CN106054019B - The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor - Google Patents
The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor Download PDFInfo
- Publication number
- CN106054019B CN106054019B CN201610345826.0A CN201610345826A CN106054019B CN 106054019 B CN106054019 B CN 106054019B CN 201610345826 A CN201610345826 A CN 201610345826A CN 106054019 B CN106054019 B CN 106054019B
- Authority
- CN
- China
- Prior art keywords
- mrow
- mtd
- mtr
- msub
- msup
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000005457 optimization Methods 0.000 claims abstract description 26
- 230000007547 defect Effects 0.000 claims abstract description 16
- 230000000295 complement effect Effects 0.000 claims description 46
- 238000013178 mathematical model Methods 0.000 claims description 26
- 230000000694 effects Effects 0.000 claims description 16
- 238000012423 maintenance Methods 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 14
- 238000002940 Newton-Raphson method Methods 0.000 claims description 11
- 230000001364 causal effect Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 9
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000002955 isolation Methods 0.000 claims description 4
- 238000012806 monitoring device Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 2
- 238000013528 artificial neural network Methods 0.000 description 3
- 229910004444 SUB1 Inorganic materials 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 102100036464 Activated RNA polymerase II transcriptional coactivator p15 Human genes 0.000 description 1
- 101000713904 Homo sapiens Activated RNA polymerase II transcriptional coactivator p15 Proteins 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Mobile Radio Communication Systems (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Locating Faults (AREA)
Abstract
The invention discloses a kind of online Fault Locating Method of power distribution network high fault tolerance based on failure confactor, the electric current for collecting each feeder switch of power distribution network using main website gets over limit information；Set up switch function collection；Set up the nonlinear complementarity optimization fault location model of distribution network failure positioning；Set up the Non-Linear Programming fault location model for meeting KKT extremum conditions；Obtained using Lagrange multiplier and continuous space nonlinear optimization extreme value theorem with faulty confactor, realize the fault location nonlinear equation group model of fault location and FTU defect identifications.The present invention realizes single or multiple failure high fault tolerance positioning to distribution feeder fault section, realize the accurate recognition of the FTU setting positions of latent defect, realize the online fault location of large-scale complex power distribution network, repair based on condition of component to FTU devices provides theoretical direction, has the advantages that to realize that convenient, reliability is high, fault-tolerance ability is strong, fault location efficiency high, has strong adaptability to multiple failure.
Description
Technical Field
The invention relates to the technical field of intelligent power distribution networks, in particular to a high fault tolerance online fault positioning method for a power distribution network based on fault auxiliary factors, which is used for positioning single or multiple faults and accurately identifying the position of a potential defect FTU device in a feeder line fault section of the power distribution network.
Background
In the process of electric energy transmission and distribution, a power distribution network is an important link between a power system and users. With the rapid development of economy, the requirements of users on power supply reliability and power supply quality are continuously improved, the fault location of the power distribution network is used as the premise that a feeder fault area accurately identifies and recovers the power supply of the users, the fault location of the feeder of the power distribution network is quickly and accurately found out, and the method has an important effect on improving the self-healing performance and the power supply reliability of a power distribution system. However, as the structure and surrounding environment of the power distribution network tend to be complex, the probability of occurrence of faults and the probability of multiple faults are increased and significantly enhanced, and how to utilize the uncertainty of fault location information and how to effectively improve the accuracy, rapidity and fault tolerance of fault identification of the power distribution network becomes a key problem to be solved urgently to improve the intelligence level of the power distribution network.
For a long time, the fault point searching method based on manual line patrol consumes a large amount of manpower and material resources, and the power supply reliability of the power distribution network is seriously influenced due to long time consumption and increased power failure time. In order to effectively shorten the fault location time of the power distribution network and improve the accuracy of fault location, an electric power operation management department relies on a distribution line to install a large number of automatic section switches and electric power intelligent monitoring terminals Feeder Terminal units-FTUs to improve the automation and intelligence level of the power distribution network, and therefore the Feeder line sections are quickly located and isolated when the power distribution network fails.
The commonly used fault location method based on the automatic switch comprises the following steps: reclosing and section switching are realized through reasonable time matching. The method needs to repeatedly find out the fault point for multiple times of power failure and power restoration, has the advantages of avoiding manual participation of fault positioning and improving the fault positioning efficiency, but has a complicated time setting process, and the fault time and range are expanded due to multiple times of manual power failure in the positioning process.
Another common fault location method based on an automatic switch is as follows: the method includes the steps that collected overcurrent information is directly monitored by the FTU device, based on the incidence relation between a fault feeder line and overcurrent, the position of a fault section is found through building a fault positioning mathematical model and a corresponding algorithm, and then section switches at two ends of the fault feeder line are directly opened to isolate the fault section. The method has the advantages of no need of power failure operation in the fault positioning process, simple principle, convenience in realization, high accuracy and the like.
