CN117411190B - Power distribution network topology identification method, equipment, system and medium based on multi-source information - Google Patents

Power distribution network topology identification method, equipment, system and medium based on multi-source information Download PDF

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CN117411190B
CN117411190B CN202311714322.8A CN202311714322A CN117411190B CN 117411190 B CN117411190 B CN 117411190B CN 202311714322 A CN202311714322 A CN 202311714322A CN 117411190 B CN117411190 B CN 117411190B
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information
topology
distribution network
power distribution
power
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CN117411190A (en
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屈爱艳
成亚伟
石志超
赵琦
杜强
辛帅
武拴娥
宋世涛
马跃
刘彩坤
葛艳蕊
于永福
梁福月
张玉龙
赵永强
张佩霞
于笑博
张世磊
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Shijiazhuang Kelin Electric Co Ltd
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Shijiazhuang Kelin Electric Co Ltd
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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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00016—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
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Abstract

The invention provides a power distribution network topology identification method, equipment, a system and a medium based on multi-source information, which are characterized in that first topology information of a target power distribution network at the last moment and multi-source information of the target power distribution network at the current moment are firstly obtained, wherein the multi-source information comprises the following steps: SCADA measurement information, mu PMU measurement information and AMI measurement information; determining whether the current moment is a topology change moment according to mu PMU measurement information, and determining second topology information of the target power distribution network according to mu PMU measurement information, first topology information and a first neural network established in advance when the current moment is the topology change moment; and determining third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information. Preliminary topology prediction is carried out by using more accurate mu PMU measurement information, then the predicted topology is corrected by combining SCADA measurement information and AMI measurement information, and the characteristics of multi-source information are comprehensively considered, so that accurate topology identification of the power distribution network is realized.

Description

Power distribution network topology identification method, equipment, system and medium based on multi-source information
Technical Field
The invention belongs to the technical field of operation management of power systems, and particularly relates to a power distribution network topology identification method, equipment, a system and a medium based on multi-source information.
Background
A large number of distributed power supplies, electric automobile charging piles and energy storage systems are connected into the power distribution network in a distributed mode, and input and output of electric energy are flexibly and dispersedly achieved. In order to meet the power transmission and distribution requirements of a more complex power distribution network system, it is important to timely and accurately acquire the real-time state of the power distribution network.
In the prior art, SCADA is commonly used to implement topology identification of a power distribution network. SCADA (Supervisory Control And DataAcquisition, data acquisition and monitoring control system) has wide application fields, mature technical development and plays an important role in the application of power systems. The SCADA system is used as a real-time data source for power system automation, and can take RTU (Remote TerminalUnit ) and microcomputer protection device as cores, and control, signal, measurement, charging and other loops of a transformer substation are brought into a computer system, so that the identification of the topology of the power distribution network is realized, and the reliability of a secondary system is improved. However, when remote signaling data of the SCADA of the power distribution network are collected, false report and non-report exist frequently, the data source is single, the correctness of the generated network topology has larger uncertainty, and the identification reliability of the operation topology of the power distribution network is not high.
Disclosure of Invention
In view of the above, the invention provides a method, equipment, a system and a medium for identifying the topology of a power distribution network based on multi-source information, which aim to solve the problem of low reliability in identifying the operation topology of the power distribution network in the prior art.
A first aspect of an embodiment of the present invention provides a method for identifying a topology of a power distribution network based on multi-source information, including:
acquiring first topology information of a target power distribution network at the previous moment and multi-source information of the target power distribution network at the current moment, wherein the multi-source information comprises: SCADA measurement information, mu PMU measurement information and AMI measurement information;
determining whether the current moment is a topology change moment according to mu PMU measurement information, and determining second topology information of the target power distribution network according to mu PMU measurement information, first topology information and a first neural network established in advance when the current moment is the topology change moment;
and determining third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information.
A second aspect of an embodiment of the present invention provides a power distribution network topology identification device based on multi-source information, including:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring first topology information of a target power distribution network at the previous moment and multi-source information of the target power distribution network at the current moment, and the multi-source information comprises: SCADA measurement information, mu PMU measurement information and AMI measurement information;
the judging module is used for determining whether the current moment is the topology change moment according to the mu PMU measurement information, and determining second topology information of the target power distribution network according to the mu PMU measurement information, the first topology information and a first neural network established in advance when the current moment is the topology change moment;
the determining module is used for determining third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information.
