CN111551820B - System identification method for rapid frequency adjustment of power distribution network based on synchronous phasor measurement - Google Patents

System identification method for rapid frequency adjustment of power distribution network based on synchronous phasor measurement Download PDF

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CN111551820B
CN111551820B CN202010317212.8A CN202010317212A CN111551820B CN 111551820 B CN111551820 B CN 111551820B CN 202010317212 A CN202010317212 A CN 202010317212A CN 111551820 B CN111551820 B CN 111551820B
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power
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frequency
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CN111551820A (en
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王成山
刘喆林
李鹏
于浩
孔祥玉
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Tianjin University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H02J3/241—The oscillation concerning frequency
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units

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Abstract

A system identification method for rapid frequency adjustment of a power distribution network based on synchronous phasor measurement comprises the following steps: acquiring various information of an active power distribution network with frequency modulation resources participating in system frequency adjustment; based on the network measurement information at the frequency adjustment triggering moment, adopting power distribution network state estimation calculation to obtain the network running state at the frequency adjustment triggering moment; calculating a sensitivity matrix between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement; the method comprises the steps of obtaining synchronous phasor measurement data at a frequency adjustment check moment, calculating synchronous phasor measurement power measurement change between the frequency adjustment check moment and a frequency adjustment trigger moment, establishing a least square identification model based on branch power measurement change and frequency modulation resource node power change of the synchronous phasor measurement according to a sensitivity matrix, solving power change of each frequency modulation resource node to be identified, and outputting a result. The invention realizes the power change identification of the frequency modulation resource node without measurement.

Description

System identification method for rapid frequency adjustment of power distribution network based on synchronous phasor measurement
Technical Field
The invention relates to a method for identifying fast frequency modulation power. In particular to a system identification method for rapid frequency adjustment of a power distribution network based on synchronous phasor measurement.
Background
In recent years, a large number of new technologies and new equipment are introduced to enable fine operation control and scheduling of a power distribution network. The measurement technology is used as a support link for the operation of the power grid, and the accuracy and the rapidity of the measurement technology directly determine the granularity of the observability and the controllability of the power grid on a time scale. The novel measuring device for the power distribution network represented by synchronous phasor measurement has the characteristics of high measuring precision (measuring error is less than 0.5%), high measuring frequency (millisecond-level measurement), synchronous measuring sampling and the like, and enables identification and verification of a large number of power distribution network operation scenes with requirements on precision and time scale to be possible.
The rapid frequency adjustment of the power distribution network is a typical scene, and the energy storage, the electric automobile and even the distributed power supply in the power distribution network have the characteristics of high response speed, strong adjustment capability and the like, and can be used as a frequency adjustment resource to rapidly respond to the frequency modulation requirement of the network. In the process of participating in the rapid frequency adjustment, each frequency modulation resource performs power response within a time limit range specified by a frequency modulation instruction, and whether the actual response of each frequency modulation resource meets the frequency modulation requirement or not depends on the identification and verification of a measurement system and pays for a frequency adjustment service according to the identification and verification. Under the scene, if the network adopts synchronous phasor measurement with rapidity and accuracy, the rapid and accurate detection of the power change of the synchronous phasor measurement point can be realized. However, since the synchrophasor measurement apparatus is limited by the cost and installation configuration, the measurement point may not be directly installed on the fm resource node, and the power variation of the measurement point cannot directly reflect the power variation of the fm resource node. Therefore, how to fully utilize the existing synchronous phasor measurement variation in the network to identify the power variation of each fm resource, especially when there is no direct measurement at the fm resource node, becomes the key of fast power adjustment identification. On the other hand, in the process of identifying the frequency modulation power change by monitoring the synchronous phasor measurement change, the optimal configuration of the synchronous phasor measurement in the power distribution network can be further guided. Therefore, the system identification method for the rapid frequency adjustment of the power distribution network based on the synchronous phasor measurement is provided, the rapid high-precision measurement data of the synchronous phasor measurement are fully utilized, the indirect power change identification of the frequency modulation resource nodes at the system level is realized, and the method has important practical significance for the operation monitoring and the optimal configuration of the power distribution network.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a system identification method for power distribution network rapid frequency adjustment based on synchronous phasor measurement, which can realize power change identification of frequency modulation resource nodes without measurement.
