A low-voltage distribution area equivalent inertia evaluation method

Abstract

The application discloses a kind of equivalent inertia evaluation methods of low-voltage distribution area, it is related to electric power distribution field, by obtaining the distributed multi-dimensional characteristic parameter of low-voltage distribution area, carry out variation modal decomposition and filter effective modal data and carry out superposition processing to obtain multi-dimensional characteristic reconstruction vector, to multi-dimensional characteristic reconstruction vector carry out Kalman filter correction, and based on the best state of Kalman filter correction carries out vector derivation to obtain multi-dimensional physical quantity derivative value, obtains the equivalent conductance and equivalent susceptance of low-voltage distribution area and constructs full-coupling impedance identification model, to multi-dimensional physical quantity derivative value carries out dynamic impedance identification processing, obtains dynamic impedance parameter, according to equivalent conductance and equivalent susceptance carries out internal equivalent frequency calculation, based on calculation carries out inertia parameter differential observation and evaluation processing, obtains the equivalent inertia evaluation data of low-voltage distribution area. With the effect of realizing the accurate quantification of low-voltage distribution area inertia, provide decision basis for frequency stability control.

Description

Technical Field

[0001] This invention relates to the technical field of power distribution, and in particular to a method for evaluating the equivalent inertia of a low-voltage power distribution substation. Background Technology

[0002] Currently, with the advancement of new power distribution system construction, a large number of distributed photovoltaic, decentralized wind power, and electric vehicle charging piles are connected to low-voltage distribution substations, changing the operating characteristics of traditional power distribution networks. For example, synchronous generators have natural rotational inertia, which can provide anti-disturbance support for the power grid. However, distributed energy sources are mostly connected to the power grid through power electronic converters, which have extremely small rotational inertia. This results in a significant decrease in the overall inertia level of low-voltage distribution substations, which exhibits fragmented and dynamically fluctuating characteristics. Therefore, accurately assessing the equivalent inertia of low-voltage substations is of great significance for frequency stability control and source-load coordinated scheduling of the power distribution network.

[0003] Existing inertia assessment methods are mostly focused on high-voltage transmission networks or large-scale renewable energy power plants. Based on linearized models dominated by synchronous machines, they are difficult to adapt to the characteristics of low-voltage distribution substations, such as high coupling between "source-grid-load-storage", nonlinearity, three-phase imbalance, and severe harmonic interference. They are usually based on the following assumptions: line reactance X Much greater than resistance R Therefore, the effect of resistance is ignored, and the equivalent reactance of the system is calculated using the reactive power-voltage decoupling characteristic. However, on the 0.4kV side of the low-voltage distribution substation, the line resistance cannot be ignored, i.e., the high resistance ratio characteristic. R / X The large ratio indicates a strong coupling between active and reactive power. Directly using the pure reactance model from the high-voltage side would lead to significant evaluation errors. Furthermore, the complex measurement environment in low-voltage distribution substations contains a large amount of high-frequency noise and non-stationary pulse interference caused by inverter switching frequencies. Traditional low-pass filter models often suffer from large phase lag or poor tracking ability for abrupt changes when processing such non-stationary signals, making it difficult to meet the high-precision requirements of differential signals in inertia evaluation. Summary of the Invention

[0004] To address the problem that existing technologies suffer from large inertia assessment errors and poor robustness in low-voltage distribution transformer areas under low-voltage, high-impedance, and high-noise environments, making it impossible to accurately quantify the overall inertia level of the transformer area, this application provides an equivalent inertia assessment method for low-voltage distribution transformer areas. This method can establish a dynamic equivalent inertia assessment model by integrating multi-dimensional characteristic parameters of distributed resources, thereby achieving accurate quantification of the inertia of low-voltage distribution transformer areas and providing a decision-making basis for frequency stability control.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A method for evaluating the equivalent inertia of a low-voltage distribution radio area, the method comprising: The distributed multidimensional feature parameters of the low-voltage distribution transformer area are obtained, and the distributed multidimensional feature parameters are subjected to variational mode decomposition and the effective mode data are selected and superimposed to obtain the multidimensional feature reconstruction vector. The multidimensional feature reconstruction vector is corrected by Kalman filtering, and the derivative of the multidimensional feature reconstruction vector is calculated based on the optimal state after Kalman filtering correction to obtain the derivative value of the multidimensional physical quantity. The equivalent conductance and equivalent susceptance of the low-voltage distribution substation are obtained to construct a fully coupled impedance identification model. The multidimensional physical quantity conductance values ​​are then subjected to dynamic impedance identification processing to obtain dynamic impedance parameters. Based on the equivalent conductance and equivalent susceptance, the internal equivalent frequency is calculated, and the inertia parameter differential observation and evaluation processing is performed based on the calculation to obtain the equivalent inertia evaluation data of the low-voltage distribution substation.

