CN113836748A - Geomagnetic disturbance-oriented disaster prevention management method and device, storage medium and equipment - Google Patents
Geomagnetic disturbance-oriented disaster prevention management method and device, storage medium and equipment Download PDFInfo
- Publication number
- CN113836748A CN113836748A CN202111210979.1A CN202111210979A CN113836748A CN 113836748 A CN113836748 A CN 113836748A CN 202111210979 A CN202111210979 A CN 202111210979A CN 113836748 A CN113836748 A CN 113836748A
- Authority
- CN
- China
- Prior art keywords
- geomagnetic
- power system
- bias
- node
- treatment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000002265 prevention Effects 0.000 title claims abstract description 34
- 238000007726 management method Methods 0.000 title abstract description 15
- 230000006698 induction Effects 0.000 claims abstract description 107
- 238000000034 method Methods 0.000 claims abstract description 67
- 230000008569 process Effects 0.000 claims abstract description 34
- 238000005315 distribution function Methods 0.000 claims abstract description 32
- 238000013178 mathematical model Methods 0.000 claims abstract description 32
- 238000004364 calculation method Methods 0.000 claims abstract description 30
- 238000004088 simulation Methods 0.000 claims abstract description 7
- 230000006870 function Effects 0.000 claims description 56
- 238000005457 optimization Methods 0.000 claims description 37
- 230000008859 change Effects 0.000 claims description 19
- 238000005070 sampling Methods 0.000 claims description 19
- 230000005684 electric field Effects 0.000 claims description 18
- 238000004458 analytical method Methods 0.000 claims description 2
- 230000005389 magnetism Effects 0.000 claims 2
- 230000007935 neutral effect Effects 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000005358 geomagnetic field Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Hardware Design (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Computational Biology (AREA)
- Algebra (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application discloses a geomagnetic disturbance-oriented disaster prevention management method, a geomagnetic disturbance-oriented disaster prevention management device, a geomagnetic disturbance-oriented disaster prevention management storage medium and geomagnetic induction current samples are calculated according to line induction voltages of all nodes and obtained through simulation based on geomagnetic conductivity data. Based on the respective geomagnetic induction current samples, a probability distribution function of the geomagnetic induction current is determined. And analyzing the bias data to obtain the bias current of the node. And substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current. And substituting each actual geomagnetic induction current into the magnetic biasing treatment mathematical model to obtain a calculation result output by the magnetic biasing treatment mathematical model. And adjusting the states of all the treatment devices in the power system to ensure that the total number of the treatment devices in the open state is equal to the optimal solution shown by the calculation result, thereby avoiding the excessive treatment devices from participating in the treatment process under the condition of ensuring that the number of the treatment devices participating in the treatment process of the power system meets the treatment requirement.
Description
Technical Field
The present application relates to the field of power, and in particular, to a method, an apparatus, a storage medium, and a device for controlling geomagnetic disturbance.
Background
The increase of the transformer reactive loss caused by Geomagnetically Induced Current (GIC) will destroy the reactive balance of the system, causing the transformer damage and even major power failure accidents. The global synchronism of the geomagnetic storm enables transformer GIC reactive loss to have full-network mass-distribution performance, and in addition, the uncertainty of the geomagnetic field intensity in the geomagnetic storm enables the geomagnetic storm to have randomness. In real life, the running state of the power system is gradually close to the stability limit, and the reactive fluctuation caused by the geomagnetic storm with strong randomness can bring large impact to the power system, so that large-scale accidents of the power system are induced.
At present, researchers put forward three main treatment methods of installing a resistor, a capacitor and a resistor capacitor on a neutral point of a transformer, wherein the GIC possibly threatens the safe and stable operation of a system when a geomagnetic storm occurs. However, the configuration of these governance devices is often based on the maximum value of the GIC in a certain geomagnetic storm event, and the fluctuation of the GIC is not considered, which may cause the problem of too strong pertinence of the governance devices, and the problems of "over-governance" and "under-governance" are likely to occur, and the stable requirement of the power system cannot be met.
Disclosure of Invention
The application provides a geomagnetic disturbance-oriented disaster prevention treatment method, a geomagnetic disturbance-oriented disaster prevention treatment device, a geomagnetic disturbance-oriented disaster prevention treatment storage medium and geomagnetic disturbance-oriented disaster prevention treatment equipment, and aims to avoid excessive treatment devices from participating in a treatment process under the condition that the number of the treatment devices participating in the treatment process of a power system meets treatment requirements.
