Load parameter identification method and device, medium and equipment

Abstract

The invention discloses a load parameter identification method and device, a medium and equipment, and relates to the field of power equipment. Parameters of the model can be optimized, and the accuracy of parameter identification is improved. The load parameter identification method comprises the following steps: acquiring actual measurement recording data of a power system, wherein the actual measurement recording data comprises current, voltage, active power and reactive power of a load node; constructing a comprehensive load model in which the dynamic load model and the static load model are connected in parallel, and determining an objective function of the comprehensive load model; inputting current and voltage in the actually measured recording data into the comprehensive load model to obtain output data of the comprehensive load model, and calculating a function value of a target function based on the output data and active power and reactive power in the actually measured recording data; and optimizing the comprehensive load model based on a dung beetle optimization algorithm until the function value of the target function meets a preset condition, and obtaining an identification parameter of the power system.

Description

Technical Field

[0001] The present application relates to the field of power equipment, and in particular to a load parameter identification method, device, medium and equipment. Background Art

[0002] At present, digital simulation calculation plays an irreplaceable role in power systems in the fields of power grid planning, operation, control and personnel training. The accuracy of the simulation results depends on the conformity of the component models and parameters used. If an inappropriate load model is selected in the power system simulation, the simulation results will deviate from the actual situation, thereby wasting planning funds and possibly leading to errors in operational decisions. Therefore, in the dynamic simulation process, it is crucial to select a suitable load model to describe the load in a specific area.

[0003] There are two main methods of load modeling in power systems: component-based and measurement-based. Component-based load modeling first requires statistics on the characteristics of various typical loads, the proportion of load equipment, and the components of loads, and then derives the mathematical models and parameters of various typical loads. Finally, the statistical data is combined to establish the model of the load node. However, the load components will change over time, the statistical workload is large, and the reactive power voltage characteristics cannot be accurately obtained. Therefore, it is less used in practical applications. In contrast, measurement-based load modeling regards the power system as a random system, first determines the model structure, then identifies the model parameters based on the measurement data, and verifies its generalization ability. This method requires the installation of load characteristic recording devices at the load nodes, and usually obtains data for identification under large disturbances. Measurement-based load modeling does not require a lot of statistical work, and can solve the problem of complex load components by using input-output models, so it can be widely used in practice, but it is prone to local convergence when identifying parameters, resulting in low parameter accuracy. Summary of the invention

[0004] The present application provides a load parameter identification method, device, and medium equipment, which can optimize the objective function through the dung beetle algorithm, obtain the optimal load model parameters, and improve the accuracy of parameter identification.

[0005] In a first aspect, the present application provides a load parameter identification method, comprising:

[0006] Acquire measured and recorded wave data of the power system, wherein the measured and recorded wave data includes current, voltage, active power and reactive power of the load node;

[0007] Constructing a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determining an objective function of the comprehensive load model;

[0008] Inputting the current and voltage in the measured recorded data into the comprehensive load model to obtain output data of the comprehensive load model, and calculating the function value of the objective function based on the output data and the active power and reactive power in the measured recorded data;

[0009] The comprehensive load model is optimized based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

[0010] According to the load parameter identification method provided in this embodiment, the dung beetle optimization algorithm is applied to the parameter identification process of the load model. The global exploration and local development capabilities of the dung beetle optimization algorithm are utilized to optimize the parameters to a greater extent, thereby improving the accuracy of parameter identification and load modeling.

[0011] In an exemplary embodiment, the comprehensive load model is optimized based on the dung beetle optimization algorithm until the function value of the objective function meets a preset condition, thereby obtaining the identification parameters of the power system, including:

[0012] Generating an initialization population through a good point set, wherein the initialization population includes a plurality of dung beetle positions;

[0013] The dung beetle position is updated based on the dung beetle optimization algorithm until the function value meets the preset condition, thereby obtaining an optimal dung beetle position, which is a parameter value of an identification parameter.

