WO2019080930A1 - 电网等风险状态检修决策方法 - Google Patents

电网等风险状态检修决策方法

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Publication number
WO2019080930A1
WO2019080930A1 PCT/CN2018/112130 CN2018112130W WO2019080930A1 WO 2019080930 A1 WO2019080930 A1 WO 2019080930A1 CN 2018112130 W CN2018112130 W CN 2018112130W WO 2019080930 A1 WO2019080930 A1 WO 2019080930A1
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Prior art keywords
equipment
repaired
grid
maintenance
risk
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2018/112130
Other languages
English (en)
French (fr)
Inventor
冯昊
毛南平
余立军
陆晓芬
劳咏昶
潘福荣
严浩军
苏正川
袁野
王慧芳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo Electric Power Design Institute
Zhejiang University ZJU
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
Ningbo Electric Power Design Institute
Zhejiang University ZJU
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Publication date
Application filed by Ningbo Electric Power Design Institute, Zhejiang University ZJU, Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd, Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd filed Critical Ningbo Electric Power Design Institute
Priority to US16/306,023 priority Critical patent/US20210224755A1/en
Publication of WO2019080930A1 publication Critical patent/WO2019080930A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/043—Distributed expert systems; Blackboards
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/001—Arrangements for handling faults or abnormalities, e.g. emergencies or contingencies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the present disclosure belongs to the technical field of power systems, for example, to a risk state maintenance decision method such as a power grid.
  • the state maintenance is an inspection mode in which the deterioration state of the device is judged by monitoring the change trend of the state parameter of the device, and the maintenance is performed after the device degradation state is obvious.
  • Scientific state maintenance not only extends the economic life of the equipment, but also ensures safe and reliable operation of the power grid and improves the effectiveness and economy of maintenance.
  • the maintenance plan is formulated, based on the results of the state evaluation, it is determined whether the equipment is overhauled, and the maintenance mode of the equipment, such as overall maintenance, partial maintenance and general maintenance, etc., and how to arrange the maintenance period scientifically and reasonably is a research hotspot. That is, the equipment to be repaired is arranged in a reasonable maintenance period through the power grid state maintenance decision.
  • the state maintenance decision is a maintenance plan optimization based on the current state evaluation result of the device, including the determination of the maintenance mode and the maintenance time period.
  • the related technologies for the study of power grid state maintenance decisions are mainly divided into two categories: First, the number of alternative maintenance schemes is limited, each scheme is evaluated by different decision-making methods, and the schemes are prioritized or an optimal scheme is selected from them.
  • This kind of research method is only applicable to the equipment-level maintenance decision with limited number of maintenance schemes; the second is to establish a target model, and adopt a decision algorithm to make decision on equipment maintenance time or maintenance order when considering some constraints.
  • the class research method tends to blindly pursue the minimum total operational risk during the entire maintenance cycle of the power system. There may be great differences in operational risks at different time periods. For example, the operational risk may be low in some time periods, and the system operation risk is too high in some maintenance periods. The risk event becomes a reality and will bring huge losses.
  • the present disclosure provides a risk state maintenance decision-making method based on a minimum cumulative risk degree, such as a power grid, to improve maintenance safety.
  • a risk status maintenance decision-making method such as a power grid, comprising: calculating the contribution of each equipment to be repaired to the grid risk under the current operation mode of the power grid, and determining the contribution of each equipment to be repaired to the grid risk as a setting corresponding to the equipment to be repaired
  • the value of the maintenance decision order of the plurality of equipment to be repaired is arranged according to the set value from the largest to the smallest; the plurality of equipment to be repaired are sequentially made to be repaired according to the arranged maintenance decision order.
  • the decision method of each equipment i to be repaired is: obtaining a plurality of candidate service intervals, calculating a cumulative risk value of each of the candidate service areas, and arranging the equipment to be inspected i to be the smallest cumulative risk value.
  • An optional maintenance interval and enters the inspection step; determining whether there is a time period, the number of equipments in the time period exceeds the number of devices that the power grid allows to be repaired at the same time; if there is, the optional maintenance interval of the arrangement is deleted, The equipment to be inspected i is rearranged in an alternative maintenance interval with the remaining small cumulative risk value, and the inspection step is repeated; if not, the equipment to be inspected i is determined.
  • each of the maintenance device to be i d contribution to the grid by the equation risk i Calculated D i is a fault state set containing the fault of the equipment i to be repaired, The contribution of the equipment i to be repaired to the operational risk under fault state k,
  • the calculation formula is:
  • RF Xi is the grid risk caused by the failure of the equipment to be repaired i, and is represented by the product of the failure rate of the equipment i to be repaired and the grid load loss caused by the failure of the equipment i to be repaired;
  • RF (X1, X2, ... Xh) is The total grid risk under fault state k is represented by the product of the probability of failure state k and the grid load loss under fault state k; for any fault state k ⁇ D i ,h is the fault device under grid fault state k
  • the number (RF X1 + RF X2 + ... RF Xh ) is the sum of the grid risk values for each faulty device in the grid fault state k.
