WO2017016021A1 - 一种基于影响增量的状态枚举可靠性评估方法及其装置 - Google Patents
一种基于影响增量的状态枚举可靠性评估方法及其装置 Download PDFInfo
- Publication number
- WO2017016021A1 WO2017016021A1 PCT/CN2015/088389 CN2015088389W WO2017016021A1 WO 2017016021 A1 WO2017016021 A1 WO 2017016021A1 CN 2015088389 W CN2015088389 W CN 2015088389W WO 2017016021 A1 WO2017016021 A1 WO 2017016021A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- power system
- state
- influence
- sensitivity
- increment
- Prior art date
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—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—Methods to deal with contingencies, e.g. abnormalities, faults or failures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
- G01R19/2513—Arrangements for monitoring electric power systems, e.g. power lines or loads; Logging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/40—Testing power supplies
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- 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
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Definitions
- the present invention relates to the field of power system reliability assessment, and in particular, to a state enumeration reliability evaluation method based on impact increment and a device thereof.
- the current common methods for power system reliability assessment are divided into state enumeration method and Monte Carlo simulation method.
- the state enumeration method calculates the probability and impact of each power system state by enumerating all possible power system states, and then calculates the reliability index of the power system.
- the number of power system states increases exponentially.
- state enumeration can quickly and efficiently calculate reliability metrics, but for complex large power systems, it is difficult to enumerate all power system states. Therefore, for large power systems, high-order faults are often ignored to improve computational efficiency. However, this will result in a decrease in the accuracy of the resulting reliability index, especially for power systems with high probability of component failure.
- the state enumeration method is more suitable for power systems with small scale, simple structure and low component failure probability.
- the Monte Carlo simulation method is also called random sampling method.
- the method obtains the state of the power system by sampling the state of each component in the power system, and then calculates the reliability index.
- the Monte Carlo simulation method can be divided into sequential Monte Carlo method and non-sequential Monte Carlo method.
- Monte Carlo simulation method is a statistical test method, which is relatively intuitive and easy to understand. Its characteristic is that the sampling times are not affected by the scale and complexity of the power system, and it is easy to handle the random variation characteristics of the load. However, its error is closely related to the number of simulations. In order to obtain a reliability index with higher accuracy, it is necessary to increase the number of simulations and prolong the calculation time. Therefore, the Monte Carlo simulation method is less efficient in dealing with a simple power system, and is more suitable for a power system with a larger scale, a higher component failure probability or multiple fault effects that cannot be ignored.
- Analytic method and Monte Carlo simulation method each have their own advantages, and the applicable situations complement each other. Therefore, the hybrid method that combines the two is an ideal evaluation method.
- the hybrid method is characterized by the use of an analytical method in the case of an analytical method, and the Monte Carlo method is applied beyond the scope of the analytical method. And in the application of Monte Carlo method, the information provided by the analytical method is used as much as possible to reduce the calculation time and improve the calculation precision.
- the invention provides a state enumeration reliability evaluation method based on influence increment and a device thereof, and the invention improves the meter Calculating the accuracy and computational efficiency of the reliability index reduces the complexity of the calculation reliability index, as described below:
- a state enumeration reliability evaluation method based on impact increments comprising the following steps:
- the breadth-first search method is used to verify the reachability of all elements in the independent adjacency matrix corresponding to the selected power system state. If there is an unreachable element, the influence of the selected power system state is incremented to zero, and the power system state is reselected;
- the influence of the power system state under all load levels is evaluated by the optimal power flow algorithm, and the influence expectation of the power system state under each load level is obtained, thereby obtaining the influence increment of the power system state;
- the power system reliability index is obtained by affecting the increment.
- the method before the step of verifying the reachability of all elements in the independent adjacency matrix corresponding to the selected power system state by the breadth-first search method, the method further includes:
- the sensitivity of each device impedance to the power flow of each branch is obtained by the perturbation method, and the independence between the devices is determined according to the sensitivity;
- a power system state is selected from the state set, and an independent adjacency matrix of the power system state is created by the independence between the devices.
- the method further includes: inputting power system data, device reliability data, and preset parameters, and initializing the fault order.
- the preset parameters include: a maximum fault search order and a device independence sensitivity threshold.
