CN109342885A - A kind of localization method and system of DC distribution net line fault - Google Patents
A kind of localization method and system of DC distribution net line fault Download PDFInfo
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- 238000013178 mathematical model Methods 0.000 claims abstract description 177
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 28
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- 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
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- 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
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The invention discloses a kind of localization method of DC distribution net line fault, the localization method includes: firstly, establishing the mathematical model of faulty line；Then, fitness function is constructed using the mathematical model of the faulty line；Finally, identifying based on genetic algorithm to the fitness function, the abort situation of the faulty line is determined.The present invention eliminates the influence of transition resistance by the mathematical model of built faulty line, improve the accuracy of positioning, and construct fitness function, parameter identification problem is converted by fault-location problem, and then parameter identification is carried out using genetic algorithm, it determines abort situation, avoids as the wrong interference caused by positioning of data acquisition, further improve the accuracy of positioning.
Description
Technical Field
The invention relates to the field of direct-current power distribution networks, in particular to a method and a system for positioning line faults of a direct-current power distribution network.
Background
With the access of a large amount of renewable energy sources to a power grid, a flexible direct-current power distribution network based on a Voltage Source Converter (VSC) becomes a research hotspot, and when a direct-current system is abnormal or fails, the fault process is rapid, the damage is great, and a higher requirement is provided for fault location. The fault location of the direct current system can be mainly divided into a traveling wave method and a non-traveling wave method. The principle of fault location by utilizing traveling waves is that fault location is realized by detecting the transmission time difference of transient traveling waves between a fault point and a measuring point. However, the method has a large manual demand in the implementation process, is difficult to realize automation, has a high requirement on the sampling frequency, and can cause positioning failure when the amplitude of the traveling wave head is limited. Furthermore, the researchers propose a non-traveling wave method to perform fault location, and the currently widely discussed non-traveling wave method mainly includes: the method for realizing fault location by using the single-end electric quantity has the advantages of higher location precision and small calculated quantity, is easily influenced by the regulation effect of an end-to-end converter, and needs to improve the application accuracy in practical engineering. Therefore, how to improve the strong anti-transient resistance capability of the fault location of the dc system, and further improve the accuracy of the fault location becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a method and a system for positioning a line fault of a direct-current power distribution network, which are used for improving the strong anti-transition resistance capability of fault positioning of a direct-current system and further improving the accuracy of fault positioning.
In order to achieve the purpose, the invention provides the following scheme:
a method for positioning line faults of a direct current power distribution network comprises the following steps:
establishing a mathematical model of a fault line;
constructing a fitness function by using the mathematical model of the fault line;
and identifying the fitness function based on a genetic algorithm, and determining the fault position of the fault line.
Optionally, the establishing a mathematical model of the fault line specifically includes:
acquiring a first equivalent circuit of the fault line;
establishing a first start-end mathematical model of the fault line according to the first equivalent circuit, wherein the first start-end mathematical model is represented by the following formula:
establishing a first end mathematical model of the fault line according to the first equivalent circuit, wherein the first end mathematical model is represented by the following formula:
combining the first starting end mathematical model and the first end mathematical model to obtain a first mathematical model of the fault line, wherein the first mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let the resistance of the line per unit length be r,then R is1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
Optionally, constructing a fitness function by using the mathematical model of the fault line specifically includes:
discretizing the first mathematical model of the fault line to obtain a first discrete model, wherein the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, wherein the first fitness function is shown as the following formula:
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) A function value representing the first mathematical model for the kth iteration.
Optionally, the establishing a mathematical model of the fault line specifically includes:
acquiring a second equivalent circuit of the fault line;
establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, wherein the second starting end mathematical model is shown as the following formula:
establishing a second end mathematical model of the faulty line according to the second equivalent circuit, wherein the second end mathematical model is represented by the following formula:
combining the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, wherein the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
Optionally, constructing a fitness function by using the mathematical model of the fault line specifically includes:
discretizing the second mathematical model of the fault line to obtain a second discrete model, wherein the second discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the second discrete model by adopting a GA algorithm to obtain a second fitness function, wherein the second fitness function is shown as the following formula:
in the formula, S' (R)1,R2,L1,L2) As a function of the second fitness measure, fk(R1,R2,L1,L2) A function value representing the second mathematical model for the kth iteration.
