CN114048859A - Power distribution network fault positioning method based on quantum annealing algorithm - Google Patents
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Abstract
The invention relates to a power distribution network fault positioning method based on a quantum annealing algorithm, which comprises the following steps of: setting an initialization parameter for positioning the power distribution network fault based on a quantum annealing algorithm; assuming fault information and constructing an evaluation function of fault positioning; performing iterative calculation by combining with Metropolis criterion according to an evaluation function of fault positioning until an iteration termination condition is met; and (4) executing the temperature-reducing operation and outputting a global optimal solution, namely outputting to obtain a power distribution network fault section. Compared with the prior art, the quantum annealing algorithm is introduced into the fault location method of the power distribution network containing a large amount of DGs, the optimal solution better than that of the simulated annealing algorithm can be obtained at a higher probability, the accuracy and the applicability of fault location diagnosis of the power distribution network are effectively improved, the fault location of the power distribution network containing DG multi-branch feeder lines can be accurately carried out, and the method has higher theoretical value and engineering significance.
Description
Technical Field
The invention relates to the technical field of power distribution network fault location, in particular to a power distribution network fault location method based on a quantum annealing algorithm.
Background
The distribution network responsible for distributing the electrical energy is an important component of the electrical power system and is the last link towards the users. The stable operation of distribution network is closely related with the benefit of consumer, however under the influence of various condition factors, some faults that influence normal operation inevitably appear in the electric wire netting, according to relevant statistics, more than 90% of power failure accidents are all relevant with the distribution network, and short circuit fault is the most main distribution network fault.
Once a power distribution network fault occurs, the fault position is quickly and accurately positioned so as to remove the fault as soon as possible and recover power supply, which has very important significance for keeping the normal operation of a power system. And as Distributed Generation (DG) is connected to the distribution network in a large amount, the initiative of the distribution network is increased, which causes great difference between the fault characteristics of the distribution network and the traditional distribution network, and makes the traditional fault location and identification difficult to ensure reliability and sensitivity. At present, various intelligent algorithms such as an artificial neural network, a particle swarm optimization algorithm, a genetic algorithm, an immune algorithm and the like are applied to fault location of a power distribution network, and a good effect is achieved. In the intelligent algorithm direct fault positioning method, the multi-population genetic algorithm has strong searching capability on a power distribution network containing DG multi-branch feeder lines; however, the cross variation has certain blindness, which leads to that the algorithm is easy to fall into local optimum when iterative optimization is performed, and the solutions corresponding to the minimum value of the fitness function are mistakenly identified as fault sections, so that the fault sections cannot be accurately positioned.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a power distribution network fault positioning method based on a quantum annealing algorithm so as to accurately position faults of a power distribution network containing DG multi-branch feeder lines.
The purpose of the invention can be realized by the following technical scheme: a power distribution network fault positioning method based on a quantum annealing algorithm comprises the following steps:
s1, setting initialization parameters for power distribution network fault positioning based on a quantum annealing algorithm;
s2, assuming fault information and constructing an evaluation function of fault positioning;
s3, performing iterative computation by combining Metropolics criterion according to the evaluation function of fault positioning until an iteration termination condition is met;
and S4, performing a temperature-reducing operation and outputting a global optimal solution, namely outputting to obtain a power distribution network fault section.
Further, the initialization parameters set in step S1 include an initial value of annealing temperature, an initial value of magnetic field strength, a maximum number of inner loop iterations, and a maximum number of outer loop iterations.
Further, the initial value of the annealing temperature in the step S1 is set as:
T0∈(0,1K]
wherein, T0Is the initial value of the annealing temperature;
the initial value of the magnetic field strength is set as:
Γ0∈(0,3T0]
wherein, gamma is0Is the initial value of the magnetic field intensity.
Further, the step S2 specifically includes the following steps:
s21, randomly assuming fault information of the corresponding sections in the distribution network node sections;
s22, constructing an evaluation function of fault location according to the difference value between the assumed fault information and the actual FTU (Feeder Terminal Unit) state value.
