Method and system for evaluating reliability of power distribution information physical system

Abstract

The invention provides a power distribution information physical system reliability evaluation method and system, and the method comprises the steps: building a multi-dimensional information system network model which comprises an equipment operation state model, a packet loss model, a time delay model and an error code model, building a node adjacency matrix according to an equipment node connection relation, obtaining a node network transmission matrix based on the node adjacency matrix, and carrying out the reliability evaluation of a power distribution information physical system. Calculating expected values of availability of four attributes of the multi-dimensional information system; based on the calculated expected values of the availability of the four attributes of the multi-dimensional information system, a fault point area caused by manual operation due to unreliable information system performance and prolonged power failure time of the upstream and downstream of a fault point are obtained; generating a physical operation scene based on the prolonged power failure time and correcting the physical operation scene; and carrying out fault recovery decision calculation on the corrected physical operation scene to finally obtain a reliability evaluation index.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution information technology, and in particular relates to a reliability assessment method and system for a power distribution cyber-physical system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of the smart grid concept, the information and physical systems of distribution networks are deeply integrated, gradually developing into distribution cyber-physical systems. Monitoring, data collection, and control based on the distribution network's information system can effectively prevent the further deterioration of physical system faults and help improve energy efficiency.

[0004] The reliability of the distribution network's information system impacts the reliability of its physical system. With the integration of a large number of distributed energy resources into the distribution network, the measurement, calculation, and control of these units have intensified the degree of cyber-physical coupling and increased the risk of cross-spatial propagation of CPDS faults. This is the cyber-physical power distribution system (CPDS).

[0005] Research on cyber-physical systems for distribution networks has considered the impact of information failures and transmission interference on distribution network reliability, starting from the functional characteristics of information systems. Some studies have established component-level reliability models by considering failures in the physical distribution system and the multidimensional information system (DIS) within the distribution cyber-physical system (CPDS). Other studies have analyzed outages in the distribution physical system (PDS) and the multidimensional information system (DIS) by considering the components, topology, and network-physical coupling characteristics of the CPDS. Other studies have established reliability analysis models for intelligent distribution feeder fault detection, isolation, and service restoration, incorporating multidimensional information system (DIS) failure analysis into distribution physical system (PDS) PDS planning. Other studies have established PDS evaluation models to quantify the impact of component monitoring and fault handling on the reliability analysis of the distribution cyber-physical system (CPDS). Other studies have applied fault tree methods to consider the impact of DIS reliability on CPDS fault handling, distributed generation, and electric vehicle scheduling. These studies focus on discretizing the coupling effects of the DIS and PDS, while communication details and the corresponding reliability analysis are often simplified or not clearly described.

[0006] In addition, some literature has established a stochastic-flow network (SFN) model, which uses multiple states to represent the degree of deterioration of the performance of the multidimensional information system DIS. On this basis, a DIS model that considers the communication network topology and information flow quality (packet loss, bit error, delay) is extended. Then, by combining the expected value of bridging and communication network unavailability with the fault traversal of the physical system, a reliability analytical calculation method for the distribution network cyber-physical system (CPDS) constrained by the multidimensional information system DIS network is obtained. However, this method cannot take into account the randomness of distribution physical system PDS failures and the strong temporal nature of resources such as new energy and energy storage system (ESS).

[0007] Although there have been many advances in the research on CPDS reliability assessment, there are still some unresolved issues, including: (1) Most CPDS reliability studies that consider isolated operation of new energy sources only discretize the DIS switch control modeling of the FLISR process at the moment of PDS failure in the N-1 simulation process, without considering the continuity of DIS. The DIS availability modeling is not refined enough, and the node attributes are single.

[0008] (2) Some studies have analyzed and expressed the impact of physical system failures by using the FLISR fault handling process as an interface between PDS and DIS, with the availability of the information system guiding the impact of physical system failures. However, since the FLISR recovery process is strongly related to the decision-making of available resources such as new energy and energy storage, it cannot be included in the recovery decision-making process of recovery resources with strong temporal sequence.

[0009] In addition, the inventors discovered in their research that existing patent literature includes related technologies for reliability assessment of power distribution cyber-physical systems based on wireless communication networks. This method includes: 1) Considering the impact of weather on wireless access network communications, a power distribution information link effectiveness model based on wireless access networks was established; 2) Considering the impact of wireless communication failures on the physical system's self-healing process, a Failure Mode Impact Analysis (FMEA) table for the power distribution cyber-physical system was established; and 3) Considering the impact of wireless access network-based information system communication failures on island formation and operation, a power distribution cyber-physical system reliability assessment method based on Monte Carlo simulation was proposed to calculate system reliability indicators. This invention accurately assesses the reliability of power distribution cyber-physical systems based on wireless access networks, providing solid support for reliability assessment and planned operation of smart distribution networks.

[0010] The paper also discloses a risk assessment scheme for power distribution cyber-physical systems, including a comprehensive assessment method for defining risks in both information and physical systems; assessing the potential threats to the safe operation of the distribution network from information flow interruption or tampering, and revealing the evolutionary mechanism of cross-space cascading failures; and verifying its effectiveness under different attack scenarios through simulation. This approach effectively quantifies the cross-space propagation impact of information system failures on physical systems, accurately assesses the combined risk level of information, physical, and cyber-physical systems, and significantly improves the accuracy and efficiency of risk assessment. It not only identifies high-risk nodes but also quantifies the propagation mechanism of cascading failures, providing reliable support for the safe operation and planning and design of power distribution cyber-physical systems, thereby enhancing the reliability and resilience of the distribution network.