In order to achieve the above object, the present application provides the following technical solutions:
a geomagnetic disturbance-oriented disaster prevention and treatment method comprises the following steps:
simulating to obtain line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the pre-collected ground conductivity data;
calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node;
determining a probability distribution function of the geomagnetic induction current based on each geomagnetic induction current sample;
analyzing the bias data of the power system to obtain bias current of each node;
substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current;
substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function;
adjusting the state of each of the abatement devices in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
Optionally, the simulating to obtain the line induced voltage of each node in the power system when the node is affected by the geomagnetic storm based on the pre-collected ground conductivity data includes:
constructing a ground conductivity model based on pre-collected ground conductivity data;
simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the ground conductivity model by using a plane wave method;
and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
Optionally, the determining a probability distribution function of the geomagnetic induction current based on each geomagnetic induction current sample includes:
calculating a difference value between each geomagnetic induction current sample and a preset threshold value to obtain each magnetic bias current sample;
fitting the occurrence frequency of each bias current sample according to various probability density functions to obtain fitting indexes of the various probability density functions;
and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
Optionally, the analyzing the bias data of the power system to obtain the bias current of each node includes:
acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales;
and analyzing the bias data to obtain the bias current of each node.
A disaster prevention treatment device facing geomagnetic disturbance comprises:
the voltage simulation unit is used for simulating and obtaining line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the ground conductivity data collected in advance;
the sample calculation unit is used for calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node;
a function determination unit configured to determine a probability distribution function of the geomagnetic induction current based on each of the geomagnetic induction current samples;
the current analysis unit is used for analyzing the magnetic bias data of the power system to obtain the magnetic bias current of each node;
the current calculation unit is used for substituting each magnetic bias current into the probability distribution function to obtain each actual geomagnetic induction current;
the target optimization unit is used for substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function;
and the quantity adjusting unit is used for adjusting the states of the treatment devices in the power system so that the total number of the treatment devices in the opening state is equal to the optimal solution.
Optionally, the voltage simulation unit is specifically configured to:
constructing a ground conductivity model based on pre-collected ground conductivity data;
simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the ground conductivity model by using a plane wave method;
and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
Optionally, the function determining unit is specifically configured to:
calculating a difference value between each geomagnetic induction current sample and a preset threshold value to obtain each magnetic bias current sample;
fitting the occurrence frequency of each bias current sample according to various probability density functions to obtain fitting indexes of the various probability density functions;
and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
Optionally, the current analyzing unit is specifically configured to:
acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales;
and analyzing the bias data to obtain the bias current of each node.
A computer-readable storage medium including a stored program, wherein the program executes the method for disaster prevention management for geomagnetic disturbance.
A disaster prevention treatment device for geomagnetic disturbance comprises: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for running the program, wherein the program is run to execute the disaster prevention and treatment method facing the geomagnetic disturbance.
According to the technical scheme, the line induction voltage of each node in the power system is simulated and obtained when the node is influenced by the geomagnetic storm based on the ground conductivity data collected in advance. And calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node. Based on the respective geomagnetic induction current samples, a probability distribution function of the geomagnetic induction current is determined. And analyzing the bias data of the power system to obtain the bias current of each node. And substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current. And substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model. The magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization objective, and the calculation result comprises an optimal solution of the multi-objective optimization function. Adjusting the state of each abatement device in the power system such that the total number of abatement devices in the on state is equal to the optimal solution. Based on the scheme that this application shows, substitute the probability distribution function with the magnetic biasing current, obtain actual geomagnetism induced current, and substitute multi-objective optimization function with actual geomagnetism induced current, obtain the optimal solution of the treatment device's of participating in the electric power system treatment process quantity, adjust the state of each treatment device among the electric power system, make the total number of the treatment device that is in the open state equal to the optimal solution, thereby under the condition that ensures that the treatment device's of participating in the electric power system treatment process quantity satisfies the treatment demand, avoid too much treatment device to participate in the treatment process, prevent the wasting of resources.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for disaster prevention and treatment for geomagnetic disturbance according to an embodiment of the present application;
fig. 2 is a frequency distribution histogram provided in an embodiment of the present application;
fig. 3 is a schematic flow chart of another method for managing geomagnetic disturbance oriented disaster prevention according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a geomagnetic disturbance oriented disaster prevention and treatment apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, a schematic flow chart of a method for disaster prevention and treatment for geomagnetic disturbance according to an embodiment of the present application includes the following steps:
s101: and constructing a ground conductivity model based on the ground conductivity data collected in advance.