  • the amount of grid loss under fault state k is calculated by a DC optimized power flow model.
  • the proportional parameter K i , C i is the curvature parameter of the failure rate model of the equipment i to be repaired, S i is the inspection start period of the equipment to be inspected i, and b i is the number of maintenance durations of the equipment i to be inspected.
  • the method further includes the step of acquiring the calculation parameter, wherein the calculation parameter comprises: the equipment to be overhauled i, the maintenance mode of the equipment to be repaired i, the number of equipment MA that is allowed to be repaired at each time interval of the power grid, and the maintenance of the equipment to be repaired i
  • the duration number b i the initial health status score value HI i0 of the equipment i to be repaired, the health repair factor ⁇ i after the maintenance of the equipment i to be repaired, the proportional parameter K i and the curvature parameter C i of the equipment i failure rate model to be overhauled, Grid structure parameters, grid node load forecast values during the maintenance cycle.
  • a storage medium storing computer executable instructions for performing a risk state overhaul decision method such as any of the above-described power grids.
  • FIG. 1 is a flowchart of a risk state maintenance decision method based on a minimum cumulative risk degree based on an embodiment.
  • the grid risk consists of maintenance risks and failure risks.
  • the maintenance risks reflect the risks caused by maintenance, and the failure risks reflect the risks caused by equipment failures due to inadequate power grid maintenance.
  • M is the maintenance cycle
  • m, n are any two different time periods in the maintenance period
  • R(T m ) and R(T n ) are the operational risks of the grid in the mth maintenance period and the nth maintenance period, respectively.
  • this embodiment proposes an objective function with the smallest standard deviation of the grid risk for each period in the maintenance period.
  • the formula is: Where min f is the minimum function.
  • the standard deviation of risk can measure the degree of fluctuation of risk in each period. The smaller the standard deviation, the smaller the fluctuation of risk in each period. In the case of considering the total risk, the maintenance decision must also consider the minimum standard deviation of the grid risk for each period of the maintenance cycle.
  • the risk state maintenance decision method based on the minimum cumulative risk degree provided by the embodiment includes the following steps.
  • step 10 the contribution of each equipment to be repaired to the grid risk under the current operation mode of the grid is calculated, and the contribution of each equipment to be repaired to the grid risk is taken as the set value corresponding to the equipment to be repaired.
  • step 20 the order of inspection decisions for each device to be serviced is arranged in descending order of the set values.
  • step 30 the plurality of devices to be repaired are sequentially inspected according to the arranged maintenance decision order.
  • the time period refers to a unit time period
  • the maintenance interval refers to a time interval required for maintenance of the equipment to be inspected, including a plurality of consecutive time periods
  • the cumulative risk value of the equipment to be inspected refers to the equipment to be inspected at The sum of the grid risks for all time periods in the maintenance interval; when the other constraints are not considered, the sum of the grid risks of the equipment to be overhauled in all time periods in the maintenance interval is the smallest, then the interval is the optimal maintenance interval of the equipment.
  • the maintenance decision is sequentially performed for each equipment to be repaired, and each inspection period is reduced.
  • the basic principle of the risk state maintenance decision method of the power grid and the like in each embodiment is to reduce the standard deviation of the grid risk degree in each period of the maintenance period, when the decision is made on the equipment to be repaired, because the maximum number of equipments to be repaired in the same period is limited, when When the number of equipments repaired in the same period reaches the maximum limit, other equipments can no longer be repaired during this period; and if the equipment that contributes to the grid risk is given priority decision, the probability that the equipment to be repaired with a large contribution to the grid risk is limited by the maximum number of equipment It is relatively low, that is, the probability that the equipment to be repaired, which contributes a lot to the grid risk, is arranged in the optimal maintenance area is high.
  • the equipment to be repaired Since the equipment to be repaired which contributes a lot to the grid risk contributes a lot to the grid risk, the equipment to be repaired is The cumulative risk value of each maintenance interval is relatively large. When the equipment to be repaired is arranged in the optimal maintenance interval, the cumulative risk value of the equipment to be repaired in each maintenance interval and the cumulative risk value of other equipment. Relatively closer, that is, the grid risk standard for each period of the maintenance cycle It will be smaller; if one of the multiple equipments to be repaired cannot be arranged in the optimal maintenance interval due to the maximum number of equipment, select the equipment to be repaired with little contribution to the grid risk and arrange the equipment to be repaired at the most When the area is overhauled, the standard deviation of the grid risk for each period of the maintenance period is more likely to be smaller than other cases.
