CN114519259A - Reliability evaluation method for power distribution information physical system - Google Patents

Reliability evaluation method for power distribution information physical system Download PDF

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CN114519259A
CN114519259A CN202210079587.4A CN202210079587A CN114519259A CN 114519259 A CN114519259 A CN 114519259A CN 202210079587 A CN202210079587 A CN 202210079587A CN 114519259 A CN114519259 A CN 114519259A
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fault
power distribution
reliability
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周杨珺
李珊
张炜
张玉波
陈绍南
黄志都
欧阳健娜
奉斌
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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Abstract

The invention discloses a reliability evaluation method of a power distribution information physical system (CPDS), which relates to the technical field of power distribution networks. Each operation state of the system corresponds to one result, and the reliability index of the power distribution information physical system is obtained by calculating the results. The reliability evaluation method for the power distribution information physical system is clear in physical significance and high in operation and convergence speed, and overcomes the defects of long simulation solving time and low convergence speed of the Monte Carlo method.

Description

Reliability evaluation method for power distribution information physical system
Technical Field
The invention belongs to the technical field of power distribution networks, and particularly relates to power distribution network reliability evaluation under a power distribution network communication system fault.
Background
The continuous deterioration of the global greenhouse effect and the ecological environment and the emergence of the el nino result in the situation that extreme natural disasters such as typhoons and the like are more and more highly developed. Extreme natural disasters cause large-scale faults of power grid facilities, and huge power failure economic losses caused by the extreme natural disasters enable disaster coping capacity of the power grid to be widely concerned. The distribution line is closely related to the user load, is an important component of the power grid, and ensures the safety and reliability of the distribution line, thereby being the centralized embodiment of the stable and economic operation of the whole power system. However, the disaster response capability of the distribution line is weak, and the quality of the equipment of the distribution network is uneven in different places, so that it is necessary to perform disaster response capability evaluation technical research, that is, reliability evaluation on the distribution network.
The reliability evaluation methods are generally classified into two methods, simulation method and analysis method. The simulation method is an evaluation method for solving reliability indexes of random variables related to reliability evaluation by adopting a sampling technology based on a Monte Carlo simulation method, and has the advantages of low speed, long calculation time and poor convergence performance; the analytic method enumerates each fault of the power grid to obtain the probability of each condition and the caused consequence, thereby calculating the reliability index of the power grid. Traditional power distribution network reliability evaluation assumes that the communication system is completely reliable, and the fact is that the reliability of the communication system has an influence on the reliability of the power distribution network. The reliability index evaluation of the system is optimistic if the power distribution network communication system is completely reliable.
Currently, reliability evaluation of a power distribution physical system (CPDS) is studied at home and abroad. Representative examples are: 1) the reliability of an information system of the power distribution network is divided into topological reliability, time delay reliability and error code reliability, and the reliability of the CPDS is evaluated by combining a Monte Carlo simulation method. 2) And evaluating the reliability of the information system by using frequency domain-time domain transformation, and modeling the physical system of the power distribution network by using a method combining an equivalence method and a minimum path method. The current evaluation method utilizes a Monte Carlo simulation method, is slow in solving speed and poor in convergence performance, and is difficult to find out weak links of the power distribution network quickly and carry out targeted remedial measures. Therefore, a power distribution cyber-physical system reliability assessment method is needed.
Disclosure of Invention
The invention aims to provide a reliability evaluation method for a physical system of power distribution information, so that the defects of long simulation solving time and low convergence speed of the existing logistics system of a power distribution network through the Monte Carlo method are overcome.
In order to achieve the purpose, the invention provides a reliability evaluation method for a power distribution information physical system, which comprises the following steps:
establishing a reliability evaluation model of the power distribution network information physical system according to the original data of the power distribution network;
dividing the reliability evaluation model into a plurality of subunits, and respectively calculating the state occupation probability vector of each unit;
constructing a state transition probability matrix of the power distribution network information physical system according to the probability vector;
calculating the result of each subunit in the fault state according to the state probability transition matrix;
and calculating the reliability index of the power grid information physical system according to the result.
