CN111064180B - Medium-voltage distribution network topology detection and identification method based on AMI (advanced mechanical arm) power flow matching - Google Patents
Medium-voltage distribution network topology detection and identification method based on AMI (advanced mechanical arm) power flow matching Download PDFInfo
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- 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
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- 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
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- 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
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- 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
The invention relates to a radial medium voltage distribution network topology detection and identification method based on AMI power flow matching, which comprises the following steps: step 1, providing a medium-voltage distribution network topology detection index based on the matching condition of an AMI voltage amplitude measurement value and a tide estimation value, and performing medium-voltage distribution network topology detection; and 2, performing medium-voltage distribution network topology identification based on AMI power flow optimal matching. The invention can realize the real-time detection and identification of the topology change of the medium-voltage distribution network by only AMI measurement without additionally configuring a synchronous phasor measurement unit.
Description
Technical Field
The invention belongs to the technical field of power system parameter identification, relates to a medium-voltage distribution network topology detection and identification method, and particularly relates to a medium-voltage distribution network topology detection and identification method based on AMI (advanced mechanical arm) tide matching.
Background
In order to realize optimal economic operation and rapid recovery of faults, the topology of the medium-voltage distribution network is frequently changed. However, the remote signaling data acquisition of the power distribution network often has the conditions of false alarm and non-alarm, the data source is single and is easily influenced by communication interruption, the power distribution main station has larger uncertainty based on the network topology generated by the remote signaling, and the problems of lower model quality, incomplete model, disordered topology communication, incapability of sensing model change and the like exist, so that the model maintenance is not timely and the accuracy is not high. Therefore, development of a topology identification tool is needed to realize reliable detection and accurate identification of the operation topology of the power distribution network, and a foundation is laid for analysis and calculation of various power networks and advanced application.
The transmission network data acquisition and monitoring system is perfect, and a dispatcher can monitor the network topology structure in real time and identify and correct topology errors through the state estimator. However, in the medium-voltage distribution network, the redundancy of measurement data is low, and topology identification is difficult to be performed by adopting a method based on state estimation. The construction of an advanced measurement system (Advanced Measurement Infrastructure, AMI) system provides a new opportunity for the topology identification of the medium-voltage distribution network.
The following documents are found in the prior art: the topology verification method of the low-voltage distribution network based on AMI measurement is provided; the feature of measuring the second moment by researching the voltage provides a radial power distribution network topology learning algorithm from the leaf node to the root node; establishing a maximum likelihood estimation model of all line running states based on a linear approximate power flow equation, and solving by adopting a projection gradient algorithm; some documents contemplate the use of miniature synchrophasor measurement units (μpmus) to identify the topology of the distribution network; detecting topology change of the power distribution network by adopting mu PMU measurement; the power distribution network tide jacobian matrix robust estimation and topology identification method based on PMU is provided; assuming that all nodes are configured with mu PMU, and identifying the topology of the power distribution network by estimating a node admittance matrix; the power distribution network operation topology identification method based on DSCADA and mu PMU data fusion is characterized in that topology change time is determined based on mu PMU phase angle measurement, a possible topology set is constructed based on node voltage change, each node voltage phase is estimated for each possible topology, and actual topology is identified based on a topology similarity identification model. The above methods each have a certain effect, however, the linear power flow based method model has approximation errors, and the mu PMU based method will result in high investment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the AMI power flow matching-based medium-voltage distribution network topology detection and identification method which is reasonable in design, accurate and reliable in identification result, high in identification precision and capable of realizing the topology change of the medium-voltage distribution network only by AMI measurement.
The invention solves the practical problems by adopting the following technical scheme:
a radial medium voltage distribution network topology detection and identification method based on AMI power flow matching comprises the following steps:
step 1, providing a medium-voltage distribution network topology detection index based on the matching condition of an AMI voltage amplitude measurement value and a tide estimation value, and performing medium-voltage distribution network topology detection;
and 2, performing medium-voltage distribution network topology identification based on AMI power flow optimal matching.