The technical scheme adopted by the invention is as follows: a system identification method for rapid frequency adjustment of a power distribution network based on synchronous phasor measurement comprises the following steps:
1) aiming at an active power distribution network with frequency modulation resources participating in system frequency adjustment, a network node set N, a branch set L, impedance parameters of all branches, topological connection relations, position information of loads, distributed power supplies and energy storage and the network are obtainedConfiguration information of network measurement point, frequency modulation resource node set
Figure BDA0002459888400000011
Figure BDA0002459888400000012
Obtaining a fast frequency adjustment time limit T and a frequency adjustment trigger time TsFrequency adjustment check time teWherein the frequency is adjusted at the check time te＝ts+T；
2) Adjusting the trigger time t based on the frequencysThe network measurement information of the network is calculated by adopting the state estimation of the power distribution network to obtain the frequency adjustment triggering time tsIncluding the voltage amplitude U (t) of each node of the networks) And phase angle theta (t)s) Active power P at the end of each branchlt(ts) And reactive power Qlt(ts)；
3) Adjusting the trigger time t according to the frequency obtained in step 2)sCalculating a sensitivity matrix between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement;
4) obtaining a frequency adjustment check time teCalculating the frequency adjustment check time teAnd the frequency regulation trigger time tsAccording to the sensitivity matrix obtained in the step 3), a least square identification model based on the branch power measurement change and the frequency modulation resource node power change of the synchronous phasor measurement is established, the power change of each frequency modulation resource node to be identified is solved, and a result is output.
The system identification method for the rapid frequency adjustment of the power distribution network based on the synchronous phasor measurement constructs a sensitivity matrix between the power of a synchronous phasor measurement branch and the power of a frequency modulation resource node, and establishes a least square identification model of the measurement change of the synchronous phasor measurement power and the power change of the frequency modulation resource node. The method provided fully utilizes the network running state before frequency adjustment and the synchronous phasor measurement change in the network before and after frequency adjustment, realizes the power change identification of the frequency modulation resource node without measurement, and provides effective support for credible frequency adjustment verification, running monitoring and the like of the power distribution network.
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FIG. 1 is a flow chart of a system identification method for fast frequency regulation of a power distribution network based on synchrophasor measurement according to the present invention;
fig. 2 is a diagram of network topology connections, fm resource distribution, and measurement configuration of an example IEEE33 node.
Detailed Description
The following describes the system identification method for fast frequency adjustment of a power distribution network based on synchrophasor measurement in detail with reference to the embodiments and the accompanying drawings.
As shown in fig. 1, the system identification method for fast frequency adjustment of a power distribution network based on synchronous phasor measurement according to the present invention includes the following steps:
1) aiming at an active power distribution network with frequency modulation resources participating in system frequency adjustment, a network node set N, a branch set L, impedance parameters of branches, topological connection relations, position information of loads, distributed power supplies and energy storage, configuration information of network measurement points and a frequency modulation resource node set of the power distribution network are obtained
Figure BDA0002459888400000021
Figure BDA0002459888400000022
The frequency modulation resource node set comprises flexible load nodes, energy storage nodes and distributed power supply nodes which participate in frequency adjustment; obtaining a fast frequency adjustment time limit T and a frequency adjustment trigger time TsFrequency adjustment check time teWherein the frequency is adjusted at the check time te＝ts+ T; the quick frequency regulation time limit T is a time scale of a second level.
2) Adjusting the trigger time t based on the frequencysThe network measurement information of the network is calculated by adopting the state estimation of the power distribution network to obtain the frequency adjustment triggering time tsIncluding the voltage amplitude U (t) of each node of the networks) And a phase angle θ: (ts) Active power P at the end of each branchlt(ts) And reactive power Qlt(ts) (ii) a Wherein the content of the first and second substances,
the network measurement information comprises a synchronous phasor measurement set zPMUData acquisition and supervisory control system measurement set ZSCADAAnd advanced metrology system measurement set ZAMI。
The calculation model of the power distribution network state estimation is represented as follows:
min[z(ts)-h(x)]TR-1[z(ts)-h(x)]
s.t.c(x)＝0
wherein, z (t)s)＝{(zPMU(ts))T (zSCADA(ts))T (zAMI(ts))T}T，zPMU(ts)、zSCADA(ts)、zAMI(ts) Respectively, the frequency regulation trigger time tsThe measurement of the synchronous phasor of the power distribution network, the measurement of a data acquisition and monitoring system and the measurement of an advanced metering system are carried out, wherein x is an operation state variable to be solved, h (x) is a quantity measurement z (t)s) And a measurement function between the measured operation state variable x to be solved, wherein R is a covariance matrix of measurement errors, and c (x) is a network zero injection constraint function.