At present, a great deal of research is carried out on a power distribution network fault positioning method based on information acquired by an FTU device, and the adopted modeling theory and fault identification method mainly comprise an artificial neural network, a rough set theory, a data mining technology, a unified matrix algorithm, a group intelligent optimization algorithm and the like. The fault positioning method based on the artificial neural network generally has the characteristics of fault tolerance, strong universality and the like, but the fault positioning method needs to select and train fault samples during fault positioning, the reasonability of the fault positioning method can directly influence the accuracy and the fault tolerance of the fault positioning, and when the distribution network structure changes, retraining is needed to track the distribution network topological structure with the characteristics of complexity and variability, so that the fault positioning efficiency is low; the method based on the rough set theory and the data mining technology has relatively complex modeling principle and is inconvenient for engineering application; when the unified matrix algorithm and the group intelligent optimization algorithm are used for constructing the fault positioning model, the method has the remarkable advantages of simple principle, convenience in implementation and the like, and is widely researched and favored in engineering and applied.
The fault locating process of the matrix algorithm is realized through matrix relation operation, so that the method has the advantages of strong numerical stability, high fault identification efficiency and good real-time performance, but the modeling principle is complex when complex multiple faults of the power distribution network are considered; the method based on the group intelligent algorithm is limited by the dependence on the random group intelligent algorithm, so that the defect of low positioning efficiency exists, the reliability of a fault positioning result is reduced due to the instability of a factor value, and the fault range is indirectly enlarged.
However, basic information sources FTUs relied on by the unified matrix algorithm and the group intelligent optimization algorithm are influenced by external environment factors of equipment working, information loss or distortion easily occurs, reliability of the method is directly reduced, and misjudgment of faults are generated. Therefore, the fault positioning accuracy of the algorithm is improved from the dual aspects of the work reliability of the FTU equipment and the fault tolerance of the fault positioning algorithm.
At present, its reliability improvement is mainly realized through the periodic overhaul to the work of FTU device, and it can lead to some equipment not should overhaul and overhauls and cause artificial reliability to descend, also can cause the huge waste of financial resources and material resources simultaneously, and in addition, the two works of FTU device overhaul and fault location are isolated going on always for there is the poor not enough of harmony between the two, promptly: the maintenance is not maintained, so that the operation reliability of the FTU is reduced, and the accuracy of a fault positioning algorithm is reduced due to the increase of the possibility of information distortion. Therefore, the maintenance of the FTU device is changed from regular maintenance to state maintenance, and it is still a problem to be solved to improve the coordination between the FTU device maintenance and the fault location source information.
From the above discussion, it can be seen that, in the existing power distribution network fault location method based on information collected by automated terminals such as FTU, etc.: the power distribution network graph theory identification method lacks strong adaptability to information distortion or loss; the distribution network fault positioning method based on the artificial neural network is difficult to meet the positioning requirements of distribution network topology change and multiple faults; the power distribution network fault positioning method based on the group intelligent algorithm has the inherent defects of low positioning efficiency, poor numerical stability and the like. Therefore, it is a key to be urgently solved to provide a highly fault-tolerant and highly adaptable FTU power distribution network fault method that integrates the above advantages and reflects coordination consistency between FTU overhaul and fault location source information.
Disclosure of Invention
In order to solve the technical problems, the invention provides a high fault tolerance online fault positioning method for a power distribution network based on fault auxiliary factors based on automatic acquisition terminals such as FTUs (fiber to the Unit), which realizes high fault tolerance positioning of single or multiple faults for a feeder fault section of the power distribution network, and simultaneously realizes accurate identification of the position of an FTU device with potential defects, can effectively realize online fault positioning of a large-scale complex power distribution network, and provides theoretical guidance for state maintenance of the FTU device.
In order to achieve the purpose, the technical scheme of the invention is as follows: a high fault tolerance online fault positioning method for a power distribution network based on fault auxiliary factors comprises the following steps:
the method comprises the following steps: dynamically monitoring the current of a monitoring point of the power distribution network by using a current monitoring device in a period of 15 minutes, and judging whether fault overcurrent exists or not by comparing the current with a set normal current reference value; when fault overcurrent exists, each independent comparator outputs an alarm value 1, otherwise, a value 0 is output; collecting fault overcurrent threshold values of all monitoring points through a control main station to form a current alarm information set;
step two: when the control master station collects fault overcurrent information, firstly, a nonlinear complementary optimization fault positioning model is established by using a current alarm information set and a power distribution network topological structure based on algebraic relation description, an approximation relation theory and a complementary theory; then, based on an equivalent transformation theory, converting the nonlinear complementary optimization fault positioning model into a nonlinear programming fault positioning model meeting the KKT extreme value condition by using a complementary smooth function; further, establishing a power distribution network fault location nonlinear equation set mathematical model based on a fault auxiliary factor method by utilizing Lagrange multipliers, disturbance factors and KKT extreme value conditions; finally, identifying the feeder line section position by adopting an iteration method to obtain the characteristic parameter value of a Lagrange multiplier, and realizing defect state evaluation and distortion position identification of the FTU device;
step three: an SCADA system of the monitoring center sends a switching-off command to an adjacent automatic switch of the fault feeder line section, so that the isolation of the feeder line fault section is realized; meanwhile, a state overhaul implementation plan is provided for operation and maintenance personnel according to the defect state evaluation and the distortion position of the FTU device.
