CN117421859A - Power distribution network disaster damage situation sensing method and system based on multi-source data fusion - Google Patents
Power distribution network disaster damage situation sensing method and system based on multi-source data fusion Download PDFInfo
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Abstract
Provided are a power distribution network disaster damage situation awareness method and system based on multi-source data fusion. Firstly, collecting waterlogging and ponding depths of all areas, and generating a failure rate curve of power distribution network equipment under rainstorm waterlogging; then, collecting field measurement data to generate a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging; then collecting feedback information of a user side, and generating a client-level disaster sensing model of the distribution network under rainstorm waterlogging; and carrying out weighted summation on the model data obtained in the steps, and carrying out multi-source data fusion; and finally, performing sensing processing on the disaster situation of the power distribution network through a disaster situation sensing model of the power distribution network obtained after multi-source data fusion. The method and the system improve the disaster damage situation awareness capability of the power distribution network, generate a power distribution network disaster damage situation awareness model changing along with the storm time, and provide more comprehensive power distribution network information support for a dispatcher and a decision maker so as to facilitate further rescue in the disaster and recovery work after the disaster.
Description
Technical Field
The invention belongs to the technical field of situation awareness of power distribution networks, and particularly relates to a method for multi-source data fusion of a power distribution network.
Background
The distribution network is an important component part in urban infrastructure, and has the characteristics of multiple voltage levels, complex network structure, multiple equipment types, multiple operation points, relatively poor safety environment and the like. The basic data of the current domestic power distribution network is poor, the informatization means is behind, and the basic data are scattered in different systems because the power distribution network management involves different departments of development, farm electricity, operation and inspection, marketing, scheduling and the like; data standards and models are inconsistent among systems, and furthermore, a data multidimensional sharing mechanism is lacked.
However, extreme events typified by natural disasters such as rainstorm and waterlogging threaten the safe and reliable operation of the power infrastructure, so elasticity has become an inevitable requirement for the development of the power system. Elasticity is the capability of a power system to cope with various disasters and damages, namely, the system can be prevented in advance when suffering various impact events, can resist in advance, and can be quickly recovered after the event. When the elastic power grid faces to an extreme event, the elastic power grid can predict, resist, adapt and respond to the extreme event, and quickly recover from the extreme event, so that the load loss of the system can be effectively reduced.
The current power distribution network recovery scheme is effective for typical power failure events, and the current recovery scheme provides a dynamic fault recovery strategy for the power distribution network with the distributed power supply; the power distribution network emergency recovery coordination control method based on the multi-agent technology (MAS) is provided; a post-disaster on-line load recovery decision method for an urban power distribution network; the method establishes the relationship between fault location and the states of the switch and the nodes so as to optimize the network topology structure, and ensures the operation safety of the unit under the condition that a plurality of faults exist in the network or potential fault areas are not detected. The method has good effect on the quick recovery technology of the elastic power distribution network, but under the condition of changeable storm disasters, communication facilities mutually dependent on the power grid can be damaged, so that the loss information of the power distribution network is incomplete, the recovery process faces more difficulties, the differences can lead to the limitation of the current recovery practice in the aspect of coping with extreme weather events, and the time characteristics and the space characteristics of the multi-source data of the power distribution network in the process of extreme disasters are not completely considered yet, so that further research is needed.
Under the condition that information is missing, support of multi-source data is needed to obtain real-time disaster situation of the power distribution network, and how to integrate multi-source data such as system measurement data, meteorological data and user side feedback is a problem worthy of research when the multi-source data is utilized to research the quick recovery technology of the elastic power distribution network under urban storm waterlogging, and the disaster situation sensing capability of the power distribution network is improved.
Disclosure of Invention
The invention provides a power distribution network disaster damage situation sensing method, which aims to solve the defects that loss information of a power distribution network is lost and a power grid is difficult to recover in severe weather in the prior art.
