CN113743756A - Synchronous measurement system and synchronous measurement algorithm of comprehensive energy system - Google Patents

Synchronous measurement system and synchronous measurement algorithm of comprehensive energy system Download PDF

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CN113743756A
CN113743756A CN202110980374.4A CN202110980374A CN113743756A CN 113743756 A CN113743756 A CN 113743756A CN 202110980374 A CN202110980374 A CN 202110980374A CN 113743756 A CN113743756 A CN 113743756A
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CN113743756B (en
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王峰
张世栋
李立生
孙勇
张林利
黄敏
苏国强
刘合金
刘洋
李帅
于海东
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

The invention belongs to the technical field of energy internet measurement, and discloses a synchronous measurement system of a comprehensive energy system, which comprises: the system comprises a basic data acquisition unit, a synchronous measurement unit and an energy comprehensive dispatching center; the basic data acquisition unit is used for acquiring sampling data of each energy subsystem; a synchronous measurement algorithm is arranged in the synchronous measurement unit, the synchronous measurement algorithm obtains a fitting function of the slow varying system according to actual sampling data and synchronous time of the slow varying system, and sampling data of the slow varying system at the synchronous time is calculated according to the fitting function; the output signal of the synchronous measurement unit is transmitted to the energy comprehensive dispatching center. The embodiment of the invention can synchronously measure the diversified data of each subsystem in the comprehensive energy system, and the data have the same sampling interval and precision, so that the basic sampling data of the power network, the cooling/heating network and the natural gas network are effectively fused, and the high sharing of the comprehensive energy system is realized.

Description

Synchronous measurement system and synchronous measurement algorithm of comprehensive energy system
Technical Field
The invention relates to the technical field of energy internet measurement, in particular to a synchronous measurement system of a comprehensive energy system and a synchronous measurement algorithm.
Background
The contradiction between the continuous increase of energy demand and the shortage of energy is increasingly serious, and the gradual formation of a comprehensive energy system for complementary optimization of various energy sources is promoted. The method has the advantages that diversified data of the comprehensive energy system are collected in a unified mode and extracted accurately, and the method is of great significance for effectively mastering the operation situation of the comprehensive energy system and achieving combined dispatching of all energy subsystems. Because the development of each energy subsystem forming the comprehensive energy system has difference, the energy supply of each energy subsystem is independently planned and operated, and each measuring system only has single energy data acquisition and analysis processing capacity. The sensing data of the fast-changing system represented by electricity has high precision and high sampling rate, and the synchronism and the real-time performance are good; the slow-changing system represented by cold, heat and gas has low requirements on data accuracy and synchronism, the measured data of each system cannot be effectively fused, and the interconnected and operated comprehensive energy system has the characteristics of large volume, multiple types, multiple time scales and the like and is difficult to realize the bidirectional flow of energy flow and information flow.
Therefore, how to realize the synchronous measurement of the diversified data in the integrated energy system is a problem to be solved urgently at present.
Disclosure of Invention
In order to solve the above problems, the present invention provides a synchronous measurement system for an integrated energy system, which can achieve synchronous measurement of diversified data in the integrated energy system.
According to a first aspect of an embodiment of the present invention, a synchronous measurement algorithm is provided.
In one embodiment, the synchronous measurement algorithm comprises the following steps:
acquiring a fitting function of the slow varying system according to actual sampling data and the synchronous time of the slow varying system;
and calculating the sampling data of the slow varying system at the synchronous moment according to the fitting function.
Optionally, the fitting function coefficients are calculated according to: so that the fitting function fits with the smallest value error.