[0011] The evaluation methods described in the aforementioned existing patent literature fail to consider the impact of higher-order faults, resulting in inaccurate results. Alternatively, they fail to fully account for the impact of information systems, also resulting in inadequate evaluations. These methods fail to fully integrate refined information system modeling with physical layer reliability assessments. Summary of the Invention

[0012] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a reliability assessment method for a power distribution cyber-physical system, which integrates information network distribution and physical fault location, isolation, and recovery to achieve reliability assessment of a power distribution cyber-physical system.

[0013] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, a reliability assessment method for a power distribution cyber-physical system is disclosed, comprising: Establish a multidimensional information system network model, including: equipment operation status model, packet loss model, delay model and error model. Build a node adjacency matrix based on the device node connection relationship. Based on the node adjacency matrix, the node network transmission matrix can be obtained. Then, the expected value of the availability of the four attributes of the multidimensional information system is calculated. Based on the expected value of the availability of the four attributes of the multi-dimensional information system, the extended power outage time in the fault area and upstream and downstream of the fault point caused by unreliable information system performance and manual operation is obtained; Generate physical operating scenarios based on extended outage duration and modify physical operating scenarios; Perform fault recovery decision calculations on the modified physical operation scenario and ultimately obtain reliability evaluation indicators.

[0014] As a further technical solution, when establishing a multi-dimensional information system network model, it specifically includes: Abstract all devices in the information system as nodes, and abstract the information system as a cell array of node connection relationships. Each element of the array reflects multiple attributes of the device node, including operating status, packet loss rate, delay time, and bit error number. For any device , the node model uses a composite vector To describe; establish a four-attribute model for each device node.

[0015] As a further technical solution, the equipment operation state model is described using a two-state model, the two state models being respectively: an equipment availability model and an unavailability model of the information system; The packet loss model uses packet loss rate and packet loss state probability distribution to characterize the packet loss state, and uses a Markov chain-based model to simulate network packet loss; The delay model: the information link delay availability is characterized by a delay threshold, and the information flow delay is divided into link delay and exchange delay; the link delay is the propagation delay; the exchange delay is calculated using a normal distribution; The bit error model includes header data bit errors and payload data bit errors.

[0016] As a further technical solution, we calculate the expected values ​​of the availability of the four attributes of the multidimensional information system. Since the delay time and the number of error bits are continuous, we need to calculate their expected availability based on the source and sink nodes. The specific calculation steps are as follows: 1) Input the packet loss, delay, bit error threshold and total number of simulations for all information system devices , set the simulation counter ; 2) Sample the four attributes of all nodes, set the four attributes corresponding to the nodes that are unavailable to run to 0, and form a transmission matrix ; 3) Use the improved depth-first search algorithm to search for paths between source and sink nodes that satisfy the four properties; 4) After traversing all source and sink node paths, count the number of valid communications for each set of source and sink nodes. , until the total number of simulations is reached ; 5) Calculate the communication availability of each source and sink node, and the communication availability between nodes i and j.

[0017] As a further technical solution, an improved depth-first search algorithm is used to search for paths between source and sink nodes that meet the four properties, as follows: Set the path source node and sink node in sequence, search all paths between the source and sink nodes, start the traversal search from the source node, and calculate whether the cumulative packet loss, delay, and bit error of the current searched path reaches the threshold. If the threshold is reached, search other paths and mark the search node. Through the search algorithm, all path sets between the source and sink nodes can be obtained, and the path with the smallest delay is selected as the path between the source and sink nodes. Then, unmark the search node and traverse the source and sink node paths in sequence.

[0018] As a further technical solution, the modified physical operation scenario specifically includes: The line is divided into multiple zones based on the switch distribution of the line. In the event of a fault, the operating status of the equipment in each zone is consistent. According to the divided areas, the simulated system operation scenario is modified as follows: According to the time of each device failure recorded when the operation scenario is generated, the fault area is determined for each device failure time, and the repair time and fault point are recorded. upstream area The impact of the power outage time on each area is calculated by using the downtime within the fault area, the power outage duration in the fault area where load cannot be transferred, the power outage duration in the area downstream of the fault point where load transfer may occur, and the corrected state duration sampling function. Then, the variables in the array corresponding to the moments affected by these areas are corrected to 0. At this point, the timing operation scenarios of each area are obtained.

[0019] As a further technical solution, after modifying the physical operation scenario, each region is abstracted into a virtual node. The load of the virtual node is the sum of the loads of the region. The virtual connection status of each region is determined based on the upstream and downstream connection relationships of each region. After the regions are equivalent to topological connections, restoration resource decisions are made with a calculation cycle of one day: an objective function and corresponding constraints are constructed; the constraints include: power balance constraints, energy storage operation constraints, load shedding constraints, and new energy output constraints.

[0020] Secondly, a reliability assessment system for a power distribution cyber-physical system is disclosed, including: The expected value calculation of availability is configured to: establish a multi-dimensional information system network model, including: a device operation status model, a packet loss model, a delay model, and a bit error model; establish a node adjacency matrix based on the device node connection relationship; obtain a node network transmission matrix based on the node adjacency matrix; and then calculate the expected value of the availability of the four attributes of the multi-dimensional information system; The physical operation scenario acquisition module is configured to: derive the extended power outage time of the fault point area and upstream and downstream of the fault point caused by unreliable information system performance and manual operation based on the expected value of the availability of four attributes of the multi-dimensional information system; The fault recovery decision calculation module is configured to: generate a physical operation scenario based on the extended power outage time and modify the physical operation scenario; Perform fault recovery decision calculations on the modified physical operation scenario and ultimately obtain reliability evaluation indicators.