The invention adopts the following technical scheme:
a power distribution network disaster damage situation sensing method based on multi-source data fusion comprises the following steps:
step 1: collecting waterlogging and ponding depths of all areas, and generating a failure rate curve of power distribution network equipment under rainstorm waterlogging;
step 2: collecting field measurement data and generating a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging;
step 3: collecting feedback information of a user side, and generating a client-level disaster damage perception model of the distribution network under rainstorm waterlogging;
step 4: carrying out weighted summation on the model data obtained in the step 1-3, and carrying out multi-source data fusion;
step 5: and performing sensing processing on the disaster situation of the power distribution network through a disaster situation sensing model of the power distribution network obtained after multi-source data fusion.
In step 1, the waterlogging and ponding depth of each area is collected and used for calculating failure rate of power distribution equipment.
When the water accumulation height of the area where the power distribution equipment is located exceeds the waterlogging prevention height, the failure rate of the power distribution equipment is quickly increased, and the failure rate lambda of the power distribution equipment at the moment t is built F (t) is of the formula:
wherein d is w (t) is the depth of grid ponding at time w; d (D) w The waterlogging prevention height is unified for the design of the power distribution station (room) and the box-type transformer substation; d (D) Bw The cable joint of the high-voltage switch cabinet in the station is high in landmark; ζ is the attenuation coefficient; gamma is the damping coefficient.
Zeta is 5000 and gamma is 1/5000.
Probability of direct failure P of a power distribution device during Δt time F (Δt) is:
P F (Δt)＝1-exp(-λ F (Δt)Δt)
λ F and (delta t) is the failure rate of the power distribution equipment in delta t time.
Establishing an indirect failure model of the power distribution equipment, wherein for the electric main equipment, the reference time-varying failure rate is as follows:
wherein beta, eta is the shape parameter of the equipment; t is the year of use, in years;
the high-voltage switch cabinet beta is 5.02, and eta is 25.
Indirect failure probability P of power distribution equipment in delta t time H (Δt) is:
P H (Δt)＝1-exp(-λ 0 (Δt)Δt)
λ 0 and (delta t) is a power distribution equipment reference time-varying fault rate within delta t time.
Within delta t time, failure probability P of power distribution network node i i J (t) is:
wherein n is the number of high-voltage switch cabinets of the power distribution network node i.
The direct failure model and the indirect failure model of the distribution equipment under the rainstorm waterlogging are mutually independent, and the comprehensive failure probability P of the power grid node i at the t moment is obtained i (t) is of the formula:
P i (t)＝1-(1-P F (t))(1-P i H (t))
P H (t) is the direct failure probability of the power distribution equipment at the moment t; p (P) F And (t) is the indirect failure probability of the power distribution equipment at the moment t.
In step 2, the status of each node of the power distribution network is determined by the fault current indicator.
The circuit breaker and the isolating switch are arranged, and the circuit between the nodes is a feeder section according to the circuit breaker and the isolating switch;
assigning a direction to each feeder area, generating a feeder network matrix [ N ]:
when node i is the starting node of the k feeder region, N kl ＝1；
When node i is the termination node of the k feeder region, N kl ＝-1；
When node i is the start or stop node of the non-k feeder region, N kl ＝0。
Defining a fault vector [ F ]:
f when the sum fault current is consistent with the appointed direction of the feeder line area according to the overcurrent information of the switch equipment l ＝1；
F when the fault current is opposite to the designated direction of the feeder line region l ＝-1；
When the sum fault current does not pass through node l, F l ＝0。
For a line having a T-shaped structure, the following method is adopted:
from [ N ]]Extracting corresponding row vectors from the matrix to generate a new matrix [ N ] T ]Fault recognition matrix [ F T ]Is [ N ] T ]And [ F] T Inner product.
Generating fault segment identification vector F s ]＝[N][F]When Fs is k If the node is not less than 1, the k feeder line area fails, otherwise, the normal operation area is formed, and the node contained in the feeder line area is in a normal state;
for the T moiety, [ F ] T ]And at least one value of the corresponding row is-1, the T structure area is in normal operation, the nodes contained in the feeder line area are in normal state, and otherwise, the nodes are in fault area.
