Relay protection device constant value optimization method, system, equipment, medium and product

Abstract

The invention discloses a relay protection device constant value optimization method and system, equipment, a medium and a product, and relates to the technical field of relay protection constant value management. When an optimization page receives a constant value optimization request, electrical working condition parameters and an operation state of a relay protection device are obtained; constructing an initial constant value population of the relay protection device based on a preset constraint condition, adjusting a preset fitness function according to the operation state to obtain a corresponding target fitness function, and performing iterative optimization on the initial constant value population based on the target fitness function and according to the electrical working condition parameters to obtain an optimal constant value population; and obtaining a corresponding target constant value optimization parameter and displaying the target constant value optimization parameter on an optimization page. The technical problems that a traditional relay protection device constant value optimization method mainly depends on an empirical formula and off-line calculation and is difficult to adapt to dynamic changes of a modern power grid, and the operation reliability of a relay protection device is reduced are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of relay protection setting value management, and in particular to a relay protection device setting value optimization method, system, device, medium and product. BACKGROUND

[0002] With the expansion of the power system, the wide access of new energy and the complexity of the power grid structure, the rationality and optimization of the setting value of the relay protection device, which is the key equipment to ensure the safe and stable operation of the power system, become particularly important.

[0003] The traditional relay protection device setting value optimization method mainly relies on empirical formula and offline calculation, which is difficult to adapt to the dynamic changes of the modern power grid, may cause protection misoperation or refusal, and further trigger chain failure or even large-scale power outage, thereby reducing the reliability of the operation of the relay protection device. SUMMARY

[0004] The present application provides a relay protection device setting value optimization method, system, device, medium and product, which solves the technical problem that the traditional relay protection device setting value optimization method mainly relies on empirical formula and offline calculation, which is difficult to adapt to the dynamic changes of the modern power grid, may cause protection misoperation or refusal, and further trigger chain failure or even large-scale power outage, thereby reducing the reliability of the operation of the relay protection device.

[0005] The first aspect of the present application provides a relay protection device setting value optimization method, comprising:

[0006] When the optimization page receives a setting value optimization request, the electrical working condition parameters and the operating state of the relay protection device are obtained;

[0007] Based on the preset constraint condition, the initial setting value population of the relay protection device is constructed;

[0008] According to the operating state, the preset fitness function is adjusted to obtain the corresponding target fitness function;

[0009] Based on the target fitness function, the initial setting value population is iteratively optimized according to the electrical working condition parameters to obtain the corresponding target setting value optimization parameter and display it on the optimization page.

[0010] Optionally, the step of adjusting the preset fitness function according to the operating state to obtain the corresponding target fitness function comprises:

[0011] Based on the preset operating state setting value mapping rule, the corresponding setting value weight is determined according to the operating state;

[0012] The setting value weight is input into the preset fitness function to obtain the corresponding target fitness function.

[0013] Optionally, the step of determining the target fitness function value corresponding to the initial setting population based on the target fitness function and the action time limit, the action current threshold, the sensitivity coefficient corresponding to each individual in the initial setting population and the electrical working condition parameter, comprises:

[0014] determining the target fitness function value corresponding to the initial setting population based on the action time limit, the action current threshold, the sensitivity coefficient corresponding to each individual in the initial setting population and the electrical working condition parameter;

[0015] updating the initial setting population according to each target fitness function value to obtain a new initial setting population;

[0016] judging whether the updating times of the initial setting population are less than a preset updating threshold;

[0017] if the updating times are less than the updating threshold, jumping to execute the step of determining the target fitness function value corresponding to the initial setting population based on the action time limit, the action current threshold, the sensitivity coefficient corresponding to each individual in the initial setting population and the electrical working condition parameter;

[0018] if the updating times are greater than or equal to the updating threshold, selecting the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value of each target fitness function value as the target setting optimization parameter and displaying the target setting optimization parameter on the optimization page.

[0019] Optionally, the step of determining the target fitness function value corresponding to the initial setting population based on the target fitness function and the action time limit, the action current threshold, the sensitivity coefficient corresponding to each individual in the initial setting population and the electrical working condition parameter, comprises:

[0020] determining a corresponding quick action function value according to the action time limit, the action current threshold and the electrical working condition parameter corresponding to each individual in the initial setting population;

[0021] inputting the action time limit, the sensitivity coefficient and the electrical working condition parameter corresponding to each individual in the initial setting population into a preset selection function to obtain a plurality of selection function values;

[0022] inputting the sensitivity coefficient, the action current threshold and the electrical working condition parameter corresponding to each individual in the initial setting population into a preset sensitivity function to obtain a plurality of sensitivity function values;

[0023] inputting each quick action function value and the corresponding selection function value and sensitivity function value into the target fitness function to obtain a plurality of target fitness function values.

