Feeder load active power-voltage coupling characteristic identification method and device

Abstract

The invention provides a feeder load active power-voltage coupling characteristic identification method and device, and relates to the field of power system load characteristic identification. The method comprises the steps that an actual power grid simulation model is constructed, real-time data of each node when the actual power grid simulation model is interfered by a target disturbance signal is acquired, and the target disturbance signal is a pseudo-random binary disturbance signal borne by the actual power grid simulation model; respectively calculating the Pearson's correlation coefficient of the real-time data of each node, and selecting target data from the real-time data of all nodes by using the Pearson's correlation coefficient of the real-time data of each node, the target data being used for identifying feeder load regulation characteristic parameters; and on the basis of the target data, constructing a feeder load regulation characteristic model, and identifying parameters of the feeder load regulation characteristic model by using a generalized least square method. The current load characteristic identification accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of power system load characteristic identification, and in particular to a feeder load active-voltage coupling characteristic identification method and device. Background Art

[0002] The power consumption of the power load (including active power and reactive power) will change with the change of the load working voltage. In order to obtain the effective voltage and power data, the commonly used technical means is to use the voltage regulating equipment to actively apply the voltage disturbance step voltage or ramp voltage disturbance to obtain the voltage and power data, and then use the identification algorithm to obtain the active-voltage coupling characteristics of the feeder load. Commonly used identification algorithms are divided into traditional identification algorithms and intelligent optimization algorithms. Traditional identification algorithms include least squares method, maximum likelihood method, Kalman filter method and gradient optimization method. Intelligent algorithms include particle swarm algorithm, genetic algorithm, clonal selection algorithm, etc., which have both global search and local search capabilities.

[0003] The voltage disturbance used in existing research is single-form. Although large-amplitude step and ramp disturbances can effectively stimulate load power changes in the time domain, their short spectrum makes it impossible to fully stimulate the complete mode of feeder load regulation characteristics. At the same time, the natural fluctuation of load power always exists and the amplitude is random. The load power changes caused by small-amplitude voltage disturbances are easily buried in the load power noise. Existing disturbance-based research often ignores the important issue of noise resistance.

[0004] Under the background of intelligent distribution network construction characterized by high observability and high controllability, accurate control of feeder voltage becomes possible, providing a valuable opportunity for large-scale development of feeder load power control resources. In order to empower high-proportion new energy distribution networks, increase system control resources and control flexibility, analyze the potential of feeder load regulation resources in my country, and demonstrate how to develop feeder load regulation resources at low cost and high efficiency in my country, it is urgent to solve. On the other hand, feeder load regulation characteristics are crucial for efficient application of feeder load power control, but there are many difficulties in its identification: first, the lack of frequent active voltage changes during normal operation of the system makes it difficult to obtain effective voltage and power identification input data. How to stimulate the load LTV characteristics in real time without affecting the operation, obtain effective data, and realize the online identification of feeder load regulation characteristics needs to be studied; secondly, there are natural random fluctuations in load power, how to distinguish LTV characteristics from other influencing factors, and realize accurate identification of feeder load regulation characteristics should also be considered; at the same time, a large number of motor loads make the feeder load have dynamic regulation characteristics, how to design a reasonable feeder load regulation model, and realize the comprehensive characterization and identification of the feeder load power regulation process also needs to be solved. Summary of the invention

[0005] The present application provides a method and device for identifying active power-voltage coupling characteristics of a feeder load, so as to solve the problem of inaccurate identification of active power-voltage coupling characteristics of a feeder load in the prior art.

[0006] In a first aspect, the present application provides a method for identifying active power-voltage coupling characteristics of a feeder load, comprising:

[0007] Constructing an actual power grid simulation model, and obtaining real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, wherein the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power;

[0008] Calculating the Pearson correlation coefficient of the real-time data of each node respectively, and selecting target data from the real-time data of all nodes by using the Pearson correlation coefficient of the real-time data of each node, wherein the target data is used for identifying the characteristic parameters of the feeder load regulation;

[0009] Based on the target data, a feeder load regulation characteristic model is constructed, and the parameters of the feeder load regulation characteristic model are identified using a generalized least squares method.