In step 3, matching the telephone number of each call to a specific customer location by the outage management system OMS, when enough faulty calls are collected, the OMS predicting trip protection devices and faulty line sections;
the user-side call probability model can be described as:
P(y＝1)＝aexp(b·Y)+c
y is a node damage flag, when y=1, indicating that node y is damaged; y is the number of calls, a, b and c are parameters, and the number of calls Y has a direct relation with the probability of node damage.
a is-1.0457, b is-0.48106, and c is 1.0191.
In step 4, the probability of damage in the given weather situation is directly derived from the failure rate curve of the distribution network system for the distribution network elements.
Arranging observations from M information sources in a vector x (m) The following two probabilities can be obtained:
local posterior probability: p (y|x) (m) )
Likelihood function: p (x) (m) |y)
And (3) applying linear combination of local posterior probability to carry out multisource fusion on the power distribution network:
wherein w is s The weight of the mth information reflects the importance of the information.
In step 5, generating a disaster damage situation scene of the power distribution network, wherein the solving formula is as follows:
where N is the set of fault area nodes,for node j failure probability, X j A binary variable that is the state of node j, 1 representing damaged, 0 representing intact;
and solving to obtain a group of binary variables related to each node, namely a disaster damage situation scene of the power distribution network.
The utility model also discloses a disaster damage situation awareness system of the power distribution network based on the method, which comprises a power distribution network equipment failure rate curve generation module, a measurement data acquisition module, a power distribution network feeder-level disaster damage awareness model generation module, a feedback information acquisition module, a power distribution network client-level disaster damage awareness model generation module, a multi-source data fusion module and a disaster damage situation awareness processing module.
The power distribution network equipment failure rate curve generation module is used for generating a power distribution network equipment failure rate curve under rainstorm waterlogging by collecting waterlogging and water accumulation depths of all areas;
the measurement data acquisition module acquires field measurement data;
the power distribution network feeder line level disaster damage sensing model generation module generates a power distribution network feeder line level disaster damage sensing model under rainstorm waterlogging through the data acquired by the measurement data acquisition module;
the feedback information acquisition module acquires feedback information of a user side;
the power distribution network client-level disaster damage sensing model generation module generates a power distribution network client-level disaster damage sensing model under rainstorm waterlogging through the user-side feedback information acquired by the feedback information acquisition module;
the multi-source data fusion module performs weighted summation on the obtained model data and performs multi-source data fusion;
and the disaster situation sensing processing module senses and processes the disaster situation of the power distribution network through a power distribution network disaster situation sensing model obtained after multi-source data fusion.
Compared with the prior art, the method has the advantages that the existing power distribution network recovery strategy does not completely consider the time characteristics and the space characteristics of the multi-source data of the power distribution network in the extreme disaster process, and is not more ready for predicting the disaster damage situation of the power distribution network based on the multi-source data. The invention adopts a multisource data fusion technology, obtains ponding depth data of different time and different places through real-time weather, and generates element vulnerability curves of distribution network lines under rainstorm waterlogging. And generating a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging by on-site measurement data. And generating a client-level disaster sensing model of the distribution network under the rainstorm waterlogging by using user side feedback information, namely telephone feedback information of the user on the fault. By combining multi-source data, under the condition of a rainstorm and waterlogging disaster with information loss, more accurate disaster damage states of the power distribution network are acquired as far as possible through three data of measurement data, meteorological data and user calling data, disaster damage situation sensing capability of the power distribution network is improved, a power distribution network disaster damage situation sensing model changing along with the rainstorm time is generated, and more comprehensive power distribution network information support is provided for a dispatcher and a decision maker, so that rescue in the disaster and recovery work after the disaster are facilitated.
Drawings
FIG. 1 is a flow of a disaster damage situation awareness method of a power distribution network with multi-source data fusion;
FIG. 2 is a power distribution equipment direct failure probability model;
FIG. 3 is a feeder level assessment model;
FIG. 4 is a multi-source data fusion model;
FIG. 5 is an IEEE33 distribution network node diagram;
fig. 6 is a block diagram of a disaster damage situation awareness system of a power distribution network, and the block diagram comprises a power distribution network equipment failure rate curve generation module 1, a measurement data acquisition module 2, a power distribution network feeder-level disaster damage awareness model generation module 3, a feedback information acquisition module 4, a power distribution network client-level disaster damage awareness model generation module 5, a multi-source data fusion module 6 and a disaster damage situation awareness processing module 7.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
Step one: collecting waterlogging and ponding depths of all areas, and generating a failure rate curve of power distribution network equipment under rainstorm waterlogging;
and collecting waterlogging ponding depth of each area for calculating failure rate of the power distribution equipment.