The specific implementation process for constructing the ground conductivity model based on the ground conductivity data is common knowledge familiar to those skilled in the art, and will not be described herein again.
S102: and (3) simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the earth electric conductivity model by using a plane wave method.
The calculation of the geomagnetic storm induced earth electric field generated under the earth conductivity model by using the plane wave method is a common knowledge familiar to those skilled in the art, and will not be described herein again.
S103: and calculating the line induction voltage of each node in the power system when the ground magnetic storm affects according to the induction ground electric field of the ground magnetic storm, the line path and the line length.
The specific calculation process of the line induction voltage of each node in the power system under the influence of the geomagnetic storm is calculated according to the induction ground electric field of the geomagnetic storm, the line path and the line length, and is shown in formula (1).
In formula (1), U represents a line induced voltage, R represents a line path, E represents an induced ground field, and L represents a line length.
As can be seen from the above formula (1), the line induced voltage is substantially an integral value of the induced ground field of the geomagnetic storm along the direction of the predetermined line, and since the induced ground field of the geomagnetic storm is constant, the integral value can be considered to be related to the geographical positions of the two ends of the predetermined line (i.e. the starting point and the ending point of the line), i.e. the line induced voltage is the sum of the first voltage (i.e. the induced voltage of the line in the north-south direction) and the second voltage (i.e. the induced voltage of the line in the east-west direction), and therefore, the above formula (1) can be converted into the formula (2) to facilitate the calculation of the line induced voltage.
U＝ENLN+EELE (2)
In the formula (2), ENRepresenting the magnitude of the north-bound earth electric field, EERepresents the value of the east-oriented electric field, LNRepresenting the northbound length of the line, LERepresenting the east length of the line.
S104: and calculating to obtain a GIC sample of each node according to the line induction voltage of each node.
Wherein, a specific calculation process of the GIC sample of each node is calculated according to the line induced voltage of each node, as shown in formula (3).
Inode＝(I+YZ)-1J (3)
In the formula (3), InodeA GIC sample representing any node in the power system, a node being an index of the node, I being an N × N identity matrix, N representing a total number of nodes, Y representing an admittance matrix, Z representing a ground impedance matrix, J representing an equivalent current source in the line, and J ═ J [ -J ]1,J2,...,JN]，[J1,J2,...,JN]Is shown in equation (4).
In the formula (4), RijRepresents the equivalent resistance between node i and node j, and i is 1,2ijRepresents the equivalent voltage between node i and node j, and the equivalent voltage of node is equal to the line induced voltage of node.
S105: and calculating the difference value between each GIC sample and a preset threshold value to obtain each bias current sample.
Wherein, under the influence of geomagnetic storm, each bias current sample should be greater than 0.
S106: and fitting the occurrence frequency of each bias current sample according to each probability density function to obtain the fitting indexes of each probability density function.
The types of the probability density function include gamma distribution, log-normal distribution, rayleigh distribution, exponential distribution, and weber distribution.
Note that the expression of the fitting index is as shown in formula (5).
In the formula (5), IFittingRepresenting the fit index, and M representing the number of groups in the frequency distribution histogram of the bias current sample (i.e., differently valued biases)Total number of current samples), generally speaking, a frequency distribution histogram is generated based on the frequency of occurrence of differently valued bias current samples, i 1,2, M,
for the probability density function, y, used in the fitting processiRepresents
The obtained value corresponding to the probability density function is fitted,
representing the height of the ith histogram in the frequency histogram (i.e. the frequency of occurrence of arbitrarily valued bias current samples),
representing the center position of the frequency distribution histogram. A specific histogram of the frequency distribution can be seen in fig. 2.
S107: the probability density function with the minimum fit index is identified as the probability distribution function of the GIC.
Wherein, the smaller the fitting index is, the higher the fitting accuracy of the representative probability density function is, and the more fit to the probability distribution of the real GIC.
S108: and acquiring the magnetic bias data of the power system according to the sampling time scale, and analyzing the magnetic bias data to obtain the magnetic bias current of each node.
Wherein, the sampling time scale is determined according to the reference change rate and the relative information loss rate of the GIC sample under different time scales.