A third aspect of an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method for identifying a topology of a power distribution network based on multi-source information as described above in the first aspect when the computer program is executed.
A fourth aspect of an embodiment of the present invention provides a topology identification system, including: mu PMU measurement device, AMI measurement device, SCADA measurement device, and electronic device according to the third aspect.
A fifth aspect of an embodiment of the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for identifying a topology of a power distribution network based on multi-source information according to the first aspect above.
The embodiment of the invention provides a method, equipment, a system and a medium for identifying power distribution network topology based on multi-source information, which are used for firstly acquiring first topology information of a target power distribution network at the previous moment and multi-source information of the target power distribution network at the current moment, wherein the multi-source information comprises the following steps: SCADA measurement information, mu PMU measurement information and AMI measurement information; determining whether the current moment is a topology change moment according to mu PMU measurement information, and determining second topology information of the target power distribution network according to mu PMU measurement information, first topology information and a first neural network established in advance when the current moment is the topology change moment; and determining third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information. Preliminary topology prediction is carried out by using more accurate mu PMU measurement information, then the predicted topology is corrected by combining SCADA measurement information and AMI measurement information, and the characteristics of multi-source information are comprehensively considered, so that accurate topology identification of the power distribution network is realized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a topology identification system according to an embodiment of the present invention;
fig. 2 is a flowchart of an implementation of a method for identifying a topology of a power distribution network based on multi-source information according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a power distribution network topology identification device based on multi-source information according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
Fig. 1 is a schematic structural diagram of a topology identification system according to an embodiment of the present invention. As shown in fig. 1, in some embodiments, the topology identification system includes a mu PMU measurement device 11, an AMI measurement device 12, a SCADA measurement device 13, and a target electronic device 14.
The mu PMU measuring device 11 can receive GPS signals, a micro-synchronous phasor measuring unit (micro-phasor measurement units, mu PMU) can add time scales for synchronous measurement data, effective information such as power, phase and power angle can be calculated, the data precision can reach 0.05, and the data delay can reach microsecond level. The AMI measurement device 12 may be a smart meter, and is installed on each load branch, and is configured to collect AMI (Advanced Metering Infrastructure, advanced measurement system) measurement data of the load on each branch. The SCADA measurement device 13 collects SCADA measurement data through the RTU, and specifically includes node injection power, branch power, node voltage amplitude, branch current amplitude, switching value, and the like. SCADA measurement equipment 13 is typically installed in feed switches, distribution transformer outlets and open closed loop cabinets. The target electronic device 14 may be a terminal or a server, the terminal may be a computer of a power management or scheduling center, and the server may be a physical server or a cloud server, which is not limited herein.
Fig. 2 is a flowchart of an implementation of a method for identifying a topology of a power distribution network based on multi-source information according to an embodiment of the present invention. As shown in fig. 2, in some embodiments, a method for identifying a topology of a power distribution network based on multi-source information is applied to the electronic device shown in fig. 1, and the method includes:
s210, acquiring first topology information of a target power distribution network at the previous moment and multi-source information of the target power distribution network at the current moment, wherein the multi-source information comprises: SCADA measurement information, mu PMU measurement information, and AMI measurement information.
In the embodiment of the invention, when the load or the distributed energy in the power distribution network is not input/output to/from the power distribution network, the topology of the power distribution network is known, namely the first topology information is changed after the input/output event occurs at a certain moment, and the topology needs to be identified again in order to ensure the operation of the power distribution network.
In the embodiment of the invention, the measurement precision of the mu PMU measurement equipment is higher, and the cost is higher, so that the mu PMU measurement equipment is usually only deployed in key nodes of a power distribution network, and the coverage rate is lower. SCADA is an important subsystem in the energy management system of the power distribution network, and has high coverage rate, low data precision and poor synchronism. AMI measurement equipment has high data precision and high coverage rate, but the sampling period is long, and the requirement of quickly identifying the topology of the power distribution network cannot be met. The invention collects the data of the 3 sources at the same time, and respectively processes the data according to the characteristics of different data, thereby realizing the accurate identification of the power distribution network topology.
