New energy station simulation method, device and equipment and readable storage medium

Abstract

The invention discloses a new energy station simulation method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining shared parameters of each target unit and operation data in different operation states; predicting mining forbidding parameters based on the shared parameters and the operation data of the target unit; building a unit simulation model of the target unit based on the shared parameters and the mining forbidding parameters of the target unit; on the basis of simulation operation information and operation data of the unit simulation model in different operation states, optimizing the mining forbidding parameters of the target unit, and returning to execute the step of constructing the unit simulation model of the target unit on the basis of the shared parameters and the mining forbidding parameters of the target unit until a preset stop condition is met; and constructing a new energy station simulation model based on the unit simulation model of each target unit. Therefore, the target new energy station can be simulated in combination with the shared parameters of different units in the target new energy station and the optimized and verified mining prohibition parameters, and the application universality and the simulation precision are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power grids, and more specifically, to a new energy power station simulation method, device, equipment, and readable storage medium. Background Art

[0002] With the increasing demand for energy transformation, new energy power stations have become a core consideration factor for power system planning and power dispatching.

[0003] However, the inherent characteristics of new energy power generation determine that it faces many challenges. The power generation of new energy power stations is strongly affected by natural conditions such as light intensity, wind force, water level drop, and tidal changes, resulting in significant fluctuations in power generation. This volatility brings many uncertainties to the stable operation of the power system. In order to deeply analyze the power generation characteristics of new energy power stations, effectively respond to their impact on the power system, and achieve reliable operation and optimal dispatching of the power system, it is particularly urgent to accurately simulate new energy power stations. However, new energy power stations are composed of different units, and in addition to white box modules, some black box modules are also included in the same unit, resulting in high simulation difficulty of new energy power stations. Summary of the Invention

[0004] In view of this, the present application provides a new energy power station simulation method, device, equipment, and readable storage medium for simulating new energy power stations.

[0005] To achieve the above object, the following solutions are proposed:

[0006] A new energy power station simulation method includes:

[0007] Determine all target units corresponding to the target new energy power station;

[0008] Obtain the shared parameters of each target unit and the operation data in different operation states;

[0009] For each target unit, based on the shared parameters and each operation data of the target unit, predict the forbidden extraction parameters of the target unit; construct a unit simulation model of the target unit based on the shared parameters and the forbidden extraction parameters of the target unit; optimize the forbidden extraction parameters of the target unit based on the simulation operation information and each operation data of the unit simulation model in different operation states, and return to execute the step of constructing a unit simulation model of the target unit based on the shared parameters and the forbidden extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition;

[0010] Construct a new energy power station simulation model based on the unit simulation models of each target unit.

[0011] Optionally, obtain the operation data of each target unit under different operation states, including:

[0012] Obtain the output oscillogram of each target unit under normal operation state, and the output oscillogram of each target unit under different fault states.

[0013] Optionally, predicting the prohibited extraction parameters of the target unit based on the shared parameters and various operation data of the target unit includes:

[0014] Obtain the adjacent prohibited extraction parameters of the adjacent unit with the same type as the target unit;

[0015] Combined with an optimization algorithm, predict the prohibited extraction parameters of the target unit based on the adjacent prohibited extraction parameters, the shared parameters and various operation data of the target unit.

[0016] Optionally, optimizing the prohibited extraction parameters of the target unit based on the simulation operation information and various operation data of the unit simulation model under different operation states includes:

[0017] Combined with an optimization algorithm, calculate the fitness value of the unit simulation model based on the simulation operation information and operation data corresponding to the same operation state;

[0018] Based on the fitness value, optimize the prohibited extraction parameters of the unit simulation model.

[0019] Optionally, constructing a new energy power station simulation model based on the unit simulation models of each target unit includes:

[0020] Cluster each unit simulation model based on the shared parameters and prohibited extraction parameters of each unit simulation model;

[0021] Generate a representative model for the corresponding category based on each unit simulation model belonging to the same category;

[0022] Construct a new energy power station simulation model according to the representative model corresponding to each target unit and the connection mode of each target unit.