It should be noted that, according to different time scales, part of the bias data is lost in the process of acquiring the bias data of the power system, and in order to prevent the lost data from being too large, the lost bias data under different time scales needs to be measured to obtain the data loss under different time scales, and the time scale with the least data loss is selected as the sampling time scale, so that the accuracy of sampling the bias data is improved.
In the embodiment of the present application, the data loss amount may be determined by two indexes, i.e., a reference change rate and a relative information loss rate, and the smaller the reference change rate and the smaller the relative information loss rate, the smaller the data loss amount is represented. Therefore, the reference change rate and the relative information loss rate of the GIC sample under each time scale are calculated in advance, and the time scale with the minimum reference change rate and the minimum relative information loss rate is selected from each time scale to serve as the sampling time scale.
Generally speaking, the increase of the time scale can cause the loss of the stability and continuity information of the GIC change, in order to measure the influence of the increase of the time scale on the stability of the GIC fluctuation, an index of a reference change rate is provided, the smaller the reference change rate is, the smaller the stability of the GIC fluctuation by the time scale is, and specifically, the expression of the reference change rate is shown as a formula (6).
In the formula (6), SkRepresenting the base rate of change, k being the index of the time scale, SBRepresenting a predetermined reference load, Δ GIC being a first order difference component of the GIC samples, UBRepresenting a preset reference voltage.
In the bias data obtained under different time scale sampling, the average first-order difference component and the probability of the position where the difference component is 0 can both reflect the fluctuation condition of the bias data. Therefore, the bias data under different time scales are compared with the standard data, the relative information loss rate under different time scales is calculated, the smaller the relative information loss rate is, the smaller the fluctuation of the time scales to the bias data is, specifically, taking the time scales as 5s and 1min as examples, and the expression of the relative information loss rate is shown in formula (7).
In the formula (7), λ represents a relative information loss rate, Δ5sRepresenting the average first order difference component, Δ, at a time scale of 5S1minRepresents the average first order difference component, Δ, at a time scale of 1min0Represents a preset reference first-order difference component,
representing the probability of the time scale being 5s when the time difference component is 0,
representing the probability of the time scale being 1min with a time difference component of 0, GIC0Representing a preset reference difference component.
Specifically, taking three time scales of 5s, 1min and 5min as an example, the reference change rate and the relative information loss rate of the GIC sample under the three time scales are shown in table 1. As can be seen from table 1, when the time scale is 5S, the probability that the reference change rate is less than 1% is the largest, that is, the reference change rate is determined to be the smallest when the time scale is 5S, and in addition, the relative information loss rate is the smallest when the time scale is 5S. For this purpose, 5s is determined as the sampling time scale.
TABLE 1
The above table 1 is for illustration only.
S109: and substituting each bias current into the probability distribution function to obtain each actual GIC.
S110: and substituting each actual GIC into a pre-constructed bias control mathematical model to obtain a calculation result output by the bias control mathematical model.
The magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in a governance process of the power system as an optimization target. In addition, the calculation result comprises an optimal solution of the multi-objective optimization function.
Specifically, the expression of the multi-objective optimization function is shown in formula (8).
In the formula (8), IALLRepresenting the sum of the bias currents at the neutral point of the transformer, E representing a predetermined desired value, N representing the number of transformers grounded at the neutral point, IiRepresenting the actual GIC, R flowing through the i-th transformer neutralsRepresenting the sum of the values of the adjustable resistors in the abatement device, M representing the number of abatement devices participating in the abatement process of the power system, RiRepresenting the resistance value of the ith abatement device.
In terms of effect, the purpose of disaster prevention management is to expect that the smaller the bias current of the neutral point, the better the bias current, so that the used management devices are necessarily many, and in terms of operation and maintenance, the purpose of disaster prevention management is to expect that the fewer the installed management devices, the better the bias current, the lower the bias current can not be. Therefore, the two objective functions shown in this equation (8) are contradictory and restrictive.
In addition, the bias influence of the GIC and the grounding electrode is comprehensively considered, and the proposed transformer bias control scheme cannot use a unified control standard because the action ranges and the influence degrees of the GIC and the grounding electrode are different, and the bias current of the power grid caused by the two conditions needs to be separately controlled, so that the constraint conditions of the two objective functions in the formula (8) also need to be respectively set. In particular, IiThe constraint of (2) is shown in formula (9), RiThe constraint of (2) is shown in equation (10).