The sampling period of the mu PMU measuring device, the SCADA measuring device and the AMI measuring device can be 1s, 10s and 60s. Mu PMU measuring equipment is deployed on M nodes of the power distribution network, SCADA measuring equipment is deployed on N nodes of the power distribution network, and AMI measuring equipment is deployed on O nodes of the power distribution network. There are cases where two or more kinds of measurement devices are installed simultaneously on a certain node.
S220, determining whether the current moment is the topology change moment according to the mu PMU measurement information, and determining second topology information of the target power distribution network according to the mu PMU measurement information, the first topology information and the first neural network established in advance when the current moment is the topology change moment.
The load/energy source variation of the distribution network can influence the operation of each key node, and when the change is detected by the mu PMU measuring equipment, the current moment is recorded as the topology change moment, and the topology identification process is started. Although the sampling frequency of the SCADA measuring equipment and the AMI measuring equipment is low, the real-time performance of topology calculation is poor, the condition of rapid topology change of the power distribution network is difficult to adapt, and the SCADA measuring equipment and the AMI measuring equipment are widely arranged in the power distribution network, so that the data volume required to be acquired for each calculation is large, and the calculated volume is high.
In the embodiment of the invention, the topology of the power distribution network can be primarily identified through the mu PMU measurement information of a small number of key nodes, so as to obtain second topology information. The first neural network may be a convolutional neural network model, a feed forward neural network model, or the like, and is not limited herein.
In some embodiments, the mu PMU measurement information includes power information, voltage information; the voltage information may specifically include a phase, an amplitude, and a power angle; s220 specifically includes: determining whether the current moment is the topology change moment according to the voltage information in the mu PMU measurement information; and when the current moment is the topology change moment, determining second topology information of the target power distribution network according to the power information, the voltage information, the first topology information and the first neural network which are established in advance in the mu PMU measurement information.
In the embodiment of the invention, according to the voltage information in the mu PMU measurement information, whether the current moment is the topology change moment is determined, which can be realized by the following formula:
(1)
wherein,U i (t) Is the firstiThe magnitude of the voltage on the individual nodes,is the firstiThe phase of the voltage on the individual nodes,θ i (t) Is the firstiThe power angle measured at the individual nodes is,w 1 、w 2 、w 3 in order to set the weight of the weight in the preset,afor a preset decision threshold, P is a decision value, when p=1 is detected on any node, the current moment is a topology change moment, and when p=0 is detected on all nodes, the topology is not changed.i∈(1，M)。
In some embodiments, determining the second topology information of the target power distribution network according to the power information, the voltage information, the first topology information and the first neural network established in advance in the mu PMU measurement information includes: according to the voltage information in the mu PMU measurement information, determining disturbance parameters of the target power distribution network, and carrying out load flow calculation according to the power information, the voltage information and the first topology information in the mu PMU measurement information to obtain power estimation values of all nodes; correcting the estimated value of each node power according to the disturbance parameters and a pre-established first neural network; and determining second topology information according to the first topology information and the corrected estimated value of each node power.
In the embodiment of the invention, when the load or the distributed power supply is switched, the power system enters a transient process, the transient process can be regarded as disturbance of the power system, and the disturbance parameters can comprise the power quality change characteristics and the disturbance time sequence. Wherein the power quality change characteristics may include, but are not limited to, at least one of: voltage fluctuations, frequency deviations, voltage distortion rates, three-phase imbalance.
In the embodiment of the invention, the disturbance time sequence of each node can be determined according to the voltage change time of each node, and the correlation between all nodes of the power system and M nodes for deploying mu PMU measuring equipment is prestored in the electronic equipment. When a certain node changes in transient state, the node with higher correlation is influenced necessarily, so that the range of the branch where the load/distributed energy source switching is located can be approximately determined by calculating the disturbance time sequence of each node for deploying mu PMU measuring equipment.
The method comprises the steps of defining load grades according to the load size, measuring the change of the power quality in the load switching process under each type and the load grade in advance, establishing a first knowledge graph based on the load type-power quality characteristic-load grade triplet, and finding the load type and the load grade of the load which is possibly switched according to the measured power quality characteristic in actual use.
For example, when the leftmost mu PMU measurement device in fig. 1 detects a disturbance first, it may be determined that load switching occurs in each load branch connected to the device, and at this time, the type and level of the load switching on the branch at the current moment may be predicted approximately by using the power quality change feature and the first knowledge graph. Wherein the load type includes civil load, commercial load, industrial load, and the like.