[0021] One or more of the above technical solutions have the following beneficial effects: The technical solution of the present invention obtains the information system network availability based on a multidimensional information system network model, and calculates the extended power outage time of the fault point area and the upstream and downstream of the fault point considering the impact of DIS. A physical simulation operation scenario correction scheme is proposed. The physical operation scenario is corrected according to the extended power outage time of each fault point area and the upstream and downstream of the fault point, and the area is redivided to obtain a scenario suitable for recovery decision-making. Finally, the recovery process decision calculation of all scenarios is carried out according to the time sequence to obtain an accurate reliability indicator.

[0022] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 This is the CPDS structure of an embodiment of the present invention; Figure 2 A sampling diagram of the operation scenario of the embodiment of the present invention; Figure 3 This is a schematic diagram of fault partitioning according to an embodiment of the present invention; Figure 4 This is a schematic diagram of fault correction according to an embodiment of the present invention; Figure 5 This is an equivalent topological diagram of the region according to an embodiment of the present invention; Figure 6 This is a flow chart of CPDS reliability evaluation according to an embodiment of the present invention; Figure 7 Schematic diagram of the improved IEEE RTS BUS6 F4 feeder system according to an embodiment of the present invention; Figure 8 A fault order diagram generated by sequential sampling according to an embodiment of the present invention; Figure 9Schematic diagram of the impact of network availability on CPDS reliability indicators according to an embodiment of the present invention; Figure 10 Schematic diagram of the effect of ESS rated capacity multiples on SAIFI according to an embodiment of the present invention; Figure 11 Schematic diagram of the impact of ESS rated capacity multiples on SAIDI and EENS according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0027] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0028] Explanation of terms: CPDS: short for cyber physical power distribution system, Chinese: power distribution cyber physical system; PDS: short for power distribution system, Chinese: physical distribution system; DIS: short for Dimensional Information System, Chinese: multidimensional information system; FLISR: Fault location, isolation, and supply restoration. SFN: short for stochastic-flow network, Chinese: random flow network; ESS: short for energy storage system, Chinese: new energy storage system; DG: short for distributed Generation, Chinese: distributed resources; IED: Intelligent Electronic Device; ONU: Optical Network Unit; POS: Passive optical splitter; OLT: Optical Line Terminal; CB: circuit breaker; TS: Tie switch.

[0029] With the advancement of smart grid research, power distribution systems are gradually evolving into cyberphysical power distribution systems (CPDSs). Refined modeling and reliability assessment methods for these systems are crucial. This example proposes a reliability assessment method for CPDSs that combines the impact of information system performance with sequential scenario simulation and correction. This method uses fault location and isolation as the interface between the physical power distribution system (PDS) and the multidimensional information system (DIS), deriving the expected impact of DIS performance on PDS reliability. A physical operation scenario simulation and correction method is proposed, in which the impact of DIS performance on outage duration is incorporated into PDS operation scenarios for recovery decisions regarding physical system operation. First, a multidimensional DIS availability model is established using Monte Carlo methods. Then, the impact of information system performance is considered to generate and correct physical operation scenarios. Finally, the distribution system is divided into regions based on topology, and a distribution network restoration model is designed to facilitate recovery resource decisions and calculate scenario reliability indicators. The reliability evaluation results of CPDS of the improved IEEERTS BUS6 F4 bus system are presented and analyzed, and the key factors affecting CPDS reliability are identified and discussed.

[0030] Example 1 This embodiment discloses a reliability assessment method for a power distribution cyber-physical system, comprising the following steps: Step 1: Analyze the FLISR process in CPDS considering DIS performance.

[0031] Step 2: Simulation of physical system operation scenarios and correction algorithms.

[0032] Step 3: A reliability assessment method for the revised scenario was established, and a recovery decision model was built to maximize the utilization of recovery resources and calculate reliability indicators.

[0033] Step 5: Numerical results are given for the improved IEEE RTS BUS6 F4 feeder distribution system. Factors affecting the reliability of CPDS are analyzed.

[0034] In step 1, the impact of DIS performance on the duration of CPDS fault outage: Affected by the performance of the DIS, the duration of the fault outage of the CPDS during the FLISR process is prolonged, thus affecting the reliability indicators of the load point. Therefore, it is necessary to accurately characterize the FLISR process and the DIS model.

[0035] 1-1) FLISR process of CPDS CPDS consists of DIS and PDS, such as Figure 1 As shown in the figure, the multi-dimensional information system DIS is mainly implemented using industrial Ethernet, including the control layer, namely the control center, the switching layer (including optical fibers, switches, routers, and servers), and the access layer (including intelligent electronic devices (IEDs), optical network units (ONUs), passive optical splitters (POSs), and optical line terminals (OLTs)).

[0036] The multidimensional information system (DIS) processes information from the physical distribution system (PDS) as follows: IEDs collect measurement data from devices in each PDS zone and transmit it to the control center via a communications network. The control center analyzes the data and issues commands to the connected IEDs in the zone where the devices are located. The IEDs then control the physical system. The distribution network can be divided into multiple zones based on switch positions, enabling operations such as positioning, remote control, and feedback.

[0037] by Figure 1 Taking a fault as an example, this paper describes the impact of DIS performance on the duration of switching operations during a PDS fault. This assumes that local relay protection is completely reliable. Protection and circuit breaker operations are ignored, and only manual operation time is included. The time required is primarily for fault location, fault isolation, and fault recovery.

[0038] (1) Fault location: When a fault occurs in Area 2, Figure 2 The circuit breaker (CB) operated by the relay trips, and the tie switch (TS) is disconnected in the same way. Area 1 and Area 3 stop supplying power. If the telemetry is normal, the fault point can be correctly located based on the correctly transmitted telemetry information uploaded by each IED within the power outage range. The location time is about 0. Otherwise, the fault location time is the manual location time. .