The specific method of the step 1 is as follows:
based on the topology s stored by the current system, after carrying out load flow calculation by utilizing AMI active and reactive power measurement to obtain a voltage amplitude estimated value, comparing the voltage amplitude estimated value with the AMI voltage amplitude measured value, and taking the mismatch degree between the voltage amplitude estimated value and the AMI voltage amplitude measured value as a medium-voltage distribution network topology detection index J(s) for detecting topology change or not:
1) If J(s) does not fluctuate much in the last moment, the system is considered to have no topology change;
2) If J(s) increases significantly or changes suddenly over the previous time, the system is considered to have changed topology.
Moreover, the topology detection index of the medium-voltage distribution network in the step 1 is as follows:
J(s)＝[V AMI -V Est (s)] T R -1 [V AMI -V Est (s)] (1)
wherein: r is a covariance matrix of voltage amplitude measurement errors, V AMI 、V Est The voltage amplitude measurement vector and the estimation vector are respectively.
V Est And obtaining through tide calculation:
V Est (s i )＝PF(s,P AMI ,Q AMI ) (2)
wherein: PF (·) is the load flow calculation operator, P AMI 、Q AMI AMI active and reactive measurement vectors are respectively used.
Moreover, the specific steps of the step 2 include:
(1) A variable of 0-1 is introduced for each medium voltage distribution line whether to be put into operation:
(2) And (3) taking the minimum weighted square sum of the difference between the AMI voltage amplitude measurement value and the power flow estimated value as a target, taking a radial structure as a constraint, and establishing the following medium-voltage distribution network operation topology identification model:
if the measurement accuracy is low, topology identification is performed by adopting a plurality of section measurements, and a multi-section topology identification model is established:
wherein: n is the number of measured sections;
(3) The multi-section topology identification model is solved by adopting a real number coding genetic algorithm, and the specific steps comprise:
1) Setting population number, crossing rate and variation rate, and encoding individual real numbers to generate an initial population;
2) Carrying out load flow calculation on each individual, calculating an fitness function value of each individual, and punishing the individual which does not meet the constraint of the radiation structure according to the constraint out-of-limit condition; if the tide is not converged, setting the individual fitness function to be positive infinity;
3) In the current population, based on the fitness function value of each individual, adopting a tournament selection strategy and an elite preservation strategy to perform individual selection to generate a pairing pool;
4) Performing extended Laplace cross operation and extended power distribution variation operation, and converting continuous decision variables into integers by adopting a random truncation strategy to generate a new generation population;
5) If the maximum iteration number is reached, ending the algorithm; otherwise, executing the step (2).
The invention has the advantages and beneficial effects that:
1. the invention provides a radial medium voltage distribution network topology detection and identification method based on AMI power flow matching, which comprises the following steps: firstly, based on the matching condition of an AMI voltage amplitude measurement value and a power flow estimated value, a topology detection index and a topology detection method of a medium-voltage distribution network are provided; secondly, a medium-voltage distribution network topology identification method based on AMI tide optimal matching is provided; finally, the effectiveness of the proposed method is verified by means of standard distribution network calculation examples. The invention can realize the real-time detection and identification of the topology change of the medium-voltage distribution network by only AMI measurement without additionally configuring a synchronous phasor measurement unit.
2. The topology detection method provided by the invention has robustness on the AMI measurement accuracy, and even if the AMI measurement accuracy is lower, the topology change can be reliably detected based on one section measurement.
3. The topology identification method is suitable for AMI measurement scenes with different accuracies: when the AMI measurement accuracy is higher, topology identification can be performed by only one measurement section, and when the AMI measurement accuracy is lower, the identification accuracy can be improved by comprehensively utilizing measurement at a plurality of moments, and the influence of measurement noise is reduced.
