Method and device for calculating gas supply reliability of natural gas pipeline system

Abstract

The application provides a gas supply reliability calculation method and device for a natural gas pipeline system, and the method comprises the following steps: constructing a BN network (Bayesian network) according to the state of a pipe section, the state of a compressor and the topological structure in the natural gas pipeline system; determining the maximum gas supply amount of the natural gas pipeline system under different states, the instantaneous failure probability of the pipe section and the compressor according to the BN network; and calculating the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor. The application overcomes the determination of low solving efficiency and high calculation cost caused by traditional Monte Carlo mass sampling, and realizes the fast inference of the gas supply reliability of the pipeline network system only according to the real-time reliability of the key equipment in the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of natural gas transmission, in particular to the technical field of reliability calculation of natural gas pipeline network, and specifically relates to a gas supply reliability calculation method and device for a natural gas pipeline system. BACKGROUND

[0002] In recent years, the proportion of natural gas in energy consumption has rapidly increased, and the pipeline network stably transports natural gas to downstream residential users and factories for the purpose of ensuring social production and promoting social and economic development. The reliable operation of the natural gas pipeline network plays a crucial role in energy supply safety and economic stability.

[0003] The gas supply reliability of a natural gas pipeline refers to the ability to deliver natural gas to end users within a specified time under given environmental and operating conditions. According to different definitions of time periods, the gas supply reliability of a natural gas pipeline includes design period and operation period. For the design period, researchers mainly study the structure of the network, the reliability of the units, and the redundancy of the key equipment, with the purpose of improving operational reliability and reducing accident risks. As for the operation period, an important task is to calculate the pipeline gas supply capacity under the interference of external uncertainties. However, for multi-scenario and multi-unit natural gas pipeline systems, there are still certain limitations in the existing technology in terms of providing an accurate and fast gas supply reliability evaluation method.

[0004] The main problems include the following two points: On the one hand, natural gas pipelines have long distances, large spatial spans, and numerous and complex equipment, with obvious differences in the operating environment and working state of each device, making it difficult to accurately describe the degradation process of the equipment and facilities and to quantitatively evaluate the probability of the unit working normally at a certain time. On the other hand, in the existing technology, the reliability calculation method represented by the reliability block diagram focuses on the logical structure of the units within the system and emphasizes the analysis of the system reliability in the design phase. However, the actual natural gas transmission process is coupled with a complex water and thermal process, and further exploration of a gas supply reliability evaluation method suitable for natural gas pipeline systems is needed. SUMMARY

[0005] In view of the problems in the prior art, the gas supply reliability calculation method and device for a natural gas pipeline system proposed by the present application overcomes the technical pain points of low solution efficiency and high calculation cost caused by traditional Monte Carlo mass sampling, and realizes the rapid inference of the gas supply reliability of the pipeline network system only based on the real-time reliability of important pipe sections and compressors within the natural gas pipeline network system. The present application promotes the basic research of the online evaluation theory of the natural gas pipeline network, and also has high use value due to its relatively low computational burden and high estimation accuracy.

[0006] In a first aspect, the present application provides a gas supply reliability calculation method for a natural gas pipeline system, comprising:

[0007] constructing a BN network according to a pipe segment state, a compressor state, and a topology structure in a natural gas pipeline system;

[0008] determining a maximum gas supply amount of the natural gas pipeline system in different states, and a transient failure probability of the pipe segment and the compressor according to the BN network;

[0009] calculating a gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the transient failure probability of the pipe segment, and the transient failure probability of the compressor.

[0010] In one embodiment, the constructing a BN network according to a pipe segment state, a compressor state, and a topology structure in a natural gas pipeline system comprises:

[0011] generating nodes of the BN network according to the pipe segment state and the compressor state;

[0012] generating edges between the nodes according to the topology structure;

[0013] constructing the BN network according to the nodes.

[0014] In one embodiment, the determining a transient failure probability of the pipe segment and the compressor according to the BN network comprises:

[0015] determining a Markov process of the pipe segment according to the BN network and a current state of the pipe segment;

[0016] determining the transient failure probability of the pipe segment according to the Markov process of the pipe segment;

[0017] determining a Markov process of the compressor according to the BN network and a current state of the compressor;

[0018] determining the transient failure probability of the compressor according to the Markov process of the compressor;

[0019] the Markov process comprises a normal state, a degradation state, and an interruption state.

[0020] In one embodiment, the determining a maximum gas supply amount of the natural gas pipeline system in different states according to the BN network comprises:

[0021] calculating an edge probability of the BN network;

[0022] calculating an occurrence probability of a user gas shortage of the natural gas pipeline system when a pipe segment is in the interruption state according to the edge probability;

[0023] determining the maximum gas supply amount according to the occurrence probability.

[0024] In an embodiment, the calculating the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor comprises:

[0025] determining the prior probability of the BN network according to the instantaneous failure probability;

[0026] determining the conditional probability of the BN network according to the maximum gas supply amount;

[0027] calculating the gas supply reliability of the natural gas pipeline system according to the prior probability and the conditional probability.

[0028] In a second aspect, the present application provides a device for calculating the gas supply reliability of a natural gas pipeline system, which comprises:

[0029] a network constructing module, configured to construct a BN network according to the state of a pipe section, the state of a compressor and the topology structure in the natural gas pipeline system;

[0030] a gas supply amount determining module, configured to determine the maximum gas supply amount of the natural gas pipeline system in different states, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor according to the BN network;

[0031] a reliability calculating module, configured to calculate the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor.