Optionally, the step of obtaining a fitting function of the slow varying system according to the actual sampling data and the synchronization time of the slow varying system includes:
step (1), defining the synchronization time
Figure BDA0003228862920000021
Fitting function in neighborhood
Figure BDA0003228862920000022
Comprises the following steps:
Figure BDA0003228862920000023
wherein the content of the first and second substances,
Figure BDA0003228862920000024
for synchronizing the time
Figure BDA0003228862920000025
The sampling points in the neighborhood of the sample,
Figure BDA0003228862920000026
representing estimated values of the fitting function, bi(t) is the basis element of the fitting function, i 1,2i(t) is the coefficient of the base element,
Figure BDA0003228862920000027
step (2), determining a base element expression;
step (3), defining an error function of the fitting function;
step (4), solving error function pair
Figure BDA0003228862920000028
Obtaining a base element coefficient corresponding to the minimum value point of the fitting function error, and calculating a coefficient which enables the fitting function error to be minimum;
and (5) acquiring a fitting function according to the obtained coefficient, substituting the synchronous time, and calculating sampling data of the synchronous time.
Optionally, the base element b (t) is a monomial, polynomial, or trigonometric function.
Optionally, the error function defining the fitting function in step (3) is:
Figure BDA0003228862920000029
wherein, f (t)j) For time t in slowly varying systemsjJ is 1,2,3, …, N sample points are
Figure BDA00032288629200000210
Discrete points in time neighborhood，
Figure BDA00032288629200000211
Fitting the value to the fitting function with time tjIs calculated as a weighted function of the squared error of the sampled data.
Optionally, the error function is represented in matrix form:
Δ＝(Bk-f)TW(Bk-f) (7)
wherein the content of the first and second substances,
f＝(f1,f2,...,fN)T＝(f(t1),f(t2),...,f(tN))T (8)
Figure BDA0003228862920000031
Figure BDA0003228862920000032
Figure BDA0003228862920000033
optionally, the weighting function satisfies:
Figure BDA0003228862920000034
Δ t is the support domain radius of the weighting function.
Optionally, the weighting function uses a normal distribution function:
Figure BDA0003228862920000035
wherein the content of the first and second substances,
Figure BDA0003228862920000036
is the relative radius, σ ∈ [1,9 ]]Is a shape parameter.
Optionally, the weighting function uses a gaussian weight function:
Figure BDA0003228862920000037
wherein the content of the first and second substances,
Figure BDA0003228862920000041
is the relative radius, σ ∈ [1,9 ]]Is a shape parameter.
Optionally, the weighting function uses a spline function:
Figure BDA0003228862920000042
wherein the content of the first and second substances,
Figure BDA0003228862920000043
are relative radii.
Optionally, the coefficients of the base elements corresponding to the minimum value points of the fitting function error obtained in step (4) are:
Figure BDA0003228862920000044
namely:
Figure BDA0003228862920000045
wherein the content of the first and second substances,
Figure BDA0003228862920000046
the coefficients that minimize the error of the fit value of the fit function are:
Figure BDA0003228862920000047
optionally, the synchronous measurement algorithm calculates sampling data of the slow change system at the synchronous time according to a sampling interval of the fast change system or according to a uniform sampling interval set by the energy comprehensive scheduling center.
According to a second aspect of the embodiments of the present invention, a system for synchronously measuring an integrated energy system is provided.
In one embodiment, the integrated energy system synchronous measurement system includes: the system comprises a basic data acquisition unit, a synchronous measurement unit and an energy comprehensive dispatching center;
the basic data acquisition unit is used for acquiring sampling data of each energy subsystem;
the synchronous measurement unit is internally provided with the synchronous measurement algorithm of the embodiment, the synchronous measurement algorithm obtains a fitting function of the slow varying system according to actual sampling data and synchronous time of the slow varying system, and the sampling data of the slow varying system at the synchronous time is calculated according to the fitting function;
the output signal of the synchronous measurement unit is transmitted to the energy comprehensive dispatching center.
Optionally, the synchronous measurement unit calculates energy data of the slow varying system according to the sampled data of the slow varying system at the synchronous time, which is obtained through calculation.
Optionally, the synchronous measurement unit transmits the sampling data of the slow varying system at the synchronous time to the energy comprehensive scheduling center, and the energy comprehensive scheduling center calculates and obtains the energy data of the slow varying system according to the sampling data of the slow varying system at the synchronous time.
According to a third aspect of embodiments of the present invention, there is provided a computer apparatus.
