CN110649600A - Multi-state power system optimization construction method based on fuzzy generation function - Google Patents
Multi-state power system optimization construction method based on fuzzy generation function Download PDFInfo
- Publication number
- CN110649600A CN110649600A CN201910882399.3A CN201910882399A CN110649600A CN 110649600 A CN110649600 A CN 110649600A CN 201910882399 A CN201910882399 A CN 201910882399A CN 110649600 A CN110649600 A CN 110649600A
- Authority
- CN
- China
- Prior art keywords
- state power
- power system
- series
- reliability
- screening
- 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.)
- Granted
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
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a multi-state power system optimization construction method based on a fuzzy generating function. Aiming at a plurality of multi-state power systems, calculating the rough reliability of each multi-state power system by adopting a fuzzy generation function method; the first screening is carried out by utilizing a first screening mode, the second screening is carried out by utilizing a second screening mode, and the number of the multi-state power systems is reduced for two times; and finally, solving the accurate reliability of each multi-state power system by adopting a Markov chain and a general generating function so as to obtain an optimal multi-state power system, and implementing and building the power system according to the optimal multi-state power system. The time for obtaining is greatly shortened, the screening process has more accurate accuracy, and the method is suitable for the optimal reliability calculation of the system and is more suitable for the actual situation.
Description
Technical Field
The invention relates to a method for calculating the reliability of a power system, in particular to a fuzzy generation function-based method for optimally constructing a multi-state power system in the multi-state power system.
Background
The reliability technology is developed from the aerospace industry and the electronic industry after world war II, and has penetrated into many industrial departments such as aerospace, electronics, chemical engineering, machinery and the like at present. The penetration of reliability technologies into the power industry and the electrical equipment manufacturing industry began in the mid-20 th century, the 60 s, and developed very rapidly in the future. The function of an electric power system is to economically provide qualified electric energy to consumers as reliably as possible, which reliability can be defined as the ability to provide qualified, continuous electric energy to consumers, usually expressed in terms of probability. Qualified means that the frequency and voltage of the electric energy must be kept within the specified ranges.
The power system reliability evaluation is to calculate and analyze the probability and the consequence of possible fault states to obtain a series of indexes reflecting the system reliability level. However, in a practical system having hundreds or even thousands of elements, the number of possible fault conditions is enormous. Due to the constraints of computation time and computation resources, it is not possible to evaluate all possible fault conditions in a practical evaluation. Therefore, the state enumeration method only screens for evaluation of fault states that contribute significantly to system reliability. The most common selection method is to cut off the number of failure occurrences, i.e., select 2 or less than 3 failure occurrences, and ignore the more significant failure occurrences. The advantage of this method is that the sum of the probabilities of the selected states is close to 1 and the number is small. However, in an actual system, due to the different outage probabilities of the components, some high-weight faults may have a higher occurrence probability than low-weight faults. Taking the example of an IEEE-RTS system with 71 elements, when the elements are modeled as 2 states, consider that the number of system states for N-3 is 57226 and the sum of the probabilities is 0.95110503. In fact, the first 57226 states with higher probability include 16786 0-3 failure states and 40440 4-6 failure states, and the sum of the probabilities is 0.98976138. The probability of the high-probability fault states is high, the consequences are serious, the reliability of the system is greatly influenced, and the high-probability faults can be ignored by carrying out state screening by cutting off the fault weight.
As can be seen from the above, in the reliability analysis of the power system, the final result is greatly affected by the number of the state selections, which means that the reliability analysis of the power system in the multi-state model is a necessary research direction.
In the existing optimal reliability calculation method for the multi-state power system, the most common method is the GA algorithm, and if a system with the highest reliability is desired to be obtained from a plurality of multi-state power systems, an exhaustive method is often adopted, that is, the reliability of all systems is calculated, and the method consumes a long calculation time, several hours or even several days. Secondly, the screening of a general system is primary screening, but the screening often has limited conditions, so the primary screening causes the problem of low screening precision.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a multi-state power system optimization construction method based on a fuzzy generation function. The method of the invention improves the traditional method from the aspects of time and precision, is applied to a series of multi-state power systems, can obtain the multi-state power system with the highest reliability, and has the time consumption far smaller than the traditional algorithm.
