
the fuzzy iterative phase coefficient of high voltage
digital electric energy metering is identified. The
phase estimation
r
θ
of multi-phase electric energy
digital measurement is:
() ()
r
Q
vn vn
θ
=+
(8)
Among them,
()vn is the disturbed white noise
of high-voltage digital electric energy measurement
and verification.
()
Q
vn
is the imaginary part of the
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
650
disturbed white noise
()vn
of 10KV High-voltage
digital electric energy measurement and verification.
The first point of the 10KV High-voltage digital
electric energy metering verification sequence
()rn
is adopted to make the sequence length even
1N − .
The iterative model
R
of 10KV High-voltage digital
electric energy metering is obtained by using phase
space reorganization technology:
()
r
j
k
Rrn
s
θ
=+
(9)
The power consumption
j
k
s
of RF information
is:
()
kjk
jk
k
R
As
ps
ϕ
++
=
(10)
Among them, the amplitude term and phase term
of 10KV High voltage digital electric energy
measurement and inspection are
k
A
and
k
ϕ
respectively.
Build the weight calculation model of 10KV High
voltage digital electric energy metering. It is set
0
k
as
the discrete spectrum component of electric energy.
And the sum
1
ϕ
and
2
ϕ
is used to represent the
10KV High-voltage digital electric energy
transmission sequence
1
()
R
k
and the phase
2
()
R
k
at
the maximum spectrum line respectively. Then the
calculation model of electric energy metering weight
is as follows:
()
()
()
() ()
12
12
0
jk
ps
tRkRk
k
ϕϕ
η
⋅+
=++
(11)
3.2 10kV High Voltage Digital Electric
Energy Metering Verification
Based on the power metering weight calculated
above, combined with the deep learning method, the
optimization of 10KV High-voltage digital power
metering verification is realized.
Taking the coefficients of electric power, working
voltage and output power gain as constraint variables
(Yang 2018, Zhang 2018, Wang 2018), the
independent variables of the verification constraint of
high-voltage digital electric energy metering
'
K
are
corrected. The verification constraint is:
()
[
]
'
un
KnF
ε
=Φ + +
(12)
The winding phase of the output power gain
()
un
nΦ
is obtained by taking the electric power,
working voltage and output power gain as the
constrained characteristic component (Wang 2019,
Gao 2019, Wei 2019). When the unwinding is
correct, the output power gain control coefficient is
() ()
un
nnΦ=Φ
or
() () ( )
un
nn n
φφ
Φ=+−
. For phase
estimation
ε
, the estimated value can be calculated
according to the verification results of the three port
electronic system.
Based on the estimation of independent DC
coefficient of multi- energy storage,the verification
steady-state coefficient
()
qw of 10KV High-
voltage digital electric energy metering is as follows:
() ()
{
}
()
,1
'
it
ns
qw K g u Y
−
=+ +
(13)
In it,
()
,1it
s
Y
−
represents the value of 10KV High-
voltage digital electric energy metering verification
coefficient detected at time
1t −
, and
()
n
g
u
is the
feedback coefficient of 10KV High-voltage digital
electric energy metering verification output.
Considering the difference of 10KV High voltage
digital electric energy metering verification,
likelihood estimation of steady-state coefficient for
verification of 10KV High voltage digital electric
energy metering is like this:
()
()
()
1w
n
fz
A
xqw
i
=
=+
(14)
Using the similarity fusion method, the dynamic
adjustment coefficient of 10KV High voltage digital
electric energy metering output is obtained as
n
i
.
()
f
z is the 10KV High voltage digital electric
energy metering output after the t+ 1 iteration (Wang,
2019, Chen, 2019, Zeng, 2019).
At this time, the response expression of 10KV
High voltage digital electric energy verification is:
()
33
I
MR JAx
×
=∈ ++
(15)
The optimized characteristic solution is:
()
t
J
I
M
ex
+
=
(16)
In it,
33
M
R
×
∈
is the positive definite matrix of
10KV High voltage digital electric energy metering
verification; J is a constant;
()
t
ex
is the guide error
vector of 10KV High voltage digital electric energy
metering.
To sum up, the verification of 10KV High voltage
digital electric energy metering is realized. The
implementation structure block diagram of the
verification device is shown in Figure 2.
Research on Verification of 10kV High Voltage Digital Electric Energy Measurement based on RF Synchronization Technology
651
r(n)
[]
Arg ⋅
Z
-1
Z
-1
Z
-1
Z
-1
...
Z
-1
Z
-1
Z
-1
Z
-1
...
1
r
w
−
1
r
w
− 1
r
w
− 1
r
w
−
...
1
N
r
θ
Figure 2: Realization structure diagram of 10kV High
voltage digital electric energy metering verification device.
4 EXPERIMENTAL TEST AND
ANALYSIS
In the experiment, the terminal voltage gain of 10KV
High-voltage digital electric energy metering is set to
be 7KV to 10V. The output power of high-voltage
digital electric energy metering device is 120W, and
the maximum power point voltage offset is 15V. The
output gain distribution curve of 10KV High-voltage
digital electric energy metering inspection under
different variable coefficients is obtained, as shown
in Figure 3.