  • a risk tracking method may be used to calculate the contribution of the equipment to be repaired to the grid risk. For example, it may be: for any fault state determined in the fault state set, track the contribution of each faulty device to the grid operation risk in the fault state, and then comprehensively combine the risk values in all fault states in the fault state set according to the contribution size. Assigned to each equipment to be repaired. For example, the size of the contribution of each equipment to be repaired to the grid risk is represented by ⁇ d i ⁇ , including d 1 , d 2 , d 3 ... d i ...
  • d 1 represents the contribution of the equipment 1 to be repaired to the risk of the grid, ie the set value of the equipment 1 to be repaired
  • d 2 represents the contribution of the equipment 2 to be repaired to the risk of the grid, ie the setting of the equipment to be repaired 2 Value
  • d 3 represents the contribution of the equipment 3 to be repaired to the risk of the grid, that is, the set value of the equipment to be repaired 3
  • d x is the contribution of the equipment to be repaired x to the risk of the grid, that is, the set value of the equipment x to be repaired
  • the value of x is the total amount of equipment to be inspected; d i may be any one between d 1 and d x .
  • D i is a fault state set containing the fault of the equipment i to be repaired, The contribution of the equipment i to be repaired to the risk of grid operation under fault state k,
  • the calculation formula is:
  • RF Xi is the grid risk caused only by the failure of the equipment to be repaired i, and is represented by the product of the failure rate of the equipment i to be repaired and the amount of grid loss caused by the failure of the equipment to be repaired i;
  • RF (X1, X2, ... Xh) The total grid risk under fault state k is represented by the product of the probability of failure state k and the grid load loss under fault state k; for any fault state k ⁇ D i ,h is the fault under grid fault state k
  • the number of devices, (RF X1 + RF X2 + ... RF Xh ), is the sum of the grid risk values for each faulty device in the grid fault state k.
  • the fault state set including the fault of the equipment i to be repaired if the fault causing a fault state includes the fault of the equipment to be repaired i, the fault state belongs to a fault state set containing the fault of the equipment i to be repaired, for example
  • the fault causing the fault state k is the fault of the equipment 1 to be repaired
  • the fault causing the fault state x is the fault of the equipment 1 to be repaired and the fault of the equipment to be repaired 2
  • the fault causing the fault state y is the fault of the equipment to be repaired 1 and the equipment to be repaired 2
  • step 20 for example, after the descending order of ⁇ d i ⁇ , the corresponding device sequence number is returned to form a device sequence ⁇ H(j) ⁇ to be overhauled, and ⁇ H(j) ⁇ includes H(1), H. (2), H(3)..., H(j)..., according to ⁇ H(j) ⁇ , the decision is made sequentially, and the maintenance decision of the equipment to be repaired can be sequentially performed in the order that j is gradually increased from 1, that is, according to the device sequence H (1), H(2), H(3), ... are sequentially determined. Assuming that ⁇ d i ⁇ is descendingly arranged as d 3 , d 1 , d 2 , ...
  • the device sequence corresponding to d 3 is H(1), that is, the decision order of the device 3 to be repaired is ranked first; 1 H is a sequence corresponding to the device (2), i.e., a sequence of decisions to be maintenance equipment in the second row; D 2 corresponding to the device serial H (3), i.e. until the maintenance apparatus 2 decision in third order, sequentially analogy.
  • the decision method of the single equipment to be repaired i is: obtaining a plurality of alternative maintenance intervals, calculating a cumulative risk value of each candidate service interval, and arranging the equipment to be repaired i in an alternative maintenance interval with the smallest cumulative risk value, and Entering the inspection step: judging whether there is a period of time, the number of equipments inspected during the period exceeds the number of equipment that the grid allows to be repaired simultaneously during the period; if it exists, the optional maintenance interval of the arrangement is deleted, and the equipment i is rearranged in the remaining The optional maintenance interval with the second smallest risk value is accumulated, and the inspection step is repeated; if it does not exist, the equipment to be repaired i is determined.
  • the continuous interval of any duration period b i in the maintenance period is an optional maintenance interval of the device i
  • b i is the number of durations required for the equipment i to be repaired.
  • the number of durations required for each equipment maintenance is known; the time period as a unit time can be set according to the actual situation, such as 1 hour, 1 day, 1 week, etc., generally 1 day as the unit, the equipment to be repaired i is in preparation
  • the cumulative risk value of the selected maintenance interval is the sum of the grid risks for each period of the alternative maintenance interval.
  • D(T m ) is the fault state set under the condition that the power grid considers maintenance during the T m period.