As shown in fig. 1, the technical solution of the present invention is as follows:
the first step is as follows: aiming at a plurality of known multi-state power systems, considering different points of element series-parallel elements in the multi-state power systems, and calculating the rough reliability of each multi-state power system by adopting a fuzzy generation function method;
the multi-state power system is a power system with multiple states and comprises various elements, a main element and an additional element, wherein the elements comprise a generator, a transmission line, a step-up transformer, a step-down transformer, a current converter and the like. A multi-state is defined as a power system, referred to as a multi-state power system, in which the system and its components may exhibit multiple performance levels.
The present invention is directed to a plurality of multi-state power systems, each having a different number of components, and thus each having a different configuration.
The rough reliability obtained by the fuzzy generating function method is very close to the accurate reliability of the system, but the calculation time is far shorter than that of the accurate reliability.
The second step is that: the first screening processing is carried out on the rough reliability of all the multi-state power systems by utilizing a first screening mode, so that the number of the multi-state power systems is reduced for the first time, and the calculation amount of the subsequent accurate reliability is reduced through screening;
the third step: secondly, a second screening mode is utilized to carry out secondary screening processing on the rough reliability of the multi-state power system obtained by the second screening, so that the number of the multi-state power system is reduced for the second time, and the calculation amount of the subsequent accurate reliability is reduced again through screening;
the fourth step: and aiming at each multi-state power system obtained by screening in the third step, solving the accurate reliability of each multi-state power system by adopting a Markov chain and a general generating function so as to obtain a system with the highest reliability, taking the system as an optimal multi-state power system, taking the accurate reliability of the optimal multi-state power system as the optimal reliability, and constructing the power system according to the optimal multi-state power system.
The invention carries out twice screening through the steps of the second step and the third step, thereby greatly reducing the number of the computing systems and improving the accuracy of the reliability screening computation.
The first step is specifically:
1.1) first, the equivalent processing of the series-parallel structure of the multi-state power system is performed:
the multi-state power system is equivalent to be mainly formed by a main element and an additional element in series-parallel connection, wherein the main element is each basic series element distributed in the multi-state power system in series, a series of basic series elements are all connected in series to the same end of the multi-state power system, and the additional element is a parallel element connected to each basic series element in parallel; a plurality of additional elements can be connected in parallel or not connected in parallel to one basic series element, one basic series element and all the additional elements connected in parallel to the basic series element form a series-parallel substructure, so that the multi-state power system is equivalent to each series-parallel substructure, and the n series-parallel substructures are connected in series to form the multi-state power system; wherein within one series-parallel substructure, each element is connected in parallel.
The main element, i.e. the basic series element, is for example a generator, a step-up transformer, a step-down transformer, a transmission line on the main network side, a transmission line on the distribution network side, an ac/dc converter, etc.
The additional elements are parallel elements connected in parallel to the respective basic series elements, so that they are of the same type as the basic series elements connected in parallel. For example, additional elements connected in parallel to the basic series element of the type of generator, also of the type of generator, such as thermal generators, hydroelectric generators, wind generators, etc.
1.2) on the basis of the structure of the element series-parallel connection element in the multi-state power system, processing the rough reliability of the series-parallel connection substructure by adopting a Fuzzy generating function method (Fuzzy universal generation function), and obtaining the rough reliability of each series-parallel connection substructure:
1.3) the whole multi-state power system is equivalently formed by connecting a plurality of series-parallel substructures in series, and the rough reliability of the whole multi-state power system is obtained by processing the rough reliability of each series-parallel substructure by a fuzzy generating function method;
in specific implementation, firstly, all single elements in the series-parallel substructure are regarded as single parallel elements, and calculation processing is carried out by adopting a parallel element processing mode in a fuzzy generation function method to obtain the rough reliability of the series-parallel substructure; and then, taking the single serial-parallel substructure as a serial element, and performing calculation processing by adopting a serial element processing mode in a fuzzy generation function method to obtain the rough reliability of the whole multi-state power system.
