Method and device for determining loop closing time based on power grid and electronic equipment

Abstract

The invention discloses a power grid-based loop closing time determination method and apparatus, and an electronic device. The method comprises the following steps: under the condition that a loop closing operation instruction is received, obtaining a steady-state current mean value of a loop closing point in a target power grid and a voltage grade of the loop closing point; determining a target simulation model from a plurality of power grid simulation models based on the steady-state current mean value and the voltage grade; determining loop closing current predicted values in a plurality of time ranges based on the historical loop closing data of the loop closing points; and determining a target time range from the plurality of time ranges based on the loop closing current predicted values of the plurality of time ranges and a target simulation model, the target time range being a time range in which loop closing operation is allowed to be executed. Through the method and the device, the problem that the accuracy of the determined time range is low when the feasible time range of the loop closing operation of the power grid is determined in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power grid closing loop, in particular, to a method and device for determining closing loop time of a power grid and an electronic device. BACKGROUND

[0002] Due to the limitation of historical power transmission capacity, the city power grid in China previously generally adopts the operation mode of "closed loop design and open loop operation". The city power grid of 110kV and below voltage levels mostly adopts the operation mode of radial double power supply form: each load point contains an access power supply and a standby power supply. In general, the load is powered by a single power supply. Once the operating line fails, the closing operation can be performed, and the standby power supply is put into use, thereby partially ensuring the reliability and continuity of power supply.

[0003] With the expansion of city size and the continuous improvement of city power grid construction capacity, the large city power grid in China has more realized double power supply or multi-power supply for load, and has reached the 10kV voltage level. The cross-regional closing loop operation scenario has become the daily work of dispatchers at all levels of large city power grid. At the same time, the city-level power grid dispatching range of large city power grid has covered the 220kV voltage level. The closing loop operation of city dispatch and district dispatch reduces the overall administrative cost and time cost of alternating current. The voltage closing loop operation usually adopts the following mode: the closing loop operation of the two 220kV stations on both sides, that is, first strengthening the electrical connection of the two sides of the closing loop, and then performing the 110kV line closing loop operation. Because the 220kV level power grid line and transformer design has large redundancy, and it is operated with the upper 500kV electromagnetic ring network, the closing loop operation between 220kV is relatively safe.

[0004] Since the 220kV line closing loop has a greater impact on the power flow of the power grid, it will inevitably lead to changes in the power flow of the entire regional power grid after closing loop. Therefore, it is necessary to select a relatively appropriate closing loop time to perform the closing loop operation. At present, the related art relies on manual determination of the feasible time range of the closing loop operation of the power grid, thereby having the problem of low accuracy of the determined time range.

[0005] In view of the above problems in the related art, no effective solution has been proposed so far. SUMMARY

[0006] The main purpose of the present application is to provide a method and device for determining closing loop time of a power grid and an electronic device, so as to solve the problem of low accuracy of the determined time range when determining the feasible time range of the closing loop operation of the power grid in the related art.

[0007] In order to achieve the above object, according to one aspect of the present application, a method for determining a closing time based on a power grid is provided. The method comprises: in the case of receiving a closing operation instruction, obtaining a steady-state current mean value of a closing point in a target power grid, a voltage level at which the closing point is located; determining a target simulation model from a plurality of power grid simulation models based on the steady-state current mean value and the voltage level; determining closing current prediction values of a plurality of time ranges based on historical closing data of the closing point of the closing point; determining a target time range from the plurality of time ranges based on the closing current prediction values of the plurality of time ranges and the target simulation model, wherein the target time range refers to a time range in which the closing operation is allowed to be performed.

[0008] Further, the method for determining a closing time based on a power grid further comprises: calculating a ratio between the steady-state current mean value and a preset rated current to obtain a first ratio, and calculating a ratio between the voltage level and a preset reference voltage to obtain a second ratio; determining a current weight coefficient and a voltage weight coefficient respectively matched with the voltage level; determining a model selection factor based on the current weight coefficient and the first ratio, the voltage weight coefficient and the second ratio; determining the target simulation model from the plurality of power grid simulation models based on a value interval to which the model selection factor belongs.

[0009] Further, the method for determining a closing time based on a power grid further comprises: in the case that the model selection factor is greater than or equal to a preset first threshold value, determining a first simulation model as the target simulation model; in the case that the model selection factor is less than the first threshold value, determining a second simulation model as the target simulation model.

[0010] Further, the method for determining a closing time based on a power grid further comprises: determining at least one candidate time range from the plurality of time ranges based on a size relationship between the closing current prediction values of the plurality of time ranges and a preset second threshold value; for each candidate time range, dividing the candidate time range into a plurality of sub-time periods; for each sub-time period, calculating a closing current prediction value of the sub-time period based on an equivalent impedance of the target simulation model by using a closing current superposition method; determining the target time range from the at least one candidate time range based on the closing current prediction values of the sub-time periods.

[0011] Further, the method for determining the closing-in time of the power grid further comprises: determining a target candidate time range from the at least one candidate time range based on a size relationship between the closing-in current prediction value of the sub-time period in the candidate time range and the third threshold; for each target candidate time range, determining a standard deviation of the closing-in current fluctuation rate of the target candidate time range according to the closing-in current prediction value of each sub-time period in the target candidate time range; determining a distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range, wherein the distributed power refers to a distributed power in a closing-in area of the target power grid; determining a target time range from the target candidate time range based on the standard deviation of the closing-in current fluctuation rate and the distributed power output change rate.

[0012] Further, the method for determining the closing-in time of the power grid further comprises: in a case where the closing-in current prediction values of the N continuous sub-time periods in the candidate time range are all less than the third threshold, determining the candidate time range as a target candidate time range.

[0013] Further, the method for determining the closing-in time of the power grid further comprises: for each time range, determining a closing-in current prediction value of a time termination point of the time range according to a load current mean value in the time range, a historical maximum voltage difference on both sides of the closing-in point, and a historical minimum equivalent impedance between both sides of the closing-in point; and determining the closing-in current prediction value of the time termination point of each time range as the closing-in current prediction value of the time range.

[0014] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for determining the closing-in time of the power grid is provided. The device comprises: an acquisition module configured to acquire a steady-state current mean value of a closing-in point in a target power grid and a voltage level at which the closing-in point is located when a closing-in operation instruction is received; a first determination module configured to determine a target simulation model from a plurality of power grid simulation models based on the steady-state current mean value and the voltage level; a second determination module configured to determine closing-in current prediction values of a plurality of time ranges based on historical closing-in data of the closing-in point; and a third determination module configured to determine a target time range from the plurality of time ranges based on the closing-in current prediction values of the plurality of time ranges and the target simulation model, wherein the target time range refers to a time range in which the closing-in operation is allowed to be performed.

[0015] Further, the first determining module further comprises: a first calculating submodule, configured to calculate a ratio between the steady-state current mean value and the preset rated current to obtain a first ratio, and calculate a ratio between the voltage level and the preset reference voltage to obtain a second ratio; a first determining submodule, configured to determine the current weight coefficient and the voltage weight coefficient respectively matched with the voltage level; a second determining submodule, configured to determine the model selection factor based on the current weight coefficient and the first ratio, the voltage weight coefficient and the second ratio; and a third determining submodule, configured to determine the target simulation model from the plurality of power grid simulation models based on the value interval to which the model selection factor belongs.

[0016] Further, the third determining submodule further comprises: a first determining unit, configured to determine the first simulation model as the target simulation model in a case where the model selection factor is greater than or equal to a preset first threshold value; and a second determining unit, configured to determine the second simulation model as the target simulation model in a case where the model selection factor is less than the first threshold value.

[0017] Further, the third determining module further comprises: a fourth determining submodule, configured to determine at least one candidate time range from the plurality of time ranges based on a size relationship between the closed-loop current prediction values of the plurality of time ranges and a preset second threshold value; a dividing submodule, configured to divide each candidate time range into a plurality of sub-time periods; a second calculating submodule, configured to calculate, for each sub-time period, the closed-loop current prediction value of the sub-time period based on the equivalent impedance of the target simulation model by using the closed-loop current superposition method; and a fifth determining submodule, configured to determine the target time range from the at least one candidate time range based on the closed-loop current prediction values of the sub-time periods.