Preferably, the reliability assessment model is divided into: a physical layer, a network layer and a decision layer.
Preferably, the raw data of the power distribution network includes: the topological graphs of the physical subsystem and the information subsystem of the power distribution network and the fault rate and the repair time of the components of the power distribution network.
Preferably, the consequences of a fault condition in each subunit include outage time and number of blackouts.
Preferably, the reliability evaluation model of the power distribution network information physical system is established by element modeling and network link modeling.
Preferably, the network link modeling comprises:
forming an reachable matrix by analyzing a topological structure of a network in the reliability evaluation model, analyzing the connectivity of the master station to each IED, and determining a channel;
respectively judging the delay reliability of the channels, and selecting one channel with the highest delay reliability as an optimal channel;
and evaluating the error code reliability of the optimal channel, thereby completing the modeling and evaluation of a network link model.
Preferably, calculating the consequences in the fault state of each subunit according to the state probability transition matrix specifically includes:
according to the process of fault treatment of the power distribution network, dividing each minimum isolation area into four conditions according to the connection relation between each minimum isolation area and a power supply;
when the network subsystem breaks down, the power failure time and the power failure times are calculated according to the four conditions caused by the failure, the affected area is divided again, and the power failure time and the power failure times are calculated according to the four conditions after the area is divided again.
Preferably, the dividing of each minimum isolation region into four cases according to the connection relationship between each minimum isolation region and the power supply specifically includes:
after the fault occurs, recording an area which is not influenced by the fault and keeps connected with a main power supply as a type A area, wherein the outage time of the load in the type A area is equal to 0;
after a fault occurs, the connection between the main power supply and the fault area is lost, then the connection area between the main power supply and the fault area is recovered after the isolating switch is operated to isolate the fault area and is recorded as a B-type area, and the outage time of a load in the B-type area is equal to the operation time of the isolating switch;
after a fault occurs, the connection with a main power supply is lost, then after the fault is isolated, a region which is connected with a standby power supply through a tie switch and is recovered to be electrified is marked as a C-type region, and the outage time of a load in the C-type region is equal to the operation time of the tie switch;
and recording an area for recovering power supply as a D-type area until the fault is eliminated, wherein the outage time of the load in the D-type area is equal to the repair time of the fault.
Preferably, characterised in that the repartitioning of the affected area comprises the following steps:
if the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the power supply to have the fault, searching the closest isolating switch capable of acting on the upstream of the minimum path; and the minimum path of the power supply is the shortest path between the fault and the power supply.
If the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the interconnection switch to have the fault, searching the closest isolating switch capable of acting on the minimum path to the downstream; and the minimum path of the interconnection switch is the shortest path between the fault and the interconnection switch.
Preferably, the reliability index includes: the average power failure frequency index of the power distribution network, the average power failure duration index of the power distribution network, the average power supply availability index and the average power failure duration index of a user.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the reliability evaluation method for the CPDS, the CPDS is divided into three mutually exclusive subsystems, and the probabilities of the subsystems in different operation states are calculated respectively, so that the probabilities of the whole CPDS in different operation states are obtained. Each operation state of the system corresponds to an effect (power failure times and power failure time), and the reliability index of the power distribution information physical system is obtained by calculating the effects. The reliability evaluation method for the power distribution information physical system is clear in physical significance and high in operation and convergence speed, and overcomes the defects of long simulation solving time and low convergence speed of the Monte Carlo method.
2. In the invention, the reliability evaluation model is divided into a plurality of subunits, and the state occupation probability vector of each unit is respectively calculated, namely, the physical significance is clear by enumerating each fault condition of the power distribution network and calculating the power failure time and the power failure times according to the fault condition.
3. Compared with the traditional power distribution network reliability calculation method, the method considers the influence of the information system fault in the power distribution network on the fault processing of the power distribution network, and the calculation result is more accurate.