1.4) arranging the rough reliability of all the multi-state power systems in descending order from big to small to obtain a first order sequence and drawing a descending order arrangement curve required by an order optimization algorithm.
The second step is specifically as follows:
processing a first sequencing sequence by adopting a first screening mode, specifically, selecting the first g multi-state power systems with high rough reliability from the first sequencing sequence according to the rough reliability, so as to reduce the number of all the multi-state power systems to g, wherein the size of g is obtained by adopting the following formula:
wherein N is originalA is a first screening setting number, i is the ith number from 1 to a, i is 1,2, …, a,
for screening probability parameters, it is generally taken to be 95%.
Because the number of the original multi-state power systems in the first step is often very large, the algorithm of the invention has two screening modes, firstly, a large number of systems to be screened are reduced to a small number of systems to be screened by the first screening mode, and then, the second screening mode is utilized to carry out second deletion processing on the systems to be screened with the small number. Meanwhile, the third step is used for reordering, and the sorting result of the multi-state power system which can be accurately calculated by the fourth step is obtained by screening, so that the accuracy of the final result is improved.
The third step is specifically as follows:
aiming at the first g systems obtained by screening in the second step, the rough reliability obtained by calculation in the first step is arranged in descending order from big to small to obtain a second sequencing sequence;
processing the second sorting sequence by adopting a second screening mode, specifically, selecting the first s multi-state power systems with high rough reliability from the g multi-state power systems according to the rough reliability in the second sorting sequence, so as to reduce the number of all the multi-state power systems to s again, wherein the size of s is obtained by adopting the following formula:
wherein e is the base of the natural logarithm, k is the second screening setting parameter, z0Rho, eta and gamma are characteristic parameters of the first, second, third and fourth ordering sequences,
indicating rounding up.
Compared with the second screening, the first screening of the invention is more suitable for the condition that the number of the systems to be screened is more, so that under the condition that the number of the systems to be screened is more, the first screening mode can still keep the previous a multi-state power systems with higher accurate reliability in the previous g systems obtained by screening through more accurate rough reliability, thereby ensuring the accuracy of the screening. In the second screening, under the condition that the number of the systems to be screened is reduced through the first screening, through the more accurate rough reliability, the second screening mode can still keep the former k multi-state power systems with higher accurate reliability in the former s systems obtained through screening, so that the screening accuracy is ensured.
The fourth step is specifically as follows:
and aiming at the s multi-state power systems obtained by screening in the third step, solving the accurate reliability of each multi-state power system by adopting a Markov chain and a general generating function so as to obtain a system with the highest reliability, wherein the system is the optimal multi-state power system. And implementing and building the power system according to the optimal multi-state power system.
The invention greatly reduces the number of multi-state power systems which need accurate reliability calculation by classified screening through the steps of the second step and the third step, and improves the accuracy of reliability screening calculation by selecting a proper screening mode according to the number of the systems to be screened through two times of screening.
The invention has the beneficial effects that:
compared with the prior art, the method and the device have the advantages that the time for obtaining the optimal multi-state power system, namely the multi-state power system with the highest reliability, is greatly shortened.
Compared with the prior art, the two screening processes provided by the invention have more accurate accuracy compared with the traditional one screening process, and the screening accuracy is ensured.
Compared with the prior art, the fuzzy generation function method is more suitable for the system compared with a general reliability calculation method, the used time is short, and the method is suitable for the reliability calculation algorithm of the system and is more suitable for the actual situation.
Drawings
FIG. 1 is a logical block diagram of the method of the present invention.
Fig. 2 is a multi-state power system with configuration type number 1 according to an embodiment of the present invention.
Fig. 3 is a multi-state power system of configuration type number 2 according to an embodiment of the present invention.
Fig. 4 is a multi-state power system of configuration type number 3 according to an embodiment of the present invention.
Fig. 5 is a multi-state power system with configuration type number 4 according to an embodiment of the present invention.