Further, the process of establishing the nonlinear complementary optimization fault location model based on the algebraic relationship description, the approximate relationship theory and the complementary theory is as follows: finding out all fault equipment directly related to the current alarm information by using a cause and effect correlation analysis theory, and establishing a cause and effect equipment set of the automatic switch; establishing a switching function set based on an undirected graph connectivity theory, a power flow transmission mechanism and algebraic relation description; based on a quadratic approximation relation theory, a nonlinear complementary optimization fault positioning model for power distribution network fault positioning is established through 0-1 complementary constraint conditions by using the index of the minimum sum of accumulated values of the switch function concentrated feeder state characteristic values of the automatic switch and the square sum of the difference between the characteristic values of the current alarm state with the time scale uploaded by the FTU device.
Furthermore, the high fault tolerance online fault positioning method for the power distribution network based on the fault auxiliary factor is characterized in that the switch function set for establishing the current out-of-limit information based on the algebraic relation description is realized by adopting algebraic operator addition (+) or subtraction (-) operation.
Furthermore, the high fault tolerance online fault positioning method for the power distribution network based on the fault auxiliary factor is characterized in that a switching function is constructed by utilizing addition operation instead of logic OR operation, and a mathematical model of the switching function is described based on algebraic relationsCan be expressed as:
wherein S isiIs the ith oneMotorized switch, omegaiTo automate the causal equipment set of the switch i,is omegaiThe number of cause and effect equipment in the system is X, X is a state variable column matrix of each power distribution network feeder line, and omega is a cause and effect equipment set omega of all automatic switchesiThe value of x (i) is 0 or 1, i is 1, 2, … …, and N is the number of the automatic switches.
Further, an approximation mathematical model of the nonlinear complementary optimization fault location model for power distribution network fault location is established through 0-1 complementary constraint conditions and is expressed as follows:
wherein,the alarm information is uploaded by an automatic switch i.
Further, according to the mutual exclusivity of the fault information state of the feeder line, constructing an auxiliary complementary constraint condition as follows: x ═ 1-X ═ 0; based on the optimization index and the complementary constraint condition with the minimum residual sum of squares in the continuous space, establishing a complementary constraint optimization power distribution network fault positioning model as follows:
further, the method for establishing the nonlinear programming fault location model comprises the following steps: introducing a Fischer-Burmeister complementation function:wherein a and b represent complementary variables, satisfy a ⊥ b as 0, increase perturbation factor mu to obtain corrected complementary functionIs shown as(μ,a,b)∈R3R is a natural number set; replacing the 0-1 complementary constraint condition with the corrected complementary function, and considering that the fault information state of the feeder line has mutual exclusivity to obtainEstablishing a nonlinear programming fault positioning model meeting the KKT extreme value condition:
further, the power distribution network high fault tolerance online fault locating method based on the fault auxiliary factor is characterized in that the method for standardizing the mathematical model of the power distribution network fault locating nonlinear equation set comprises the following steps: using lagrange multiplier lambdaiEstablishing a nonlinear equation set model for power distribution network fault location based on the KKT extreme value condition:
furthermore, c is an acceleration factor, X is a state variable array matrix of each distribution network feeder line, a fault auxiliary factor mathematical model for fault location is c mu X,then phiFB(mu, X,1-X) + c mu lambda is a state evaluation factor for defect identification of the FTU automatic device; order:a is a coefficient matrix formed by a switching function, and the normalized form of a nonlinear equation set model is as follows:
further, the mathematical model for converting the standardized form of the nonlinear equation set model of the power distribution network fault location into the Newton-Raphson method iterative solution with the second-order convergence characteristic is as follows:
further, the method for performing the iterative solution of the nonlinear equation set model for fault location by the newton-raphson method comprises the following steps:
1. selecting acceleration factors c ∈ (0.5,2), (X)(0),λ(0),μ(0))＝1；
2. Judgment | | | H (X)(k),λ(k),μ(k))||2If the value is 0, the algorithm is terminated, otherwise, the step 3 is carried out;
3. calculating [ Delta X ] by using a mathematical model of Newton-Raphson method iterative solution with second-order convergence characteristics(k),Δλ(k),Δμ(k)]T；
4. Calculating [ X ] by using a mathematical model of Newton-Raphson method iterative solution with second-order convergence characteristics(k+1),λ(k+1),μ(k+1)]TAnd calculate | | | H (X)(k),λ(k),μ(k))||2Step 2.
Has the advantages that: compared with the prior art, the fault-tolerant method is realized by adopting algebraic relation modeling and approximation relation theory, is easier to consider fault tolerance compared with a matrix algorithm based on graph theory knowledge, simultaneously gets rid of the dependence of the traditional optimization fault positioning method based on logic relation modeling on a group intelligent algorithm, realizes high fault-tolerant positioning of single or multiple faults on a feeder line fault section of the power distribution network, simultaneously realizes accurate identification of the position of an FTU (fiber to the Unit) device with potential defects, can effectively realize online fault positioning of a large-scale complex power distribution network, provides theoretical guidance for state maintenance of the FTU device, and has the advantages of convenience in realization, high reliability, strong fault tolerance capability, high fault positioning efficiency, strong adaptability to multiple faults and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a circuit diagram of a single power supply radial distribution network in normal operation of the present invention.
Fig. 2 is a circuit diagram of a single-power radial distribution network during fault operation of the present invention.
