Reliability Assessment Method for Power Distribution Cyber-Physical System

Abstract

The present invention discloses a reliability assessment method for a power distribution cyber-physical system (CPDS), which relates to the technical field of distribution network. The power distribution cyber-physical system is divided into three mutually exclusive subsystems, and the probability of each subsystem being in different operating states is calculated respectively, thereby obtaining the probability of the entire distribution network cyber-physical system being in different operating states. Each operating state of the system corresponds to a consequence, and the reliability index of the power distribution cyber-physical system is obtained by calculating these consequences. The reliability assessment method for the power distribution cyber-physical system has clear physical meaning, fast calculation and convergence speed, and overcomes the shortcomings of the Monte Carlo method, such as long simulation solution time and slow convergence speed.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network, and in particular relates to distribution network reliability evaluation under distribution network communication system failure. Background Art

[0002] The continuous deterioration of the global greenhouse effect and ecological environment, as well as the emergence of El Niño, have led to an increasing incidence of extreme natural disasters such as typhoons. Extreme natural disasters have caused large-scale failures in power grid facilities, and the huge economic losses caused by power outages have attracted widespread attention to the disaster response capabilities of the power grid. Distribution lines are closely related to user loads and are an important part of the power grid. Ensuring the safety and reliability of distribution lines is a concentrated reflection of the stability and economic operation of the entire power system. However, at the same time, the disaster response capabilities of distribution lines are relatively weak, and the equipment quality of distribution networks in different places is uneven. Therefore, it is necessary to conduct a technical study on the disaster response capability assessment of the distribution network, that is, reliability assessment.

[0003] Reliability assessment methods are generally divided into two types, simulation method and analytical method. The simulation method is an assessment method based on the Monte Carlo simulation method, which uses sampling technology to obtain reliability indicators for random variables involved in reliability assessment. It is slow, takes a long time to calculate, and has poor convergence performance. The analytical method enumerates each type of fault in the power grid, derives the probability of each situation and the consequences, and then calculates the reliability index of the power grid. It has the advantages of clear physical concepts, fast calculation speed, and accurate results. The traditional distribution network reliability assessment assumes that the communication system is completely reliable, but the fact is that the reliability of the communication system will affect the reliability of the distribution network. Assuming that the distribution network communication system is completely reliable will inevitably lead to a more optimistic assessment of the system reliability index.

[0004] At present, the reliability assessment of power distribution cyber-physical system (CPDS) has been studied at home and abroad. The representative ones are: 1) The reliability of the information system of the distribution network is divided into topological reliability, delay reliability and error reliability, and the reliability of CPDS is evaluated by Monte Carlo simulation. 2) The reliability of the information system is evaluated by frequency domain-time domain transformation, and the physical system of the distribution network is modeled by combining the equivalent method with the minimum path method. The current evaluation methods all use the Monte Carlo simulation method, which has a slow solution speed and poor convergence performance. It is difficult to quickly find the weak links of the distribution network and take targeted remedial measures. Therefore, a reliability assessment method for power distribution cyber-physical system is needed. Summary of the invention

[0005] The purpose of the present invention is to provide a reliability assessment method for a power distribution cyber-physical system, thereby overcoming the shortcomings of the existing distribution network logistics system evaluated by the Monte Carlo method, which has long simulation solution time and slow convergence speed.

[0006] To achieve the above object, the present invention provides a method for evaluating the reliability of a power distribution cyber-physical system, comprising the following steps:

[0007] Establish a reliability assessment model for the cyber-physical system of the distribution network based on the original data of the distribution network;

[0008] Dividing the reliability assessment model into a plurality of sub-units, and calculating the state occupancy probability vector of each unit respectively;

[0009] Constructing a state transition probability matrix of the power distribution network cyber-physical system according to the probability vector;

[0010] Calculate the consequences of each subunit failure state according to the state probability transfer matrix;

[0011] A reliability index of the power grid cyber-physical system is calculated based on the consequences.

[0012] Preferably, the reliability assessment model is divided into three units: a physical layer, a network layer and a decision layer.

[0013] Preferably, the original data of the distribution network includes: a topological diagram of the physical subsystem and information subsystem of the distribution network, and a failure rate and repair time of components of the distribution network.

[0014] Preferably, the consequences of each subunit failure state include power outage time and power outage times.

[0015] Preferably, establishing a reliability assessment model for the information-physical system of the distribution network is divided into component modeling and network link modeling.