Fig. 6 is a multi-state power system of configuration type number 5 of an embodiment of the present invention.
Fig. 7 is a multi-state power system of configuration type number 6 according to an embodiment of the present invention.
Fig. 8 is a multi-state power system of configuration type number 7 of an embodiment of the present invention.
Fig. 9 is a multi-state power system of configuration type number 8 of an embodiment of the present invention.
Fig. 10 is a multi-state power system of configuration type number 9 of an embodiment of the present invention.
Fig. 11 is a multi-state power system of configuration type number 10 according to an embodiment of the present invention.
Fig. 12 is a multi-state power system of configuration type number 11 according to an embodiment of the present invention.
Fig. 13 is a multi-state power system of configuration type number 12 of an embodiment of the present invention.
Fig. 14 is a multi-state power system of configuration type number 13 of an embodiment of the present invention.
Fig. 15 is a multi-state power system of configuration type number 14 of an embodiment of the present invention.
Fig. 16 is a multi-state power system of configuration type number 15 of an embodiment of the present invention.
Fig. 17 is a multi-state power system of configuration type number 16 according to an embodiment of the present invention.
Fig. 18 is a multi-state power system of configuration type number 17 of an embodiment of the present invention.
Fig. 19 is a multi-state power system of configuration type number 18 of an embodiment of the present invention.
Fig. 20 is a multi-state power system of configuration type number 19 of an embodiment of the present invention.
Fig. 21 is a multi-state power system of configuration type number 20 of an embodiment of the present invention.
Fig. 22 is a multi-state power system of configuration type number 21 of an embodiment of the present invention.
Fig. 23 is a multi-state power system of configuration type number 22 of an embodiment of the present invention.
Fig. 24 is a multi-state power system of configuration type number 23 of an embodiment of the present invention.
Fig. 25 is a multi-state power system of configuration type number 24 of an embodiment of the present invention.
Fig. 26 is a multi-state power system of configuration type number 25 of an embodiment of the present invention.
Fig. 27 is a multi-state power system of configuration type number 26 of an embodiment of the present invention.
Fig. 28 is a multi-state power system of configuration type number 27 of an embodiment of the present invention.
Fig. 29 is a multi-state power system of configuration type number 28 of an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
The examples of the invention are as follows:
the first step is as follows: there are 28 system configurations in each of which a block represents a respective power element, two series connections of blocks represent series connections of power elements, and two parallel connections of blocks represent parallel connections of power elements. Each system architecture represents a class of power system architecture, the topology between the various components on the power generation, transformation, transmission, distribution and consumption sides. And solving the rough reliability of each system by using a fuzzy generating function method.
For example, in the multi-state power system with configuration type number 1 in fig. 2, a total of 5 series-parallel substructures are included. The first series-parallel substructure comprises two parallel elements, which represent the generators in the actual power system, i.e. the power system comprises two generators. The second series-parallel sub-structure contains an element representing a step-up transformer in the actual power system, and the third series-parallel sub-structure contains an element representing a transmission line from the step-up transformer to the step-down transformer in the actual power system. The fourth series-parallel sub-structure contains an element representing the step-down transformer of the actual power system. The fifth series-parallel sub-structure contains an element representing the transmission line of the actual power system from the step-down transformer to the consumer side.
The second step is that: and performing primary screening treatment in a primary screening mode based on the rough reliability of each system to obtain the g of 20. The first 20 high coarse reliability multi-state power systems thus have their corresponding coarse reliabilities shown in table 1.
TABLE 1 coarse reliability of the first 20 systems
The third step: the coarse reliability of the first 10 high coarse reliability multi-state power systems is shown in the table by obtaining s as 10 through the second screening method
Table 210 coarse reliability of the system
The fourth step: aiming at the multi-state power system obtained by screening in the third step, an optimal multi-state power system is obtained by adopting a Markov chain and a general generating function method, wherein the following table shows the accurate reliability of the first 10 systems obtained by screening.
Table 310 accurate reliability of the system
It can be seen from table 3 that the system 27 has the highest reliability, so the system 27 is the optimal system.