Further, the evaluation function of fault location is specifically:
wherein, Fit(SB) For each solution in the solution group, SBThe switching state of each measurement and control point in the power distribution network, I is the actual state of each measurement and control point FTU,
the expected state of each measurement and control point in the power distribution network is a function of the state of each device.
Further, the step S3 specifically includes the following steps:
s31, calculating a difference value between the assumed state value of the power distribution network fault and the actual FTU state value, if the difference value is less than 0, executing a step S33, otherwise executing a step S32;
s32, judging whether the set iteration conditions are met, if so, executing a step S33, otherwise, returning to execute the step S2;
and S33, judging whether the current internal loop iteration number is larger than the set maximum internal loop iteration number, if so, executing the step S4, otherwise, returning to the step S1.
Further, the iteration condition set in step S32 is specifically:
exp(ΔH)＜random(0,1)
wherein Δ H is the difference between the current quantum energy and the previous quantum energy.
Further, the step S4 is to perform the annealing operation by decreasing the magnetic field and the temperature.
Further, during the temperature-reducing operation performed in step S4, the magnetic field strength is continuously reduced according to the following formula:
Γ＝Γ0-Γ0/M
wherein gamma is the current magnetic field intensity, and M is the current external circulation iteration times;
the temperature is constantly decreasing according to the following formula:
T＝T0-T0/M
wherein T is the current annealing temperature.
Further, when the global optimum solution is output in step S4, the corresponding magnetic field strength is 0.
Compared with the prior art, the method introduces the quantum annealing algorithm into the fault location method of the power distribution network containing a large amount of DGs, and can solve the problem of easy falling into local optimization by assuming fault information and constructing the evaluation function of fault location, so that the global optimal solution is accurately and quickly searched, the reliability and the sensitivity of power distribution network location are favorably improved for the power distribution network accessed with a large amount of DGs, and the fault section of the power distribution network is accurately obtained.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram comparing the working principles of a conventional simulated annealing algorithm and a quantum annealing algorithm;
FIG. 3 is a schematic diagram of an embodiment of an application process;
fig. 4 is a schematic diagram of a fault section positioning process of the power distribution network in the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a method for locating a fault of a power distribution network based on a quantum annealing algorithm includes the following steps:
s1, setting initialization parameters for power distribution network fault location based on a quantum annealing algorithm, where the set initialization parameters include an initial annealing temperature value, an initial magnetic field strength value, a maximum number of inner loop iterations, and a maximum number of outer loop iterations, and in this embodiment, the initial annealing temperature value is set as:
T0∈(0,1K]
in the formula, T0Is the initial value of the annealing temperature;
the initial value of the magnetic field strength is set as:
Γ0∈(0,3T0]
in the formula, gamma0Setting the magnetic field intensity as an initial value;
s2, assuming fault information, and constructing an evaluation function of fault location, specifically:
firstly, randomly assuming fault information of a corresponding section in a node section of a power distribution network;
then, according to the difference value between the assumed fault information and the actual FTU (Feeder Terminal) state value, an evaluation function of fault location is constructed:
wherein, Fit(SB) For each solution in the solution group, SBThe switching state of each measurement and control point in the power distribution network, I is the actual state of each measurement and control point FTU,
the expected state of each measurement and control point in the power distribution network is a function of the state of each device;
s3, according to the evaluation function of fault location, combining with Metropolis criterion to carry out iterative computation until the iteration termination condition is met, specifically:
s31, calculating a difference value between the assumed state value of the power distribution network fault and the actual FTU state value, if the difference value is less than 0, executing a step S33, otherwise executing a step S32;
s32, judging whether the set iteration condition is met:
exp(ΔH)＜random(0,1)
wherein, Δ H is the difference between the current quantum energy and the previous quantum energy;
if yes, executing step S33, otherwise, returning to execute step S2;
s33, judging whether the current internal loop iteration number is larger than the set maximum internal loop iteration number, if so, executing a step S4, otherwise, returning to the step S1;
s4, executing the temperature reduction operation and outputting a global optimal solution, namely outputting to obtain a fault section of the power distribution network, specifically, performing the temperature reduction operation by continuously reducing the magnetic field and the temperature, wherein in the process of executing the temperature reduction operation, the magnetic field intensity is continuously reduced according to the following formula:
Γ＝Γ0-Γ0/M
wherein gamma is the current magnetic field intensity, and M is the current external circulation iteration times;
the temperature is constantly decreasing according to the following formula:
T＝T0-T0/M
wherein T is the current annealing temperature;
when the output obtains the global optimal solution, the corresponding magnetic field intensity is 0.