Drawings
FIG. 1 (a) is a single line diagram of a 33 node power distribution network prior to topology change of the present invention;
FIG. 1 (b) is a single line diagram of a topology-changed 33-node power distribution network of the present invention;
FIG. 2 is a schematic diagram of the topology detection result of the present invention;
Detailed Description
Embodiments of the invention are described in further detail below with reference to the attached drawing figures:
a radial medium voltage distribution network topology detection and identification method based on AMI power flow matching comprises the following steps:
step 1, providing a medium-voltage distribution network topology detection index based on the matching condition of an AMI voltage amplitude measurement value and a tide estimation value, and performing medium-voltage distribution network topology detection;
the specific method of the step 1 is as follows:
the AMI system may periodically obtain injected active, reactive and voltage amplitude information for each 10kV node, where: each node is independent in active and reactive power, and does not contain network topology information; and the voltage amplitude value contains network topology information. Thus, it is possible to detect whether a topology change has occurred based on the degree of matching between the active, reactive and voltage amplitude measurements.
Based on the topology s stored in the current system, after carrying out load flow calculation by utilizing AMI active and reactive power measurement to obtain a voltage amplitude estimated value, comparing the voltage amplitude estimated value with the AMI voltage amplitude measured value, and taking the mismatch degree between the voltage amplitude estimated value and the AMI voltage amplitude measured value as a topology detection index J(s) for detecting topology change or not:
1) If J(s) does not fluctuate much in the last moment, the system is considered to have no topology change;
2) If J(s) increases significantly or changes suddenly over the previous time, the system is considered to have changed topology.
In this embodiment, in order to measure the degree of mismatch between the voltage amplitude measurement value and the power flow estimation value, the following topology detection index is defined:
J(s)＝[V AMI -V Est (s)] T R -1 [V AMI -V Est (s)] (1)
wherein: r is a covariance matrix of voltage amplitude measurement errors, V AMI 、V Est The voltage amplitude measurement vector and the estimation vector are respectively.
V Est And obtaining through tide calculation:
V Est (s i )＝PF(s,P AMI ,Q AMI ) (2)
wherein: PF (·) is the load flow calculation operator, P AMI 、Q AMI AMI active and reactive measurement vectors are respectively;
thus, the topology detection algorithm is: based on the topology stored by the system at the current moment, after AMI active, reactive and voltage amplitude measurement is obtained at each moment, a topology detection index J(s) of the medium-voltage distribution network is calculated:
1) If J(s) does not fluctuate much in the last moment, the system is considered to have no topology change;
2) If J(s) increases significantly or changes suddenly over the previous time, the system is considered to have changed topology.
And 2, performing medium-voltage distribution network topology identification based on AMI power flow optimal matching.
The specific steps of the step 2 include:
(1) a variable of 0-1 is introduced for each medium voltage distribution line whether to be put into operation:
(2) and (3) taking the minimum weighted square sum of the difference between the AMI voltage amplitude measurement value and the power flow estimated value as a target, taking a radial structure as a constraint, and establishing the following medium-voltage distribution network operation topology identification model:
if the measurement accuracy is low, topology identification can be performed by adopting a plurality of section measurements, and a multi-section topology identification model is established so as to reduce the influence of measurement noise;
the multi-section topology identification model is as follows:
wherein: n is the number of measured sections.
(3) The multi-section topology identification model is solved by adopting a real number coding genetic algorithm, and the solving steps are as follows:
1) Setting population number, crossing rate and variation rate, and encoding individual real numbers to generate an initial population;
2) Carrying out load flow calculation on each individual, calculating an fitness function value of each individual, and punishing the individual which does not meet the constraint of the radiation structure according to the constraint out-of-limit condition; if the tide is not converged, setting the individual fitness function to be positive infinity;
3) In the current population, based on the fitness function value of each individual, adopting a tournament selection strategy and an elite preservation strategy to perform individual selection to generate a pairing pool;
4) Performing extended Laplace cross operation and extended power distribution variation operation, and converting continuous decision variables into integers by adopting a random truncation strategy to generate a new generation population;
5) If the maximum iteration number is reached, ending the algorithm; otherwise, executing the step (2).