In some embodiments, the computer device comprises a memory storing a computer program and a processor implementing the steps of the above-described synchronization measurement algorithm when executing the computer program.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the sampling data of the slow change system at the synchronous moment obtained by calculation, the energy data of the slow change system can be calculated, the diversified data of each subsystem in the comprehensive energy system can be measured synchronously, the data have the same sampling interval and precision, so that the basic sampling data of the power network, the cooling/heating network and the natural gas network are effectively fused, the high sharing of the comprehensive energy system is realized, and a reliable basis is provided for the efficient and accurate regulation and control of the comprehensive energy system.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram illustrating an integrated energy system synchronization measurement system in accordance with an exemplary embodiment;
FIG. 2 is a flow chart illustrating a synchronous metrology algorithm in accordance with an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating a weighting function in accordance with an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating a given sampling instant and a synchronization instant in accordance with an exemplary embodiment;
FIG. 5 is a schematic diagram illustrating a configuration of a computer device, according to an example embodiment.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the embodiments herein includes the full ambit of the claims, as well as all available equivalents of the claims. The terms "first," "second," and the like, herein are used solely to distinguish one element from another without requiring or implying any actual such relationship or order between such elements. In practice, a first element can also be referred to as a second element, and vice versa. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such structure, apparatus, or device. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a structure, device or apparatus that comprises the element. The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like herein, as used herein, are defined as orientations or positional relationships based on the orientation or positional relationship shown in the drawings, and are used for convenience in describing and simplifying the description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may include, for example, mechanical or electrical connections, communications between two elements, direct connections, and indirect connections via intermediary media, where the specific meaning of the terms is understood by those skilled in the art as appropriate.
Herein, the term "plurality" means two or more, unless otherwise specified.
Herein, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B represents: a or B.
Herein, the term "and/or" is an associative relationship describing objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.
Fig. 1 shows an embodiment of the integrated energy system synchronous measurement system of the present invention.
In this embodiment, the integrated energy system synchronous measurement system includes: the system comprises a basic data acquisition unit, a synchronous measurement unit and an energy comprehensive dispatching center.
The basic data acquisition unit is used for acquiring sampling data of each energy subsystem, and comprises sensors, such as a voltage transformer, a current transformer, a temperature transmitter, a pressure transmitter, a volume flow meter and the like, which are respectively arranged in the energy subsystems such as a power network, a cooling/heating network, a natural gas network and the like.
A Synchronous Measurement algorithm is built in a Synchronous Measurement Unit (SMU), and the Synchronous Measurement algorithm calculates sampling data of the slow varying system at a Synchronous moment according to the sampling data of the slow varying system acquired by the basic data acquisition Unit. According to the sampling data of the slow-changing system at the synchronous moment obtained by the calculation of the synchronous measuring unit, energy data of the slow-changing system can be calculated, such as the thermal power of a natural gas network and the thermal power of a cooling/heating network. The energy data of the slow-changing system can be obtained by calculation of the synchronous measuring unit, and the synchronous measuring unit transmits the energy data to the energy comprehensive dispatching center. The energy data of the slow-changing system can also be obtained by calculation of an energy comprehensive scheduling center, the synchronous measurement unit transmits the sampling data of the slow-changing system at the synchronous moment to the energy comprehensive scheduling center, and the energy comprehensive scheduling center obtains the energy data of the slow-changing system by calculation according to the sampling data of the slow-changing system at the synchronous moment.
Optionally, the sampling data acquired by the basic data acquisition unit, or the sampling data of the slow-varying system at the synchronous time acquired by the synchronous measurement unit, or the energy data of the slow-varying system acquired by the synchronous measurement unit may be transmitted to the energy comprehensive scheduling center as an output signal of the synchronous measurement unit. For example, sampled values of voltage, current, temperature, pressure, flow or calculated values of electrical power and thermal power can be selected as output signals of the synchronous measuring unit.
Optionally, the synchronous measurement unit of each energy subsystem communicates with the integrated energy dispatching center through a regional communication network. Optionally, the regional communication network is a double-layer ring network composed of optical fibers.