[0032] In an embodiment, the network constructing module comprises:

[0033] a node generating unit, configured to generate nodes of the BN network according to the state of the pipe section and the state of the compressor;

[0034] an edge generating unit, configured to generate edges between nodes according to the topology structure;

[0035] a network constructing unit, configured to construct the BN network according to the nodes.

[0036] In an embodiment, the gas supply amount determining module comprises:

[0037] a pipe section process determining unit, configured to determine the Markov process of the pipe section according to the BN network and the current state of the pipe section;

[0038] a pipe section probability determining unit, configured to determine the instantaneous failure probability of the pipe section according to the Markov process of the pipe section;

[0039] a compressor process determining unit configured to determine a Markov process of the compressor according to the BN network and a current state of the compressor;

[0040] a compressor probability determining unit configured to determine an instantaneous failure probability of the compressor according to the Markov process of the compressor;

[0041] the Markov process comprises a normal state, a degradation state and an interruption state.

[0042] In an embodiment, the gas amount determining module further comprises:

[0043] an edge probability calculating unit configured to calculate an edge probability of the BN network;

[0044] an occurrence probability calculating unit configured to calculate an occurrence probability of the natural gas pipeline system in which a pipe section is in the interruption state according to the edge probability;

[0045] a maximum gas supply amount determining unit configured to determine the maximum gas supply amount according to the occurrence probability.

[0046] In an embodiment, the reliability calculating module comprises:

[0047] a prior probability determining unit configured to determine a prior probability of the BN network according to the instantaneous failure probability;

[0048] a conditional probability determining unit configured to determine a conditional probability of the BN network according to the maximum gas supply amount;

[0049] a reliability calculating unit configured to calculate a gas supply reliability of the natural gas pipeline system according to the prior probability and the conditional probability.

[0050] In a third aspect, the present application provides an electronic device, comprising a memory, a processor and a determining machine program stored in the memory and executable on the processor, wherein the processor implements the steps of the gas supply reliability calculation method of the natural gas pipeline system when executing the program.

[0051] In a fourth aspect, the present application provides a determining machine readable storage medium, which stores a determining machine program, wherein the determining machine program is executable on a processor to implement the steps of the gas supply reliability calculation method of the natural gas pipeline system.

[0052] From the above description, the natural gas pipeline system gas supply reliability calculation method and device provided by the embodiment of the application first constructs a BN network according to the pipe section state, compressor state and topology structure in the natural gas pipeline system; then determines the maximum gas supply amount, instantaneous failure probability of the pipe section and the compressor of the natural gas pipeline system in different states according to the BN network; and finally calculates the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, instantaneous failure probability of the pipe section and instantaneous failure probability of the compressor. The application overcomes the determination of low solving efficiency and high calculation cost caused by the traditional Monte Carlo mass sampling, realizes the rapid inference of the gas supply reliability of the pipe network system only according to the real-time reliability of the key equipment in the system, and promotes the basic research of the online evaluation theory of the natural gas pipe network. On the one hand, the application promotes the basic research of the online evaluation theory of the natural gas pipe network, and on the other hand, due to the relatively low calculation burden and high estimation accuracy, the application also has high use value. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0054] Figure 1 The flowchart of the natural gas pipeline system gas supply reliability calculation method in the embodiment of the application;

[0055] Figure 2 The flowchart of step 100 in the embodiment of the application;

[0056] Figure 3 The Bayesian network structure diagram in the embodiment of the application;

[0057] Figure 4 The flowchart of step 200 in the embodiment of the application Figure 1 ;

[0058] Figure 5 The device state transition process diagram in the embodiment of the application;

[0059] Figure 6 The flowchart of step 200 in the embodiment of the application Figure 2 ;

[0060] Figure 7 The flowchart of step 300 in the embodiment of the application;

[0061] Figure 8 The flowchart of the natural gas pipeline system gas supply reliability calculation method in the specific application example of the application;

[0062] Figure 9 Fig. 1 is a diagram showing the probability of different states of users in a specific application example of the present application changing over time;

[0063] Figure 10 Fig. 2 is a diagram showing the global gas shortage loss of a system in a specific application example of the present application;

[0064] Figure 11 Fig. 3 is a diagram showing the composition of a gas supply reliability calculation device for a natural gas pipeline system in an embodiment of the present application;

[0065] Figure 12 Fig. 4 is a diagram showing the composition of a network construction module 10 in an embodiment of the present application;

[0066] Figure 13 Fig. 5 is a diagram showing the composition of a gas supply amount determination module 20 in an embodiment of the present application Figure 1 ;

[0067] Figure 14 Fig. 6 is a diagram showing the composition of a gas supply amount determination module 20 in an embodiment of the present application Figure 2 ;

[0068] Figure 15 Fig. 7 is a diagram showing the composition of a reliability calculation module 30 in an embodiment of the present application;

[0069] Figure 16 Fig. 8 is a diagram showing the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to make the objects, technical solutions, and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0071] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0072] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and in the claims and the above description of the drawings are intended to cover both the exclusive and the non-exclusive inclusion of the stated steps or elements, i.e., the inclusion of the stated steps or elements is not exclusive of other steps or elements not specifically recited. It should be noted that the embodiments and features of the embodiments in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0073] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0074] In the prior art, the problems in the background art have attracted attention in the engineering field. From the publication of the current research results, there are mainly two methods: 1. Based on commercial software to evaluate the gas supply capacity of the pipeline system; 2. Convert the topological structure of the pipe network into graph theory, and introduce the maximum flow algorithm to calculate the maximum gas supply capacity of the pipeline. Specifically:

[0075] For the first method, based on the idea of the former, Wei ChaoYu proposed a quantitative calculation method of natural gas pipeline transportation capacity. This method can first simulate the state transition process and the duration of each operating state based on the Monte Carlo method, and then analyze the actual flow variation law through the commercial software SPS (Stoner Pipeline Simulator) for the unsteady flow of the system after the transition to other states. By combining the hydraulic analysis with the simulation of the state transition process, two reliability indicators are calculated. A detailed procedure for evaluating the gas supply capacity of the pipeline system is proposed, and its feasibility is verified through two case studies.