3) Adjusting the trigger time t according to the frequency obtained in step 2)sCalculating a sensitivity matrix between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement; wherein the content of the first and second substances,
the branch power based on the synchronous phasor measurement is dynamic measurement data of millisecond level.
The calculating of the sensitivity matrix between the branch power based on the synchronous phasor measurement and the frequency modulation resource node power comprises the following steps:
(3.1) defining preamble legs L for all nodes of the networkinPreamble node BinSet of subsequent branches LoutSet of successor nodes BoutSet of branches of unique paths from each node to the source node
Figure BDA0002459888400000031
Node set of unique paths from each node to source node
Figure BDA0002459888400000032
For any node k belonging to N, acquiring a preamble branch L of the node k according to the topological connection relation of the power distribution networkkPreamble node BkSet of subsequent branches
Figure BDA0002459888400000033
Set of successor nodes
Figure BDA0002459888400000034
Set of legs of unique path from node k to source node
Figure BDA0002459888400000035
Node k to source node unique path node set
Figure BDA0002459888400000036
Wherein the node
Figure BDA0002459888400000037
Source node
Figure BDA0002459888400000038
(3.2) adjusting the trigger time t based on the frequencysCalculating the active power loss P of the network branchlossActive power P of branch endltSensitivity of (2): for any branch L belonging to L, any phase sequence p, q belonging to { A, B, C }, the frequency regulation trigger time t belongs to L, andsp-phase active power loss of branch l
Figure BDA0002459888400000039
Active power of branch end q phase with branch l
Figure BDA00024598884000000310
Sensitivity of (2)
Figure BDA00024598884000000311
Comprises the following steps:
Figure BDA00024598884000000312
in the formula (I), the compound is shown in the specification,
Figure BDA00024598884000000313
rpq、xpqthe resistance and the reactance of the p phase and the q phase of the branch l are respectively represented, when p is equal to q, the p phase self-resistance and the self-reactance of the branch l are represented, when p is equal to q, the mutual resistance and the mutual reactance of the p phase and the q phase of the branch l are represented, i is a terminal node of the branch l, U is a terminal node of the branch l, andi,pfor adjusting the frequency by triggering time tsP-phase voltage amplitude, U, of terminal node i of branch li,qFor adjusting the frequency by triggering time tsQ-phase voltage amplitude, theta, of terminal node i of branch li,pqFor adjusting the frequency by triggering time tsThe phase angle difference of the p and q phases of the end node i of branch l,
Figure BDA00024598884000000314
for adjusting the frequency by triggering time tsThe branch end p-phase reactive power of the branch l,
Figure BDA00024598884000000315
for adjusting the frequency by triggering time tsThe branch end t phase active power of the branch l,
Figure BDA00024598884000000316
for adjusting the frequency by triggering time tsT-phase reactive power at the tail end of the branch I;
(3.3) for any FM resource node
Figure BDA00024598884000000317
Successively and respectively calculating branch set of unique paths from frequency modulation resource node j to source node
Figure BDA00024598884000000318
The sensitivity of the active power of each branch and the active power of the frequency modulation resource node j comprises the following steps:
(3.3.1) calculating preamble branch L of frequency modulation resource node jjThe sensitivity of the active power of the frequency modulation resource node j is as follows:
(3.3.1.1) for any phase sequence p, q ∈ { A, B, C }, preamble branch of FM resource node j
Figure BDA00024598884000000319
P-phase terminal active power of
Figure BDA00024598884000000320
Q-phase active power of node j of frequency modulation resource
Figure BDA00024598884000000321
Sensitivity of (2)
Figure BDA00024598884000000322
Is composed of
Figure BDA00024598884000000323
(3.3.1.2) for any phase sequence p, q ∈ { A, B, C }, preamble branch of FM resource node j
Figure BDA0002459888400000041
P-phase head end active power
Figure BDA0002459888400000042
Q-phase active power of node j of frequency modulation resource
Figure BDA0002459888400000043
Sensitivity of (2)
Figure BDA0002459888400000044
Is composed of
Figure BDA0002459888400000045
(3.3.2) separately compute nodes
Figure BDA0002459888400000046