[0010] In a second aspect, the present application provides a feeder load active-voltage coupling characteristic identification device, comprising:

[0011] A data acquisition module, used to construct an actual power grid simulation model and acquire real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, wherein the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power;

[0012] A calculation module, used to calculate the Pearson correlation coefficient of the real-time data of each node respectively, and select target data from the real-time data of all nodes using the Pearson correlation coefficient of the real-time data of each node, wherein the target data is used for identifying the characteristic parameters of feeder load regulation;

[0013] A parameter identification module is used to construct a feeder load regulation characteristic model based on the target data, and identify the parameters of the feeder load regulation characteristic model using a generalized least squares method.

[0014] The present application provides a method and device for identifying the active power-voltage coupling characteristics of feeder loads, by constructing an actual power grid simulation model, and obtaining the real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power; the Pearson correlation coefficient of the real-time data of each node is calculated respectively, and the Pearson correlation coefficient of the real-time data of each node is used to select the target data from the real-time data of all nodes, and the target data is used for identifying the parameters of the feeder load regulation characteristics; based on the target data, a feeder load regulation characteristic model is constructed, and the parameters of the feeder load regulation characteristic model are identified using the generalized least squares method. The present application constructs an actual power grid simulation model, and real-time data interaction can provide dynamic load data, so that the feeder load regulation characteristic model can reflect the current load characteristics, thereby improving the accuracy of the feeder load regulation characteristic identification; an accurate actual power grid simulation model can help operators formulate strategies in a timely manner and respond quickly, and is more adaptable. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 It is a flow chart of the implementation of the feeder load active-voltage coupling characteristic identification method provided in the embodiment of the present application;

[0017] Figure 2 is a signal schematic diagram of a pseudo-random binary signal provided in an embodiment of the present application;

[0018] Figure 3 is a schematic diagram of real-time data interaction provided by an embodiment of the present application;

[0019] Figure 4 It is a schematic diagram of feeder load regulation based on the CVR coefficient model provided in an embodiment of the present application;

[0020] Figure 5 It is a schematic diagram of feeder load regulation based on a third-order discrete transfer function model provided in an embodiment of the present application;

[0021] Figure 6 It is a schematic diagram of the identification result of the actual power and the output result under the PRBS signal disturbance based on the CVR model provided in the embodiment of the present application;

[0022] Figure 7It is a schematic diagram of the identification result of the actual power and the output result under the PRBS signal disturbance based on the third-order discrete transfer function model provided in the embodiment of the present application;

[0023] Figure 8 It is a structural schematic diagram of a feeder load active-voltage coupling characteristic identification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0026] The voltage and power data during the normal operation of the power grid system are difficult to be used for the identification of the feeder load regulation characteristics. In order to obtain effective voltage and power data and realize the online identification of the feeder load regulation characteristics, it is necessary to actively apply voltage disturbances to stimulate the load power response voltage change (Load-to-voltage process, LTV process). However, current research mostly uses voltage regulating equipment to actively apply voltage disturbances, step voltages or ramp voltage disturbances. On the one hand, although these disturbances produce angle amplitude disturbances in the time domain, their disturbance signal spectrum is relatively short, requiring the input signal to be able to continuously excite the model dynamics, that is, the disturbance signal spectrum should be able to cover the model spectrum. On the other hand, the actual data obtained can only be obtained offline and then identified, and it is difficult to ensure the real-time nature of the data.

[0027] Based on the above problems, the embodiment of the present application proposes an online identification strategy of feeder load power regulation characteristics based on micro-disturbance:

[0028] First, it is analyzed and verified that normal operating data cannot be used for online identification of the feeder load power regulation characteristics. Therefore, the embodiment of the present application actively applies the designed pseudo-random binary signal (Pseudo-Random Binary Sequence, PRBS) to fully stimulate the LTV process without affecting the normal operation of the power grid system, thereby realizing the online acquisition of the data required for the identification of the feeder load regulation characteristics.

[0029] At the same time, the interaction between the Supervisory Control And Data Acquisition (SCADA) and the Real-Time Digital Simulation (RTDS) can be used to obtain the field data required for identification, that is, the effective data, in real time online.

[0030] The feeder load characteristics are expanded from traditional static loads to comprehensive loads, and a dual-model system of feeder load regulation characteristics based on conservation voltage reduction (CVR) and transfer function is constructed to accurately describe the steady-state and transient processes of load regulation, reflecting the comprehensive identification of feeder load regulation characteristics.

[0031] Further considering the influence of noise, the embodiment of the present application proposes a model parameter identification strategy based on the generalized least squares (GLS) method, which can distinguish the output power from the noise signal and is not affected by colored noise, thereby realizing accurate identification of the feeder load regulation characteristics. That is, the generalized least squares algorithm is used to filter the original data using the identified noise model, thereby realizing noise whitening and accurate identification of the load model.

[0032] The identification technology proposed in the embodiment of the present application can update the load characteristics online when the load composition changes, and the identification process is not affected by colored noise, and can accurately model the transient and steady-state regulation characteristics of the feeder load, and flexibly meet the needs of different applications. And it can comprehensively and accurately characterize the model structure of the feeder load regulation characteristics. The proposed identification strategy conforms to the development trend of high observability and high controllability of the power grid, and can be implemented using a variety of voltage regulation and measurement devices.

[0033] Figure 1 The implementation flow chart of the feeder load active-voltage coupling characteristic identification method provided in the embodiment of the present application is described in detail as follows:

[0034] In step 101, an actual power grid simulation model is constructed, and real-time data of each node is obtained when the actual power grid simulation model is disturbed by a target disturbance signal. The target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power.

[0035] Among them, the voltage and power data of the power grid under normal operation are difficult to be used for the identification of feeder load regulation characteristics. In order to obtain effective voltage and power data and realize online identification of feeder load regulation characteristics, it is necessary to actively apply voltage disturbance to stimulate the LTV process.

[0036] The white noise spectrum covers the entire frequency domain and is constant, and is considered to be the optimal disturbance signal. However, since it is difficult for actual equipment to operate according to the white noise law, the embodiment of the present application chooses a pseudo-random binary signal PRBS with excellent statistical characteristics and easy to generate as the voltage disturbance signal.

[0037] Among them, the pseudo-random binary signal PRBS is a positive and negative two-level signal. Figure 2 , the pseudo-random binary signal PRBS is a three-parameter signal, including amplitude α, interval period δt and sequence length N p . When the sequence length is large enough or the amplitude is small enough, the average value of the pseudo-random binary signal PRBS is close to 0, and the autocorrelation function is close to the pulse function, which is similar to the statistical characteristics of white noise. The embodiment of the present application applies a pseudo-random binary signal PRBS voltage disturbance to fully stimulate various modes of the feeder load without affecting the operation of the system, thereby helping the operating personnel to obtain the data required for the identification of the feeder load regulation characteristics, convey the disturbance signal through real-time communication, and collect node data.

[0038] In the embodiment of the present application, an actual power grid simulation model is constructed according to the actual power grid, and a pseudo-random binary signal PRBS is used as a target disturbance signal to be applied to the actual power grid simulation model, and real-time data of each node of the actual power grid simulation model after being disturbed by the target disturbance signal is obtained. The real-time data includes voltage data and power data of each node.

[0039] In a possible implementation, building an actual power grid simulation model may include:

[0040] Use data acquisition and monitoring control systems to collect field data from actual power grids;

[0041] Using the data acquisition and monitoring control system to transmit the field data of the actual power grid to the real-time digital simulator in accordance with 104 protocol;

[0042] Utilize real-time digital simulators and field data to build a simulation model of the actual power grid.

[0043] In order to obtain voltage and power data in real time, the embodiment of the present application needs to establish a real-time communication connection. The specific connection method is real-time interaction between the simulation model and SCADA. For example, refer to Figure 3The master station and RTDS transmit data in real time. The master station can collect field data such as current and voltage of each bus in the actual power grid. The master station transmits the field data to RTDS through the 104 protocol and the GTNET interface card. The actual power grid is simulated using the field data collected by RTDS and the master station to obtain the actual power grid simulation model. The pseudo-random binary signal PRBS is used as the target disturbance signal to apply disturbance to the actual power grid simulation model using RTDS, and the real-time data of each node of the actual power grid simulation model when it is disturbed by the target disturbance signal is obtained, so as to perform strategy operations on the actual power grid.

[0044] The embodiment of the present application applies a pseudo-random binary signal PRBS voltage disturbance through vibration, fully stimulates the LTV process without affecting the normal operation of the power grid system, and realizes the online acquisition of data required for feeder load regulation characteristic identification; at the same time, real-time field data can be acquired in real time by interacting with SCADA and RTDS, thereby improving the accuracy of the construction of the actual power grid simulation model, and providing accurate support for the subsequent feeder load regulation characteristic identification.

[0045] In step 102, the Pearson correlation coefficient of the real-time data of each node is calculated respectively, and the Pearson correlation coefficient of the real-time data of each node is used to select target data from the real-time data of all nodes, and the target data is used for feeder load regulation characteristic parameter identification.

[0046] In an embodiment of the present application, for the real-time data of each node obtained in step 101, the Pearson correlation coefficient of the real-time data of each node is calculated respectively, and then the Pearson correlation coefficient of the real-time data of each node is used to select data for feeder load regulation characteristic parameter identification from the real-time data of all nodes.

[0047] In a possible implementation, respectively calculating the Pearson correlation coefficient of the real-time data of each node may include:

[0048] The voltage and power of each node are input into the Pearson correlation coefficient formula respectively to obtain the Pearson correlation coefficient of the corresponding node. The Pearson correlation coefficient formula is:

[0049]

[0050] Among them, r is the Pearson correlation coefficient, X i is the power of the ith group of each node, Y i is the voltage of the ith group of each node, is the average power of each node, is the average voltage of each node, n is the sample size of the real-time data of each node, and i is the number of nodes.

[0051] After acquiring real-time data, since the voltage and power data collected on site are numerous and messy, it is necessary to extract useful data to distinguish between the two situations of voltage response load power change (VTL) process and LTV process. The embodiment of the present application uses the data of the LTV process for feeder load regulation characteristics identification.

[0052] Let’s further analyze these two situations:

[0053] For the VTL process, the voltage change and power change show a negative correlation, for example, the line voltage will decrease when the load is heavy, that is, the load power increases and the bus voltage decreases.

[0054] For the LTV process, voltage change is positively correlated with power change, for example, load power decreases when bus voltage decreases.

[0055] In addition, the Pearson correlation coefficient is a statistic used to measure the degree of linear correlation between two variables. Its value range is between -1 and +1, where -1 indicates a complete negative correlation, +1 indicates a complete positive correlation, and 0 indicates that there is no linear correlation between the two variables.

[0056] The Pearson correlation coefficient formula is:

[0057]

[0058] Optionally, for this embodiment, the voltage and power of each node are respectively input into the above Pearson correlation coefficient formula to obtain the Pearson correlation coefficient r corresponding to each node.

[0059] Among them, the range of the Pearson correlation coefficient can be set according to actual needs. For example, it can be considered that when -0.3≤r≤0.3, it indicates that there is no correlation between power and voltage at this time, which is mainly caused by random start and stop of the load and measurement noise; when r<-0.3, it means that this section is data related to the VRL process; when r>0.3, it means that this section is data of the LVT process related to the feeder load regulation characteristics, and this section of data is data that can be used for parameter identification.

[0060] In a possible implementation, using the Pearson correlation coefficient of the real-time data of each node to select target data from the real-time data of all nodes may include:

[0061] The real-time data of nodes whose Pearson correlation coefficient is greater than a preset coefficient are selected from all nodes as target data for identifying the characteristic parameters of feeder load regulation.

[0062] Optionally, real-time data corresponding to nodes whose Pearson correlation coefficients of all nodes are greater than a preset coefficient are selected as target data.

[0063] Exemplarily, when r<-0.3, it is a VRL process; when r>0.3, it is an LVT process, and the real-time data corresponding to all nodes with r>0.3 are selected as target data.

[0064] In step 103, a feeder load regulation characteristic model is constructed based on the target data, and the parameters of the feeder load regulation characteristic model are identified using the generalized least squares method.

[0065] Among them, the generalized least squares method is an algorithm improved based on the least squares method. Its basic idea is to convert colored noise into white noise, that is, to introduce a whitening filter, filter the obtained data, and then use the least squares method for identification.

[0066] In the embodiment of the present application, the target data obtained in step 102 is used to construct a feeder load regulation characteristic model, and then the generalized least squares method is used to identify the parameters of the feeder load regulation characteristic model.

[0067] In this embodiment, after obtaining identifiable target data, it is necessary to identify the feeder load regulation characteristic model. First, it is necessary to clarify the structure selection of the feeder load regulation characteristic model.

[0068] In a possible implementation, constructing a feeder load regulation characteristic model based on target data may include:

[0069] Using the target data and the voltage reduction energy-saving control coefficient, a feeder load regulation characteristic model is constructed. The feeder load regulation characteristic model is:

[0070]

[0071] Among them, CVR fac is the voltage reduction and energy saving control coefficient, ΔV is the voltage change, and ΔP is the power change corresponding to ΔV.

[0072] The feeder load regulation characteristics are generally described by the voltage reduction control (CVR) coefficient, which is defined as the ratio of the corresponding load power change to the voltage change. The CVR coefficient is concise and has a clear physical meaning. It can help operators intuitively evaluate the feeder load regulation characteristics and is widely used in various medium and long-term feeder load power control applications, such as energy saving and peak regulation.

[0073] Optionally, the target data and CVR coefficient are used to construct a feeder load regulation characteristic model, and the feeder load regulation characteristic model is:

[0074] In a possible implementation, constructing a feeder load regulation characteristic model based on target data may include:

[0075] Using the target data and the third-order discrete transfer function, a feeder load regulation characteristic model is constructed. The feeder load regulation characteristic model is:

[0076]

[0077] Among them, G LTV (z) is the third-order discrete transfer function, P(z) is the power data in the target data, V(z) is the voltage data in the target data, z is the complex variable in the third-order discrete transfer function, m1, m2, m3, n1, n2 and n3 are all parameters of the feeder load regulation characteristic model.

[0078] Considering that the motor load generally adopts a third-order model, but the actual engineering mostly uses a discrete model, this embodiment selects a third-order discrete transfer function as the feeder load regulation characteristic model. The third-order discrete transfer function can fully represent the nonlinear process of the feeder load. At the same time, the function parameters are limited, which not only reduces the complexity of parameter identification, but also enhances the generalization ability.

[0079] Optionally, the objective function and the third-order discrete transfer function are used to construct a feeder load regulation characteristic model, and the feeder load regulation characteristic model is:

[0080] For this embodiment, the model structure should be flexibly selected according to its purpose of use. When the feeder load power control technology is applied to non-real-time time scale applications, such as energy saving and peak load regulation, refer to Figure 4 , since there is no need to consider the transient regulation characteristics of the load, the simple and intuitive CVR coefficient model should be used first; when applied to real-time time scale applications, such as power fluctuation smoothing, refer to Figure 5 , the transient characteristics of the feeder load will affect the control performance, and a third-order discrete transfer function model must be used.

[0081] In a possible implementation, the parameters of the feeder load regulation characteristic model are identified using the generalized least squares method, which may include:

[0082] Performing whitening processing on the target data, and performing filtering processing on the target data after the whitening processing;

[0083] The filtered target data is input into the feeder load regulation characteristic model, and the least square method is used to identify the parameters of the feeder load regulation characteristic model after the filtered target data is input.

[0084] Optionally, after the feeder load regulation characteristic model is clarified, the model parameters need to be specifically identified by using the generalized least squares method. However, before identification, the colored noise needs to be converted into white noise, that is, a whitening filter is introduced, and the data after whitening is filtered again, and then the model is identified using the least squares method.

[0085] When noise is not considered, the equation of the feeder load transfer function model is as follows:

[0086] A(z -1 )P(k)=B(z -1 )V(k)(1)

[0087] A(z -1 )=1+a1z -1 +a2z -1 +a3z -1 (2)

[0088] B(z -1 )=b1z -1 +b2z -1 +b3z -1 (3)

[0089] Among them, P(k) is the input of the system at time k, V(k) is the output of the system at time k, that is, the load power and voltage value at time k, A(z -1 ) is the denominator function of the feeder load regulation characteristic model, B(z -1 ) is the molecular function of the feeder load regulation characteristic model, and a1, a2, a3, b1, b2 and b3 are all parameters of the feeder load regulation characteristic model.

[0090] Generally speaking, for the voltage and power of the feeder load, due to the spatiotemporal continuity of electricity consumption in the store system and the problem of power fluctuation of new energy sources, there will be a lot of colored noise in the above input and output data. Assume that the error of the voltage is e u (k), the error in power is e p (k), then the modified formula (1) can be obtained:

[0091]

[0092] Where ε(k)=A(z -1 ) p (k)-B(z -1 ) u (k) is the autocorrelated colored noise. The least square method cannot obtain an unbiased estimate of the parameters for the above equation, so the data needs to be whitened.

[0093] Colored noise can be represented by white noise and other noise models, and the autocorrelation residual of colored noise can be fitted by the autoregressive model, that is:

[0094] C(z -1 )ε(k)=e(k)

[0095] According to the analysis of the third-order discrete transfer function selected in this embodiment, its expression is:

[0096] C(z -1 )=1+c1z -1 +c2z -2 +c3z -3

[0097] Among them, C(z -1 ) is the whitening filter, ε(k) is the colored noise, e(k) is the white noise, c1, c2 and c3 are the parameters of the whitening filter, z is the complex variable in the third-order discrete transfer function, and k is the time.

[0098] This embodiment uses After the target data is whitened, the whitened target data is filtered. The filter formula for the filtering process is:

[0099]

[0100] Among them, A(z -1 ) is the denominator function of the feeder load regulation characteristic model, C(z -1 ) is the whitening filter, P(k) is the power of the system at time k, B(z -1 ) is the molecular function of the feeder load regulation characteristic model, V(k) is the voltage of the system at time k, e(k) is white noise, P f (k) is the power after filtering, V f (k) is the filtered voltage, z is the complex variable in the third-order discrete transfer function, and k is the time.

[0101] After filtering, the least square method can be used to identify the parameters of the transfer function in this embodiment.

[0102] Exemplarily, the method of this embodiment is used to obtain two feeder load regulation characteristic model identification results, respectively referring to Figure 6 and Figure 7 ,It can be seen that the pseudo-random binary signal PRBS can well stimulate the LTV process. At the same time, the GLS algorithm has a good identification effect, thus realizing the identification of feeder load well.

[0103] The present application provides a method for identifying the active power-voltage coupling characteristics of feeder loads, by constructing an actual power grid simulation model, and obtaining the real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power; the Pearson correlation coefficient of the real-time data of each node is calculated respectively, and the Pearson correlation coefficient of the real-time data of each node is used to select the target data from the real-time data of all nodes, and the target data is used for identifying the parameters of the feeder load regulation characteristics; based on the target data, a feeder load regulation characteristic model is constructed, and the parameters of the feeder load regulation characteristic model are identified using the generalized least squares method. The present application constructs an actual power grid simulation model, and real-time data interaction can provide dynamic load data, so that the feeder load regulation characteristic model can reflect the current load characteristics, thereby improving the accuracy of the feeder load regulation characteristic identification; an accurate actual power grid simulation model can help operators formulate strategies in a timely manner and respond quickly, and is more adaptable.

[0104] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0105] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0106] Figure 8 The structure diagram of the feeder load active power-voltage coupling characteristic identification device provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0107] like Figure 8 As shown, the feeder load active power-voltage coupling characteristic identification device 8 includes:

[0108] The data acquisition module 81 is used to construct an actual power grid simulation model and acquire the real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal. The target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected. The real-time data includes voltage and power.

[0109] A calculation module 82, used to calculate the Pearson correlation coefficient of the real-time data of each node respectively, and select target data from the real-time data of all nodes using the Pearson correlation coefficient of the real-time data of each node, the target data is used for feeder load regulation characteristic parameter identification;

[0110] The parameter identification module 83 is used to construct a feeder load regulation characteristic model based on the target data, and identify the parameters of the feeder load regulation characteristic model using the generalized least squares method.

[0111] The present application provides a feeder load active-voltage coupling characteristic identification device, which constructs an actual power grid simulation model and obtains the real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power; the Pearson correlation coefficient of the real-time data of each node is calculated respectively, and the Pearson correlation coefficient of the real-time data of each node is used to select the target data from the real-time data of all nodes, and the target data is used for feeder load regulation characteristic parameter identification; based on the target data, a feeder load regulation characteristic model is constructed, and the parameters of the feeder load regulation characteristic model are identified using the generalized least squares method. The present application constructs an actual power grid simulation model, and real-time data interaction can provide dynamic load data, so that the feeder load regulation characteristic model can reflect the current load characteristics, thereby improving the accuracy of feeder load regulation characteristic identification; an accurate actual power grid simulation model can timely help operating personnel formulate strategies and respond quickly, and is more adaptable.

[0112] In a possible implementation, the data acquisition module may be used to:

[0113] Use data acquisition and monitoring control systems to collect field data from actual power grids;

[0114] Using the data acquisition and monitoring control system to transmit the field data of the actual power grid to the real-time digital simulator in accordance with 104 protocol;

[0115] Utilize real-time digital simulators and field data to build a simulation model of the actual power grid.

[0116] In a possible implementation, the computing module may be used to:

[0117] The voltage and power of each node are input into the Pearson correlation coefficient formula respectively to obtain the Pearson correlation coefficient of the corresponding node. The Pearson correlation coefficient formula is:

[0118]

[0119] Among them, r is the Pearson correlation coefficient, X i is the power of the ith group of each node, Y i is the voltage of the ith group of each node, is the average power of each node, is the average voltage of each node, n is the sample size of the real-time data of each node, and i is the number of nodes.

[0120] In a possible implementation, the calculation module may also be used to:

[0121] The real-time data of nodes whose Pearson correlation coefficient is greater than a preset coefficient are selected from all nodes as target data.

[0122] In a possible implementation, the parameter identification module may be used to:

[0123] Using the target data and the voltage reduction energy-saving control coefficient, a feeder load regulation characteristic model is constructed. The feeder load regulation characteristic model is:

[0124]

[0125] Among them, CVR fac is the voltage reduction and energy saving control coefficient, ΔV is the voltage change, and ΔP is the power change corresponding to ΔV.

[0126] In a possible implementation, the parameter identification module may also be used to:

[0127] Using the target data and the third-order discrete transfer function, a feeder load regulation characteristic model is constructed. The feeder load regulation characteristic model is:

[0128]

[0129] Among them, G LTV (z) is the third-order discrete transfer function, P(z) is the power data in the target data, V(z) is the voltage data in the target data, z is the complex variable in the third-order discrete transfer function, m1, m2, m3, n1, n2 and n3 are all parameters of the feeder load regulation characteristic model.

[0130] In a possible implementation, the parameter identification module may be used to:

[0131] Performing whitening processing on the target data, and performing filtering processing on the target data after the whitening processing;

[0132] The filtered target data is input into the feeder load regulation characteristic model, and the least square method is used to identify the parameters of the feeder load regulation characteristic model after the filtered target data is input.

[0133] In a possible implementation, the parameter identification module may also be used to:

[0134] The target data is whitened using the first formula, which is:

[0135]

[0136] Among them, C(z -1 ) is the whitening filter, ε(k) is the colored noise, e(k) is the white noise, c1, c2 and c3 are the parameters of the whitening filter, z is the complex variable in the third-order discrete transfer function, and k is the time.

[0137] In a possible implementation, the parameter identification module may also be used to:

[0138] The target data after whitening is filtered using the second formula, which is:

[0139]

[0140] Among them, A(z -1 ) is the denominator function of the feeder load regulation characteristic model, C(z -1 ) is the whitening filter, P(k) is the power of the system at time k, B(z -1 ) is the molecular function of the feeder load regulation characteristic model, V(k) is the voltage of the system at time k, e(k) is white noise, P f (k) is the power after filtering, V f (k) is the filtered voltage, z is the complex variable in the third-order discrete transfer function, and k is the time.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0143] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned feeder load active-voltage coupling characteristic identification method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0144] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for identifying active power-voltage coupling characteristics of feeder loads, characterized in that: include: Constructing an actual power grid simulation model, and obtaining real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, wherein the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power; Calculating the Pearson correlation coefficient of the real-time data of each node respectively, and selecting target data from the real-time data of all nodes by using the Pearson correlation coefficient of the real-time data of each node, wherein the target data is used for identifying the characteristic parameters of the feeder load regulation; Based on the target data, a feeder load regulation characteristic model is constructed, and the parameters of the feeder load regulation characteristic model are identified using a generalized least squares method.

2. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The construction of the actual power grid simulation model comprises: Using a data acquisition and monitoring control system to collect field data of the actual power grid; Using the data acquisition and monitoring control system to transmit the field data of the actual power grid to the real-time digital simulator in accordance with the 104 protocol; The actual power grid simulation model is constructed using the real-time digital simulator and the field data.

3. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The Pearson correlation coefficient of the real-time data of each node is calculated respectively, including: The voltage and power of each node are respectively input into the Pearson correlation coefficient formula to obtain the Pearson correlation coefficient of the corresponding node. The Pearson correlation coefficient formula is: Where r is the Pearson correlation coefficient, X i is the power of the ith group of each node, Y i is the voltage of the ith group of each node, is the average power of each node, is the average voltage of each node, n is the sample size of the real-time data of each node, and i is the number of nodes.

4. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The method of selecting target data from the real-time data of all nodes by using the Pearson correlation coefficient of the real-time data of each node includes: The real-time data of nodes whose Pearson correlation coefficient is greater than a preset coefficient are selected from all nodes as the target data.

5. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The constructing of a feeder load regulation characteristic model based on the target data includes: The target data and the voltage reduction energy-saving control coefficient are used to construct the feeder load regulation characteristic model, and the feeder load regulation characteristic model is: Among them, CVR fac is the voltage reduction energy-saving control coefficient, ΔV is the voltage change, and ΔP is the power change corresponding to ΔV.

6. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The constructing of a feeder load regulation characteristic model based on the target data includes: The feeder load regulation characteristic model is constructed by using the target data and the third-order discrete transfer function. The feeder load regulation characteristic model is: Among them, G LTV (z) is the third-order discrete transfer function, P(z) is the power data in the target data, V(z) is the voltage data in the target data, z is the complex variable in the third-order discrete transfer function, m1, m2, m3, n1, n2 and n3 are all parameters of the feeder load regulation characteristic model.

7. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 1, characterized in that: The method of identifying the parameters of the feeder load regulation characteristic model by using the generalized least squares method includes: Performing whitening processing on the target data, and performing filtering processing on the target data after the whitening processing; The filtered target data is input into the feeder load regulation characteristic model, and the least square method is used to identify the parameters of the feeder load regulation characteristic model after the filtered target data is input.

8. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 7, characterized in that: The whitening process of the target data comprises: The target data is whitened using a first formula, where the first formula is: Among them, C(z -1 ) is the whitening filter, ε(k) is the colored noise, e(k) is the white noise, c1, c2 and c3 are the parameters of the whitening filter, z is the complex variable in the third-order discrete transfer function, and k is the time.

9. The method for identifying active power-voltage coupling characteristics of feeder load according to claim 7, characterized in that: The filtering process of the target data after the whitening process comprises: The target data after whitening is filtered using the second formula, where the second formula is: Among them, A(z -1 ) is the denominator function of the feeder load regulation characteristic model, C(z -1 ) is the whitening filter, P(k) is the power of the system at time k, B(z -1 ) is the molecular function of the feeder load regulation characteristic model, V(k) is the voltage of the system at time k, e(k) is white noise, P f (k) is the power after filtering, V f (k) is the filtered voltage, z is the complex variable in the third-order discrete transfer function, and k is the time.

10. A feeder load active power-voltage coupling characteristic identification device, characterized in that: include: A data acquisition module, used to construct an actual power grid simulation model and acquire real-time data of each node when the actual power grid simulation model is disturbed by a target disturbance signal, wherein the target disturbance signal is a pseudo-random binary disturbance signal to which the actual power grid simulation model is subjected, and the real-time data includes voltage and power; A calculation module, used to calculate the Pearson correlation coefficient of the real-time data of each node respectively, and select target data from the real-time data of all nodes using the Pearson correlation coefficient of the real-time data of each node, wherein the target data is used for identifying the characteristic parameters of feeder load regulation; A parameter identification module is used to construct a feeder load regulation characteristic model based on the target data, and identify the parameters of the feeder load regulation characteristic model using a generalized least squares method.

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