[0018] Further, the fifth determining submodule further comprises: a third determining unit, configured to determine the target candidate time range from the at least one candidate time range based on a size relationship between the closed-loop current prediction values of the sub-time periods in the candidate time range and a preset third threshold value; a fourth determining unit, configured to determine, for each target candidate time range, the standard deviation of the closed-loop current fluctuation rate of the target candidate time range according to the closed-loop current prediction values of the sub-time periods in the target candidate time range; a fifth determining unit, configured to determine the distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range, wherein the distributed power refers to the distributed power in the closed-loop area of the target power grid; and a sixth determining unit, configured to determine the target time range from the target candidate time range based on the standard deviation of the closed-loop current fluctuation rate and the distributed power output change rate.

[0019] Further, the third determining unit further includes a determining subunit, configured to determine the candidate time range as the target candidate time range in a case that the loop current prediction values of the N continuous sub-time periods in the candidate time range are all less than the third threshold value.

[0020] Further, the second determining module further includes a sixth determining sub-module, configured to determine, for each time range, a loop current prediction value of a time terminal point of the time range according to the average load current in the time range, the historical maximum voltage difference on both sides of the loop point, and the historical minimum equivalent impedance between both sides of the loop point; and a seventh determining sub-module, configured to determine the loop current prediction value of the time terminal point of each time range as the loop current prediction value of the time range.

[0021] To achieve the above object, according to another aspect of the present application, a computer readable storage medium is provided, which includes a stored executable program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the above-mentioned determination method of the loop closing time based on the power grid when the executable program is running.

[0022] To achieve the above object, according to another aspect of the present application, an electronic device is provided, which includes a memory storing an executable program, and a processor configured to run the program, wherein the program executes the above-mentioned determination method of the loop closing time based on the power grid when the program is running.

[0023] To achieve the above object, according to another aspect of the present application, a computer program product is provided, which includes computer instructions, and the computer instructions are executed by a processor to implement the steps of the above-mentioned determination method of the loop closing time based on the power grid.

[0024] In the embodiments of the present application, the loop current prediction values of the multiple time ranges are determined based on the historical loop data of the loop point, the effective prediction of the current values that may occur when the loop operation is performed in the future multiple time periods is achieved, the target time range is determined from the multiple time ranges based on the loop current prediction values of the multiple time ranges and the target simulation model, the selection of the feasible time range for the loop operation is performed based on the predicted data and the simulation related data, and thus the accuracy of the determined feasible time range can be effectively improved, and the dependence on the artificial experience is reduced.

[0025] Therefore, the method provided by the present application achieves the purpose of determining the feasible time range for the loop operation based on the loop current prediction values of the time ranges and the related information of the power grid simulation model, and achieves the technical effect of improving the accuracy of the determined time range, and solves the technical problem of low accuracy of the determined time range when the feasible time range for the loop operation of the power grid is determined in the related art. BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of this application, and of the description of these embodiments, are used to explain the application and do not in any way limit the application. In the drawings:

[0027] Figure 1 is a hardware structure block diagram of a computer terminal provided according to an embodiment of the application;

[0028] Figure 2 is a flow chart of a determination method of a closing time based on a power grid according to an embodiment of the application;

[0029] Figure 3 is a schematic diagram of a determination method of a closing time based on a power grid according to an embodiment of the application;

[0030] Figure 4 is a schematic diagram of a determination device of a closing time based on a power grid according to an embodiment of the application;

[0031] Figure 5 is a structure block diagram of an electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0032] In order to make the personnel in the art better understand the scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the 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 should be within the scope of protection of the present application.

[0033] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] It should be noted that the information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose authorization or refusal. For example, the system and the interface between the related users or institutions are provided with the corresponding operation portal for the user to choose to agree or refuse the automatic decision result; if the user chooses to refuse, the expert decision process is entered.

[0035] Embodiment 1

[0036] According to the embodiments of the present application, an embodiment of a method for determining the closing-in time based on the power grid is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0037] The method embodiment provided by the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for determining the closing-in time based on the power grid is shown. As shown in Figure 1 The computer terminal 10 (or mobile device) can include one or more processors 102 (the processor 102 can include but is not limited to a microcontroller unit (MCU) or a field programmable gate array (FPGA) processing device, etc.), a memory 104 for storing data, and a transmission device 106 for communication function. In addition, it can also include a display, an input / output (I / O) interface, a universal serial bus (USB) port (which can be included as one of the ports in the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or less components than those shown in Figure 1 or have a different configuration than that shown in Figure 1 .

[0038] It should be noted that the one or more processors 102 and / or other data processing circuitry described above can be generally referred to herein as "data processing circuitry". The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single standalone processing module, or incorporated in whole or in part within any of the other elements of the computer terminal 10 (or mobile device). As referred to in embodiments of the present application, the data processing circuitry functions as a processor to control, for example, the selection of the variable resistance terminal path connected to the interface.

[0039] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the determination method of the power grid-based closing time according to embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, i.e. implements the determination method of the power grid-based closing time described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0040] The transmission device 106 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0041] The display can be, for example, a touch screen type liquid crystal display (LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0042] In the above operating environment, the present application provides a determination method of a power grid-based closing time as shown in Figure 2 Figure 2 is a flowchart of the determination method of the power grid-based closing time according to Embodiment 1 of the present application. ​

[0043] Step S201, in the case of receiving the closing operation instruction, the steady-state current average of the closing point in the target power grid, the voltage level where the closing point is located are obtained.

[0044] Optionally, the electronic device, application system, server and the like can be taken as the execution subject of the present application. In the embodiment, the target processing system is taken as the execution subject to execute the above-mentioned determination method of the closing time based on the power grid.

[0045] When the staff of the power grid expects to perform the closing operation, he / she can issue an instruction through the power grid dispatching center, instructing to perform the closing operation on the specific power grid loop, so as to optimize the power transmission or cope with the emergency. The target processing system obtains the steady-state current average of the closing point in the target power grid, the voltage level where the closing point is located in the case of receiving the closing operation instruction. The closing point refers to the specific electrical connection point for the closing operation in the target power grid.

[0046] In an optional embodiment, the target processing system can obtain the following contents in the case of receiving the closing operation instruction:

[0047] Online data (i.e. real-time data): real-time voltage on both sides of the closing point, steady-state current average of the closing point, voltage level where the closing point is located, load data of the closing area where the closing point is located, total load data of the target power grid and real-time output of the distributed power in the closing area. The closing area belongs to the target power grid, and the closing area specifically includes a first closing area and a second closing area, and the first closing area and the second closing area correspond to the closing areas on both sides of the closing point.

[0048] Historical data: historical simulation result data of the closing point, previous day / previous week / previous year same day load data of the closing point, the historical data can be processed by temperature compensation.

[0049] Planning data: load prediction curve (such as active power prediction curve, reactive power prediction curve, voltage prediction curve and the like) of the closing area in the future 24 hours, distributed power output prediction value and other possible data. The planning data is issued by the power grid dispatching center.

[0050] Optionally, the online data can be collected by the synchronous phasor measurement device, and the measurement time alignment error is ≤1 millisecond; the historical data can be established by three-dimensional index according to the voltage level, load rate and new energy penetration rate; the compensation coefficient of the temperature compensation processing can be linearly related to the daily average temperature change.

[0051] In an optional embodiment, the following data cleaning process can be performed on the received data: time periods with current mutation greater than or equal to 20% are directly removed; missing data is interpolated using the average of the previous 5 minutes; and the temperature compensation coefficient is stored according to the season, with the compensation coefficient in winter being 0.05-0.1 higher than that in summer.

[0052] The above data can be stored in a target database. The target database storage structure is "time stamp; current theoretical value and historical calculation value", the association rule is "stored in time sequence; data rolling update", and the data storage optimization method is "stored by time partition, a data file is generated every day, and the index structure includes: date, voltage level, and closing point number".