Preamble branch L ofkThe sensitivity of the active power of the frequency modulation resource node j is as follows:
(3.3.2.1) for any phase sequence p, q e { A, B, C }, the preamble branch of node k
Figure BDA0002459888400000047
P-phase terminal active power of
Figure BDA0002459888400000048
Q-phase active power of node j of frequency modulation resource
Figure BDA0002459888400000049
Sensitivity of (2)
Figure BDA00024598884000000410
Satisfy the requirement of
Figure BDA00024598884000000411
Wherein l is a calculation subsequent branch of the node k,
Figure BDA00024598884000000412
(3.3.2.2) for any phase sequence p, q e { A, B, C }, the preamble branch of node k
Figure BDA00024598884000000413
P-phase head end active power
Figure BDA00024598884000000414
Q-phase active power of node j of frequency modulation resource
Figure BDA00024598884000000415
Sensitivity of (2)
Figure BDA00024598884000000416
Satisfy the requirement of
Figure BDA00024598884000000417
Wherein l is a calculation subsequent branch of the node k,
Figure BDA00024598884000000418
m∈{A,B,C}，m≠p，m≠q；
(3.4) generating a sensitivity matrix S between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement, comprising:
(3.4.1) measurement of z for arbitrary synchrophasorsl∈ZPMUFrequency modulated resource node
Figure BDA00024598884000000419
Setting phase sequence p, q belonging to { A, B, C }, setting synchronous phasor measurement zlThe sensitivity matrix element S (z) between the active power of the p-phase branch and the active power of the q-phase of the frequency modulation resource node jl,p,j,q)＝0；
(3.4.2) for any FM resource node
Figure BDA00024598884000000420
Branch set traversing unique path from frequency modulation resource node j to source node
Figure BDA00024598884000000421
For arbitrary branch
Figure BDA00024598884000000422
Respectively judging whether the head end and the tail end of the branch I have synchronous phasor measurement:
(3.4.2.1) if there is a head end synchrophasor measurement for branch l
Figure BDA00024598884000000423
Setting head end synchronous phasor measurement for any phase sequence p, q ∈ { A, B, C }, and obtaining a new set of initial end synchronous phasor measurement
Figure BDA00024598884000000424
Sensitivity matrix element between p-phase branch active power and q-phase active power of frequency modulation resource node j
Figure BDA00024598884000000425
Comprises the following steps:
Figure BDA0002459888400000051
(3.4.2.2) if there is an end synchrophasor measurement for branch l
Figure BDA0002459888400000052
Setting terminal synchronous phasor measurement for any phase sequence p, q ∈ { A, B, C }, setting
Figure BDA0002459888400000053
Sensitivity matrix element between p-phase branch active power and q-phase active power of frequency modulation resource node j
Figure BDA0002459888400000054
Is composed of
Figure BDA0002459888400000055
4) Obtaining a frequency adjustment check time teCalculating the frequency adjustment check time teAnd the frequency regulation trigger time tsAccording to the sensitivity matrix obtained in the step 3), a least square identification model based on the branch power measurement change and the frequency modulation resource node power change of the synchronous phasor measurement is established, the power change of each frequency modulation resource node to be identified is solved, and a result is output.
The establishing of the least square identification model of branch power measurement change and frequency modulation resource node power change based on synchronous phasor measurement and solving of the power change of each frequency modulation resource node to be identified comprises the following steps:
(4.1) the least square identification model of branch power measurement change and frequency modulation resource node power change of synchronous phasor measurement is as follows:
min([ΔPPMU-SΔPF]T[ΔPPMU-SΔPF])
wherein, Δ PPMUIndicating the frequency adjustment check time teAnd the frequency regulation trigger time tsThe power measurement change of the synchronous phasor measurement is performed, S represents a sensitivity matrix between the branch power of the synchronous phasor measurement and the node power of the frequency modulation resource, and delta PFRepresenting the power change of each frequency modulation resource node to be identified;
(4.2) solving the power change delta P of each frequency modulation resource node to be identifiedFThe mathematical expression of (a) is:
ΔPF＝(STS)-1STΔPPMU
wherein S isTIs the transpose of the sensitivity matrix between the branch power of the synchronous phasor measurement and the frequency modulation resource node power.