Example verification
a) Reliability accuracy verification
Table 4 for solving all 28 systems of accurate reliability using mahalanobis chain and generic generating function
It can be seen from table 4 that the system 27 has the highest reliability, consistent with the calculated results.
b) Comparing the arrangement order
The top 10 systems are 28, 27, 26, 25, 24, 23, 22, 21, 20, 18 in terms of accuracy reliability.
The first 10 systems, named 27, 28, 26, 25, 24, 23, 22, 21, 20, 18, of the two screens according to the coarse and finer reliability remain the same. This demonstrates the accuracy of the two screens based on coarse reliability.
This results in a 27 th multi-state power system being the optimal multi-state power system.
c) Advantages of the invention
In the specific implementation of this embodiment, the same reused traditional method (the traditional method is to simply adopt a mahalanobis chain and a general generating function to sequentially process and calculate each multi-state power system to obtain accurate reliability, and then the maximum one is selected), the sequence optimization and mahalanobis chain based multi-state power system reliability analysis method of application No. 2017109442805, and the sequence optimization and monte carlo based multi-state power system reliability analysis method of application No. 2017109442792 are used as comparison, and the time comparison results obtained by the method of the present invention are as follows:
TABLE 5 comparison of the results of the different processes
In terms of calculated amount and calculation time, different screening rules are adopted in the two screening processes, and the second screening mode can greatly reduce the number of the electric power systems, and the reduction degree of the number of the electric power systems is greater than that of the electric power systems screened for the second time in the two patents with the application numbers of 2017109442805 and 2017109442792.
And the time used by the rough reliability calculation algorithm adopted by the invention is far less than the time required by the rough reliability calculation of the two patents of application numbers 2017109442805 and 2017109442792. Resulting in the overall time required for the present invention being less than the overall time of the two patents of application No. 2017109442805 and application No. 2017109442792.
From the rough reliability calculation methods of the two patents of application numbers 2017109442805 and 2017109442792, it can be seen that the more internal parallel elements, the larger the rough reliability, and the more internal parallel elements, the larger the deviation of the obtained rough reliability from the precise reliability, that is, the more internal parallel elements, the power system may be erroneously retained due to the too high rough reliability, so that the system with high precise reliability may not be retained due to the relatively small rough reliability.
The power system numbered 27, however, due to its small number of internal parallel elements, is rejected by two screenings because the small coarse reliability cannot be retained under the coarse reliability calculation methods of the two patents of application numbers 2017109442805 and 2017109442792. The results obtained finally by the two patents of application No. 2017109442805 and No. 2017109442792 do not correspond to reality.
The accuracy of the rough reliability calculation algorithm adopted by the invention is higher than that of the two patents of application number 2017109442805 and application number 2017109442792, so the rough reliability calculated by the invention is closer to the accurate reliability. The end result of the invention is in line with reality.
Therefore, the time used by the method is approximately one third of that of the traditional method, compared with the two patent methods, the method reduces the calculation time of rough reliability due to the calculation characteristics of the fuzzy generating function, reduces the number of multi-state power systems due to the first screening mode and the second screening mode, reduces the calculation time of precise reliability, greatly reduces the overall calculation time, improves the calculation precision of the rough reliability due to the fuzzy generating function, and improves the precision of the overall screening. Therefore, the method has higher accuracy and high reliability, greatly shortens the time for obtaining, is more suitable for actual conditions, and has obvious technical effect.
Claims (5)
1. A multi-state power system optimization construction method based on a fuzzy generating function is characterized in that:
the first step is as follows: aiming at a plurality of multi-state power systems, considering different points of element series-parallel elements in the multi-state power systems, and calculating the rough reliability of each multi-state power system by adopting a fuzzy generation function method;
the second step is that: carrying out primary screening processing aiming at the rough reliability of all the multi-state power systems by utilizing a first screening mode, thereby reducing the number of the multi-state power systems for the first time;
the third step: secondly, screening the rough reliability of the multi-state power system obtained by the second screening step by using a second screening mode, so that the number of the multi-state power system is reduced for the second time;
the fourth step: and aiming at each multi-state power system obtained by screening in the third step, solving the accurate reliability of each multi-state power system by adopting a Markov chain and a general generating function so as to obtain a system with the highest reliability, and constructing the power system by taking the system as an optimal multi-state power system according to the optimal multi-state power system.