Optionally, the identifying the fitness function based on the genetic algorithm to determine the fault location of the fault line specifically includes:
calculating a fitness function value of each discrete position;
selecting a discrete position with a fitness function value larger than a preset first threshold value as a candidate fault position;
performing cross and variation operation on the candidate fault positions to obtain optimized fault positions;
calculating a fitness function value of each optimized fault position, wherein the iteration times are increased by 1;
judging whether the difference value between the position with the maximum fitness function value in the optimized fault positions and the position with the maximum fitness function value obtained in the last iteration is smaller than a second threshold value or not, and obtaining a first judgment result;
if the first judgment result is yes, taking the position with the maximum fitness function value as a fault position;
if the first judgment result is negative, judging whether the iteration times are smaller than the maximum iteration times to obtain a second judgment result;
if the second judgment result is yes, returning to the step of selecting a discrete position with the fitness function value larger than a preset first threshold value as a candidate fault position;
and if the second judgment result is negative, taking the position with the maximum fitness function value as a fault position.
A system for locating a line fault in a dc power distribution network, the system comprising:
the mathematical model establishing module is used for establishing a mathematical model of the fault line;
the fitness function constructing module is used for constructing a fitness function by utilizing the mathematical model of the fault line;
and the fault position determining module is used for identifying the fitness function based on a genetic algorithm and determining the fault position of the fault line.
Optionally, the mathematical model building module specifically includes:
the first equivalent circuit acquisition submodule is used for acquiring a first equivalent circuit of the fault line;
a first start-end mathematical model building submodule, configured to build a first start-end mathematical model of the faulty line according to the first equivalent circuit, where the first start-end mathematical model is expressed as follows:
a first end mathematical model building submodule, configured to build a first end mathematical model of the faulty line according to the first equivalent circuit, where the first end mathematical model is expressed as follows:
a first mathematical model building submodule, configured to combine the first starting-end mathematical model and the first ending-end mathematical model to obtain a first mathematical model of the faulty line, where the first mathematical model is expressed as follows:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
Optionally, the fitness function constructing module specifically includes:
the first mathematical model discretization submodule is used for discretizing the first mathematical model of the fault line to obtain a first discrete model, and the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
the first discrete model optimization submodule is used for optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, and the first fitness function is shown as the following formula;
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) A function value representing the first mathematical model for the kth iteration.
Optionally, the mathematical model building module specifically includes:
the second equivalent circuit acquisition submodule is used for acquiring a second equivalent circuit of the fault line;
the second starting end mathematical model establishing submodule is used for establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, and the second starting end mathematical model is as follows:
a second end mathematical model building submodule, configured to build a second end mathematical model of the faulty line according to the second equivalent circuit, where the second end mathematical model is expressed as follows:
a second mathematical model establishing submodule, configured to combine the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, where the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method for positioning line faults of a direct-current power distribution network, which comprises the following steps: firstly, establishing a mathematical model of a fault line; then, constructing a fitness function by using the mathematical model of the fault line; and finally, identifying the fitness function based on a genetic algorithm, and determining the fault position of the fault line. According to the invention, the influence of the transition resistance is eliminated through the established mathematical model of the fault line, the positioning accuracy is improved, the fitness function is constructed, the fault positioning problem is converted into the parameter identification problem, the parameter identification is further carried out by adopting the genetic algorithm, the fault position is determined, the interference caused by the error data acquisition to the positioning is avoided, and the positioning accuracy is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for locating a line fault in a dc power distribution network according to the present invention;
FIG. 2 is a circuit diagram of a first equivalent circuit provided by the present invention;
FIG. 3 is a circuit diagram of a second equivalent circuit provided by the present invention;
FIG. 4 is a flow chart of the identification of the fitness function based on a genetic algorithm provided by the present invention;
fig. 5 is a structural diagram of a dc distribution network line fault location system provided by the present invention;
FIG. 6 is an experimental system diagram of the positioning method and system of the present invention;
FIG. 7 is a simulation circuit diagram of the positioning method and system set up by the present invention;
fig. 8 is a graph showing the result of a convergence rate experiment of the inter-electrode short-circuit fault location and the single-electrode ground fault location provided by the present invention;
fig. 9 is a diagram illustrating the results of an experiment on the accuracy of the inter-electrode short-circuit fault location and the single-electrode ground fault location provided by the present invention;
fig. 10 is a diagram of experimental results of 18 line fault location by the positioning method and system provided by the present invention.