[0023] Optionally, generating a representative model for the corresponding category based on each unit simulation model belonging to the same category includes:

[0024] Based on each unit simulation model belonging to the same category, determine the central model of the corresponding category;

[0025] For each category of central models, in combination with the electromagnetic transient theory, generate the circuit internal node power functions and circuit boundary node power functions of the central models. Based on the circuit internal node power functions and circuit boundary node power functions, reduce the order of the central models, and after the order reduction, obtain the representative models of the categories.

[0026] Optionally, it further includes:

[0027] Respond to the unit change operation of the target new energy power station, determine the changed units, and determine the initial units from each target unit;

[0028] Collect the shared parameters of the changed units and the operation data under different operation states;

[0029] Based on the shared parameters of the changed units and each operation data, update the parameters of the unit simulation model corresponding to the initial unit in the new energy power station simulation model of the target new energy power station, so as to update the new energy power station simulation model.

[0030] A new energy power station simulation device includes:

[0031] A determination module, configured to determine all target units corresponding to the target new energy power station;

[0032] An acquisition module, configured to acquire the shared parameters of each target unit and the operation data under different operation states;

[0033] An optimization module, for each target unit, based on the shared parameters of the target unit and each operation data, predict the prohibited extraction parameters of the target unit; construct the unit simulation model of the target unit based on the shared parameters and prohibited extraction parameters of the target unit; based on the simulation operation information and each operation data of the unit simulation model under different operation states, optimize the prohibited extraction parameters of the target unit, and return to execute the step of constructing the unit simulation model of the target unit based on the shared parameters and prohibited extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition;

[0034] A construction module, configured to construct a new energy power station simulation model based on the unit simulation models of each target unit.

[0035] A new energy power station simulation device includes a memory and a processor;

[0036] The memory is used to store programs;

[0037] The processor is configured to execute the programs to implement each step of the above new energy power station simulation method.

[0038] A readable storage medium stores a computer program thereon. When the computer program is executed by a processor, each step of the above-mentioned new energy power station simulation method is implemented.

[0039] As can be seen from the above technical solution, the new energy power station simulation method provided by this application can determine all target units corresponding to the target new energy power station; obtain the shared parameters of each target unit and the operation data under different operation states; for each target unit, predict the prohibited extraction parameters of the target unit based on the shared parameters and each operation data of the target unit; construct a unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit; based on the simulation operation information and each operation data of the unit simulation model under different operation states, optimize the prohibited extraction parameters of the target unit, and return to execute the step of constructing the unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition; based on this, this application can predict and optimize the prohibited extraction parameters by using the available shared parameters and the operation data under different operation states, breaking through the modeling dilemma caused by the inability to directly obtain some parameters. It is ensured that even in the case of data acquisition limitations, this application can still construct a unit simulation model including the influence of prohibited extraction parameters, improving the application universality of this application. Optimize the prohibited extraction parameters according to the simulation operation information and actual operation data of the unit simulation model, and continuously adjust the prohibited extraction parameters to better conform to the actual operation situation by iteratively constructing the unit simulation model, improving the reliability of the prohibited extraction parameters, thereby improving the accuracy of the unit simulation model, further ensuring the characterization ability of the unit simulation model for the dynamic behavior and static performance of the target unit, and improving the simulation accuracy. Subsequently, based on the unit simulation models of each target unit, construct a new energy power station simulation model; based on this, this application constructs a new energy power station simulation model based on each unit simulation model that has been parameter-optimized and verified, ensuring that the overall new energy power station simulation model can more accurately reflect various complex situations in the actual operation of the power station, including the interaction between units and the dynamic behavior and static performance of the overall power station, providing a more reliable basis for power system planning and dispatching analysis, helping relevant personnel make more reasonable and effective decisions, and improving the stability and reliability of the power system. It can be seen that this application can simulate the target new energy power station by combining the shared parameters of different units in the target new energy power station and each optimized and verified prohibited extraction parameter, improving the application universality and simulation accuracy. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided accompanying drawings.