  • the maintenance condition means: during the Tm period, there may be other equipment to be repaired to be overhauled, D(T m ) is The set of fault states taking into account the equipment that has been scheduled for maintenance; E k (T m ) is the unplanned loss of load caused by the fault state k during the T m maintenance period; P k (T m ) is at T The m period considers the probability of the fault state k of the grid in the case of maintenance; the fault state is a fault caused by a single equipment to be repaired or two or more equipment to be repaired.
  • the probability that the fault state k occurs in the time period T m is calculated as follows: Where: N F is the number of equipment to be repaired that is out of service in fault state k, N Q -N F is the number of equipment to be repaired working in fault state k, and ⁇ i (T m ) is the period of equipment to be repaired i at T m Failure rate, the model expression is ⁇ j (T m ) is the failure rate of the equipment to be repaired j during the T m period.
  • the grid has a failure rate model expression as HI i (T m ) is the health status score value of the equipment i to be repaired, K i is the proportional parameter of the equipment failure rate model of the equipment to be repaired, and C i is the curvature parameter of the equipment failure rate model of the equipment to be repaired;
  • the health status score values before and after the maintenance are different, so in this embodiment, the health repair factor ⁇ i after the maintenance of the equipment i to be repaired is introduced.
  • the fault state set of the equipment to be repaired is enumerated to the second-order fault, which can cover the faults with high risk of the power grid, and eliminate high-order faults with small probability of occurrence, and realize reasonable The balance between sex and computing speed.
  • the embodiment may further include the step of acquiring the calculation parameters before the step 10, wherein the obtaining the calculation parameters includes: the equipment to be overhauled i, the maintenance mode of the equipment to be repaired i, and the equipment that is allowed to be repaired at each time interval of the power grid.
  • number MA until repair apparatus of i for several periods repair B i, the initial state of health scores HI i0 to be maintenance equipment i after to be maintenance equipment i repair health repair factor beta] i, the proportion to be maintenance i failure rate model device