Similarly, the distributed power supplies can be divided according to the generated energy, a second knowledge graph is established based on the power supply type-power quality characteristic-power generation level triplet, and the power supply type and the power generation level of the power supply which can possibly be switched can be found in the knowledge graph according to the measured power quality characteristic in actual use.
For example, when the mu PMU measurement device on the far right in FIG. 1 detects a disturbance first, it can be determined that distributed power switching occurs in each power branch connected with the mu PMU measurement device, and at this time, the switching power type and the power generation level on the branch at the current moment can be predicted approximately through the power quality change feature and the second knowledge graph. The power source type comprises wind power, photovoltaic and the like.
In the embodiment of the invention, the power flow calculation can be performed according to the power and the voltage of each node measured by mu PMU measuring equipment, the power and the voltage of all nodes of the power distribution network are deduced by combining the network topology before switching, and the deduced power is the estimated value of the power of each node. Because switching occurs, the network topology is different from the original topology, so that the power estimated value of each node obtained by the power flow calculation is inaccurate, the power flow calculation is only performed once, and repeated iteration is not needed.
In the embodiment of the invention, the switching branch range, the power supply/load type and the load level/power generation level obtained based on the disturbance parameters are input into a first pre-established neural network, so that the estimated value of the power of each node can be corrected, and then the first topology information is changed to obtain the second topology information.
The first neural network outputs a plurality of results, namely correction values of the estimated values of the power of a plurality of groups of nodes, and simultaneously outputs the confidence coefficient of each group of results. Correspondingly, a plurality of second topologies of the target power distribution network are obtained, and the second topologies form second topology information together.
In the embodiment of the invention, the historical data measured by the mu PMU measuring equipment during load/distributed energy switching in a historical period is recorded, disturbance parameters in a historical state are calculated according to the historical data, the historical switching branch range, the power supply/load type, the load grade/power generation grade are calculated, and a training set is formed by combining network topologies before and after switching at the historical moment, so that the neural network is trained.
In the embodiment, the topology identification process can be completed only by one time of tide calculation, two knowledge maps and one neural network operation, and the calculated amount is small.
In some embodiments, determining the second topology information of the target power distribution network according to the power information, the voltage information, the first topology information and the first neural network established in advance in the mu PMU measurement information includes: according to the voltage information in the mu PMU measurement information, determining disturbance parameters of a target power distribution network, and according to the voltage information, the power information, the disturbance parameters and a first neural network established in advance, determining switching information; and determining second topology information according to the switching information and the first topology information.
In the embodiment of the invention, once power flow calculation can still be performed according to the voltage information and the power information, and then the power and the voltage of each node are input into the first neural network in combination with the disturbance parameters to determine the switching information. The switching information comprises a switching node sequence number, a switching node power variation and a switching node voltage variation. According to the first neural network, the relevant information of several nodes where switching may occur can be predicted approximately. And modifying the first topology information to obtain a plurality of second topologies, and forming second topology information.
Accordingly, the training set used in the training process of the neural network will also be changed, and the training manner is the same as the above manner, which will not be described herein.
And S230, determining third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information.
In the embodiment of the present invention, since a plurality of second topologies are obtained in S230, it is necessary to further determine the final network topology of the target power distribution network. The method can be realized by SCADA measurement information and AMI measurement information. Before this, all data needs to be time synchronized,
in some embodiments, the method further comprises: and taking mu PMU measurement information as a reference, and performing time synchronization processing on the SCADA measurement information and the AMI measurement information.
After the time synchronization is completed, since the sampling frequency of the SCADA measurement information and the AMI measurement information is low, when the SCADA measurement information and the AMI measurement information are used in the subsequent steps, it is necessary to linearly interpolate the used data.
In some embodiments, determining third topology information of the target power distribution network at the current time according to the second topology information, the SCADA measurement information and the AMI measurement information includes: and determining third topology information of the target power distribution network at the current moment according to the corrected estimated value of the power of each node, the SCADA measurement information, the AMI measurement information and the second topology information.