Fig. 3 is a flowchart of a method for positioning a high fault tolerance online fault of a power distribution network based on a fault auxiliary factor.
In FIGS. 1 and 2, S1For the inlet line breakers of substations S2、S3、……、S7For feeder section switches, the long squares represent circuit breakers and the short squares represent load switches.
Detailed Description
The invention will be further explained with reference to the drawings and examples.
As shown in fig. 1, fig. 2 and fig. 3, when a line of a power distribution network fails, the method for positioning a high fault tolerance online fault of the power distribution network based on a fault auxiliary factor according to the present invention may be adopted, and includes the following steps:
step 1: based on a time synchronization device, taking 15 minutes as a control period, dynamically monitoring the current of monitoring points of the power distribution network by using a current monitoring device, comparing the current with a set normal current reference value, judging whether fault overcurrent exists, outputting an alarm value 1 by each independent comparator when the fault overcurrent exists, otherwise outputting a value 0, and collecting fault overcurrent threshold values of all the monitoring points by controlling a master station to form a current alarm information set.
As shown in fig. 1 and 2, S1For the incoming breaker of substation SUB1, feeder sections 1-7 are formed by incoming breaker S1SUB1 of the substation S2-S7The feeder line section switch is a feeder line automatic switch. Assume that feeder 5 and feeder 7 fail at the same time, and assume two cases: (1) no FTU information distortion exists, and the sequence number S of the section switch is used1、S2、……、S7The current alarm information set formed according to the undirected graph connectivity theory, the power flow transmission mechanism and the comparison method is as follows: [1111111](ii) a (2) Presence of S1、S2Distortion or S of two-bit information1、S2、S3When the three-dimensional information is distorted, current alarm information sets formed according to an undirected graph connectivity theory, a power flow transmission mechanism and a comparison method are respectively as follows: [0011111]And [ 0001111 ]]。
Step 2: when the control master station collects fault overcurrent information, firstly, a nonlinear complementary optimization fault positioning model is established by using a current alarm information set and a power distribution network topological structure based on algebraic relation description, an approximation relation theory and a complementary theory; then, based on an equivalent transformation theory, converting the nonlinear complementary optimization fault positioning model into a nonlinear programming fault positioning model meeting the KKT extreme value condition by using a complementary smooth function; further, establishing a power distribution network fault positioning nonlinear equation set mathematical model based on a fault auxiliary factor method by utilizing Lagrange multipliers, disturbance factors and KKT extreme value conditions; and finally, identifying the position of the feeder line section by adopting an iteration method to obtain the characteristic parameter value of the Lagrange multiplier, thereby realizing defect state evaluation and distortion position identification of the FTU device.
1) Firstly, all possible fault equipment directly related to current alarm information of faults uploaded on a monitoring point, namely cause-and-effect related equipment, are found out by adopting a cause-and-effect related analysis theory, and a cause-and-effect equipment set of each automatic switch is established. As shown in fig. 2, according to undirected graph connectivity theory and power flow transmission mechanism, if a fault overcurrent occurs in a certain automatic switch K and a fault of a short circuit occurs in a feeder line section i is directly related, the feeder line section i is a causal device of the automatic switch K. When the circuit breaker S1When the alarm information of the monitoring point is uploaded, according to network topology connectivity and a power flow transmission mechanism, the monitoring point can know that the short-circuit fault of the feeder lines 1-7 is possibly caused, and the short-circuit fault is caused to the breaker S1A cause and effect device of current alarm information. By the same token, a section switch S can be obtained2～S7The causal equipment of (a), the set of causal equipment established is shown in table 1.
TABLE 1 cause and effect device set of automation switches
2) And constructing a switch function according to the cause and effect equipment and the sequence of each automatic switch, wherein the cause and effect equipment and the corresponding automatic switch alarm information are required to be directly reflected to establish a switch function set. According to the determination method of the cause and effect related devices, the cause and effect devices have the parallel superposition characteristic, namely, the cause and effect devices can be subjected to short circuit faults alone or faults simultaneously, and fault overcurrent of the automatic switch can be caused. The switching function set for establishing the current out-of-limit information based on the algebraic relation description is realized by adopting algebraic operator addition (+) or subtraction (-) and contains the parallel superposition characteristic of the coupling effect of the operation state information of the causal association equipment on the uploaded current alarm information so as to jump out the dependence on the group intelligent optimization algorithm. In algebraic operation, the + operation implies the parallel superposition characteristic, so the invention uses the + operation to replace the logical OR operation to construct the switching function. OmegaiFor automatic switching of iA set of causal equipment is set up,is omegaiNumber of causal equipment. According to the construction method of the switching function, when N automatic monitoring terminals are provided, the mathematical model of the switching function is described based on the algebraic relationCan be expressed as:
wherein Ω is a set of all automation switch causal equipment ΩiIn the set, x (i) is the running state information of the adjacent feeder lines of the automatic switch i, and the value is 0 or 1, i is 1, 2, … …, and N is the number of the automatic switches.
3) And establishing a nonlinear complementary optimization fault positioning model for power distribution network fault positioning through 0-1 complementary constraint conditions by using the indexes of the sum of the accumulated characteristic values of the state of the feeder line in the switching function of the automatic switch and the minimum sum of the squares of the difference between the characteristic values of the alarm state with the time scale uploaded by the FTU terminal based on the quadratic approximation relation theory.
For the alarm information uploaded by the automatic switch i, the approximation mathematical model can be expressed as follows:
the fault information states of the feeder lines have mutual exclusivity, namely the values of the fault states x (i) of the same feeder line cannot be 0 or 1 at the same time, so that auxiliary complementary constraint conditions can be constructed as follows:
X⊥(1-X)＝0。
based on the optimization index and the complementary constraint condition with the minimum residual sum of squares in the continuous space, the established complementary constraint optimization power distribution network fault location model can be expressed as follows:
4) equivalently converting the complementary function with the disturbance factor into a continuous space and satisfying a nonlinear programming power distribution network fault location mathematical model of a KKT condition: first, a complementary function Fischer-Burmeister function is introduced, i.e.And increasing the perturbation factor mu, mathematical models with complementary smooth functions of the perturbation factor are generally used(μ,a,b)∈R3(ii) a Secondly, considering that the fault information state of the feeder has mutual exclusivity, therefore, the fault information state can be obtainedAnd finally, establishing a nonlinear programming power distribution network fault positioning mathematical model as follows:
5) using lagrange multiplier lambdaiAnd establishing a nonlinear equation set model for power distribution network fault location based on the KKT extreme value condition:
c is an acceleration factor, X is a state variable column matrix of each distribution network feeder line, and then fault auxiliary factors of fault locationThe mathematical model is c mu X, phiFBAnd (mu, X,1-X) + c mu lambda is a state evaluation factor for defect identification of the FTU automatic device. Order:a is a coefficient matrix formed by a switching function, the element value is 0 or 1, and the normalized form of the nonlinear equation set model is as follows:
the power distribution network fault location nonlinear equation set model based on the fault auxiliary factor is as follows: h (X, λ, μ) ═ 0.
6) Solving by adopting a Newton-Raphson method with second-order convergence characteristics, wherein the Newton-Raphson method is used for solving the fault location model by iteration, and the mathematical model of the fault location model comprises the following steps:
the solving steps are as follows:
1. selecting acceleration factors c ∈ (0.5,2), (X)(0),λ(0),μ(0))＝1；
2. Judgment | | | H (X)(k),λ(k),μ(k))||2If the value is 0, the algorithm is terminated, otherwise, the step 3 is carried out;
3. calculating [ Delta X ] using the above formula(k),Δλ(k),Δμ(k)]T；
4. Using the above formula to calculate [ X(k+1),λ(k+1),μ(k+1)]TAnd calculate | | | H (X)(k),λ(k),μ(k))||2Step 2.
7) And solving a nonlinear equation set based on a Newton-Raphson method, and obtaining a fault positioning result when the embodiment has no information distortion and information distortion when the algorithm is terminated. If a feeder line section fails, the status characteristic value of the feeder line is defined as 1, otherwise, the status characteristic value is 0, and for the fault location results of the specific examples of fig. 1 and 2 without information distortion and with information distortion, as shown in table 2:
TABLE 2 Fault location simulation results
And step 3: according to the feeder line fault section positioning result completed in the step 2, the SCADA system of the monitoring center sends a brake-off command to the automatic switch close to the fault feeder line section, so that the isolation of the feeder line fault section is realized; and providing a state maintenance implementation plan for operation and maintenance personnel according to the defect state evaluation and the distortion position of the FTU device.
And (3) according to the positioning result of the feeder fault section completed in the step (2), the values of x (5) and x (7) are known to be 1. Namely, the feeder line 5 and the feeder line 7 have short-circuit faults, the SCADA system of the monitoring center sends a brake-separating command to the automatic switches at two ends of the fault feeder line sections 5 and 7, and isolation of the feeder line fault sections 5 and 7 is achieved. When no information is distorted, all values of the Lagrangian multipliers lambda are 0, when information is distorted, the corresponding Lagrangian multipliers lambda of the distortion position are not 0, and the distortion position can be judged by using the position of the lambda value which is not 0. From the above-described fault location method, it can be determined that the distortion position assumed in step 1 is S1、S2Or S1、S2、S3And further provides a state maintenance implementation plan of the FTU device for operation and maintenance personnel.
The examples given above are intended to illustrate the invention and its practical application, without limiting the invention in any way, and any person skilled in the art will, without departing from the scope of the invention, easily conceive of changes and alterations to the above techniques and methods or equivalent examples considered equivalent variations.