-5
0
5
10
15
-15
-10
-5
0
5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
R
A
C
θ
γ
θ
1, 6
C
γ
θθ
==−
2, 0.015625C
γ
==
0.93875RA =
(a)
-1
0
1
2
3
-8
-7
-6
-5
-4
0.92
0.925
0.93
0.935
0.94
0.945
0.95
C
θ
γ
θ
0, 5
C
γ
θθ
==−
1, 0.03125C
γ
==
0.94375RA =
R
A
(b)
-1
-0.5
0
0.5
1
-6
-5.5
-5
-4.5
-4
0.92
0.925
0.93
0.935
0.94
0.945
0.95
0.955
C
θ
γ
θ
0.25, 4.75
C
γ
θθ
==−
1.189207, 0.037163C
γ
==
0.94875RA =
R
A
(c)
Figure 3: Output gain distribution of 10kV High voltage
digital electric energy metering verification.
According to the analysis of Figure 3, the output
gain and stability of 10KV High-voltage digital
electric energy metering verification by this method
are large. The output root mean square error of 10KV
High-voltage digital electric energy metering is
tested, and the comparison results are shown in
Figure 4.
Number of snapshots
Traditional MUSIC way
Method in the
paper
Root
mean
squar
e
error
Figure 4 Comparison of output error of 10kV High voltage
digital electric energy metering calibration.
According to the analysis of Figure 4, the output
root mean square error of 10KV High-voltage digital
electric energy metering verification by this method
is low.
5 CONCLUSION
A verification method of 10KV High voltage digital
electric energy measurement based on RF
synchronization technology is proposed. Based on the
planning and measured data of power grid, according
to the increasing amplitude of electric heating power,
BDEDM 2022 - The International Conference on Big Data Economy and Digital Management
652
the RF information acquisition model of 10KV High-
voltage digital electric energy measurement is
constructed. And a new quadratic planning objective
function for the verification of 10KV High-voltage
digital electric energy measurement is obtained to
realize 10KV High-voltage digital electric energy
measurement. It is concluded that the output error of
10KV High voltage digital electric energy
measurement verification by this method is small and
the stability is high.
REFERENCES
Dong Xianlun, Li Xueqing. A Novel distributed database
solution based on MySQL[C]//Proceedings of the 2015
7th International Conference on Information
Technology in Medicine and Education. Huangshan: I
EEE, 2015:329-333.
Guo Zun, Li Gengyin, Zhou Ming, Wang Jianxiao.
Decentralized coordinated robust optimal scheduling
model for wind power consumption [J]. Chinese
Journal of electrical engineering, 2020, 40 (20): 6442-
6454.
Jin Jiahui, Song Aibo, Gong Huan, et al. Distributed storage
system for electric power data based on HBase[J]. Big
Data Mining and Analytics, 2018, 1(4): 324-334.
Jing Chaoxia, Chen Dapeng, Tan Huijuan, Wang Hongyi.
Improved benders algorithm for maximum power
supply capacity of urban transmission network [J].
Chinese Journal of electrical engineering, 2019, 39
(15): 4373-4384.
Li Bin, Du Mengyuan, Zhu Yun, et al. Intelligent
distribution network state estimation based on quasi
real-time data [J]. Journal of electro-technics, 2016,31
(1): 34-44.
Newsham G R. Measurement and verification of energy
conservation measures using whole-building electricity
data from four identical office towers[J]. Applied
Energy, 2019, 255(Dec.1):113882.1-113882.18.
Wang Cheng, Gao Rui, Wei Wei, et al. Risk-based
distributionally robust optimal gas-power flow with
Wasserstein distance[J]. IEEE Transactions on Power
Systems,2019,34(3):2190-2204.
Wang Yuhong, Chen Yang, Zeng Qi, et al. Research on
contribution ranking and suppression method of ultra-
low frequency oscillation of hydro-power units [J].
Electrical measurement and instrumentation, 2019,56
(7): 7-14,29.
Wu Qingxi, Peng Hui, Wang Jin, et al. Design and key
technology of distributed real-time database for power
grid regulation cluster [J]. Power system automation,
2017,41 (22): 89-95.
Yang Jingwei, Zhang Ning, Wang Yi, et al. Multi energy
system for renewable energy consumption: review and
prospect [J]. Power system automation, 2018,42 (4):
11-24.
Yin Rui, Guo Jiangtao, Wang Xiaolei, Wang Tianjun, pan
Jianli. Design of association rule mining algorithm for
smart grid [J]. Microcomputer application, 2020,36
(05): 85-88.
Yu Chunping, Bai Jingfen, Zhou Jianbo, et al. Remote
calibration technology of digital electric energy
metering secondary equipment based on IEC 61850 [J].
Electrical measurement and instrumentation, 2019.
Zhan Side, Hu Wei, Wei Zhinong, et al. Stochastic optimal
power flow of electricity gas interconnected integrated
energy system based on opportunity constrained
programming [J]. Power automation equipment,
2018,38 (9): 121-128.
Zhang Lingtao, Zhao Lin, Zhang Liang, et al. Load
balancing framework for distributed computing cluster
with power big data [J]. Power grid technology,
2019,43 (1): 259-265.
Zhu Xingyu, Zhang Qianwu, Cao Bingyao. Design of
traffic test platform based on dpdk [J]. Microcomputer
application, 2020,36 (05): 99-102.
Research on Verification of 10kV High Voltage Digital Electric Energy Measurement based on RF Synchronization Technology
653