  • the grid structure parameters and the grid node load prediction value in the maintenance period are the parameters required for the DC optimized load flow model to calculate the load loss under a certain operating state of the grid.

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Abstract

一种电网等风险状态检修决策方法，包括：计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将所述每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值（10）；按照所述设定值从大到小的顺序对多个所述待检修设备的检修决策顺序进行排列（20）；根据排列后的所述检修决策顺序对多个所述待检修设备依次进行检修决策（30）。通过确定电网当前运行方式下每个待检修设备对电网风险的贡献程度；按贡献程度由大到小排序确定待检修设备决策顺序；按待检修设备决策顺序基于最小累积风险度依次确定每个设备的检修时段，进而提高检修安全性。

Description

电网等风险状态检修决策方法
本公开要求申请日为2017年10月27日、申请号为201711021537.6的中国专利申请的优先权，该申请的全部内容通过引用结合在本公开中。
技术领域
本公开属于电力系统技术领域，例如涉及一种电网等风险状态检修决策方法。
背景技术
状态检修是通过监测设备的状态参数的变化趋势来判断设备的劣化状态，在设备劣化状态明显后实施检修的一种检修模式。科学的状态检修不仅能延长设备的经济寿命，也能保证电网安全可靠运行，提高维修的有效性和经济性。检修计划制定时，以状态评价结果为依据，决定设备是否检修，以及决定设备的检修方式，如整体性检修、局部性检修和一般性检修等，而如何科学合理的安排检修时段是研究热点，即通过电网状态检修决策将待检修设备安排在合理的检修时段中。因此，状态检修决策是基于设备当前状态评价结果进行的检修方案优选，包括检修方式和检修时段的确定。相关技术对于电网状态检修决策的研究主要分为两大类：一是备选检修方案数目有限，通过不同的决策方法对每个方案进行评价，排列出方案的优先次序或从中选择一个最优方案，该类研究方法仅适用于检修方案数目有限的设备级检修决策；二是建立一个目标模型，在考虑一些约束条件的情况下采用一种决策算法，对设备检修时段或检修次序 进行决策，该类研究方法倾向于一味追求电力系统整个检修周期内的总运行风险最小，可能出现不同时段运行风险差异很大，如可能一些时段运行风险很低，而一些检修时段系统运行风险过高，一旦高风险事件变为现实，将会带来巨大损失。
发明内容
本公开提供一种基于最小累积风险度的电网等风险状态检修决策方法，提高检修安全性。
一种电网等风险状态检修决策方法，包括：计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值；按照设定值从大到小的顺序对多个待检修设备的检修决策顺序进行排列；根据排列后的检修决策顺序对多个待检修设备依次进行检修决策。
在一实施例中，每个待检修设备i的决策方法为：获取多个备选检修区间，计算每个备选检修区间的累积风险度值，将待检修设备i安排在累积风险度值最小的备选检修区间，并进入检验步骤；判断是否存在一时段，该时段检修的设备数超过电网允许在该时段内同时检修的设备数；如果存在，则删除此次安排的备选检修区间，将待检修设备i重新安排在剩余累积风险度值次小的备选检修区间，并重复检验步骤；如果不存在，则待检修设备i决策完毕。
在一实施例中，每个待检修设备i对电网风险的贡献大小d i通过公式
Figure PCTCN2018112130-appb-000001
计算，D i为包含待检修设备i故障的故障状态集合，
Figure PCTCN2018112130-appb-000002
为待检修设备i对故障状态k下运行风险的贡献大小，
Figure PCTCN2018112130-appb-000003
的计算公式为：
Figure PCTCN2018112130-appb-000004
其中，RF Xi为由待检修设备i故障引起的电网风险，由待检修设备i的故障率和待检修设备i故障引起的电网失负荷量的乘积表示；RF(X1，X2，…Xh)为故障状态k下的总电网风险，由故障状态k发生的概率和故障状态k下的电网失负荷量的乘积表示；对于任一故障状态k∈D i，h为电网故障状态k下的故障设备数，(RF X1+RF X2+…RF Xh)为电网故障状态k下的每个故障设备的电网风险值之和。
在一实施例中，故障状态k下的电网失负荷量通过直流优化潮流模型计算。
在一实施例中，在备选检修区间，待检修设备i的累积风险度值通过叠加备选检修区间内所有时段的电网风险R(T m)得出，R(T m)通过公式R(T m)＝RM(T m)+RF(T m)计算，其中，RM(T m)为电网在T m时段的检修风险，用电网检修引起的失负荷量表示；RF(T m)为电网在T m时段的故障风险，是故障发生的概率和故障引起的失负荷量的综合，通过
Figure PCTCN2018112130-appb-000005
计算，其中，D(T m)为电网在T m时段考虑检修情况下的故障状态集；E k(T m)为电网在T m检修时段由故障状态k造成的非计划性失负荷量，P k(T m)为在T m时段考虑检修情况下电网发生故障状态k的概率；故障状态k在时段T m发生的概率通过公式