[0053] In step S202, a target simulation model is determined from a plurality of power grid simulation models based on the steady-state current mean value and the voltage level.

[0054] Optionally, the target processing system can calculate a model selection factor corresponding to the closing point based on the steady-state current mean value and the voltage level, and determine the target simulation model from the plurality of power grid simulation models based on the model selection factor. The power grid simulation model refers to a simulation model of a closing area, and the power grid simulation model includes simulation models corresponding to the first closing area and the second closing area respectively, and the accuracies of different power grid simulation models are different.

[0055] For example, when the model selection factor is greater than or equal to a specific threshold, a refined model containing the topology of the adjacent three-level substation is loaded. When the model selection factor is less than the specific threshold, an equivalent impedance network model is used.

[0056] In step S203, closing current prediction values of a plurality of time ranges are determined based on historical closing data of the closing point.

[0057] Optionally, the historical closing data can include historical maximum voltage difference on both sides of the closing point, historical minimum equivalent impedance between both sides of the closing point, and the like.

[0058] For each time range, the target processing system can determine a closing current prediction value of the time range based on the historical closing data of the closing point and the load current prediction mean value of the closing area in the time range. The plurality of time ranges have the same time length, which can be divided into time periods at a fixed time interval.

[0059] The time termination points of the plurality of time ranges are future time points. The closing current prediction value refers to the current value that may occur when the closing operation is performed in a specific future time range.

[0060] In step S204, a target time range is determined from the plurality of time ranges based on the closing current prediction values of the plurality of time ranges and the target simulation model, wherein the target time range refers to a time range that allows the closing operation to be performed.

[0061] The target processing system can screen multiple time ranges based on the loop current prediction values to obtain candidate time ranges. For example, time ranges with loop current prediction values exceeding a certain current threshold are screened out.

[0062] After obtaining the candidate time ranges, for each candidate time range, it can be divided into finer-grained sub-time periods, and then for each sub-time period, the superposition method is used to calculate the loop current prediction value of the sub-time period based on the equivalent impedance of the target simulation model, so as to judge whether the candidate time range meets the corresponding condition based on the loop current prediction value of the sub-time period, and determine the candidate time range that meets the condition as the target time range.

[0063] After determining the target time range, the target time range can be fed back to the power grid dispatching center so that the staff can know and perform loop operation within the corresponding time range.

[0064] In the embodiments of the present application, the loop current prediction values of multiple time ranges are determined based on the historical loop data of the loop point, which realizes effective prediction of the current values that may occur when loop operation is performed in multiple future time periods. The target time range is determined from the multiple time ranges based on the loop current prediction values of the multiple time ranges and the target simulation model, which realizes screening of the feasible time range for loop operation based on the predicted data and simulation-related data, thereby effectively improving the accuracy of the determined feasible time range and reducing the dependence on manual experience.

[0065] Therefore, the method provided by the present application achieves the purpose of determining the feasible time range for loop operation based on the loop current prediction values of each time range and the relevant information of the power grid simulation model, and realizes the technical effect of improving the accuracy of the determined time range, thereby solving the technical problem of low accuracy of the determined time range for loop operation of the power grid in related technologies.

[0066] Optionally, in the method for determining the loop time of the power grid provided in the embodiments of the present application, the target simulation model is determined from the multiple power grid simulation models based on the steady-state current mean value and the voltage level, which includes: calculating the ratio between the steady-state current mean value and the preset rated current to obtain a first ratio, and calculating the ratio between the voltage level and the preset reference voltage to obtain a second ratio; determining the current weight coefficient and the voltage weight coefficient matched with the voltage level respectively; determining the model selection factor based on the current weight coefficient and the first ratio, the voltage weight coefficient and the second ratio; and determining the target simulation model from the multiple power grid simulation models based on the value interval to which the model selection factor belongs.

[0067] For example, the target processing system can calculate the model selection factor by the following formula:

[0068] a = k1(I_avg / I_base) + k2(U / U_base)

[0069] wherein a represents a model selection factor, k1 represents a current weight coefficient, k2 represents a voltage weight coefficient, I_avg represents a steady-state current average, I_base represents a preset rated current, U represents a voltage level, and U_base represents a preset reference voltage (e.g., 220 kV). The sum of the current weight coefficient and the voltage weight coefficient is 1. That is, the steady-state current average, the voltage level, and the model selection factor are positively correlated.

[0070] In an optional embodiment, the weight coefficients are dynamically assigned according to the voltage level. For example, when the voltage level is greater than or equal to 110 kV, the current weight coefficient is 0.7 and the voltage weight coefficient is 0.3; when the voltage level is less than or equal to 35 kV, the current weight coefficient is 0.4 and the voltage weight coefficient is 0.6.

[0071] In an optional embodiment, the setting of the weight coefficients k1 / k2 is based on: regression analysis is performed on a historical closing operation database to establish the influence weight of the current-voltage pair model error. For example, for a 110 kV closing point, when I_avg / I_base>1, the current factor dominates the error (weight k1=0.7), and when U / U_base<0.5, the voltage factor weight is increased to k2=0.5. The model template calling process includes a data verification step: if the real-time I_avg exceeds a preset threshold I_max (I_max=1.3I_base), the switching to the refined model is forced. The template file is stored in an E format structure, including: ① line parameter table (R, X, B), ② transformer tap position, and ③ distributed power supply switching state.

[0072] After the model selection factor is determined, a target simulation model can be determined from a plurality of power grid simulation models based on the value interval to which the model selection factor belongs. For example, the model selection factor is compared with a preset first threshold, and the target simulation model is determined from the plurality of power grid simulation models based on the comparison result.

[0073] In an optional embodiment, the plurality of power grid simulation models can include: a refined model including line parameters, transformer impedance, and distributed power supply access points, and a model simplified as a π-type equivalent impedance network.

[0074] It should be noted that, in the above manner, the most suitable simulation model is automatically matched according to the specific state of different closing points, thereby improving the accuracy of determining the target simulation model.

[0075] Optionally, in the method for determining the closing time based on the power grid provided in the embodiments of the present application, the plurality of power grid simulation models include a first simulation model and a second simulation model, the simulation precision of the first simulation model is higher than that of the second simulation model, and the target simulation model is determined from the plurality of power grid simulation models based on the value range to which the model selection factor belongs, including: in the case where the model selection factor is greater than or equal to a preset first threshold value, determining the first simulation model as the target simulation model; and in the case where the model selection factor is less than the first threshold value, determining the second simulation model as the target simulation model.

[0076] For example, the preset first threshold value can be 1. When the model selection factor is greater than or equal to 1, a refined model containing the topology of the adjacent third-level transformer substation is loaded, the model integrates the line positive sequence parameters, the transformer short-circuit impedance and the equivalent impedance of the distributed power supply, and it can provide high-precision calculation results and capture subtle changes that may affect the closing operation, thereby ensuring the accuracy and safety of the calculation. When the model selection factor is less than 1, an equivalent impedance network model is used, only the R-L series parameters of the closing path are reserved, and it is suitable for fast calculation and preliminary evaluation.

[0077] In an optional embodiment, the first simulation model includes a main power grid and a distribution network. The first simulation model contains detailed parameters of the 220kV / 110kV transformer substation: line resistance R, reactance X, and ground capacitance C; transformer short-circuit impedance Z_T (unit value); and distributed power supply equivalent impedance Z_DG.

[0078] Optionally, the data of the first simulation model can be obtained from a system real-time library to obtain measured values. When it is necessary to consider the access of the distributed power supply, the equivalent impedance Z_DG = V_inv / I_inv is calculated based on the grid-connected inverter outlet voltage V_inv and the output current I_inv, and the influence of the power factor compensation capacitor is considered. In the distributed power supply point that does not meet the real-time detection requirement, the equivalent impedance Z_DG is modeled in the manner of inverter maximum power point tracking characteristics, and the 1.2 times overload capacity is considered.