Claims (3)
1. A high fault tolerance online fault positioning method for a power distribution network based on fault auxiliary factors is characterized by comprising the following steps:
the method comprises the following steps: dynamically monitoring the current of a monitoring point of the power distribution network by using a current monitoring device in a period of 15 minutes, and judging whether fault overcurrent exists or not by comparing the current with a set normal current reference value; when fault overcurrent exists, each independent comparator outputs an alarm value 1, otherwise, a value 0 is output; collecting fault overcurrent threshold values of all monitoring points through a control main station to form a current alarm information set;
step two: when the control master station collects fault overcurrent information, firstly, a nonlinear complementary optimization fault positioning model is established by using a current alarm information set and a power distribution network topological structure based on algebraic relation description, an approximation relation theory and a complementary theory; then, based on an equivalent transformation theory, converting the nonlinear complementary optimization fault positioning model into a nonlinear programming fault positioning model meeting the KKT extreme value condition by using a complementary smooth function; further, establishing a power distribution network fault location nonlinear equation set mathematical model based on a fault auxiliary factor method by utilizing Lagrange multipliers, disturbance factors and KKT extreme value conditions; finally, identifying the feeder line section position by adopting an iteration method to obtain the characteristic parameter value of a Lagrange multiplier, and realizing defect state evaluation and distortion position identification of the FTU device;
step three: an SCADA system of the monitoring center sends a switching-off command to an adjacent automatic switch of the fault feeder line section, so that the isolation of the feeder line fault section is realized; meanwhile, a state maintenance implementation plan is provided for operation and maintenance personnel according to the defect state evaluation and the distortion position of the FTU device;
the process of establishing the nonlinear complementary optimization fault positioning model based on the algebraic relationship description, the approximate relationship theory and the complementary theory is as follows: finding out all fault equipment directly related to the current alarm information by using a cause and effect correlation analysis theory, and establishing a cause and effect equipment set of the automatic switch; establishing a switching function set based on an undirected graph connectivity theory, a power flow transmission mechanism and algebraic relation description; based on a quadratic approximation relation theory, establishing a nonlinear complementary optimization fault positioning model for power distribution network fault positioning through 0-1 complementary constraint conditions by using the index of the minimum sum of accumulated values of the switch function concentrated feeder state characteristic values of the automatic switch and the square sum of the difference between the characteristic values of the current alarm state with the time scale uploaded by the FTU device;
switch function is constructed by utilizing addition operation instead of logical OR operation, and mathematical model of switch function is described based on algebraic relationCan be expressed as:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>K</mi> <msub> <mi>&Omega;</mi> <mi>i</mi> </msub> </msub> </msubsup> <mi>x</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>N</mi> <mo>;</mo> <mi>x</mi> <mo>&Element;</mo> <mi>&Omega;</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
wherein S isiFor the ith automatic switch, ΩiTo automate the causal equipment set of the switch i,set omega for causal equipmentiThe number of cause and effect equipment in the system is X, the X is a state variable column matrix of each power distribution network feeder line, and omega is omega of a cause and effect equipment set omega of all automatic switchesiThe x (i) is the running state information of the adjacent feeder of the automatic switch i, and the value of the x (i) is 0 or1, i is 1, 2, … … and N, wherein N is the number of the automatic switches;
establishing an approximation mathematical model of a nonlinear complementary optimization fault location model of power distribution network fault location according to 0-1 complementary constraint conditions, wherein the approximation mathematical model is represented as follows:
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>min&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msubsup> <mi>&delta;</mi> <mi>i</mi> <mn>2</mn> </msubsup> <mo>=</mo> <msubsup> <mi>min&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msup> <mrow> <mo>&lsqb;</mo> <msub> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>*</mo> </msubsup> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> <mo>,</mo> </mrow>
wherein,uploading alarm information for the automatic switch i;
according to the mutual exclusivity of the fault information state of the feeder line, constructing an auxiliary complementary constraint condition as follows: x ═ 1-X ═ 0;
based on the optimization index and the complementary constraint condition with the minimum residual sum of squares in the continuous space, establishing a complementary constraint optimization power distribution network fault positioning model as follows:
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>min&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </msubsup> <msup> <mrow> <mo>&lsqb;</mo> <msub> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>-</mo> <msubsup> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>*</mo> </msubsup> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>X</mi> <mo>&perp;</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>X</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>
the method for establishing the nonlinear programming fault positioning model comprises the following steps: introducing a Fischer-Burmeister complementation function:wherein a and b represent complementary variables, satisfy a ⊥ b is 0, increase the perturbation factor mu, and obtain the corrected complementary function expressed asR is a natural number set; replacing 0-1 complementary constraint conditions with the corrected complementary function, and considering fault information of the feeder lineThe information state has mutual exclusivity and is obtainedEstablishing a nonlinear programming fault positioning model meeting the KKT extreme value condition:
the method for establishing the power distribution network fault location nonlinear equation set mathematical model comprises the following steps: using lagrange multiplier lambdaiEstablishing a nonlinear equation set model for power distribution network fault location based on the KKT extreme value condition:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mn>2</mn> <mo>&lsqb;</mo> <msub> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> </msub> <mo>(</mo> <mi>X</mi> <mo>)</mo> <mo>-</mo> <msubsup> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>*</mo> </msubsup> <mo>&rsqb;</mo> <mfrac> <mrow> <mo>&part;</mo> <msub> <mi>I</mi> <msub> <mi>S</mi> <mi>i</mi> </msub> </msub> <mrow> <mo>(</mo> <mi>X</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mo>&part;</mo> <mi>x</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mfrac> <mrow> <mo>&lsqb;</mo> <mn>1</mn> <mo>-</mo> <mn>2</mn> <mi>x</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <msub> <mi>&lambda;</mi> <mi>i</mi> </msub> </mrow> <msqrt> <mrow> <mi>x</mi> <msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>&lsqb;</mo> <mn>1</mn> <mo>-</mo> <mi>x</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mn>4</mn> <msup> <mi>&mu;</mi> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>=</mo> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> <mo>-</mo> <msqrt> <mrow> <mi>x</mi> <msup> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>&lsqb;</mo> <mn>1</mn> <mo>-</mo> <mi>x</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mn>4</mn> <msup> <mi>&mu;</mi> <mn>2</mn> </msup> </mrow> </msqrt> <mo>=</mo> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mi>&mu;</mi> <mo>=</mo> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mi>N</mi> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
c is an acceleration factor, X is a state variable array matrix of each distribution network feeder line, a fault auxiliary factor mathematical model for fault location is c mu X,then phiFB(mu, X,1-X) + c mu lambda is a state evaluation factor for defect identification of the FTU automatic device; order:a is a coefficient matrix formed by a switching function, and the normalized form of a nonlinear equation set model is as follows:
<mrow> <mi>H</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>,</mo> <mi>&lambda;</mi> <mo>,</mo> <mi>&mu;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>A</mi> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>X</mi> </mtd> </mtr> <mtr> <mtd> <mi>&lambda;</mi> </mtd> </mtr> <mtr> <mtd> <mi>&mu;</mi> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msubsup> <mi>I</mi> <mi>s</mi> <mo>*</mo> </msubsup> <mo>+</mo> <mi>c</mi> <mi>&mu;</mi> <mi>Y</mi> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&Phi;</mi> <mrow> <mi>F</mi> <mi>B</mi> </mrow> </msub> <mo>(</mo> <mi>&mu;</mi> <mo>,</mo> <mi>X</mi> <mo>,</mo> <mn>1</mn> <mo>-</mo> <mi>X</mi> <mo>)</mo> <mo>+</mo> <mi>c</mi> <mi>&mu;</mi> <mi>&lambda;</mi> </mtd> </mtr> <mtr> <mtd> <mi>&mu;</mi> </mtd> </mtr> </mtable> </mfenced> <mo>.</mo> </mrow>
2. the method for positioning the high fault tolerance online fault of the power distribution network based on the fault auxiliary factor as claimed in claim 1, wherein the mathematical model formed by the standardization of the nonlinear equation set model for positioning the fault of the power distribution network and converted into the newton-raphson method iterative solution with the second-order convergence characteristic is as follows:
<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msub> <mi>H</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>H</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>H</mi> <mrow> <mn>2</mn> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mrow> <mo>(</mo> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>,</mo> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mo>-</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>X</mi> </msub> <msub> <mi>H</mi> <mn>1</mn> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&lambda;</mi> </msub> <msub> <mi>H</mi> <mn>1</mn> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&mu;</mi> </msub> <msub> <mi>H</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>X</mi> </msub> <msub> <mi>H</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&lambda;</mi> </msub> <msub> <mi>H</mi> <mn>2</mn> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&mu;</mi> </msub> <msub> <mi>H</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>X</mi> </msub> <msub> <mi>H</mi> <mrow> <mn>2</mn> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&lambda;</mi> </msub> <msub> <mi>H</mi> <mrow> <mn>2</mn> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> <mtd> <mrow> <msub> <mo>&dtri;</mo> <mi>&mu;</mi> </msub> <msub> <mi>H</mi> <mrow> <mn>2</mn> <mi>N</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <msup> <mi>&Delta;X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>&Delta;&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>&Delta;&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>2
<mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>&Delta;</mi> <msup> <mi>X</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <mi>&Delta;</mi> <msup> <mi>&lambda;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>&Delta;&mu;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>.</mo> </mrow>
3. the method for positioning the high fault tolerance online fault of the power distribution network based on the fault auxiliary factor as claimed in claim 2, wherein the method for performing the iterative solution of the nonlinear equation set model for fault positioning by the newton-raphson method comprises the following steps:
1. selecting acceleration factors c ∈ (0.5,2), (X)(0),λ(0),μ(0))＝1；
2. Judgment | | | H (X)(k),λ(k),μ(k))||2If the value is 0, the algorithm is terminated, otherwise, the step 3 is carried out;
3. calculating [ Delta X ] by using a mathematical model of Newton-Raphson method iterative solution with second-order convergence characteristics(k),Δλ(k),Δμ(k)]T；