- the step of determining the independence between the devices according to the sensitivity is specifically:
- the sensitivity index of the impedance of one faulty device to the branch power flow distribution is greater than the device independence sensitivity threshold, and the sensitivity index of the impedance of the other faulty device to the branch power flow distribution is greater than the device independence.
- the two faulty devices are not independent.
- a state enumeration reliability evaluation device based on influence increment comprising:
- the verification module is configured to verify the reachability of all elements in the independent adjacency matrix corresponding to the selected power system state by the breadth-first search method, and if there is an unreachable element, the influence of the selected power system state is incremented to zero. Select the power system status;
- the first obtaining module is configured to evaluate the influence of the state of the power system under all load levels by using an optimal power flow algorithm if the unreachable element is not present, obtain the influence expectation of the state of the power system under each load level, and then obtain the state of the power system. Impact increment
- the second obtaining module is configured to obtain the power system reliability indicator by affecting the increment when all the power system states in the state set have been analyzed and the maximum fault search order has been reached.
- the device further comprises:
- a third obtaining module configured to obtain, by using a perturbation method, sensitivity of each device impedance to each branch power flow
- a module is created for selecting a power system state from a set of states, and creating an independent adjacency matrix of power system states by independence between devices.
- the device further comprises:
- Input and initialization modules for inputting power system data, device reliability data and preset parameters, and initializing the fault order.
- the preset parameters include: a maximum fault search order and a device independence sensitivity threshold.
- the determining module includes:
- the technical solution provided by the present invention has the beneficial effects that the core of the present invention is to replace the influence of the enumerated power system state with the influence increment, and can effectively improve the weight of the low-order fault state in the reliability index;
- the lower-order state calculates a more accurate reliability index; the invention proves that the calculation of the reliability index can ignore the influence increment of the high-order fault and greatly improve the calculation efficiency.
- 1 is a flow chart of a state enumeration reliability evaluation method based on an influence increment
- FIG. 2 is a schematic diagram of a state enumeration reliability evaluation device based on an influence increment
- FIG. 3 is another schematic diagram of a state enumeration reliability evaluation device based on an influence increment
- FIG. 4 is another schematic diagram of a state enumeration reliability evaluation device based on an influence increment
- Figure 5 is a schematic diagram of a determination module
- FIG. 6 is a topological structure diagram of an IEEE 118 node system
- FIG. 7a is a schematic diagram showing a convergence curve of the obtained EENS index when the method, the traditional state enumeration method and the Monte Carlo method are applied to the IEEE 118 node system;
- FIG. 7b is a schematic diagram showing a comparison of the convergence curves of the obtained PLC indicators when the method, the traditional state enumeration method and the Monte Carlo method are applied to the IEEE 118 node system;
- FIG. 8a is a schematic diagram showing a comparison of the relative error convergence curves of the EENS index obtained by the method, the traditional state enumeration method and the Monte Carlo method applied to the IEEE 118 node system;
- FIG. 8b is a schematic diagram showing a comparison of the relative error convergence curves of the obtained PLC indicators when the method, the traditional state enumeration method and the Monte Carlo method are applied to the IEEE 118 node system;
- Figure 9a is a schematic diagram showing the comparison of the convergence curves of the obtained EENS indicators when the method, the traditional state enumeration method and the Monte Carlo method are applied to the PEGASE 1354 node system;
- Figure 9b is a schematic diagram showing the comparison of the convergence curves of the obtained PLC indicators when the method, the traditional state enumeration method and the Monte Carlo method are applied to the PEGASE 1354 node system;
- Figure 10a is a schematic diagram showing the comparison of the relative error convergence curves of the EENS index obtained by the method, the traditional state enumeration method and the Monte Carlo method applied to the PEGASE 1354 node system;
- Fig. 10b is a schematic diagram showing the comparison of the relative error convergence curves of the obtained PLC indicators when the method, the traditional state enumeration method and the Monte Carlo method are applied to the PEGASE 1354 node system.
- 1 inspection module
- 2 first acquisition module
- 3 a second acquisition module
- 4 a third acquisition module
- the state enumeration reliability evaluation method based on the impact increment includes the following steps:
- step 104 Verifying the reachability of all elements in the independent adjacency matrix D s by the breadth-first search method. If there is an unreachable element, the power system state s corresponding to the faulty device may be divided into at least two independent subsets, and the steps are performed. 103; otherwise, perform step 105;
- the corresponding reliability indicators can be obtained, as follows:
- the obtained reliability index is the EENS index.
- the obtained index is the PLC index.
- step 106 Calculate the influence increment ⁇ I s of the power system state s; check whether all power system states in the k-th order state set ⁇ A k have been analyzed, and if yes, perform step 107; if not, execute step 103;
- the method improves the accuracy and calculation efficiency of the calculation reliability index by the above steps 101-108, and reduces the complexity of the calculation reliability index.
- the power system data includes: power system nodes, branches, generator sets parameters, load levels of each node, annual load change curves, etc.; equipment reliability data includes: unavailability of equipment such as lines, transformers, generator sets, etc.;
- the parameters include: maximum fault search order N CTG and device independence sensitivity threshold ⁇ s .
- A represents a set of power system equipment
- the power system state s is a set of faulty devices used to indicate the state of the power system at the time of failure of these devices
- Card(s) represents the order of failure of the state of the power system s.
- the reachability is defined as: in the undirected connected graph determined by the independent adjacency matrix D s , if a certain node V 1 can be connected to another node V 2 through the edge in the graph, then V 1 is called Up to V 2 .
- the faulty device in the power system state s may be divided into at least two mutually independent subsets, so that the impact increment ⁇ I s is 0, Performing the calculation, performing step 205; otherwise, if all the nodes are mutually reachable, step 207 is performed;
- P l is the probability of load level l
- n l is the total number of load levels.
- n s is the total number of faulty devices in the power system state s; ⁇ s k is the set of k-order subsets of the power system state s; u is an element in ⁇ s k ; ⁇ I u is the load of u Loss increment.
- ⁇ s k is defined as follows:
- Is a subset symbol
- s 1 is a subset of the power system state s
- Card(s 1 ) represents the fault order of the power system state s 1 .
- step 209 Check whether all power system states in the state set ⁇ A k have been analyzed, if yes, go to step 210, otherwise go to step 205;
- R is the system reliability indicator
- P i is the unavailability of device i
- N is the total number of devices in the system.
- the calculation method of the sensitivity S PZ of each device in step 202 is:
- a device failure can be equivalent to a sudden rise in impedance of the device from a nominal value to infinity.
- Equipment failures have a direct impact on the power flow distribution of the power system. Therefore, the sensitivity of the device impedance to the power flow of each branch of the power system can be used to describe the independence between the faulty device and the branches of the power system.
- the sensitivity index is labeled as S PZ , and the sensitivity index can be calculated by using the perturbation method. The process of calculating the sensitivity is well known to those skilled in the art, and will not be described in detail in the embodiments of the present invention.
- the calculation method of the independence device flag d ij between the faulty devices in step 203 is:
- the independence flag between the faulty devices i and j is d ij .
- ⁇ s is the preset parameter device independence sensitivity threshold
- A represents the power system equipment set
- S PZ (h, i) is the sensitivity index of the fault device i impedance to the branch h tidal current distribution
- S PZ (h, j) is the sensitivity index of the impedance of the faulty device j to the flow distribution of the branch h.
- the faulty device in the power system state s can be divided into at least two mutually independent subsets, and thus the influence increment ⁇ I s is 0,
- the basic proof process is as follows:
- the node is proved to have at least one set of faulty devices and other faulty devices in the state of the power system, and thus the faulty devices in the power system state s can be divided into at least two mutually independent subsets s 1 and s 2 .
- the formula for calculating the reliability index of the power system in step 211 is as follows:
- the reliability indicator can be expressed as
- ⁇ is the set of all power system states that may occur in the power system; I(s) is the influence function of the power system state s; P(s) is the probability of occurrence of the power system state s.
- the reliability index R is the product of the probability of failure of the device and its loss, plus the probability of the normal operation of the device and the product of the loss at that time.
- the reliability index calculation can be formulated into a form based on the influence increment, in which all normal running probabilities are eliminated, and the fault impact is replaced by the influence increment.
- the incremental influence ⁇ I s of the high-order fault state can be expressed in the form of a formula, and the equation can be further simplified to
- R 2 I ⁇ +P 1 ⁇ I 1 +P 2 ⁇ I 2 +P 1 P 2 ⁇ I 12 ⁇ *MERGEFORMAT(12)
- the power system contains n+1 devices.
- the reliability index R n+1 of the new power system can be derived from the original power system indicator R n .
- ⁇ n+1 ⁇ indicates the state of the power system with only the newly added equipment failure
- P n+1 They are the availability and unavailability of newly added equipment
- k' and k 1 represent the order of failure.
- k' and k 1 should be calculated from the 0th order; For the k 1st order sub-set of the power system state s, the definition is as shown in the formula; u is An element in the middle. The equation can be further transformed into the following form:
- k 2 represents the number of failure orders; It is a k 2 order sub-set of the power system state s, and its definition is as shown in the equation.
- the influence increment ⁇ I s of any state s can be calculated according to the system reliability index. Different reliability indicators can be obtained depending on the state influence function I s used.
- the method improves the accuracy and calculation efficiency of the calculation reliability index by the above steps 201-211, and reduces the complexity of the calculation reliability index.
- a state enumeration reliability evaluation device based on influence increment see FIG. 2, the device includes:
- the verification module 1 is configured to verify the reachability of all elements in the independent adjacency matrix corresponding to the selected power system state by the breadth-first search method, and if there is an unreachable element, the influence of the selected power system state is zero. Reselect the power system status;
- the first obtaining module 2 is configured to: if there is no unreachable element, evaluate the influence of the power system state under all load levels by using an optimal power flow algorithm, obtain the influence expectation of the power system state under each load level, and obtain the power system state. Incremental impact;
- the second obtaining module 3 is configured to obtain the power system reliability index by affecting the increment when all the power system states in the state set have been analyzed and the maximum fault search order has been reached.
- the device further includes:
- the third obtaining module 4 is configured to obtain, by using a perturbation method, sensitivity of each device impedance to each branch power flow;
- Determining module 5 determining the independence between the devices according to the sensitivity
- the creation module 6 is configured to select a power system state from the state set, and create an independent adjacency matrix of the power system state by the independence between the devices.
- the device further includes:
- the input and initialization module 7 is configured to input power system data, device reliability data and preset parameters, and initialize the fault order.
- the preset parameters include: a maximum fault search order and a device independence sensitivity threshold.
- the determining module 5 includes:
- the determining sub-module 51 is configured to: if there is one branch, the sensitivity index of the impedance of the faulty device to the branch power flow distribution is greater than the device independence sensitivity threshold, and the impedance of the other faulty device is sensitive to the branch power flow distribution When the indicator is greater than the device independence sensitivity threshold, the two faulty devices are not independent.
- modules and sub-modules can be implemented by a device having a computing function, such as a single-chip microcomputer or a PC.
- a computing function such as a single-chip microcomputer or a PC.
- the embodiment of the present invention does not limit the type and type of the device.
- the device improves the accuracy and calculation efficiency of the calculation reliability index by the verification module 1, the first acquisition module 2, the second acquisition module 3, the third acquisition module 4, the determination module 5, the creation module 6, the input and initialization module 7, Reduce the complexity of calculating reliability indicators.
- the implementation method and practical effects of the present invention will be described below with reference to an example.
- This example is tested on an IEEE 118 node test system, and its network topology is shown in Figure 6.
- the test system consists of 118 nodes, 54 generator sets, 186 branches, 54 generator nodes, and 64 load nodes.
- the total installed capacity and load demand are 9966 MW and 4242 MW, respectively.
- This example verifies the efficiency and accuracy of the method by comparing the method with the traditional state enumeration method and the Monte Carlo method.
- the EENS and PLC indicators of the test system can be calculated, as shown in Table 1.
- IISE comparative analysis of the method
- MCS Monte Carlo method
- SE state enumeration method
- the search depth N CTG of the method is also set to 2; in the Monte Carlo method, the convergence criterion total number of samples N MCS is set to 10 6 . Due to the large sample size, the Monte Carlo method can produce sufficiently accurate results, so the results of its calculations can be used as a benchmark for evaluating the accuracy of other examples.
- the evaluation results of the above three methods are shown in Table 1, Figure 7a, Figure 7b, Figure 8a and Figure 8b.
- Table 1 shows the evaluation results of two reliability indicators (EENS and PLC). It can be seen that the Monte Carlo method and the method are very close, and their relative error is about 1% (the error of EENS is 0.8182% and the error of PLC is 1.3157%). The error of the two indicators obtained by the traditional enumeration method exceeds 6% (the error of EENS is 6.2357% and the error of PLC is 7.2863%), which is much higher than this method. At the same time, the CPU time consumed by this method is much smaller than the other two algorithms, indicating that this method is more efficient than the traditional evaluation method.
- the Monte Carlo method and the method are very close, and their relative error is about 1% (the error of EENS is 0.8182% and the error of PLC is 1.3157%). The error of the two indicators obtained by the traditional enumeration method exceeds 6% (the error of EENS is 6.2357% and the error of PLC is 7.2863%), which is much higher than this method. At the same time, the CPU time consumed
- Fig. 7a and Fig. 7b respectively show the convergence curves of EENS and PLC obtained by Monte Carlo method
- Fig. 8a and Fig. 8b respectively show the relative error convergence curves of these two indicators.
- the calculation results of this method and the state enumeration method are also given in these figures.
- the calculation accuracy of this method is much higher than that of the state enumeration method, and the calculation time of this method is about 1/10 of the state enumeration method.
- the Monte Carlo method requires about 10 4 seconds for the relative error to be stable within 1%, and the method can achieve the same accuracy in 100 seconds, which is about 1/100 of the Monte Carlo method.
- the EENS and PLC indicators of the test system can be calculated, as shown in Table 2.
- IISE comparative analysis of the method
- MCS Monte Carlo method
- SE state enumeration method
- the search depth N CTG of the method is also set to 1; in the Monte Carlo method, the convergence criterion total number of samples N MCS is set to 10 5 . Due to the large sample size, the Monte Carlo method can produce sufficiently accurate results, so the results of its calculations can be used as a benchmark for evaluating the accuracy of other examples.
- the evaluation results of the above three methods are shown in Table 2, Fig. 9a, Fig. 9b, Fig. 10a and Fig. 10b.
- Table 2 shows the evaluation results of two reliability indicators (EENS and PLC). It can be seen that the Monte Carlo method and the method are very close, and their relative error is about 2% (the error of EENS is 1.4590% and the error of PLC is 2.1644%). The two indicators obtained by the traditional enumeration method have a very large error of about 98% (the error of EENS is 98.1514% and the error of PLC is 98.0834%), which is much higher than other methods. At the same time, because only the first-order fault is considered, the operation time of ISE and SE is similar, but it is much shorter than the MCS. It shows that this method is more efficient than the traditional evaluation method in this power system.
- the Monte Carlo method and the method are very close, and their relative error is about 2% (the error of EENS is 1.4590% and the error of PLC is 2.1644%).
- the two indicators obtained by the traditional enumeration method have a very large error of about 98% (the error of EENS is 98.1514%
- Fig. 9a and Fig. 9b respectively show the convergence curves of EENS and PLC obtained by Monte Carlo method
- Fig. 10a and Fig. 10b respectively show the relative error convergence curves of these two indicators.
- the calculation results of this method and the state enumeration method are also given in these figures. It can be seen from Fig. 10a and Fig. 10b that the calculation accuracy of the method is much higher than that of the state enumeration method, and the calculation time of the method is similar to the state enumeration method. It can be seen from the relative error convergence curve that the Monte Carlo method requires about 3 ⁇ 10 4 seconds to stabilize the relative error within 2%, and the method can achieve the same accuracy in 1500 seconds, which is about 1/ of the Monte Carlo method. 20.
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
Description
Claims (10)
- 一种基于影响增量的状态枚举可靠性评估方法,其特征在于,所述方法包括以下步骤:通过广度优先搜索法检验所选电力系统状态对应的独立性邻接矩阵中所有元素的可达性,若存在不可达元素,则所选电力系统状态的影响增量为零,重新选择电力系统状态;若不存在不可达元素,则通过最优潮流算法评估所有负荷水平下的电力系统状态的影响,获取电力系统状态在各负荷水平下的影响期望,进而获取电力系统状态的影响增量;当状态集中所有电力系统状态已被分析,且已达到最大故障搜索阶数时,通过影响增量获取电力系统可靠性指标。
- 根据权利要求1所述的一种基于影响增量的状态枚举可靠性评估方法,其特征在于,在通过广度优先搜索法检验所选电力系统状态对应的独立性邻接矩阵中所有元素的可达性的步骤之前,所述方法还包括:通过微扰法获取各设备阻抗对各支路潮流的灵敏度,根据灵敏度确定各设备间的独立性;从状态集中选择一个电力系统状态,通过设备间的独立性创建电力系统状态的独立性邻接矩阵。
- 根据权利要求2所述的一种基于影响增量的状态枚举可靠性评估方法,其特征在于,所述方法还包括:输入电力系统数据,设备可靠性数据和预置参数,并初始化故障阶数。
- 根据权利要求3所述的一种基于影响增量的状态枚举可靠性评估方法,其特征在于,所述预置参数包括:最大故障搜索阶数和设备独立性灵敏度阈值。
- 根据权利要求4所述的一种基于影响增量的状态枚举可靠性评估方法,其特征在于,所述根据灵敏度确定各设备间的独立性的步骤具体为:若存在一条支路,使得一故障设备的阻抗对支路潮流分布的灵敏度指标大于所述设备独立性灵敏度阈值,且另一故障设备的阻抗对支路潮流分布的灵敏度指标大于所述设备独立性灵敏度阈值时,两故障设备之间不独立。
- 一种基于影响增量的状态枚举可靠性评估装置,其特征在于,所述装置包括:检验模块,用于通过广度优先搜索法检验所选电力系统状态对应的独立性邻接矩阵中所有元素的可达性,若存在不可达元素,则所选电力系统状态的影响增量为零,重新选择电力 系统状态;第一获取模块,用于若不存在不可达元素,通过最优潮流算法评估所有负荷水平下的电力系统状态的影响,获取电力系统状态在各负荷水平下的影响期望,进而获取电力系统状态的影响增量;第二获取模块,用于当状态集中所有电力系统状态已被分析,且已达到最大故障搜索阶数时,通过影响增量获取电力系统可靠性指标。
- 根据权利要求6所述的一种基于影响增量的状态枚举可靠性评估装置,其特征在于,所述装置还包括:第三获取模块,用于通过微扰法获取各设备阻抗对各支路潮流的灵敏度;确定模块,根据灵敏度确定各设备间的独立性;创建模块,用于从状态集中选择一个电力系统状态,通过设备间的独立性创建电力系统状态的独立性邻接矩阵。
- 根据权利要求7所述的一种基于影响增量的状态枚举可靠性评估装置,其特征在于,所述装置还包括:输入和初始化模块,用于输入电力系统数据,设备可靠性数据和预置参数,并初始化故障阶数。
- 根据权利要求8所述的一种基于影响增量的状态枚举可靠性评估装置,其特征在于,所述预置参数包括:最大故障搜索阶数和设备独立性灵敏度阈值。
- 根据权利要求9所述的一种基于影响增量的状态枚举可靠性评估装置,其特征在于,所述确定模块包括:确定子模块,用于若存在一条支路,使得一故障设备的阻抗对支路潮流分布的灵敏度指标大于所述设备独立性灵敏度阈值,且另一故障设备的阻抗对支路潮流分布的灵敏度指标大于所述设备独立性灵敏度阈值时,两故障设备之间不独立。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/747,522 US20180375373A1 (en) | 2015-07-28 | 2015-08-28 | Impact increments-based state enumeration reliability assessment approach and device thereof |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510456039.9A CN105071381B (zh) | 2015-07-28 | 2015-07-28 | 一种基于影响增量的状态枚举可靠性评估方法及其装置 |
CN201510456039.9 | 2015-07-28 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2017016021A1 true WO2017016021A1 (zh) | 2017-02-02 |
Family
ID=54500694
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2015/088389 WO2017016021A1 (zh) | 2015-07-28 | 2015-08-28 | 一种基于影响增量的状态枚举可靠性评估方法及其装置 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20180375373A1 (zh) |
CN (1) | CN105071381B (zh) |
WO (1) | WO2017016021A1 (zh) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950894A (zh) * | 2020-08-11 | 2020-11-17 | 杜金其 | 一种基于改进蒙特卡洛法的电力系统可靠性评估方法 |
Families Citing this family (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105680442B (zh) * | 2016-03-07 | 2018-06-15 | 重庆大学 | 考虑潮流和灵敏度一致性等值的期望缺供电量评估方法 |
CN107508279B (zh) * | 2017-08-08 | 2020-02-18 | 国网山东省电力公司荣成市供电公司 | 一种电力网络的稳定性仿真方法 |
CN110298766B (zh) * | 2019-07-02 | 2021-11-02 | 广东电网有限责任公司 | 适于继电保护整定计算的负荷筛选方法、装置及设备 |
CN111244944B (zh) * | 2020-01-23 | 2023-04-18 | 天津大学 | 一种基于影响增量的交直流配电网可靠性评估方法 |
CN111313411B (zh) * | 2020-03-11 | 2023-08-04 | 国网天津市电力公司 | 基于重要抽样影响增量的电力系统可靠性评估方法及装置 |
CN111651911B (zh) * | 2020-04-17 | 2022-09-02 | 北京理工大学 | 一种集总元件阻抗灵敏度快速计算方法及优化方法 |
CN111673291B (zh) * | 2020-06-03 | 2021-11-05 | 广东省智能制造研究所 | 一种激光切割机精度保持性的评估方法 |
CN112039211B (zh) * | 2020-09-07 | 2022-03-01 | 国网四川省电力公司电力科学研究院 | 智能变电站二次安措可靠性与复杂度的优化方法 |
CN112288326B (zh) * | 2020-11-23 | 2023-04-18 | 天津大学 | 一种适用于输电系统韧性评估的故障场景集削减方法 |
CN112668177B (zh) * | 2020-12-25 | 2022-12-16 | 天津大学 | 一种对多重故障的配电系统进行可靠性评估方法 |
CN113177717B (zh) * | 2021-05-06 | 2022-08-26 | 天津大学 | 一种基于影响增量灵敏度的输电系统韧性快速评估方法 |
CN113609721B (zh) * | 2021-07-13 | 2023-11-07 | 天津大学 | 多类型极端灾害电气互联系统韧性计算方法及装置 |
CN113887989B (zh) * | 2021-10-15 | 2024-01-16 | 中国南方电网有限责任公司超高压输电公司柳州局 | 电力系统可靠性评估方法、装置、计算机设备和存储介质 |
CN115374634A (zh) * | 2022-08-22 | 2022-11-22 | 常熟理工学院 | 基于交叉控制变量法的电网可靠性快速评估方法及系统 |
CN116720324A (zh) * | 2023-05-15 | 2023-09-08 | 中铁第四勘察设计院集团有限公司 | 基于预测模型的牵引变电所关键设备故障预警方法及系统 |
CN116579181B (zh) * | 2023-06-02 | 2023-11-24 | 天津大学 | 一种基于激活约束的电力系统可靠性快速评估方法及装置 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103324744A (zh) * | 2013-07-03 | 2013-09-25 | 国家电网公司 | 基于配电环网自动成图的拓扑搜索方法 |
CN103632313A (zh) * | 2013-12-05 | 2014-03-12 | 国家电网公司 | 一种基于pmu数据的电网动态可观方法 |
CN103902814A (zh) * | 2014-03-10 | 2014-07-02 | 中国南方电网有限责任公司 | 基于动态分区的电力系统运行状态检测方法 |
JP2014138488A (ja) * | 2013-01-17 | 2014-07-28 | Espec Corp | パワーサイクル試験装置 |
CN104156883A (zh) * | 2014-08-01 | 2014-11-19 | 重庆大学 | 基于分块枚举法的风电场集电系统可靠性评估方法 |
CN104518488A (zh) * | 2014-12-30 | 2015-04-15 | 广西大学 | 用于配电网可靠性分析的负荷点故障区域类型划分方法 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9300134B2 (en) * | 2012-06-26 | 2016-03-29 | Eleon Energy, Inc. | Methods and systems for power restoration planning |
CN103914788B (zh) * | 2014-03-04 | 2017-08-08 | 广东电网公司电力科学研究院 | 电网多环节系统的可靠性评估方法 |
CN104376504B (zh) * | 2014-11-06 | 2017-10-27 | 国家电网公司 | 一种基于解析法的配电系统概率可靠性评估方法 |
-
2015
- 2015-07-28 CN CN201510456039.9A patent/CN105071381B/zh active Active
- 2015-08-28 WO PCT/CN2015/088389 patent/WO2017016021A1/zh active Application Filing
- 2015-08-28 US US15/747,522 patent/US20180375373A1/en not_active Abandoned
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2014138488A (ja) * | 2013-01-17 | 2014-07-28 | Espec Corp | パワーサイクル試験装置 |
CN103324744A (zh) * | 2013-07-03 | 2013-09-25 | 国家电网公司 | 基于配电环网自动成图的拓扑搜索方法 |
CN103632313A (zh) * | 2013-12-05 | 2014-03-12 | 国家电网公司 | 一种基于pmu数据的电网动态可观方法 |