2. The method for optimally constructing the multi-state power system based on the fuzzy generating function as claimed in claim 1, wherein: the first step is specifically:
1.1) first, the equivalent processing of the series-parallel structure of the multi-state power system is performed: the multi-state power system is equivalent to be mainly composed of a main element and an additional element in series-parallel connection, wherein the main element is each basic series element distributed in the multi-state power system in series, a series of basic series elements are connected together in series, and the additional element is a parallel element connected to each basic series element in parallel; a plurality of additional elements can be connected in parallel or not connected in parallel to one basic series element, one basic series element and all the additional elements connected in parallel to the basic series element form a series-parallel substructure, so that the multi-state power system is equivalent to each series-parallel substructure, and the n series-parallel substructures are connected in series to form the multi-state power system;
1.2) on the basis of the structure of the element series-parallel connection element in the multi-state power system, processing the rough reliability of the series-parallel connection substructure by adopting a fuzzy generation function method to obtain the rough reliability of each series-parallel connection substructure:
1.3) the whole multi-state power system is equivalently formed by connecting a plurality of series-parallel substructures in series, and the rough reliability of the whole multi-state power system is obtained by processing the rough reliability of each series-parallel substructure by a fuzzy generating function method;
1.4) arranging the rough reliability of all the multi-state power systems in descending order from big to small to obtain a first order sequence and drawing a descending order arrangement curve required by an order optimization algorithm.
3. The method for optimally constructing the multi-state power system based on the fuzzy generating function as claimed in claim 1, wherein: the second step is specifically as follows:
processing a first sequencing sequence by adopting a first screening mode, specifically, selecting the first g multi-state power systems with high rough reliability from the first sequencing sequence according to the rough reliability, so as to reduce the number of all the multi-state power systems to g, wherein the size of g is obtained by adopting the following formula:
where N is the number of the original total multi-state power systems, a is the first screening set number, i is the ith number from 1 to a, i is 1,2, …, a,
to screen probability parameters.
4. The method for optimally constructing the multi-state power system based on the fuzzy generating function as claimed in claim 1, wherein: the third step is specifically as follows:
aiming at the first g systems obtained by screening in the second step, the rough reliability obtained by calculation in the first step is arranged in descending order from big to small to obtain a second sequencing sequence;
processing the second sorting sequence by adopting a second screening mode, specifically, selecting the first s multi-state power systems with high rough reliability from the g multi-state power systems according to the rough reliability in the second sorting sequence, so as to reduce the number of all the multi-state power systems to s again, wherein the size of s is obtained by adopting the following formula:
wherein e is the base of the natural logarithm, k is the second screening setting parameter, z0Rho, eta and gamma are characteristic parameters of the first, second, third and fourth ordering sequences,indicating rounding up.