[0014] In an exemplary embodiment, the generating an initialization population through a good point set includes:

[0015] Extract the first prime number greater than or equal to 2D+3 from the prime number sequence:

[0016] q=min{p∈prime1|p≥2D+3}

[0017] Wherein, prime1=primes(100·D), prime1 is an array containing a prime number sequence not greater than 100·D, each element of which is a prime number; D is the number of identification parameters;

[0018] Calculate the good point set based on the first prime number:

[0019] If 1≤i≤T and 1≤j≤D

[0020] Among them, GD[i,j] is the element in the matrix T×D; mod(...) is the modulus operation, which takes the value in the brackets modulo 1 so that the result is between 0 and 1; T is the number of iterations, that is, the population size;

[0021] The elements in the good point set are mapped to the value range of the identification parameter to obtain an initialized population.

[0022] In an exemplary embodiment, the static load model is:

[0023]

[0024] Where: The quadratic term of the voltage in the brackets above represents a constant impedance load, the first order term represents a constant current load, and the zero order term represents a constant power load. pz , k pI , k pp are the percentage of active power of constant impedance, constant current and constant power in the total active power, and satisfy k pz +k pI +k pp =1; k qz , k qI , k qp is the corresponding reactive power coefficient, and k qz +k qI +k qp =1. s and Q s is the active and reactive power of the static load model. P0 is the initial active power of the load model, and Q0 is the initial reactive power of the load model, which are obtained through measured waveform data.

[0025] The constant impedance k under the active load pz , constant current k pI , with the constant impedance k under the reactive load qz , constant current k qI are the identification parameters of the power system.

[0026] In an exemplary embodiment, it further includes:

[0027] Solving the stator equation on the stator side and the rotor equation on the rotor side of the power system to obtain a dynamic load model;

[0028] The stator equation is:

[0029]

[0030] The rotor equation is:

[0031]

[0032] in, T m =(Aω 2 +Bω+C)T0;X=X s +X m ;

[0033] Solving for this yields:

[0034]

[0035] Among them, R s , X s Indicates the resistance and leakage reactance of the stator winding; X m Represents the mutual inductance of the stator and rotor; R r , X r Represents the resistance and leakage reactance of the rotor winding; E' q represents the potential of the equivalent motor in the q-axis transient state; E' d represents the potential of the equivalent motor in the d-axis transient state; A+B+C=1; w represents the speed of the equivalent motor; H represents the inertia time constant of the equivalent motor;

[0036]

[0037] Among them, the resistance of the stator winding R s And leakage reactance X s , stator-rotor mutual inductance X m , the rotor winding resistance R r And leakage reactance X r , A, B and inertia time constant H are the identification parameters of the power system.

[0038] In an exemplary embodiment, the objective function is:

[0039]

[0040] is the active power and reactive power output by the comprehensive load model at time i, P m,i , Q m,i is the active power and reactive power actually measured at time i, and n represents the number of samples in the measured recorded data.

[0041] In an exemplary embodiment, the identification parameters further include:

[0042]

[0043] Among them, P0' represents the initial active power of the equivalent motor; P0 represents the initial active power of the load; K pm Represents the allocation parameter of initial active power; M 1f Represents the rated initial load factor; S MB Represents the rated capacity of the induction motor.

[0044] In a second aspect, the present application provides a load parameter identification device, comprising:

[0045] A data measurement and recording module is used to obtain the measured and recorded wave data of the power system, wherein the measured and recorded wave data includes the current, voltage, active power and reactive power of the load node;

[0046] A model building module, used to build a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determine an objective function of the comprehensive load model;

[0047] A variable optimization module, used for inputting the current and voltage in the measured wave recording data into the comprehensive load model to obtain output data of the comprehensive load model, and calculating the function value of the objective function based on the output data and the active power and reactive power in the measured wave recording data;

[0048] The parameter output module is used to optimize the comprehensive load model based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

[0049] In a third aspect, the present application provides an electronic device, the electronic device comprising a memory and one or more processors. The memory stores one or more computer programs, the computer programs comprising instructions, and when the instructions are executed by the processor, the electronic device can perform the load parameter identification method as in the first aspect.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on an electronic device, the electronic device executes the load parameter identification method as in the first aspect.