In the embodiment of the invention, for each second topology, a first difference matrix is calculated according to SCADA measurement information of N nodes and estimated values of node powers of the N nodes after correction. And calculating a second difference matrix according to the AMI measurement information of the O nodes and the estimated value of the corrected node power of the O nodes. And finally, calculating an error value from the first difference matrix and the second difference matrix corresponding to each second topology, and taking the second topology with the smallest error value as third topology information of the target power distribution network.
And subtracting the estimated value of the node power from the measurement information of each node in the process, and sequentially calculating each node after absolute value processing to obtain a first difference matrix and a second difference matrix. The average value of all data in the first difference matrix and the second difference matrix is the error value. The first difference matrix has a size of 1×n and the second difference matrix has a size of 1×o.
In addition, the error value may be calculated by:
(2)
wherein Q is L An error value for the L second topology; q (Q) 1L Is the first error value of the L second topology, Q 2L A second error value for an L second topology; ΔP Lj Installing a j node of SCADA measurement equipment in the second topology for a j value corresponding to the first difference matrix of the L second topology; ΔP Lk For the kth value corresponding to the first difference matrix for the lth second topology,and the kth node of the AMI measurement equipment is installed in the second topology.
λ j The correlation between the j-th node and the first node represents the influence of the switching of the first node on the j-th node, and the influence is measured through a pre-experiment. Lambda (lambda) k Is the correlation between the kth node and the first node. The first node is a node where load/distributed power switching occurs in the second topology. The first nodes of each second topology are different.
P SCADAj SCADA measurement information, P, for a jth node AMIk AMI measurement information for kth node, P Lj An estimated value of the node power of the jth node under the L-th second topology, P Lk Is an estimate of the node power of the kth node in the lth second topology.
In summary, the beneficial effects of the invention are as follows:
1. preliminary topology prediction is carried out by using more accurate mu PMU measurement information, then the predicted topology is corrected by combining SCADA measurement information and AMI measurement information, and the characteristics of multi-source information are comprehensively considered, so that accurate topology identification of the power distribution network is realized.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a power distribution network topology identification device based on multi-source information according to an embodiment of the present invention. As shown in fig. 3, in some embodiments, the power distribution network topology identification device 3 based on multi-source information includes:
the obtaining module 310 is configured to obtain first topology information of the target power distribution network at a previous time and multi-source information of the target power distribution network at a current time, where the multi-source information includes: SCADA measurement information, mu PMU measurement information and AMI measurement information;
the determining module 320 is configured to determine whether the current time is a topology change time according to the mu PMU measurement information, and determine second topology information of the target power distribution network according to the mu PMU measurement information, the first topology information and a first neural network established in advance when the current time is the topology change time;
the determining module 330 is configured to determine third topology information of the target power distribution network at the current moment according to the second topology information, the SCADA measurement information and the AMI measurement information.
Optionally, the mu PMU measurement information includes power information and voltage information; the voltage information may specifically include a phase, an amplitude, and a power angle; a determining module 320, configured to determine whether the current time is a topology change time according to the voltage information in the mu PMU measurement information; and when the current moment is the topology change moment, determining second topology information of the target power distribution network according to the power information, the voltage information, the first topology information and the first neural network which are established in advance in the mu PMU measurement information.
Optionally, the determining module 320 is specifically configured to: determining disturbance parameters of the target power distribution network according to the voltage information in the mu PMU measurement information; carrying out load flow calculation according to the power information, the voltage information and the first topology information in the mu PMU measurement information to obtain power estimation values of all nodes; correcting the estimated value of each node power according to the disturbance parameters and a pre-established first neural network; and determining second topology information according to the first topology information and the corrected estimated value of each node power.
Optionally, the determining module 330 is configured to: and determining third topology information of the target power distribution network at the current moment according to the corrected estimated value of the power of each node, the SCADA measurement information, the AMI measurement information and the second topology information.
Optionally, the second topology information includes a plurality of second topologies; a determining module 330, configured to: for each second topology, calculating a first difference matrix according to the SCADA measurement information and the corrected estimated value of the node power; for each second topology, calculating a second difference matrix according to the AMI measurement information and the corrected estimated value of the node power; and calculating an error value from the first difference matrix and the second difference matrix corresponding to each second topology, and taking the second topology with the smallest error value as third topology information of the target power distribution network.