[0024] Optionally, the electrical working condition parameter comprises a working condition weight coefficient and a fault current amplitude, and the step of determining a corresponding quick action function value according to the action time limit, the action current threshold and the electrical working condition parameter corresponding to each individual in the initial setting value population respectively comprises:

[0025] respectively, the action current threshold corresponding to each individual in the initial setting value population is subtracted from the fault current amplitude to obtain a plurality of first differences;

[0026] respectively, the absolute value of each first difference is multiplied by a preset current deviation penalty coefficient to obtain a plurality of first products;

[0027] Based on the working condition weight coefficient, the action time limit and the first product corresponding to each individual in the initial setting value population are weighted to obtain a plurality of quick action function values.

[0028] Optionally, the step of selecting the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value in each target adaptive function value as the target setting value optimization parameter and displaying it on the optimization page comprises:

[0029] selecting the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value in each target adaptive function value as the target setting value optimization parameter;

[0030] loading the target setting value optimization parameter into the feedback component in the optimization page;

[0031] rendering the feedback component to generate an optimization page containing the target setting value optimization parameter.

[0032] The second aspect of the application provides a relay protection device setting value optimization system, comprising:

[0033] The acquisition module is used to acquire the electrical working condition parameter and the running state of the relay protection device when the optimization page receives a setting value optimization request.

[0034] The construction module is used to construct an initial setting value population of the relay protection device based on a preset constraint condition.

[0035] The adjustment module is used to adjust a preset adaptive function according to the running state to obtain a corresponding target adaptive function.

[0036] The optimization module is used to iteratively optimize the initial setting value population based on the target adaptive function and the electrical working condition parameter to obtain a corresponding target setting value optimization parameter and display it on the optimization page.

[0037] The third aspect of the present application provides an electronic device, comprising a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the relay protection device setting value optimization method according to any one of the preceding aspects.

[0038] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the relay protection device setting value optimization method according to any one of the preceding aspects.

[0039] The fifth aspect of the present application provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the relay protection device setting value optimization method according to any one of the preceding aspects.

[0040] From the above technical solutions, the present application has the following advantages:

[0041] The present application obtains the electrical working condition parameters and the running state of the relay protection device when the optimization page receives the setting value optimization request, constructs the initial setting value population of the relay protection device based on the preset constraint condition, adjusts the preset fitness function according to the running state to obtain the corresponding target fitness function, iteratively optimizes the initial setting value population based on the target fitness function according to the electrical working condition parameters, obtains the corresponding target setting value optimization parameter, and displays it on the optimization page. The technical problem that the traditional relay protection device setting value optimization method mainly relies on empirical formula and offline calculation, and is difficult to adapt to the dynamic changes of the modern power grid, and reduces the reliability of the relay protection device operation is overcome. Compared with the traditional setting value optimization method, the present application iteratively optimizes the initial setting value population based on the target fitness function according to the electrical working condition parameters, obtains the corresponding target setting value optimization parameter, and displays it on the optimization page. The setting value can be dynamically adjusted according to the electrical working condition parameters and the running state of the relay protection device, so as to realize the dynamic changes of the power grid and improve the reliability of the relay protection device operation. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0043] Figure 1 A flowchart of the steps of a relay protection device setting value optimization method provided for the first embodiment of the present application;

[0044] Figure 2 A step flow chart of a relay protection device setting value optimization method provided for the second embodiment of the present application is shown in the figure.

[0045] Figure 3 A structure block diagram of a relay protection device setting value optimization system provided for the third embodiment of the present application is shown in the figure.

[0046] Figure 4 A structure block diagram of an electronic device provided for the fourth embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0047] The embodiments of the present application provide a relay protection device setting value optimization method, system, device, medium and product, and are used for solving the technical problem that the traditional relay protection device setting value optimization method mainly depends on experience formula and offline calculation, is difficult to adapt to the dynamic change of the modern power grid, may cause protection misoperation or refusal to operate, and further causes chain failure or even large-scale power failure, and reduces the reliability of the relay protection device operation.