[0076] Regarding the second method, Pavel Praks published a research article titled "Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure" in the journal "Reliability Engineering and System Safety" in 2017. The article establishes a probabilistic evaluation model for the reliability of natural gas pipeline networks based on Monte Carlo simulation using graph theory. First, the model uses graph theory to transform the pipeline network topology into a graph representation; then, it uses maximum flow simulation to calculate the maximum gas supply capacity of the pipeline network under given operating conditions, while simultaneously calculating the actual gas supply to each user; finally, it repeats the above steps using Monte Carlo simulation to calculate the system gas supply reliability at different time points, thereby identifying weak paths and nodes in the pipeline network system and quantitatively evaluating the gas supply reliability of each user node in the pipeline network system.

[0077] The limitations of the two methods mentioned above are as follows: Method 1 is limited by the solution efficiency caused by the problem size. This algorithm is only applicable to natural gas pipelines with shorter lengths and fewer devices, and cannot be applied to large-scale natural gas pipeline networks with multiple devices and multiple states; Method 2 has too single evaluation index, focusing on the impact of the probability of accident occurrence, and ignoring the importance of accident consequences and accident risks in system management.

[0078] To address the aforementioned technical challenges, embodiments of the present invention provide a specific implementation method for calculating the gas supply reliability of a natural gas pipeline system. See [link to details]. Figure 1 The method specifically includes the following:

[0079] Step 100: Construct a BN network based on the pipe segment status, compressor status, and topology of the natural gas pipeline system;

[0080] Step 200: Determine the maximum gas supply of the natural gas pipeline system under different conditions, the instantaneous failure probability of the pipeline segment and the compressor based on the BN network;

[0081] Specifically, a stochastic capacity network model is first established based on the BN network, and graph theory and maximum flow algorithm are combined to evaluate the maximum gas supply capacity of the natural gas pipeline system under different scenarios.

[0082] Step 300: Calculate the gas supply reliability of the natural gas pipeline system based on the maximum gas supply, the instantaneous failure probability of the pipeline section, and the instantaneous failure probability of the compressor.

[0083] Specifically, the reliability evaluation index of the natural gas pipeline system is calculated by using a Bayesian method and based on the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor to calculate the gas supply reliability thereof.

[0084] From the above description, the method and device for calculating the gas supply reliability of the natural gas pipeline system provided by the embodiments of the present application firstly construct a BN network according to the pipe section state, the compressor state and the topological structure of the natural gas pipeline system; then determine the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor of the natural gas pipeline system in different states according to the BN network; and finally calculate the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor. The present application has the following beneficial effects:

[0085] (1) The method for evaluating the gas supply reliability of the natural gas pipeline based on the probabilistic inference is established for the first time.

[0086] (2) The evaluation connotation of the gas supply reliability of the natural gas pipeline is enriched, and the quantitative analysis of the maximum gas supply capacity of the pipeline system in different scenarios is realized from the two angles of the local and global gas shortage risks.

[0087] (3) Compared with the traditional sampling-based method, the present application can significantly improve the calculation efficiency, save the cost and reduce the calculation burden.

[0088] In an embodiment, referring to Figure 2 , the step 100 comprises:

[0089] Step 101: generating nodes of the BN network according to the pipe section state and the compressor state;

[0090] Step 102: generating edges between the nodes according to the topological structure;

[0091] The topological structure here refers to the connection relationship between the users and the pipelines and compressors in the natural gas pipeline network system.

[0092] Step 103: constructing the BN network according to the nodes.

[0093] In the Bayesian structure, the variables are represented as nodes, and their dependency relationships are encoded in the graph. The structure of the BN describes the causal relationship between the nodes and reveals the intrinsic dynamic characteristics of the system. A key is to learn the dependency relationship of the Bayesian network from the data. Given that the flow direction in the pipeline network is fixed and the mode of unit failure is easy to obtain, the Bayesian network structure is established based on expert knowledge.

[0094] Figure 3A simple pipeline system topology is shown. The pipeline system includes four elements: gas source, compression station, pipeline and customer. It is assumed that the gas source can provide a steady flow to the user. The Bayesian model focuses on the random failure of the pipeline and compression station. Obviously, the failure of the pipeline or compression station will cause the user to be short of gas. The relationship between the unit failure and the gas shortage can be modeled by a Bayesian network. The Bayesian model of the system is shown in Figure 3 Figure 3 In the figure, the nodes represent the states of the pipeline segment, the compressor station and the end user. The P1 node, the C node represent the pipeline failure and the compression station failure, respectively. The CU node represents the gas shortage event of the customer. The arrows between the nodes represent their dependency relationship.