[0041] Figure 1 Flowchart of a new energy power station simulation method disclosed in an embodiment of the present application;

[0042] Figure 2 Block diagram of the structure of a new energy power station simulation device disclosed in an embodiment of the present application;

[0043] Figure 3 Hardware block diagram of a new energy power station simulation device disclosed in an embodiment of the present application. Detailed implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0045] The embodiment of the present application provides a new energy power station simulation method. This new energy power station simulation method can be integrated into an automated script and applied to platforms such as the DIgSILENT PowerFactory platform, PSSE platform, PSCAD platform, RTDS platform, or MATLAB platform, and can also be applied to various computer terminals or intelligent terminals. Its execution entity can be the processor or server of a computer terminal or an intelligent terminal.

[0046] Among them, the PSCAD platform is mainly used for the dynamic simulation of power systems; the RTDS platform, as a real-time digital simulation system, has the advantage of real-time dynamic simulation capabilities; the MATLAB platform has strong data processing and algorithm optimization capabilities.

[0047] At the same time, through a unified data exchange interface, seamless collaboration between the PSCAD platform, RTDS platform, and MATLAB platform can be achieved to ensure the consistency of the new energy power station simulation model in static and dynamic simulations.

[0048] Next, in combination with Figure 1 A detailed introduction to the new energy power station simulation method of the present application is as follows:

[0049] Step S1: Determine all target units corresponding to the target new energy power station.

[0050] Specifically, the new energy power station that needs to be simulated can be used as the target new energy power station, and each new energy unit included in the target new energy power station can be used as each target unit.

[0051] In the same new energy power station, there can be different types of target units or the same type of target units.

[0052] Step S2: Obtain the shared parameters of each target unit and the operation data under different operation states.

[0053] Specifically, each target unit may include a white box module and a black box module, and the shared parameters corresponding to the white box module can be collected; that is, the shared parameters are the publicly available and collectable parameters of the corresponding target unit.

[0054] Since the parameters in the black box module have the characteristic of being uncollectable, the voltage data, current data, power data, temperature data and other operation data of the target unit under different operation states can be collected, so as to obtain the prohibited collection data through debugging of the operation data.

[0055] Among them, the shared parameters collected by each sensor and the operation data under different operation states can be obtained, and preprocessing operations such as noise filtering, missing value filling and data standardization processing are performed on each collected shared parameter and each operation data.

[0056] Step S3: For each target unit, based on the shared parameters and each operation data of the target unit, predict the prohibited collection parameters of the target unit; based on the shared parameters and prohibited collection parameters of the target unit, construct the unit simulation model of the target unit; based on the simulation operation information and each operation data of the unit simulation model under different operation states, optimize the prohibited collection parameters of the target unit, and return to the step of constructing the unit simulation model of the target unit based on the shared parameters and prohibited collection parameters of the target unit until the latest unit simulation model meets the preset stop condition.

[0057] Specifically, the unit simulation models of each target unit can be constructed in sequence.

[0058] During the construction process, methods such as deep learning or support vector machine (SVM) can be combined to generate feasible prohibited collection parameters according to the shared parameters and each operation data of the target unit;

[0059] Based on the shared parameters and prohibited extraction parameters of the same target unit, a unit simulation model corresponding to the target unit is constructed. According to the simulation operation information of the unit simulation model under different operating conditions and the various operation data of the target unit, combined with the gradient descent method, the prohibited extraction parameters are optimized and adjusted until the unit simulation model converges.

[0060] The prohibited extraction parameters of each optimized target unit can be synchronized to each platform through the data exchange interface to ensure the unity of the new energy power station simulation models of each platform.

[0061] Each simulation operation information can indicate the operation condition of the corresponding unit simulation model under the corresponding operating condition;

[0062] Each operation data can indicate the operation condition of the corresponding target unit under the corresponding operating condition.

[0063] Step S4: Based on the unit simulation models of each target unit, a new energy power station simulation model is constructed.

[0064] Specifically, according to the connection mode of each target unit, the unit simulation models of each target unit are processed to construct a new energy power station simulation model.

[0065] The unit simulation models, prohibited extraction data, and shared data of each target unit can be stored.