Figure PCTCN2018112130-appb-000006
计算，其中：N F为故障状态k中停运的待检修设备数量，N Q-N F为故障状态k中工作的待检修设备数量，λ i(T m)为待检修设备i在T m时段的故障率。
在一实施例中，待检修设备i的故障率公式为
Figure PCTCN2018112130-appb-000007
待检修设备i决策完毕后，若T m≤S i，则待检修设备i在T m时段的健康状态评分值 HI i(T m)＝HI i0；若T m≥S i+b i，则HI i(T m)＝β iHI i0，HI i0为待检修设备i的初始健康状态评分值，β i为待检修设备i检修后的健康修复因子，K i为待检修设备i故障率模型的比例参数K i，C i为待检修设备i故障率模型的曲率参数，S i为待检修设备i检修起始时段，b i为待检修设备i的检修持续时段数。
在一实施例中，所述计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将所述每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值的步骤之前，所述方法还包括获取计算参数的步骤，其中计算参数包括：待检修设备i，待检修设备i的检修方式，电网每个时段允许同时检修的设备数MA，待检修设备i的检修持续时段数b i，待检修设备i的初始健康状态评分值HI i0，待检修设备i检修后的健康修复因子β i，待检修设备i故障率模型的比例参数K i和曲率参数C i，电网结构参数，检修周期内的电网节点负荷预测值。
一种存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行上述任一电网等风险状态检修决策方法。
附图说明
下面对描述实施例中所需要用到的附图进行介绍。
图1为一实施例提供的一种基于最小累积风险度的电网等风险状态检修决策方法的流程图。
具体实施方式
下面结合实施例对本公开进行说明。
电网风险由检修风险和故障风险组成，其中，检修风险反映由于检修而引起的风险，故障风险则反映由于电网检修不足，设备发生故障而引起的风险。
理想的电网等风险检修模型的目标函数可表示为：R(T m)＝R(T n)，m，n∈{1，2，…，M}且m≠n，其中，M为检修周期内的时段数；m、n为检修周期内任意2个不同的时段；R(T m)、R(T n)分别为电网在第m个检修时段和第n个检修时段的运行风险。实际上，电网风险在检修周期每个时段完全相等是难以实现的，因此本实施例提出了检修周期内每个时段的电网风险度标准差最小的目标函数，公式为：
Figure PCTCN2018112130-appb-000008
其中，min f为取最小函数。
风险度标准差可以衡量每个时段风险度的波动程度，标准差越小，每个时段风险度的波动也越小。检修决策在考虑总风险小的情况下，还需兼顾考虑检修周期内每个时段的电网风险度标准差最小。
如图1所示，本实施例提供的基于最小累积风险度的电网等风险状态检修决策方法包括以下步骤。
在步骤10中，计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值。
在步骤20中，按照设定值从大到小的顺序对每个待检修设备的检修决策顺序进行排列。
在步骤30中，根据排列后的检修决策顺序对多个待检修设备依次进行检修决策。
本实施例中，时段指的是单位时间段，检修区间指的是待检修设备检修所需的时间区间，包含多个连续的时段；待检修设备的累积风险度值指的是待检修设备在检修区间内所有时段的电网风险之和；不考虑其他限制条件时，待检修设备在检修区间内所有时段的电网风险之和最小，则该区间为该设备的最优检修区间。
本实施例通过计算每个待检修设备对电网风险的贡献大小，并以此为依据确定设备的检修决策顺序，根据检修决策顺序对每个待检修设备依次进行检修决策，降低检修周期内每个时段的电网风险度标准差。其中，本实施例电网等风险状态检修决策方法降低检修周期内每个时段的电网风险度标准差的基本原理是，当对待检修设备进行决策时，由于同一时段检修的最大设备数有限制，当同一时段检修的设备数达到最大限制时，此时段不能再检修其他设备；而如果对电网风险贡献大的设备进行优先决策，那么，对电网风险贡献大的待检修设备受最大设备数限制的几率就相对比较低，即对电网风险贡献大的待检修设备被安排在最优检修区间的几率就高，由于对电网风险贡献大的待检修设备本身对电网风险贡献大，所以该待检修设备在每个检修区间的累积风险度值都相对比较大，将该待检修设备安排在最优检修区间时，则该待检修设备在每个检修区间的累积风险度值与其他设备的累积风险度值相对会更趋于接近，也就是检修周期内每个时段的电网风险度标准差会更小；如果因最大设备数限制，多个待检修设备中需要有一个不能被安排在最优检修区间时，选择对电网风险贡献小的待检修设备并将该待检修设备安排在非最优检修区域时，检修周期内每个时段的电网风险度标准差相对其他情况要小的可能性更大。
在步骤10中，可以采用风险追踪方法，计算待检修设备对电网风险的贡献大小。例如可以为：对故障状态集合中任一确定的故障状态，追踪该故障状态下每个故障设备对电网运行风险的贡献大小，然后综合故障状态集合中所有故障状态下的风险值，按贡献大小分配给每个待检修设备。例如可以为：在考虑电网所有可能故障状态的情况下，每个待检修设备对电网风险的贡献大小用{d i}表示，包含d 1，d 2，d 3...d i...d x；其中d 1表示待检修设备1对电网风险的贡献大小，即待检修设备1的设定值，d 2表示待检修设备2对电网风险的贡献大小， 即待检修设备2的设定值，d 3表示待检修设备3对电网风险的贡献大小，即待检修设备3的设定值，d x为待检修设备x对电网风险的贡献大小，即待检修设备x的设定值，x的值为待检修设备的总量；d i可以为d 1与d x之间的任意一个。在考虑电网所有可能故障状态的情况下，单个待检修设备i对电网风险的贡献大小d i通过公式 计算，D i为包含待检修设备i故障的故障状态集合，