[0016] Preferably, the network link modeling includes:

[0017] By analyzing the topological structure of the network in the reliability assessment model, a reachability matrix is ​​formed, the connectivity from the master station to each IED is analyzed, and the channel is determined;

[0018] Determine the delay reliability of the channels respectively, and select the channel with the highest delay reliability as the optimal channel;

[0019] The error reliability of the optimal channel is evaluated, thereby completing the modeling and evaluation of the network link model.

[0020] Preferably, the consequences of each subunit failure state are calculated according to the state probability transfer matrix, specifically including:

[0021] According to the process of distribution network fault handling, each minimum isolation area is divided into four situations according to the connection relationship between each minimum isolation area and the power supply;

[0022] When a network subsystem fails, the consequences of the failure are calculated based on the four situations, including the power outage time and number of outages, and the affected area is re-divided. After the area is re-divided, the power outage time and number of outages are calculated based on the four situations.

[0023] Preferably, each minimum isolation area is divided into four cases according to the connection relationship between each minimum isolation area and the power supply, specifically including:

[0024] After a fault occurs, the area that is not affected by the fault and remains connected to the main power supply is recorded as a Class A area, and the downtime time of the load in the Class A area is equal to 0;

[0025] The area that loses connection with the main power supply after a fault occurs and then restores connection with the main power supply after the disconnector is operated to isolate the fault area is recorded as a Class B area, and the outage time of the load in the Class B area is equal to the operation time of the disconnector;

[0026] The area that loses connection with the main power supply after a fault occurs and then restores power through connection with the backup power supply through a tie switch after the fault is isolated is recorded as a Class C area. The outage time of the load in the Class C area is equal to the tie switch operation time;

[0027] The area where power supply is restored only after the fault is eliminated is recorded as a Class D area, and the outage time of the load in the Class D area is equal to the fault repair time.

[0028] Preferably, it is characterized in that re-dividing the affected area comprises the following steps:

[0029] If a network fault causes the switch closest to the fault on the minimum path between the fault and the power supply to fail, the nearest operable isolating switch is sought upstream on the minimum path; wherein the minimum path of the power supply is the shortest path between the fault and the power supply.

[0030] If a network failure causes the switch closest to the fault on the minimum path between the fault and the tie switch to fail, then the nearest operable disconnector can be found downstream on this minimum path; wherein the tie switch minimum path is the shortest path between the fault and the tie switch.

[0031] Preferably, the reliability indicators include: an average power outage frequency indicator of the distribution network, an average power outage duration indicator of the distribution network, an average power supply availability indicator, and an average power outage duration indicator for households.

[0032] Compared with the existing technology, the present invention has the following beneficial effects:

[0033] 1. The reliability assessment method of the power distribution information physical system CPDS provided by the present invention is to split the power distribution information physical system CPDS into three mutually exclusive subsystems, and calculate the probability of each subsystem being in different operating states, so as to obtain the probability of the entire distribution network information physical system in different operating states. Each operating state of the system corresponds to a consequence (the number of power outages and the power outage time), and the reliability index of the power distribution information physical system is obtained by calculating these consequences. The reliability assessment method of the power distribution information physical system has clear physical meaning, fast calculation and convergence speed, and overcomes the shortcomings of the Monte Carlo method, such as long simulation solution time and slow convergence speed.

[0034] 2. In the present invention, the reliability assessment model is divided into multiple sub-units, and the state occupancy probability vector of each unit is calculated separately, that is, by enumerating each fault condition of the distribution network and calculating the power outage time and the number of power outages according to the fault condition, the physical meaning is clear.

[0035] 3. Compared with the traditional distribution network reliability calculation method, the present invention takes into account the impact of information system failures in the distribution network on distribution network fault processing, and the calculation result is more accurate.

[0036] 4. Compared with the distribution network reliability calculation method based on the Monte Carlo method, the present invention has faster calculation and convergence speed and more accurate calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flow chart of a reliability assessment method of a power distribution cyber-physical system according to the present invention;

[0039] Figure 2 is a schematic diagram of the structure of the power distribution cyber-physical system of the present invention;

[0040] Figure 3 It is a topological diagram of the power distribution cyber-physical system of the present invention. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solutions in the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, the reliability assessment method of the power distribution cyber-physical system provided by the present invention comprises the following steps:

[0043] S1. Establish a reliability assessment model for the information-physical system of the distribution network based on the original data of the distribution network;

[0044] S2. Divide the reliability assessment model into multiple sub-units, and calculate the state occupancy probability vector of each unit respectively; specifically, the reliability assessment model is divided into three units: physical layer, network layer and decision layer. Correspondingly, the state occupancy probability vector ∏ PH (t),∏ COM (t),∏ DEC (t);

[0045] S3. Constructing a state transition probability matrix of the distribution network cyber-physical system according to the probability vector;

[0046] S4, calculating the consequences of each subunit failure state according to the state probability transfer matrix;

[0047] The consequences of each subunit failure state include power outage duration and number of outages;

[0048] S5. Calculate the reliability index of the power grid cyber-physical system according to the consequences.