[0048] In order to make the invention purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0049] Please refer to Figure 1 , Figure 1 A step flow chart of a relay protection device setting value optimization method provided for the first embodiment of the present application is shown in the figure.

[0050] The relay protection device setting value optimization method provided by the present application comprises:

[0051] Step 101, when the optimization page receives a setting value optimization request, the electrical working condition parameters and the running state of the relay protection device are obtained.

[0052] The electrical working condition parameters refer to a key data set representing the electrical characteristics of the power line and the running electrical characteristics of the relay protection device, including but not limited to working condition weight coefficient, fault current amplitude, topological correlation degree, distributed power penetration rate, upper protection action time limit, etc.

[0053] The running state refers to the current running state of the relay protection device, including but not limited to normal running state, extreme running state and high penetration of distributed power.

[0054] In the embodiment of the present application, when the optimization page receives a fixed value optimization request, the electrical working condition parameters and the operating state of the relay protection device are acquired in real time by a preset remote monitoring device (such as an FTU / DTU, i.e., a feeder terminal unit / distribution terminal unit).

[0055] It should be noted that the optimization page refers to a man-machine interaction page used for initiating and processing relay protection device fixed value optimization related operations.

[0056] Step 102, constructing an initial fixed value population of the relay protection device based on preset constraint conditions.

[0057] In the embodiment of the present application, the initial fixed value population of the relay protection device is constructed based on preset constraint conditions, wherein each individual in the initial fixed value population corresponds to a fixed value optimization parameter.

[0058] It should be noted that the fixed value optimization parameter includes an action time limit, an action current threshold, and a sensitivity coefficient.

[0059] It should be noted that the constraint condition is specifically:

[0060]

[0061]

[0062]

[0063] Among them, is the action time limit, is the minimum action time limit, is the action current threshold, is the upper level protection action time limit, is the coordination time difference, is the sensitivity coefficient.

[0064] Step 103, adjusting the preset fitness function according to the operating state to obtain a corresponding target fitness function.

[0065] In the embodiment of the present application, the operating state is input into a preset fixed value weight list to obtain a corresponding fixed value weight. The fixed value weight is input into a preset fitness function to obtain a corresponding target fitness function.

[0066] It should be noted that the target fitness function is specifically:

[0067]

[0068] Among them, is the target fitness function value, is the quick action function value, is the selection function value, For the sensitivity function value, The first fixed weighting coefficient, This is the second fixed-value weighting coefficient. This is the third fixed-value weighting coefficient.

[0069] Step 104: Based on the target fitness function, iteratively optimize the initial setpoint population according to the electrical operating condition parameters to obtain the corresponding target setpoint optimization parameters and display them on the optimization page.

[0070] The target setpoint optimization parameters refer to the setpoint parameters that achieve the optimal target fitness value and satisfy the constraints after iterative optimization using a genetic algorithm. These parameters include the optimized action time limit, action current threshold, and sensitivity coefficient.

[0071] In this embodiment of the invention, based on the target fitness function, the target fitness function value corresponding to the initial setpoint population is determined according to the action time limit, action current threshold, sensitivity coefficient, and electrical condition parameters corresponding to each individual in the initial setpoint population. A genetic algorithm is used to update the initial setpoint population based on each target fitness function value, resulting in a new initial setpoint population. It is then determined whether the number of updates to the initial setpoint population is less than a preset update threshold. If the number of updates is less than the update threshold, the process jumps to the step of determining the target fitness function value corresponding to the initial setpoint population based on the target fitness function, the action time limit, action current threshold, sensitivity coefficient, and electrical condition parameters corresponding to each individual in the initial setpoint population. If the number of updates is greater than or equal to the update threshold, the action time limit, action current threshold, and sensitivity coefficient corresponding to the minimum value among the target fitness function values ​​are selected as the target setpoint optimization parameters and displayed on the optimization page.

[0072] In this embodiment of the invention, when the optimization page receives a setting optimization request, it obtains the electrical operating parameters and operating status of the relay protection device. An initial setting population for the relay protection device is constructed based on preset constraints. A preset fitness function is adjusted according to the operating status to obtain a corresponding target fitness function. Based on the target fitness function, the initial setting population is iteratively optimized according to the electrical operating parameters to obtain the corresponding target setting optimization parameters, which are then displayed on the optimization page. This overcomes the technical problem that traditional relay protection device setting optimization methods mainly rely on empirical formulas and offline calculations, making it difficult to adapt to the dynamic changes of modern power grids and reducing the reliability of relay protection device operation. Compared with traditional setting optimization methods, this invention, by iteratively optimizing the initial setting population based on the target fitness function and electrical operating parameters to obtain the corresponding target setting optimization parameters and displaying them on the optimization page, allows for dynamic adjustment of the setting based on the electrical operating parameters and operating status of the relay protection device, thereby realizing the dynamic changes of the power grid and improving the reliability of relay protection device operation.