Optionally, the synchronous measurement algorithm performs synchronous measurement calculation on the actual sampling data of the slow varying system according to the sampling interval of the fast varying system, and calculates the sampling data of the slow varying system at the synchronous time.
Optionally, the synchronous measurement algorithm performs synchronous measurement calculation on the actual sampling data of the slow-varying system according to a uniform sampling interval set by the energy comprehensive scheduling center, and calculates the sampling data of the slow-varying system at the synchronous time.
The sensing data of the fast variable system has high precision and high sampling rate, and the synchronism and the real-time performance are good, the synchronous measurement unit can directly obtain the sampling data of the fast variable system at the synchronous moment through the basic data acquisition unit, or the synchronous measurement unit can also obtain the sampling data of the fast variable system at the synchronous moment through the calculation of a synchronous measurement algorithm.
The embodiment of the invention constructs the synchronous measurement system suitable for the comprehensive energy system, the system can synchronously measure diversified data of each subsystem in the comprehensive energy system, the data have the same sampling interval and precision, so that the basic sampling data of a power network, a cooling/heating network and a natural gas network are effectively fused, the high sharing of the comprehensive energy system is realized, and a reliable basis is provided for the efficient and accurate regulation and control of the comprehensive energy system.
In one embodiment, as shown in fig. 2, the above-mentioned synchronization measurement algorithm includes the following steps:
acquiring a fitting function of the slow varying system according to actual sampling data and synchronous time of the slow varying system, wherein the coefficient of the fitting function is set according to the minimum error of a fitting value of the fitting function;
and calculating the sampling data of the slow varying system at the synchronous moment according to the fitting function.
Optionally, the step of obtaining a fitting function of the slow varying system according to the actual sampling data and the synchronization time of the slow varying system includes:
step (1) of carrying out a treatment,defined at the synchronous moment
Figure BDA0003228862920000091
Fitting function in neighborhood
Figure BDA0003228862920000092
Comprises the following steps:
Figure BDA0003228862920000093
wherein the content of the first and second substances,
Figure BDA0003228862920000094
for synchronizing the time
Figure BDA0003228862920000095
A sample point in the neighborhood, the sample point being a given sample point,
Figure BDA0003228862920000096
representing estimated values of the fitting function, bi(t) is the basis element of the fitting function, i 1,2i(t) is the coefficient of the base element,
Figure BDA0003228862920000097
Figure BDA0003228862920000098
and (2) determining a base element expression.
Optionally, when the base element b (t) is a monomial, the base element expression is:
bT＝(1,t) m＝2 (2)
bT＝(1,t,t2) m＝3 (3)
bT＝(1,t,t2,t3) m＝4 (4)
by analogy, the base element expression when m is other values can be obtained.
And (3) defining an error function of the fitting function as:
Figure BDA0003228862920000099
wherein, f (t)j) For time t in slowly varying systemsjSampled data of, at time tjThe sampling data of (1) is known sampling data, j is 1,2,3, …, N, N sampling points are
Figure BDA0003228862920000101
A discrete point in the neighborhood of the time of day,
Figure BDA0003228862920000102
fitting the value to the fitting function with time tjIs centered at tjThe weighting function has a strong supporting effect on the sampling points of the synchronization time, and the schematic of the weighting function is shown in fig. 3.
Optionally, to ensure strong support of the weighting function, positive in the support domain, otherwise 0, the weighting function satisfies:
Figure BDA0003228862920000103
where Δ t is the support domain radius of the weighting function.
In the support domain of the weighting function, the fit value of the fitting function decreases as the distance between the fitting point and the known point increases. Because the weighting function is automatically segmented, the sampling data can be locally fitted without dividing a synchronous time interval.