Ii≤18A (9)
0＜Ri≤5Ω (10)
Optionally, in order to improve the accuracy of the calculation result, a non-dominated Sorting Genetic Algorithm (NSGA-ii) may be used to optimize the magnetic bias treatment mathematical model. Specifically, the bias control mathematical model after NSGA-II optimization is shown as a formula (11).
In formula (11), ImaxRepresents the maximum value of the sum of the bias currents, | AiI represents IALLThe value of (c).
Specifically, the process of optimizing the magnetic bias treatment mathematical model by using NSGA-II comprises the following steps:
1. inputting power grid bias data (namely actual GICs flowing through neutral points of various transformers);
2. constructing a target optimization model (namely formula (11));
3. setting relevant parameters of NSGA-II, and initializing a population (namely, the number of the treatment devices represented by individuals in the population);
4. performing non-dominated sorting on each individual in the population to obtain a first generation subgroup;
5. iteratively executing a preset step to obtain a non-dominated solution; wherein, the presetting step is as follows: merging the parent individuals and the offspring individuals to obtain new individuals; carrying out non-dominated sorting and congestion degree calculation on each new individual; generating a new parent population; selecting, crossing and mutating individuals in the population;
6. and (5) demodulating parameters of the whole target optimization model according to the non-dominant mode.
It should be noted that the above specific implementation process is only for illustration.
S111: adjusting the state of each abatement device in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
The state of each treatment device in the power system is adjusted, so that the total number of the treatment devices in the open state is equal to the optimal solution, the number of the treatment devices participating in the treatment process of the power system can be ensured to meet the treatment requirement (namely the sum of the bias currents of the power system reaches the minimum value), excessive treatment devices are prevented from participating in the treatment process, and the problems of over-treatment and under-treatment existing in the treatment process are effectively solved.
In summary, with the scheme shown in this embodiment, under the condition that the number of the management devices participating in the management process of the power system is ensured to meet the management requirement, the excessive management devices are prevented from participating in the management process, and resource waste is prevented.
It should be noted that, in the foregoing embodiment, step S101 is an optional implementation manner of the method for managing geomagnetic disturbance oriented disaster prevention as shown in this application. In addition, S102 mentioned in the above embodiment is also an optional implementation manner of the method for disaster prevention and treatment for geomagnetic disturbance in the present application. For this reason, the flow mentioned in the above embodiment can be summarized as the method shown in fig. 3.
As shown in fig. 3, a schematic flow chart of another method for controlling geomagnetic disturbance based on disaster prevention according to an embodiment of the present application includes the following steps:
s301: and simulating to obtain the line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the ground conductivity data collected in advance.
S302: and calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node.
S303: based on the respective geomagnetic induction current samples, a probability distribution function of the geomagnetic induction current is determined.
S304: and analyzing the bias data of the power system to obtain the bias current of each node.
S305: and substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current.
S306: and substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model.
The magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in a governance process of the power system as an optimization target. The calculation result comprises an optimal solution of the multi-objective optimization function.
S307: adjusting the state of each abatement device in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
To sum up, the bias current is substituted into the probability distribution function to obtain the actual geomagnetic induction current, the actual geomagnetic induction current is substituted into the multi-objective optimization function to obtain the optimal solution of the number of the treatment devices participating in the treatment process of the power system, the states of the treatment devices in the power system are adjusted, the total number of the treatment devices in the open state is equal to the optimal solution, and therefore under the condition that the number of the treatment devices participating in the treatment process of the power system meets the treatment requirement, the excessive treatment devices are prevented from participating in the treatment process, and resource waste is prevented.
Corresponding to the method for controlling geomagnetic disturbance in disaster prevention provided by the embodiment of the present application, the embodiment of the present application further provides a device for controlling geomagnetic disturbance in disaster prevention.
As shown in fig. 4, an architecture diagram of a geomagnetic-disturbance-oriented disaster prevention and treatment apparatus provided in an embodiment of the present application includes:
and the voltage simulation unit 100 is used for simulating and obtaining the line induction voltage of each node in the power system when the power system is influenced by the geomagnetic storm based on the ground conductivity data collected in advance.
The voltage simulation unit 100 is specifically configured to: constructing a ground conductivity model based on pre-collected ground conductivity data; simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the earth electric conductivity model by using a plane wave method; and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
The sample calculating unit 200 is configured to calculate a geomagnetic induction current sample of each node according to the line induction voltage of each node.
A function determining unit 300 configured to determine a probability distribution function of the geomagnetic induction current based on each of the geomagnetic induction current samples.