[0095] In one embodiment, referring to Figure 4 , step 200 includes:

[0096] Step 201: determining the Markov process of the pipeline segment according to the BN network and the current state of the pipeline segment;

[0097] According to the basic assumption of the Markov process, the next state of the pipeline segment only depends on the current state. Taking a certain pipeline segment as an example, the pipeline segment is in state i at time s, and within a sufficiently small Dt time interval, the pipeline segment undergoes a state transition, and the probability of being in state j at the next time s+t is independent of the state before the initial state of the pipeline segment. The state transition process can be expressed as:

[0098]

[0099] Step 202: determining the instantaneous failure probability of the pipeline segment according to the Markov process of the pipeline segment;

[0100] Specifically, based on formula (1), the conditional probability is:

[0101] P{X(v+t)=j|X(t)=i}, i,j=0,1,2,…N (2)

[0102] is called the transition probability of the Markov process. If the transition probability does not depend on time t, but only depends on the transition time interval v, the Markov process is homogeneous or stable, which can be denoted as:

[0103] P{X(v+t)=j|X(t)=i}=p ij (v), t,v>0 i,j=0,1,2,…N (3)

[0104] A Markov process with a stable transition probability is memoryless. The state transition rate a ij represents the probability that the unit is in state i at time t and in state j at time t+Dt, which can be expressed as: ​

[0105]

[0106] Assuming that the evolution process of the unit has Markov property, the probability of the unit being in each state at time t can be recorded as vector P(t)=[P1(t),P2(t),…,P n (t)] and the probability of the unit being in each state at time t+dt can be recorded as vector P(t+dt)=[P1(t+dt),P2(t+dt),…,P n (t+dt)]. Assuming that dt is small enough, at most one state transition occurs within dt, then the state transition equation expressed by vector is:

[0107]

[0108] wherein P ij represents the probability of the unit being in state i at time t and in state j at time t+dt, referred to as state transition probability; P ij represents the probability of the running state of the unit not being transitioned within dt.

[0109] The relationship between the unit state transition rate and the state transition probability is shown in the formula.

[0110]

[0111] Further, the first-order linear ordinary differential equation for determining the unit state can be obtained:

[0112]

[0113] For formula (1)-(8), according to the complexity of the system, the first-order differential linear equation can be solved in multiple ways, and the general solution is Laplace transform. The Laplace transform of state probability P j (t),j=0,1,2…N,t>0 is recorded as Correspondingly, the Laplace transform of the time derivative of P j (t) is:

[0114]

[0115] The Laplace basic equation of the Markov process is:

[0116] sP(s)-C=P(s)xA (10)

[0117] Step 203: determining the Markov process of the compressor according to the BN network and the current state of the compressor;

[0118] Step 204: determining the instantaneous failure probability of the compressor according to the Markov process of the compressor;

[0119] Step 203 is similar to step 201, and step 204 is similar to step 202, which will not be repeated here.

[0120] In the present application, the Markov process of the pipeline and the compressor includes: normal state, degradation state and interruption state.

[0121] According to the unit failure analysis and engineering experience, due to different failure modes, each unit in the natural gas pipeline network has multiple operating states, and these operating states often can be converted to each other with a certain probability. In order to describe the random evolution process of the unit operating state, the discrete Markov process is introduced in the present research.

[0122] Referring to Figure 5 For the pipe section and the compressor, the normal state, the degradation state and the interruption state are used to represent the state change of the pipeline during operation. The degradation state refers to the change of the material performance of the pipe section due to the increase of the service time, the long-term stress, load and temperature, resulting in the decrease of the maximum gas transmission capacity of the pipe section (such as corrosion, crack). On the other hand, sudden geological disasters or accidents can cause the pipe section to break or rupture, which can completely lose the gas transmission function and reduce the maximum gas transmission capacity of the pipe section to 0.

[0123] Corrosion, fatigue, erosion and other factors can cause the degradation of the compressor performance, resulting in the decrease of the normal gas supply capacity of the compressor station. The normal state, the degradation state and the failure state are used to describe the state of the compressor station. When the compressor station is in the degradation state, the compressor station retains the flow capacity, and the maximum gas transmission capacity of the adjacent pipe section will decrease to a certain level, and the maximum gas transmission capacity is calculated by the hydraulic model. The interruption state of the compressor station refers to the complete loss of the flow capacity of the compressor station due to accidents.

[0124] In an embodiment, referring to Figure 6 , step 200 further comprises:

[0125] Step 205: calculating the edge probability of the BN network;

[0126] In the Bayesian network, the node represents an event. When there is a causal relationship between events, the nodes are connected by arrows, and the starting node of the arrow represents the cause event, and the ending node of the arrow represents the result event. The structure of the Bayesian network directly reflects the causal logic between variables and reveals the internal dynamic action law of the system.

[0127] Specifically, the basic requirement for querying variable distribution using Bayesian network is to obtain the occurrence probability of root node and the conditional probability between nodes. The relationship between pipe network unit failure and user gas shortage is characterized by Bayesian network, as shown in the following formula. The core of Bayesian statistical inference is Bayesian theorem, which can update the existing information through the collected data. Mathematically, the Bayesian theorem can be expressed as follows:

[0128]

[0129] wherein

[0130] m(y) =∫f(y|θ)p(θ)dθ (12)

[0131] The function p(θ|y) is called the posterior density function; p(θ) is called the prior density function; m(y) is the marginal density function of data, and f(y|θ) is the sampling density function of data.

