Reliability assessment method and system for hydrogen-electricity coupled micro-energy grid based on fault visualization

Abstract

A reliability assessment method and system for a hydrogen-electricity coupled micro-energy network with fault visualization obtains the first top-level fault event and the first fault cause, the second top-level fault event and the second fault cause, and the third top-level fault event and the third fault cause as nodes to establish a reliability assessment network model that characterizes the relationship between the nodes; a statistical method is used to determine the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated based on the fault data and risk data of the micro-energy network and the failure probability of each node and its constraints; the fault probability of the fault node after verification, the fault repair time of the fault node, and the average failure duration of the fault node are used as the first reliability index, the second reliability index, and the third reliability index of the fault node, respectively, and compared with the reliability index of the hydrogen-electricity coupled micro-energy network, the reliability of the hydrogen-electricity coupled micro-energy network is evaluated on the basis of achieving fault visualization.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen-electricity coupled micro-energy networks, and in particular to a reliability assessment method and system for hydrogen-electricity coupled micro-energy networks with fault visualization. Background Art

[0002] As the "dual carbon goals" are implemented, hydrogen energy is expected to become a vital component of the energy sector. In recent years, with the gradual maturity of hydrogen energy application technology and the increasing pressure to address climate change globally, electric-hydrogen coupling technology has continued to advance, rapidly integrating hydrogen into the entire micro-energy system.

[0003] Existing technologies for the reliability assessment of hydrogen-electricity coupled micro-energy grids include: using operational data collected by equipment sensors to establish a coupled mathematical model to obtain reliability indicators; using training data to train a hybrid neural network to monitor abnormal operating conditions of the hydrogen-electricity coupled system; using linear regression and deviation analysis to improve data analysis accuracy; then using neural network algorithms to verify data in real time; and using Bayesian network algorithms to optimize abnormality testing processes and improve the reliability of intelligent robot detection; combining dynamic Bayesian network time series analysis to predict and identify changes in specific geological structures, improving data processing accuracy and prediction reliability; and constructing a Bayesian network structure by integrating a causal reasoning module to accurately demonstrate the complex relationships between various production and energy sources, providing a solution for real-time monitoring and automatic regulation of energy consumption. However, existing reliability assessment methods for micro-energy grids primarily consider the impact of the integrated energy system as a whole, lacking a focus on hydrogen energy, which is flammable, explosive, and easily diffusible.

[0004] Therefore, there is an urgent need for a technical solution that can solve the problem that the existing reliability assessment method for micro-energy grids mainly considers the overall impact of the integrated energy system and lacks a focus on hydrogen energy with unstable physical properties. Summary of the Invention

[0005] To address the deficiencies in the prior art, the present invention provides a reliability assessment method and system for a hydrogen-electricity coupled micro-energy network with fault visualization. This method performs a local focused assessment of the reliability of hydrogen energy with unstable physical properties within the micro-energy network, and combines the assessment results with the overall reliability assessment results of the integrated energy system. This allows for an accurate assessment of the reliability of the hydrogen-electricity coupled micro-energy network based on fault visualization.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a reliability assessment method for a hydrogen-electric coupled micro-energy network with fault visualization. The energy structure of the micro-energy network includes a hydrogen energy sub-network, an electric energy sub-network, and a thermal energy sub-network. The hydrogen energy sub-network and the electric energy sub-network are coupled via an electric-hydrogen coupling element, and the electric energy sub-network and the thermal energy sub-network are coupled via an electric-thermal coupling element. The method includes:

[0008] Obtaining a first top-level fault event and a first fault cause of the hydrogen energy sub-network, a second top-level fault event and a second fault cause of the electric energy sub-network, and a third top-level fault event and a third fault cause of the thermal energy sub-network as nodes to establish a reliability assessment network model that characterizes the relationship between the nodes;

[0009] Collect micro energy grid fault data and micro energy grid risk data;

[0010] Using statistical methods, the failure probability of each node in the reliability assessment network model is determined based on the fault data and risk data of the micro-energy network. Based on the failure probability of each node and its constraints, the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated are determined based on the reliability assessment network model. Fault visualization is performed based on the fault nodes and occurrence paths.