[0006] In a preferred embodiment, this application can be further configured as follows: obtaining the distributed multidimensional feature parameters of the low-voltage distribution transformer area, performing variational mode decomposition on the distributed multidimensional feature parameters, filtering effective mode data, and performing superposition processing to obtain a multidimensional feature reconstruction vector, specifically includes: The distributed multidimensional feature parameters are preprocessed to generate several multidimensional feature mode components. The preprocessing expression is as follows: (1) in, This represents the distributed multidimensional characteristic parameters of the original signal in a low-voltage distribution substation. express Multidimensional feature modal components at time intervals It is the number of modes in the variational mode decomposition. Represents the decomposition residue; The correlation between the multidimensional feature modal components and the distributed multidimensional feature parameters is quantified to obtain the correlation coefficient between the multidimensional feature modal components and the distributed multidimensional feature parameters. The correlation coefficient is compared with a preset correlation threshold. Based on the comparison results, effective multidimensional feature modal components are selected and modal component superposition is performed to obtain a multidimensional feature reconstruction vector.

[0007] In a preferred embodiment, this application can be further configured as follows: comparing the correlation coefficient with a preset correlation threshold, filtering valid multidimensional feature modal components based on the comparison result, and performing modal component superposition processing to obtain a multidimensional feature reconstruction vector, further includes: Obtain the physical quantity magnitude and rate of change of the physical quantity magnitude of the multidimensional feature reconstruction vector, and construct the state vector of the multidimensional feature reconstruction vector. The expression of the state vector is as follows: (2) in, The amplitude of the reconstructed physical quantity. The rate of change of the magnitude of the physical quantity; Construct the state transition equation of the state vector, and construct the measurement equation based on the state transition equation to perform noise removal processing on the multidimensional feature reconstruction vector to obtain the noise-removed multidimensional feature reconstruction vector. The state transition equation is expressed as follows: (3) in, Here is the state transition matrix. The input is the sampling interval. The process noise covariance matrix; The measurement equation is expressed as follows: (4) in, The reconstructed signal value is the reconstructed vector of the multidimensional feature reconstruction vector. To measure the noise covariance matrix, This is the measurement matrix.

[0008] In a preferred embodiment, this application can be further configured such that: the quantification of the correlation between the multidimensional feature modal components and the distributed multidimensional feature parameters to obtain the correlation coefficient between the multidimensional feature modal components and the distributed multidimensional feature parameters specifically includes: The formula for calculating the correlation coefficient is as follows: (5) in, Indicates the number of sampling points. and These represent the mean values ​​between the multidimensional feature modal components and the distributed multidimensional feature parameters, respectively. and These represent the sampling points. Multimodal feature components and distributed multidimensional feature parameters.

[0009] In a preferred embodiment, this application can be further configured as follows: The step of performing Kalman filtering correction on the multidimensional feature reconstruction vector, and then calculating the derivative of the multidimensional feature reconstruction vector based on the optimal state after Kalman filtering correction to obtain the derivative value of the multidimensional physical quantity, specifically includes: The prior state and prior error covariance are calculated for the multidimensional feature reconstruction vector at the current time to obtain the predicted value of the prior state and the predicted value of the prior error covariance. The prior state prediction expression is as follows: (6) The prior error covariance prediction expression is shown below: (7) in, Indicates by The optimal state at time t is calculated The predicted value of the prior state at the sampling time. Here is the state transition matrix. Indicates by The highest priority test error covariance at time Calculated The predicted value of the prior error covariance at the sampling time. The process noise covariance matrix; The Kalman gain coefficient is calculated by obtaining the real-time observation value of the multidimensional feature reconstruction vector. Based on the Kalman gain coefficient, the prior state prediction value is corrected to obtain the best state estimation vector after Kalman filtering correction. The Kalman gain coefficient is calculated as follows: (8) The correction expression for the prior state prediction is shown below: (9) The expression for the optimal state estimation vector is as follows: (10) in, Indicates the Kalman gain coefficient. For the measurement matrix, To measure the noise covariance matrix, This represents the prior state correction value. This represents the reconstructed observation at the current moment; Based on the optimal state estimation vector, the derivatives of the multidimensional physical quantities of the low-voltage distribution transformer area are calculated to obtain the derivative values ​​of the multidimensional physical quantities at the junction of the low-voltage distribution transformer area.