Figure PCTCN2018112130-appb-000010
为待检修设备i在故障状态k下对电网运行风险的贡献大小，
Figure PCTCN2018112130-appb-000011
的计算公式为：
Figure PCTCN2018112130-appb-000012
其中，RF Xi为仅由待检修设备i故障引起的电网风险，由待检修设备i的故障率和待检修设备i故障引起的电网失负荷量的乘积表示；RF(X1，X2，…Xh)为故障状态k下的总电网风险，由故障状态k发生的概率和故障状态k下的电网失负荷量的乘积表示；对于任一故障状态k∈D i，h为电网故障状态k下的故障设备数，(RF X1+RF X2+…RF Xh)为电网故障状态k下的每个故障设备的电网风险值之和。
在此，对包含待检修设备i故障的故障状态集合做一个说明：如果引起一个故障状态的故障中包含待检修设备i故障，则该故障状态属于包含待检修设备i故障的故障状态集合，例如：引起故障状态k的故障为待检修设备1故障；引起故障状态x的故障为待检修设备1故障和待检修设备2故障；引起故障状态y的故障为待检修设备1故障、待检修设备2故障和待检修设备3故障；那么包含待检修设备1故障的故障状态集合为故障状态k、故障状态x和故障状态y。
在步骤20中，例如可以为：对{d i}进行降序排列后，返回对应的设备序号形成待检修的设备序列{H(j)}，{H(j)}包括H(1)，H(2)，H(3)…，H(j)…，根据{H(j)}进行依次决策，可以依次按照j从1逐渐增加的顺序进行待检修设备的检修决 策，即按设备序列H(1)，H(2)，H(3)，…的顺序进行依次决策。假设{d i}降序排列为d 3，d 1，d 2，...d x，那么d 3对应的设备序列是H(1)，即待检修设备3的决策顺序排在第一；d 1对应的设备序列为H(2)，即待检修设备1的决策顺序排在第二；d 2对应的设备序列为H(3)，即待检修设备2的决策顺序排在第三，依次类推。
在步骤30中，根据检修决策排列顺序对多个待检修设备依次进行检修决策，决策流程可以为：安排第一个检修设备时，j＝1，并进入单设备决策步骤：对序列号为H(j)的待检修设备按单个待检修设备i的决策方法进行决策，决策完毕后，判断待检修设备是否为最后一个，如果不是，j＝j+1，并重复单设备决策步骤，如果是，则待检修设备均已决策完毕，结束所有步骤。单个待检修设备i的决策方法为：获取多个备选检修区间，计算每个备选检修区间的累积风险度值，将待检修设备i安排在累积风险度值最小的备选检修区间，并进入检验步骤：判断是否存在一时段，该时段检修的设备数超过电网允许在该时段内同时检修的设备数；如果存在，则删除此次安排的备选检修区间，将设备i重新安排在剩余累积风险度值次小的备选检修区间，并重复检验步骤；如果不存在，则待检修设备i决策完毕。
在这里，检修周期内任一持续时段数为b i的连续区间均为设备i的备选检修区间，b i为设备i检修所需的持续时段数。每个设备检修所需的持续时段数是已知的；时段作为单位时间可以根据实际情况设定，如1小时、1天、1周等，一般以1天为单位，待检修设备i在备选检修区间的累积风险度值为备选检修区间每个时段的电网风险之和。
在备选检修区间，待检修设备i的累积风险度值通过叠加备选检修区间内所有时段的电网风险R(T m)得出，而T m时段的电网风险R(T m)可通过公式：R(T m)＝RM(T m)+RF(T m)计算，其中，RM(T m)为电网在T m时段的检修风险，用电网 检修引起的失负荷量表示；RF(T m)为电网在T m时段的故障风险，是故障发生的概率和故障引起的失负荷量的综合。因此有
Figure PCTCN2018112130-appb-000013
其中，D(T m)为电网在T m时段考虑检修情况下的故障状态集，考虑检修情况的意思是：Tm时段，有可能已有其他待检修设备被安排检修，D(T m)是将已被安排检修的设备考虑在内的故障状态集；E k(T m)为电网在T m检修时段由故障状态k造成的非计划性失负荷量；P k(T m)为在T m时段考虑检修情况下电网发生故障状态k的概率；故障状态为由单个待检修设备或两个及以上待检修设备引起的故障。根据状态枚举法，故障状态k在时段T m发生的概率计算如下：
Figure PCTCN2018112130-appb-000014
其中：N F为故障状态k中停运的待检修设备数量，N Q-N F为故障状态k中工作的待检修设备数量，λ i(T m)为待检修设备i在T m时段的故障率，其模型表达式为
Figure PCTCN2018112130-appb-000015
λ j(T m)为待检修设备j在T m时段的故障率。
相关技术中，电网已有故障率模型表达式为
Figure PCTCN2018112130-appb-000016
HI i(T m)为待检修设备i的健康状态评分值，K i为待检修设备i故障率模型的比例参数，C i为待检修设备i故障率模型的曲率参数；由于待检修设备i检修前后的健康状态评分值有所不同，所以本实施例中引入待检修设备i检修后的健康修复因子β i。因此待检修设备i在检修前，即T m≤S i，则待检修设备i在T m时段的健康状态评分值HI i(T m)＝HI i0，HI i0为待检修设备i的初始健康状态评分值；在检修完成后，即T m≥S i+b i，则HI i(T m)＝β iHI i0，β i可以通过检修类型得出，对整体性检修、局部性检修、一般性检修三类停电检修方式下β i分别取1.5、1.3和1.2，S i为设备i检修起始时段。