[0051] In a fifth aspect, the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the load parameter identification method as described in the first aspect.

[0052] It can be understood that the beneficial effects that can be achieved by the load parameter identification device, electronic device, computer-readable storage medium, and computer program product provided above can refer to the beneficial effects in the first aspect and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of a load parameter identification method provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of the model in the load parameter identification method provided in the embodiment of the present application;

[0055] Figure 3A schematic diagram of a parameter optimization process in a load parameter identification method provided in an embodiment of the present application;

[0056] Figure 4 A schematic diagram of the structure of a load parameter identification device provided in an embodiment of the present application;

[0057] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first chip and the second chip are only used to distinguish different chips, and their order is not limited. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the difference. It should be noted that in the embodiments of the present application, the words "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0059] It should be noted that the “at…” in the embodiments of the present application may be the instant when a certain situation occurs, or may be a period of time after the occurrence of a certain situation, and the embodiments of the present application do not specifically limit this.

[0060] The implementation of this embodiment will be described in detail below with reference to the accompanying drawings.

[0061] This embodiment provides a load parameter identification method. By way of example, the load parameter identification method can be applied to various electronic devices such as computers (PCs), tablet computers, virtual reality / augmented reality devices, wearable devices, industrial computers, and vehicle computers; it can also be applied to servers, cloud computing, server clusters, etc., and this embodiment does not make any special limitations on this.

[0062] Figure 1 A flow chart of a load parameter identification method provided in an embodiment of the present application is shown.

[0063] like Figure 1 As shown, the load parameter identification method may include the following steps:

[0064] Step 101: Acquire measured wave data of the power system, wherein the measured wave data includes current, voltage, active power and reactive power of a load node.

[0065] The measured wave data of the power system can be recorded by PMU and other devices to obtain the measured data of load node voltage, current, active power, reactive power, etc. Exemplarily, the measured wave data is preprocessed, such as normalization.

[0066] The measured wave data obtained is variable S, which contains n sample values. The minimum value of variable S in the measured wave data is S min , the maximum value is S max , the variable S is scaled to a new target range, such as [a, b], by minimum-maximum normalization, and the processed data is obtained:

[0067] S normalized =(SS min ) / (S max -S min )*(ba)+a (1)

[0068] After the above operations, the normalized data is guaranteed to be within the target range, and the relative order and distribution of the original data are guaranteed, eliminating the impact of different dimensions or amplitude differences on data analysis and modeling, making comparison, statistics and model training easier.

[0069] Step 102: construct a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determine an objective function of the comprehensive load model.

[0070] First, the model structure and objective function need to be determined, and then the input and output data are used to identify the parameters using an optimization method with the minimization of the objective function value as the optimization principle.

[0071] The power system includes the dynamic part of the motor and the static load part. The static load model is described by a polynomial model:

[0072]

[0073] Where: The quadratic term of the voltage in the brackets above represents a constant impedance load, the first term represents a constant current load, and the zero-order term represents a constant power load. pz , k pI , k pp are the percentage of active power of constant impedance, constant current and constant power in the total active power, and satisfy k pz +k pI +k pp =1; k qz , k qI, k qp is the corresponding reactive power coefficient, and k qz +k qI +k qp =1. s and Q s is the active and reactive power of the static ZIP part. P0 is the initial active power of the load model, and Q0 is the initial reactive power of the load model, which are obtained through measured waveform data.

[0074] The structure of the dynamic load model is as follows Figure 2 As shown in the figure, it consists of two parts: the stator side and the rotor side, as follows:

[0075] The stator equation is:

[0076]

[0077] The rotor equation is:

[0078]

[0079] in: T m =(Aω 2 +Bω+C)T0;X=X s +X m ; I d and I q is the stator current.