CN103902814A (zh) * | 2014-03-10 | 2014-07-02 | 中国南方电网有限责任公司 | 基于动态分区的电力系统运行状态检测方法 |
CN104156883A (zh) * | 2014-08-01 | 2014-11-19 | 重庆大学 | 基于分块枚举法的风电场集电系统可靠性评估方法 |
CN104518488A (zh) * | 2014-12-30 | 2015-04-15 | 广西大学 | 用于配电网可靠性分析的负荷点故障区域类型划分方法 |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950894A (zh) * | 2020-08-11 | 2020-11-17 | 杜金其 | 一种基于改进蒙特卡洛法的电力系统可靠性评估方法 |
Also Published As
Publication number | Publication date |
---|---|
CN105071381B (zh) | 2017-04-12 |
CN105071381A (zh) | 2015-11-18 |
US20180375373A1 (en) | 2018-12-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2017016021A1 (zh) | 一种基于影响增量的状态枚举可靠性评估方法及其装置 | |
Liu et al. | Recognition and vulnerability analysis of key nodes in power grid based on complex network centrality | |
US10503839B2 (en) | Detecting state estimation network model data errors | |
CN105976257A (zh) | 基于隶属度函数的模糊综合评价法的电网脆弱性评估方法 | |
CN107238765A (zh) | 基于加速性能退化参数的led集成驱动电源可靠性分析方法 | |
CN109638838A (zh) | 电网关键断面识别方法、装置及电子设备 | |
CN106529791A (zh) | 一种电力系统中支路重要度的评估方法 | |
CN109670611A (zh) | 一种电力信息系统故障诊断方法及装置 | |
CN106021097A (zh) | 基于测试特征的软件可靠性指标区间估计方法 | |
CN109657913B (zh) | 一种考虑分布式电源的输配电网联合风险评估方法 | |
CN106228459A (zh) | 基于蒙特卡洛的等值可靠性评估方法 | |
Binelo et al. | Mathematical modeling and parameter estimation of battery lifetime using a combined electrical model and a genetic algorithm | |
CN107204616B (zh) | 基于自适应稀疏伪谱法的电力系统随机状态估计方法 | |
Barzegkar-Ntovom et al. | Methodology for evaluating equivalent models for the dynamic analysis of power systems | |
Correa-Henao et al. | Representation of electric power systems by complex networks with applications to risk vulnerability assessment | |
CN102738794B (zh) | 基于赛德尔式递推贝叶斯估计的电网拓扑错误辨识方法 | |
CN103957114A (zh) | 一种基于变异系数的网络抗毁性评估方法 | |
Vasudevan et al. | Improved state estimation by optimal placement of measurement devices in distribution system with ders | |
CN108334721A (zh) | 基于mMIFS-U的两阶段电力系统关键特征选择方法及装置 | |
CN109683036A (zh) | 一种用于数据中心的配电系统故障模拟方法及系统 | |
CN114139985A (zh) | 计及电力网电气耦合的智能电网控制系统薄弱点识别方法 | |
Kabiri et al. | Enhancing power system state estimation by incorporating equality constraints of voltage dependent loads and zero injections | |
CN114386510A (zh) | 一种电力系统量测错误辨识方法及系统 | |
CN109861214B (zh) | 判断区域电网暂态功角稳定薄弱线路的方法、系统 | |
CN106529805A (zh) | 一种基于发电机重要度的发电系统可靠性评估方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 15899373 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 15899373 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 01.08.2018) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 15899373 Country of ref document: EP Kind code of ref document: A1 |