Detailed Description
The invention aims to provide a method and a system for positioning a line fault of a direct-current power distribution network, which are used for improving the strong anti-transition resistance capability of fault positioning of a direct-current system and further improving the accuracy of fault positioning.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, a method for locating a line fault of a dc power distribution network includes the following steps:
step 101, establishing a mathematical model of the fault line.
When the fault of the fault circuit is an inter-electrode short-circuit fault, the establishing a mathematical model of the fault line in step 101 specifically includes:
when an interelectrode short circuit fault occurs, due to the self-protection function of the IGBT, the converter is locked instantly when the fault occurs, the influence of the current on the alternating current side can be ignored, and the current on the line is completely from the discharge of the capacitor. A first equivalent circuit of the faulty line is obtained as shown in fig. 2.
Establishing a first start-end mathematical model of the fault line according to the first equivalent circuit, wherein the first start-end mathematical model is represented by the following formula:
establishing a first end mathematical model of the fault line according to the first equivalent circuit, wherein the first end mathematical model is represented by the following formula:
combining the first starting end mathematical model and the first end mathematical model to obtain a first mathematical model of the fault line, wherein the first mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
When the fault of the faulty line is a single-pole ground fault, the establishing 101 of the mathematical model of the faulty line specifically includes:
acquiring a second equivalent circuit of the fault line; at this time, the second equivalent circuit is as shown in fig. 3.
Establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, wherein the second starting end mathematical model is shown as the following formula:
establishing a second end mathematical model of the faulty line according to the second equivalent circuit, wherein the second end mathematical model is represented by the following formula:
combining the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, wherein the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
And 102, constructing a fitness function by using the mathematical model of the fault line.
When the fault of the fault circuit is an inter-electrode short circuit fault, in step 102, constructing a fitness function by using the mathematical model of the fault line specifically includes:
discretizing the first mathematical model of the fault line to obtain a first discrete model, wherein the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, wherein the first fitness function is shown as the following formula:
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) A function value representing the first mathematical model for the kth iteration.
When the fault of the fault line is a single-pole ground fault, the constructing a fitness function by using the mathematical model of the fault line in step 102 specifically includes:
discretizing the second mathematical model of the fault line to obtain a second discrete model, wherein the second discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the second discrete model by adopting a GA algorithm to obtain a second fitness function, wherein the second fitness function is shown as the following formula:
in the formula, S' (R)1,R2,L1,L2) As a function of the second fitness measure, fk(R1,R2,L1,L2) A function value representing the second mathematical model for the kth iteration.
And 103, identifying the fitness function based on a genetic algorithm, and determining the fault position of the fault line.
Step 103, identifying the fitness function based on the genetic algorithm, and determining the fault location of the fault line, as shown in fig. 4, specifically including:
sampling voltages at two ends of a capacitor at two sides of a fault line, initializing each parameter, and calculating a fitness function value of each discrete position according to a formula (10) or (7); when the line fault is an inter-electrode short-circuit fault, the parameter in the formula (10) is obtained by using a formula (8), and when the line fault is a unipolar ground fault, the parameter in the formula (7) is obtained by using a formula (9).
Selecting a discrete position with a fitness function value larger than a preset first threshold value as a candidate fault position; the method specifically comprises the steps of sorting and selecting the discrete positions according to the size of the fitness function value.
Performing cross and variation operation on the candidate fault positions to obtain optimized fault positions;
calculating a fitness function value of each optimized fault position, wherein the iteration times are increased by 1;
judging whether the difference value between the position with the maximum fitness function value in the optimized fault positions and the position with the maximum fitness function value obtained in the last iteration is smaller than a second threshold value or not, and obtaining a first judgment result;
if the first judgment result is yes, taking the position with the maximum fitness function value as a fault position;
if the first judgment result is negative, judging whether the iteration times are smaller than the maximum iteration times to obtain a second judgment result;
if the second judgment result is yes, returning to the step of selecting a discrete position with the fitness function value larger than a preset first threshold value as a candidate fault position;
and if the second judgment result is negative, taking the position with the maximum fitness function value as a fault position.
As shown in fig. 5, the present invention further provides a system for locating a line fault of a dc power distribution network, where the system includes:
and a mathematical model establishing module 501, configured to establish a mathematical model of the faulty line.