[0079] For example, the first simulation model contains:

[0080] (1) adjacent 3-level transformer substation parameters: 220kV bus impedance Z_bus and parallel capacitor bank switching state;

[0081] (2) distributed power supply access point: equivalent internal resistance Z_DG = (V_nom 2 ) / S_DG, where V_nom is the rated voltage of the distributed power supply, and S_DG is the rated capacity.

[0082] The first simulation model N-1 verification implementation step is: traversing single element fault scenarios (total of 12) of adjacent 3-level substations, including: ① main transformer fault exit, ② single circuit line trip, ③ bus voltage loss. Calculate the loop current variation ΔI for each scenario, and require ΔI / I_ref≤8%. Dynamic modeling of the distributed power supply access point: when the distributed power supply DG output changes, update its equivalent impedance Z_DG=V 2 / (P+jQ), where P / Q is the real-time collected active / reactive power.

[0083] In an optional embodiment, the second simulation model includes a distribution network. The distribution network in the second simulation model is simplified as an equivalent impedance network: the 35kV / 10kV line adopts a π-type equivalent circuit (R-L series); adjacent load nodes are combined as equivalent load power S_eq.

[0084] In an optional embodiment, in the second simulation model, the line combination rule is: the multiple parallel lines under the same feeder are simplified as a single line, its equivalent resistance R_eq=ΣR_i / n (n is the number of parallel lines), and the reactance X_eq=ΣX_i / n, and the ground capacitance is ignored, where R_i represents the resistance of the i-th node under the line, and X_i represents the reactance of the i-th node under the line. When the load nodes are combined, the simultaneous coefficient method is adopted: S_eq=K_sim×ΣS_i, where K_sim is taken as 0.7-0.9 according to the load type, S_i represents the load of the i-th node under the line, and S_eq represents the apparent equivalent result. The detailed steps of load equivalence are: for multiple distribution transformers under the same bus, combine their secondary side loads. The equivalent load power S_eq=ΣS_i×K_t, where K_t is the simultaneity (taken as 0.7-0.9), and the power factor is taken as the weighted average value. Error control of line parameter simplification: the difference between the voltage loss ΔU% of the simplified equivalent line and the original network is required to be ≤σ%, and σ can be usually taken as 5. The verification method is to compare the power flow calculation results before and after simplification.

[0085] It should be noted that by constructing a dynamic scaling model combination based on the comprehensive weight of the current mean value and the voltage level, fine modeling of the high voltage level (including line parameters and distributed power access) and fast calculation balance of the equivalent impedance network of the low voltage level are realized, so as to break through the calculation bottleneck of the traditional fixed model. In the relatively high voltage environment, the fine model containing more details is used to ensure safety; in the relatively low voltage environment, the simplified model is used to improve efficiency. This dynamic model selection mechanism can intelligently adjust the fineness of the simulation model according to the real-time running state of the closing loop point, accurately evaluate the feasibility of the high-voltage closing operation, and quickly screen out the feasible time window of the low-voltage closing operation, greatly improving the automation level and intelligent decision-making ability of the cross-regional closing operation of the large urban power grid, significantly reducing the work burden of the power grid dispatching personnel, optimizing the time window selection of the closing operation, and enhancing the stability and safety of the power grid operation.

[0086] Optionally, in the method for determining the closing time based on the power grid provided in the embodiments of the present application, the target time range is determined from the plurality of time ranges based on the closing current prediction value of the plurality of time ranges and the target simulation model, comprising: determining at least one candidate time range from the plurality of time ranges based on the size relationship between the closing current prediction value of the plurality of time ranges and the preset second threshold value; for each candidate time range, dividing the candidate time range into a plurality of sub-time periods; for each sub-time period, calculating the closing current prediction value of the sub-time period based on the equivalent impedance of the target simulation model using the closing current superposition method; and determining the target time range from the at least one candidate time range based on the closing current prediction value of each sub-time period.

[0087] Optionally, the target processing system can divide the remaining period of the day into a plurality of time ranges at a fixed interval of four hours. The aforementioned day refers to the date on which the closing operation instruction is received.

[0088] For each time range, the target processing system can determine whether the closing current prediction value of the time range is less than the second threshold value, so as to determine the time range as a candidate time range in the case that the closing current prediction value of the time range is less than the second threshold value.

[0089] In an optional embodiment, the second threshold value = the early warning threshold value x the threshold value coefficient. Different voltage levels correspond to different second threshold values. For example, the correlation between the voltage level and the early warning threshold value and the threshold value coefficient is shown in Table 1:

[0090] Table 1

[0091] Voltage class Rated current (A) Pre-alarm threshold (A) Threshold factor 220 kV 1250 1000 0.8 110 kV 710 550 0.78 35 kV 350 280 0.8 10 kV 120 100 0.83

[0092] After the candidate time range is determined, the candidate time range is divided into multiple sub-time periods. Optionally, the candidate time range can be divided according to the voltage level. For example, in the case of a voltage level ≥ 110 kV, the initial interval of time division is 30 minutes, that is, the time length of the sub-time period is 30 minutes, which can be reduced to 5 minutes, for example, when the current change rate ≥ 1.5% / min, switching to 5 minutes. In the case of a voltage level ≤ 35 kV, the initial interval of time division is 60 minutes, that is, the time length of the sub-time period is 60 minutes, which can be reduced to 30 minutes, for example, when the current change rate ≥ 3% / min, switching to 15 minutes.

[0093] Optionally, the target processing system can first calculate the equivalent impedance Z_eq of the target simulation model according to the target simulation model. Then, for each sub-time period, the closed-loop current prediction value of the sub-time period is calculated based on the equivalent impedance of the target simulation model using the closed-loop current superposition method. For example, the load current component I_load is calculated according to the following formula:

[0094] I_load = P / (1.73V)

[0095] wherein P is the difference of real-time active power on both sides of the closed-loop point, and P can be determined based on the load prediction curve in the planning data.

[0096] The crossing current component I_cross is calculated according to the following formula:

[0097] I_cross = ΔV / Z_eq

[0098] wherein ΔV is the voltage difference on both sides of the closed-loop point, and ΔV can be determined based on the load prediction curve in the planning data. Z_eq is the equivalent impedance of the target simulation model.

[0099] The closed-loop current prediction value I_total of the sub-time period is calculated according to the following formula:

[0100]

[0101] wherein θ represents the phase difference θ between the load current I_load and the crossing current I_cross, and the vector sum is calculated using complex operation. The phase angle difference θ can be obtained by calculating the voltage phase angle.

[0102] After the closed-loop current prediction value of each sub-time period is determined, the target time range is determined from at least one candidate time range based on the closed-loop current prediction value of each sub-time period. For example, in the case that the closed-loop current prediction values of the continuous N sub-time periods in the candidate time range are all less than the third threshold value, the candidate time range is determined as the target time range.

[0103] It should be noted that by comparison with the second threshold, the time range that obviously does not meet the condition can be quickly excluded. By further subdividing the candidate range, using the superposition method and the equivalent impedance parameters of the target simulation model for detailed analysis, and finally determining the safe and feasible target time range based on the closed-loop current prediction value in the sub-time period, this phased and refined time window screening method not only significantly improves the efficiency of determining the feasible time window of the closed-loop operation, but also improves the accuracy of determining the feasible time window, ensures that the operation is carried out in a time period with stable current change and controllable load, thereby greatly improving the operation safety and effectively avoiding the risk in power grid dispatching.

[0104] Optionally, in the method for determining the closing-in time of the power grid provided in the embodiments of the present application, the target time range is determined from the at least one candidate time range based on the closed-loop current prediction value of each sub-time period, comprising: determining the target candidate time range from the at least one candidate time range based on the size relationship between the closed-loop current prediction value of the sub-time period in the candidate time range and the preset third threshold; for each target candidate time range, determining the standard deviation of the closed-loop current fluctuation rate of the target candidate time range according to the closed-loop current prediction value of each sub-time period in the target candidate time range; determining the distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range, wherein the distributed power refers to the distributed power in the closing-in area of the target power grid; determining the target time range from the target candidate time range based on the standard deviation of the closed-loop current fluctuation rate and the distributed power output change rate.