[0011] Based on the confidence interval, the failure probability of the faulty node in the reliability assessment network model is verified, and the failure probability of the faulty node, the fault repair time of the faulty node, and the average fault duration of the faulty node after verification are used as the first reliability index, the second reliability index, and the third reliability index of the faulty node respectively;

[0012] The reliability of the hydrogen-electricity coupled micro-energy network is evaluated by using the first reliability index, the second reliability index, the third reliability index of the fault node and the reliability index of the hydrogen-electricity coupled micro-energy network.

[0013] Preferably, the first top-level fault event includes: a fault event caused by hydrogen, and the first fault factor includes: a fault factor causing the fault event caused by hydrogen;

[0014] The second top-level fault events include: power system collapse or outage, and the second fault factors include: fault factors that cause the power system collapse or outage;

[0015] The third top-level fault events include: the thermal system loses control and cannot continuously provide heating, and the third fault factors include: the fault factors that cause the thermal system to lose control and cannot continuously provide heating.

[0016] Preferably, a reliability assessment network model that characterizes the relationship between nodes is established, including:

[0017] Taking the first top-level fault event and the first fault cause as the root node, generate layer by layer layer subnodes, and the electric-hydrogen coupling element is used as the first Child nodes in the layer;

[0018] From Starting from the child nodes in the layer, generate layer by layer The second top-level fault event and the second fault cause of the power sub-network are taken as the first Leaf nodes in a layer;

[0019] From Starting from the child nodes in the layer, generate layer by layer The second top-level fault event and the second fault cause of the power sub-network are taken as the first The leaf node in the layer uses the electrothermal coupling element as the first The leaf nodes in the layer; ;

[0020] From Starting from the leaf nodes in the layer, generate layer by layer The third top-level fault event and the third fault cause of the thermal energy sub-network are used as the first Leaf nodes in a layer;

[0021] in, 、 、 and All are positive integers.

[0022] Preferably, the relationships between nodes represented by the reliability assessment network model include: and relationship, joint relationship, and causal relationship.

[0023] Preferably, a statistical method is used to determine the failure probability of each node in the reliability assessment network model based on the failure data and risk data of the micro energy network, including:

[0024] Extract characteristic information from micro-energy grid fault data as prior information;

[0025] According to the prior information, the fault prior distribution is obtained based on the reliability evaluation network model;

[0026] Extract characteristic information from micro-energy grid risk data as posterior information;

[0027] According to the posterior information and the fault prior distribution, the fault posterior distribution is obtained based on the reliability evaluation network model;

[0028] According to the fault posterior distribution, the failure probability of each node in the reliability assessment network model is obtained.

[0029] Preferably, according to the failure probability of each node and its constraints, the failure event to be evaluated and its occurrence path are determined based on the reliability evaluation network model, including:

[0030] Establish the constraint conditions of the failure probability of each node to satisfy the following relationship:

[0031] ,

[0032] Where, is the observed value of the fault population sample The corresponding coupling coefficient; is the observed value of the fault population sample The corresponding probability of failure; and are the observed values ​​of the fault population sample The corresponding lower and upper limits of the probability of failure;

[0033] When the probability of failure Greater than , it is determined to be a high probability failure event ,in, is the number of tests; high probability failure events are taken as the failure events to be evaluated, and high probability failure events are obtained based on the reliability evaluation network model The path of occurrence.

[0034] Preferably, the failure probability of each node in the reliability assessment network model is verified based on the confidence interval, including:

[0035] The confidence interval of the fault probability of the fault node is determined according to the fault posterior distribution, and the fault probability of the fault node is screened using the confidence interval; when the confidence interval is not less than the set interval threshold, the fault probability of the fault node is retained; conversely, when the confidence interval is less than the set interval threshold, the fault probability of the fault node is removed.

[0036] Preferably, based on the reliability assessment network model, it is determined whether the faulty node whose fault occurrence probability has been removed is the underlying event of each sub-network; if the faulty node is a non-bottom-level event of each sub-network, then based on the occurrence path of the fault event to be evaluated, the underlying event of each sub-network corresponding to the faulty node is determined in the reliability assessment network model; according to Bayes' theorem, the failure rate of the underlying event of each sub-network corresponding to the faulty node is used to calculate the failure probability of the faulty node; conversely, if the faulty node is the bottom-level event of each sub-network, then the failure rate of the bottom-level event of each sub-network is used as the failure probability of the faulty node.