Referring to fig. 2, a direct failure model of the power distribution equipment is established, when the water accumulation height of the area where the power distribution equipment is located exceeds the waterlogging prevention height, the failure rate of the power distribution equipment is rapidly increased, and the failure rate lambda of the power distribution equipment at the moment t is increased F (t) is of the formula:
wherein d is w (t) is the depth of grid ponding at time w; d (D) w For distribution stations (rooms), cabinetsThe design of the transformer substation unifies the waterlogging prevention height; d (D) Bw The cable joint of the high-voltage switch cabinet in the station is high in landmark; ζ is the attenuation coefficient; gamma is the damping coefficient.
Zeta is 5000 and gamma is 1/5000.
Probability of direct failure P of a power distribution device during Δt time F (Δt) is:
P F (Δt)＝1-exp(-λ F (Δt)Δt)
λ F and (delta t) is the failure rate of the power distribution equipment in delta t time.
Establishing an indirect failure model of the power distribution equipment, wherein for the electric main equipment, the reference time-varying failure rate is as follows:
wherein beta, eta is the shape parameter of the equipment; t is the year of use, in years;
the high-voltage switch cabinet beta is 5.02, and eta is 25.
Indirect failure probability P of power distribution equipment in delta t time H (Δt) is:
P H (Δt)＝1-exp(-λ 0 (Δt)Δt)
λ 0 and (delta t) is a power distribution equipment reference time-varying fault rate within delta t time.
Within delta t time, failure probability P of power distribution network node i i J (t) is:
wherein n is the number of high-voltage switch cabinets of the power distribution network node i.
The direct failure model and the indirect failure model of the distribution equipment under the rainstorm waterlogging are mutually independent, and the comprehensive failure probability P of the power grid node i at the t moment is obtained i (t) is of the formula:
P i (t)＝1-(1-P F (t))(1-P i H (t))
P H (t) is the direct failure probability of the power distribution equipment at the moment t; p (P) F And (t) is the indirect failure probability of the power distribution equipment at the moment t.
Step two: collecting field measurement data and generating a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging;
referring to fig. 3, the status of each node of the distribution network is determined by a fault current indicator.
The circuit breaker and the isolating switch are arranged, and the circuit between the nodes is a feeder section according to the circuit breaker and the isolating switch;
assigning a direction to each feeder area, generating a feeder network matrix [ N ]:
when node i is the starting node of the k feeder region, N kl ＝1；
When node i is the termination node of the k feeder region, N kl ＝-1；
When node i is the start or stop node of the non-k feeder region, N kl ＝0。
Defining a fault vector [ F ]:
f when the sum fault current is consistent with the appointed direction of the feeder line area according to the overcurrent information of the switch equipment l ＝1；
F when the fault current is opposite to the designated direction of the feeder line region l ＝-1；
When the sum fault current does not pass through node l, F l ＝0。
For a line having a T-shaped structure, the following method is adopted:
from [ N ]]Extracting corresponding row vectors from the matrix to generate a new matrix [ N ] T ]Fault recognition matrix [ F T ]Is [ N ] T ]And [ F] T Inner product.
Generating fault segment identification vector F s ]＝[N][F]When Fs is k If the node is not less than 1, the k feeder line area fails, otherwise, the normal operation area is formed, and the node contained in the feeder line area is in a normal state;
for the T moiety, [ F ] T ]Corresponding to the row, at least one value is-1, the T structure area is normal operation, and the feeder line area is enclosedThe contained node is in a normal state, otherwise, the node is in a fault area.
Step three: collecting feedback information of a user side, and generating a client-level disaster damage perception model of the distribution network under rainstorm waterlogging;
matching the telephone number of each call to a specific customer location by a shutdown management system OMS, the OMS predicting trip protectors and faulty line segments when sufficient faulty calls are collected;
the user-side call probability model can be described as:
P(y＝1)＝aexp(b·Y)+c
y is a node damage flag, when y=1, indicating that node y is damaged; y is the number of calls, a, b and c are parameters, and the number of calls Y has a direct relation with the probability of node damage.
a is-1.0457, b is-0.48106, and c is 1.0191.
Step four: weighting and summing the model data obtained in the steps, and carrying out multi-source data fusion;
as shown in fig. 4, for the distribution network elements, the probability of damage in a given weather situation is directly derived from the distribution network system failure rate curve.
Arranging observations from M information sources in a vector x (m) The following two probabilities can be obtained:
local posterior probability: p (y|x) (m) )
Likelihood function: p (x) (m) |y)
And (3) applying linear combination of local posterior probability to carry out multisource fusion on the power distribution network:
wherein w is s The weight of the mth information reflects the importance of the information.
Step five: and performing sensing processing on the disaster situation of the power distribution network through a disaster situation sensing model of the power distribution network obtained after multi-source data fusion.
Generating a disaster damage situation scene of the power distribution network, wherein the solving formula is as follows:
where N is the set of fault area nodes,for node j failure probability, X j A binary variable that is the state of node j, 1 representing damaged, 0 representing intact;
and solving to obtain a group of binary variables related to each node, namely a disaster damage situation scene of the power distribution network.
Referring to fig. 6, the application also discloses a disaster damage situation sensing system of the power distribution network based on the above, which comprises a power distribution network equipment failure rate curve generating module 1, a measurement data acquisition module 2, a power distribution network feeder-level disaster damage sensing model generating module 3, a feedback information acquisition module 4, a power distribution network client-level disaster damage sensing model generating module 5, a multi-source data fusion module 6 and a disaster damage situation sensing processing module 7;
the power distribution network equipment failure rate curve generation module 1 generates a power distribution network equipment failure rate curve under rainstorm waterlogging by collecting waterlogging and water accumulation depths of all areas;
the measurement data acquisition module 2 acquires field measurement data;
the power distribution network feeder-level disaster damage sensing model generation module 3 generates a power distribution network feeder-level disaster damage sensing model under rainstorm waterlogging through the data acquired by the measurement data acquisition module 2;
the feedback information acquisition module 4 acquires feedback information of a user side;
the power distribution network client-level disaster damage sensing model generation module 5 generates a power distribution network client-level disaster damage sensing model under rainstorm waterlogging through the user-side feedback information acquired by the feedback information acquisition module 4;
the multi-source data fusion module 6 performs weighted summation on the obtained model data and performs multi-source data fusion;
and the disaster situation awareness processing module 7 carries out awareness processing on the disaster situation of the power distribution network through a power distribution network disaster situation awareness model obtained after multi-source data fusion.
Compared with the prior art, the method has the advantages that the existing power distribution network recovery strategy does not completely consider the time characteristics and the space characteristics of the multi-source data of the power distribution network in the extreme disaster process, and is not more ready for predicting the disaster damage situation of the power distribution network based on the multi-source data. The invention adopts a multisource data fusion technology, obtains ponding depth data of different time and different places through real-time weather, and generates element vulnerability curves of distribution network lines under rainstorm waterlogging. And generating a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging by on-site measurement data. And generating a client-level disaster sensing model of the distribution network under the rainstorm waterlogging by using user side feedback information, namely telephone feedback information of the user on the fault. By combining multi-source data, under the condition of a rainstorm and waterlogging disaster with information loss, more accurate disaster damage states of the power distribution network are acquired as far as possible through three data of measurement data, meteorological data and user calling data, disaster damage situation sensing capability of the power distribution network is improved, a power distribution network disaster damage situation sensing model changing along with the rainstorm time is generated, and more comprehensive power distribution network information support is provided for a dispatcher and a decision maker, so that rescue in the disaster and recovery work after the disaster are facilitated.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (23)
1. The power distribution network disaster damage situation sensing method based on multi-source data fusion is characterized by comprising the following steps of:
step 1: collecting waterlogging and ponding depths of all areas, and generating a failure rate curve of power distribution network equipment under rainstorm waterlogging;
step 2: collecting field measurement data and generating a feeder line level disaster sensing model of the distribution network under rainstorm waterlogging;
step 3: collecting feedback information of a user side, and generating a client-level disaster damage perception model of the distribution network under rainstorm waterlogging;
step 4: carrying out weighted summation on the model data obtained in the step 1-3, and carrying out multi-source data fusion;
step 5: and performing sensing processing on the disaster situation of the power distribution network through a disaster situation sensing model of the power distribution network obtained after multi-source data fusion.
2. The power distribution network disaster damage situation awareness method according to claim 1, wherein:
in step 1, the waterlogging and ponding depth of each area is collected and used for calculating failure rate of power distribution equipment.
3. The power distribution network disaster damage situation awareness method according to claim 2, wherein:
when the water accumulation height of the area where the power distribution equipment is located exceeds the waterlogging prevention height, the failure rate of the power distribution equipment is quickly increased, and the failure rate lambda of the power distribution equipment at the moment t is built F (t) is of the formula:
wherein d is w (t) is the depth of grid ponding at time w; d (D) w The waterlogging prevention height is unified for the design of the power distribution station (room) and the box-type transformer substation; d (D) Bw The cable joint of the high-voltage switch cabinet in the station is high in landmark; ζ is the attenuation coefficient; gamma is the damping coefficient.
4. A method for sensing disaster damage situation of power distribution network according to claim 3, wherein:
zeta is 5000 and gamma is 1/5000.
5. The power distribution network disaster damage situation awareness method according to claim 3 or 4, wherein:
probability of direct failure P of a power distribution device during Δt time F (Δt) is:
P F (Δt)＝1-exp(-λ F (Δt)Δt)
λ F and (delta t) is the failure rate of the power distribution equipment in delta t time.
6. A method for sensing disaster damage situation of power distribution network according to claim 3, wherein:
establishing an indirect failure model of the power distribution equipment, wherein for the electric main equipment, the reference time-varying failure rate is as follows:
wherein beta, eta is the shape parameter of the equipment; t is the year of use, in years;
7. the power distribution network disaster damage situation awareness method according to claim 6, wherein:
the high-voltage switch cabinet beta is 5.02, and eta is 25.
8. The power distribution network disaster damage situation awareness method according to claim 6 or 7, wherein:
indirect failure probability P of power distribution equipment in delta t time H (Δt) is:
P H (Δt)＝1-exp(-λ 0 (Δt)Δt)
λ 0 and (delta t) is a power distribution equipment reference time-varying fault rate within delta t time.
9. The power distribution network disaster damage situation awareness method according to claim 8, wherein:
within delta t time, failure probability P of power distribution network node i i J (t) is:
wherein n is the number of high-voltage switch cabinets of the power distribution network node i.
10. The power distribution network disaster damage situation awareness method according to claim 9, wherein:
the direct failure model and the indirect failure model of the distribution equipment under the rainstorm waterlogging are mutually independent, and the comprehensive failure probability P of the power grid node i at the t moment is obtained i (t) is of the formula:
P i (t)＝1-(1-P F (t))(1-P i H (t))
P H (t) is the direct failure probability of the power distribution equipment at the moment t;P F and (t) is the indirect failure probability of the power distribution equipment at the moment t.
11. The power distribution network disaster damage situation awareness method according to claim 1, wherein:
in step 2, the status of each node of the power distribution network is determined by the fault current indicator.
12. The power distribution network disaster damage situation awareness method according to claim 11, wherein:
the circuit breaker and the isolating switch are arranged, and the circuit between the nodes is a feeder section according to the circuit breaker and the isolating switch;
assigning a direction to each feeder area, generating a feeder network matrix [ N ]:
when node i is the starting node of the k feeder region, N kl ＝1；
When node i is the termination node of the k feeder region, N kl ＝1；
When node i is the start or stop node of the non-k feeder region, N kl ＝0。
13. The power distribution network disaster damage situation awareness method according to claim 12, wherein:
defining a fault vector [ F ]:
f when the sum fault current is consistent with the appointed direction of the feeder line area according to the overcurrent information of the switch equipment l ＝1；
F when the fault current is opposite to the designated direction of the feeder line region l ＝-1；
When the sum fault current does not pass through node l, F l ＝0。
14. The power distribution network disaster damage situation awareness method of claim 13 wherein:
for a line having a T-shaped structure, the following method is adopted:
from [ N ]]Extracting corresponding row vectors from the matrix to generate a new matrix [ N ] T ]Fault recognition matrix [ F T ]Is [ N ] T ]And [ F] T Inner product.