[0105] Optionally, for each candidate time range, the target processing system can compare the closed-loop current prediction value of each sub-time period in the candidate time range with the preset third threshold respectively, to determine whether to take the candidate time range as the target candidate time range based on the comparison result of each sub-time period.

[0106] After the target candidate time range is determined, for each candidate time range, the standard deviation of the closed-loop current fluctuation rate of the target candidate time range is determined according to the closed-loop current prediction value of each sub-time period in the target candidate time range. The fluctuation rate can be defined as the difference between each prediction value and the average prediction value, for example, for the closed-loop current prediction value of each sub-time period, the ratio change relative to the average value of all prediction values in the target candidate time range is calculated, i.e. the fluctuation rate of a certain sub-time period = (current prediction value-average prediction value) / average prediction value. After the closed-loop current fluctuation rate of each sub-time period is determined, the standard deviation of the closed-loop current fluctuation rate of the target candidate time range is calculated based on the standard deviation formula.

[0107] Optionally, the distributed power source includes, but is not limited to, photovoltaic, wind power, etc. The target processing system can determine the distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range. The distributed power output prediction value can be determined from the planning data. For example, the distributed power output change rate = (maximum output - minimum output) / minimum output x 100%.

[0108] Optionally, after the standard deviation of the closing current fluctuation rate and the distributed power output change rate are determined, they can be compared with the respective corresponding threshold values, so that the target candidate time range is determined as the target time range when the standard deviation of the closing current fluctuation rate is less than or equal to the corresponding threshold value, and the distributed power output change rate is less than or equal to the corresponding threshold value. On the contrary, the target candidate time range is not determined as the target time range.

[0109] For example, in an optional embodiment, the candidate time range that meets the following conditions is determined as the target time range:

[0110] (1) The closing current prediction value of the continuous three and more sub-time periods is less than the corresponding warning threshold;

[0111] (2) The standard deviation of the closing current fluctuation rate is less than or equal to 5%;

[0112] (3) The distributed power output change rate is less than or equal to 15% / min.

[0113] It should be noted that by the above method, the closing current stability in the target candidate time range can be effectively quantified, thereby improving the accuracy of determining the target time range.

[0114] Optionally, in the method for determining the closing-in time of the power grid provided in the embodiments of the present application, the target candidate time range is determined from at least one candidate time range based on the size relationship between the closing current prediction value of the sub-time period in the candidate time range and the preset third threshold value, comprising: in the case that the closing current prediction value of the continuous N sub-time periods in the candidate time range is less than the third threshold value, the candidate time range is determined as the target candidate time range.

[0115] Optionally, N is a positive integer greater than 1. For example, N = 3.

[0116] If there is no continuous N sub-time period in the candidate time range whose closing current prediction value is less than the third threshold value, the candidate time range is not determined as the target candidate time range.

[0117] It should be noted that by the above method, the evaluation of the time range is refined, the accuracy of the screening is improved, and the accurate determination of the target candidate time range is realized.

[0118] Optionally, in the method for determining the closing time based on the power grid provided in the embodiments of the present application, the closing current prediction value of each time range is determined based on the historical closing data of the closing point, including: for each time range, determining the closing current prediction value of the time termination point of the time range according to the average load current in the time range, the historical maximum voltage difference on both sides of the closing point, and the historical minimum equivalent impedance between both sides of the closing point; and determining the closing current prediction value of the time termination point of each time range as the closing current prediction value of the time range.

[0119] Optionally, the target processing system can divide the remaining period of the day into multiple time ranges at a fixed interval of four hours. The aforementioned day refers to the date on which the closing operation instruction is received.

[0120] For example, the target processing system can calculate the closing current prediction value of the time termination point based on the following formula:

[0121]

[0122] wherein I approx represents the closing current prediction value of the time termination point, γ represents a preset error compensation coefficient, I load represents the average load current of the closing area in the current time range, for example, the average load current of the previous four hours of the time termination point. ΔV represents the historical maximum voltage difference on both sides of the closing point. Z eq represents the historical minimum equivalent impedance between both sides of the closing point. Wherein I load is the prediction value, which can be determined from the planning data.

[0123] In an optional embodiment, the closing current prediction value of the time termination point can also be calculated using the closing current superposition method according to the equivalent impedance of the target simulation model.

[0124] Optionally, the closing current prediction value of the time termination point of each time range is determined as the closing current prediction value of the time range.

[0125] It should be noted that by the above method, the closing current prediction value of the time range is accurately determined.

[0126] In an optional embodiment, the target processing system can perform sliding window anomaly detection. The sliding window is used to determine the time range, for example, window length = 4 hours (including 48 5-minute periods), step = 5 minutes (add 1 period each time). The mutation variable AI_step of the sliding window detection formula is: AI_step = |I(t)-I(t-1)| / I(t-1) x 100%. Where I(t) represents the load current value at time t, and when AI_step ≥ 20%, it is marked as an anomaly. When there are 3 consecutive abnormal periods, the system alarm is triggered. The target processing system can also include an abnormal period compensation mechanism. The abnormal period compensation mechanism can be: when a period is marked as abnormal (AI_step ≥ 20%), the system automatically replaces it with the previous period data. The interpolation method is linear extrapolation: I'(t) = I(t-1) + [I(t-1)-I(t-2)] x (t-(t-1)) / (t-1-(t-2)). The target processing system can also perform continuous anomaly processing rules: if the proportion of abnormal periods in the window is ≥ 30%, discard the window and start the manual intervention process. The target processing system can also perform data integrity verification: each sliding window must contain at least 90% valid data, otherwise trigger the data re-sampling request.

[0127] In an optional embodiment, after determining the target time range, the target processing system can output the continuous period stability and error consistency index values of the target time range simultaneously for reference by the staff. The target processing system can determine the continuous period stability based on the length of time in the target time range during which the new energy output is less than the preset output value. The higher the length of time, the worse the stability. The target processing system can calculate the error consistency index value of each sub-period in the target time range by the following formula: consistency = 1- |coarse analysis value - detailed calculation value| / max(coarse analysis value, detailed calculation value), where the coarse analysis value is the average load current of the target time range, and the detailed calculation value is the average load current of the sub-period. The maximum error consistency index value in the target time range is determined as the error consistency index value of the target time range.

[0128] In an optional embodiment, the target processing system can also make dynamic correction and result output. For example, based on the proportional relationship between the real-time measured voltage difference and the historical current deviation average, the equivalent impedance parameters of the simulation model are dynamically updated. When the distributed power output or load fluctuation is detected to be out of limit, the model reconstruction is triggered. The model reconstruction trigger condition is that for the first loop area and the second loop area, the distributed power output fluctuation is calculated respectively: ΔP_DG = |P(t)-P(t-1)| / P(t-1)×100%, where P(t) represents the output power of the distributed power at time t, and when the ΔP_DG of any loop area (i.e. the first loop area or the second loop area) is greater than or equal to 15% / min for 15 minutes, the model reconstruction is triggered. The load fluctuation detection formula is: ΔLoad = |S(t)-S(t-1)| / S(t-1)×100%, S(t) represents the load of the distributed power at time t, and when the ΔLoad of any loop area is greater than or equal to 20% for 15 minutes, the model reconstruction is triggered. In addition, the moving average method can be used for distributed power monitoring rate calculation, and the time window is 15 minutes. The threshold of load fluctuation detection is set to 20%.

[0129] In an optional embodiment, parameter updating can be performed based on the following methods:

[0130] (1) Voltage difference calculation: ΔV = |V1-V2|, where V1 / V2 is the line voltage on both sides of the loop point;

[0131] (2) The historical deviation average ΔI_hist takes the average value of the data in the last one day, and the aforementioned deviation refers to the current deviation between adjacent time points, for example, 4 o'clock relative to 3 o'clock, 3 o'clock relative to 2 o'clock, etc.

[0132] (3) Impedance correction formula: Z_eq' = Z_eq×(ΔV / ΔI_hist), Z_eq is the equivalent impedance of the simulation model.