When the fault of the fault circuit is an inter-electrode short circuit fault, the mathematical model establishing module 501 specifically includes:
the first equivalent circuit acquisition submodule is used for acquiring a first equivalent circuit of the fault line;
a first start-end mathematical model building submodule, configured to build a first start-end mathematical model of the faulty line according to the first equivalent circuit, where the first start-end mathematical model is expressed as follows:
a first end mathematical model building submodule, configured to build a first end mathematical model of the faulty line according to the first equivalent circuit, where the first end mathematical model is expressed as follows:
a first mathematical model building submodule, configured to combine the first starting-end mathematical model and the first ending-end mathematical model to obtain a first mathematical model of the faulty line, where the first mathematical model is expressed as follows:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
When the fault of the fault line is a single-pole ground fault, the mathematical model establishing module 401 specifically includes:
the second equivalent circuit acquisition submodule is used for acquiring a second equivalent circuit of the fault line;
the second starting end mathematical model establishing submodule is used for establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, and the second starting end mathematical model is as follows:
a second end mathematical model building submodule, configured to build a second end mathematical model of the faulty line according to the second equivalent circuit, where the second end mathematical model is expressed as follows:
a second mathematical model establishing submodule, configured to combine the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, where the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2Respectively near the end of the faulty lineEquivalent current value, resistance value, inductance value, voltage value at two ends of capacitance, RfIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
A fitness function constructing module 502, configured to construct a fitness function using the mathematical model of the fault line; when the fault of the fault circuit is an inter-electrode short circuit fault, the fitness function constructing module 502 specifically includes:
the first mathematical model discretization submodule is used for discretizing the first mathematical model of the fault line to obtain a first discrete model, and the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
the first discrete model optimization submodule is used for optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, and the first fitness function is shown as the following formula;
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) Represents the kth iterationA function value of a mathematical model.
And a fault location determining module 503, configured to identify the fitness function based on a genetic algorithm, and determine a fault location of the faulty line.
Example 1:
the invention builds a 6-end ring network HILS (semi-physical simulation) experimental system as shown in figure 6. The system comprises an RT-LAB real-time simulator with the model of OP5600, a DSP arithmetic unit with the model of TMS320F28335, an upper computer and the like, a built simulation line is shown in figure 7, and a fault is arranged on a line between G-VSC and W-VSC.
The G-VSC adopts a double closed-loop control mode, the outer ring adopts voltage droop control, and the inner ring adopts a constant direct current control mode. The W-VSC operates in a maximum power control mode and in some cases requires reduced power operation. The energy storage module is in a charging or discharging state. Meanwhile, in order to ensure the power balance and stable operation of the system, the energy storage unit plays the role of a balance node of the whole direct current power distribution network and is in an island operation mode if necessary. The solar cell panel is connected into the direct current distribution network through the DC-DC converter, the outer ring adopts a maximum power tracking control mode, and the inner ring adopts a constant voltage control mode. The voltage of a direct current bus is 500V, the capacitance of a direct current side of the converter is 2mF, the resistance value of a unit length line is 0.0139 omega/km, the inductance value of the unit length line is 0.159 omega/km, the length of the line is 10km, and the sampling frequency is 25 us.
Initializing population parameters of a genetic algorithm: the number of the parameters to be identified is 4, so that the search dimension is 4, and the parameter value range is the product of the unit long line value and the full field length of the line; the size of the population is generally 5-10 times of the dimensionality, the size of the population is 20 because fault positioning puts higher requirements on algorithm speed, and 22 points after a fault occurs need to be taken when voltage is sampled. The cross probability is 0.6, the mutation probability is 0.01, the substitution ditches are 0.95, and the maximum genetic generation number is 30 times to ensure the protection rapidity. The initial population determination process is as follows: first, 22 sets of voltage values after a fault occurred were sampled. Secondly, as can be seen from equations (9) and (10), the number of 22 sets of voltage values corresponding to the effective equations is 20 sets. 4 unknowns need to be solved, so C204 initial populations can be calculated. Finally, 20 groups of parameter values are randomly selected from the initial population.
Example 2:
and (3) performing parameter identification by using a genetic algorithm, and respectively simulating the monopolar grounding fault condition and the bipolar short-circuit fault condition. As the number of iterations increases in the sequence,the convergence of (2) is shown in FIG. 8, which illustrates a case where the transition resistance is 10. omega. and the actual positioning distance is 5 km. It can be known that, in the 20 th iteration, when the inter-electrode short-circuit fault and the single-electrode ground fault are both solved, the 1/S value is converged, the convergence is good, and the convergence rate can be ensured.