Optionally, the determining module 320 is specifically configured to: according to the voltage information in the mu PMU measurement information, determining disturbance parameters of a target power distribution network, and according to the voltage information, the power information, the disturbance parameters and a first neural network established in advance, determining switching information; and determining second topology information according to the switching information and the first topology information.
Optionally, the power distribution network topology identification device 3 based on the multi-source information further includes: and the preprocessing module is used for carrying out time synchronization processing on the SCADA measurement information and the AMI measurement information by taking mu PMU measurement information as a reference.
The power distribution network topology identification device based on the multi-source information provided by the embodiment can be used for executing the method embodiment, the implementation principle and the technical effect are similar, and the embodiment is not repeated here.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, an electronic device 4 according to an embodiment of the present invention is provided, the electronic device 4 of the embodiment including: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The steps of the embodiments of the method for identifying a topology of a power distribution network based on multi-source information described above, such as the steps shown in fig. 2, are implemented by the processor 40 when executing the computer program 42. Alternatively, the processor 40, when executing the computer program 42, performs the functions of the modules/units of the system embodiments described above, e.g., the functions of the modules shown in fig. 3.
By way of example, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to complete the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions for describing the execution of the computer program 42 in the electronic device 4.
The electronic device 4 may be a terminal or a server and the electronic device 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting as to the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a terminal may also include an input-output device, a network access device, a bus, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store computer programs and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the invention provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in the embodiment of the power distribution network topology identification method based on multi-source information when being executed by a processor.
The computer readable storage medium stores a computer program 42, the computer program 42 comprising program instructions which, when executed by the processor 40, implement all or part of the processes of the above described embodiments, or may be implemented by means of hardware associated with the instructions of the computer program 42, the computer program 42 being stored in a computer readable storage medium, the computer program 42, when executed by the processor 40, implementing the steps of the above described embodiments of the method. The computer program 42 comprises computer program code, which may be in the form of source code, object code, executable files, or in some intermediate form, among others. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store a computer program and other programs and data required for the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims (5)

1. A method for identifying a topology of a power distribution network based on multi-source information, the method comprising:
acquiring first topology information of a target power distribution network at the previous moment and multi-source information of the target power distribution network at the current moment, wherein the multi-source information comprises: SCADA measurement information, mu PMU measurement information and AMI measurement information;
the mu PMU measurement information comprises power information and voltage information; the voltage information specifically comprises a phase, an amplitude and a power angle;
determining whether the current moment is the topology change moment according to the voltage information in the mu PMU measurement information;
when the current moment is the topology change moment, determining disturbance parameters of the target power distribution network according to the voltage information in the mu PMU measurement information; carrying out load flow calculation according to the power information, the voltage information and the first topology information in the mu PMU measurement information to obtain power estimation values of all nodes;
inputting the power of each node into a pre-established first neural network according to the switching branch range, the power supply/load type and the load level/power generation level, correcting the estimated value of the power of each node, and changing the first topology information to obtain second topology information;
the disturbance parameters comprise power quality change characteristics and disturbance time sequences; the disturbance time sequence is used for determining the range of the switching branch; the power supply type and the load level are determined according to the power quality change characteristics and the first knowledge graph; the load type and the power generation level are determined according to the power quality change characteristics and the second knowledge graph;
the second topology information includes a plurality of second topologies;
for each second topology, calculating a first difference matrix according to the SCADA measurement information and the corrected estimated value of the node power;
for each second topology, calculating a second difference matrix according to the AMI measurement information and the corrected estimated value of the node power;
and calculating an error value from the first difference matrix and the second difference matrix corresponding to each second topology, and taking the second topology with the smallest error value as third topology information of the target power distribution network.
2. The method for identifying a topology of a power distribution network based on multi-source information according to claim 1, wherein after obtaining the first topology information of the target power distribution network at the previous time and the multi-source information of the target power distribution network at the current time, the method further comprises:
and taking mu PMU measurement information as a reference, and performing time synchronization processing on the SCADA measurement information and the AMI measurement information.
3. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the multi-source information based power distribution network topology identification method according to claim 1 or 2 when the computer program is executed.
4. A topology identification system, comprising: a mu PMU measurement device, an AMI measurement device, a SCADA measurement device, and an electronic device as claimed in claim 3.
5. A computer readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the multisource information based power distribution network topology identification method according to claim 1 or 2.
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