[0133] Wherein, when the change rate of ΔV / ΔI_hist is greater than or equal to 10%, an alarm is triggered. The physical meaning derivation of the impedance correction formula is: according to the Thevenin theorem, the loop current I = ΔV / (Z_eq+Z_sys), where Z_sys is the equivalent impedance of the target grid. Assuming that the historical deviation ΔI_hist is mainly caused by the Z_eq error, the correction formula Z_eq' = Z_eq×ΔV_real / ΔV_model, where ΔV_real is the measured voltage difference, and ΔV_model = Z_eq×ΔI_hist. When the distributed power output mutation is detected, the system automatically locks the current model parameters, and starts the fast recalculation process based on the data in the last 15 minutes.

[0134] In an optional embodiment, the target processing system can design a parallel computing architecture. For example:

[0135] (1) High-voltage task processing thread allocation rule: each 220kV loop point independently occupies 1 computing thread; thread priority is set to real-time level;

[0136] (2) Calculation time delay control method: sparse matrix compression technology is used for matrix operation; pre-generated topological admittance matrix shortens the calculation time;

[0137] Thread pool optimization management strategy: set high-priority thread pool (thread number = CPU core number x 2) to process 220kV tasks, and normal thread pool (thread number = CPU core number) to process 110kV and below tasks. The calculation task allocation uses greedy algorithm: real-time monitoring of each thread load rate, and the new task is allocated to the most idle thread. Sparse matrix storage uses CSR format (Compressed Sparse Row) to store the non-zero elements of the node admittance matrix Y of the 220kV model. The condition for pre-generating the topological admittance matrix: when the power grid topology structure is continuously unchanged for 24 hours, the system automatically generates and caches the Y matrix.

[0138] In an optional embodiment, the target processing system can include a model construction module, an online calculation module, a time series analysis module, and a decision unit, and an optional workflow of the target processing system can be:

[0139] (1) The model construction module updates the parameter library every 5 minutes;

[0140] (2) The online calculation module performs full-period scanning every hour;

[0141] (3) The time series analysis module starts historical data analysis at 0 o'clock every day;

[0142] (4) The decision unit outputs the latest feasible window in real time.

[0143] An optional task scheduling time sequence control is that the model construction module starts full model verification at 00:00 every day; the online calculation module triggers incremental update every 30 minutes; and the time series analysis module starts data archiving after each loop operation. Abnormal processing priority: model reconstruction request (highest) > real-time calculation task > historical data analysis.

[0144] In an optional embodiment, the actual application process described above is illustrated by a simple example.

[0145] Example 1 (ignoring the impact of distributed power supply, actual power grid data):

[0146] Scenario: A regional power grid B partition bus 2245 switch (220 kV) on February 29, 2024, 23:55:05, steady-state closing current 762.36 A (I_avg = 762.36 A, I_base = 1200 A, U = 220 kV), no distributed power access.

[0147] Technical solution implementation:

[0148] 1. Factor calculation:

[0149] Scaling factor = 0.7 x 762.36 / 1200 + 0.3 x 220 / 220 = 0.7 x 0.6353 + 0.3 x 1 = 0.745.

[0150] 2. Model loading decision: scaling factor < 1, system loads equivalent impedance network model.

[0151] 3. Real-time calculation: equivalent impedance network only retains line R, X; calculates the total current Itotal = 743.28 A, error δ = |762.36 - 743.28| / 762.36 x 100% = 2.5%.

[0152] Beneficial effects: calculation efficiency: model simplification reduces calculation time by 20 seconds; precision guarantee: error meets engineering requirements (<5%).

[0153] Example 2 (considering the impact of distributed power, assuming example):

[0154] Scenario: same closing point, assume access to 50 MW photovoltaic power station (Z_DG = 0.85 + j0.52 Ω), output fluctuation causes real-time current to rise to I_avg = 1024.67 A.

[0155] Technical solution implementation:

[0156] 1. Recalculate factor: scaling factor = 0.7 x 1200 / 1024.67 + 0.3 x 1 = 0.7 x 0.8539 + 0.3 = 0.898.

[0157] 2. Model switching: scaling factor < 1, no need to switch precision.

[0158] 3. Dynamic correction: assume photovoltaic output mutation (ΔP_DG = 8% / min), trigger impedance correction: Zeq' = Zeq x ΔVmodel / ΔVreal = 0.82 x 3.1 / 2.8 = 0.907 Ω; corrected current calculation value Itotal = 1002.45 A, error 2.2%.

[0159] Beneficial effects: error remains <3% under output fluctuation.

[0160] The application can guarantee the calculation accuracy and online calculation real-time requirement in the presence or absence of distributed power supply, and effectively cope with the closing operation demand of complex urban power grid.

[0161] In an optional embodiment, Figure 3 is a schematic diagram of a determination method of a closing time based on a power grid according to an embodiment of the application, as Figure 3 shown, the target processing system can be used to acquire relevant data (such as steady-state current average, voltage level at the closing point, etc.) of the closing point in the case of receiving a closing operation instruction. Then, a model selection factor is calculated based on the acquired relevant data, and a target simulation model is determined according to the model selection factor. Then, it is judged whether model reconstruction is needed, if needed, the closing current is predicted after model reconstruction, if not needed, the closing current is directly predicted. Further, at least one candidate time range is determined from a plurality of time ranges based on the closing current prediction value, and a target time range is determined from the at least one candidate time range, thereby completing the effective determination of the feasible time window of the closing operation.

[0162] Therefore, the method provided by the application achieves the purpose of determining the feasible time range of the closing operation based on the closing current prediction value of each time range and the relevant information of the power grid simulation model, realizes the technical effect of improving the accuracy of the determined time range, and solves the technical problem of low accuracy of the determined time range in determining the feasible time range of the closing operation of the power grid in the related art.

[0163] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0164] Embodiment 2

[0165] The embodiment of the application also provides a determination device for a closing time based on a power grid, and it should be noted that the determination device for a closing time based on a power grid in the embodiment of the application can be used to execute the determination method for a closing time based on a power grid provided by the embodiment of the application. The determination device for a closing time based on a power grid provided by the embodiment of the application is introduced as follows.

[0166] According to the embodiment of the application, a device for implementing the determination method of the closing time based on the power grid is also provided, as Figure 4 shown, the device comprises:

[0167] The acquisition module 401 is configured to acquire a steady-state current mean value of the loop-through point in the target power grid and a voltage level where the loop-through point is located, in a case where the loop-through operation instruction is received.

[0168] The first determination module 402 is configured to determine the target simulation model from a plurality of power grid simulation models based on the steady-state current mean value and the voltage level.

[0169] The second determination module 403 is configured to determine a plurality of time range loop-through current prediction values based on historical loop-through data of the loop-through point.

[0170] The third determination module 404 is configured to determine a target time range from the plurality of time ranges based on the plurality of time range loop-through current prediction values and the target simulation model, wherein the target time range refers to a time range in which the loop-through operation is allowed to be performed.

[0171] In the embodiments of the present application, the plurality of time range loop-through current prediction values are determined based on the historical loop-through data of the loop-through point, so that effective prediction of the current values that may occur when the loop-through operation is performed in the future plurality of time ranges is achieved. The target time range is determined from the plurality of time ranges based on the plurality of time range loop-through current prediction values and the target simulation model, so that the selection of the feasible time range for the loop-through operation is performed based on the predicted data and the simulation related data, thereby effectively improving the accuracy of the determined feasible time range and reducing the dependence on artificial experience.

[0172] Therefore, the method provided in the present application achieves the purpose of determining the feasible time range for the loop-through operation based on the loop-through current prediction values of each time range and the related information of the power grid simulation model, and achieves the technical effect of improving the accuracy of the determined time range, thereby solving the technical problem of low accuracy of the determined time range for the loop-through operation of the power grid in the related art.

[0173] Optionally, in the determination device for the loop-through time of the power grid provided in the embodiments of the present application, the first determination module further includes: a first calculation submodule configured to calculate a ratio between the steady-state current mean value and a preset rated current to obtain a first ratio, and calculate a ratio between the voltage level and a preset reference voltage to obtain a second ratio; a first determination submodule configured to determine a current weight coefficient and a voltage weight coefficient matched with the voltage level respectively; a second determination submodule configured to determine a model selection factor based on the current weight coefficient and the first ratio, the voltage weight coefficient and the second ratio; and a third determination submodule configured to determine the target simulation model from the plurality of power grid simulation models based on a value interval to which the model selection factor belongs.

[0174] Optionally, in the device for determining the closing time based on the power grid provided in the embodiments of the present application, the third determining module further comprises: a first determining unit, configured to determine the first simulation model as the target simulation model when the model selection factor is greater than or equal to a preset first threshold; and a second determining unit, configured to determine the second simulation model as the target simulation model when the model selection factor is less than the first threshold.

[0175] Optionally, in the device for determining the closing time based on the power grid provided in the embodiments of the present application, the third determining module further comprises: a fourth determining submodule, configured to determine at least one candidate time range from the plurality of time ranges based on a size relationship between the closing current prediction values of the plurality of time ranges and a preset second threshold; a division submodule, configured to divide each candidate time range into a plurality of sub-time periods; a second calculating submodule, configured to calculate, for each sub-time period, a closing current prediction value of the sub-time period based on the equivalent impedance of the target simulation model by using the closing current superposition method; and a fifth determining submodule, configured to determine the target time range from the at least one candidate time range based on the closing current prediction values of the sub-time periods.

[0176] Optionally, in the device for determining the closing time based on the power grid provided in the embodiments of the present application, the fifth determining submodule further comprises: a third determining unit, configured to determine a target candidate time range from the at least one candidate time range based on a size relationship between the closing current prediction values of the sub-time periods in the candidate time range and a preset third threshold; a fourth determining unit, configured to determine, for each target candidate time range, a standard deviation of the closing current fluctuation rate of the target candidate time range according to the closing current prediction values of the sub-time periods in the target candidate time range; a fifth determining unit, configured to determine a distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range, wherein the distributed power refers to a distributed power in a closing area of the target power grid; and a sixth determining unit, configured to determine the target time range from the target candidate time range based on the standard deviation of the closing current fluctuation rate and the distributed power output change rate.

[0177] Optionally, in the device for determining the closing time based on the power grid provided in the embodiments of the present application, the third determining unit further comprises: a determining unit, configured to determine the candidate time range as the target candidate time range when the closing current prediction values of the continuous N sub-time periods in the candidate time range are all less than the third threshold.

[0178] Optionally, in the device for determining the loop closing time based on the power grid provided in this application embodiment, the second determining module further includes: a sixth determining submodule, used to determine the predicted value of the loop closing current at the time end point of each time range based on the average load current within the time range, the historical maximum voltage difference between the two sides of the loop closing point, and the historical minimum equivalent impedance between the two sides of the loop closing point; and a seventh determining submodule, used to determine the predicted value of the loop closing current at the time end point of each time range as the predicted value of the loop closing current for that time range.

[0179] It should be noted that the above-mentioned acquisition module 401, first determination module 402, second determination module 403, and third determination module 404 correspond to steps S201 to S204 in Embodiment 1. The four modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above-mentioned modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above-mentioned modules can also be part of the device and run in the computer terminal 10 provided in Embodiment 1.

[0180] Example 3

[0181] Embodiments of this application may provide an electronic device. Figure 5 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0182] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0183] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: in the case of receiving a closing operation instruction, obtaining a steady-state current average of a closing point in a target power grid and a voltage level where the closing point is located; determining a target simulation model from a plurality of power grid simulation models based on the steady-state current average and the voltage level; determining a closing current prediction value of a plurality of time ranges based on historical closing data of the closing point; determining a target time range from the plurality of time ranges based on the closing current prediction value of the plurality of time ranges and the target simulation model, wherein the target time range refers to a time range that allows the closing operation to be performed.

[0184] The processor can also call information and application programs stored in the memory through the transmission device to perform the following steps: calculating a ratio between the steady-state current average and a preset rated current to obtain a first ratio, and calculating a ratio between the voltage level and a preset reference voltage to obtain a second ratio; determining a current weight coefficient and a voltage weight coefficient that respectively match the voltage level; determining a model selection factor based on the current weight coefficient and the first ratio, and the voltage weight coefficient and the second ratio; determining the target simulation model from the plurality of power grid simulation models based on a value interval to which the model selection factor belongs.

[0185] The processor can also call information and application programs stored in the memory through the transmission device to perform the following steps: in the case that the model selection factor is greater than or equal to a preset first threshold value, determining the first simulation model as the target simulation model; in the case that the model selection factor is less than the first threshold value, determining the second simulation model as the target simulation model.

[0186] The processor can also call information and application programs stored in the memory through the transmission device to perform the following steps: determining at least one candidate time range from the plurality of time ranges based on a size relationship between the closing current prediction value of the plurality of time ranges and a preset second threshold value; for each candidate time range, dividing the candidate time range into a plurality of sub-time periods; for each sub-time period, calculating a closing current prediction value of the sub-time period based on an equivalent impedance of the target simulation model using a closing current superposition method; determining the target time range from the at least one candidate time range based on the closing current prediction value of each sub-time period.

[0187] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: determining a target candidate time range from the at least one candidate time range based on a size relationship between the loop current prediction value of each sub time period in the candidate time range and a preset third threshold value; for each target candidate time range, determining a standard deviation of the loop current fluctuation rate of the target candidate time range according to the loop current prediction value of each sub time period in the target candidate time range; determining a distributed power output change rate of the target candidate time range according to the distributed power output prediction value corresponding to the target candidate time range, wherein the distributed power refers to a distributed power in a loop area of the target power grid; and determining a target time range from the target candidate time range based on the standard deviation of the loop current fluctuation rate and the distributed power output change rate.

[0188] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: determining the candidate time range as a target candidate time range in a case where the loop current prediction values of the N continuous sub time periods in the candidate time range are all less than the third threshold value.

[0189] The processor can further call information and application programs stored in the memory through the transmission device to perform the following steps: for each time range, determining a loop current prediction value of a time terminal point of the time range according to a load current average in the time range, a historical maximum voltage difference on both sides of the loop point, and a historical minimum equivalent impedance between both sides of the loop point; and determining the loop current prediction value of the time terminal point of each time range as the loop current prediction value of the time range.

[0190] In the embodiments of the present application, the loop current prediction values of the multiple time ranges are determined based on the historical loop data of the loop point, effective prediction of the current values that may occur when the loop operation is performed in the future multiple time periods is realized, the target time range is determined from the multiple time ranges based on the loop current prediction values of the multiple time ranges and the target simulation model, the selection of the feasible time range for the loop operation is realized based on the predicted data and the simulation related data, and therefore the accuracy of the determined feasible time range can be effectively improved, and the dependence on artificial experience is reduced.

[0191] It can be seen that the method provided in the present application achieves the purpose of determining the feasible time range for the loop operation based on the loop current prediction values of the time ranges and the related information of the power grid simulation model, realizes the technical effect of improving the accuracy of the determined time range, and solves the technical problem of low accuracy of the determined time range in determining the feasible time range for the loop operation of the power grid in the related art.

[0192] Those skilled in the art can understand that Figure 5The structure shown is only schematic, and the electronic device can also be a smart phone, a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, or the like terminal device. Figure 5 It does not limit the structure of the electronic device described above. For example, the electronic device can further include more or less components (such as a network interface, a display device, etc.) than those shown in the figure, or have a different configuration from that shown in the figure. Figure 5 ​ The structure shown is only schematic, and the electronic device can also be a smart phone, a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, or the like terminal device.

[0193] Those skilled in the art can understand that all or part of the steps of the various methods of the above embodiments can be completed by instructing the terminal device related hardware through a program, and the program can be stored in a computer readable storage medium, which can include a flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0194] Embodiment 4

[0195] The embodiments of the present application also provide a storage medium. Optionally, in the present embodiment, the storage medium can be used to save the program code executed by the determination method of the loop time based on the power grid provided in the first embodiment.