The function determining unit 300 is specifically configured to: calculating the difference value between each geomagnetic induction current sample and a preset threshold value to obtain each bias current sample; fitting the occurrence frequency of each bias current sample according to each probability density function to obtain a fitting index of each probability density function; and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
The current analyzing unit 400 is configured to analyze the bias data of the power system to obtain the bias current of each node.
The current analyzing unit 400 is specifically configured to: acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales; and analyzing the bias data to obtain the bias current of each node.
And a current calculating unit 500, configured to substitute each bias current into the probability distribution function to obtain each actual geomagnetic induction current.
The target optimization unit 600 is configured to substitute each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in a governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function.
A quantity adjusting unit 700 for adjusting the status of each abatement device in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
To sum up, the bias current is substituted into the probability distribution function to obtain the actual geomagnetic induction current, the actual geomagnetic induction current is substituted into the multi-objective optimization function to obtain the optimal solution of the number of the treatment devices participating in the treatment process of the power system, the states of the treatment devices in the power system are adjusted, the total number of the treatment devices in the open state is equal to the optimal solution, and therefore under the condition that the number of the treatment devices participating in the treatment process of the power system meets the treatment requirement, the excessive treatment devices are prevented from participating in the treatment process, and resource waste is prevented.
The application also provides a computer-readable storage medium, which includes a stored program, where the program executes the above-mentioned geomagnetic disturbance-oriented disaster prevention and treatment method provided by the application.
The application also provides a disaster prevention treatment device for geomagnetic disturbance, which comprises: a processor, a memory, and a bus. The processor is connected with the memory through a bus, the memory is used for storing programs, and the processor is used for running the programs, wherein when the programs are run, the method for controlling the geomagnetic disturbance oriented disaster prevention comprises the following steps:
simulating to obtain line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the pre-collected ground conductivity data;
calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node;
determining a probability distribution function of the geomagnetic induction current based on each geomagnetic induction current sample;
analyzing the bias data of the power system to obtain bias current of each node;
substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current;
substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function;
adjusting the state of each of the abatement devices in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
Specifically, on the basis of the above embodiment, the simulating to obtain the line induced voltage of each node in the power system when being affected by the geomagnetic storm based on the ground conductivity data collected in advance includes:
constructing a ground conductivity model based on pre-collected ground conductivity data;
simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the ground conductivity model by using a plane wave method;
and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
Specifically, on the basis of the above embodiment, the determining a probability distribution function of the geomagnetic induction current based on each geomagnetic induction current sample includes:
calculating a difference value between each geomagnetic induction current sample and a preset threshold value to obtain each magnetic bias current sample;
fitting the occurrence frequency of each bias current sample according to various probability density functions to obtain fitting indexes of the various probability density functions;
and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
Specifically, on the basis of the above embodiment, the analyzing the bias data of the power system to obtain the bias current of each node includes:
acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales;
and analyzing the bias data to obtain the bias current of each node.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A geomagnetic disturbance-oriented disaster prevention and treatment method is characterized by comprising the following steps:
simulating to obtain line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the pre-collected ground conductivity data;
calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node;
determining a probability distribution function of the geomagnetic induction current based on each geomagnetic induction current sample;
analyzing the bias data of the power system to obtain bias current of each node;
substituting each bias current into the probability distribution function to obtain each actual geomagnetic induction current;
substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function;
adjusting the state of each of the abatement devices in the power system such that the total number of abatement devices in the on state is equal to the optimal solution.
2. The method of claim 1, wherein the simulating the line induced voltage of each node in the power system when affected by the geomagnetic storm based on the pre-collected ground conductivity data comprises:
constructing a ground conductivity model based on pre-collected ground conductivity data;
simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the ground conductivity model by using a plane wave method;
and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
3. The method of claim 1, wherein determining a probability distribution function of the geomagnetic induction current based on each of the geomagnetic induction current samples comprises:
calculating a difference value between each geomagnetic induction current sample and a preset threshold value to obtain each magnetic bias current sample;
fitting the occurrence frequency of each bias current sample according to various probability density functions to obtain fitting indexes of the various probability density functions;
and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
4. The method of claim 1, wherein analyzing the bias data of the power system to obtain the bias current of each node comprises:
acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales;
and analyzing the bias data to obtain the bias current of each node.