15. The power distribution network disaster damage situation awareness method of claim 14 wherein:
generating fault segment identification vector F s ]＝[N][F]When Fs is k If the node is not less than 1, the k feeder line area fails, otherwise, the normal operation area is formed, and the node contained in the feeder line area is in a normal state;
for the T moiety, [ F ] T ]And at least one value of the corresponding row is-1, the T structure area is in normal operation, the nodes contained in the feeder line area are in normal state, and otherwise, the nodes are in fault area.
16. The power distribution network disaster damage situation awareness method of claim 15 wherein:
in step 3, matching the telephone number of each call to a specific customer location by the outage management system OMS, when enough faulty calls are collected, the OMS predicting trip protection devices and faulty line sections;
the user-side call probability model can be described as:
P(y＝1)＝aexp(b·Y)+c
y is a node damage flag, when y=1, indicating that node y is damaged; y is the number of calls, a, b and c are parameters, and the number of calls Y has a direct relation with the probability of node damage.
17. The power distribution network disaster damage situation awareness method of claim 16 wherein:
a is-1.0457, b is-0.48106, and c is 1.0191.
18. A method for sensing disaster damage situation of power distribution network according to claim 3, wherein:
in step 4, the probability of damage in the given weather situation is directly derived from the failure rate curve of the distribution network system for the distribution network elements.
19. The power distribution network disaster damage situation awareness method of claim 18 wherein:
arranging observations from M information sources in a vector x (m) The following two probabilities can be obtained:
local posterior probability: p (y|x) (m) )
Likelihood function: p (x) (m) |y)
And (3) applying linear combination of local posterior probability to carry out multisource fusion on the power distribution network:
wherein w is s The weight of the mth information reflects the importance of the information.
20. The power distribution network disaster damage situation awareness method of claim 19 wherein:
in step 5, generating a disaster damage situation scene of the power distribution network, wherein the solving formula is as follows:
where N is the set of fault area nodes,for node j failure probability, X j A binary variable that is the state of node j, 1 representing damaged, 0 representing intact;
and solving to obtain a group of binary variables related to each node, namely a disaster damage situation scene of the power distribution network.
21. The power distribution network disaster damage situation awareness system based on claims 1-20 comprises a power distribution network equipment failure rate curve generation module, a measurement data acquisition module, a power distribution network feeder level disaster damage awareness model generation module, a feedback information acquisition module, a power distribution network client level disaster damage awareness model generation module, a multi-source data fusion module and a disaster damage situation awareness processing module; the method is characterized in that:
the power distribution network equipment failure rate curve generation module is used for generating a power distribution network equipment failure rate curve under rainstorm waterlogging by collecting waterlogging and water accumulation depths of all areas;
the measurement data acquisition module acquires field measurement data;
the power distribution network feeder line level disaster damage sensing model generation module generates a power distribution network feeder line level disaster damage sensing model under rainstorm waterlogging through the data acquired by the measurement data acquisition module;
the feedback information acquisition module acquires feedback information of a user side;
the power distribution network client-level disaster damage sensing model generation module generates a power distribution network client-level disaster damage sensing model under rainstorm waterlogging through the user-side feedback information acquired by the feedback information acquisition module;
the multi-source data fusion module performs weighted summation on the obtained model data and performs multi-source data fusion;
and the disaster situation sensing processing module senses and processes the disaster situation of the power distribution network through a power distribution network disaster situation sensing model obtained after multi-source data fusion.
22. A computer system comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-20.
23. A computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor realizes the steps of the method according to any of claims 1-20.
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