[0196] Optionally, in the present embodiment, the storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0197] The present application also provides a computer program product adapted to execute the steps of the determination method of the loop time based on the power grid when executed on a data processing device.

[0198] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0199] In the above embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0200] ​In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, and can be electrical or other forms.

[0201] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0202] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0203] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions 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 method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0204] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for determining the loop closing time based on a power grid, characterized in that, include: Upon receiving a loop closing operation command, obtain the average steady-state current at the loop closing point in the target power grid and the voltage level of the loop closing point. The target simulation model is determined from multiple power grid simulation models based on the mean steady-state current and the voltage level. Based on the historical loop closing data of the loop closing point, predictive values ​​of the loop closing current for multiple time ranges are determined; Based on the predicted loop current values ​​for the multiple time ranges and the target simulation model, a target time range is determined from the multiple time ranges, wherein the target time range refers to the time range within which the loop closing operation is allowed to be performed.

2. The method according to claim 1, characterized in that, The target simulation model is determined from multiple power grid simulation models based on the mean steady-state current and the voltage level, including: Calculate the ratio between the average steady-state current and the preset rated current to obtain a first ratio, and calculate the ratio between the voltage level and the preset reference voltage to obtain a second ratio; Determine the current weighting coefficient and voltage weighting coefficient that are matched to the voltage levels respectively; The model selection factor is determined based on the current weighting coefficient and the first ratio, and the voltage weighting coefficient and the second ratio. The target simulation model is determined from multiple power grid simulation models based on the value range of the model selection factor.

3. The method according to claim 2, characterized in that, The plurality of power grid simulation models includes a first simulation model and a second simulation model. The first simulation model has a higher simulation precision than the second simulation model. The determination of the target simulation model from the plurality of power grid simulation models based on the value range of the model selection factor includes: If the model selection factor is greater than or equal to a preset first threshold, the first simulation model is determined as the target simulation model; If the model selection factor is less than the first threshold, the second simulation model is determined as the target simulation model.

4. The method according to claim 1, characterized in that, Based on the predicted loop current values ​​from the multiple time ranges and the target simulation model, the target time range is determined from the multiple time ranges, including: Based on the relationship between the predicted values ​​of the closed loop current in the plurality of time ranges and the preset second threshold, at least one candidate time range is determined from the plurality of time ranges. For each candidate time range, divide the candidate time range into multiple sub-time periods; For each sub-time period, the predicted value of the closed loop current for that sub-time period is calculated based on the equivalent impedance of the target simulation model using the closed loop current superposition method. The target time range is determined from the at least one candidate time range based on the predicted loop current values ​​for each sub-time period.

5. The method according to claim 4, characterized in that, Based on the predicted loop current values ​​for each sub-time period, the target time range is determined from the at least one candidate time range, including: Based on the relationship between the predicted loop current value of the sub-time period in the candidate time range and the preset third threshold, a target candidate time range is determined from the at least one candidate time range. For each target candidate time range, the standard deviation of the loop current volatility of the target candidate time range is determined based on the predicted loop current values ​​of each sub-time period within the target candidate time range. Based on the predicted output of distributed generation corresponding to the target candidate time range, the rate of change of output of distributed generation in the target candidate time range is determined, wherein distributed generation refers to distributed generation in the loop area of ​​the target power grid. The target time range is determined from the candidate target time ranges based on the standard deviation of the loop current fluctuation rate and the output change rate of the distributed power source.

6. The method according to claim 5, characterized in that, Based on the relationship between the predicted loop current value of the sub-time period in the candidate time range and a preset third threshold, a target candidate time range is determined from the at least one candidate time range, including: If the predicted value of the loop current in N consecutive sub-time periods within the candidate time range is less than the third threshold, the candidate time range is determined as the target candidate time range.

7. The method according to claim 1, characterized in that, Based on historical loop closing data at the loop closing point, predicted loop current values ​​for multiple time ranges are determined, including: For each time range, the predicted value of the loop current at the end of the time range is determined based on the average load current within that time range, the historical maximum voltage difference across the loop point, and the historical minimum equivalent impedance between the two sides of the loop point. The predicted closed-loop current at the end point of each time range is determined as the predicted closed-loop current for that time range.

8. A device for determining the loop closing time of a power grid, characterized in that, include: The acquisition module is used to acquire the average steady-state current of the loop closing point in the target power grid and the voltage level of the loop closing point when a loop closing operation command is received. The first determining module is used to determine the target simulation model from multiple power grid simulation models based on the mean steady-state current and the voltage level; The second determining module is used to determine the predicted values ​​of the loop current for multiple time ranges based on the historical loop closing data of the loop closing point; The third determining module is used to determine a target time range from the multiple time ranges based on the predicted loop current values ​​of the multiple time ranges and the target simulation model, wherein the target time range refers to the time range within which the loop closing operation is allowed to be performed.

9. The apparatus according to claim 8, characterized in that, The first determination module includes: The first calculation submodule is used to calculate the ratio between the average steady-state current and the preset rated current to obtain a first ratio, and to calculate the ratio between the voltage level and the preset reference voltage to obtain a second ratio. The first determining submodule is used to determine the current weighting coefficient and voltage weighting coefficient that are respectively matched for the voltage levels; The second determining submodule is used to determine the model selection factor based on the current weighting coefficient and the first ratio, and the voltage weighting coefficient and the second ratio; The third determination submodule is used to determine the target simulation model from multiple power grid simulation models based on the value range of the model selection factor.

10. The apparatus according to claim 9, characterized in that, The plurality of power grid simulation models include a first simulation model and a second simulation model. The simulation precision of the first simulation model is higher than that of the second simulation model. The third determining submodule includes: The first determining unit is configured to determine the first simulation model as the target simulation model when the model selection factor is greater than or equal to a preset first threshold. The second determining unit is used to determine the second simulation model as the target simulation model when the model selection factor is less than the first threshold.

11. The apparatus according to claim 8, characterized in that, The third determination module includes: The fourth determining submodule is used to determine at least one candidate time range from the multiple time ranges based on the relationship between the predicted values ​​of the closed loop current in the multiple time ranges and a preset second threshold. The sub-module is used to divide each candidate time range into multiple sub-time periods. The second calculation submodule is used to calculate the predicted value of the closed loop current for each sub-time period based on the equivalent impedance of the target simulation model using the closed loop current superposition method. The fifth determining submodule is used to determine the target time range from the at least one candidate time range based on the predicted value of the loop current in each sub-time period.

12. The apparatus according to claim 11, characterized in that, The fifth determination submodule includes: The third determining unit is used to determine a target candidate time range from the at least one candidate time range based on the relationship between the predicted value of the loop current in the sub-time period of the candidate time range and a preset third threshold. The fourth determining unit is used to determine the standard deviation of the loop current volatility of each target candidate time range based on the predicted loop current values ​​of each sub-time period in the target candidate time range. The fifth determining unit is used to determine the rate of change of distributed power output in the target candidate time range based on the predicted output value of the distributed power source corresponding to the target candidate time range, wherein the distributed power source refers to the distributed power source in the loop area of ​​the target power grid. The sixth determining unit is used to determine the target time range from the target candidate time range based on the standard deviation of the loop current fluctuation rate and the output change rate of the distributed power source.

13. The apparatus according to claim 12, characterized in that, The third determining unit includes: A sub-unit is defined to determine the candidate time range as the target candidate time range when the predicted value of the loop current in N consecutive sub-time periods within the candidate time range is less than the third threshold.

14. The apparatus according to claim 8, characterized in that, The second determining module includes: The sixth determination submodule is used to determine the predicted value of the loop current at the end of the time range for each time range based on the average load current within that time range, the historical maximum voltage difference across the loop point, and the historical minimum equivalent impedance between the two sides of the loop point. The seventh determination submodule is used to determine the predicted closed loop current at the end point of each time range as the predicted closed loop current for that time range.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the computer-readable storage medium is located to perform the method for determining the loop closing time based on any one of claims 1 to 7.

16. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for determining the loop closing time based on the power grid as described in any one of claims 1 to 7.

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