In this embodiment, a 33 node distribution network is used for testing, and the system includes 33 nodes and 37 lines, including 5 connection lines, as shown in fig. 1 (a) and fig. 1 (b). Assuming that each branch of the system is configured with a sectionalizing switch, the AMI system can collect active, reactive and voltage amplitude data of each node every 15 minutes, and otherwise real-time measurement is not configured. All tests were performed on a personal notebook computer with i5-7200U processor and 8G memory.
The simulation is carried out on 96 times of day, and the following scene is designed:
case 1: at the 1 st to 71 st moment, the network operates in a normal operation state, and no fault occurs and no economic reconstruction exists; at 72 th moment, as the branch 5-6 has permanent faults, the power supply is recovered by adopting a network reconstruction algorithm after fault isolation, the reconstruction scheme is that the connection switches TS1, TS3, TS4 and TS5 are closed by opening 11-12, 17-18 and 28-29, and as shown in figure 1, the power supply still operates in a radial mode after fault reconstruction. In the simulation process, AMI active power, reactive power and voltage amplitude measurement are respectively set to 5%, 5% and 0.01% relative errors, and the voltage amplitude measurement weight is 1/0.0001 2 。
Case 2: AMI active power, reactive power and voltage amplitude measurement relative errors are modified to be 5%, 5% and 0.1% on the basis of Case 1, and the voltage amplitude measurement weight is 1/0.001 2 ；
Case 3: on the basis of Case 1, the relative error of AMI active power, reactive power and voltage amplitude measurement is modified to be 5%, 5% and 1%, and the voltage amplitude measurement weight is 1/0.05 2 。
(1) Topology detection test
The topology detection index curves of cases 1 to 3 are shown in fig. 2. It can be seen from the figure that the proposed topology detection index can effectively detect topology changes for different measurement error levels.
(2) Topology identification test based on AMI tide matching
Topology identification is carried out on Case 2 and Case 3 by adopting an AMI (advanced mechanical instrumentation) power flow matching-based method, the population number of a genetic algorithm is set to be 1000, and five times of calculation are carried out on each scene. The Case 2 topology identification results are shown in table 1. Since only 5 of the 37 lines are finally disconnected and the remaining 32 lines remain in radial operation, only 5 lines with a status of "split" are listed in the table. It can be seen from the table that the 3 rd to 5 th calculations succeeded in finding the optimal topology, with an average calculation time of about 3 minutes.
Table 1 topology identification result (Case 2)
The Case 3 topology identification results are shown in table 2 when 1 measurement section is used. As can be seen from the table, the correct topology is not recognized for 5 times, because the measurement error is large, the accuracy of the recognition result cannot be guaranteed based on the topology recognition result of the single measurement section, in fact, the objective function value of the real topology is 31.84, and the algorithm finds a solution smaller than the objective function of the real topology, and the "over fitting" occurs. The Case 3 topology identification results are shown in table 3 when 5 measurement sections are used. It can be seen from the table that the optimal topology was found successfully at both the 3 rd and 4 th times, with an average calculation time of about 15 minutes.
TABLE 2 topology identification result (Case 3,1 section)
TABLE 3 topology identification results (Case 3,5 sections)
Tests show that the topology identification method based on AMI power flow matching can effectively realize topology identification, and when the equivalent measurement error is large, the topology identification precision can be improved by increasing the number of measurement sections.
It should be emphasized that the embodiments described herein are illustrative rather than limiting, and that this invention encompasses other embodiments which may be made by those skilled in the art based on the teachings herein and which fall within the scope of this invention.