[0132] wherein X represents the pipe failure event, and Y represents the user gas shortage event downstream of the pipe.

[0133] Step 206: calculating the occurrence probability of user gas shortage of the natural gas pipeline system in the pipe section in the interruption state according to the marginal probability;

[0134] Specifically, the basis for querying variable distribution using Bayesian network is to obtain the conditional probability distribution of nodes, and the model parameters are calculated using Bayesian estimation:

[0135]

[0136] wherein, p(y i ) represents the marginal probability of event occurrence, p(x j |y i ) is the conditional probability distribution obtained from historical data, and p(y i |x j ) represents the posterior probability. x j represents the failure event of the jth pipe, and y i represents the user i gas shortage event. Formula (6) indicates that through Bayesian estimation, the probability that the pipe j gas shortage leads to the occurrence of user i gas shortage can be obtained.

[0137] Step 207: determining the maximum gas supply according to the occurrence probability.

[0138] Because the gas supply capacity of the pipe network differs greatly in different operating states, evaluation of the gas supply reliability of the pipe network requires evaluation of the gas supply capacity of all randomly generated operating states, which puts high requirements on the calculation efficiency of the algorithm. Although the method based on hydraulic and thermal analysis is accurate, the calculation cost is high, and the method is often not suitable for occasions requiring a large number of random simulations. Based on the above problems, the application converts the gas supply capacity calculation problem of the natural gas pipe network into a maximum flow problem in graph theory. The goal of the maximum flow problem is to calculate the maximum transmission capacity between two points in a transmission network, where the two points are called the source point and the sink point, respectively. Preferably, the application uses the following algorithm as a method for solving the maximum flow problem

[0139] For a weighted directed graph G=(V,E), where V is the set of all points in the graph, E is the set of all edges, and each edge includes two key parameters: capacity and cost. For a pipe system, the capacity value represents the maximum flow that the pipe can transport, and the cost value represents the cost of transporting the medium through the pipe, which is related to the length of the pipe. The specific implementation steps include:

[0140] (1) First, search for a connected path between the source point and the sink point, and each edge on the path has available capacity;

[0141] (2) Second, repeat the above search step until no path with additional flow capacity can be found.

[0142] In the above search step, two constraints need to be noted:

[0143] (1) For any node other than the source point and the sink point, the sum of the flow into the node must be equal to the sum of the flow out of the node;

[0144] (2) The capacity of each edge is within a predetermined range. For a natural gas pipe network, the capacity of each edge is the range of the gas transmission capacity of the gas pipeline in different operating states.

[0145] In an embodiment, referring to Figure 7 , step 300 includes:

[0146] Step 301: determining the prior probability of the BN network according to the instantaneous failure probability;

[0147] Specifically, the prior probability table of the root node of the BN network is determined according to the instantaneous failure probability.

[0148] Step 302: determining the conditional probability of the BN network according to the maximum gas supply;

[0149] The conditional probability table of the BN node is created from the random capacity network model using Monte Carlo simulation.

[0150] Step 303: calculating the gas supply reliability of the natural gas pipeline system according to the prior probability and the conditional probability.

[0151] After calculating the marginal probability distribution and the conditional probability distribution of the Bayesian network, the user gas shortage state can be calculated according to the real-time failure probability of the equipment by using Bayesian inference. According to the accuracy of the inference, the inference method includes an exact algorithm and an approximate algorithm. In the exact inference, the calculation and space complexity of the tree grow exponentially with the number of BN nodes. The exact algorithm will bear a heavy calculation burden, and the application program becomes difficult to handle. Therefore, the approximate inference belief propagation algorithm is used to calculate the gas shortage probability of the user.

[0152] The gas supply reliability index is shown in equations (7)-(8):

[0153]

[0154] wherein R c,i represents the average gas supply reliability of the user i in the time 0-T, R s,t represents the gas supply reliability of the system at the time t. P i,t represents the probability of the user i in the gas shortage state at the time t. N represents the total number of users, and T represents the total length of the maintenance management.

[0155] To further illustrate the scheme, the application further provides a specific application example of the gas supply reliability calculation method of the natural gas pipeline system by taking a natural gas pipeline network system in a certain region as an example, which is shown in Figure 8 The specific application example specifically includes the following contents.

[0156] In the natural gas pipeline network system in the region, the distances of the nodes 1-7, 3-8, 5-12 and 6-13 are 20 km, 5-6 is 150 km, and the capacity is 300*10 8 Nm 3 / a. At the same time, the super sink 14 is set, the pipeline length of each user to the super sink is 0, and the capacity is the demand of the user. Parameters: unit length pipeline failure rate: 1.21*10 -5 1 / km·month; unit pipeline length repair rate: 0.001388 (1 / km·month); compressor failure rate: 0.01. Compressor repair rate: 0.2; total time: 1 year; time interval: 1 month; cycle number: 500.

[0157] S1: determining the nodes of the Bayesian network and the network structure.

[0158] S2: calculating the marginal probability of the nodes of the Bayesian network.

[0159] S3: calculating the conditional probability of the nodes of the Bayesian network.

[0160] S4: Compute node probability distribution using Bayesian inference.

[0161] The results are shown in Figure 9 As time goes by, the failure probability of the pipeline increases. The state of users gradually changes from normal to gas shortage. Figure 9 The instantaneous probability of users in different states is shown. The global gas shortage of the system is shown in Figure 10 It is intuitively found that the average gas shortage of users increases over time due to the increase in pipeline failure probability. In addition, the state transition of the pipeline reaches a steady distribution after a period of time. The results accurately quantify the global user gas shortage and comprehensively reflect the ability of the system to stably supply users considering the failure unit.