Specific examples are given below:
the method provided by the invention is verified by adopting the improved IEEE33 node example, and the network topology connection, the frequency modulation resource distribution and the measurement configuration of the IEEE33 node example are shown in figure 2. The network has a plurality of resources such as distributed power supplies, controllable loads, energy storage and the like, wherein the distributed power supplies are positioned at nodes 14 and 18, the controllable loads are positioned at nodes 8, 22 and 32, and the energy storage is positioned at node 24; the controllable load nodes and the energy storage nodes are used as frequency modulation resources to directly participate in frequency adjustment control, the distributed power supply nodes do not participate in frequency modulation control, but the power of the distributed power supply nodes fluctuates under the influence of weather factors such as illumination, and therefore the distributed power supply nodes are brought into the frequency modulation resource nodes to participate in power identification of the system. The network measurement comprises synchronous phasor measurement and node injection power measurement, the synchronous phasor measurement is distributed on partial nodes and branches of the network, the specific distribution condition is shown in table 1, and the node injection power measurement is distributed on all the other nodes except the source node 1; the measurement errors in the network all meet normal distribution, the standard error difference of the synchronous phasor measurement is 0.05%, and the standard error difference of the node injection power measurement is 5%.
TABLE 1 distribution of network synchrophasor measurements
Figure BDA0002459888400000056
Figure BDA0002459888400000061
At a certain time ts＝[07:01:10](namely the frequency adjustment triggering moment), the system frequency exceeds the upper limit, the system operator sends a frequency modulation instruction to each frequency modulation resource node, each frequency modulation resource immediately receives the frequency modulation instruction, and the instruction requires the frequency modulation resource node to respond to the power of the frequency modulation resource node within the fast frequency adjustment time limit T of 5 seconds so as to meet the frequency stability requirement of the system.
Carrying out global state estimation on the network by using the measurement information of the frequency modulation triggering time [07:01:10] to obtain network information of the frequency modulation triggering time, wherein the network information comprises the voltage amplitude of each node and the active power of a branch circuit; and then generating a sensitivity matrix between the synchronous phasor measurement branch power and the frequency modulation resource node power. Since the IEEE33 node is a three-phase symmetric network, the mutual impedance of the branch is 0, and the sensitivity matrix between the A, B, C three-phase synchronous phasor measurement branch power and the frequency modulation resource node power is decoupled, the sensitivity matrix of each phase can be calculated separately, where the a-phase sensitivity matrix is shown in table 2.
Table 2A sensitivity matrix between the same-phase phasor measurement branch power and frequency modulated resource node power
Figure BDA0002459888400000062
Calculating the synchronous phasor measurement power changes of the frequency regulation check time [07:01:15] and the frequency regulation trigger time [07:01:10], and identifying and obtaining the active power variation of each frequency modulation resource node and the distributed power source node by using a least square identification model of the synchronous phasor measurement power changes and the frequency modulation resource node power changes, as shown in table 3.
Table 3 active power variation amount of each fm resource node/distributed power node identified
Figure BDA0002459888400000063
The deviation between the identified power variation of each fm resource and the actual power variation of each fm resource is shown in table 4. The comparison result shows that the average deviation between the power variation of each identified frequency modulation resource and the actual power variation is 1.026%, which shows that the system identification method for the rapid frequency adjustment of the power distribution network based on the synchronous phasor measurement has good precision.
Table 4 shows the deviation between the power variation of each fm resource and the actual power variation of each fm resource
Figure BDA0002459888400000071
According to the analysis, the system identification method for the rapid frequency adjustment of the power distribution network based on the synchronous phasor measurement, which is provided by the invention, realizes the power change identification of each frequency modulation resource node of the network by utilizing the sensitivity between the synchronous phasor measurement branch power and the frequency modulation resource node power and the network synchronous phasor measurement data before and after frequency modulation, and has important practical significance for the fine operation monitoring and control of the power distribution network.

Claims (8)

1. A system identification method for rapid frequency adjustment of a power distribution network based on synchronous phasor measurement is characterized by comprising the following steps:
1) active for system frequency regulation with frequency modulation resourceThe method comprises the steps of acquiring a network node set N, a branch set L, impedance parameters of all branches, topological connection relations, position information of loads, distributed power supplies and energy storage, configuration information of network measurement points and a frequency modulation resource node set of the power distribution network
Figure FDA0003418789200000012
Figure FDA0003418789200000011
Obtaining a fast frequency adjustment time limit T and a frequency adjustment trigger time TsFrequency adjustment check time teWherein the frequency is adjusted at the check time te＝ts+T；
2) Adjusting the trigger time t based on the frequencysThe network measurement information of the network is calculated by adopting the state estimation of the power distribution network to obtain the frequency adjustment triggering time tsIncluding the voltage amplitude U (t) of each node of the networks) And phase angle theta (t)s) Active power P at the end of each branchlt(ts) And reactive power Qlt(ts)；
3) Adjusting the trigger time t according to the frequency obtained in step 2)sCalculating a sensitivity matrix between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement;
4) obtaining a frequency adjustment check time teCalculating the frequency adjustment check time teAnd the frequency regulation trigger time tsAccording to the sensitivity matrix obtained in the step 3), a least square identification model based on the branch power measurement change and the frequency modulation resource node power change of the synchronous phasor measurement is established, the power change of each frequency modulation resource node to be identified is solved, and a result is output.