考虑到实际电网运行一般要求满足N-1检验，因此将待检修设备的故障状 态集枚举至2阶故障，能囊括电网风险较大的故障，剔除发生概率很小的高阶故障，实现合理性与计算速度的平衡。
为了获取所需参数，本实施例在步骤10之前还可以包括获取计算参数的步骤，其中获取计算参数包括：待检修设备i，待检修设备i的检修方式，电网每个时段允许同时检修的设备数MA，待检修设备i的检修持续时段数b i，待检修设备i的初始健康状态评分值HI i0，待检修设备i检修后的健康修复因子β i，待检修设备i故障率模型的比例参数K i和曲率参数C i，电网结构参数，检修周期内的电网节点负荷预测值。其中电网结构参数，检修周期内的电网节点负荷预测值是直流优化潮流模型计算电网某一运行状态下的失负荷量所需要的参数。

Claims (8)

  1. 电网等风险状态检修决策方法，包括：
    计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将所述每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值；
    按照所述设定值从大到小的顺序对多个所述待检修设备的检修决策顺序进行排列；
    根据排列后的所述检修决策顺序对多个所述待检修设备依次进行检修决策。
  2. 如权利要求1所述的方法，其中，所述每个待检修设备i的决策方法为：获取多个备选检修区间，计算每个备选检修区间的累积风险度值，将待检修设备i安排在累积风险度值最小的备选检修区间，并进入检验步骤；判断是否存在一时段，该时段检修的设备数超过电网允许在该时段内同时检修的设备数；如果存在，则删除此次安排的备选检修区间，将所述待检修设备i重新安排在剩余累积风险度值次小的备选检修区间，并重复检验步骤；如果不存在，则所述待检修设备i决策完毕。
  3. 如权利要求1所述的方法，其中，所述每个待检修设备i对电网风险的贡献大小d i通过公式
    Figure PCTCN2018112130-appb-100001
    计算，D i为包含待检修设备i故障的故障状态集合，
    Figure PCTCN2018112130-appb-100002
    为待检修设备i对故障状态k下运行风险的贡献大小，
    Figure PCTCN2018112130-appb-100003
    的计算公式为：
    Figure PCTCN2018112130-appb-100004
    其中，RF Xi为由待检修设备i故障引起的电网风险，由待检修设备i的故障率和待检修设备i故障引起的电网失负荷量的乘积表示；RF(X1，X2，…Xh)为故障状态k下的总电网风险，由故障状态k发生的概率和故障状态k下的电网失 负荷量的乘积表示；对于任一故障状态k∈D i，h为电网故障状态k下的故障设备数，(RF X1+RF X2+…RF Xh)为电网故障状态k下的每个故障设备的电网风险值之和。
  4. 如权利要求3所述的方法，其中，故障状态k下的电网失负荷量通过直流优化潮流模型计算。
  5. 如权利要求2所述的方法，其中，在备选检修区间，所述待检修设备i的累积风险度值通过叠加备选检修区间内所有时段的电网风险R(T m)得出，R(T m)通过公式R(T m)＝RM(T m)+RF(T m)计算，其中，RM(T m)为电网在T m时段的检修风险，用电网检修引起的失负荷量表示；RF(T m)为电网在T m时段的故障风险，是故障发生的概率和故障引起的失负荷量的综合，通过
    Figure PCTCN2018112130-appb-100005
    计算，其中，D(T m)为电网在T m时段考虑检修情况下的故障状态集；E k(T m)为电网在T m检修时段由故障状态k造成的非计划性失负荷量，P k(T m)为在T m时段考虑检修情况下电网发生故障状态k的概率；故障状态k在时段T m发生的概率通过公式
    Figure PCTCN2018112130-appb-100006
    计算，其中：N F为故障状态k中停运的待检修设备数量，N Q-N F为故障状态k中工作的待检修设备数量，λ i(T m)为待检修设备i在T m时段的故障率。
  6. 如权利要求5所述的方法，其中，所述待检修设备i的故障率公式为
    Figure PCTCN2018112130-appb-100007
    所述待检修设备i决策完毕后，若T m≤S i，则所述待检修设备i在T m时段的健康状态评分值HI i(T m)＝HI i0；若T m≥S i+b i，则HI i(T m)＝β iHI i0，HI i0为待检修设备i的初始健康状态评分值，β i为待检修设备i检修后的健康修复因子，K i为待检修设备i故障率模型的比例参数K i，C i为待检修设备i故障率模型的曲率参数，S i为待检修设备i检修起始时段，b i为待检修设备i的检修持续时段数。
  7. 如权利要求1所述的方法，其中，所述计算电网当前运行方式下每个待检修设备对电网风险的贡献大小，将所述每个待检修设备对电网风险的贡献大小作为对应待检修设备的设定值的步骤之前，所述方法还包括获取计算参数的步骤，其中计算参数包括：待检修设备i，待检修设备i的检修方式，电网每个时段允许同时检修的设备数MA，待检修设备i的检修持续时段数b i，待检修设备i的初始健康状态评分值HI i0，待检修设备i检修后的健康修复因子β i，待检修设备i故障率模型的比例参数K i和曲率参数C i，电网结构参数，检修周期内的电网节点负荷预测值。
  8. 一种存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求1-7任一项的方法。
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