[0162] In reality, the gas supply reliability of a natural gas pipeline network system is affected by different external disturbances and internal instabilities. The inherent uncertainty leads to irregularities in system characteristics, and it is not wise to apply simple management strategies in a complex dynamic environment. To further analyze, two shortage levels are defined here: mild shortage (0.75 demand < supply capacity < demand) and severe shortage (supply capacity < 0.75 demand). Correspondingly, the supply reliability of each demand point has two levels: extreme reliability and general reliability. The four scenarios are described as follows:

[0163] Scenario 1: Gas source supply is Nm 3 / d.

[0164] Scenario 2: Gas source supply is Nm 3 / d.

[0165] Scenario 3: Gas source supply is Nm 3 / d.

[0166] Scenario 4: Gas source supply is Nm 3 / d.

[0167] The gas supply reliability results of all demand points under different scenarios are shown in Table 1. The normal reliability of some nodes, such as node 4, node 9 and node 13, is equal to the extreme reliability. This result represents the single gas shortage state of the above users. When the gas supply is less than the supply, the gas source will preferentially deliver natural gas to users, with low transportation cost. The distance between the gas source and the user is related to the transportation cost. In addition, as the gas source supply decreases, the reliability of users also decreases to varying degrees. The supply reliability of node 9 decreases from 0.9874 to 0.9632. The results show that node 9 is the most vulnerable node to supply changes. It can provide comprehensive analysis for operators to understand how the risk of natural gas shortage propagates in the pipeline network and help management personnel identify vulnerable nodes.

[0168] Table 1 Supply reliability results of all users in different scenarios

[0169]

[0170] From the above description, the natural gas pipeline system supply reliability calculation method provided by the embodiment of the application firstly generates a prior probability table through the instantaneous failure probability of the calculation unit, and obtains a conditional probability table from the sample generated by the supply capacity calculation. Finally, the reliability index is formulated and the supply reliability of the natural gas pipeline network is evaluated. The application has the following contributions in the field:

[0171] 1. The natural gas pipeline supply reliability evaluation method based on probability inference is first established, and the rapid calculation of system supply reliability is realized.

[0172] 2. The natural gas pipeline network supply reliability evaluation index coupling the gas shortage probability and the gas shortage consequence is first proposed, the multi-perspective pipeline system gas shortage risk assessment of local and global is realized, so that the cognitive level of the management personnel on system management can be improved, and technical support is provided for related decision-making.

[0173] Based on the same inventive concept, the embodiment of the application also provides a natural gas pipeline system supply reliability calculation device, which can be used to realize the method described in the above embodiment, such as the following embodiment. Since the principle of solving problems of the natural gas pipeline system supply reliability calculation device is similar to that of the natural gas pipeline system supply reliability calculation method, the implementation of the natural gas pipeline system supply reliability calculation device can be referred to the implementation of the natural gas pipeline system supply reliability calculation method, and the repeated parts will not be described here. The term "unit" or "module" used below can be a combination of software and / or hardware that realizes a predetermined function. Although the system described in the following embodiment is preferably realized in software, the realization of hardware or a combination of software and hardware is also possible and is conceived.

[0174] The embodiment of the application provides a specific implementation of a natural gas pipeline system supply reliability calculation device capable of realizing a natural gas pipeline system supply reliability calculation method, which is described with reference to Figure 11 , the natural gas pipeline system supply reliability calculation device specifically includes the following contents:

[0175] The network construction module 10 is used to construct a BN network according to the pipe section state, the compressor state and the topological structure in the natural gas pipeline system;

[0176] The supply amount determination module 20 is used to determine the maximum supply amount of the natural gas pipeline system under different states, the instantaneous failure probability of the pipe section and the compressor according to the BN network;

[0177] The reliability calculation module 30 is configured to calculate the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section, and the instantaneous failure probability of the compressor.

[0178] In one embodiment, referring to Figure 12 , the network construction module 10 comprises:

[0179] The node generation unit 101 is configured to generate nodes of the BN network according to the pipe section state and the compressor state.

[0180] The edge generation unit 102 is configured to generate edges between nodes according to the topological structure.

[0181] The network construction unit 103 is configured to construct the BN network according to the nodes.

[0182] In one embodiment, referring to Figure 13 , the gas supply amount determination module 20 comprises:

[0183] The pipe section process determination unit 201 is configured to determine a Markov process of the pipe section according to the BN network and the current state of the pipe section.

[0184] The pipe section probability determination unit 202 is configured to determine the instantaneous failure probability of the pipe section according to the Markov process of the pipe section.

[0185] The compressor process determination unit 203 is configured to determine a Markov process of the compressor according to the BN network and the current state of the compressor.

[0186] The compressor probability determination unit 204 is configured to determine the instantaneous failure probability of the compressor according to the Markov process of the compressor.

[0187] The Markov process comprises a normal state, a degradation state, and an interruption state.

[0188] In one embodiment, referring to Figure 14 , the gas supply amount determination module 20 further comprises:

[0189] The edge probability calculation unit 205 is configured to calculate an edge probability of the BN network.

[0190] The occurrence probability calculation unit 206 is configured to calculate an occurrence probability of the natural gas pipeline system in which the pipe section is in the interruption state and the user lacks gas according to the edge probability.