2. The method according to claim 1, wherein the set of frequency modulation resource nodes in step 1) includes flexible load nodes, energy storage nodes, and distributed power nodes involved in frequency modulation.
3. The method for system identification of synchronized phasor measurement based fast frequency regulation for power distribution networks according to claim 1, wherein said fast frequency regulation time limit T in step 1) is a time scale in the order of seconds.
4. The method as claimed in claim 1, wherein the network measurement information in step 2) includes a synchrophasor measurement set ZPMUData acquisition and supervisory control system measurement set ZSCADAAnd advanced metrology system measurement set ZAMI。
5. The method for system identification of distribution network fast frequency regulation based on synchrophasor measurement according to claim 1, wherein the calculation model of distribution network state estimation in step 2) is represented as:
min[z(ts)-h(x)]TR-1[z(ts)-h(x)]
s.t.c(x)＝0
wherein, z (t)s)＝{(zPMU(ts))T (zSCADA(ts))T (zAMI(ts))T)T，zPMU(ts)、zSCADA(ts)、zAMI(ts) Respectively, the frequency regulation trigger time tsThe measurement of the synchronous phasor of the power distribution network, the measurement of a data acquisition and monitoring system and the measurement of an advanced metering system are carried out, wherein x is an operation state variable to be solved, h (x) is a quantity measurement z (t)s) And a measurement function between the measured operation state variable x to be solved, wherein R is a covariance matrix of measurement errors, and c (x) is a network zero injection constraint function.
6. The method for system identification of power distribution network fast frequency regulation based on synchrophasor measurement of claim 1, wherein the branch power based on synchrophasor measurement in step 3) is dynamic measurement data of millisecond level.
7. The method for system identification of distribution network fast frequency regulation based on synchrophasor measurement according to claim 1, wherein the calculating of the sensitivity matrix between branch power and frequency modulation resource node power based on synchrophasor measurement in step 3) comprises:
(3.1) defining preamble legs L for all nodes of the networkinPreamble node BinSet of subsequent branches LoutSet of successor nodes BoutSet of branches of unique paths from each node to the source node
Figure FDA0003418789200000021
Node set of unique paths from each node to source node
Figure FDA0003418789200000022
For any node k belonging to N, acquiring a preamble branch L of the node k according to the topological connection relation of the power distribution networkkPreamble node BkSet of subsequent branches
Figure FDA0003418789200000023
Set of successor nodes
Figure FDA0003418789200000024
Set of legs of unique path from node k to source node
Figure FDA0003418789200000025
Node k to source node unique path node set
Figure FDA0003418789200000026
Wherein the node
Figure FDA0003418789200000027
Figure FDA0003418789200000028
(3.2) adjusting the trigger time t based on the frequencysCalculating the active power loss P of the network branchlossActive power P of branch endltSensitivity of (2): for any branch L belonging to L, any phase sequence p, q belonging to { A, B, C }, the frequency regulation trigger time t belongs to L, andsp-phase active power loss of branch l
Figure FDA0003418789200000029
Active power of branch end q phase with branch l
Figure FDA00034187892000000210
Sensitivity of (2)
Figure FDA00034187892000000211
Comprises the following steps:
Figure FDA00034187892000000212
in the formula (I), the compound is shown in the specification,
Figure FDA00034187892000000213
rpq、xpqthe resistance and the reactance of the p phase and the q phase of the branch l are respectively represented, when p is equal to q, the p phase self-resistance and the self-reactance of the branch l are represented, when p is equal to q, the mutual resistance and the mutual reactance of the p phase and the q phase of the branch l are represented, i is a terminal node of the branch l, U is a terminal node of the branch l, andi，pfor adjusting the frequency by triggering time tsP-phase voltage amplitude, U, of terminal node i of branch li，qFor adjusting the frequency by triggering time tsQ-phase voltage amplitude, theta, of terminal node i of branch li，pqFor adjusting the frequency by triggering time tsThe phase angle difference of the p and q phases of the end node i of branch l,