In summary, the fault location method is used for fault location of a power distribution network containing a large number of distributed power sources, the existing research is the research of fault location based on a simulated annealing algorithm, and the quantum annealing algorithm is a new quantum optimization algorithm evolved on the basis of the classical simulated annealing algorithm. The basic idea is to construct an optimization algorithm using quantum fluctuations that give the quantum the ability to penetrate a barrier higher than its own energy, i.e. quantum tunneling effect. Unlike the classical simulated annealing algorithm which utilizes thermal fluctuation to search the optimal solution of the problem, the quantum annealing algorithm utilizes the quantum tunneling effect to enable the algorithm to get rid of local optimization and approach global optimization with higher probability.
Therefore, the invention provides the power distribution network fault positioning method based on the quantum annealing algorithm, so that the defect that the traditional algorithm and the intelligent algorithm are easy to fall into local optimization during optimization is avoided, and the limitation of fault addressing is broken through.
The construction model of the quantum annealing algorithm generally consists of two parts: one part is called quantum potential energy, and the objective function to be optimized is mapped into a potential field applied to the quantum system, namely the objective function is regarded as the potential energy part of Hamiltonian of the quantum system; the other part is called quantum kinetic energy, usually, an amplitude-controllable kinetic energy item is introduced to control quantum transition, the target function is globally optimized by gradually reducing the kinetic energy of a quantum system, the process is equal to the process that the quantum system particles converge on the system ground state, and the evolution expression of the quantum system is as follows
H(t)＝Hpot(t)+Hkin(t) (1)
Quantum Hamilton function: hq＝Hpot+HkinI.e. the evaluation Function (Cost Function) in the quantum annealing algorithm, wherein HpotReferred to as potential energy, i.e. the merit function in simulated annealing, HkinFor kinetic energy, the kinetic energy of the system is reduced in an iterative manner through the gradual reduction of an external transverse field, and a target function finally jumps out of local optimum by utilizing a quantum tunneling effect, so that a global optimum solution is achieved. The quantum annealing algorithm has a quantum tunneling effect which is the most essential difference for making the quantum annealing algorithm superior to the simulated annealing algorithm.
As shown in fig. 2, when the simulated annealing algorithm is to reach the global minimum point P' from the local minimum point P, it can only be implemented by crossing the potential barrier; and the quantum annealing algorithm can directly reach P' from the local minimum point P by virtue of the quantum tunneling effect. It is because of the quantum tunneling effect that quantum annealing algorithms have better performance than simulated annealing algorithms in some problems.
As shown in table 1, the present invention mainly includes the following processes when applied specifically:
TABLE 1
As shown in fig. 3, when a fault occurs at a certain point in a power distribution network line, Feeder Terminal Units (FTUs) on both sides of the fault point detect fault current information and report the fault current information to a master station (or a slave station).
The evaluation function for fault localization is as follows:
Fit(SB) The fitness corresponding to each solution in the solution group; sBThe state of each measurement and control point in the power distribution network is shown (1 is a fault in the on-off state, and 0 is a normal in the on-off state); i is the actual state of each measurement and control point FTU (1 'means that the jth measurement and control point has fault current flowing, and 0' means that the jth measurement and control point has no fault current flowing);
the expected state of each measurement and control point in the power distribution network is a function of the state of each device. The state of the measurement and control point deduced from the actual state of the fault equipment should be the smallest difference with the actual state of the measurement and control point. Therefore, finding the optimal solution group is to find the solution group that minimizes equation (2).