[0073] Please see Figure 2, Figure 2 The flowchart illustrates the steps of a relay protection device setting optimization method provided in Embodiment 2 of the present invention.

[0074] This invention provides a method for optimizing the settings of a relay protection device, comprising:

[0075] Step 201: When the optimization page receives a setting optimization request, it obtains the electrical operating parameters and operating status of the relay protection device.

[0076] In this embodiment of the invention, when a setting optimization request is received from the operation and maintenance personnel on the optimization page, the electrical operating parameters and operating status of the relay protection device are obtained.

[0077] Step 202: Construct the initial setting population of the relay protection device based on the preset constraints.

[0078] In this embodiment of the invention, based on a genetic algorithm, an initial setpoint population for a relay protection device is constructed using preset constraints. Each individual in the initial setpoint population corresponds to a setpoint optimization parameter (the setpoint optimization parameter includes the action time limit, the action current threshold, and the sensitivity coefficient).

[0079] Step 203: Adjust the preset fitness function according to the running status to obtain the corresponding target fitness function.

[0080] Furthermore, step 203 includes the following sub-steps:

[0081] S11. Based on the preset operation status fixed value mapping rules, determine the corresponding fixed value weight according to the operation status.

[0082] In this embodiment of the invention, based on a preset operating state setpoint mapping rule, the corresponding setpoint weights are determined according to the operating state. For example, when the operating state is normal operation, the first setpoint weight coefficient = 0.3, the second setpoint weight coefficient = 0.5, and the third setpoint weight coefficient = 0.2. When the operating state is extreme operation, the first setpoint weight coefficient = 0.5, the second setpoint weight coefficient = 0.3, and the third setpoint weight coefficient = 0.2. When the operating state is high penetration of distributed power sources, the first setpoint weight coefficient = 0.3, the second setpoint weight coefficient = 0.3, and the third setpoint weight coefficient = 0.4.

[0083] S12. Input the fixed weights into the preset fitness function to obtain the corresponding target fitness function.

[0084] In this embodiment of the invention, the fixed weights are substituted into a preset fitness function to obtain the corresponding target fitness function.

[0085] In step 204, based on the target adaptation function, the target adaptation function values corresponding to the initial setting value population are determined according to the action time limit, the action current threshold, the sensitivity coefficient and the electrical working condition parameters corresponding to each individual in the initial setting value population.

[0086] Further, step 204 includes the following sub-steps:

[0087] S21, respectively, according to the action time limit, the action current threshold and the electrical working condition parameters corresponding to each individual in the initial setting value population, the corresponding quick action function values are determined.

[0088] In another embodiment, the action time limit, the action current threshold and the electrical working condition parameters corresponding to each individual in the initial setting value population are respectively input into the preset quick action function to obtain a plurality of quick action function values.

[0089] It should be noted that the quick action function is specifically:

[0090]

[0091] Among them, the quick action function value, the working condition weight coefficient, the action time limit, the current deviation penalty coefficient, the action current threshold, the fault current amplitude.

[0092] Further, the electrical working condition parameters include the working condition weight coefficient and the fault current amplitude, and S21 includes the following sub-steps:

[0093] S211, respectively, the action current threshold and the fault current amplitude corresponding to each individual in the initial setting value population are differentially processed to obtain a plurality of first differences.

[0094] In the embodiment of the application, the difference between the action current threshold and the fault current amplitude corresponding to each individual in the initial setting value population is calculated to obtain a plurality of first differences.

[0095] S212, respectively, the absolute value of each first difference and the preset current deviation penalty coefficient are multiplied to obtain a plurality of first products.

[0096] The current deviation penalty coefficient refers to a fixed coefficient preset according to the tolerance of the current deviation of the relay protection device in the power system operation regulation, which is used to amplify or quantify the influence of the current deviation on the setting value reliability.

[0097] In the embodiment of the application, the multiplication between the absolute value of each first difference and the preset current deviation penalty coefficient is calculated to obtain a plurality of first products.