5. The method for optimally constructing the multi-state power system based on the fuzzy generating function as claimed in claim 1, wherein: the fourth step is specifically as follows: and aiming at the s multi-state power systems obtained by screening in the third step, solving the accurate reliability of each multi-state power system by adopting a Markov chain and a general generating function so as to obtain a system with the highest reliability, wherein the system is the optimal multi-state power system. And implementing and building the power system according to the optimal multi-state power system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910882399.3A CN110649600B (en) | 2019-09-18 | 2019-09-18 | Multi-state power system optimization construction method based on fuzzy generation function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910882399.3A CN110649600B (en) | 2019-09-18 | 2019-09-18 | Multi-state power system optimization construction method based on fuzzy generation function |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110649600A true CN110649600A (en) | 2020-01-03 |
| CN110649600B CN110649600B (en) | 2021-03-23 |
Family
ID=68991351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910882399.3A Active CN110649600B (en) | 2019-09-18 | 2019-09-18 | Multi-state power system optimization construction method based on fuzzy generation function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110649600B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909185A (en) * | 2017-10-12 | 2018-04-13 | 浙江大学 | Multimode Power System Reliability Analysis method based on sequence optimization and markov chain |
| CN108364115A (en) * | 2018-01-04 | 2018-08-03 | 浙江大学 | A kind of quick multimode Power System Reliability computational methods |
| CN108448631A (en) * | 2018-04-25 | 2018-08-24 | 浙江大学 | The Dynamic Phasors modeling method that power distribution network is accessed containing distributed generation resource based on model reduction |
-
2019
- 2019-09-18 CN CN201910882399.3A patent/CN110649600B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909185A (en) * | 2017-10-12 | 2018-04-13 | 浙江大学 | Multimode Power System Reliability Analysis method based on sequence optimization and markov chain |
| CN108364115A (en) * | 2018-01-04 | 2018-08-03 | 浙江大学 | A kind of quick multimode Power System Reliability computational methods |
| CN108448631A (en) * | 2018-04-25 | 2018-08-24 | 浙江大学 | The Dynamic Phasors modeling method that power distribution network is accessed containing distributed generation resource based on model reduction |
Non-Patent Citations (2)
| Title |
|---|
| Q．C．ZHAO等: "Vector ordinal Optimization", 《JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS》 * |
| 雷鹏: "考虑不能覆盖失效的模糊多状态系统可靠性分析", 《中国优秀硕士学位论文全文数据库》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110649600B (en) | 2021-03-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104239687B (en) | Reliability modeling and evaluation method based on aerospace product signal transmission path | |
| Duan et al. | A deep reinforcement learning based approach for optimal active power dispatch | |
| CN105868863B (en) | Electromechanical system multi-level reliability prediction method based on fuzzy judgment | |
| CN112069666B (en) | Power grid short-term reliability evaluation method based on probabilistic power flow method | |
| CN104268792A (en) | Comprehensive energy efficiency evaluation model for high-energy-bearing electricity consumption enterprise | |
| Hou et al. | Chance constraint tuning for optimal power flow | |
| CN105024645A (en) | Matrix evolution-based photovoltaic array fault location method | |
| CN110601187B (en) | Multi-state power system optimization construction method based on continuous discrete function | |
| CN112310959A (en) | Power distribution network low voltage comprehensive treatment method considering uncertainty factors and correlation thereof | |
| CN114037194B (en) | Hydroelectric power plant power generation load prediction system and method based on machine learning | |
| CN112734141B (en) | Diversified load interval prediction method and device | |
| CN110649600B (en) | Multi-state power system optimization construction method based on fuzzy generation function | |
| CN106056305A (en) | Power generation system reliability rapid assessment method based on state clustering | |
| WO2019134258A1 (en) | Reliability approximation calculation method for large-scale multi-state system having cascading structure | |
| Negri et al. | Uncertainty and Lack of Information Affecting the Training of Machine Learning Algorithms for Fault Prediction of Cable-Joints | |
| KR20220083353A (en) | Power system stability analysis system and method considering renewable energy variablity | |
| CN110782080B (en) | Electric power system structure optimization method based on population performance sorting selection | |
| CN115860263A (en) | Power grid infrastructure planning project construction period prediction method and prediction system | |
| CN107909185A (en) | Multimode Power System Reliability Analysis method based on sequence optimization and markov chain | |
| Zhu et al. | An optimization based network reduction method with generator placement | |
| CN110309586B (en) | MA-DBN-based GIS decomposed gas content prediction method | |
| CN107909184B (en) | Multimode Power System Reliability Analysis method based on sequence optimization and Monte Carlo | |
| CN107834538A (en) | Multimode power system redundancy optimization method based on sequence optimization and markov chain | |
| CN107863771A (en) | Multimode power system redundancy optimization method based on sequence optimization and Monte Carlo | |
| Yan et al. | A three-stage CE-IS Monte Carlo algorithm for highly reliable composite system reliability evaluation based on screening method |
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 |