Figure FDA00034187892000000214
for adjusting the frequency by triggering time tsThe branch end p-phase reactive power of the branch l,
Figure FDA00034187892000000215
for adjusting the frequency by triggering time tsThe branch end t phase active power of the branch l,
Figure FDA00034187892000000216
for adjusting the frequency by triggering time tsT-phase reactive power at the tail end of the branch I;
(3.3) for any FM resource node
Figure FDA00034187892000000217
Successively and respectively calculating branch set of unique paths from frequency modulation resource node j to source node
Figure FDA00034187892000000218
The sensitivity of the active power of each branch and the active power of the frequency modulation resource node j comprises the following steps:
(3.3.1) calculating preamble branch L of frequency modulation resource node jjThe sensitivity of the active power of the frequency modulation resource node j is as follows:
(3.3.1.1) for any phase sequence p, q ∈ { A, B, C }, preamble branch of FM resource node j
Figure FDA00034187892000000219
P-phase terminal active power of
Figure FDA00034187892000000220
Q-phase active power of node j of frequency modulation resource
Figure FDA00034187892000000221
Sensitivity of (2)
Figure FDA00034187892000000222
Is composed of
Figure FDA00034187892000000223
(3.3.1.2) for any phase sequence p, q ∈ { A, B, C }, preamble branch of FM resource node j
Figure FDA00034187892000000224
P-phase head end active power
Figure FDA00034187892000000225
Q-phase active power of node j of frequency modulation resource
Figure FDA00034187892000000226
Sensitivity of (2)
Figure FDA00034187892000000227
Is composed of
Figure FDA0003418789200000031
(3.3.2) separately compute nodes
Figure FDA0003418789200000032
Preamble branch L ofkThe sensitivity of the active power of the frequency modulation resource node j is as follows:
(3.3.2.1) for any phase sequence p, q e { A, B, C }, the preamble branch of node k
Figure FDA0003418789200000033
P-phase terminal active power of
Figure FDA0003418789200000034
Q-phase active power of node j of frequency modulation resource
Figure FDA0003418789200000035
Sensitivity of (2)
Figure FDA0003418789200000036
Satisfy the requirement of
Figure FDA0003418789200000037
Wherein l is a calculation subsequent branch of the node k,
Figure FDA0003418789200000038
(3.3.2.2) for any phase sequence p, q e { A, B, C }, the preamble branch of node k
Figure FDA0003418789200000039
P-phase head end active power
Figure FDA00034187892000000310
Q-phase active power of node j of frequency modulation resource
Figure FDA00034187892000000311
Sensitivity of (2)
Figure FDA00034187892000000312
Satisfy the requirement of
Figure FDA00034187892000000313
Wherein l is a calculation subsequent branch of the node k,
Figure FDA00034187892000000314
m∈{A，B，C}，m≠p，m≠q；
(3.4) generating a sensitivity matrix S between the branch power and the frequency modulation resource node power based on the synchronous phasor measurement, comprising:
(3.4.1) measurement of z for arbitrary synchrophasorsl∈ZPMUFrequency modulated resource node
Figure FDA00034187892000000315
Setting phase sequence p, q belonging to { A, B, C }, setting synchronous phasor measurement zlThe sensitivity matrix element S (z) between the active power of the p-phase branch and the active power of the q-phase of the frequency modulation resource node jl，p，j，q)＝0；
(3.4.2) for any FM resource node
Figure FDA00034187892000000316
Branch set traversing unique path from frequency modulation resource node j to source node
Figure FDA00034187892000000317
For arbitrary branch
Figure FDA00034187892000000318
Respectively judging whether the head end and the tail end of the branch I have synchronous phasor measurement:
(3.4.2.1) if there is a head end synchrophasor measurement for branch l
Figure FDA00034187892000000319
Setting head end synchronous phasor measurement for any phase sequence p, q ∈ { A, B, C }, and obtaining a new set of initial end synchronous phasor measurement
Figure FDA00034187892000000320
Sensitivity matrix element between p-phase branch active power and q-phase active power of frequency modulation resource node j
Figure FDA00034187892000000321
Comprises the following steps:
Figure FDA00034187892000000322
(3.4.2.2) if there is an end synchrophasor measurement for branch l
Figure FDA0003418789200000041
Setting terminal synchronous phasor measurement for any phase sequence p, q ∈ { A, B, C }, setting
Figure FDA0003418789200000042