[0037] Preferably, the faulty node The first reliability index, the second reliability index and the third reliability index satisfy the following relationship:

[0038] ,

[0039] In the formula, the faulty node after verification is The probability of failure For the faulty node The first reliability indicator is the fault repair time of the faulty node. For the faulty node The second reliability indicator is the average failure duration of the faulty node. For the faulty node The third reliability indicator.

[0040] Preferably, the reliability indicators of the hydrogen-electricity coupled micro-energy network include: the probability of the hydrogen-electricity coupled micro-energy network stopping supplying hydrogen, the probability of the hydrogen-electricity coupled micro-energy network obtaining hydrogen, and the energy expectation of hydrogen load shortage.

[0041] The present invention also proposes a fault-visualized hydrogen-electricity coupled micro-energy grid reliability assessment system, comprising:

[0042] Reliability assessment network establishment module, fault event visualization processing module, reliability assessment module;

[0043] a reliability assessment network establishment module, configured to obtain a first top-level fault event and a first fault cause of the hydrogen energy sub-network, a second top-level fault event and a second fault cause of the electric energy sub-network, and a third top-level fault event and a third fault cause of the thermal energy sub-network as nodes, so as to establish a reliability assessment network model representing the relationship between the nodes;

[0044] The fault event visualization processing module is used to collect micro-energy network fault data and micro-energy network risk data; using statistical methods, based on the micro-energy network fault data and micro-energy network risk data, the failure probability of each node in the reliability assessment network model is determined; based on the failure probability of each node and its constraints, the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated are determined based on the reliability assessment network model;

[0045] The reliability assessment module is used to verify the failure probability of the fault node in the reliability assessment network model based on the confidence interval, and use the verified failure probability of the fault node, the fault repair time of the fault node, and the average fault duration of the fault node as the first reliability index, second reliability index, and third reliability index of the fault node respectively; the reliability of the hydrogen-electricity coupled micro-energy network is jointly evaluated by using the first reliability index, second reliability index, third reliability index of the fault node and the reliability index of the hydrogen-electricity coupled micro-energy network.

[0046] A terminal includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0047] A computer-readable storage medium stores a computer program thereon, which implements the steps of the method when executed by a processor.

[0048] The beneficial effects of the present invention lie in at least one aspect, compared to the prior art, of the present invention's fault-visualized reliability assessment method for a hydrogen-electricity coupled micro-energy grid. By constructing hydrogen-related equipment failure events and then building a Bayesian network-based model based on these failure events, the present method prioritizes the impact of hydrogen energy on the reliability of the micro-energy grid, enabling it to further assess the reliability of the micro-energy grid based on the prior art. By analyzing the posterior distribution of faults and then deriving high-probability fault events and event occurrence paths based on the posterior distribution, the present method can visualize faults and occurrence paths, further facilitating micro-energy grid reliability assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a reliability assessment method for hydrogen-electricity coupled micro-energy network with fault visualization proposed by the present invention; DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] The present invention proposes a reliability assessment method for a hydrogen-electric coupled micro-energy network with fault visualization. The energy structure of the micro-energy network includes a hydrogen energy sub-network, an electric energy sub-network, and a thermal energy sub-network. The hydrogen energy sub-network and the electric energy sub-network are coupled via an electric-hydrogen coupling element, and the electric energy sub-network and the thermal energy sub-network are coupled via an electric-thermal coupling element.

[0052] like Figure 1 As shown, the method includes:

[0053] Step 1: Obtain the first top-level fault event and the first fault cause of the hydrogen energy sub-network, the second top-level fault event and the second fault cause of the electric energy sub-network, and the third top-level fault event and the third fault cause of the thermal energy sub-network as nodes to establish a reliability assessment network model that characterizes the relationship between the nodes.

[0054] Specifically, based on the production, transportation and use of hydrogen energy in the micro-energy network, the hydrogen-related equipment in the hydrogen energy sub-network includes: new energy hydrogen production equipment, distributed energy equipment, energy conversion equipment, and storage equipment.