[0080] Solving the dynamic part of the induction motor, its complex form is:

[0081]

[0082] Its steady state can be expressed as:

[0083]

[0084] Substituting formula (6) into formula (5) to simplify it, we get:

[0085] jsT'(E' d +jE' q )(R S +jX')+(E' d +jE' q )(R S +jX')-j(X-X')[(U d +jU q )-(E' d +jE' q )]=0 (7)

[0086] Then separate the real and imaginary parts of formula (7) to obtain:

[0087]

[0088] The final formula in the form of a difference equation is:

[0089]

[0090] The above equation (9) is transformed into a differential equation:

[0091]

[0092] in

[0093] The iteration time t of the dung beetle optimization algorithm and the above H also need to be normalized:

[0094] Then the iterative formula can be obtained as:

[0095]

[0096] in: T e(n) =E' d(n) I d(n) +E' q(n) I q(n) , I d(n) and I q(n) It is obtained through measured wave recording data. The iterative formula is:

[0097]

[0098] make:

[0099]

[0100] The solution is:

[0101]

[0102] In the above formula, R s , X s Indicates the resistance and leakage reactance of the stator winding; X m Represents the mutual inductance of the stator and rotor; R r , X r Represents the resistance and leakage reactance of the rotor winding; E' q represents the potential of the equivalent motor in the q-axis transient state; E' d represents the potential of the equivalent motor in the d-axis transient state; wherein A+B+C=1 is satisfied; w represents the speed of the equivalent motor; H represents the inertia time constant of the equivalent motor.

[0103] The above formulas (2) to (13) constitute a comprehensive load model. Among them, the constant impedance k under active load pz , constant current k pI , with the constant impedance k under the reactive load qz , constant current k qI ; Resistance of stator winding R s And leakage reactance X s , stator-rotor mutual inductance X m , the rotor winding resistance R r And leakage reactance X r , A, B and inertia time constant H are the identification parameters of the power system.

[0104] In addition to the above 12 parameters, K is also defined to convert the model parameters into normalized ones. pm and M 1f Two identification parameters are as follows:

[0105]

[0106] Where: P0' represents the initial active power of the equivalent motor; P0 represents the initial active power of the load; K pm Represents the allocation parameter of initial active power; M 1f Represents the rated initial load factor; S MB Represents the rated capacity of the induction motor.

[0107] After determining the structure of the comprehensive load model, it is necessary to select an efficient and reliable optimization algorithm for parameter identification. The essence of parameter identification is to fit the mathematical model of the system through input and output data to estimate the model parameters. Figure 3 As shown, the input of the system is voltage, and the output curve of the actual system represents the measured active and reactive power of the load under the condition of voltage disturbance; while the output curve of the simulation system represents the simulated active and reactive power of the load under the condition of voltage disturbance.

[0108] Furthermore, the objective function of the load model is as follows:

[0109]

[0110] Where: is the active and reactive power calculated by the integrated load model at time i, P m,i , Q m,i is the active and reactive power actually measured at time i, and n represents the number of samples.

[0111] Step 103 inputs the current and voltage in the measured recorded data into the comprehensive load model to obtain output data of the comprehensive load model, and calculates the function value of the objective function based on the output data and the active power and reactive power in the measured recorded data.

[0112] Based on the above formulas (1) to (16), the function value of the objective function is calculated. When the function value is less than the threshold, it is confirmed that the preset condition is met. When the function value is not less than the threshold, the identification parameters in the comprehensive load model are continuously optimized.

[0113] Step 104: Optimize the comprehensive load model based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, and obtain the identification parameters of the power system.

[0114] The Dung Beetle Optimizer (DBO) is a new type of swarm intelligence optimization algorithm. The DBO algorithm specifically consists of four parts: rolling, laying eggs, foraging, and thieves. According to the value range of each identification parameter, the search space can be determined, and the optimal solution can be found in the search space through the DBO algorithm.

[0115] This implementation specifically includes: generating an initialization population through a good point set, wherein the initialization population includes multiple dung beetle positions; updating the dung beetle positions based on the dung beetle optimization algorithm until the function value meets the preset conditions, thereby obtaining the optimal dung beetle position, and the optimal dung beetle position is the parameter value of the identification parameter.

[0116] The present invention obtains the optimal comprehensive load model parameters by using a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO). By using the good point set, the initialization of the population can be distributed in the solution space, thereby improving the diversity of the population and enabling the algorithm to better search for the global optimal solution.