Sensitivity matrix element between p-phase branch active power and q-phase active power of frequency modulation resource node j
Figure FDA0003418789200000043
Is composed of
Figure FDA0003418789200000044
8. The method according to claim 1, wherein the step 4) of establishing a least square identification model based on the branch power measurement variation and the frequency modulation resource node power variation measured by the synchrophasor measurement to solve the power variation of each frequency modulation resource node to be identified comprises:
(4.1) the least square identification model of branch power measurement change and frequency modulation resource node power change of synchronous phasor measurement is as follows:
min([ΔPPMU-SΔPF]T[ΔPPMU-SΔPF])
wherein, Δ PPMUIndicating the frequency adjustment check time teAnd the frequency regulation trigger time tsThe power measurement change of the synchronous phasor measurement is performed, S represents a sensitivity matrix between the branch power of the synchronous phasor measurement and the node power of the frequency modulation resource, and delta PFRepresenting the power change of each frequency modulation resource node to be identified;
(4.2) solving the power change delta P of each frequency modulation resource node to be identifiedFThe mathematical expression of (a) is:
ΔPF＝(STS)-1STΔPPMU
wherein S isTIs the transpose of the sensitivity matrix between the branch power of the synchronous phasor measurement and the frequency modulation resource node power.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786665A (en) * 2016-12-14 2017-05-31 国家电网公司华中分部 A kind of generating set primary frequency regulation property abnormality Online statistics analysis method
CN107966672A (en) * 2017-12-05 2018-04-27 国网重庆市电力公司电力科学研究院 The generator power transmitter test method and system of meter and synchronous phasor measurement
CN110190611A (en) * 2019-06-04 2019-08-30 国网山东省电力公司 The primary frequency modulation bearing calibration and system of grid cyclic wave change rate based on PMU
CN110850236A (en) * 2019-11-28 2020-02-28 国网福建省电力有限公司厦门供电公司 Power distribution network fault positioning method based on parameter estimation

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101661069B (en) * 2009-09-25 2011-07-20 北京四方继保自动化股份有限公司 Dynamic process real-time estimation method of weak observable non-PMU measuring point independent of state matrix
CN101750562B (en) * 2010-01-13 2012-02-29 湖北省电力公司 Non-PMU measure point dynamic process estimation method based on flow equation sensitiveness analysis
CN103701121B (en) * 2013-12-31 2015-10-21 电子科技大学 A kind of bulk power grid frequency temporal-spatial erosion process analytical system
CN107577870B (en) * 2017-09-04 2020-10-27 天津大学 Power distribution network voltage power sensitivity robust estimation method based on synchronous phasor measurement
CN107976592A (en) * 2017-10-25 2018-05-01 国电南瑞科技股份有限公司 Unit primary frequency modulation on-line testing method based on wide area measurement information
CN108199375B (en) * 2018-01-31 2021-01-22 天津大学 Intelligent power distribution network topology identification method based on synchronous phasor measurement
CN108614187B (en) * 2018-04-10 2019-10-29 清华大学 Power system oscillation source tracing method and system based on multi-modal synchronization phasor
CN109858061B (en) * 2018-11-13 2023-06-30 天津大学 Power distribution network equivalence and simplification method for voltage power sensitivity estimation
CN110676940B (en) * 2019-10-21 2021-02-02 国网上海市电力公司 Power distribution network PMU configuration method and system for improving parameter identification precision
CN110927442B (en) * 2019-12-06 2021-11-02 云南电网有限责任公司 Multimode oscillation on-line monitoring and early warning system based on edge calculation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786665A (en) * 2016-12-14 2017-05-31 国家电网公司华中分部 A kind of generating set primary frequency regulation property abnormality Online statistics analysis method
CN107966672A (en) * 2017-12-05 2018-04-27 国网重庆市电力公司电力科学研究院 The generator power transmitter test method and system of meter and synchronous phasor measurement
CN110190611A (en) * 2019-06-04 2019-08-30 国网山东省电力公司 The primary frequency modulation bearing calibration and system of grid cyclic wave change rate based on PMU
CN110850236A (en) * 2019-11-28 2020-02-28 国网福建省电力有限公司厦门供电公司 Power distribution network fault positioning method based on parameter estimation

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