CN108539795B - Flexible multi-state switch reliability modeling method considering current load uncertainty - Google Patents

Flexible multi-state switch reliability modeling method considering current load uncertainty Download PDF

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CN108539795B
CN108539795B CN201810481457.7A CN201810481457A CN108539795B CN 108539795 B CN108539795 B CN 108539795B CN 201810481457 A CN201810481457 A CN 201810481457A CN 108539795 B CN108539795 B CN 108539795B
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reliability
flexible multi
current load
load
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CN108539795A (en
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刘文霞
徐雅惠
王荣杰
韩辉
杨勇
陆翌
许烽
王朝亮
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381Dispersed generators
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Abstract

The invention discloses a flexible multi-state switch reliability modeling method considering current load uncertainty, which is characterized by comprising the following steps of: step 1: constructing a single-ended MMC physical structure reliability model; step 2: establishing a sub-module equivalent reliability model based on the current load expectation; and step 3: and establishing an eight-state reliability model of the flexible multi-state switch.

Description

Flexible multi-state switch reliability modeling method considering current load uncertainty
Technical Field
The invention relates to the technical field of reliability modeling of a flexible multi-state switch, in particular to a flexible multi-state switch reliability modeling method considering current load uncertainty.
Background
With the rapid development of the active power distribution network, the large-scale grid connection of new energy such as wind power, photovoltaic and the like can effectively reduce the network loss and reduce the environmental pollution. However, the new energy is greatly influenced by the external environment, and the uncertainty and the fluctuation of the output of the new energy cause a plurality of problems for the power distribution network. The traditional interconnection switch has a single adjusting means and is difficult to deal with the problem caused by a large amount of new energy grid connection. In this context, flexible multi-state switches have emerged. The flexible multi-state switch is a power electronic device based on a Voltage Source Converter (VSC), can be applied to a power distribution network to achieve flexible control of power flow so as to achieve the purposes of balancing power grid load and promoting renewable energy consumption, and can achieve uninterrupted power supply in a non-fault area through a switching control mode when the power distribution network fails, so that the power supply reliability of the power distribution network is improved. VSCs have a variety of topologies including series valves based on series connection of turn-off devices, MMC valves based on modular multilevel structures, chained valves based on full-bridge chained. Among them, the MMC has been widely used due to its many advantages brought by its modular structure, and is also one of the key technologies for flexible multi-state switch research.
However, in the past MMC reliability research, the failure rate of the sub-module is usually constant, that is, the failure rate of the sub-module under the rated condition is calculated, while the working state of the sub-module is constantly changed during actual operation and usually does not reach the rated operation condition, so that the reliability calculation result is more conservative. In actual operation, the state characteristic parameters influencing the reliability of the MMC are many, wherein the current load borne by the sub-modules is one of the most important factors and is not involved in the current reliability modeling. Since the IGBT is the most critical component in the sub-module reliability, it is necessary to study the effect of the current load on the IGBT reliability.
It is therefore desirable to have a flexible multi-state switch reliability modeling method that takes into account current load uncertainty to solve the problems in the prior art.
Disclosure of Invention
The invention aims to provide a flexible multi-state switch reliability modeling method considering current load uncertainty, so as to consider the influence of random current load on submodule reliability and the difference of reliability indexes of three-terminal MMC of a flexible multi-state switch, and accordingly, an eight-state reliability model of the flexible multi-state switch is established, and the reliability calculation precision of the model and a power distribution network is improved.
The modeling method comprises the following steps:
step 1: constructing a single-ended MMC physical structure reliability model;
step 2: establishing a sub-module equivalent reliability model based on the current load expectation;
the step 2 comprises the following steps:
step 2.1: through Monte Carlo simulation, the current load which changes continuously in a period of time is equivalent to the current load expected value I which is fixedavConstructing a load expected correction coefficient;
step 2.2: obtaining the equivalent fault rate of the IGBT module in the sub-module by utilizing the load expected correction coefficient obtained in the step 2.1, so as to obtain the equivalent fault rate of the sub-module;
the monte carlo simulation in step 2.1 comprises the steps of:
step 2.1.1: inputting the network structure parameters of the distribution network, the output of the distributed power supply and the normal distribution probability model information of the load, wherein the normal distribution probability model information of the load comprises a mean value and a standard deviation,
setting a sampling scale N;
step 2.1.2: carrying out Monte Carlo sampling according to the normal distribution probability characteristics of the output and the load of the distributed power supply to generate random samples of the output and the load of the distributed power supply;
step 2.1.3: carrying out load flow calculation on the system state obtained by N times of sampling;
step 2.1.4: respectively counting N pieces of sample information of the three-terminal MMC alternating-current side current according to the load flow result, and calculating probability distribution information of the three-terminal MMC alternating-current side current;
the equivalent reliability of the IGBT obtained by considering the improvement of the load expectation correction factor in step 2.2 is:
λIGBT_av=L(Iav)×λIGBT (9)
combining step 2.1, the equivalent failure rate of the sub-modules is as follows:
λSM=2×λIGBT_avCTHYSMC (10);
and step 3: and establishing an eight-state reliability model of the flexible multi-state switch.
The step 3 comprises the following steps:
step 3.1: dividing the flexible multi-state switch into 4 subsystems, namely 3 MMC subsystems and 1 device-level control protection system;
step 3.2: assuming that all 4 subsystems in the step 3.1 have two states of working and fault, combining the states of the 4 subsystems to obtain a 16-state space transfer model of the flexible multi-state switch, and further combining all the shutdown states in the 16 states to obtain an eight-state model of the flexible multi-state switch;
step 3.3: a Markov chain-based analytical method calculates the probability of occurrence and average duration of the 16 states of the flexible multi-state switch.
Preferably, the step 1 comprises the following steps:
step 1.1: establishing a reliability model of the submodule by adopting a series-parallel connection method based on an SM submodule internal structure of the MMC;
step 1.2: calculating the reliability of the bridge arm by adopting a k/n-G model;
step 1.3: and establishing a reliability model of the whole MMC by using a series-parallel connection method.
Preferably, the sub-module reliability of step 1.1 is:
λSM=2×λIGBTCTHYSMC (1)
in the formula, λIGBT、λC、λTHY、λSMCAre respectively an IGBT module, a capacitor, a bypass thyristor and a sub-module controllerThe failure rate.
Preferably, the step 2.1 further comprises the following steps:
step 2.1.5: representing the sample information of the three-terminal MMC alternating-current side current to the current load of the sub-module, and defining the current load proportionality coefficient as
Wherein, IciRepresenting a random current load; i isc0Is represented by the formula such that L (I)c0) A current load of l; β is an adjustment coefficient, and if β is 1, the load expectation correction coefficient of the structure can only reflect the influence of the magnitude of the current load; if beta is selected>1, reflecting the influence of the magnitude and the fluctuation of the current load;
step 2.1.6: setting IGBT reliability function R under consideration of current load influenceiWorking time t of element and current load I borne by elementciRelated, formula:
Ri(t)=R0(t;L(Ici)) (6)
in the formula, R0The reliability of the IGBT is not considered when the current load influence is considered;
step 2.1.7: obtaining N random samples of the alternating-current side current of the MMC by utilizing probability load flow calculation, thereby obtaining a load expectation correction coefficient:
preferably, the 4 subsystems in step 3.1 have the following four operation modes:
(1) the device normally operates;
(2) when the MMC is out of operation due to a fault at one end and the other two ends are in normal operation, power transmission can still be carried out;
(3) the MMC with two ends stops running due to faults, can run at a single end, works in a static reactive compensator mode, and only one working end performs reactive power control in a capacitance compensation mode;
(4) and when the MMCs at the three ends stop running or the device-level control protection system fails, the flexible multi-state switch stops running.
Preferably, the step 3.3 further comprises the following steps:
step 3.3.1: obtaining a state transition matrix T of the flexible multi-state switch eight-state model according to the flexible multi-state switch eight-state model established in the step 3.2:
step 3.3.2: applying the process approximation principle in the Markov analysis method:
PT=P (12)
wherein P ═ PS1,PS2,…,PS8]Is the state probability of the eight states of the flexible multi-state switch, equation (12) is rewritten as:
P(T-I)=P (13)
wherein I is an identity matrix;
step 3.3.3: adding a total probability condition that the probability sum of all system states is 1, namely:
finishing to obtain:
step 3.3.4: solving the Markov matrix equation obtained in the step 3.3.2 and the step 3.3.3 by using a linear algebra algorithm, and calculating the state probability of the eight states of the flexible multi-state switch;
step 3.3.5: the frequency and duration are calculated using the frequency duration method, and the frequency of each state Si can be calculated from equation (16):
in the formula: pSiProbability of being state i; pSlProbability of being a state directly connected to state i; lambda [ alpha ]kOr λlIs the failover rate or failover rate; mdIs the number of transitions leaving state i; meIs the number of transitions into state i, and the average duration of stay in state Si is:
the invention considers the time-varying property of alternating-current side current caused by the randomness of a distributed power supply and the load fluctuation in a system model and the electrothermal stress and damage accumulation borne by IGBT devices in bridge arm submodules of a flexible multi-state switch caused by random current load, proposes that the current load expectation is utilized to replace the random current load, and the current load which is subjected to continuous change in a period of time is equivalent to be subjected to a certain fixed current load by a Monte Carlo simulation method, so that a load expectation correction coefficient is constructed to correct the reliability parameters of the submodules. And establishing a flexible multi-state switch eight-state reliability model based on the sub-module equivalent fault rate obtained by the improved parameters and an MMC physical structure reliability model, and calculating the occurrence probability and the average duration of each state by using an analysis method based on a Markov chain. The research result is used for calculating the reliability of the power distribution network, the reliability model parameters can be changed according to the application scene, and the accuracy of the model and the reliability calculation of the power distribution network is improved.
Drawings
Fig. 1 is a view of an MMC topology.
Fig. 2 is a schematic diagram of a flexible multi-state switch access distribution network.
Fig. 3 is a diagram of a modified algorithm for a power distribution network including a flexible multi-state switch.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, but not all embodiments of the invention. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, the flexible multi-state switch reliability modeling method considering the current load uncertainty comprises the following steps:
step 1: and establishing a reliability model of the submodule based on the internal structure of the SM submodule in the MMC. In this embodiment, the MMC topology is as shown in fig. 1, and the reliability of the sub-module obtained by combining the series-parallel reliability principle is:
λSM=2×λIGBTCTHYSMC (1)(1)
in the formula, λIGBT、λC、λTHY、λSMCThe failure rates of the IGBT module, the capacitor, the bypass thyristor and the sub-module controller are respectively.
The reliability test data of the IGBT module with the model number of 6MBI450V-170-50 provided by Nanrui company is adopted for the reliability calculation of the sub-modules: the rated voltage of the submodule is 1.7kV, and the rated current is 450A. The original failure rate parameters of each component in the MMC are shown in table 1.
TABLE 1 original failure rate parameter table for each element of flexible multi-state switch
Step 2: the single bridge arm comprises a series valve bank and a reactor, and the redundancy is k/n if the series valve bank comprises n sub-modules. The reliability of the bridge arm series valve group can be calculated through a k/n: G model:
the reliability of the whole bridge arm is:
in the formula, RL0(t) is a reliability function of the bridge arm reactor.
And step 3: since any one bridge arm fault will cause the MMC to shut down, the MMC reliability can be calculated by the following formula based on the series model:
in the formula, RVBC(t)、Rcp(t)、RclAnd (t) is a reliability function of the valve base controller VBC, the control protection system and the valve cooling system respectively.
And 4, step 4: through Monte Carlo simulation, the current load which changes continuously in a period of time is equivalent to the current load which changes a certain fixed expected value IavConstructing a load expected correction factor L (I)av) The implementation adopted is as follows:
the wiring pattern of a power distribution network including a flexible multi-state switch is shown in fig. 2. 3 modified IEEE33 node distribution systems were used as random current load testing systems (IEEE33 node distribution system with a 12.66kV baseline voltage at the head end and a near 10kV voltage level at the tail end) as shown in FIG. 3. The test system and the flexible multi-state switch node have 100 nodes and 99 branches; suppose that five wind power generator sets are respectively connected with 2-14 and 2-16. The rated capacities of the nodes 2-17, 3-16 and 3-17 are respectively 300kVA, 500kVA, 300kVA and 300kVA, and the power factors are all 0.9. In the present embodiment, the load point power and the wind power output are simulated by normal distribution, wherein the standard deviation of the load is 5.0% of the corresponding expected value (rated power), and the standard deviation of the wind power output is 50.0% of the corresponding expected value. The three-terminal flexible multi-state switch is connected with nodes 1-18, 2-18 and 3-18, the capacity of the three-terminal MMC current converter is 1MVA, the voltage of a balance node is 1.05, and the three-terminal MMC adopts a PQ control mode. The sampling size of the monte carlo simulation was 500 times. In order to comprehensively reflect the influence of the current load size and the current load fluctuation on the sub-module reliability by the load expectation correction coefficient, beta is 1.5. Let Ic0The per unit value is 1.5 for the rated current.
Step 4-1: representing the current load of the submodule by the magnitude of the three-terminal MMC alternating-current side current, and defining the current load proportionality coefficient as follows:
wherein, IciRepresenting a random current load; i isc0Is represented by the formula such that L (I)c0) A current load of l; beta is an adjustment coefficient. In particular, if β is 1, the structural load expectation correction coefficient can only reflect the influence of the magnitude of the current load; if beta is selected>1, the influence of the magnitude of the current load and the fluctuation thereof can be reflected to a certain extent.
Step 4-2: setting IGBT reliability function R under consideration of current load influenceiWorking time t of element and current load I borne by elementciRelated, formula:
Ri(t)=R0(t;L(Ici)) (6)
in the formula, R0The reliability of the IGBT is not considered when the current load influences.
Step 4-3: obtaining N random samples of the alternating-current side current of the MMC by utilizing probability load flow calculation, thereby obtaining a load expectation correction coefficient:
note: to obtain a plurality of I required for the above calculationciAnd (4) random samples, wherein Monte Carlo simulation is adopted to carry out simulation calculation, and mathematical expectation and probability distribution of the three-terminal MMC alternating-current side current are respectively obtained.
The calculation steps using the monte carlo simulation method are as follows:
(1) inputting power distribution network structure parameters, distributed power supply output and normal distribution probability model information (mean value and standard deviation) of loads, and setting a sampling scale N;
(2) carrying out Monte Carlo sampling according to probability distribution characteristics of the distributed power supply and the load to generate random samples of the output force and the load of the distributed power supply;
(3) carrying out load flow calculation on the system state obtained by N times of sampling;
(4) and respectively counting N pieces of sample information of the current at the alternating current sides of the three-terminal MMC according to the load flow result, and calculating the probability distribution information of the N pieces of sample information.
And 5: the equivalent reliability of the IGBT improved by considering the load expectation correction factor is:
λIGBT_av=L(Iav)×λIGBT (9)
with reference to step 1, the equivalent failure rate in the sub-modules is:
λSM=2×λIGBT_avCTHYSMC (10)
step 6: according to the functional characteristics of the components of the flexible multi-state switch, the flexible multi-state switch can be divided into 4 subsystems, namely 3 MMC subsystems and 1 device-level control protection system; it has the following four modes of operation: (1) the device normally operates; (2) when the MMC is out of operation due to a fault at one end and the other two ends are in normal operation, power transmission can still be carried out; (3) the MMC with two ends stops running due to faults, can run at a single end, works in a static reactive compensator mode, and only one working end performs reactive power control in a capacitance compensation mode; (4) when the MMCs at the three ends stop running or the device-level control protection system fails, the whole flexible multi-state switching device stops running.
And 7: assuming that the above 4 subsystems all have and only have two states of working (1) and fault (0), combining the states of the 4 subsystems can obtain a 16-state space transition model of the whole flexible multi-state switch, as shown in fig. 3. Then, all the shutdown states in the 16 states are further merged to finally obtain an eight-state model, as shown in table 2:
TABLE 2 Flexible multi-state switch eight-state table
And 8: the probability of occurrence and the average duration of each state are calculated using a Markov chain based analytic method.
Step 8-1: obtaining a state transition matrix T of the flexible multi-state switch eight-state model according to the flexible multi-state switch eight-state space transition model established in the step 7:
step 8-2: applying markov process approximation principle:
PT=P (12)
wherein P ═ PS1,PS2,…,PS8]Is the state probability of eight states. The above formula can be rewritten as
P(T-I)=P (13)
Wherein I is an identity matrix.
Step 8-3: add the full probability condition-the sum of the probabilities for all system states is 1. Namely, it is
Finishing to obtain:
step 8-4: the Markov matrix equation obtained by the 8 th-2 th and 8 th-3 rd steps is solved by applying a linear algebra algorithm, so that the state probability of 8 states can be calculated.
And 8-5: the frequency and duration are calculated using a frequency-duration method. The frequency of each state Si can be calculated by equation (16):
in the formula: pSiProbability of being state i; pSlProbability of being a state directly connected to state i; lambda [ alpha ]kOr λlIs the rate of metastasis (failure or repair); mdIs the number of transitions leaving state i; meIs the number of transitions into state i.
The average duration of stay in state Si is:
according to the flexible multi-state switch reliability model established above, reliability parameters can be solved for power distribution network reliability evaluation.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. A flexible multi-state switch reliability modeling method considering current load uncertainty is characterized by comprising the following steps:
step 1: constructing a single-ended MMC physical structure reliability model;
step 2: establishing a sub-module equivalent reliability model based on the current load expectation;
the step 2 comprises the following steps:
step 2.1: through Monte Carlo simulation, the current load which changes continuously in a period of time is equivalent to the current load expected value I which is fixedavConstructing a load expected correction coefficient;
step 2.2: obtaining the equivalent fault rate of the IGBT module in the sub-module by utilizing the load expected correction coefficient obtained in the step 2.1, so as to obtain the equivalent fault rate of the sub-module;
the monte carlo simulation in step 2.1 comprises the steps of:
step 2.1.1: inputting power distribution network structure parameters, distributed power supply output and normal distribution probability model information of loads, wherein the normal distribution probability model information of the loads comprises a mean value and a standard deviation, and setting a sampling scale N;
step 2.1.2: carrying out Monte Carlo sampling according to the normal distribution probability characteristics of the output and the load of the distributed power supply to generate random samples of the output and the load of the distributed power supply;
step 2.1.3: carrying out load flow calculation on the system state obtained by N times of sampling;
step 2.1.4: respectively counting N pieces of sample information of the three-terminal MMC alternating-current side current according to the load flow result, and calculating probability distribution information of the three-terminal MMC alternating-current side current;
the equivalent reliability of the IGBT obtained by considering the improvement of the load expectation correction factor in step 2.2 is:
λIGBT_av=L(Iav)×λIGBT (9)
combining step 2.1, the equivalent failure rate of the sub-modules is as follows:
λSM=2×λIGBT_avCTHYSMC (10);
and step 3: establishing an eight-state reliability model of the flexible multi-state switch;
the step 3 comprises the following steps:
step 3.1: dividing the flexible multi-state switch into 4 subsystems, namely 3 MMC subsystems and 1 device-level control protection system;
step 3.2: assuming that all 4 subsystems in the step 3.1 have two states of working and fault, combining the states of the 4 subsystems to obtain a 16-state space transfer model of the flexible multi-state switch, and further combining all the shutdown states in the 16 states to obtain an eight-state model of the flexible multi-state switch;
step 3.3: a Markov chain-based analytical method calculates the probability of occurrence and average duration of the 16 states of the flexible multi-state switch.
2. The method of claim 1 for modeling flexible multi-state switch reliability that accounts for current load uncertainty, characterized by: the step 1 comprises the following steps:
step 1.1: establishing a reliability model of the submodule by adopting a series-parallel connection method based on an SM submodule internal structure of the MMC;
step 1.2: adopting k/n: calculating the reliability of the bridge arm by the G model;
step 1.3: and establishing a reliability model of the whole MMC by using a series-parallel connection method.
3. The method of claim 2 for modeling flexible multi-state switch reliability that accounts for current load uncertainty, characterized by: the reliability of the sub-modules of step 1.1 is:
λSM=2×λIGBTCTHYSMC (1)
in the formula, λIGBT、λC、λTHY、λSMCThe failure rates of the IGBT module, the capacitor, the bypass thyristor and the sub-module controller are respectively.
4. The method of claim 1 for modeling flexible multi-state switch reliability that accounts for current load uncertainty, characterized by: step 2.1 further comprises the steps of:
step 2.1.5: representing the sample information of the three-terminal MMC alternating-current side current to the current load of the sub-module, and defining the current load proportionality coefficient as
Wherein, IciRepresenting a random current load; i isc0Is represented by the formula such that L (I)c0) A current load of l; β is an adjustment coefficient, and if β is 1, the load expectation correction coefficient of the structure can only reflect the influence of the magnitude of the current load; if beta is selected>1, reflecting the influence of the magnitude and the fluctuation of the current load;
step 2.1.6: setting IGBT reliability function R under consideration of current load influenceiWorking time t of element and current load I borne by elementciRelated, formula:
Ri(t)=R0(t;L(Ici)) (6)
in the formula, R0The reliability of the IGBT is not considered when the current load influence is considered;
step 2.1.7: obtaining N random samples of the alternating-current side current of the MMC by utilizing probability load flow calculation, thereby obtaining a load expectation correction coefficient:
5. the method of claim 1 for modeling flexible multi-state switch reliability that accounts for current load uncertainty, characterized by: the 4 subsystems in step 3.1 have the following four modes of operation:
(1) the device normally operates;
(2) when the MMC is out of operation due to a fault at one end and the other two ends are in normal operation, power transmission can still be carried out;
(3) the MMC with two ends stops running due to faults, can run at a single end, works in a static reactive compensator mode, and only one working end performs reactive power control in a capacitance compensation mode;
(4) and when the MMCs at the three ends stop running or the device-level control protection system fails, the flexible multi-state switch stops running.
6. The method of claim 1 for modeling flexible multi-state switch reliability that accounts for current load uncertainty, characterized by: said step 3.3 further comprises the steps of:
step 3.3.1: obtaining a state transition matrix T of the flexible multi-state switch eight-state model according to the flexible multi-state switch eight-state model established in the step 3.2:
step 3.3.2: applying the process approximation principle in the Markov analysis method:
PT=P (12)
wherein P ═ PS1,PS2,…,PS8]Is the state probability of the eight states of the flexible multi-state switch, equation (12) is rewritten as:
P(T-I)=P (13)
wherein I is an identity matrix;
step 3.3.3: adding a total probability condition that the probability sum of all system states is 1, namely:
finishing to obtain:
step 3.3.4: solving the Markov matrix equation obtained in the step 3.3.2 and the step 3.3.3 by using a linear algebra algorithm, and calculating the state probability of the eight states of the flexible multi-state switch;
step 3.3.5: the frequency and duration are calculated using the frequency duration method, and the frequency of each state Si can be calculated from equation (16):
in the formula: pSiProbability of being state i; pSlIs the state probability directly connected to state i; lambda [ alpha ]kOr λlIs the failover rate or failover rate; mdIs the number of transitions leaving state i; meIs the number of transitions into state i, and the average duration of stay in state Si is:
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106972541A (en) * 2017-05-18 2017-07-21 贵州电网有限责任公司电力科学研究院 A kind of power distribution network multiterminal flexible interconnection switch based on mixed type submodule MMC

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106972541A (en) * 2017-05-18 2017-07-21 贵州电网有限责任公司电力科学研究院 A kind of power distribution network multiterminal flexible interconnection switch based on mixed type submodule MMC

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MMC控制系统时序逻辑与子模块故障监测;罗程等;《电力自动化设备》;20150531;第35卷(第5期);第83-88页 *
考虑子模块相关性的MMC可靠性分析方法;井皓等;《中国电机工程学报》;20170705;第37卷(第13期);第3835-3842页 *

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