[0066] As can be seen from the above technical solution, the new energy power station simulation method provided by this application can determine all target units corresponding to the target new energy power station; obtain the shared parameters of each target unit and the operation data under different operation states; for each target unit, based on the shared parameters and each operation data of the target unit, predict the prohibited extraction parameters of the target unit; construct a unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit; based on the simulation operation information and each operation data of the unit simulation model under different operation states, optimize the prohibited extraction parameters of the target unit, and return to the step of constructing the unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition; based on this, this application can predict and optimize the prohibited extraction parameters by using the available shared parameters and the operation data under different operation states, breaking through the modeling dilemma caused by the inability to directly obtain some parameters. It is ensured that even in the case of data acquisition limitations, this application can still construct a unit simulation model including the influence of prohibited extraction parameters, improving the application universality of this application. Optimize the prohibited extraction parameters according to the simulation operation information and actual operation data of the unit simulation model, and construct the unit simulation model iteratively, so that the prohibited extraction parameters can be continuously adjusted to better conform to the actual operation situation, improving the reliability of the prohibited extraction parameters, thereby improving the accuracy of the unit simulation model, further ensuring the characterization ability of the unit simulation model for the dynamic behavior and static performance of the target unit, and improving the simulation accuracy. Subsequently, based on the unit simulation models of each target unit, construct a new energy power station simulation model; based on this, this application constructs a new energy power station simulation model based on each unit simulation model that has been parameter-optimized and verified, ensuring that the overall new energy power station simulation model can more accurately reflect various complex situations in the actual operation of the power station, including the interaction between units and the dynamic behavior and static performance of the overall power station, providing a more reliable basis for power system planning and dispatching analysis, helping relevant personnel make more reasonable and effective decisions, and improving the stability and reliability of the power system. It can be seen that this application can simulate the target new energy power station by combining the shared parameters of different units in the target new energy power station and each prohibited extraction parameter that has been optimized and verified, improving the application universality and simulation accuracy.

[0067] In some embodiments of this application, the process of obtaining the operation data of each target unit under different operation states in step S2 is described in detail as follows:

[0068] S20. Obtain the output oscillogram of each target unit in the normal operation state and the output oscillogram of each target unit in different fault states.

[0069] Specifically, the fault state may include a short-circuit state, an open-circuit state, an open state, etc.

[0070] The output oscillogram can include voltage output oscillogram, current output oscillogram, etc.

[0071] As can be seen from the above technical solution, this embodiment provides an optional way to obtain the operation data of the target unit in different operation states. Through the above method, the output oscillograms in the normal operation state and the fault state can be collected for optimizing the parameters to be prohibited from collection, so as to better reflect the characteristics of the target unit through the output oscillogram and further ensure the accuracy of the parameters to be prohibited from collection.

[0072] In some embodiments of the present application, the process of predicting the parameters to be prohibited from collection of the target unit based on the shared parameters and each operation data of the target unit in step S3 is described in detail as follows:

[0073] S30. Obtain the adjacent parameters to be prohibited from collection of the adjacent unit with the same type as the target unit.

[0074] Specifically, the unit with the same manufacturer and the same rated power as the target unit can be used as the adjacent unit, and the parameters to be prohibited from collection of the adjacent unit are obtained as the adjacent parameters to be prohibited from collection.

[0075] S31. Combine an optimization algorithm to predict the parameters to be prohibited from collection of the target unit based on the adjacent parameters to be prohibited from collection, the shared parameters of the target unit, and each operation data.

[0076] Specifically, optimization algorithms such as genetic algorithm or particle swarm algorithm can be combined to comprehensively consider the adjacent parameters to be prohibited from collection, the shared parameters of the corresponding target unit, and each operation data to predict the feasible parameters to be prohibited from collection.

[0077] As can be seen from the above technical solution, this embodiment provides an optional way to predict the parameters to be prohibited from collection of the target unit based on the shared parameters and each operation data of the target unit. Through the above method, the parameters to be prohibited from collection can be further predicted by combining the parameters of other units and the optimization algorithm, further improving the reliability of the initial parameters to be prohibited from collection and accelerating the optimization process.

[0078] On this basis, in some embodiments of the present application, the process of optimizing the parameters to be prohibited from collection of the target unit based on the simulation operation information and each operation data of the unit simulation model in different operation states in step S3 is described in detail as follows:

[0079] S32. Combine an optimization algorithm to calculate the fitness value of the unit simulation model based on the simulation operation information and operation data corresponding to the same operation state.

[0080] Specifically, in combination with an optimization algorithm, the loss degree between the simulation operation information and operation data under the same operation state can be calculated, and the sum of the loss values under each operation state is calculated as the fitness value of the unit simulation model.

[0081] S33. Optimize the prohibited mining parameters of the unit simulation model based on the fitness value.

[0082] Specifically, with reference to the fitness value and in combination with an optimization algorithm, the prohibited mining parameters of the unit simulation model can be mutated to obtain optimized prohibited mining parameters.

[0083] It can be seen from the above technical solutions that this embodiment provides an optional method for optimizing the prohibited mining parameters of the target unit based on the simulation operation information and various operation data of the unit simulation model under different operation states. Through the above method, the optimization algorithm can be further integrated to adjust the prohibited mining parameters to ensure the credibility of the prohibited mining parameters.

[0084] In some embodiments of the present application, the process of step S4, constructing a new energy power station simulation model based on the unit simulation models of each target unit, is described in detail as follows:

[0085] S40. Cluster each unit simulation model based on the shared parameters and prohibited mining parameters of each unit simulation model.

[0086] Specifically, in combination with a clustering algorithm, analyze the shared parameters and prohibited mining parameters corresponding to each unit simulation model to cluster each unit simulation model.

[0087] S41. Generate a representative model for the corresponding category based on each unit simulation model belonging to the same category.

[0088] Specifically, analyze each unit simulation model corresponding to the same category and construct a representative representative model.

[0089] S42. Construct a new energy power station simulation model according to the representative model corresponding to each target unit and the connection mode of each target unit.

[0090] Specifically, determine the connection mode of each target unit, connect the representative models corresponding to each target unit, and form a new energy power station simulation model.

[0091] As can be seen from the above technical solutions, this embodiment provides an optional method for constructing a new energy power station simulation model based on the unit simulation models of each target unit. Through the above method, the present application can use the representative model to replace the similar unit simulation models as much as possible, effectively reducing the complexity of a single model, and then significantly reducing the calculation amount during the simulation operation, and improving the efficiency and fluency of the overall simulation.

[0092] In some embodiments of the present application, the process of step S41, generating a representative model for each category based on the unit simulation models belonging to the same category, is described in detail as follows:

[0093] S410. Based on the unit simulation models belonging to the same category, determine the central model of the corresponding category.

[0094] Specifically, there are various ways to determine the central model of the corresponding category based on the unit simulation models belonging to the same category.

[0095] For example, the average parameter values of the unit simulation models belonging to the same category can be calculated, and the central model of the corresponding category can be constructed with the average parameter values.

[0096] It is also possible to select the unit simulation model closest to the central tendency from the unit simulation models belonging to the same category as the prototype, and transform the prototype according to other unit simulation models. After the transformation, the central model of the corresponding category is obtained.

[0097] S411. For the central model of each category, in combination with the electromagnetic transient theory, generate the circuit internal node power function and the circuit boundary node power function of the central model. Based on the circuit internal node power function and the circuit boundary node power function, reduce the order of the central model. After the order reduction, the representative model of the category is obtained.

[0098] Specifically, the order of the central model of each category can be reduced.

[0099] In this process, in combination with the electromagnetic transient theory, analyze the circuit internal node calculation function, current response characteristic function and circuit internal node current input and output function of the central model, and generate the circuit boundary node current calculation function and the circuit internal node current calculation function based on the current response characteristic function and the circuit internal node current input and output function; comprehensively consider the circuit internal node calculation function, the circuit boundary node current calculation function and the circuit internal node current calculation function, determine the internal nodes that need to be fused, and integrate the internal nodes that need to be fused in the central model. After the integration, the representative model of the corresponding category is obtained.

[0100] As can be seen from the above technical solution, this embodiment provides an optional method for generating a representative model of a corresponding category based on the simulation models of each unit belonging to the same category. Through the above method, the central model can be further simplified by order reduction to reduce the computational amount of the representative model.

[0101] In some embodiments of the present application, considering that after building the simulation model of the new energy power station, there may be a situation where the target unit is replaced due to an operation failure. In this case, the present application can correspondingly update the simulation model of the new energy power station to improve the reliability of the simulation model. Next, the update process will be described in detail as follows:

[0102] S5. Respond to the unit change operation of the target new energy power station, determine the changed unit, and determine the initial unit from each target unit.

[0103] Specifically, the automation script can respond to the unit change operation of the target new energy power station, determine the new unit as the changed unit, and identify the initial unit from each target unit.

[0104] S6. Collect the shared parameters and operation data of the changed unit under different operation states.

[0105] Specifically, the shared parameters and operation data of the changed unit collected by each sensor can be obtained.

[0106] S7. Based on the shared parameters and each operation data of the changed unit, update the parameters of the unit simulation model corresponding to the initial unit in the new energy power station simulation model of the target new energy power station to update the new energy power station simulation model.

[0107] Specifically, based on the simulation operation information of the unit simulation model corresponding to the initial unit under different operation states and each operation data of the changed unit, the prohibited sampling parameters corresponding to the unit simulation model can be optimized until the latest unit simulation model converges.

[0108] Each shared parameter and prohibited sampling parameter can be converted into JSON or XML as the data format through the data exchange interface, and the middleware is introduced for protocol conversion, and the converted data is uploaded to the control center to synchronize the shared parameters and prohibited sampling parameters of the changed unit.

[0109] Data interaction can also be directly carried out between each platform.

[0110] For example, the PSCAD platform and the RTDS platform can carry out data interaction through the TCP / IP or shared memory mechanism.

[0111] For another example, MATLAB can utilize a TCP server to listen for data from the PSCAD platform and the RTDS platform, and through the tcpserver function, parse and store the data.

[0112] It is also possible to use the UDP Receive module in MATLAB's Simulink to directly receive real-time data from the PSCAD platform and the RTDS platform.

[0113] As can be seen from the above technical solutions, compared with the previous embodiment, this embodiment provides an update process for the new energy power station simulation model, and correspondingly adjusts the unit simulation model in the new energy power station simulation model to ensure the accuracy of the new energy power station simulation model.

[0114] Next, it will be combined with Figure 2 The new energy power station simulation device provided by this application will be introduced in detail below. The new energy power station simulation device provided below can be compared with the new energy power station simulation method provided above.

[0115] Referring to Figure 2 It can be found that the new energy power station simulation device may include:

[0116] A determination module 10, configured to determine all target units corresponding to the target new energy power station;

[0117] An acquisition module 20, configured to acquire the shared parameters of each target unit and the operation data in different operation states;

[0118] An optimization module 30, configured to, for each target unit, predict the prohibited extraction parameters of the target unit based on the shared parameters and each operation data of the target unit; construct the unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit; optimize the prohibited extraction parameters of the target unit based on the simulation operation information and each operation data of the unit simulation model in different operation states, and return to execute the step of constructing the unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition;

[0119] A construction module 40, configured to construct a new energy power station simulation model based on the unit simulation models of each target unit.

[0120] Furthermore, the acquisition module 20 may include:

[0121] An output oscillogram acquisition unit, configured to acquire the output oscillogram of each target unit in the normal operation state and the output oscillogram of each target unit in different fault states.

[0122] Furthermore, the optimization module 30 may include:

[0123] An adjacent prohibited mining parameter acquisition unit, configured to acquire the adjacent prohibited mining parameters of adjacent units of the same type as the target unit;

[0124] A prohibited mining parameter prediction unit, configured to combine an optimization algorithm and predict the prohibited mining parameters of the target unit based on the adjacent prohibited mining parameters, the shared parameters of the target unit, and each operation data.

[0125] Furthermore, the optimization module 30 may further include:

[0126] A fitness value calculation unit, configured to combine an optimization algorithm and calculate the fitness value of the unit simulation model based on the simulation operation information and operation data corresponding to the same operation state;

[0127] A prohibited mining parameter optimization unit, configured to optimize the prohibited mining parameters of the unit simulation model based on the fitness value.

[0128] Furthermore, the construction module 40 may include:

[0129] A unit simulation model clustering unit, configured to cluster each unit simulation model based on the shared parameters and prohibited mining parameters of each unit simulation model;

[0130] A representative model generation unit, configured to generate a representative model of a corresponding category based on each unit simulation model belonging to the same category;

[0131] A new energy station simulation model construction unit, configured to construct a new energy station simulation model according to the representative model corresponding to each target unit and the connection mode of each target unit.

[0132] Furthermore, the representative model generation unit may include:

[0133] A first representative model generation subunit, configured to determine a central model of a corresponding category based on each unit simulation model belonging to the same category;

[0134] A second representative model generation subunit, configured to, for the central model of each category, combine the electromagnetic transient theory to generate the circuit internal node power function and the circuit boundary node power function of the central model, and based on the circuit internal node power function and the circuit boundary node power function, reduce the order of the central model, and after the order reduction, obtain the representative model of the category.

[0135] Furthermore, the new energy station simulation device may further include:

[0136] An initial unit determination unit, configured to respond to the unit change operation of the target new energy station, determine the changed unit, and determine the initial unit from each target unit;

[0137] A shared parameter acquisition unit for acquiring the shared parameters of the changed unit and the operation data in different operation states;

[0138] A shared parameter utilization unit for updating the parameters of the unit simulation model corresponding to the initial unit in the new energy power station simulation model of the target new energy power station based on the shared parameters of the changed unit and each operation data, so as to update the new energy power station simulation model.

[0139] The new energy power station simulation device provided by the embodiments of the present application can be applied to new energy power station simulation devices, such as PC terminals, cloud platforms, servers, server clusters, etc. Optionally, Figure 3 The hardware structure block diagram of the new energy power station simulation device is shown. Refer to Figure 3 , the hardware structure of the new energy power station simulation device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0140] In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete mutual communication through the communication bus 4;

[0141] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0142] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0143] Wherein, the memory stores a program, and the processor can call the program stored in the memory. The program is used for:

[0144] Determine all target units corresponding to the target new energy power station;

[0145] Obtain the shared parameters of each target unit and the operation data in different operation states;

[0146] For each target unit, based on the shared parameters and various operation data of the target unit, predict the prohibited mining parameters of the target unit; based on the shared parameters and prohibited mining parameters of the target unit, construct a unit simulation model of the target unit; based on the simulation operation information and various operation data of the unit simulation model under different operation states, optimize the prohibited mining parameters of the target unit, and return to the step of constructing the unit simulation model of the target unit based on the shared parameters and prohibited mining parameters of the target unit until the latest unit simulation model meets the preset stop condition;

[0147] Based on the unit simulation models of each target unit, construct a new energy station simulation model.

[0148] Optionally, the refinement function and expansion function of the program can be referred to the above description.

[0149] The embodiment of the present application also provides a readable storage medium, which can store a program suitable for being executed by a processor, and the program is used for:

[0150] Determine all target units corresponding to the target new energy station;

[0151] Obtain the shared parameters of each target unit and the operation data under different operation states;

[0152] For each target unit, based on the shared parameters and various operation data of the target unit, predict the prohibited mining parameters of the target unit; based on the shared parameters and prohibited mining parameters of the target unit, construct a unit simulation model of the target unit; based on the simulation operation information and various operation data of the unit simulation model under different operation states, optimize the prohibited mining parameters of the target unit, and return to the step of constructing the unit simulation model of the target unit based on the shared parameters and prohibited mining parameters of the target unit until the latest unit simulation model meets the preset stop condition;

[0153] Based on the unit simulation models of each target unit, construct a new energy station simulation model.

[0154] Optionally, the refinement function and expansion function of the program can be referred to the above description.

[0155] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0156] The various embodiments in this specification are described in a progressive manner, and the key points of each embodiment are the differences from other embodiments. For the same or similar parts among the various embodiments, reference may be made to each other.

[0157] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments of the present application can be combined with each other. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A new energy power station simulation method, characterized in that Including: Determine all target units corresponding to the target new energy power station; Obtain the shared parameters of each target unit and the operation data under different operation states; For each target unit, based on the shared parameters and each operation data of the target unit, predict the prohibited extraction parameters of the target unit; construct a unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit; based on the simulation operation information and each operation data of the unit simulation model under different operation states, optimize the prohibited extraction parameters of the target unit, and return to the step of constructing the unit simulation model of the target unit based on the shared parameters and the prohibited extraction parameters of the target unit until the latest unit simulation model meets the preset stop condition; Construct a new energy power station simulation model based on the unit simulation models of each target unit.

2. The new energy power station simulation method according to claim 1, characterized in that Obtain the operation data of each target unit under different operation states, including: Obtain the output oscillogram of each target unit in the normal operation state and the output oscillogram of each target unit in different fault states.

3. The new energy power station simulation method according to claim 1, characterized in that, The predicting the prohibited extraction parameters of the target unit based on the shared parameters and each operation data of the target unit includes: Obtain the adjacent prohibited extraction parameters of the adjacent units of the same type as the target unit; Combined with the optimization algorithm, based on the adjacent prohibited extraction parameters, the shared parameters and each operation data of the target unit, predict the prohibited extraction parameters of the target unit.

4. The new energy power station simulation method according to claim 3, characterized in that, The optimizing the prohibited extraction parameters of the target unit based on the simulation operation information and each operation data of the unit simulation model under different operation states includes: Combined with the optimization algorithm, calculate the fitness value of the unit simulation model based on the simulation operation information and the operation data corresponding to the same operation state; Based on the fitness value, optimize the prohibited extraction parameters of the unit simulation model.

5. The new energy power station simulation method according to claim 1, wherein The constructing a new energy power station simulation model based on the unit simulation models of each target unit includes: Cluster each unit simulation model based on the shared parameters and the prohibited extraction parameters of each unit simulation model; Generate a representative model for the corresponding category based on each unit simulation model belonging to the same category; Construct a new energy power station simulation model according to the representative model corresponding to each target unit and the connection mode of each target unit.

6. The new energy power station simulation method according to claim 5, wherein The generating a representative model for the corresponding category based on each unit simulation model belonging to the same category includes: Based on each unit simulation model belonging to the same category, determine the central model of the corresponding category; For the central model of each category, combined with the electromagnetic transient theory, generate the circuit internal node power function and the circuit boundary node power function of the central model, and based on the circuit internal node power function and the circuit boundary node power function, reduce the order of the central model, and the reduced-order model is the representative model of the category.

7. The new energy power station simulation method according to claim 1, wherein Also including: Respond to the unit change operation of the target new energy power station, determine the changed unit, and determine the initial unit from each target unit; Collect the shared parameters and the operation data under different operation states of the changed unit; Based on the shared parameters and various operation data of the changed unit, update the parameters of the unit simulation model corresponding to the initial unit in the new energy power station simulation model of the target new energy power station, so as to update the new energy power station simulation model.

8. A new energy power station simulation device, characterized in that, It includes: A determination module, configured to determine all target units corresponding to the target new energy power station; An acquisition module, configured to acquire the shared parameters of each target unit and the operation data under different operation states; An optimization module, for each target unit, based on the shared parameters and various operation data of the target unit, predict the prohibited mining parameters of the target unit; construct the unit simulation model of the target unit based on the shared parameters and prohibited mining parameters of the target unit; based on the simulation operation information and various operation data of the unit simulation model under different operation states, optimize the prohibited mining parameters of the target unit, and return to execute the step of constructing the unit simulation model of the target unit based on the shared parameters and prohibited mining parameters of the target unit until the latest unit simulation model meets the preset stop condition; A construction module, configured to construct a new energy power station simulation model based on the unit simulation models of each target unit.

9. A new energy power station simulation device, characterized in that, It includes a memory and a processor; The memory is used to store programs; The processor is configured to execute the program to implement each step of the new energy power station simulation method described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, each step of the new energy power station simulation method described in any one of claims 1-7 is implemented.

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