[0039] (2) Fault isolation: After the positioning is completed, the control center issues a control command to disconnect switches SS1 and SS2 at both ends of the fault point. If the remote control information transmission is normal and the switch is not faulty at this time, the switch action time is about 0; otherwise, manual switching is required and the fault isolation time is The success or failure of the fault isolation information feedback to the control center affects the restoration of power supply. If the feedback to the control center is not successful, manual inspection and correction of the on-site remote signal information are required. Manual inspection time is required. .

[0040] (3) Fault recovery: After successful isolation, it is necessary to make power restoration decisions for the upstream and downstream areas (Area1 and Area3) of the fault area based on resources. For the upstream area, when control information can be sent to the CB normally, the control center shuts down the CB to restore power. The restoration operation time is approximately 0. If the distributed resources downstream are sufficient to restore power, the downstream restoration operation time is also 0. Otherwise, the restoration time of the downstream non-fault area is the manual switching operation time. .

[0041] 1-2) Multidimensional DIS Network Model The DIS device has been introduced in step 1-1). All devices in the information system (including optical fibers) are abstracted as nodes, and the information system is abstracted as a cell array of node connection relationships. Each element of the array reflects multiple attributes of the device node, including operating status, packet loss rate, delay time, and bit error number. The node model uses a composite vector To describe.

[0042] (1) Where, The node operation status (1 is normal, 0 is interrupted); is the packet loss rate, is the delay time, is the bit error rate.

[0043] The cell array above includes multiple types of information, not all of which are integers (int). It can be either numeric or symbolic. The cell array can be used here to conveniently store "operating status, packet loss rate, delay time, and number of bit errors" information.

[0044] (1) Operational status model: A two-state model is used to describe DIS equipment availability. and unavailability Expressed as: (2) Where, and For devices failure rate and repair rate.

[0045] After obtaining the four models of all network nodes, they can be used to establish the node network transmission matrix, and then perform sampling simulation to obtain the communication availability between any nodes i and j , which is then used for sampling simulation and correction of running scenarios.

[0046] (2) Packet loss model: The packet loss rate and probability distribution of packet loss state are used to characterize the packet loss state. The Gilbert-Elliot model based on Markov chain is used to simulate network packet loss. The transmission density matrix of the Markov model is established based on historical data, and the steady-state distribution is solved by combining the Kolmogorov equation: (3) Where, For nodes In state Packet loss rate when For nodes In state The steady-state probability when ; is the total number of states. and The sequence pairs described describe the performance of the nodes that allow data flow to pass through. The steady-state probability and the packet loss rate at that time can be determined through random sampling. The packet loss availability can be obtained by comparing it with the packet loss rate threshold.

[0047] (3) Delay model: The information link delay availability is characterized by the delay threshold. The information flow delay is affected by device propagation, forwarding, queuing, etc., including link delay and exchange delay The link delay is the propagation delay, which is calculated as: (4) Where: is the line length, is the propagation speed of electromagnetic waves in the communication medium. The exchange delay is calculated using the normal distribution: (5) Where: is the mean of the delay statistics, is the variance. Applying the inverse transform to sample it gives the analog delay.

[0048] (4) Error model: The error model includes header data errors and payload data errors. The error availability is characterized by the threshold of the number of erroneous bits in the data stream. If the threshold is exceeded, it is considered that the error has caused the information transmission failure. The bit error is simulated using Poisson distribution as follows: (6) Where, is the number of error bits per unit time, The expected number of error bits per unit time is obtained by multiplying the total number of bits transmitted per unit time by the bit error rate. Since the Poisson distribution has no cumulative distribution function, the number of error bits is obtained by discrete accumulation instead of integration and then sampling simulation calculation. .

[0049] After establishing the four-attribute model for each device node, without considering human factors such as network attacks, the node adjacency matrix can be established based on the device node connection relationship. Replace the adjacency matrix Row elements, we can get the node network transmission matrix: (7) Transmission Matrix No. Rank The four attributes of the column represent the device node availability.

[0050] The above method facilitates analysis of the impact of node i and quickly determines the impact on transmission performance through the elements in the i-th row.

[0051] Monte Carlo methods are used to calculate the expected availability of the four attributes of a multidimensional DIS. Since delay and error rates are continuous, their expected availability needs to be calculated based on the source and sink nodes. The source and sink nodes are the starting and ending nodes, with the source node sending information and the sink node receiving information. The specific calculation steps are as follows: 1) Input the packet loss, delay, bit error threshold and simulation total of all DIS devices , set the simulation counter .

[0052] 2) Sample the four attributes of all nodes, set the four attributes corresponding to the nodes that are unavailable to run to 0, and form a transmission matrix .

[0053] 3) Use the improved depth-first search algorithm to search for paths between source and sink nodes that satisfy the four properties. The algorithm is as follows: Set the path source node srcNode and sink node sinkNode in sequence, search all paths between the source and sink nodes, start the traversal search from the source node, and calculate whether the cumulative packet loss, delay, and bit error of the path currently searched and stored reaches the threshold. If the threshold is reached, search other paths and mark the search node. Through the search algorithm, all path sets between the source and sink nodes can be obtained, and the path with the smallest delay is selected as the path between the source and sink nodes. Then, unmark the search node and traverse the source and sink node paths in sequence.

[0054] 4) After traversing all source and sink node paths once, , count the number of effective communications for each group of source and sink nodes Until the total number of simulations is reached .

[0055] 5) Calculate the communication availability of each source and sink node. The communication availability between nodes i and j is: (8) Where, For simulation nodes and The number of effective communications between is the total number of simulations, A ij Calculated by those factors of the transfer matrix.