[0191] The maximum gas supply amount determination unit 207 is configured to determine the maximum gas supply amount according to the occurrence probability.

[0192] In an embodiment, referring to Figure 15 The reliability calculation module 30 comprises:

[0193] A prior probability determination unit 301 is configured to determine a prior probability of the BN network according to the instantaneous failure probability;

[0194] A conditional probability determination unit 302 is configured to determine a conditional probability of the BN network according to the maximum gas supply amount;

[0195] A reliability calculation unit 303 is configured to calculate the gas supply reliability of the natural gas pipeline system according to the prior probability and the conditional probability.

[0196] As can be seen from the above description, the gas supply reliability calculation device for the natural gas pipeline system provided by the embodiment of the present application firstly establishes a loss function for characterizing the loss process of the natural gas pipeline network according to the current gas supply amount, the initial gas supply amount, the instantaneous loss amount after the disturbance, the loss speed and the disturbance time; then, constructs a natural gas pipeline network resilience evaluation parameter according to the gas supply amount after the disturbance of the natural gas pipeline network ends, the initial gas supply amount, the gas supply critical value, the gas supply amount drop time during the disturbance process and the repair time; and finally evaluates the resilience of the natural gas pipeline network according to the loss function and the natural gas pipeline network resilience evaluation parameter.

[0197] The present application evaluates the gas supply resilience of the natural gas pipeline network system after the occurrence of a deterministic disturbance event from the perspective of resilience, develops a model of the loss process and the recovery process of the natural gas pipeline network system based on the actual characteristics of the natural gas pipeline network system after suffering from a deterministic failure event, establishes a gas supply attenuation model of the pipeline network, which can depict the process of different attenuation rates, different attenuation starting points and endpoints, and also depicts the recovery process of the natural gas pipeline network system based on the actual repair mode; performs community-like division on the node regions possibly affected by the failure node, so as to realize the amplification of the change of the gas supply resilience.

[0198] The embodiment of the present application also provides a specific implementation of an electronic device capable of realizing all the steps in the gas supply reliability calculation method for the natural gas pipeline system in the above-mentioned embodiments, referring to Figure 16 The electronic device specifically comprises the following contents:

[0199] A processor 1201, a memory 1202, a communications interface 1203 and a bus 1204;

[0200] The processor 1201, the memory 1202 and the communication interface 1203 complete communication with each other through the bus 1204; the communication interface 1203 is used for realizing information transmission between the server-side device and the client-side device and other related devices;

[0201] The processor 1201 is used for calling the determination machine program in the memory 1202, and the processor realizes all steps in the gas supply reliability calculation method of the natural gas pipeline system in the above embodiment when executing the determination machine program, for example, the processor realizes the following steps when executing the determination machine program:

[0202] Step 100: constructing a BN network according to the state of the pipe section, the state of the compressor and the topology structure in the natural gas pipeline system;

[0203] Step 200: determining the maximum gas supply amount of the natural gas pipeline system in different states, the instantaneous failure probability of the pipe section and the compressor according to the BN network;

[0204] Step 300: calculating the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor.

[0205] Embodiments of the present application also provide a determination machine readable storage medium capable of realizing all steps in the gas supply reliability calculation method of the natural gas pipeline system in the above embodiment, and the determination machine readable storage medium has a determination machine program stored thereon, and the determination machine program realizes all steps in the gas supply reliability calculation method of the natural gas pipeline system in the above embodiment when executed by a processor, for example, the processor realizes the following steps when executing the determination machine program:

[0206] Step 100: constructing a BN network according to the state of the pipe section, the state of the compressor and the topology structure in the natural gas pipeline system;

[0207] Step 200: determining the maximum gas supply amount of the natural gas pipeline system in different states, the instantaneous failure probability of the pipe section and the compressor according to the BN network;

[0208] Step 300: calculating the gas supply reliability of the natural gas pipeline system according to the maximum gas supply amount, the instantaneous failure probability of the pipe section and the instantaneous failure probability of the compressor.

[0209] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. Especially, for the hardware+program type embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0210] The above described embodiments of the present description have been described. Other embodiments are within the scope of the following claims. In some cases, the acts or steps recited in the claims can be performed in a different order than those in the embodiments and still achieve desirable results. Additionally, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous or necessary.

[0211] Although the present application provides method operation steps as the embodiments or flowcharts, more or less operation steps can be included based on routine or non-creative labor. The order of steps listed in the embodiments is only one of the many ways to execute the steps, and does not represent the only way to execute the steps. When the device or client product is executed in practice, the method order shown in the embodiments or the figures can be executed in sequence or in parallel (for example, in the environment of parallel processor or multi-thread processing).

[0212] For the convenience of description, the above device is described as various modules described respectively in function. Of course, when implementing the embodiments of the present description, the functions of the modules can be implemented in the same or multiple software and / or hardware, or the modules implementing the same function can be implemented by the combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division mode, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0213] Those skilled in the art also know that in addition to implementing the controller in the form of pure deterministic machine readable program code, the same function can also be implemented by logically programming the method steps to make the controller in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0214] In a typical configuration, the determination device includes one or more processors (CPU), input / output interface, network interface and memory.

[0215] Memory can include a non-transitory memory in a determinate machine-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory, etc. Memory is an example of a determinate machine-readable medium.