[0055] The top-level fault events in the preferred embodiment of the present invention are shown in Table 1, and the fault causes are shown in Table 2.

[0056] Table 1 List of top-level fault events

[0057]

[0058] Table 2 List of fault causes

[0059]

[0060] Specifically, the first top-level fault event includes, but is not limited to, a fault event caused by hydrogen, and the first fault factor includes, but is not limited to, a fault factor that causes the hydrogen-induced fault event. The second top-level fault event includes, but is not limited to, a power system collapse or shutdown, and the second fault factor includes, but is not limited to, a fault factor that causes the power system collapse or shutdown. The third top-level fault event includes, but is not limited to, a thermal system that loses control and cannot continuously provide heat, and the third fault factor includes, but is not limited to, a fault factor that causes the thermal system to lose control and cannot continuously provide heat.

[0061] Specifically, a reliability assessment network model is established, including:

[0062] Taking the first top-level fault event and the first fault cause as the root node, generate layer by layer layer subnodes, and the electric-hydrogen coupling element is used as the first Child nodes in the layer;

[0063] In a non-limiting preferred embodiment, The first fault layer is the one that connects hydrogen energy and electric energy. For example, when facing fuel cells, fuel cells can cause faults in both electric energy and hydrogen energy. Therefore, fuel cells are used as the first fault layer. Child nodes in the layer.

[0064] From Starting from the child nodes in the layer, generate layer by layer The second top-level fault event and the second fault cause of the power sub-network are taken as the first The leaf node in the layer uses the electrothermal coupling element as the first The leaf nodes in the layer; .

[0065] From Starting from the leaf nodes in the layer, generate layer by layer The third top-level fault event and the third fault cause of the thermal energy sub-network are used as the first Leaf nodes in a layer.

[0066] in, 、 、 and All are positive integers.

[0067] The reliability assessment network model established by the present invention can characterize the various relationships between hydrogen energy sub-network faults, electric energy sub-network faults and thermal energy sub-network faults, including but not limited to: and relationship, joint relationship, and causal relationship; and according to the energy structure of the micro-energy network, the probability of the existence of causal relationship between the sub-network faults is the highest. Therefore, when establishing the reliability assessment network model, the hydrogen energy sub-network fault is taken as the main trunk. On the basis of the trunk, based on the causal relationship between the electric energy sub-network fault and the hydrogen energy sub-network fault, and the causal relationship between the thermal energy sub-network fault and the electric energy sub-network fault, the reliability assessment network model is generated layer by layer, and the relationship between the sub-networks represented by the model is arranged according to the actual situation of the micro-energy network.

[0068] In a non-limiting preferred embodiment, generating a reliability assessment network model layer by layer based on a Bayesian network includes:

[0069] 1) The first top-level fault event and the first fault cause of the hydrogen energy sub-network, the second top-level fault event and the second fault cause of the electric energy sub-network, and the third top-level fault event and the third fault cause of the thermal energy sub-network are used as variables for reliability assessment and serve as nodes in the reliability assessment network model;

[0070] 2) Based on the relationship between variables, corresponding nodes are connected with directed edges;

[0071] 3) For any node, when the probability distribution of its parent node is given, the conditional probability of the node is used as the probability distribution of the node.

[0072] Step 2: Collect micro energy grid fault data and micro energy grid risk data.

[0073] Specifically, micro-energy network failure data includes but is not limited to: empirical data on micro-energy network failures, historical data on micro-energy network failures, and predicted data on micro-energy network failures; micro-energy network risk data includes but is not limited to: empirical data on micro-energy network risks, historical data on micro-energy network risks, and predicted data on micro-energy network risks.

[0074] Step 3: Use statistical methods to determine the failure probability of each node in the reliability assessment network model based on the fault data and risk data of the micro-energy network. Based on the failure probability of each node and its constraints, determine the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated based on the reliability assessment network model, and perform fault visualization based on the fault node and occurrence path.

[0075] Specifically, step 3 includes:

[0076] Step 3.1, extracting characteristic information from micro-energy grid fault data as prior information;

[0077] Those skilled in the art may adopt different feature extraction methods according to actual needs to extract relevant feature information from micro energy grid fault data.