[0117] Generating an initialized population through a good point set specifically includes: extracting a first prime number greater than or equal to 2D+3 from a prime number sequence; calculating a good point set based on the first prime number; and mapping elements in the good point set to a value range of an identification parameter to obtain an initialized population.

[0118] The first prime number is:

[0119] q=min{p∈prime1|p≥2D+3} (17)

[0120] Wherein, prime1=primes(100·D), prime1 is an array containing a prime number sequence not greater than 100·D, wherein each element is a prime number; and D is the number of identification parameters.

[0121] Calculate the good point set:

[0122]

[0123] Among them, GD[i,j] is the element in the matrix T×D; mod(...) is the modulo operation, which takes the value in the brackets modulo 1 so that the result is between 0 and 1; T is the number of iterations, that is, the population size.

[0124] The above results are linearly mapped to the upper and lower limits of D:

[0125] GD_NEW=GD·(Ub-Lb)+Lb (19)

[0126] Where: GD_NEW is the final result after mapping, Ub and Lb represent the upper and lower limits of the value range of the corresponding identification parameters. Generating a random initial set into an initial set with good distribution characteristics can improve the update rate of the dung beetle optimization algorithm and avoid falling into the local optimum. For the parameter identification strategy of load modeling, it not only improves its identification speed but also improves its identification accuracy.

[0127] Calculate the fitness values ​​of all DBs in the matrix according to the objective function; update the position through the rolling ball DB model; then determine the spawning area and update the spawning DB position again; then determine the foraging area and update the foraging DB position; then update the thief DB position; repeat the above steps until the function value of the objective function is less than the threshold, and output the global optimal solution and its fitness value. The global optimal solution is the final parameter value.

[0128] By performing parameter identification and optimization using the comprehensive conformance model in this embodiment, the speed and accuracy of identification can be improved.

[0129] Furthermore, this embodiment also provides a load parameter identification device, which can be used to execute the above load parameter identification method. Figure 4As shown, the load parameter identification device 400 includes: a data measurement and recording module 401, which is used to obtain the measured recorded data of the power system, and the measured recorded data includes the current, voltage, active power and reactive power of the load node; a model construction module 402, which is used to construct a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determine the objective function of the comprehensive load model; a variable optimization module 403, which is used to input the current and voltage in the measured recorded data into the comprehensive load model to obtain the output data of the comprehensive load model, and calculate the function value of the objective function based on the output data and the active power and reactive power in the measured recorded data; a parameter output module 404, which is used to optimize the comprehensive load model based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

[0130] In an exemplary embodiment, the parameter output module specifically includes a population initialization module, which is used to generate an initialized population through a good point set, wherein the initialized population includes multiple dung beetle positions; the dung beetle positions are updated based on the dung beetle optimization algorithm until the function value meets the preset conditions, thereby obtaining an optimal dung beetle position, and the optimal dung beetle position is the parameter value of the identification parameter.

[0131] In an exemplary embodiment, the population initialization module is specifically used to: extract the first prime number greater than or equal to 2D+3 from the prime number sequence:

[0132] q=min{p∈prime1|p≥2D+3}

[0133] Wherein, prime1=primes(100·D), prime1 is an array containing a prime number sequence not greater than 100·D, wherein each element is a prime number; and D is the number of identification parameters.

[0134] Calculate the good point set based on the first prime number:

[0135] If 1≤i≤T and 1≤j≤D

[0136] Among them, GD[i,j] is the element in the matrix T×D; mod(...) is the modulo operation, which takes the value in the brackets modulo 1 so that the result is between 0 and 1; T is the number of iterations, that is, the population size.

[0137] The elements in the good point set are mapped to the value range of the identification parameter to obtain an initialized population.