[0216] Embodiments of the present specification can be described in the general context of a determinate machine-executable instructions, such as program modules, being executed by a determinate machine. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Embodiments of the present specification can also be practiced in distributed determinate environments where tasks are performed by remote processing devices that are linked through a communication network. In a distributed determinate environment, program modules can be located in both local and remote determinate machine storage media including memory storage devices.

[0217] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the description of the method embodiments. In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of the present specification. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0218] The above only describes the embodiments of the embodiments of the present specification and does not limit the embodiments of the present specification. The embodiments of the present specification can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification shall be included in the scope of claims of the embodiments of the present specification.

Claims

1. A method for calculating the gas supply reliability of a natural gas pipeline system, characterized in that, include: Construct a Bayesian network based on the pipe segment status, compressor status, and topology of the natural gas pipeline system; The step of determining the instantaneous failure probability of the pipeline segment and the compressor under different states of the natural gas pipeline system based on the Bayesian network includes: The Markov process of the pipe segment is determined based on the Bayesian network and the current state of the pipe segment; The instantaneous failure probability of the pipe segment is determined based on the Markov process of the pipe segment; The Markov process of the compressor is determined based on the Bayesian network and the current state of the compressor; The instantaneous failure probability of the compressor is determined based on the Markov process of the compressor; The Markov process includes: normal state, degenerate state, and interrupted state; The metrics for measuring gas supply reliability include: Among them, R c,i R represents the average gas supply reliability for user i during the time interval 0-T. s,t P represents the gas supply reliability of the system at time t; i,t This represents the probability that user i is in a gas shortage state at time t; N represents the total number of users, and T represents the total maintenance and management time. Determining the maximum gas supply of the natural gas pipeline system under interruption conditions based on the Bayesian network includes: Calculate the edge probabilities of the Bayesian network; The probability of gas shortage for users in the natural gas pipeline system under the interruption state is calculated based on the marginal probability. The maximum gas supply is determined based on the probability of occurrence. The calculation of the gas supply reliability of the natural gas pipeline system based on the maximum gas supply capacity, the instantaneous failure probability of the pipeline section, and the instantaneous failure probability of the compressor includes: The prior probability of the Bayesian network is determined based on the instantaneous failure probability; The conditional probability of the Bayesian network is determined based on the maximum gas supply. The gas supply reliability of the natural gas pipeline system is calculated based on the prior probability and the conditional probability.

2. The gas supply reliability calculation method according to claim 1, characterized in that, The construction of a Bayesian network based on the pipe segment status, compressor status, and topology of the natural gas pipeline system includes: The nodes of the Bayesian network are generated based on the pipe segment status and the compressor status; Generate edges between multiple nodes based on the topology; Based on the nodes, the Bayesian network is constructed.

3. A gas supply reliability calculation device for a natural gas pipeline system, characterized in that, include: The network construction module is used to construct Bayesian networks based on the pipe segment status, compressor status, and topology of the natural gas pipeline system. A failure probability determination module is used to determine the instantaneous failure probability of the pipeline segment and the compressor under different states of the natural gas pipeline system based on the Bayesian network, including: Pipe segment process determination unit, used to determine the Markov process of the pipe segment based on the Bayesian network and the current state of the pipe segment; The pipe segment probability determination unit is used to determine the instantaneous failure probability of the pipe segment based on the Markov process of the pipe segment; A compressor process determination unit is used to determine the Markov process of the compressor based on the Bayesian network and the current state of the compressor. A compressor probability determination unit is used to determine the instantaneous failure probability of the compressor based on the Markov process of the compressor; The Markov process includes: normal state, degenerate state, and interrupted state; The metrics for measuring gas supply reliability include: Among them, R c,i R represents the average gas supply reliability for user i during the time interval 0-T. s,t P represents the gas supply reliability of the system at time t; i,t This represents the probability that user i is in a gas shortage state at time t; N represents the total number of users, and T represents the total maintenance and management time. A gas supply determination module is used to determine the maximum gas supply of the natural gas pipeline system under interruption conditions based on the Bayesian network, including: An edge probability calculation unit is used to calculate the edge probabilities of the Bayesian network; The probability calculation unit is used to calculate the probability of gas shortage for users in the natural gas pipeline system under the interruption state based on the marginal probability. A maximum gas supply determination unit is used to determine the maximum gas supply based on the occurrence probability. The reliability calculation module is used to calculate the gas supply reliability of the natural gas pipeline system based on the maximum gas supply capacity, the instantaneous failure probability of the pipeline section, and the instantaneous failure probability of the compressor, including: A prior probability determination unit is used to determine the prior probability of the Bayesian network based on the instantaneous failure probability. A conditional probability determination unit is used to determine the conditional probability of the Bayesian network based on the maximum gas supply. A reliability calculation unit is used to calculate the gas supply reliability of the natural gas pipeline system based on the prior probability and the conditional probability.

4. The gas supply reliability calculation device according to claim 3, characterized in that, The network construction module includes: A node generation unit is used to generate nodes of the Bayesian network based on the pipe segment status and the compressor status. An edge generation unit is used to generate edges between multiple nodes based on the topology. A network construction unit is used to construct the Bayesian network based on the nodes.

5. An electronic device, comprising a memory, a processor, and a deterministic machine program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the gas supply reliability calculation method for the natural gas pipeline system according to any one of claims 1 to 2.

6. A deterministic machine-readable storage medium having a deterministic machine program stored thereon, characterized in that, When the determination machine program is executed by the processor, it implements the steps of the gas supply reliability calculation method for the natural gas pipeline system according to any one of claims 1 to 2.

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