[0078] Step 3.2: Based on the prior information, the fault prior distribution is obtained based on the reliability evaluation network model;

[0079] Step 3.3, extract characteristic information from the micro-energy grid risk data as posterior information;

[0080] Those skilled in the art may adopt different feature extraction methods according to actual needs to extract relevant feature information from micro energy grid risk data.

[0081] In step 3.4, according to the posterior information and the fault prior distribution, the fault posterior distribution is obtained based on the reliability evaluation network model.

[0082] In step 3.5, the failure probability of each node in the reliability assessment network model is obtained based on the posterior distribution of the failure.

[0083] Specifically, step 3.5 includes:

[0084] set up is a sample from the fault population of Fault samples, is the observed value of each sample, and Satisfying the set probability distribution, in a non-limiting preferred embodiment, Satisfies the binomial distribution , the binomial distribution describes the binomial distribution The possibility of taking values, the fault prior distribution adopts the conjugate prior distribution; Parameters in The prior distribution of ,but The probability density function of Satisfies the following relationship:

[0085] ,

[0086] Where, is the probability of an event occurring in the binomial distribution, is a Beta distribution, and .

[0087] Then the likelihood function satisfies the following relationship:

[0088] ,

[0089] Where, is the likelihood function, is the total sample of faults The observed value of is the number of experiments, For the The interval corresponding to the observed value of the fault population sample in the experiment.

[0090] Taking into account the small but uncertain probability of human error, the probability density function satisfies the following relationship:

[0091] ,

[0092] Where, For super parameters The value range of for The prior distribution function of for The prior distribution function of .

[0093] Then the likelihood function satisfies the following relationship:

[0094] ,

[0095] According to Bayes' theorem, The posterior density function Satisfies the following relationship:

[0096] ,

[0097] Where, for The value range of .

[0098] The posterior density function is integrated to obtain the failure probability of each node in the reliability assessment network model.

[0099] Step 3.6: Determine the node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated based on the reliability evaluation network model.

[0100] Specifically, step 3.6 includes:

[0101] Establish the constraint conditions of the failure probability of each node to satisfy the following relationship:

[0102] ,

[0103] Where, is the observed value of the fault population sample The corresponding coupling coefficient; is the observed value of the fault population sample The corresponding probability of failure; and are the observed values ​​of the fault population sample The corresponding lower limit and upper limit of the probability of failure.

[0104] When the probability of failure Greater than , it is determined to be a high probability failure event , taking high probability failure events as the failure events to be evaluated, and obtaining high probability failure events based on the reliability evaluation network model path of occurrence.

[0105] Extract the fault node corresponding to the fault event to be evaluated.

[0106] The present invention obtains the fault posterior distribution through analysis, and then obtains high-probability fault events and event occurrence paths based on the fault posterior distribution, so that the method can visualize the faults and occurrence paths, further facilitating the reliability assessment of the micro-energy network.

[0107] Step 4: Verify the failure probability of the faulty node in the reliability assessment network model based on the confidence interval, and use the verified failure probability of the faulty node, the fault repair time of the faulty node, and the average failure duration of the faulty node as the first reliability index, the second reliability index, and the third reliability index of the faulty node, respectively.

[0108] Specifically, step 4 includes:

[0109] Step 4.1: Verify the failure probability of the faulty node in the reliability assessment network model based on the confidence interval.

[0110] The confidence interval of the fault probability of the fault node is determined according to the fault posterior distribution, and the confidence interval is used to screen the fault probability of the fault node; when the confidence interval is not less than the set interval threshold, the larger the confidence interval, the greater the reliability of the fault posterior distribution of the fault node, and the greater the reliability of the fault probability of the fault node, and the fault probability of the fault node is retained; conversely, when the confidence interval is less than the set interval threshold, the smaller the confidence interval, the lower the reliability of the fault probability of the fault node, and the fault probability of the fault node is removed.

[0111] Based on the reliability assessment network model, it is determined whether the faulty node whose fault occurrence probability has been removed is the underlying event of each sub-network. If the faulty node is a non-bottom-level event of each sub-network, then based on the occurrence path of the fault event to be evaluated, the underlying event of each sub-network corresponding to the faulty node is determined in the reliability assessment network model. According to Bayes' theorem, the failure rate of the underlying event of each sub-network corresponding to the faulty node is used to calculate the failure probability of the faulty node. Conversely, if the faulty node is the bottom-level event of each sub-network, the failure rate of the bottom-level event of each sub-network is used as the failure probability of the faulty node.