Optionally, the error function is represented in matrix form:
Δ＝(Bk-f)TW(Bk-f) (7)
wherein the content of the first and second substances,
f＝(f1,f2,...,fN)T＝(f(t1),f(t2),...,f(tN))T (8)
Figure BDA0003228862920000104
Figure BDA0003228862920000105
Figure BDA0003228862920000106
alternatively, the weighting function may use a normal distribution function:
Figure BDA0003228862920000111
alternatively, the weighting function may use a gaussian weight function:
Figure BDA0003228862920000112
alternatively, the weighting function may use a spline function:
Figure BDA0003228862920000113
in the alternative embodiment described above, the first and second,
Figure BDA0003228862920000114
for relative radius, Δ t is the support domain radius, σ ∈ [1,9 ]]For the shape parameter, adjusting the size of the shape parameter may change the rate of change of the weighting function over the support field.
Step (4), solving error function pair
Figure BDA0003228862920000115
By making the partial derivative of (1) 0, the error of the fitting function can be obtainedCoefficient of base element corresponding to the small value point:
Figure BDA0003228862920000116
namely:
Figure BDA0003228862920000117
wherein the content of the first and second substances,
Figure BDA0003228862920000118
the coefficients that minimize the error of the fit value of the fit function are calculated as:
Figure BDA0003228862920000119
and (5) acquiring a fitting function according to the obtained coefficient, substituting the required synchronous time, and calculating sampling data of the synchronous time.
The synchronous measurement algorithm of the present invention is further described below by taking thermodynamic system temperature data as an example.
Given a sampling interval Δ t1The corresponding temperature is θ ═ θ1,θ2,...,θN)T＝(θ(t1),θ(t2),...,θ(tN))TThe corresponding given sampling instant is t ═ t (t)1,t2,...,tN)TThe required sampling interval is Deltat1And/2, the required synchronization time is t ═ t1′,t2′,...,tN′)TThe given sampling instant and synchronization instant are schematically shown in fig. 4.
Step (1), defining a fitting function of global approximation as theta (t), and then, at the synchronous moment
Figure BDA0003228862920000121
In the neighborhoodLocal approximation function of
Figure BDA0003228862920000122
Comprises the following steps:
Figure BDA0003228862920000123
wherein the content of the first and second substances,
Figure BDA0003228862920000124
representing the fitting function as an estimate of temperature, bi(t) (i ═ 1, 2.., m) is the basis element of the fitting function, m is the number of basis elements, k is the number of basis elementsi(t) is the coefficient of the base element.
And (2) determining a base element expression. Using a quadratic monomial as the basis element for the temperature fitting function, m being 3, i.e. bT＝(1,t,t2)。
And (3) defining an error function of the temperature fitting function as follows:
Figure BDA0003228862920000125
wherein, θ (t)j)∈θ，j＝1,2,3,…,N，tj∈t，
Figure BDA0003228862920000126
As a weighted function of the squared error of the fitted temperature value from the known temperature value, centered at tjThe weighting function has a strong supporting effect on the sampling point of the synchronous moment.
The error function is represented in matrix form:
Δ＝(Bk-θ)TW(Bk-θ) (25)
Figure BDA0003228862920000131
Figure BDA0003228862920000132
Figure BDA0003228862920000133
the weighting function is selected to be a gaussian weight function:
Figure BDA0003228862920000134
wherein the content of the first and second substances,
Figure BDA0003228862920000135
for the relative radius, Δ t is the support domain radius, σ is the shape parameter, and σ is taken to be 3.
Step (4), solving the error function pair of the temperature fitting function
Figure BDA0003228862920000136
And making the partial derivative of the error function be 0, so as to obtain the base element coefficient corresponding to the minimum value point of the error function:
Figure BDA0003228862920000137
namely:
Figure BDA0003228862920000138
wherein the content of the first and second substances,
Figure BDA0003228862920000141
Figure BDA0003228862920000142
the coefficients that minimize the error of the fit value of the fitting function are therefore:
Figure BDA0003228862920000143
step 5, according to
Figure BDA0003228862920000144
Computing
Figure BDA0003228862920000145
And use
Figure BDA0003228862920000146
And calculating the sampling data of the required synchronization moment.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing static information and dynamic information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement the steps of the above-described synchronization measurement algorithm.