Example 3:
for determining the accuracy of parameter identification, curve fitting needs to be performed on the formula (8) and the formula (9), and the curve fitting needs to be written into functional forms such as the formulas (11) to (12). The final recognition result is substituted into a formula, and a case where the transition resistance is 10 Ω and the actual positioning distance is 5km in the case of a bipolar short circuit is taken as an example. The fitting result obtained by using the formula (11) is shown in fig. 9, where the abscissa represents time and the ordinate represents the value of the function y.
The simulation data and the fitting curve are basically superposed, which shows that the identification result is good.
Example 4:
the faults were set for 18 cases, respectively, and curve fitting was performed according to (11) to (12), resulting in fig. 10. The abscissa represents the value of 18 cases and the ordinate represents the value of y for an average of 20 moments in each case. The formula for y is shown as (13). The value of j depends on the fault type, and when the fault type is an inter-electrode short-circuit fault, j is 1; otherwise, j is 2.
As can be seen from fig. 10, the average y value at each time corresponding to each fault is [ -0.2-0.2], and the error is very small.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method for positioning line faults of a direct-current power distribution network, which comprises the following steps: firstly, establishing a mathematical model of a fault line; then, constructing a fitness function by using the mathematical model of the fault line; and finally, identifying the fitness function based on a genetic algorithm, and determining the fault position of the fault line. According to the invention, the influence of the transition resistance is eliminated through the established mathematical model of the fault line, the positioning accuracy is improved, the fitness function is constructed, the fault positioning problem is converted into the parameter identification problem, the parameter identification is further carried out by adopting the genetic algorithm, the fault position is determined, the interference caused by the error data acquisition to the positioning is avoided, and the positioning accuracy is further improved. According to the embodiment provided by the invention, the positioning precision provided by the invention is very high, the error is below 1%, the interference of sampling information error in the sampling process is eliminated, and the robustness is very strong.
The invention can reduce the influence caused by the change of the production structure and better track the existing products in the market. By the method, agricultural products entering the index calculation range every year in the index calculation are newly added or eliminated according to the macroscopic condition of the agricultural products in the previous year, and the method can better solve the problem of difference caused by annual production and consumption structure change of various agricultural products.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the implementation manner of the present invention are explained by applying specific examples, the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof, the described embodiments are only a part of the embodiments of the present invention, not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.
Claims (10)
1. A method for positioning a line fault of a direct current power distribution network is characterized by comprising the following steps:
establishing a mathematical model of a fault line;
constructing a fitness function by using the mathematical model of the fault line;
and identifying the fitness function based on a genetic algorithm, and determining the fault position of the fault line.
2. The method according to claim 1, wherein the establishing of the mathematical model of the fault line specifically comprises:
acquiring a first equivalent circuit of the fault line;
establishing a first start-end mathematical model of the fault line according to the first equivalent circuit, wherein the first start-end mathematical model is represented by the following formula:
establishing a first end mathematical model of the fault line according to the first equivalent circuit, wherein the first end mathematical model is represented by the following formula:
combining the first starting end mathematical model and the first end mathematical model to obtain a first mathematical model of the fault line, wherein the first mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Being near the endsLength of line, D ═ D1+D2And D is the total length of the line.
3. The method according to claim 2, wherein the constructing the fitness function using the mathematical model of the fault line specifically comprises:
discretizing the first mathematical model of the fault line to obtain a first discrete model, wherein the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, wherein the first fitness function is shown as the following formula:
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) A function value representing the first mathematical model for the kth iteration.
4. The method according to claim 1, wherein the establishing of the mathematical model of the fault line specifically comprises:
acquiring a second equivalent circuit of the fault line;
establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, wherein the second starting end mathematical model is shown as the following formula:
establishing a second end mathematical model of the faulty line according to the second equivalent circuit, wherein the second end mathematical model is represented by the following formula:
combining the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, wherein the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
5. The method according to claim 4, wherein the constructing the fitness function using the mathematical model of the fault line specifically comprises:
discretizing the second mathematical model of the fault line to obtain a second discrete model, wherein the second discrete model is represented by the following formula:
wherein,k represents the number of iterations;
and optimizing the second discrete model by adopting a GA algorithm to obtain a second fitness function, wherein the second fitness function is shown as the following formula:
in the formula, S' (R)1,R2,L1,L2) As a function of the second fitness measure, fk(R1,R2,L1,L2) A function value representing the second mathematical model for the kth iteration.