[0056] 1-3): Impact of DIS performance on power outage duration in various areas at the fault point After obtaining the node communication expectation value of the multi-dimensional DIS network and combining it with the FLISR process of CPDS, the extended power outage time of the fault point area and the upstream and downstream of the fault point caused by unreliable DIS performance and manual operation can be derived, including: (1) Fault point upstream area Downtime within: (9) Where, is the expected availability of the control center, obtained from historical statistical values, From the control center to the network device node The availability expectation, It is the time for manual fault location. is the fault repair time, obtained by sequential Monte Carlo sampling of faulty equipment.

[0057] The explanation of the conditions under which manual operation time is required in the fault location, isolation, and recovery process is in the blue part of the text. Through the analysis of this part, the manual operation time caused by information system failures in the fault area and upstream and downstream can be deduced as formula (9), formula (10), and formula (11).

[0058] Formula (10) is used for scenario simulation, and formulas (9) and (11) are used to correct the upstream and downstream states of the physical layer simulation running scenario to obtain accurate physical layer states for subsequent recovery decision calculations.

[0059] (2) Duration of power outage in the fault area where load cannot be transferred: (10) Where, is the manual fault isolation time, It is the time for troubleshooting and correction after remote signaling failure.

[0060] (3) Duration of power outage in the area downstream of the fault point where load transfer may occur: (11) Where, It is the time it takes for power to be manually restored to the non-fault area downstream of the fault point. It is a 01 variable for the downstream load transfer status. If there is a transfer, it is 1. Because it involves correction and decision-making, the transfer can be performed by default. When there is no transfer, the power outage scenario in the operation scenario will also default to the status of 0.

[0061] The impact value calculated in step 1 is used for scenario simulation and correction in step 2. Step 1 can obtain the expected availability of information nodes from the control center to each area, which can be used in formulas (9), (10), and (11). The power outage time in the fault area is added to the simulation link, see formula (10), and the upstream and downstream power outage time is written into the correction link, see formulas (9) and (11).

[0062] Step 2: Simulation operation scenario generation and modification; 2-1) Run scenario simulation sampling Sequential Monte Carlo is generally used to generate the timing of the physical operation scenario of the device. The device is modeled with two states, and the state duration sampling is shown in formula (12): (12) Where, and are the normal operation time and fault duration obtained by sampling equipment, and is the failure rate and repair rate of the equipment, A computer-generated random number between 0 and 1.

[0063] The time series scenario generation is to sample the normal operation time of each device, record the time of device failure, and then sample the repair time (fault duration), record the repair time, and use 01 variables to fill in the fault and normal duration until the sum of the accumulated time meets the simulation time requirement. Finally, the operating status of all devices is superimposed to obtain the operating status of the entire system, such as Figure 2 shown.

[0064] This sampling process does not include the switching action and manual isolation time. Therefore, the formula (12) is modified in combination with the impact of DIS performance: (13) According to (13), the system timing operation status can be simulated. It is worth mentioning that the correction of (13) only considers the repair time correction of the fault point, and does not include the correction of the interruption time in the area where the faulty equipment is located and the upstream and downstream areas.

[0065] 2-2) Operation scenario division and correction According to the distribution of switches on the line, the line can be divided into multiple areas. When a fault occurs, the operating status of the equipment in each area is consistent. Figure 3 As shown in the figure, when a fault occurs at the location shown in the figure, the interruption time of all devices in Area 2 and the interruption time of the faulty device, as well as the interruption time of the upstream and downstream areas of the fault point affected by the DIS performance can be calculated according to formulas (9) to (11).

[0066] Run scenarios on the simulated system based on the divided areas To make corrections, follow these steps: According to the time of each device failure recorded when the operation scenario is generated, for each time of each device failure, determine the area where the failure is located, and calculate the impact of the power outage time caused by the failure on each area based on the recorded repair time and (9), (10), (11), (13), and then correspond the time of these areas affected in the array The variables in are corrected to 0. At this point, the time series operation scenarios of each region are obtained.

[0067] The above process incorporates the manual switching operation time to obtain an accurate physical operation scenario, which facilitates the recovery decision calculation at the physical layer.

[0068] like Figure 4 As shown in the figure, since the switch is sandwiched between two areas, the switch is simultaneously divided into two areas during area division. When the switch fails, both areas are considered to be the fault areas.

[0069] Step 3: CPDS reliability assessment method 3-1) Regional topological equivalence After simulating and correcting the operation scenarios of each region, each region is abstracted into a virtual node. The load of the virtual node is the total load of the region. The virtual connection status of the region is determined based on the upstream and downstream connection relationship of each region. Figure 3 The partition is re-equalized into a topological structure, such as Figure 5 As shown in Figure 2, area 2 is in an interrupted state due to a fault. The virtual line connected to the equivalent virtual node 2 is disconnected. An optimization problem can be designed to consider the recovery decision problem of resources such as DG and ESS.

[0070] 3-2) Regional load restoration decision After equating regions to topological connections, the following recovery resource decision-making method is developed with one day as a calculation cycle: (1) Objective function (14) Where, A regional equivalent virtual node exist The load that is removed at the moment is the total load of the area. For 24 hours, is the total number of equivalent virtual nodes, that is, the number of regions.

[0071] In this step, the objective function and constraints are established by computer, and the solution can obtain the minimum load shedding amount. The advantage is that it can maximize the utilization of timing resources such as energy storage and DG. Recovery refers to the restoration of power supply to the interrupted load through energy storage and DG.

[0072] Constraints a) Power balance constraints (15) Where, and Represents a virtual node Upstream virtual branch Downstream virtual branch exist Active power at the moment, For virtual nodes exist Active power at the moment, 、 For the node The connected generator and DG are The output active power at all times, 、 For the node Connected energy storage Since the virtual line only represents the connection relationship and has no actual impedance, network losses are not considered. Reactive power balance is not considered in the reliability standard calculation example, so it is not expanded here.