Those skilled in the art will appreciate that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing devices to which aspects of the present invention may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above synchronization measurement algorithm when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which computer program, when executed by a processor, implements the steps in the above described synchronization measurement algorithm.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The present invention is not limited to the structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Claims (16)

1. A synchronous measurement algorithm, comprising the steps of:
acquiring a fitting function of the slow varying system according to actual sampling data and the synchronous time of the slow varying system;
and calculating the sampling data of the slow varying system at the synchronous moment according to the fitting function.
2. The method of claim 1, wherein the step of measuring the time difference between the two measurements,
the fitting function coefficients are calculated according to the following steps: so that the fitting function fits with the smallest value error.
3. The method of claim 1, wherein the step of measuring the time difference between the two measurements,
the step of obtaining the fitting function of the slow varying system according to the actual sampling data and the synchronous time of the slow varying system comprises the following steps:
step (1), defining the synchronization time
Figure FDA0003228862910000011
Fitting function in neighborhood
Figure FDA0003228862910000012
Comprises the following steps:
Figure FDA0003228862910000013
wherein the content of the first and second substances,
Figure FDA0003228862910000014
for synchronizing the time
Figure FDA0003228862910000015
The sampling points in the neighborhood of the sample,
Figure FDA0003228862910000016
representing estimated values of the fitting function, bi(t) is the basis element of the fitting function, i 1,2i(t) is the coefficient of the base element,
Figure FDA0003228862910000017
step (2), determining a base element expression;
step (3), defining an error function of the fitting function;
step (4), solving error function pair
Figure FDA0003228862910000018
Obtaining a base element coefficient corresponding to the minimum value point of the fitting function error, and calculating a coefficient which enables the fitting function error to be minimum;
and (5) acquiring a fitting function according to the obtained coefficient, substituting the synchronous time, and calculating sampling data of the synchronous time.
4. The method of claim 3, wherein the step of determining the phase of the signal,
the base element b (t) is a monomial.
5. The method of claim 3, wherein the step of determining the phase of the signal,
the error function defining the fitting function in the step (3) is as follows:
Figure FDA0003228862910000021
wherein, f (t)j) For time t in slowly varying systemsjJ is 1,2,3, …, N sample points are
Figure FDA0003228862910000022
A discrete point in the neighborhood of the time of day,
Figure FDA0003228862910000023
fitting the value to the fitting function with time tjIs calculated as a weighted function of the squared error of the sampled data.
6. The method of claim 5, wherein the step of determining the phase of the signal,
expressing the error function in a matrix form:
Δ＝(Bk-f)TW(Bk-f) (7)
wherein the content of the first and second substances,
f＝(f1,f2,...,fN)T＝(f(t1),f(t2),...,f(tN))T (8)
Figure FDA0003228862910000024
Figure FDA0003228862910000025
Figure FDA0003228862910000026
7. the method of claim 5, wherein the step of determining the phase of the signal,
the weighting function satisfies:
Figure FDA0003228862910000027
Δ t is the support domain radius of the weighting function.
8. The method of claim 7, wherein the step of measuring the time difference between the two measurements,
the weighting function uses a normal distribution function:
Figure FDA0003228862910000031
wherein the content of the first and second substances,
Figure FDA0003228862910000032
is the relative radius, σ ∈ [1,9 ]]Is a shape parameter.
9. The method of claim 7, wherein the step of measuring the time difference between the two measurements,
the weighting function uses a gaussian weight function:
Figure FDA0003228862910000033
wherein the content of the first and second substances,
Figure FDA0003228862910000034
is the relative radius, σ ∈ [1,9 ]]Is a shape parameter.
10. The method of claim 7, wherein the step of measuring the time difference between the two measurements,
the weighting function uses a spline function:
Figure FDA0003228862910000035
wherein the content of the first and second substances,
Figure FDA0003228862910000036
are relative radii.