[0098] S213, based on the working condition weight coefficient, respectively, on each individual in each initial setting value population Corresponding action time limit and the first multiplication value are weighted operation, obtain multiple quick action function value.

[0099] In the embodiment of the application, based on the working condition weight coefficient, respectively, on each individual in each initial setting value population Corresponding action time limit and the first multiplication value are weighted operation, obtain multiple quick action function value. For example, quick action function value = working condition weight coefficient * action time limit + (1-working condition weight coefficient) * first multiplication value.

[0100] S22, respectively, on each individual in initial setting value population Corresponding action time limit, sensitivity coefficient and electrical working condition parameter input preset selection function, obtain multiple selection function value.

[0101] In the embodiment of the application, respectively, on each individual in initial setting value population Corresponding action time limit, sensitivity coefficient and electrical working condition parameter input preset selection function, obtain multiple selection function value.

[0102] It should be noted that the selection function is specifically:

[0103]

[0104] Among them, The selection function value is, The topological correlation degree is, The upper protection action time limit is, The sensitivity compensation coefficient is, The sensitivity coefficient is.

[0105] The topological correlation degree refers to the correlation degree of the relay protection device and its upper relay protection equipment.

[0106] S23, respectively, on each individual in initial setting value population Corresponding sensitivity coefficient, action current threshold and electrical working condition parameter input preset sensitivity function, obtain multiple sensitivity function value.

[0107] In the embodiment of the application, respectively, on each individual in initial setting value population Corresponding sensitivity coefficient, action current threshold and electrical working condition parameter input preset sensitivity function, obtain multiple sensitivity function value.

[0108] It should be noted that the sensitivity function is specifically:

[0109]

[0110] Among them, The sensitivity function value is, The distributed power penetration rate is, The minimum fault current is, is a reference coefficient (usually 1.5), is an action current threshold value.

[0111] S24, respectively, each quick function value and the corresponding selection function value and sensitivity function value input target adaptive function, get a plurality of target adaptive function value.

[0112] In the embodiments of the application, each quick function value and the corresponding selection function value and sensitivity function value are input into the target adaptive function to obtain a plurality of target adaptive function values.

[0113] Step 205, updating the initial setting population according to each target adaptive function value to obtain a new initial setting population.

[0114] In the embodiments of the application, based on the genetic algorithm, the initial setting population is updated according to each target adaptive function value to obtain a new initial setting population. For example, the individuals in the initial setting population are sorted from high to low according to the target adaptive function value corresponding to each individual in the initial setting population, and the individuals with high target adaptive value are selected to form a parent population. Then, the core operation of the genetic algorithm is performed on the parent population: first, the parent individuals participating in the crossover are determined by the roulette selection or the tournament selection strategy, and then the single-point crossover or two-point crossover method is used to recombine the gene of the setting parameter of the parent individual to generate the basic individuals of the offspring population. After that, according to the preset mutation probability (such as 0.01-0.05), the setting parameters of part of the individuals in the offspring population are randomly adjusted to increase the population diversity and avoid falling into a local optimal solution. After the crossover and mutation operations are completed, the offspring population and the remaining high-adaptive parent individuals are combined to form a candidate population, and the candidate population is then screened again according to the target adaptive function value to remove individuals with low adaptive value, and finally a new initial setting population with the same size as the initial setting population is obtained.

[0115] Step 206, determining whether the number of updates of the initial setting population is less than a preset update threshold value.

[0116] The update threshold value refers to the upper limit value of the iteration number pre-configured in the system according to the complexity of the distribution network line, the relay protection setting accuracy requirement and the genetic algorithm running efficiency, and the value is 100.

[0117] In the embodiments of the application, it is determined whether the number of updates of the initial setting population is less than 100.

[0118] Step 207, if the number of updates is less than the update threshold value, then jump to execute the step of determining the target adaptive function value corresponding to the initial setting population based on the target adaptive function according to the action time limit, the action current threshold value, the sensitivity coefficient and the electrical working condition parameters corresponding to each individual in the initial setting population.

[0119] In the embodiment of the present application, if the number of updates is less than the update threshold, the execution jumps to step 204-step 206.

[0120] Step 208, if the number of updates is greater than or equal to the update threshold, the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value in each target adaptive function value are selected as the target setting value optimization parameters and displayed on the optimization page.

[0121] Further, step 208 includes the following sub-steps:

[0122] S31, the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value in each target adaptive function value are selected as the target setting value optimization parameters.

[0123] In the embodiment of the present application, if the number of updates is greater than or equal to the update threshold, the setting value optimization parameters (including the action time limit, the action current threshold and the sensitivity coefficient) corresponding to the minimum value in each target adaptive function value are selected as the target setting value optimization parameters.

[0124] S32, the target setting value optimization parameters are loaded into the feedback component in the optimization page.

[0125] The feedback component refers to a functional module integrated in the optimization page, which is used to receive and visually display the target setting value optimization parameters and associated data, has the ability of data analysis, multi-form presentation (numerical value, table, chart) and operation interaction (confirmation, re-optimization), and helps the operation and maintenance personnel to intuitively obtain the optimization result.

[0126] In the embodiment of the present application, the target setting value optimization parameters are converted into a structured data format recognizable by the feedback component, and the converted target setting value optimization parameters are input into the feedback component in the optimization page.

[0127] S33, the feedback component is rendered to generate the optimization page containing the target setting value optimization parameters.

[0128] In the embodiment of the present application, the optimization page containing the target setting value optimization parameters is generated by rendering the feedback component.

[0129] It is worth mentioning that the operation and maintenance personnel can manually check the target setting value optimization parameters displayed on the optimization page, and then adjust the relay protection device. Or the relay protection device can be adjusted according to the target setting value optimization parameters through the setting value management platform.

[0130] In the embodiment of the present application, when the optimization page receives a fixed value optimization request, the electrical working condition parameters and the running state of the relay protection device are acquired, the initial fixed value population of the relay protection device is constructed based on the preset constraint condition, the preset fitness function is adjusted according to the running state to obtain the corresponding target fitness function, the initial fixed value population is iteratively optimized based on the target fitness function according to the electrical working condition parameters to obtain the corresponding target fixed value optimization parameter and display the target fixed value optimization parameter on the optimization page. The technical problem that the traditional fixed value optimization method of the relay protection device mainly relies on the experience formula and the offline calculation and is difficult to adapt to the dynamic change of the modern power grid and reduces the reliability of the relay protection device operation is overcome. Compared with the traditional fixed value optimization method, the target fixed value optimization parameter is obtained by iteratively optimizing the initial fixed value population based on the target fitness function according to the electrical working condition parameters, and the target fixed value optimization parameter is displayed on the optimization page, the fixed value can be dynamically adjusted according to the electrical working condition parameters and the running state of the relay protection device, so that the dynamic change of the power grid is realized, and the reliability of the relay protection device operation is improved.

[0131] Please refer to Figure 3 , Figure 3 The structure block diagram of a relay protection device fixed value optimization system provided in the third embodiment of the present application is shown.

[0132] The relay protection device fixed value optimization system provided by the present application comprises:

[0133] The acquisition module 301 is configured to acquire the electrical working condition parameters and the running state of the relay protection device when the optimization page receives a fixed value optimization request.

[0134] The construction module 302 is configured to construct the initial fixed value population of the relay protection device based on the preset constraint condition.

[0135] The adjustment module 303 is configured to adjust the preset fitness function according to the running state to obtain the corresponding target fitness function.

[0136] The optimization module 304 is configured to iteratively optimize the initial fixed value population based on the target fitness function according to the electrical working condition parameters to obtain the corresponding target fixed value optimization parameter and display the target fixed value optimization parameter on the optimization page.

[0137] Further, the adjustment module 303 comprises:

[0138] The mapping submodule is configured to determine the corresponding fixed value weight according to the running state based on the preset running state fixed value mapping rule.

[0139] The adjustment submodule is configured to input the fixed value weight into the preset fitness function to obtain the corresponding target fitness function.

[0140] Further, the optimization module 304 comprises:

[0141] a target fitness function submodule configured to determine target fitness function values corresponding to the initial setting population based on the target fitness function and the action time limit, the action current threshold, the sensitivity coefficient and the electrical working condition parameter corresponding to each individual in the initial setting population;

[0142] an updating submodule configured to update the initial setting population according to the target fitness function values to obtain a new initial setting population;

[0143] an analysis submodule configured to determine whether the number of times of updating the initial setting population is less than a preset updating threshold;

[0144] If the number of times of updating is less than the updating threshold, the step of determining the target fitness function values corresponding to the initial setting population based on the target fitness function and the action time limit, the action current threshold, the sensitivity coefficient and the electrical working condition parameter corresponding to each individual in the initial setting population is executed.

[0145] a display submodule configured to select the action time limit, the action current threshold and the sensitivity coefficient corresponding to the minimum value of the target fitness function values as the target setting optimization parameters and display the target setting optimization parameters on an optimization page if the number of times of updating is greater than or equal to the updating threshold.

[0146] Further, the target fitness function submodule comprises:

[0147] a quick action unit configured to determine corresponding quick action function values based on the action time limit, the action current threshold and the electrical working condition parameter corresponding to each individual in the initial setting population;

[0148] a selection unit configured to input the action time limit, the sensitivity coefficient and the electrical working condition parameter corresponding to each individual in the initial setting population into a preset selection function to obtain a plurality of selection function values;

[0149] a sensitivity unit configured to input the sensitivity coefficient, the action current threshold and the electrical working condition parameter corresponding to each individual in the initial setting population into a preset sensitivity function to obtain a plurality of sensitivity function values;

[0150] a fitness unit configured to input each quick action function value, the corresponding selection function value and the sensitivity function value into the target fitness function to obtain a plurality of target fitness function values;

[0151] Further, the electrical working condition parameter comprises a working condition weight coefficient and a fault current amplitude, and the quick action unit comprises:

[0152] a difference subunit configured to perform difference processing on the action current threshold and the fault current amplitude corresponding to each individual in the initial setting population to obtain a plurality of first differences;

[0153] A multiplication subunit is configured to multiply each absolute value of the first difference value by a preset current deviation penalty coefficient to obtain a plurality of first multiplication values.

[0154] A weighting subunit is configured to perform a weighting operation on each action time limit and each first multiplication value corresponding to each individual in each initial setting population based on a working condition weight coefficient to obtain a plurality of quick action function values.

[0155] Further, the display sub-module comprises:

[0156] A selection unit is configured to select an action time limit, an action current threshold and a sensitivity coefficient corresponding to the minimum value in each target fitness function value as a target setting optimization parameter.

[0157] A loading unit is configured to load the target setting optimization parameter to a feedback component in an optimization page.

[0158] A rendering unit is configured to render the feedback component to generate the optimization page containing the target setting optimization parameter.

[0159] Please refer to Figure 4 , Figure 4 a structural block diagram of an electronic device provided in Embodiment Four of the present application.

[0160] An electronic device in an embodiment of the present application comprises a memory 401 and a processor 402, the memory 401 storing a computer program; the computer program is executed by the processor 402 to enable the processor 402 to perform the setting optimization method of the relay protection device according to any one of the above embodiments.

[0161] The memory 401 can be an electronic memory such as a flash memory, an EEPROM (Electrically Erasable Programmable Read-Only Memory), an EPROM, a hard disk or a ROM. The memory 401 has a storage space 403 for program codes 413 for performing any of the method steps in the above described methods. For example, the storage space 403 for program codes can comprise individual program codes 413 for implementing the various steps in the above described methods, respectively. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card or a floppy disk. The program codes can be compressed, for example, in a suitable form. These codes, when run by a computing processing device, cause the computing processing device to perform the individual steps in the above described methods. These program codes can be read from or written to one or more computer program products. These computer program products include program code carriers such as a hard disk, a compact disk (CD), a memory card or a floppy disk. The program codes can be compressed, for example, in a suitable form. These codes, when run by a computing processing device, cause the computing processing device to perform the individual steps in the above described methods for relay protection device setting optimization.

[0162] The embodiment five of the present application further provides a computer readable storage medium, which has stored thereon a computer program, and the computer program is executed by a processor to implement the method for relay protection device setting optimization according to any of the above described embodiments.

[0163] The embodiment six of the present application further provides a computer program product, which comprises a computer program stored on a non-transitory computer readable storage medium, and the computer program comprises program instructions, and when the program instructions are executed by a computer, the computer is caused to perform the method for relay protection device setting optimization according to any of the above described embodiments.

[0164] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above described system, device and unit can refer to the corresponding processes in the above described method embodiments, and will not be described here.

[0165] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0166] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0167] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0168] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

[0169] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the settings of a relay protection device, characterized in that, include: When the optimization page receives a setting optimization request, it obtains the electrical operating parameters and operating status of the relay protection device. The initial setting population of the relay protection device is constructed based on preset constraints. The preset fitness function is adjusted according to the operating state to obtain the corresponding target fitness function; Based on the target fitness function, the initial setpoint population is iteratively optimized according to the electrical operating condition parameters to obtain the corresponding target setpoint optimization parameters, which are then displayed on the optimization page.