Claims (3)
1. A radial medium voltage distribution network topology detection and identification method based on AMI power flow matching is characterized in that: the method comprises the following steps:
step 1, providing a medium-voltage distribution network topology detection index based on the matching condition of an AMI voltage amplitude measurement value and a tide estimation value, and performing medium-voltage distribution network topology detection;
step 2, performing medium-voltage distribution network topology identification based on AMI power flow optimal matching;
the specific steps of the step 2 include:
(1) The variable 0-1 is introduced into the operation or disconnection of each medium-voltage distribution line:
(2) And (3) taking the minimum weighted square sum of the difference between the AMI voltage amplitude measurement value and the power flow estimated value as a target, taking a radial structure as a constraint, and establishing the following medium-voltage distribution network operation topology identification model:
if the measurement accuracy is low, topology identification is performed by adopting a plurality of section measurements, and a multi-section topology identification model is established:
wherein: n is the number of measured sections; j(s) is a topology detection index of the medium-voltage distribution network; r is a covariance matrix of voltage amplitude measurement errors, V AMI 、V Est The voltage amplitude measurement vector and the estimation vector are respectively;
(3) The multi-section topology identification model is solved by adopting a real number coding genetic algorithm, and the specific steps comprise:
1) Setting population number, crossing rate and variation rate, and encoding individual real numbers to generate an initial population;
2) Carrying out load flow calculation on each individual, calculating an fitness function value of each individual, and punishing the individual which does not meet the constraint of the radiation structure according to the constraint out-of-limit condition; if the tide is not converged, setting the individual fitness function to be positive infinity;
3) In the current population, based on the fitness function value of each individual, adopting a tournament selection strategy and an elite preservation strategy to perform individual selection to generate a pairing pool;
4) Performing extended Laplace cross operation and extended power distribution variation operation, and converting continuous decision variables into integers by adopting a random truncation strategy to generate a new generation population;
5) If the maximum iteration number is reached, ending the algorithm; otherwise, executing the step (2).
2. The method for detecting and identifying the topology of the radial medium voltage distribution network based on AMI power flow matching according to claim 1, wherein the method comprises the following steps: the specific method of the step 1 is as follows:
based on the topology s stored by the current system, after carrying out load flow calculation by utilizing AMI active and reactive power measurement to obtain a voltage amplitude estimated value, comparing the voltage amplitude estimated value with the AMI voltage amplitude measured value, and taking the mismatch degree between the voltage amplitude estimated value and the AMI voltage amplitude measured value as a medium-voltage distribution network topology detection index J(s) for detecting topology change or not:
1) If J(s) does not fluctuate much in the last moment, the system is considered to have no topology change;
2) If J(s) increases significantly or changes suddenly over the previous time, the system is considered to have changed topology.
3. The method for detecting and identifying the topology of the radial medium voltage distribution network based on AMI power flow matching according to claim 1, wherein the method comprises the following steps: the topology detection index of the medium-voltage distribution network in the step 1 is as follows:
J(s)＝[V AMI -V Est (s)] T R -1 [V AMI -V Est (s)](1)
wherein: r is a covariance matrix of voltage amplitude measurement errors, V AMI 、V Est The voltage amplitude measurement vector and the estimation vector are respectively;
V Est and obtaining through tide calculation:
V Est (s i )＝PF(s,P AMI ,Q AMI ) (2)
wherein: PF (·) is the load flow calculation operator, P AMI 、Q AMI AMI active and reactive measurement vectors are respectively used.
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| CN112488874B (en) * | 2020-12-07 | 2022-12-09 | 清华大学 | Data-driven distribution network topology estimation and line parameter identification method |
| CN112736911B (en) * | 2020-12-29 | 2023-04-14 | 国网河北省电力有限公司经济技术研究院 | Power distribution network topological structure identification method and system based on PMU measurement similarity |
| CN112800567A (en) * | 2021-03-09 | 2021-05-14 | 广州汇通国信科技有限公司 | Treatment method based on artificial neural network power data diagnosis |
| US11824354B2 (en) * | 2021-08-24 | 2023-11-21 | Bigwood Technology, Inc. | Online state estimation and topology identification using advanced metering infrastructure (AMI) measurements |
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