[0138] In an exemplary embodiment, the static load model is:

[0139]

[0140] The quadratic term of the voltage in the brackets above represents a constant impedance load, the first order term represents a constant current load, and the zero order term represents a constant power load. pz , k pI , k pp are the percentage of active power of constant impedance, constant current and constant power in the total active power, and satisfy k pz +k pI +k pp =1; k qz , k qI , k qp is the corresponding reactive power coefficient, and k qz +k qI +k qp =1. s and Q s is the active and reactive power of the static load model. P0 is the initial active power of the load model, and Q0 is the initial reactive power of the load model, which are obtained through measured waveform data.

[0141] The constant impedance k under the active load pz , constant current k pI , with the constant impedance k under the reactive load qz , constant current k qI are the identification parameters of the power system.

[0142] In an exemplary embodiment, the model building module is specifically used to:

[0143] The dynamic load model is obtained by solving the stator equation on the stator side and the rotor equation on the rotor side of the power system; wherein the stator equation is:

[0144]

[0145] The rotor equation is:

[0146]

[0147] in, T m =(Aω 2 +Bω+C)T0;X=X s +X m ;

[0148] Solving for this yields:

[0149]

[0150] Among them, R s, X s Indicates the resistance and leakage reactance of the stator winding; X m Represents the mutual inductance of the stator and rotor; R r , X r Represents the resistance and leakage reactance of the rotor winding; E' q represents the potential of the equivalent motor in the q-axis transient state; E' d represents the potential of the equivalent motor in the d-axis transient state; A+B+C=1; w represents the speed of the equivalent motor; H represents the inertia time constant of the equivalent motor.

[0151]

[0152] Among them, the resistance of the stator winding R s And leakage reactance X s , stator-rotor mutual inductance X m , the rotor winding resistance R r And leakage reactance X r , A, B and inertia time constant H are the identification parameters of the power system.

[0153] In an exemplary embodiment, the objective function is:

[0154]

[0155] is the active power and reactive power output by the comprehensive load model at time i, P m,i , Q m,i is the active power and reactive power actually measured at time i, and n represents the number of samples in the measured recorded data.

[0156] In an exemplary embodiment, the identification parameters further include:

[0157]

[0158] Among them, P0' represents the initial active power of the equivalent motor; P0 represents the initial active power of the load; K pm Represents the allocation parameter of initial active power; M 1f Represents the rated initial load factor; S MB Represents the rated capacity of the induction motor.

[0159] The specific details of each module or unit in the above-mentioned load parameter identification device have been described in detail in the corresponding load parameter identification method, so they will not be repeated here.

[0160] The embodiment of the present application also provides an electronic device, Figure 5 A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown. Figure 5The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0161] like Figure 5 As shown, electronic device 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In RAM 603, various programs and data required for system operation are also stored. CPU 601, ROM 602 and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0162] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0163] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the embodiment of the present application are executed.

[0164] For example, when the computer program is executed by the central processing unit (CPU) 601, it can be executed as follows: obtaining measured recorded data of the power system, the measured recorded data including the current, voltage, active power and reactive power of the load node; constructing a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determining the objective function of the comprehensive load model; inputting the current and voltage in the measured recorded data into the comprehensive load model to obtain the output data of the comprehensive load model, and calculating the function value of the objective function based on the output data and the active power and reactive power in the measured recorded data; optimizing the comprehensive load model based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

[0165] It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0166] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0167] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0168] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and the one or more programs include instructions, and when the instructions are executed by the electronic device, the electronic device implements the method described in the above embodiment.

[0169] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0170] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A load parameter identification method, characterized in that: include: Acquire measured and recorded wave data of the power system, wherein the measured and recorded wave data includes current, voltage, active power and reactive power of the load node; Constructing a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determining an objective function of the comprehensive load model; Inputting the current and voltage in the measured recorded data into the comprehensive load model to obtain output data of the comprehensive load model, and calculating the function value of the objective function based on the output data and the active power and reactive power in the measured recorded data; The comprehensive load model is optimized based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

2. The load parameter identification method according to claim 1, characterized in that: The integrated load model is optimized based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system, including: Generating an initialization population through a good point set, wherein the initialization population includes a plurality of dung beetle positions; The dung beetle position is updated based on the dung beetle optimization algorithm until the function value meets the preset condition, thereby obtaining an optimal dung beetle position, which is a parameter value of an identification parameter.

3. The load parameter identification method according to claim 2, characterized in that: The generating of the initialization population by using the good point set includes: Extract the first prime number greater than or equal to 2D+3 from the prime number sequence: q=min{p∈prime1|p≥2D+3} Wherein, prime1=primes(100·D), prime1 is an array containing a prime number sequence not greater than 100·D, each element of which is a prime number; D is the number of identification parameters; Calculate the good point set based on the first prime number: Among them, GD[i,j] is the element in the matrix T×D; mod(...) is the modulus operation, which takes the value in the brackets modulo 1 so that the result is between 0 and 1; T is the number of iterations, that is, the population size; The elements in the good point set are mapped to the value range of the identification parameter to obtain an initialized population.

4. The load parameter identification method according to claim 1, wherein the static load model is: in: The quadratic term of the voltage in the brackets above represents a constant impedance load, the first order term represents a constant current load, and the zero order term represents a constant power load. pz , k pI , k pp are the percentage of active power of constant impedance, constant current and constant power in the total active power, and satisfy k pz +k pI +k pp =1; k qz , k qI , k qp is the corresponding reactive power coefficient, and k qz +k qI +k qp =1;P s and Q s is the active and reactive power of the static load model; P0 is the initial active power of the load model, Q0 is the initial reactive power of the load model, which is obtained through measured waveform data; The constant impedance k under the active load pz , constant current k pI , with the constant impedance k under the reactive load qz , constant current k qI are the identification parameters of the power system.

5. The load parameter identification method according to claim 1, wherein: Also includes: Solving the stator equation on the stator side and the rotor equation on the rotor side of the power system to obtain a dynamic load model; The stator equation is: The rotor equation is: in, T m =(Aω 2 +Bω+C)T0;X=X s +X m ; Solving for this yields: Among them, R s , X s Indicates the resistance and leakage reactance of the stator winding; X m Represents the mutual inductance of the stator and rotor; R r , X r Represents the resistance and leakage reactance of the rotor winding; E' q represents the potential of the equivalent motor in the q-axis transient state; E' d represents the potential of the equivalent motor in the d-axis transient state; A+B+C=1; w represents the speed of the equivalent motor; H represents the inertia time constant of the equivalent motor; Among them, the resistance of the stator winding R s And leakage reactance X s , stator-rotor mutual inductance X m , the rotor winding resistance R r And leakage reactance X r , A, B and inertia time constant H are the identification parameters of the power system.

6. The load parameter identification method according to claim 1, wherein: The objective function is: is the active power and reactive power output by the comprehensive load model at time i, P m,i , Q m,i is the active power and reactive power actually measured at time i, and n represents the number of samples in the measured recorded data.

7. The load parameter identification method according to claim 1, characterized in that: The identification parameters also include: Among them, P0' represents the initial active power of the equivalent motor; P0 represents the initial active power of the load; K pm Represents the allocation parameter of initial active power; M 1f Represents the rated initial load factor; S MB Represents the rated capacity of the induction motor.

8. A load parameter identification device, characterized in that: include: A data measurement and recording module is used to obtain the measured and recorded wave data of the power system, wherein the measured and recorded wave data includes the current, voltage, active power and reactive power of the load node; A model building module, used to build a comprehensive load model in which a dynamic load model and a static load model are connected in parallel, and determine an objective function of the comprehensive load model; A variable optimization module, used for inputting the current and voltage in the measured wave recording data into the comprehensive load model to obtain output data of the comprehensive load model, and calculating the function value of the objective function based on the output data and the active power and reactive power in the measured wave recording data; The parameter output module is used to optimize the comprehensive load model based on the dung beetle optimization algorithm until the function value of the objective function meets the preset conditions, thereby obtaining the identification parameters of the power system.

9. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the load parameter identification method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory, wherein one or more computer programs are stored in the memory, and the one or more computer programs include instructions. When the instructions are executed by the electronic device, the electronic device executes the load parameter identification method described in any one of claims 1 to 7.

Images