4. Calculating [ X ] by using a mathematical model of Newton-Raphson method iterative solution with second-order convergence characteristics(k+1),λ(k+1),μ(k+1)]TAnd calculate | | | H (X)(k),λ(k),μ(k))||2Step 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610345826.0A CN106054019B (en) | 2016-05-23 | 2016-05-23 | The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610345826.0A CN106054019B (en) | 2016-05-23 | 2016-05-23 | The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106054019A CN106054019A (en) | 2016-10-26 |
| CN106054019B true CN106054019B (en) | 2017-09-01 |
Family
ID=57177425
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610345826.0A Expired - Fee Related CN106054019B (en) | 2016-05-23 | 2016-05-23 | The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106054019B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106526419B (en) * | 2016-11-03 | 2017-09-22 | 河南工程学院 | The online Fault Locating Method of power distribution network fault-tolerance based on prediction alignment technique |
| CN106771875B (en) * | 2016-12-30 | 2020-07-28 | 国网天津市电力公司 | Fault-tolerant fault positioning method for power distribution network |
| CN109470998B (en) * | 2018-12-11 | 2020-06-30 | 江苏电力信息技术有限公司 | Distribution network medium-voltage fault judgment method based on multi-dimensional fault characteristic quantity |
| CN111044847B (en) * | 2019-12-30 | 2022-01-28 | 河南工程学院 | Complex power distribution network fault tolerance online fault positioning method based on probability evaluation |
| CN111413583B (en) * | 2020-03-19 | 2023-08-25 | 国网湖北省电力有限公司荆门供电公司 | Real-time linear integer programming method for positioning power distribution network section |
| CN112271726B (en) * | 2020-10-15 | 2022-12-09 | 北京交通大学 | Power distribution system fault recovery method considering electricity-water-gas coupling relation |
| CN114660419B (en) * | 2022-05-24 | 2022-11-04 | 国网山西省电力公司大同供电公司 | Multi-loop circuit ground fault line selection method based on accurate compensation of arc suppression coil |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102565624A (en) * | 2011-12-21 | 2012-07-11 | 陕西电力科学研究院 | Fault-tolerant fault positioning method for distribution network |
| CN103076540A (en) * | 2012-12-28 | 2013-05-01 | 辽宁省电力有限公司沈阳供电公司 | Fault-tolerance correction method for matrix algorithm fault location result of power distribution network |
| CN104764980A (en) * | 2015-04-22 | 2015-07-08 | 福州大学 | Positioning method for distribution circuit fault section based on BPSO and GA |
| CN105486983A (en) * | 2016-01-03 | 2016-04-13 | 国网江西省电力科学研究院 | Fault-tolerance and distributed power supply contained power distribution network fault locating method |
-
2016
- 2016-05-23 CN CN201610345826.0A patent/CN106054019B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102565624A (en) * | 2011-12-21 | 2012-07-11 | 陕西电力科学研究院 | Fault-tolerant fault positioning method for distribution network |
| CN103076540A (en) * | 2012-12-28 | 2013-05-01 | 辽宁省电力有限公司沈阳供电公司 | Fault-tolerance correction method for matrix algorithm fault location result of power distribution network |
| CN104764980A (en) * | 2015-04-22 | 2015-07-08 | 福州大学 | Positioning method for distribution circuit fault section based on BPSO and GA |
| CN105486983A (en) * | 2016-01-03 | 2016-04-13 | 国网江西省电力科学研究院 | Fault-tolerance and distributed power supply contained power distribution network fault locating method |
Non-Patent Citations (1)
| Title |
|---|
| 潜在等式约束的配电网遗传算法故障定位;郭壮志等;《现代电力》;20070630;第24卷(第3期);24－27页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106054019A (en) | 2016-10-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106054019B (en) | The online Fault Locating Method of power distribution network high fault tolerance based on failure confactor | |
| CN103245881B (en) | Power distribution network fault analyzing method and device based on tidal current distribution characteristics | |
| CN101413981B (en) | Electric power system operation standby reliability testing system | |
| CN107180314B (en) | Operation and maintenance business model modeling method based on primary and secondary system incidence relation | |
| CN103872681A (en) | Online real-time loop closing method based on integration of major network and distribution network | |
| CN109241169A (en) | The multi-source heterogeneous data fusion geo-database integration method of power distribution network operation information | |
| CN110493031A (en) | A kind of substation control system network device state on-line monitoring method | |
| CN103269279A (en) | Master station and substation combined topology identification method | |
| CN113837423A (en) | Power grid operation situation prediction method based on energy internet electric power big data | |
| CN110363334A (en) | Grid-connected grid line loss prediction technique based on Grey Neural Network Model | |
| CN106408016A (en) | Distribution network power outage time automatic identification model construction method | |
| CN112014687A (en) | Layered positioning method for fault section of active power distribution network containing distributed power supply | |
| Zhang et al. | Distributed Architecture of Power Grid Asset Management and Future Research Directions | |
| CN116561638B (en) | Protective pressing plate non-correspondence checking method based on neural network learning and state evaluation | |
| Yang et al. | Technology research on panoramic situation awareness of operation state of smart distribution network | |
| Liu et al. | Multi-source electricity information fusion methods: A survey | |
| Hu et al. | Research on automatic optimization configuration strategy based on knowledge base | |
| Yang et al. | Trend Analysis and Fault Diagnosis of Equipment State Based on Transformer Operation Mechanism Modeling | |
| CN113011047A (en) | Reliability analysis method based on secondary system of intelligent substation of 220kV or below | |
| Domínguez et al. | Analysis of electric power consumption using Self-Organizing Maps. | |
| Hu et al. | Optimization analysis of intelligent substation monitoring information based on improved PSO | |
| Xiang et al. | Application of Artificial Intelligence Technology in Power System Stability Assessment | |
| Zhu et al. | Research on real-time dispatch reliability assessment algorithm of smart grid based on neural network | |
| Zhou et al. | Power Grid transient stability prediction method based on improved CNN under big data background | |
| Liu et al. | Research on Key Technologies of dynamic risk assessment in whole process of mega city power grid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20170901 Termination date: 20180523 |