[0049] The above-mentioned distribution network information physical system CPDS reliability assessment method divides the distribution network information physical system into three mutually exclusive subsystems, and calculates the probability of each subsystem being in different operating states, thereby obtaining the probability of the entire distribution network information physical system in different operating states. Each operating state of the system corresponds to a consequence (number of power outages and power outage time), and the reliability index of the distribution network information physical system is obtained by calculating these consequences. The reliability assessment method of the distribution network information physical system has clear physical meaning, fast calculation and convergence speed, and overcomes the shortcomings of long simulation solution time and slow convergence speed of the Monte Carlo method.

[0050] Figure 2 The structure of the power distribution cyber-physical system is shown. The original data of the distribution network includes: the topological diagram of the physical subsystem and the information subsystem of the distribution network, and the failure rate and repair time of the components of the distribution network.

[0051] Specifically, in step S1, a reliability assessment model of the information-physical system of the distribution network is established according to the original data of the distribution network, including component modeling and network link modeling, that is, a physical subsystem and a network subsystem.

[0052] In the process of component modeling, the reliability models of physical components (such as distribution lines, transformers, etc.) and information components (such as transmission lines, switches, etc.) in the power distribution cyber-physical system CPDS are established through the two-state models of "available" and "unavailable".

[0053] In the process of network link modeling, the reliability of the network link model is measured by topology reliability C o,p (x), delay reliability D o,p (x i ,y) and the correct reliability judgment E o,p (x i ). Specifically include:

[0054] Analyze the topology of the network subsystem, form a reachability matrix, analyze the connectivity from the master station to each IED, and determine the channel;

[0055] In fact, there may be multiple channels in the communication link from the master station to a specific IED. By selecting the channel with the highest delay reliability as the optimal channel and then evaluating the error reliability of the channel, the reliability evaluation model is modeled and evaluated, thereby improving the reliability of the reliability evaluation model.

[0056] A channel x i The delay reliability evaluation of is evaluated by satisfying the following formula:

[0057]

[0058] In the above formula, o and p are channel x respectively. i The starting and ending nodes of a message are y, and y is the default delay threshold. If the transmission time exceeds y, the information transmission fails. is the channel delay probability density function, which is equivalent to the convolution of the delay probability density functions of each component in the channel, that is:

[0059]

[0060] In the above formula, f0(t)…f p (t) is the channel x i The delay probability density function of each component in;

[0061] A channel x i The error reliability evaluation of is evaluated by satisfying the following formula:

[0062]

[0063] In the above formula, e k (x i ) and e k,k+1 (x i ) represent the channel xi The probability that no error occurs in the transmission between node k and line k, k+1.

[0064] In step S2, the failure rate and repair rate of the components that make up the power distribution information physical system satisfy the exponential distribution and are independent of the time t. Therefore, the evolution of the operating state of the power distribution information physical system under component failure is expressed as a homogeneous Markov process with continuous time {X(t), t≥0} and discrete state S={0,1,2,…,n}. The operating state refers to the set of working states (available or unavailable) of all components in the system. Assume that the state of the power distribution information physical system at time t is X(t)=i, and due to component failure, the state at time t+Δt is X(t+Δt)=j. Then the conditional probability that the power distribution information physical system is in the state i at time t and in the state j at time t+Δt is expressed as:

[0065] P{X(t+Δt)=j|X(t)=i}=P{X(Δt)=j|X(0)=i}

[0066] In the above formula, i,j∈S. The above formula indicates that the probability of being in state j at time t+Δt depends only on the current state (the state at the current time t is i), and has nothing to do with the state before time t. Conditional probability is also called transition probability, that is, the above formula can be expressed as p ij (Δt). Therefore, the probability that a system is in state j at time t1 is expressed as:

[0067] in

[0068]

[0069] In the above formula, π j (t1) is the probability mass function (PMF) of the system being in state j at time t1, that is, the probability that the system is in state j at time t1. π i (0) is the PMF of the system in state i at time 0, p ij (0, t1) represents the conditional probability that the system is in state i at time 0 and in state j at time t1, and n represents the total number of states of the system. Therefore, the state occupancy probability vector π(t1) of a system can be expressed as:

[0070] π(t1)=[π1(t1) π2(t1)…π j (t1)…π n (t1)]

[0071] Given the initial state occupancy probability vector π(0) of the system, which usually indicates that the probability of the system being in a normal working state is 1 and the probability of other fault states is 0, then π(t1) can be calculated by the following formula:

[0072]

[0073] In the above formula, A represents the state transition density matrix of the system, which is calculated using the following formula:

[0074] in

[0075] i≠j and i,j∈[1,n]

[0076] In the above formula, i, j are integers; n is the total number of states of the system; q ii is the probability that the process does not change during Δt, given that the process is in state i at the beginning of the interval; q ij is the probability that the process changes during Δt given that the process is in state i at the beginning of the interval, which is related to the failure rate or repair rate of the component that causes this state;

[0077] The T matrix is ​​the state transition probability matrix of the system, and its relationship with the A matrix is:

[0078] T=A+E

[0079] In the above formula, E is the unit matrix.

[0080] Therefore, the physical layer PH (t1), network layer ∏ COM (t1) and decision-making level DEC (t1) The state occupancy probability vector of the three sub-units is expressed as:

[0081]

[0082]

[0083]

[0084] In the above formula, is the PMF of the physical layer in state M at time t1; is the PMF of the network layer in state N at time t1; since the decision layer is composed only of the distribution master station and is a single-element system, the state occupancy probability vector of the decision layer is a two-dimensional vector, where is the PMF of the decision-making layer in working state at time t1, is the PMF of the decision layer that is in a failed state at time t1.

[0085] In step S3, the state transition probability matrix MDRM of CPDS is calculated as follows:

[0086]

[0087] In the above formula, the MDRM matrix is ​​an M×N matrix, which represents all possible combinations of operating states in the CPDS. Each element in the MDRM represents the probability that the CPDS is in a three-layer duration Markov chain (CTMC) unit combination state, so that the state corresponding to each element in the MDRM can be risk assessed.

[0088] In step S4, the consequences of each subunit failure state are calculated according to the state probability transfer matrix, specifically including:

[0089] S41. The faults of the physical subsystem are considered to be at most the first order, and the network subsystem is also considered to be at most the first order. Therefore, according to the process of distribution network fault handling, each minimum isolation area is divided into four situations according to the connection relationship between each minimum isolation area and the power supply, including:

[0090] S411. After a fault occurs, the area that is not affected by the fault and remains connected to the main power supply is recorded as a Class A area, and the downtime time of the load in the Class A area is equal to 0;

[0091] S412. The area that loses connection with the main power supply after the fault occurs and then recovers the connection with the main power supply after the disconnector is operated to isolate the fault area is recorded as a Class B area. The outage time of the load in the Class B area is equal to the operation time of the disconnector, recorded as t b ;

[0092] S413. The area that loses connection with the main power supply after the fault occurs and then restores power through the connection switch to the backup power supply after the fault is isolated is recorded as a Class C area. The load outage time in the Class C area is equal to the connection switch operation time, recorded as t c ;

[0093] S414. The area where power supply is restored only after the fault is eliminated is recorded as a Class D area. The load outage time in the Class D area is equal to the fault repair time, recorded as t d ;

[0094] S42. When a network subsystem fails, the consequences caused by the failure are calculated according to the four situations for power outage time and power outage frequency, and the affected areas (areas B, C, and D) are re-divided. After the areas are re-divided, the power outage time and power outage frequency are calculated again according to the four situations. Re-dividing the affected areas includes the following steps:

[0095] S421. If a network fault causes a switch closest to the fault on the minimum path between the fault and the power supply to fail, then find the nearest operable isolating switch upstream on the minimum path; wherein the minimum path of the power supply is the shortest path between the fault and the power supply.

[0096] S422. If the network failure causes the switch closest to the fault on the minimum path between the fault and the tie switch to fail, then the nearest operable disconnector can be found downstream on the minimum path; wherein the tie switch minimum path is the shortest path between the fault and the tie switch.

[0097] For a specific state of CPDS, assuming that the physical layer subunit state is i and the network layer subsystem state is j, the probability that CPDS is in state (i, j) is MDRM (i，j) (t1), in this case the total power outage time of all loads is t (i，j) , the total number of power outages for all loads is n (i，j) Then, the frequency f of CPDS in state (i, j) is (i，j) (t1) is:

[0098]

[0099] Among them, T PH is the state transition probability matrix of the physical layer subunit, T PH(k,i) is the element in the kth row and ith column of the matrix.

[0100] In step S5, the reliability indicators of CPDS include: average power outage frequency index SAIFI of distribution network, average power outage duration index SAIDI of distribution network, average power supply availability index ASAI and average power outage duration index CAIDI of household. The calculation methods are as follows:

[0101]

[0102]

[0103]

[0104]

[0105] In the above formula, N is the total number of system users.

[0106] The following further describes the CPDS reliability calculation in conjunction with the specific parameter data of the operation of specific distribution network components. The present invention uses Python program to write the CPDS model and its algorithm so that those skilled in the art can better understand the present invention:

[0107] The CPDS reliability calculation process of this embodiment is as follows: Figure 1 As shown, the topology diagram of the physical subsystem and network subsystem of the embodiment is as follows Figure 3As shown, the parameters of the physical subsystem in the embodiment are shown in Table 1, and the parameters of the information subsystem are shown in Table 2. There are 250 users at load point A, 100 users at load point B, and 50 users at load point C. The isolation switching time of the section switch is 0.5 hours each time, and the alternative power supply switching time is 1.0 hour.

[0108] Table 1 Parameters of the physical subsystem

[0109]

[0110] Table 2 Parameters of the information subsystem

[0111]

[0112]

[0113] In step S1, the original data of the distribution network is read, including parameters such as the nodes at both ends of each component of the distribution network, the failure rate, and the average repair time, so as to establish a reliability assessment model of the distribution network cyber-physical system.

[0114] In step S2, t can be 100s, indicating that the power grid is in a steady state. According to the above calculation formula, the calculated results are as follows:

[0115] ∏ 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]

[0116] ∏ 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]

[0117] ∏ DEC (t) = [0.9999988, 0.0000012]

[0118] After calculation in step S3, the MDRM matrix of the system can be obtained as follows:

[0119] MDRM=[0.9609288415,0.0000640619,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0 024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611,0.0000640619,0.0000640619,0.0000640619,0.0024023221,0.0024023221,0.0024023221,0.0012011611,0.0012011611

[0120] 0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.000000000,0.000000000,0.000000000,0.0000000000,0.0000000000 000000,0.000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.000000000,0.000000000,0.0000000000

[0121] 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

[0122] 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

[0123] 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

[0124] 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

[0125] 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

[0126] 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

[0127] 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

[0128] 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

[0129] 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

[0130] 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

[0131] 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

[0132] 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

[0133] 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

[0134] 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

[0135] 0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000 000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000]

[0136] Step S4 is only an example for illustration. For example, if the M2 feeder in the physical subsystem fails and the information subsystem is not faulty, it is calculated that the total power outage time of all loads in this case is 325 hours and the number of power outages is 400.

[0137] The calculation results of step S5 are shown in Table 3. By comparing the reliability indicators before and after considering the information subsystem, it can be found that the reliability of the information subsystem has a certain impact on the reliability of CPDS.

[0138] Table 3 Calculation results

[0139]

[0140]

[0141] The feasibility of the method of the present invention can be verified by the calculation results of the Monte Carlo method. Since the calculation time of the method of the present invention is 0.5 seconds and the calculation time of the Monte Carlo method is 40.6 hours, in summary, the reliability evaluation method of the present invention can reliably evaluate the reliability of CPDS, effectively improve the calculation speed, have the characteristics of clear physical meaning, fast calculation and convergence speed, and can overcome the shortcomings of long simulation solution time and slow convergence speed of the Monte Carlo method.

[0142] What is disclosed above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A reliability assessment method for a power distribution cyber-physical system, characterized in that: The following steps are involved: Establish a reliability assessment model for the cyber-physical system of the distribution network based on the original data of the distribution network; The reliability assessment model is divided into multiple sub-units, and the state occupancy probability vector of each unit is calculated respectively; the reliability assessment model is divided into three units: physical layer, network layer and decision layer. Correspondingly, the state occupancy probability vector ∏ PH (t),∏ COM (t),∏ DEC (t); Constructing a state transition probability matrix of the power distribution network cyber-physical system according to the probability vector; Physical layer PH (t1), network layer ∏ COM (t1) and decision-making level DEC (t1) The state occupancy probability vector of the three sub-units is expressed as: In the above formula, is the PMF of the physical layer in state M at time t1; is the PMF of the network layer in state N at time t1; since the decision layer is composed only of the distribution master station and is a single-element system, the state occupancy probability vector of the decision layer is a two-dimensional vector, where is the PMF of the decision-making layer in working state at time t1, is the PMF of the decision layer that is in a failed state at time t1; The state transition probability matrix MDRM is calculated as follows: In the above formula, the MDRM matrix is ​​an M×N matrix, which represents all possible combinations of operating states in the CPDS; each element in the MDRM represents the probability that the CPDS is in a three-layer duration Markov chain (CTMC) unit combination state, so that the state corresponding to each element in the MDRM can be risk assessed; Calculate the consequences of each subunit failure state according to the state transition probability matrix; For a specific state of CPDS, assuming that the physical layer subunit state is i and the network layer subsystem state is j, the probability that CPDS is in state (i, j) is MDRM (i，j) (t1), in this case the total power outage time of all loads is t (i，j) , the total number of power outages for all loads is n (i，j)； Then, the frequency f of CPDS in state (i, j) is (i，j) (t1) is: Among them, T PH is the state transition probability matrix of the physical layer subunit, T PH(k,i) is the element in the kth row and ith column of the matrix; A reliability index of the power grid cyber-physical system is calculated based on the consequences.

2. The reliability assessment method of power distribution cyber-physical system according to claim 1, characterized in that: The original data of the distribution network includes: topological diagrams of the physical subsystem and information subsystem of the distribution network, and the failure rate and repair time of the components of the distribution network.

3. The reliability assessment method of power distribution cyber-physical system according to claim 1, characterized in that: The consequences of each subunit failure state include power outage time and number of power outages.

4. The reliability assessment method of power distribution cyber-physical system according to claim 1, characterized in that: The reliability assessment model of the distribution network cyber-physical system is divided into component modeling and network link modeling.

5. The reliability assessment method of power distribution cyber-physical system according to claim 4, characterized in that: The network link modeling includes: By analyzing the topological structure of the network in the reliability assessment model, a reachability matrix is ​​formed, the connectivity from the master station to each IED is analyzed, and the channel is determined; Determine the delay reliability of the channels respectively, and select the channel with the highest delay reliability as the optimal channel; The error reliability of the optimal channel is evaluated, thereby completing the modeling and evaluation of the network link model.

6. The reliability assessment method of power distribution cyber-physical system according to claim 1, characterized in that: The consequences of each subunit failure state are calculated according to the state transition probability matrix, specifically including: According to the process of distribution network fault handling, each minimum isolation area is divided into four situations according to the connection relationship between each minimum isolation area and the power supply; When a network subsystem fails, the consequences of the failure are calculated based on the four situations, including the power outage time and number of outages, and the affected area is re-divided. After the area is re-divided, the power outage time and number of outages are calculated based on the four situations.

7. The reliability assessment method of power distribution cyber-physical system according to claim 6, characterized in that: According to the connection relationship between each minimum isolation area and the power supply, each minimum isolation area is divided into four situations, including: After a fault occurs, the area that is not affected by the fault and remains connected to the main power supply is recorded as a Class A area, and the downtime time of the load in the Class A area is equal to 0; The area that loses connection with the main power supply after a fault occurs and then restores connection with the main power supply after the disconnector is operated to isolate the fault area is recorded as a Class B area, and the outage time of the load in the Class B area is equal to the operation time of the disconnector; The area that loses connection with the main power supply after a fault occurs and then restores power through connection with the backup power supply through a tie switch after the fault is isolated is recorded as a Class C area. The outage time of the load in the Class C area is equal to the tie switch operation time; The area where power supply is restored only after the fault is eliminated is recorded as a Class D area, and the outage time of the load in the Class D area is equal to the fault repair time.

8. The reliability assessment method of a power distribution cyber-physical system according to claim 6, characterized in that: Rezoning the affected area involves the following steps: If the network fault causes the switch closest to the fault on the minimum path between the fault and the power supply to fail, then the nearest operable isolating switch is found upstream of the minimum path; wherein the minimum path of the power supply is the shortest path between the fault and the power supply; If a network failure causes the switch closest to the fault on the minimum path between the fault and the tie switch to fail, then the nearest operable disconnector can be found downstream on this minimum path; wherein the tie switch minimum path is the shortest path between the fault and the tie switch.

9. The reliability assessment method of power distribution cyber-physical system according to claim 1, characterized in that: The reliability indicators include: an average power outage frequency indicator of the distribution network, an average power outage duration indicator of the distribution network, an average power supply availability indicator, and an average power outage duration indicator for each household.

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