Fig. 4 shows a fault section locating process in an embodiment, which includes: (1) establishing a mathematical model for fault location; (2) realizing effective mapping of a quantum annealing algorithm and a power distribution network objective function (switching function); (3) the fault location of the power distribution network is realized through the mixed calculation of classical calculation and quantum calculation.
With the massive access of Distributed Generation (DG), the reliability and sensitivity are difficult to guarantee by the traditional power distribution network fault location method. Aiming at the problem, the power distribution network fault positioning method based on the quantum annealing algorithm is introduced, so that the method is more suitable for fault positioning of 0-1 discrete variables, and the applicability of power distribution network fault positioning can be effectively improved;
the invention considers that the existing fault positioning method based on the intelligent algorithm is easy to fall into local optimum when iterative optimization is carried out, the quantum annealing algorithm can overcome the defect that the traditional search algorithm is easy to fall into local extreme points by virtue of the unique quantum tunneling effect, has the capability of penetrating a potential barrier higher than the energy of the quantum annealing algorithm by virtue of quantum fluctuation, and is expected to approach or even reach the global optimum solution with a higher probability.
Claims (10)
1. A power distribution network fault positioning method based on a quantum annealing algorithm is characterized by comprising the following steps:
s1, setting initialization parameters for power distribution network fault positioning based on a quantum annealing algorithm;
s2, assuming fault information and constructing an evaluation function of fault positioning;
s3, performing iterative computation by combining Metropolics criterion according to the evaluation function of fault positioning until an iteration termination condition is met;
and S4, performing a temperature-reducing operation and outputting a global optimal solution, namely outputting to obtain a power distribution network fault section.
2. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm, according to claim 1, wherein the initialization parameters set in the step S1 include an initial annealing temperature value, an initial magnetic field strength value, a maximum number of inner loop iterations, and a maximum number of outer loop iterations.
3. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 2, wherein the initial value of the annealing temperature in the step S1 is set as:
T0∈(0,1K]
wherein, T0Is the initial value of the annealing temperature;
the initial value of the magnetic field strength is set as:
Γ0∈(0,3T0]
wherein, gamma is0Is the initial value of the magnetic field intensity.
4. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 1, wherein the step S2 specifically includes the following steps:
s21, randomly assuming fault information of the corresponding sections in the distribution network node sections;
and S22, constructing an evaluation function of fault positioning according to the difference value between the assumed fault information and the actual FTU state value.
5. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 4, wherein the evaluation function of the fault positioning is specifically as follows:
wherein, Fit(SB) For each solution in the solution group, SBThe switching state of each measurement and control point in the power distribution network, I is the actual state of each measurement and control point FTU,
the expected state of each measurement and control point in the power distribution network is a function of the state of each device.
6. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 5, wherein the step S3 specifically comprises the following steps:
s31, calculating a difference value between the assumed state value of the power distribution network fault and the actual FTU state value, if the difference value is less than 0, executing a step S33, otherwise executing a step S32;
s32, judging whether the set iteration conditions are met, if so, executing a step S33, otherwise, returning to execute the step S2;
and S33, judging whether the current internal loop iteration number is larger than the set maximum internal loop iteration number, if so, executing the step S4, otherwise, returning to the step S1.
7. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 6, wherein the iteration condition set in the step S32 is specifically:
exp(ΔH)＜random(0,1)
wherein Δ H is the difference between the current quantum energy and the previous quantum energy.
8. The method for locating the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 3, wherein the step S4 is implemented by decreasing the magnetic field and the temperature continuously to perform the annealing operation.
9. The method for locating the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 8, wherein during the annealing operation performed in the step S4, the magnetic field strength is continuously decreased according to the following formula:
Γ＝Γ0-Γ0/M
wherein gamma is the current magnetic field intensity, and M is the current external circulation iteration times;
the temperature is constantly decreasing according to the following formula:
T＝T0-T0/M
wherein T is the current annealing temperature.
10. The method for positioning the fault of the power distribution network based on the quantum annealing algorithm as claimed in claim 9, wherein when the global optimal solution is output in step S4, the corresponding magnetic field strength is 0.
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