4. Compared with a distribution network reliability calculation method based on a Monte Carlo method, the method has the advantages that the calculation and convergence speed is higher, and the calculation result is more accurate.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a method for evaluating reliability of a physical system of power distribution information according to the present invention;
FIG. 2 is a schematic diagram of the physical system of the electrical distribution information of the present invention;
fig. 3 is a topology diagram of the power distribution cyber-physical system of the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the reliability evaluation method for the physical system of power distribution information provided by the present invention includes the following steps:
s1, establishing a reliability evaluation model of the power distribution network information physical system according to the power distribution network original data;
s2, dividing the reliability evaluation model into a plurality of subunits, and respectively calculating the state occupation probability vector of each unit; specifically, the reliability evaluation model is divided into: three units of physical layer, network layer and decision layer, corresponding to the state occupation probability vector IIPH(t)、∏COM(t)、∏DEC(t)；
S3, constructing a state transition probability matrix of the power distribution network information physical system according to the probability vector;
s4, calculating the result of each subunit in the fault state according to the state probability transition matrix;
the consequences in the fault state of each subunit comprise power failure time and power failure times;
and S5, calculating the reliability index of the power grid information physical system according to the result.
According to the reliability evaluation method for the CPDS, the distribution information physical system is divided into three mutually exclusive subsystems, and the probability of each subsystem in different operation states is calculated respectively, so that the probability of the whole distribution information physical system in different operation states is obtained. Each operation state of the system corresponds to an effect (power failure times and power failure time), and the reliability index of the power distribution information physical system is obtained by calculating the effects. The reliability evaluation method for the power distribution information physical system is clear in physical significance and high in operation and convergence speed, and overcomes the defects of long simulation solving time and low convergence speed of the Monte Carlo method.
Fig. 2 shows the structure of a power distribution cyber-physical system, and the power distribution cyber-physical system raw data includes: the topological graphs of the physical subsystem and the information subsystem of the power distribution network and the fault rate and the repair time of the components of the power distribution network.
Specifically, in step S1, the reliability evaluation model of the power distribution network information physical system, which includes element modeling and network link modeling, is established according to the power distribution network raw data, that is, the physical subsystem and the network subsystem.
In the process of element modeling, reliability models of physical elements (such as distribution lines, transformers and the like) and information elements (such as transmission lines, switches and the like) in the distribution information physical system CPDS are established through an available state model and an unavailable state model.
In the process of network link modeling, the reliability of the network link model passes through the topological reliability Co,p(x) Delay reliability Do,p(xiY) and error-free reliability judgment Eo,p(xi). The method specifically comprises the following steps:
analyzing the topology of a network subsystem to form a reachable matrix, analyzing the connectivity of a master station to each IED, and determining a channel;
in fact, a plurality of channels may exist in a communication link from the master station to a specific IED, and one channel with the highest delay reliability is selected as an optimal channel, and error code reliability is evaluated on the channel, so that modeling evaluation on a reliability evaluation model is completed, and reliability of the reliability evaluation model is improved.
One channel xiThe delay reliability evaluation of (a) is evaluated by satisfying the following formula:
Figure BDA0003485362100000061
in the above formula, o and p are channels x respectivelyiY is a default delay threshold, and if the transmission time exceeds y, the information transmission fails.
Figure BDA0003485362100000062
The channel delay probability density function is equivalent to the convolution of the delay probability density functions of each element in the channel, namely:
Figure BDA0003485362100000063
in the above formula, f0(t)…fp(t) is channel xiDelay probability density function of each element;
one channel xiThe error reliability evaluation of (a) is evaluated by satisfying the following formula:
Figure BDA0003485362100000064
in the above formula, ek(xi) And ek,k+1(xi) Respectively represent channels xiThe probability that no error occurs in the transmission of the middle node k and the line k, k + 1.