6. The method according to claim 3 or 5, wherein the identifying the fitness function based on the genetic algorithm to determine the fault location of the fault line specifically comprises:
calculating a fitness function value of each discrete position;
selecting a discrete position with a fitness function value larger than a preset first threshold value as a candidate fault position;
performing cross and variation operation on the candidate fault positions to obtain optimized fault positions;
calculating a fitness function value of each optimized fault position, wherein the iteration times are increased by 1;
judging whether the difference value between the position with the maximum fitness function value in the optimized fault positions and the position with the maximum fitness function value obtained in the last iteration is smaller than a second threshold value or not, and obtaining a first judgment result;
if the first judgment result is yes, taking the position with the maximum fitness function value as a fault position;
if the first judgment result is negative, judging whether the iteration times are smaller than the maximum iteration times to obtain a second judgment result;
if the second judgment result is yes, returning to the step of selecting a discrete position with the fitness function value larger than a preset first threshold value as a candidate fault position;
and if the second judgment result is negative, taking the position with the maximum fitness function value as a fault position.
7. A system for locating line faults in a dc power distribution network, the system comprising:
the mathematical model establishing module is used for establishing a mathematical model of the fault line;
the fitness function constructing module is used for constructing a fitness function by utilizing the mathematical model of the fault line;
and the fault position determining module is used for identifying the fitness function based on a genetic algorithm and determining the fault position of the fault line.
8. The system according to claim 7, wherein the mathematical model building module specifically includes:
the first equivalent circuit acquisition submodule is used for acquiring a first equivalent circuit of the fault line;
a first start-end mathematical model building submodule, configured to build a first start-end mathematical model of the faulty line according to the first equivalent circuit, where the first start-end mathematical model is expressed as follows:
a first end mathematical model building submodule, configured to build a first end mathematical model of the faulty line according to the first equivalent circuit, where the first end mathematical model is expressed as follows:
a first mathematical model building submodule, configured to combine the first starting-end mathematical model and the first ending-end mathematical model to obtain a first mathematical model of the faulty line, where the first mathematical model is expressed as follows:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
9. The system according to claim 8, wherein the fitness function constructing module specifically includes:
the first mathematical model discretization submodule is used for discretizing the first mathematical model of the fault line to obtain a first discrete model, and the first discrete model is represented by the following formula:
wherein,k represents the number of iterations;
the first discrete model optimization submodule is used for optimizing the first discrete model by adopting a GA algorithm to obtain a first fitness function, and the first fitness function is shown as the following formula;
in the formula, S (R)1,R2,L1,L2) Is a first fitness function, fk(R1,R2,L1,L2) A function value representing the first mathematical model for the kth iteration.
10. The system according to claim 7, wherein the mathematical model building module specifically includes:
the second equivalent circuit acquisition submodule is used for acquiring a second equivalent circuit of the fault line;
the second starting end mathematical model establishing submodule is used for establishing a second starting end mathematical model of the fault line according to the second equivalent circuit, and the second starting end mathematical model is as follows:
a second end mathematical model building submodule, configured to build a second end mathematical model of the faulty line according to the second equivalent circuit, where the second end mathematical model is expressed as follows:
a second mathematical model establishing submodule, configured to combine the second starting end mathematical model and the second end mathematical model to obtain a second mathematical model of the fault line, where the second mathematical model is represented by the following formula:
wherein, I1，R1，L1，C1，Vdc1Respectively an equivalent current value, a resistance value, an inductance value, a capacitance value and a voltage value at two ends of a capacitor which are close to the starting end of the fault line; i is2，R2，L2，C2，Vdc2The equivalent current value, the resistance value, the inductance value, the voltage value at two ends of the capacitance, R, close to the tail end of the fault linefIs a transition resistance value; let R be the line resistance per unit length1＝r×D1，R2＝r×D2Let the line inductance per unit length be L, then L1＝l×D1，L2＝l×D2，D1For the length of the line near the start, D1Length of line near end, D ═ D1+D2And D is the total length of the line.
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