11. The method of claim 6, wherein the step of determining the position of the target is performed by a computer,
the coefficients of the substrate elements corresponding to the minimum value points of the fitting function error obtained in the step (4) are as follows:
Figure FDA0003228862910000037
namely:
Figure FDA0003228862910000038
wherein the content of the first and second substances,
Figure FDA0003228862910000039
the coefficients that minimize the error of the fit value of the fit function are:
Figure FDA0003228862910000041
12. the method of claim 1, wherein the step of measuring the time difference between the two measurements,
and the synchronous measurement algorithm calculates the sampling data of the slow change system at the synchronous moment according to the sampling interval of the fast change system or the uniform sampling interval set by the energy comprehensive scheduling center.
13. A synchronous measurement system of an integrated energy system is characterized by comprising: the system comprises a basic data acquisition unit, a synchronous measurement unit and an energy comprehensive dispatching center;
the basic data acquisition unit is used for acquiring sampling data of each energy subsystem;
the synchronous measurement unit is provided with a synchronous measurement algorithm according to any one of claims 1 to 12, the synchronous measurement algorithm obtains a fitting function of the slow varying system according to actual sampling data and synchronous time of the slow varying system, and calculates sampling data of the slow varying system at the synchronous time according to the fitting function;
the output signal of the synchronous measurement unit is transmitted to the energy comprehensive dispatching center.
14. The integrated energy system synchronous measurement system of claim 13,
and the synchronous measurement unit calculates the energy data of the slow change system according to the sampling data of the slow change system at the synchronous moment obtained by calculation.
15. The integrated energy system synchronous measurement system of claim 13,
and the synchronous measurement unit transmits the sampling data of the slow varying system at the synchronous moment to the energy comprehensive dispatching center, and the energy comprehensive dispatching center calculates and obtains the energy data of the slow varying system according to the sampling data of the slow varying system at the synchronous moment.
16. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the synchronization measurement algorithm of any one of claims 1 to 12.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114440866A (en) * 2021-12-17 2022-05-06 武汉元生创新科技有限公司 Sensor data calibration method and calibration system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060253267A1 (en) * 2005-05-04 2006-11-09 Giovanni Bonvicini Method of information analysis
CN109190789A (en) * 2018-07-19 2019-01-11 清华大学 Long-term wind power power forecasting method, device, computer equipment and storage medium
US20200309829A1 (en) * 2019-04-01 2020-10-01 Dake He On-line monitoring system for the performance of the measurement equipment in the entire power grid based on wide-area synchronous measurement
CN111811398A (en) * 2020-06-28 2020-10-23 上海大学 Multi-surface measurement method based on phase shift characteristic polynomial high-precision fitting
CN112073460A (en) * 2020-08-03 2020-12-11 国网山东省电力公司电力科学研究院 Data acquisition unit, data transmission method and comprehensive energy monitoring system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060253267A1 (en) * 2005-05-04 2006-11-09 Giovanni Bonvicini Method of information analysis
CN109190789A (en) * 2018-07-19 2019-01-11 清华大学 Long-term wind power power forecasting method, device, computer equipment and storage medium
US20200309829A1 (en) * 2019-04-01 2020-10-01 Dake He On-line monitoring system for the performance of the measurement equipment in the entire power grid based on wide-area synchronous measurement
CN111811398A (en) * 2020-06-28 2020-10-23 上海大学 Multi-surface measurement method based on phase shift characteristic polynomial high-precision fitting
CN112073460A (en) * 2020-08-03 2020-12-11 国网山东省电力公司电力科学研究院 Data acquisition unit, data transmission method and comprehensive energy monitoring system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
刘学欣;陆伟成;: "门电路延时查找表的凸平滑算法", 计算机工程与应用, no. 25, 1 September 2008 (2008-09-01) *
张介秋, 陈砚圃, 梁昌洪, 韩峰岩: "交流电电气参量高精度测量的加权算法", 仪器仪表学报, no. 02, 15 April 2005 (2005-04-15) *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114440866A (en) * 2021-12-17 2022-05-06 武汉元生创新科技有限公司 Sensor data calibration method and calibration system
CN114440866B (en) * 2021-12-17 2024-02-20 武汉元生创新科技有限公司 Sensor data calibration method and calibration system

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