In step S2, the failure rate and the repair rate of the components constituting the electrical distribution cyber-physical system satisfy an exponential distribution regardless of the time t, so that the evolution of the operation state of the electrical distribution cyber-physical system under the component failure is represented as a homogeneous markov process having a continuous time { x (t) ≧ 0} and a discrete state S ═ 0,1,2, …, n }. The run state refers to the set of operating states (available or unavailable) of all components in the system. The state of the constituent distribution information physical system at time t is set to X (t) ═ i, and the state at time t + Δ t is set to X (t + Δ t) ═ j due to a component failure. Then the conditional probability that the state of the electrical distribution cyber-physical system is i at time t and j at time t + Δ t is expressed as:
P{X(t+Δt)＝j|X(t)＝i}＝P{X(Δt)＝j|X(0)＝i}
in the above formula, i, j ∈ S. The above equation indicates that the probability of being in state j at time t + Δ t depends only on the current state (i for the current time t) and is independent of the state before time t. Conditional probabilities, also called transition probabilities, i.e. the above expression can be expressed as pij(Delta t). Thus, a system is at t1The probability that a time is in state j is expressed as:
Figure BDA0003485362100000071
wherein
Figure BDA0003485362100000072
In the above formula, pij(t1) Is that the system is at t1Probability Mass Function (PMF) at time j, i.e. system at t1Probability of a time being in state j. Pii(0) Is the PMF, p of the system in state i at time 0ij(0,t1) Indicating that the state of the system at time 0 is i and at t1The time state is the conditional probability of j, and n represents the total number of states of the system. Thus, the state occupation probability vector pi (t) of a system1) Can be prepared byExpressed as:
π(t1)＝[π1(t1) π2(t1)…πj(t1)…πn(t1)]
given an initial state occupancy probability vector of the system, pi (0), this state occupancy probability vector generally represents a probability of 1 for a system in a normal operating state, and a probability of 0 for other fault states, then pi (t)1) It can be calculated by the following equation:
Figure BDA0003485362100000073
in the above equation, a represents the state transition density matrix of the system, which is calculated by the following equation:
Figure BDA0003485362100000074
wherein
Figure BDA0003485362100000075
i ≠ j and i, j ∈ [1, n ]]
In the above formula, i, j are integers; n is the total number of states of the system; q. q.siiIs the probability that the process does not change during Δ t, given that the process is in state i at the beginning of the interval; q. q.sijIs the probability that the process will change during Δ t, given that the process is in state i at the beginning of the interval, in relation to the failure rate or repair rate of the component that caused the state;
the T matrix is a state transition probability matrix of the system, and the relation between the T matrix and the A matrix is as follows:
T＝A+E
in the above formula, E is an identity matrix.
Thus, physical layer IIPH(t1) Network layer piCOM(t1) And decision layer IIDEC(t1) The state occupancy probability vector for the three subunits is represented as:
Figure BDA0003485362100000076
Figure BDA0003485362100000081
Figure BDA0003485362100000082
in the above formula, the first and second carbon atoms are,
Figure BDA0003485362100000083
for the physical layer at t1PMF in state M at the moment;
Figure BDA0003485362100000084
at t for the network layer1PMF in state N at the moment; since the decision layer is only composed of the power distribution main station and is a single-element system, the state occupation probability vector of the decision layer is a two-dimensional vector, wherein
Figure BDA0003485362100000085
For decision layer at t1The PMF that is in an active state at the moment,
Figure BDA0003485362100000086
for decision layer at t1PMF in failure state at the moment.
In step S3, the calculation method of the state transition probability matrix MDRM of the CPDS is:
Figure BDA0003485362100000087
in the above formula, the MDRM matrix is an M × N matrix, which represents all possible operation state combinations in the CPDS. Each element in the MDRM represents a probability that the CPDS is in a combined state of a markov chain (CTMC) unit of three-layer duration, so that a risk assessment can be performed on a corresponding state of each element in the MDRM.
In step S4, calculating the consequences in the failure state of each subunit according to the state probability transition matrix, specifically including:
s41, considering the failure of the physical subsystem to the first order at most, and considering the failure of the network subsystem to the first order at most, so that according to the process of processing the failure of the power distribution network, the minimum isolation regions are divided into four cases according to the connection relationship between the minimum isolation regions and the power supply, which specifically includes:
s411, after a fault occurs, recording an area which is not influenced by the fault and is kept connected with a main power supply as a class A area, wherein the outage time of a load in the class A area is equal to 0;
s412, after the fault occurs, the connection between the main power supply and the fault area is lost, then the connection area between the main power supply and the fault area is recovered after the isolating switch is operated to isolate the fault area, and the area in which the outage time of the load in the B type area is equal to the operation time of the isolating switch and is recorded as tb；
S413, after a fault occurs, the fault is disconnected with a main power supply, then after fault isolation, a region which is connected with a standby power supply through a tie switch and is recovered to be electrified is recorded as a C-type region, the outage time of a load in the C-type region is equal to the operation time of the tie switch and is recorded as tc；
S414, recording an area which is not recovered to be supplied with power until the fault is eliminated as a D-type area, wherein the outage time of the load in the D-type area is equal to the repair time of the fault and is recorded as td；
S42, when the network subsystem breaks down, calculating the power failure time and the power failure times according to the four conditions for the consequences caused by the failure, and re-dividing the affected area (B, C, D area), and calculating the power failure time and the power failure times according to the four conditions after re-dividing the area, wherein the re-dividing the affected area comprises the following steps:
s421, if the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the power supply to have the fault, searching the closest isolating switch capable of acting on the upstream of the minimum path; and the minimum path of the power supply is the shortest path between the fault and the power supply.
S422, if the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the tie switch to have the fault, searching the closest isolating switch capable of acting on the minimum path to the downstream; and the minimum path of the tie switch is the shortest path between the fault and the tie switch.
For a specific state of the CPDS, assuming that the physical layer subunit state is i and the network layer subsystem state is j, the probability that the CPDS is in the state (i, j) is MDRM(i，j)(t1) In this case, the total of the power-off time of all the loads is t(i，j)The sum of the power failure times of all the loads is n(i，j). Then, the frequency f of the CPDS in state (i, j)(i，j)(t1) Comprises the following steps:
Figure BDA0003485362100000091
wherein, TPHIs a state transition probability matrix, T, of a physical layer subunitPH(k,i)The k-th row and i-th column of the matrix.
In step S5, the reliability index of the CPDS includes: the average power failure frequency index SAIFI of the power distribution network, the average power failure duration index SAIDI of the power distribution network, the average power supply availability index ASAI and the average power failure duration index CAIDI of a user. The calculation methods are respectively as follows:
Figure BDA0003485362100000092
Figure BDA0003485362100000093
Figure BDA0003485362100000094
Figure BDA0003485362100000101
in the above formula, N is the total number of system users.
The following further describes the calculation of the CPDS reliability by combining specific parameter data of specific power distribution network element operation, and the CPDS model and its algorithm are written by using Python program in the present invention, so that those skilled in the art can understand the present invention more:
the CPDS reliability calculation flow in this embodiment is shown in fig. 1, the topology diagram of the physical subsystem and the network subsystem in this embodiment is shown in fig. 3, the parameters of the physical subsystem in this embodiment are shown in table 1, and the parameters of the information subsystem are shown in table 2. Load point a has 250 users, load point B has 100 users, and load point C has 50 users. The isolation switching time of the section switch is 0.5 hour each time, and the switching time of the alternative power supply is 1.0 hour.
TABLE 1 parameters of physical subsystems
Figure BDA0003485362100000102
TABLE 2 parameters of the information subsystem
Figure BDA0003485362100000103
Figure BDA0003485362100000111
In step S1, the reading of the original data of the distribution network includes: and forming parameters such as nodes at two ends of each element of the power distribution network, fault rate, average repair time and the like, and establishing a reliability evaluation model of the power distribution network information physical system.
In step S2, t may take 100S, which indicates that the power grid is in a steady state, and the following result is obtained by calculation according to the above calculation formula:
∏PH(t)＝[0.99962,0.00000,0.00000,0.00000,0.00003,0.00006,0.00009,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00003,0.00010,0.00007,0.00000]
∏COM(t)＝[0.96129,0.00006,0.00240,0.00240,0.00240,0.00120,0.00120,0.00240,0.00240,0.00240,0.00120,0.00120,0.00240,0.00240,0.00240,0.00120,0.00120,0.00006,0.00006,0.00006,0.00240,0.00240,0.00240,0.00120,0.00120]
∏DEC(t)＝[0.9999988,0.0000012]
the MDRM matrix of the system can be obtained through the calculation of step S3 as follows:
MDRM＝[0.9609288415,0.0000640619,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611,0.0000640619,0.0000640619,0.0000640619,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000274238,0.0000000018,0.0000000686,0.0000000686,0.0000000686,0.0000000343,0.0000000343,0.0000000686,0.0000000686,0.0000000686,0.0000000343,0.0000000343,0.0000000686,0.0000000686,0.0000000686,0.0000000343,0.0000000343,0.0000000018,0.0000000018,0.0000000018,0.0000000686,0.0000000686,0.0000000686,0.0000000343,0.0000000343
0.0000548475,0.0000000037,0.0000001371,0.0000001371,0.0000001371,0.0000000686,0.0000000686,0.0000001371,0.0000001371,0.0000001371,0.0000000686,0.0000000686,0.0000001371,0.0000001371,0.0000001371,0.0000000686,0.0000000686,0.0000000037,0.0000000037,0.0000000037,0.0000001371,0.0000001371,0.0000001371,0.0000000686,0.0000000686
0.0000822713,0.0000000055,0.0000002057,0.0000002057,0.0000002057,0.0000001028,0.0000001028,0.0000002057,0.0000002057,0.0000002057,0.0000001028,0.0000001028,0.0000002057,0.0000002057,0.0000002057,0.0000001028,0.0000001028,0.0000000055,0.0000000055,0.0000000055,0.0000002057,0.0000002057,0.0000002057,0.0000001028,0.0000001028
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000
0.0000329085,0.0000000022,0.0000000823,0.0000000823,0.0000000823,0.0000000411,0.0000000411,0.0000000823,0.0000000823,0.0000000823,0.0000000411,0.0000000411,0.0000000823,0.0000000823,0.0000000823,0.0000000411,0.0000000411,0.0000000022,0.0000000022,0.0000000022,0.0000000823,0.0000000823,0.0000000823,0.0000000411,0.0000000411
0.0000987256,0.0000000066,0.0000002468,0.0000002468,0.0000002468,0.0000001234,0.0000001234,0.0000002468,0.0000002468,0.0000002468,0.0000001234,0.0000001234,0.0000002468,0.0000002468,0.0000002468,0.0000001234,0.0000001234,0.0000000066,0.0000000066,0.0000000066,0.0000002468,0.0000002468,0.0000002468,0.0000001234,0.0000001234
0.0000658170,0.0000000044,0.0000001645,0.0000001645,0.0000001645,0.0000000823,0.0000000823,0.0000001645,0.0000001645,0.0000001645,0.0000000823,0.0000000823,0.0000001645,0.0000001645,0.0000001645,0.0000000823,0.0000000823,0.0000000044,0.0000000044,0.0000000044,0.0000001645,0.0000001645,0.0000001645,0.0000000823,0.0000000823
0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000]
step S4 is only an example. Taking the case that the M2 feeder line in the physical subsystem fails and the information subsystem fails, the power failure time of the sum of all loads in the case is 325 hours, and the power failure times are 400 times.
The calculation result of step S5 is shown in table 3, and the reliability of the information subsystem can be found to have a certain influence on the reliability of the CPDS by comparing and considering the reliability indexes before and after the information subsystem.
TABLE 3 results of calculation
Figure BDA0003485362100000141
Figure BDA0003485362100000151
The feasibility of the method can be verified by the calculation result of the Monte Carlo method. In conclusion, the reliability evaluation method can reliably evaluate the reliability of the CPDS, effectively improves the calculation speed, has the characteristics of clear physical significance and the like, is high in operation and convergence speed, and can overcome the defects of long simulation solving time and low convergence speed of the Monte Carlo method.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.

Claims (10)

1. A reliability evaluation method for a power distribution information physical system is characterized by comprising the following steps:
establishing a reliability evaluation model of the power distribution network information physical system according to the original data of the power distribution network;
dividing the reliability evaluation model into a plurality of subunits, and respectively calculating the state occupation probability vector of each unit;
constructing a state transition probability matrix of the power distribution network information physical system according to the probability vector;
calculating the result of each subunit in the fault state according to the state probability transition matrix;
and calculating the reliability index of the power grid information physical system according to the result.
2. The reliability assessment method of the power distribution cyber-physical system according to claim 1, wherein the reliability assessment model is divided into: a physical layer, a network layer and a decision layer.
3. The physical system reliability assessment method according to claim 1, wherein said distribution network raw data comprises: the topological graphs of the physical subsystem and the information subsystem of the power distribution network and the fault rate and the repair time of the components of the power distribution network.
4. The physical system reliability assessment method according to claim 1, wherein the consequences of each subunit failure state include outage time and number of blackouts.
5. The reliability assessment method for the power distribution cyber-physical system according to claim 1, wherein the reliability assessment model for the cyber-physical system of the power distribution cyber-physical system is established by being divided into component modeling and network link modeling.
6. The physical system reliability assessment method according to claim 5, wherein said network link modeling comprises:
forming an reachable matrix by analyzing a topological structure of a network in the reliability evaluation model, analyzing the connectivity of the master station to each IED, and determining a channel;
respectively judging the delay reliability of the channels, and selecting one channel with the highest delay reliability as an optimal channel;
and evaluating the error code reliability of the optimal channel, thereby completing the modeling and evaluation of a network link model.
7. The reliability assessment method for the power distribution information physical system according to claim 1, wherein the calculating of the consequences in the fault state of each subunit according to the state probability transition matrix specifically comprises:
according to the process of fault treatment of the power distribution network, dividing each minimum isolation area into four conditions according to the connection relation between each minimum isolation area and a power supply;
when the network subsystem breaks down, the power failure time and the power failure times are calculated according to the four conditions caused by the failure, the affected area is divided again, and the power failure time and the power failure times are calculated according to the four conditions after the area is divided again.
8. The physical system reliability assessment method according to claim 7, wherein the minimum isolation regions are divided into four cases according to the connection relationship between the minimum isolation regions and the power supply, specifically comprising:
after the fault occurs, recording an area which is not influenced by the fault and keeps connected with a main power supply as a type A area, wherein the outage time of the load in the type A area is equal to 0;
after a fault occurs, the connection between the main power supply and the fault area is lost, then the connection area between the main power supply and the fault area is recovered after the isolating switch is operated to isolate the fault area and is recorded as a B-type area, and the outage time of a load in the B-type area is equal to the operation time of the isolating switch;
after a fault occurs, the connection with a main power supply is lost, then after the fault is isolated, a region which is connected with a standby power supply through a tie switch and is recovered to be electrified is marked as a C-type region, and the outage time of a load in the C-type region is equal to the operation time of the tie switch;
and recording an area for recovering power supply as a D-type area until the fault is eliminated, wherein the outage time of the load in the D-type area is equal to the repair time of the fault.
9. The physical system reliability assessment method according to claim 1, wherein the repartitioning of the affected area comprises the steps of:
if the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the power supply to have the fault, searching the closest isolating switch capable of acting on the upstream of the minimum path; and the minimum path of the power supply is the shortest path between the fault and the power supply.
If the network fault causes the fault and the action of the switch closest to the fault on the minimum path of the interconnection switch to have the fault, searching the closest isolating switch capable of acting on the minimum path to the downstream; and the minimum path of the tie switch is the shortest path between the fault and the tie switch.
10. The physical system reliability assessment method according to claim 1, wherein said reliability indicators comprise: the average power failure frequency index of the power distribution network, the average power failure duration index of the power distribution network, the average power supply availability index and the average power failure duration index of a user.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115801546A (en) * 2023-02-09 2023-03-14 四川大学 Reliability evaluation method for power distribution network information physical system considering information disturbance

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115801546A (en) * 2023-02-09 2023-03-14 四川大学 Reliability evaluation method for power distribution network information physical system considering information disturbance

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