5. The utility model provides a device is administered in disaster prevention towards earth magnetism disturbance which characterized in that includes:
the voltage simulation unit is used for simulating and obtaining line induction voltage of each node in the power system under the influence of the geomagnetic storm based on the ground conductivity data collected in advance;
the sample calculation unit is used for calculating a geomagnetic induction current sample of each node according to the line induction voltage of each node;
a function determination unit configured to determine a probability distribution function of the geomagnetic induction current based on each of the geomagnetic induction current samples;
the current analysis unit is used for analyzing the magnetic bias data of the power system to obtain the magnetic bias current of each node;
the current calculation unit is used for substituting each magnetic bias current into the probability distribution function to obtain each actual geomagnetic induction current;
the target optimization unit is used for substituting each actual geomagnetic induction current into a pre-constructed magnetic bias treatment mathematical model to obtain a calculation result output by the magnetic bias treatment mathematical model; the magnetic biasing governance mathematical model comprises a multi-objective optimization function taking the number of governance devices participating in the governance process of the power system as an optimization target; the calculation result comprises an optimal solution of the multi-objective optimization function;
and the quantity adjusting unit is used for adjusting the states of the treatment devices in the power system so that the total number of the treatment devices in the opening state is equal to the optimal solution.
6. The apparatus of claim 5, wherein the voltage simulation unit is specifically configured to:
constructing a ground conductivity model based on pre-collected ground conductivity data;
simulating and calculating the induced ground electric field, the line path and the line length of the geomagnetic storm occurring under the ground conductivity model by using a plane wave method;
and calculating the line induction voltage of each node in the power system under the influence of the geomagnetic storm according to the induction ground electric field, the line path and the line length.
7. The apparatus according to claim 5, wherein the function determination unit is specifically configured to:
calculating a difference value between each geomagnetic induction current sample and a preset threshold value to obtain each magnetic bias current sample;
fitting the occurrence frequency of each bias current sample according to various probability density functions to obtain fitting indexes of the various probability density functions;
and identifying the probability density function with the minimum fitting index as a probability distribution function of the geomagnetic induction current.
8. The apparatus of claim 5, wherein the current resolution unit is specifically configured to:
acquiring bias magnetic data of the power system according to a sampling time scale; the sampling time scale is determined according to the reference change rate and the relative information loss rate of the geomagnetic induction current sample under different time scales;
and analyzing the bias data to obtain the bias current of each node.
9. A computer-readable storage medium, comprising a stored program, wherein the program executes the method for disaster prevention and treatment for geomagnetic disturbance according to any one of claims 1 to 4.
10. The utility model provides a disaster prevention treatment equipment towards earth magnetism disturbance which characterized in that includes: a processor, a memory, and a bus; the processor and the memory are connected through the bus;
the memory is used for storing a program, and the processor is used for executing the program, wherein the program executes the method for disaster prevention and treatment for geomagnetic disturbance according to any one of claims 1 to 4 when running.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111210979.1A CN113836748B (en) | 2021-10-18 | 2021-10-18 | Geomagnetic disturbance-oriented disaster prevention and management method, device, storage medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111210979.1A CN113836748B (en) | 2021-10-18 | 2021-10-18 | Geomagnetic disturbance-oriented disaster prevention and management method, device, storage medium and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113836748A true CN113836748A (en) | 2021-12-24 |
| CN113836748B CN113836748B (en) | 2024-01-30 |
Family
ID=78965216
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111210979.1A Active CN113836748B (en) | 2021-10-18 | 2021-10-18 | Geomagnetic disturbance-oriented disaster prevention and management method, device, storage medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113836748B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350102A (en) * | 2023-09-20 | 2024-01-05 | 国网上海市电力公司 | Subway electric power system management method, device, equipment and readable storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150075907A (en) * | 2013-12-26 | 2015-07-06 | (주)에스이티시스템 | Method and apparatus for predicting geomagnetically induced current |