[0112] In a non-limiting preferred embodiment, the parameter The value range of Find the interval corresponding to the observed value of the fault population sample , so that the posterior probability As large as possible, and When the posterior probability is as small as possible, the loss function satisfies the following relationship:

[0113] ,

[0114] Where, and are all non-negative weights, is an interval length, is the indicator function.

[0115] Then the posterior risk satisfies the following relationship:

[0116] ,

[0117] Where, is the posterior risk, is the length of the posterior interval, is the posterior probability.

[0118] Will Considered as a random variable, binomial parameter distribution It is approximately a Beta distribution, satisfying the following relationship:

[0119] ,

[0120] For a given sample observation and , if there are two statistics and , so that the following relationship is satisfied:

[0121] ,

[0122] The confidence level is ; interval( , ) is a parameter Confidence interval of .

[0123] In step 4.2, the verified fault probability of the faulty node, the fault repair time of the faulty node, and the average fault duration of the faulty node are used as the first reliability index, the second reliability index, and the third reliability index of the faulty node, respectively.

[0124] Specifically, the reliability index of the faulty node is closely related to the reliability index of the hydrogen-electricity coupled micro-energy network, reflecting the degree of reliability of hydrogen energy supply to each faulty node in the hydrogen-electricity coupled micro-energy network. The primary, secondary, and tertiary reliability indexes of the faulty node enable a localized, focused assessment of the reliability of physically unstable hydrogen energy within the micro-energy network.

[0125] The faulty node after verification The probability of failure , Fault repair time of the faulty node and the average failure duration of failed nodes They are respectively used as the first reliability index, the second reliability index and the third reliability index of the fault node.

[0126] In a non-limiting preferred embodiment, the fault repair time of a faulty node represents the average time from fault to normal operation, and the average fault duration of a faulty node represents the average power outage time of the faulty node in the system within a year.

[0127] Faulty node The first reliability index, the second reliability index and the third reliability index satisfy the following relationship:

[0128] ,

[0129] Step 5: Use the first reliability index, the second reliability index, the third reliability index of the faulty node, and the reliability index of the hydrogen-electricity coupled micro-energy network to jointly evaluate the reliability of the hydrogen-electricity coupled micro-energy network.

[0130] Specifically, the reliability indicators of faulty nodes alone cannot fully reflect the overall hydrogen supply reliability level of the hydrogen-electricity coupled micro-energy network. Therefore, the reliability indicators of the hydrogen-electricity coupled micro-energy network are needed to objectively evaluate the system's ability to provide continuous power supply.

[0131] Reliability indicators of hydrogen-electricity coupled micro-energy grid include:

[0132] 1) The probability of hydrogen supply being stopped by the hydrogen-electricity coupled micro-energy grid , satisfying the following relationship:

[0133] ,

[0134] Where, Indicates a faulty node Uptime, For the faulty node The hydrogen outage time when a fault occurs.

[0135] 2) The probability of obtaining hydrogen through hydrogen-electricity coupling micro-energy grid , satisfying the following relationship:

[0136] ,

[0137] 3) Duration of hydrogen outage in hydrogen-electricity coupled micro-energy grid , satisfying the following relationship:

[0138] ,

[0139] Where, For the faulty node The probability of hydrogen suspension.

[0140] 4) Hydrogen load shortfall energy expectation , satisfying the following relationship:

[0141] ,

[0142] In the formula, 1 means that there is loss during the operation of hydrogen energy equipment. is the simulation period, For the Year time Faulty node The amount of hydrogen load reduction due to electric-hydrogen coupling, is the number of years, is the number of failed nodes.

[0143] In a non-limiting preferred embodiment, those skilled in the art can adopt a variety of evaluation methods such as scoring method, weighted summation method, hierarchical analysis method, etc., and use the first reliability index, second reliability index, third reliability index of the fault node, and the reliability index of the hydrogen-electricity coupled micro-energy network to jointly conduct reliability evaluation of the hydrogen-electricity coupled micro-energy network.