2. The method for optimizing the setting of a relay protection device according to claim 1, characterized in that, The step of adjusting the preset fitness function according to the operating state to obtain the corresponding target fitness function includes: Based on the preset operation state fixed value mapping rules, the corresponding fixed value weight is determined according to the operation state; The fixed weights are input into a preset fitness function to obtain the corresponding target fitness function.

3. The relay protection device setting optimization method according to claim 1, characterized in that, The step of iteratively optimizing the initial setpoint population based on the target fitness function and the electrical operating condition parameters to obtain the corresponding target setpoint optimization parameters and displaying them on the optimization page includes: Based on the target fitness function, the target fitness function value corresponding to the initial setpoint population is determined according to the action time limit, action current threshold, sensitivity coefficient and electrical condition parameters corresponding to each individual in the initial setpoint population; The initial fixed-value population is updated according to each of the target fitness function values ​​to obtain a new initial fixed-value population; Determine whether the number of updates to the initial fixed-value population is less than a preset update threshold; If the number of updates is less than the update threshold, then proceed to the step of determining the target fitness function value corresponding to the initial setpoint population based on the target fitness function, according to the action time limit, action current threshold, sensitivity coefficient and electrical condition parameters corresponding to each individual in the initial setpoint population; If the number of updates is greater than or equal to the update threshold, then the action time limit, action current threshold and sensitivity coefficient corresponding to the minimum value among the target fitness function values ​​are selected as target setpoint optimization parameters and displayed on the optimization page.

4. The relay protection device setting optimization method according to claim 3, characterized in that, The step of determining the target fitness function value corresponding to the initial setpoint population based on the target fitness function, according to the action time limit, action current threshold, sensitivity coefficient, and electrical condition parameters corresponding to each individual in the initial setpoint population, includes: The corresponding kinetic function value is determined based on the action time limit, action current threshold and electrical condition parameters of each individual in the initial fixed value population; The action time limit, sensitivity coefficient and electrical condition parameters corresponding to each individual in the initial fixed value population are respectively input into a preset selection function to obtain multiple selection function values; The sensitivity coefficient, operating current threshold, and electrical condition parameters corresponding to each individual in the initial fixed-value population are respectively input into a preset sensitivity function to obtain multiple sensitivity function values; Each of the aforementioned kinetic function values, along with the corresponding selection function value and sensitivity function value, is input into the target adaptation function to obtain multiple target adaptation function values.

5. The relay protection device setting optimization method according to claim 4, characterized in that, The electrical operating condition parameters include operating condition weighting coefficients and fault current amplitudes. The step of determining the corresponding fast-acting function value based on the action time limit, action current threshold, and electrical operating condition parameters corresponding to each individual in the initial setpoint population includes: The action current threshold corresponding to each individual in the initial fixed value population is respectively processed with the fault current amplitude to obtain multiple first differences; The absolute value of each of the first differences is multiplied by a preset current deviation penalty coefficient to obtain multiple first multiplication values; Based on the working condition weight coefficient, the action time limit and the first multiplication value corresponding to each individual in each initial fixed value population are weighted and calculated to obtain multiple kinetic function values.

6. The relay protection device setting optimization method according to claim 3, characterized in that, The step of selecting the minimum value among the target fitness function values, corresponding to the action time limit, action current threshold, and sensitivity coefficient, as the target setpoint optimization parameters and displaying them on the optimization page includes: The minimum value among the various target fitness function values ​​is selected as the action time limit, action current threshold, and sensitivity coefficient as the target setpoint optimization parameters; The target setpoint optimization parameters are loaded into the feedback component within the optimization page; Render the feedback component to generate an optimization page containing the target setpoint optimization parameters.

7. A relay protection device setting optimization system, characterized in that, include: The data acquisition module is used to obtain the electrical operating parameters and operating status of the relay protection device when the optimization page receives a setpoint optimization request; A construction module is used to construct the initial setting population of the relay protection device based on preset constraints; The adjustment module is used to adjust the preset fitness function according to the running state to obtain the corresponding target fitness function; The optimization module is used to iteratively optimize the initial setpoint population based on the target fitness function and the electrical operating condition parameters to obtain the corresponding target setpoint optimization parameters and display them on the optimization page.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the relay protection device setting optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the relay protection device setting optimization method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the relay protection device setting optimization method as described in any one of claims 1-6.

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