[0073] b) Energy storage operation constraints (16) (17) (18) (19) (20) Where, and Energy storage The charge and discharge status at the moment is represented by the variable 01. Representing energy storage Always charge; and They represent the upper limit of the charging and discharging power of energy storage, and Energy storage The charging and discharging power at each moment, For energy storage The capacity of the moment; and are the upper and lower limits of energy storage capacity respectively. and The initial and final capacity of the energy storage on this day.

[0074] c) Load shedding constraints (twenty one) Where, for Moment virtual node The load, 01 variable, indicating a node Whether the load is cut off, Representation node Load cut off.

[0075] d) Constraints on new energy output (twenty two) Where, for The output value of new energy connected to the node at any time, It is the output value of new energy.

[0076] 3-3) CPDS Reliability Assessment Process Based on the DIS network model and Monte Carlo method, a CPDS reliability evaluation method considering multi-dimensional network model is established. Figure 6 As shown in the figure, it is divided into three parts: DIS network modeling, simulation operation scenario correction, and fault scenario recovery decision.

[0077] Sample the expected link availability of DIS information from Part 1 and calculate the expected time for manual troubleshooting due to communication unavailability; Part 2 generates PDS system operation scenarios based on the Sequential Monte Carlo method, divides areas according to switches, and modifies the physical system operation scenarios; Specifically, a preliminary set of operating scenarios is obtained by sampling using the Sequential Monte Carlo method. The set of operating scenarios is then modified according to formulas (9) and (11), and the impact moment is modified to 0 (1 indicates normal at that moment, and 0 indicates fault at that moment).

[0078] Part 3 performs fault recovery decision calculations on the fault scenarios in the revised operating scenario set, and ultimately obtains reliability evaluation indicators.

[0079] The computer establishes the objective function and constraints for fault recovery decision making. The main software used is MATLAB, YALMIP, and GUROBI. Through the recovery decision calculation, the load shedding amount of the system under each recovery scenario can be calculated, and then the reliability index of the system can be statistically calculated.

[0080] The basic load point indicators used in traditional distribution network reliability assessment are: load point outage rate , Load point outage duration and the average annual outage time of load points The system indicators can then be obtained: system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), average service availability index (ASAI) and expected unavailable energy index (EENS).

[0081] Case Analysis Case Description The test system used in this example is the improved IEEE RTS BUS6 F4 feeder system. Figure 7 As shown. The total active load of PDS is 4.8155MW. The system is equipped with 5 section switches and 3 tie switches, divided into 6 areas, equipped with 0.6MWh energy storage capacity, rated power of 0.75MW, and DG output data are selected from typical daily data. The test system DIS node packet loss is divided into 5 states: 0%, 2.5%, 5%, 7.5% and 10%, and the corresponding steady-state probabilities are 0.9, 0.068, 0.03, 0.001 and 0.001 respectively; the node delay mean is 63.58 ms, the variance is 11 ms2; the node bit error rate is The number of payload and header data bits transmitted per unit time is 20 Mbits and 2 Mbits, respectively. Information communication thresholds: 10% node packet loss threshold, 10% end-to-end packet loss threshold; 73.53 ms node delay threshold, 600 ms end-to-end delay threshold; 3500 bits payload data error threshold, 20 bits header data error threshold. The manual correction times th1 to th4 due to network telesignaling and telecontrol failures are 1 hour, 0.5 hour, 0.4 hour, and 0.5 hour, respectively.

[0082] The case analysis was compiled in MATLAB R2020b, modeled using the YALMIP toolbox, and solved using the GUROBI solver. All analyses were performed on a laptop (Intel Core i5-13th Gen 3.0GHz, 16GB RAM).

[0083] Analysis of the impact of DIS performance on CPDS reliability 1) To reflect the impact of random failures and DIS networks, three scenarios are considered for reliability analysis: Scenario 1: Considering the impact of DIS, the physical scenario simulation adopts N-1 sampling, which is also the most commonly used physical system sampling in CPDS reliability analysis. Scenario 2: Assuming that DIS is completely reliable, the correction process is simulated considering the physical scenario; Scenario 3: Considering the impact of DIS, the correction process is simulated considering the physical scenario.

[0084] It is worth mentioning that all operating scenarios in this paper assume that CPDS operates in a three-remote mode, that is, considering the telesignaling and remote control of the DIS. Unlike the discretized simulation of the information system after a physical failure in traditional CPS reliability analysis, the calculation expectation of DIS availability can ensure the continuity of the information network system during CPDS reliability analysis.

[0085] The fault order diagram obtained by sequential sampling of all equipment for 200 years is shown in the figure below. Figure 8 As shown in the figure, in addition to the first-order faults, there are still some high-order faults. The N-1 sequential sampling simulation cannot take into account the impact of such scenarios. Although some scholars have studied the method of reducing the order of high-order faults, they cannot be fully combined with variables with strong temporal characteristics. Therefore, it is necessary to analyze the reliability of the scenario recovery decision.

[0086] Table 1 shows the average duration of load points in each region under these three scenarios. and the average annual outage time of load points .

[0087] Table 1 Load point reliability index

[0088] Table 2 System reliability indicators

[0089] Comparing Case 1 and Case 3, we can find that when only considering the N-1 operating scenario, the reliability indicators of the load point and the system are relatively small. This is because in the high-order fault scenario, there are multiple areas with simultaneous power outages after the scenario correction, resulting in more load power supply terminals. In addition, the annual average number of faults at the load point is This is also related to the N-1 simulation, which cannot consider high-order faults. The method in this paper can consider high-order faults and make full decisions on recovery resources, thus obtaining more accurate reliability indicators. It also provides an alternative N-1 sampling simulation method for CPDS reliability analysis.

[0090] Comparing Case 2 with Case 3, we can find that the availability of DIS has an impact on the reliability index of the load point. When DIS is completely reliable, the load point and The DIS network availability between the IEDs and the control center in each area is significantly reduced, making the result unrealistic. The Monte Carlo sampling is used to obtain the DIS network availability between the IEDs and the control center in each area, as shown in Table 3.

[0091] Table 3 Network availability between regions and control center

[0092] The load data error threshold is set to a relatively large value, and the expected value of network availability between the region and the control center performs well. However, as shown in Table 1, network unavailability has a significant impact on the reliability indicators of the load point, indicating that the availability of the DIS determines the reliability of the PDS. Traditionally, the discrete nature of non-sequential sampling of information systems using PDS failure scenarios cannot reasonably assess the availability of information systems under failure scenarios. However, the method proposed in this paper can quantify information system availability using expected values ​​in time-series scenarios, enabling reasonable judgment of operating scenarios and obtaining relatively reasonable evaluation results.

[0093] Table 4 Impact of continuous bit errors and packet loss on DIS availability

[0094] Since delays and bit errors are continuous, delays and packet losses in the transmission process in the DIS network are continuous. When the IED where the switch is located is farther away from the server, the availability is lower, which in turn affects the reliability of CPDS.

[0095] In order to further demonstrate the intuitive impact of DIS availability on CPDS reliability, the network availability between the IEDs and the control center in each area is reduced from 100% to 95% at a rate of 1%, and the rest are set according to the above case 3. The reliability indicators are shown in Table 5 and Figure 8 shown.

[0096] Table 5 Impact of network availability on CPDS reliability indicators

[0097] From Table 5 and Figure 9 It can be seen that the DIS network availability has a significant impact on the CPDS reliability index. Therefore, it is very necessary to analyze the DIS network availability instead of using fixed discrete values ​​to simulate whether the information network is available.

[0098] Analysis of the impact of recovery resources on CPDS reliability: This paper also analyzes the impact of new energy, energy storage and other resources on the reliability of CPDS.

[0099] Table 6 Impact of DG penetration on CPDS reliability indicators

[0100] pass Figure 10 、 Figure 11 As can be seen, with the increase in renewable energy penetration and energy storage capacity, the CPDS reliability indicators SAIFI, SAIDI, and EENS all decrease. As the proportion of distributed DG access increases, the consideration of highly time-sensitive recovery resources such as DG and ESS has a significant impact on CPDS reliability indicators. Methods that fail to consider the continuous recovery process will be significantly limited. The method proposed in this paper not only refines the DIS network availability model but also characterizes the maximum power supply capacity of recovery resources in fault scenarios, providing an implementation method for highly time-sensitive DG output and continuous decision-making based on recovery resources.

[0101] This paper proposes a reliability analysis method for CPDS, using the FLISR fault handling process as the interface between the PDS and DIS. A multidimensional DIS network model is established, communication availability is derived based on Monte Carlo and DIS models, and an analytical expression for the expected time of manual maintenance affected by information is established. A sequential operation scenario simulation and correction process is proposed, and a decision-making method for sequential resource restoration is designed. Finally, reliability indicators are calculated using a standard case study, and the impact of multiple factors, such as DIS network availability and DG resources, on the reliability indicators is analyzed. Results demonstrate that the proposed refined DIS model is crucial to the overall CPDS reliability and that reliability analysis without incorporating highly sequential restoration resources is ultimately limited. The proposed CPDS reliability assessment method, which considers highly sequential resources such as the DIS and DG / ESS, not only describes DIS communication characteristics such as packet loss, delay, and bit errors during DIS transmission, effectively connecting the PDS and DIS reliability models, but also characterizes the impact of renewable energy penetration and restoration capabilities on the safe and reliable operation of the PDS.

[0102] This embodiment's sub-technical solution establishes a reliability assessment method for power distribution cyber-physical systems that integrates information network distribution with physical fault location, isolation, and recovery. Based on the DIS network communication model, DIS network availability is determined, and the expected time for location and isolation considering the impact of the DIS is calculated. A physical simulation operation scenario correction method is proposed. The physical operation scenario is corrected based on the expected time for each location and isolation, and the region is re-divided to obtain scenarios suitable for recovery decisions. Finally, the recovery process decisions for all scenarios are calculated according to the time sequence to obtain an accurate reliability indicator.

[0103] A distribution network sequential reliability assessment modeling and evaluation method considering the impact of DIS performance and distributed generation (DG), ESS and other recovery resources is proposed. This method incorporates the impact of DIS performance into the PDS operation scenario generation process, and incorporates the impact of DIS performance and recovery resources into the PDS operation scenario modification process, solving the limitation that PDS operation scenario decision can only simulate N-1.

[0104] A sequential sampling simulation operation scenario and correction method for the impact of DIS performance and timing recovery resources on load point interruption time are proposed. Based on the expected value of interruption time in the fault area obtained by the multidimensional DIS network model and the Monte Carlo method, a simulation operation scenario is obtained, and the operation scenario of the upstream and downstream areas at each fault moment is corrected.

[0105] A recovery decision model was designed. Using simulated and corrected operating scenarios, the recovery resources at each moment were restored to maximize the upper limit of the recovery capacity of strong timing recovery resources in the scheduling cycle where the fault occurred, and the reliability indicators under each corrected timing fault scenario were calculated.

[0106] Example 2 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0107] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0108] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0109] Example 4 The purpose of this embodiment is to provide a power distribution cyber-physical system reliability assessment system, including: The expected value calculation of availability is configured to: establish a multi-dimensional information system network model, including: a device operation status model, a packet loss model, a delay model, and a bit error model; establish a node adjacency matrix based on the device node connection relationship; obtain a node network transmission matrix based on the node adjacency matrix; and then calculate the expected value of the availability of the four attributes of the multi-dimensional information system; The physical operation scenario acquisition module is configured to: derive the extended power outage time of the fault point area and upstream and downstream of the fault point caused by unreliable information system performance and manual operation based on the expected value of the availability of four attributes of the multi-dimensional information system; The fault recovery decision calculation module is configured to: generate a physical operation scenario based on the extended power outage time and modify the physical operation scenario; Perform fault recovery decision calculations on the modified physical operation scenario and ultimately obtain reliability evaluation indicators.

[0110] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments. The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0111] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0112] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A reliability assessment method for a power distribution cyber-physical system, characterized by: include: Establish a multidimensional information system network model, including: equipment operation status model, packet loss model, delay model and error model. Build a node adjacency matrix based on the device node connection relationship. Based on the node adjacency matrix, the node network transmission matrix can be obtained. Then, the expected value of the availability of the four attributes of the multidimensional information system is calculated. Based on the expected value of the availability of the four attributes of the multi-dimensional information system, the extended power outage time in the fault area and upstream and downstream of the fault point caused by unreliable information system performance and manual operation is obtained; Generate physical operating scenarios based on extended outage duration and modify physical operating scenarios; Perform fault recovery decision calculations on the modified physical operation scenario and ultimately obtain reliability evaluation indicators.

2. The reliability assessment method of a power distribution cyber-physical system according to claim 1, wherein: When establishing a multidimensional information system network model, it specifically includes: Abstract all devices in the information system as nodes, and abstract the information system as a cell array of node connection relationships. Each element of the array reflects multiple attributes of the device node, including operating status, packet loss rate, delay time, and bit error number. For any device , the node model uses a composite vector To describe; establish a four-attribute model for each device node.

3. The reliability assessment method of a power distribution cyber-physical system according to claim 2, wherein: Calculate the expected value of the availability of the four attributes of the multidimensional information system. Since the delay time and the number of error bits are continuous, the expected availability needs to be calculated based on the source and sink nodes. The specific calculation steps are as follows: 1) Input the packet loss, delay, bit error threshold and total number of simulations for all information system devices , set the simulation counter ; 2) Sample the four attributes of all nodes, set the four attributes corresponding to the nodes that are unavailable to run to 0, and form a transmission matrix ; 3) Use the improved depth-first search algorithm to search for paths between source and sink nodes that satisfy the four properties; 4) After traversing all source and sink node paths, count the number of valid communications for each set of source and sink nodes. , until the total number of simulations is reached ; 5) Calculate the communication availability of each source and sink node, and the communication availability between nodes i and j.

4. The reliability assessment method of a power distribution cyber-physical system according to claim 3, wherein: Use the improved depth-first search algorithm to search for paths between source and sink nodes that satisfy the four properties, as follows: Set the path source node and sink node in sequence, search all paths between the source and sink nodes, start the traversal search from the source node, and calculate whether the cumulative packet loss, delay, and bit error of the current searched path reaches the threshold. If the threshold is reached, search other paths and mark the search node. Through the search algorithm, all path sets between the source and sink nodes can be obtained, and the path with the smallest delay is selected as the path between the source and sink nodes. Then, unmark the search node and traverse the source and sink node paths in sequence.

5. The reliability assessment method of a power distribution cyber-physical system according to claim 1, wherein: The modified physical operation scenario specifically includes: The line is divided into multiple zones based on the switch distribution of the line. In the event of a fault, the operating status of the equipment in each zone is consistent. According to the divided areas, the simulated system operation scenario is modified as follows: According to the time of each device failure recorded when the operation scenario is generated, the fault area is determined for each device failure time, and the repair time and fault point are recorded. upstream area The impact of the power outage time on each area is calculated by using the downtime within the fault area, the power outage duration in the fault area where load cannot be transferred, the power outage duration in the area downstream of the fault point where load transfer may occur, and the corrected state duration sampling function. Then, the variables in the array corresponding to the moments affected by these areas are corrected to 0. At this point, the timing operation scenarios of each area are obtained.

6. The reliability assessment method of a power distribution cyber-physical system according to claim 5, wherein: After modifying the physical operation scenario, each region is abstracted into a virtual node. The load of the virtual node is the sum of the loads of the region. The virtual connection status of each region is determined based on the upstream and downstream connection relationships of each region. After the regions are equivalent to topological connections, restoration resource decisions are made with a calculation cycle of one day: an objective function and corresponding constraints are constructed; the constraints include: power balance constraints, energy storage operation constraints, load shedding constraints, and new energy output constraints.

7. A reliability assessment system for a power distribution cyber-physical system, characterized by: include: The expected value calculation of availability is configured to: establish a multi-dimensional information system network model, including: a device operation status model, a packet loss model, a delay model, and a bit error model; establish a node adjacency matrix based on the device node connection relationship; obtain a node network transmission matrix based on the node adjacency matrix; and then calculate the expected value of the availability of the four attributes of the multi-dimensional information system; The physical operation scenario acquisition module is configured to: derive the extended power outage time of the fault point area and upstream and downstream of the fault point caused by unreliable information system performance and manual operation based on the expected value of the availability of four attributes of the multi-dimensional information system; The fault recovery decision calculation module is configured to: generate a physical operation scenario based on the extended power outage time and modify the physical operation scenario; Perform fault recovery decision calculations on the modified physical operation scenario and ultimately obtain reliability evaluation indicators.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method described in any one of claims 1 to 6 are implemented.

10. 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 described in any one of claims 1 to 6 are performed.

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