[0144] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0145] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0146] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0147] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A reliability assessment method for hydrogen-electricity coupled micro-energy network with fault visualization. The energy structure of the micro-energy network includes hydrogen energy sub-network, electric energy sub-network and thermal energy sub-network. The hydrogen energy sub-network and the electric energy sub-network are coupled via an electric-hydrogen coupling element, and the electric energy sub-network and the thermal energy sub-network are coupled via an electric-thermal coupling element; the characteristics are: Methods include: The first top-level fault event and the first fault cause of the hydrogen energy sub-network, the second top-level fault event and the second fault cause of the electric energy sub-network, and the third top-level fault event and the third fault cause of the thermal energy sub-network are obtained as nodes to establish a reliability assessment network model that characterizes the relationship between the nodes; wherein the first top-level fault event includes: a fault event caused by hydrogen, and the first fault factor includes: a fault factor that causes the fault event caused by hydrogen; the second top-level fault event includes: a power system collapse or shutdown, and the second fault factor includes: a fault factor that causes the power system collapse or shutdown; the third top-level fault event includes: a thermal system loses control and cannot continuously supply heat, and the third fault factor includes: a fault factor that causes the thermal system loses control and cannot continuously supply heat; Collect micro energy grid fault data and micro energy grid risk data; Using statistical methods, the failure probability of each node in the reliability assessment network model is determined based on the fault data and risk data of the micro-energy network. Based on the failure probability of each node and its constraints, the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated are determined based on the reliability assessment network model. Fault visualization is performed based on the fault nodes and occurrence paths. The failure probability of the faulty node in the reliability assessment network model is verified based on the confidence interval, and the verified failure probability of the faulty node, the fault repair time of the faulty node, and the average failure duration of the faulty node are used as the first reliability index, the second reliability index, and the third reliability index of the faulty node, respectively, including: 1) The probability of hydrogen supply being stopped by the hydrogen-electricity coupled micro-energy grid , satisfying the following relationship: Where, Indicates a faulty node Uptime, For the faulty node The hydrogen outage time when the failure occurs; 2) The probability of obtaining hydrogen through hydrogen-electricity coupling micro-energy grid , satisfying the following relationship: 3) Duration of hydrogen outage in hydrogen-electricity coupled micro-energy grid , satisfying the following relationship: Where, For the faulty node The probability of hydrogen suspension; 4) Hydrogen load shortfall energy expectation , satisfying the following relationship: In the formula, 1 means that there is loss during the operation of hydrogen energy equipment. is the simulation period, For the Year time Faulty node The amount of hydrogen load reduction due to electric-hydrogen coupling, is the number of years, is the number of failed nodes; The reliability of the hydrogen-electricity coupled micro-energy network is evaluated by using the first reliability index, the second reliability index, the third reliability index of the fault node and the reliability index of the hydrogen-electricity coupled micro-energy network.

2. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 1 is characterized in that: Establish a reliability assessment network model that characterizes the relationship between nodes, including: Taking the first top-level fault event and the first fault cause as the root node, generate layer by layer layer subnodes, and the electric-hydrogen coupling element is used as the first Child nodes in the layer; From Starting from the child nodes in the layer, generate layer by layer The second top-level fault event and the second fault cause of the power sub-network are taken as the first Leaf nodes in a layer; From Starting from the child nodes in the layer, generate layer by layer The second top-level fault event and the second fault cause of the power sub-network are taken as the first The leaf node in the layer uses the electrothermal coupling element as the first The leaf nodes in the layer; ; From Starting from the leaf nodes in the layer, generate layer by layer The third top-level fault event and the third fault cause of the thermal energy sub-network are used as the first Leaf nodes in a layer; in, 、 、 and All are positive integers.

3. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 2 is characterized in that: The relationships between nodes represented by the reliability assessment network model include: and relationship, joint relationship, and causal relationship.

4. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 1 is characterized in that: Using statistical methods, based on the failure data and risk data of the micro-energy grid, the failure probability of each node in the reliability assessment network model is determined, including: Extract characteristic information from micro-energy grid fault data as prior information; According to the prior information, the fault prior distribution is obtained based on the reliability evaluation network model; Extract characteristic information from micro-energy grid risk data as posterior information; According to the posterior information and the fault prior distribution, the fault posterior distribution is obtained based on the reliability evaluation network model; According to the fault posterior distribution, the failure probability of each node in the reliability assessment network model is obtained.

5. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 4 is characterized in that: According to the failure probability of each node and its constraints, the failure event to be evaluated and its occurrence path are determined based on the reliability evaluation network model, including: Establish the constraint conditions of the failure probability of each node to satisfy the following relationship: Where, is the observed value of the fault population sample The corresponding coupling coefficient; is the observed value of the fault population sample The corresponding probability of failure; and are the observed values ​​of the fault population sample The corresponding lower and upper limits of the probability of failure; When the probability of failure Greater than , it is determined to be a high probability failure event ,in, is the number of tests; high probability failure events are taken as the failure events to be evaluated, and high probability failure events are obtained based on the reliability evaluation network model The path of occurrence.

6. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 4 is characterized in that: Verify the failure probability of each node in the reliability assessment network model based on the confidence interval, including: The confidence interval of the fault probability of the fault node is determined according to the fault posterior distribution, and the fault probability of the fault node is screened using the confidence interval; when the confidence interval is not less than the set interval threshold, the fault probability of the fault node is retained; conversely, when the confidence interval is less than the set interval threshold, the fault probability of the fault node is removed.

7. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 6 is characterized in that: Based on the reliability assessment network model, determine whether the faulty node whose fault probability has been removed is the underlying event of each sub-network. If the faulty node is not an underlying event of each sub-network, then based on the occurrence path of the fault event to be evaluated, determine the underlying event of each sub-network corresponding to the faulty node in the reliability assessment network model. According to Bayes' theorem, the failure probability of the faulty node is calculated using the failure rate of the underlying events of each sub-network corresponding to the faulty node; On the contrary, if the faulty node is the underlying event of each sub-network, the failure rate of the underlying event of each sub-network is used as the failure probability of the faulty node.

8. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 1 is characterized in that: Faulty node The first reliability index, the second reliability index and the third reliability index satisfy the following relationship: In the formula, the faulty node after verification is The probability of failure For the faulty node The first reliability indicator is the fault repair time of the faulty node. For faulty nodes The second reliability indicator is the average failure duration of the faulty node. For faulty nodes The third reliability indicator.

9. The reliability assessment method of hydrogen-electricity coupled micro-energy network with fault visualization according to claim 1 is characterized in that: The reliability indicators of the hydrogen-electricity coupled micro-energy grid include: the probability of the hydrogen-electricity coupled micro-energy grid stopping hydrogen supply, the probability of the hydrogen-electricity coupled micro-energy grid obtaining hydrogen, and the energy expectation of hydrogen load shortage.

10. A reliability assessment system for a hydrogen-electricity coupled micro-energy network with fault visualization, applying the reliability assessment method for a hydrogen-electricity coupled micro-energy network with fault visualization according to any one of claims 1 to 9, characterized in that: The system includes: Reliability assessment network establishment module, fault event visualization processing module, reliability assessment module; a reliability assessment network establishment module, configured to obtain a first top-level fault event and a first fault cause of the hydrogen energy sub-network, a second top-level fault event and a second fault cause of the electric energy sub-network, and a third top-level fault event and a third fault cause of the thermal energy sub-network as nodes, so as to establish a reliability assessment network model representing the relationship between the nodes; The fault event visualization processing module is used to collect micro-energy network fault data and micro-energy network risk data; using statistical methods, based on the micro-energy network fault data and micro-energy network risk data, the failure probability of each node in the reliability assessment network model is determined; based on the failure probability of each node and its constraints, the fault node corresponding to the fault event to be evaluated and the occurrence path of the fault event to be evaluated are determined based on the reliability assessment network model; The reliability assessment module is used to verify the failure probability of the fault node in the reliability assessment network model based on the confidence interval, and use the verified failure probability of the fault node, the fault repair time of the fault node, and the average fault duration of the fault node as the first reliability index, second reliability index, and third reliability index of the fault node respectively; the reliability of the hydrogen-electricity coupled micro-energy network is jointly evaluated by using the first reliability index, second reliability index, third reliability index of the fault node and the reliability index of the hydrogen-electricity coupled micro-energy network.

11. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

Images