| CN104821560A (en) * | 2015-04-14 | 2015-08-05 | 华北电力大学 | Novel interphase distance III segment protecting method in consideration of influence of geomagnetically induced current |
| CN108010693A (en) * | 2017-12-26 | 2018-05-08 | 华北电力大学 | The suppressing method and device of transformer DC magnetic bias |
| US20200400735A1 (en) * | 2019-06-20 | 2020-12-24 | Afshin REZAEI ZARE | System and method for geomagnetic disturbance determination for power systems |
| CN112653135A (en) * | 2020-12-14 | 2021-04-13 | 国网内蒙古东部电力有限公司检修分公司 | Optimization method for governing geomagnetic storm power grid disasters by adopting small resistors |
| CN113034002A (en) * | 2021-03-26 | 2021-06-25 | 国网江苏省电力有限公司电力科学研究院 | Method for analyzing small-disturbance voltage stability of power system by geomagnetic storm |
-
2021
- 2021-10-18 CN CN202111210979.1A patent/CN113836748B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150075907A (en) * | 2013-12-26 | 2015-07-06 | (주)에스이티시스템 | Method and apparatus for predicting geomagnetically induced current |
| CN104821560A (en) * | 2015-04-14 | 2015-08-05 | 华北电力大学 | Novel interphase distance III segment protecting method in consideration of influence of geomagnetically induced current |
| CN108010693A (en) * | 2017-12-26 | 2018-05-08 | 华北电力大学 | The suppressing method and device of transformer DC magnetic bias |
| US20200400735A1 (en) * | 2019-06-20 | 2020-12-24 | Afshin REZAEI ZARE | System and method for geomagnetic disturbance determination for power systems |
| CN112653135A (en) * | 2020-12-14 | 2021-04-13 | 国网内蒙古东部电力有限公司检修分公司 | Optimization method for governing geomagnetic storm power grid disasters by adopting small resistors |
| CN113034002A (en) * | 2021-03-26 | 2021-06-25 | 国网江苏省电力有限公司电力科学研究院 | Method for analyzing small-disturbance voltage stability of power system by geomagnetic storm |
Non-Patent Citations (2)
| Title |
|---|
| 刘春明 等: "抑制变压器地磁感应电流的电容隔直装置安装位置优化", 电力系统自动化, no. 16 * |
| 刘春明 等: "抑制电网地磁感应电流的电容电阻优化布置方法", 《电网技术》, vol. 42, no. 5 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117350102A (en) * | 2023-09-20 | 2024-01-05 | 国网上海市电力公司 | Subway electric power system management method, device, equipment and readable storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113836748B (en) | 2024-01-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7979239B2 (en) | System and method for monitoring and managing electrical power transmission and distribution networks | |
| Wang et al. | Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties | |
| CN110930052A (en) | Method, system, equipment and readable storage medium for predicting failure rate of power transformation equipment | |
| Raposo et al. | Optimal meter placement algorithm for state estimation in power distribution networks | |
| CN111507509A (en) | Risk assessment method for extreme events of power system | |
| Wu et al. | A gene importance based evolutionary algorithm (GIEA) for identifying critical nodes in cyber–physical power systems | |
| CN112149873A (en) | Low-voltage transformer area line loss reasonable interval prediction method based on deep learning | |
| CN112329997A (en) | Power demand load prediction method and system, electronic device, and storage medium | |
| Rossi et al. | Stochastic evaluation of distribution network hosting capacity: Evaluation of the benefits introduced by smart grid technology | |
| Shakeri et al. | Determining accurate area of vulnerability for reliable voltage sag assessment considering wind turbine ride-through capability | |
| CN113836748A (en) | Geomagnetic disturbance-oriented disaster prevention management method and device, storage medium and equipment | |
| CN108647838A (en) | A kind of power grid Situation Awareness method based on Random Matrices Theory and online limit of sequence learning machine | |
| Beyza et al. | Characterising the security of power system topologies through a combined assessment of reliability, robustness, and resilience | |
| Wang et al. | Load model of high power magnet power supply for EAST based on GMPSO-BP neural network | |
| EP3497766B1 (en) | Method and device for wind power curtailment optimization for day-ahead operational planning of a power network | |
| Gao et al. | High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension | |
| Hatziargyriou et al. | Probabilistic load flow for assessment of voltage instability | |
| CN112633665A (en) | Lightning protection decision method for power distribution network based on analytic hierarchy process | |
| Chihota et al. | Modelling and simulation of uncertainty in the placement of distributed energy resources for planning applications | |
| Sushma et al. | Observability constrained GA approach for optimal PMU placement considering zero injection modeling | |
| Wu et al. | Extreme bifurcation interval with deep PV penetration considering correlated net bus load forecast errors | |
| CN113517693B (en) | Weak voltage transient stability node analysis method and device | |
| Guevara et al. | Identification of weak buses for proper placement of reactive compensation through sensitivity analysis using a neural network surrogate model | |
| Raschke et al. | Vulnerability of the Swiss electric power transmission grid against natural hazards | |
| CN111130098B (en) | Risk assessment method for power distribution network system with distributed power supplies |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |