CN108539795B  Flexible multistate switch reliability modeling method considering current load uncertainty  Google Patents
Flexible multistate switch reliability modeling method considering current load uncertainty Download PDFInfo
 Publication number
 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
 Authority
 CN
 China
 Prior art keywords
 state
 reliability
 flexible multi
 current load
 load
 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.)
 Active
Links
 238000004364 calculation method Methods 0.000 claims description 13
 238000005070 sampling Methods 0.000 claims description 10
 238000000342 Monte Carlo simulation Methods 0.000 claims description 9
 239000011159 matrix material Substances 0.000 claims description 9
 238000004458 analytical method Methods 0.000 claims description 6
 238000004422 calculation algorithm Methods 0.000 claims description 4
 QDNXSIYWHYGMCDUHFFFAOYSAN 2(methylamino)1(3methylphenyl)propan1one Chemical compound data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 CNC(C)C(=O)C1=CC=CC(C)=C1 QDNXSIYWHYGMCDUHFFFAOYSAN 0.000 claims description 3
 230000005540 biological transmission Effects 0.000 claims description 3
 239000003990 capacitor Substances 0.000 claims description 3
 238000000034 method Methods 0.000 claims description 3
 229910021426 porous silicon Inorganic materials 0.000 claims description 3
 230000003068 static Effects 0.000 claims description 3
 230000000875 corresponding Effects 0.000 description 3
 238000010586 diagram Methods 0.000 description 2
 206010027476 Metastasis Diseases 0.000 description 1
 238000009825 accumulation Methods 0.000 description 1
 238000001816 cooling Methods 0.000 description 1
 230000000694 effects Effects 0.000 description 1
 238000005265 energy consumption Methods 0.000 description 1
 238000003912 environmental pollution Methods 0.000 description 1
 238000011156 evaluation Methods 0.000 description 1
 230000004048 modification Effects 0.000 description 1
 238000006011 modification reaction Methods 0.000 description 1
 230000001737 promoting Effects 0.000 description 1
 238000004088 simulation Methods 0.000 description 1
 238000006467 substitution reaction Methods 0.000 description 1
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
 H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
 H02J3/381—Dispersed generators

 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]
Abstract
The invention discloses a flexible multistate switch reliability modeling method considering current load uncertainty, which is characterized by comprising the following steps of: step 1: constructing a singleended MMC physical structure reliability model; step 2: establishing a submodule equivalent reliability model based on the current load expectation; and step 3: and establishing an eightstate reliability model of the flexible multistate switch.
Description
Technical Field
The invention relates to the technical field of reliability modeling of a flexible multistate switch, in particular to a flexible multistate switch reliability modeling method considering current load uncertainty.
Background
With the rapid development of the active power distribution network, the largescale 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 multistate switches have emerged. The flexible multistate 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 nonfault 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 turnoff devices, MMC valves based on modular multilevel structures, chained valves based on fullbridge 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 multistate switch research.
However, in the past MMC reliability research, the failure rate of the submodule is usually constant, that is, the failure rate of the submodule under the rated condition is calculated, while the working state of the submodule 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 submodules 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 submodule reliability, it is necessary to study the effect of the current load on the IGBT reliability.
It is therefore desirable to have a flexible multistate 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 multistate 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 threeterminal MMC of a flexible multistate switch, and accordingly, an eightstate reliability model of the flexible multistate 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 singleended MMC physical structure reliability model;
step 2: establishing a submodule 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 fixed_{av}Constructing a load expected correction coefficient;
step 2.2: obtaining the equivalent fault rate of the IGBT module in the submodule by utilizing the load expected correction coefficient obtained in the step 2.1, so as to obtain the equivalent fault rate of the submodule;
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 threeterminal MMC alternatingcurrent side current according to the load flow result, and calculating probability distribution information of the threeterminal MMC alternatingcurrent side current;
the equivalent reliability of the IGBT obtained by considering the improvement of the load expectation correction factor in step 2.2 is:
λ_{IGBT_av}＝L(I_{av})×λ_{IGBT} (9)
combining step 2.1, the equivalent failure rate of the submodules is as follows:
λ_{SM}＝2×λ_{IGBT_av}+λ_{C}+λ_{THY}+λ_{SMC} (10)；
and step 3: and establishing an eightstate reliability model of the flexible multistate switch.
The step 3 comprises the following steps:
step 3.1: dividing the flexible multistate switch into 4 subsystems, namely 3 MMC subsystems and 1 devicelevel 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 16state space transfer model of the flexible multistate switch, and further combining all the shutdown states in the 16 states to obtain an eightstate model of the flexible multistate switch;
step 3.3: a Markov chainbased analytical method calculates the probability of occurrence and average duration of the 16 states of the flexible multistate switch.
Preferably, the step 1 comprises the following steps:
step 1.1: establishing a reliability model of the submodule by adopting a seriesparallel 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/nG model;
step 1.3: and establishing a reliability model of the whole MMC by using a seriesparallel connection method.
Preferably, the submodule reliability of step 1.1 is:
λ_{SM}＝2×λ_{IGBT}+λ_{C}+λ_{THY}+λ_{SMC} (1)
in the formula, λ_{IGBT}、λ_{C}、λ_{THY}、λ_{SMC}Are respectively an IGBT module, a capacitor, a bypass thyristor and a submodule controllerThe failure rate.
Preferably, the step 2.1 further comprises the following steps:
step 2.1.5: representing the sample information of the threeterminal MMC alternatingcurrent side current to the current load of the submodule, and defining the current load proportionality coefficient as
Wherein, I_{ci}Representing a random current load; i is_{c0}Is 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 influence_{i}Working time t of element and current load I borne by element_{ci}Related, formula:
R_{i}(t)＝R_{0}(t；L(I_{ci})) (6)
in the formula, R_{0}The reliability of the IGBT is not considered when the current load influence is considered;
step 2.1.7: obtaining N random samples of the alternatingcurrent 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 devicelevel control protection system fails, the flexible multistate 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 multistate switch eightstate model according to the flexible multistate switch eightstate 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 ═ P_{S1},P_{S2},…,P_{S8}]Is the state probability of the eight states of the flexible multistate switch, equation (12) is rewritten as:
P(TI)＝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 multistate 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: p_{Si}Probability of being state i; p_{Sl}Probability of being a state directly connected to state i; lambda [ alpha ]_{k}Or λ_{l}Is the failover rate or failover rate; m_{d}Is the number of transitions leaving state i; m_{e}Is the number of transitions into state i, and the average duration of stay in state Si is:
the invention considers the timevarying property of alternatingcurrent 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 multistate 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 multistate switch eightstate reliability model based on the submodule 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 multistate switch access distribution network.
Fig. 3 is a diagram of a modified algorithm for a power distribution network including a flexible multistate 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 multistate 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 submodule obtained by combining the seriesparallel reliability principle is:
λ_{SM}＝2×λ_{IGBT}+λ_{C}+λ_{THY}+λ_{SMC} (1)(1)
in the formula, λ_{IGBT}、λ_{C}、λ_{THY}、λ_{SMC}The failure rates of the IGBT module, the capacitor, the bypass thyristor and the submodule controller are respectively.
The reliability test data of the IGBT module with the model number of 6MBI450V17050 provided by Nanrui company is adopted for the reliability calculation of the submodules: 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 multistate 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 submodules. 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, R_{L0}(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, R_{VBC}(t)、R_{cp}(t)、R_{cl}And (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 I_{av}Constructing 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 multistate 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 multistate switch node have 100 nodes and 99 branches; suppose that five wind power generator sets are respectively connected with 214 and 216. The rated capacities of the nodes 217, 316 and 317 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 threeterminal flexible multistate switch is connected with nodes 118, 218 and 318, the capacity of the threeterminal MMC current converter is 1MVA, the voltage of a balance node is 1.05, and the threeterminal 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 submodule reliability by the load expectation correction coefficient, beta is 1.5. Let I_{c0}The per unit value is 1.5 for the rated current.
Step 41: representing the current load of the submodule by the magnitude of the threeterminal MMC alternatingcurrent side current, and defining the current load proportionality coefficient as follows:
wherein, I_{ci}Representing a random current load; i is_{c0}Is 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 42: setting IGBT reliability function R under consideration of current load influence_{i}Working time t of element and current load I borne by element_{ci}Related, formula:
R_{i}(t)＝R_{0}(t；L(I_{ci})) (6)
in the formula, R_{0}The reliability of the IGBT is not considered when the current load influences.
Step 43: obtaining N random samples of the alternatingcurrent 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 calculation_{ci}And (4) random samples, wherein Monte Carlo simulation is adopted to carry out simulation calculation, and mathematical expectation and probability distribution of the threeterminal MMC alternatingcurrent 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 threeterminal 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:
λ_{IGBT_av}＝L(I_{av})×λ_{IGBT} (9)
with reference to step 1, the equivalent failure rate in the submodules is:
λ_{SM}＝2×λ_{IGBT_av}+λ_{C}+λ_{THY}+λ_{SMC} (10)
step 6: according to the functional characteristics of the components of the flexible multistate switch, the flexible multistate switch can be divided into 4 subsystems, namely 3 MMC subsystems and 1 devicelevel 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 devicelevel control protection system fails, the whole flexible multistate 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 16state space transition model of the whole flexible multistate switch, as shown in fig. 3. Then, all the shutdown states in the 16 states are further merged to finally obtain an eightstate model, as shown in table 2:
TABLE 2 Flexible multistate switch eightstate table
And 8: the probability of occurrence and the average duration of each state are calculated using a Markov chain based analytic method.
Step 81: obtaining a state transition matrix T of the flexible multistate switch eightstate model according to the flexible multistate switch eightstate space transition model established in the step 7:
step 82: applying markov process approximation principle:
PT＝P (12)
wherein P ═ P_{S1},P_{S2},…,P_{S8}]Is the state probability of eight states. The above formula can be rewritten as
P(TI)＝P (13)
Wherein I is an identity matrix.
Step 83: add the full probability conditionthe sum of the probabilities for all system states is 1. Namely, it is
Finishing to obtain:
step 84: the Markov matrix equation obtained by the 8 th2 th and 8 th3 rd steps is solved by applying a linear algebra algorithm, so that the state probability of 8 states can be calculated.
And 85: the frequency and duration are calculated using a frequencyduration method. The frequency of each state Si can be calculated by equation (16):
in the formula: p_{Si}Probability of being state i; p_{Sl}Probability of being a state directly connected to state i; lambda [ alpha ]_{k}Or λ_{l}Is the rate of metastasis (failure or repair); m_{d}Is the number of transitions leaving state i; m_{e}Is the number of transitions into state i.
The average duration of stay in state Si is:
according to the flexible multistate 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 multistate switch reliability modeling method considering current load uncertainty is characterized by comprising the following steps:
step 1: constructing a singleended MMC physical structure reliability model;
step 2: establishing a submodule 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 fixed_{av}Constructing a load expected correction coefficient;
step 2.2: obtaining the equivalent fault rate of the IGBT module in the submodule by utilizing the load expected correction coefficient obtained in the step 2.1, so as to obtain the equivalent fault rate of the submodule;
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 threeterminal MMC alternatingcurrent side current according to the load flow result, and calculating probability distribution information of the threeterminal MMC alternatingcurrent side current;
the equivalent reliability of the IGBT obtained by considering the improvement of the load expectation correction factor in step 2.2 is:
λ_{IGBT_av}＝L(I_{av})×λ_{IGBT} (9)
combining step 2.1, the equivalent failure rate of the submodules is as follows:
λ_{SM}＝2×λ_{IGBT_av}+λ_{C}+λ_{THY}+λ_{SMC} (10)；
and step 3: establishing an eightstate reliability model of the flexible multistate switch;
the step 3 comprises the following steps:
step 3.1: dividing the flexible multistate switch into 4 subsystems, namely 3 MMC subsystems and 1 devicelevel 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 16state space transfer model of the flexible multistate switch, and further combining all the shutdown states in the 16 states to obtain an eightstate model of the flexible multistate switch;
step 3.3: a Markov chainbased analytical method calculates the probability of occurrence and average duration of the 16 states of the flexible multistate switch.
2. The method of claim 1 for modeling flexible multistate 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 seriesparallel 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 seriesparallel connection method.
3. The method of claim 2 for modeling flexible multistate switch reliability that accounts for current load uncertainty, characterized by: the reliability of the submodules of step 1.1 is:
λ_{SM}＝2×λ_{IGBT}+λ_{C}+λ_{THY}+λ_{SMC} (1)
in the formula, λ_{IGBT}、λ_{C}、λ_{THY}、λ_{SMC}The failure rates of the IGBT module, the capacitor, the bypass thyristor and the submodule controller are respectively.
4. The method of claim 1 for modeling flexible multistate 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 threeterminal MMC alternatingcurrent side current to the current load of the submodule, and defining the current load proportionality coefficient as
Wherein, I_{ci}Representing a random current load; i is_{c0}Is 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 influence_{i}Working time t of element and current load I borne by element_{ci}Related, formula:
R_{i}(t)＝R_{0}(t；L(I_{ci})) (6)
in the formula, R_{0}The reliability of the IGBT is not considered when the current load influence is considered;
step 2.1.7: obtaining N random samples of the alternatingcurrent 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 multistate 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 devicelevel control protection system fails, the flexible multistate switch stops running.
6. The method of claim 1 for modeling flexible multistate 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 multistate switch eightstate model according to the flexible multistate switch eightstate 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 ═ P_{S1},P_{S2},…,P_{S8}]Is the state probability of the eight states of the flexible multistate switch, equation (12) is rewritten as:
P(TI)＝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 multistate 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: p_{Si}Probability of being state i; p_{Sl}Is the state probability directly connected to state i; lambda [ alpha ]_{k}Or λ_{l}Is the failover rate or failover rate; m_{d}Is the number of transitions leaving state i; m_{e}Is the number of transitions into state i, and the average duration of stay in state Si is:
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201810481457.7A CN108539795B (en)  20180518  20180518  Flexible multistate switch reliability modeling method considering current load uncertainty 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201810481457.7A CN108539795B (en)  20180518  20180518  Flexible multistate switch reliability modeling method considering current load uncertainty 
Publications (2)
Publication Number  Publication Date 

CN108539795A CN108539795A (en)  20180914 
CN108539795B true CN108539795B (en)  20210212 
Family
ID=63472319
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201810481457.7A Active CN108539795B (en)  20180518  20180518  Flexible multistate switch reliability modeling method considering current load uncertainty 
Country Status (1)
Country  Link 

CN (1)  CN108539795B (en) 
Families Citing this family (3)
Publication number  Priority date  Publication date  Assignee  Title 

CN110098754B (en) *  20190425  20201106  国网冀北电力有限公司  MMC redundancy submodule effective utilization rate calculation method considering standby redundancy 
CN110739679A (en) *  20190827  20200131  华北电力大学  Flexible multistate switch reliability analysis method based on Bayesian network method 
CN111162555B (en) *  20200106  20210518  许继集团有限公司  Reliability evaluation method and device for MMC flexible direct current converter valve and converter valve design method and device 
Citations (1)
Publication number  Priority date  Publication date  Assignee  Title 

CN106972541A (en) *  20170518  20170721  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 

2018
 20180518 CN CN201810481457.7A patent/CN108539795B/en active Active
Patent Citations (1)
Publication number  Priority date  Publication date  Assignee  Title 

CN106972541A (en) *  20170518  20170721  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 
NonPatent Citations (2)
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8388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38353842页 * 
Also Published As
Publication number  Publication date 

CN108539795A (en)  20180914 
Similar Documents
Publication  Publication Date  Title 

CN108539795B (en)  Flexible multistate switch reliability modeling method considering current load uncertainty  
CN108616143B (en)  Flexible multistate switch reliability modeling method considering voltage load sharing mechanism  
Zhu et al.  Adaptive power flow method for distribution systems with dispersed generation  
Zhang et al.  Optimal reactive power dispatch considering costs of adjusting the control devices  
CN109256970B (en)  MMCMTDC transmission system monopolar grounding fault current calculation method  
Billinton et al.  Adequacy assessment of composite power systems with HVDC links using Monte Carlo simulation  
Xiong et al.  Modeling and transient behavior analysis of an inverterbased microgrid  
CN109240124A (en)  A kind of electric power stability control strategy analogue system  
Peng et al.  Static security risk assessment for islanded hybrid AC/DC microgrid  
Ram et al.  Voltage stability analysis using Lindex under various transformer tap changer settings  
Aminifar et al.  Extended reliability model of a unified power flow controller  
Taylor et al.  A reactive contingency analysis algorithm using MW and MVAR distribution factors  
CN108400615A (en)  A kind of photovoltaic generating system low voltage crossing characteristic analysis method  
Liu et al.  Reliability model of MMC‐based flexible interconnection switch considering the effect of loading state uncertainty  
Quintana et al.  Overload and voltage control of power systems by line switching and generation rescheduling  
CN108390378B (en)  MMCUPFC reliability modeling method  
Liu et al.  Reliability modeling of MMCbased flexible interconnection controller considering the uncertainty of current loading  
CN110427635A (en)  LCCHVDC optimizes electromagnetical transient emulation method  
Li et al.  A new method for reference network considering contingent events based on line outage distribution factor  
FotuhiFiruzabad et al.  Power system reliability enhancement using unified power flow controllers  
CN110707720B (en)  Method for solving feeder line fault by using power electronic device SOP  
Gunda et al.  On convergence of conventional and metaheuristic methods for securityconstrained OPF analysis  
Shchetinin et al.  Locational security impact factors for riskconstrained AC OPF  
Rai et al.  Artificial neural network application for prediction of reactive power compensation under line outage contingency  
Matar et al.  Dynamic model reduction of large power systems based on coherency aggregation techniques and blackbox optimization 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
PB01  Publication  
SE01  Entry into force of request for substantive examination  
SE01  Entry into force of request for substantive examination  
GR01  Patent grant  
GR01  Patent grant 