[0010] In a preferred embodiment, this application can be further configured as follows: the step of performing Kalman filtering correction on the multidimensional feature reconstruction vector, and calculating the derivative of the multidimensional feature reconstruction vector based on the optimal state after Kalman filtering correction to obtain the derivative value of the multidimensional physical quantity, further includes: The prediction bias is corrected based on the Kalman gain coefficient for the predicted prior error covariance. The correction expression for the predicted prior error covariance is as follows: (11) in, This represents the correction value for the predicted value of the prior error covariance. Represents the identity matrix with the same dimension as the system state.

[0011] In a preferred embodiment, this application can be further configured as follows: The step of obtaining the equivalent conductance and equivalent susceptance of the low-voltage distribution transformer area to construct a fully coupled impedance identification model, and performing dynamic impedance identification processing on the multidimensional physical quantity conductance values ​​to obtain dynamic impedance parameters, specifically includes: The expression for the fully coupled impedance identification model is as follows: (12) in, To identify the inertial time constant of the differential observer based on impedance, , The equivalent conductance and equivalent susceptance are estimated at the current moment. , These are the gain coefficients corresponding to the equivalent conductance and equivalent susceptance. Indicates meritorious residual work. Indicates the residual of no work done. The normalized rate of change of voltage amplitude. This refers to the angular frequency deviation at the junction. This represents the voltage vector obtained from the measurement at the transformer substation. The derivative values ​​of the multidimensional physical quantities are input into the fully coupled impedance identification model to calculate the equivalent resistance and equivalent reactance of the transformer area, thereby obtaining the dynamic impedance parameters.

[0012] In a preferred embodiment, this application can be further configured as follows: The step of inputting the derivative values ​​of the multidimensional physical quantities into the fully coupled impedance identification model to calculate the equivalent resistance and equivalent reactance of the transformer area, thereby obtaining dynamic impedance parameters, specifically includes: The calculation expression for the dynamic impedance parameter is as follows: (13) in, , These are the equivalent conductance and equivalent susceptance calculated using the fully coupled impedance identification model, respectively. This represents the dynamic equivalent resistance. This represents the dynamic equivalent reactance.

[0013] In a preferred embodiment, this application can be further configured as follows: the internal equivalent frequency calculation based on the equivalent conductance and the equivalent susceptance, and the inertia parameter differential observation and evaluation processing based on the calculation to obtain the equivalent inertia evaluation data of the low-voltage distribution substation, specifically includes: The expression for calculating the internal equivalent frequency is as follows: (14) in, This indicates the equivalent frequency within the low-voltage distribution radio area. This indicates the measurement frequency at the control point in the distribution area. This represents the dynamic equivalent resistance. Represents dynamic equivalent reactance. This indicates the residual value of the checkpoints in the Taiwan area. This indicates the reactive residual at the checkpoint in the Taiwan area. This represents the voltage vector obtained from the measurement at the transformer substation. The equivalent inertia of the low-voltage distribution substation is quantized based on the internal equivalent frequency to obtain the equivalent inertia parameter. The expression for the equivalent inertia parameter is as follows: (15) in, Let be the equivalent inertia to be determined. Equivalent frequency within the substation area rate of change, This represents the total power output of all power sources within the transformer substation area. For the electromagnetic power output to the outside, This is the equivalent damping coefficient; The equivalent inertia parameter is observed at a microscopic level, and the observation expression is as follows: (16) in, Dead zone function to prevent numerical drift, The time constant for inertial observation. The rate of change of the equivalent inertia constant. The rate of change of the equivalent frequency within the transformer substation area. Indicates power residual; Based on the microscopic observation results, the equivalent inertia parameter is converted into an inertial time constant for equivalent inertia evaluation, and the equivalent inertia evaluation data of the low-voltage distribution substation is obtained. The conversion expression for the inertial time constant is as follows: (17) in, This is the system's reference angular frequency.

[0014] In a preferred embodiment, this application can be further configured such that: during the observation process of performing microscopic observation of the equivalent inertia parameter, the method further includes: The power residual expression is as follows: (18) in, for Steady-state value before disturbance This represents the power at the sudden change threshold.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application innovatively adopts the VMD-KF joint algorithm for signal preprocessing, which directly extracts the smooth first derivative with high signal-to-noise ratio while suppressing environmental noise, effectively avoiding the noise amplification effect of the traditional differential method. Furthermore, considering the non-negligible resistance of low-voltage lines, a fully coupled complex power observer is constructed to identify the equivalent resistance and reactance in real time, correcting the fundamental deviation of the model. Finally, based on the internal virtual frequency after impedance voltage drop correction, a parameter adaptive observer is used to achieve accurate decoupling evaluation of the transformer area inertia. 2. This application synchronously collects high-frequency time-series data of voltage, frequency, and power at the low-voltage side of the distribution transformer to achieve non-intrusive monitoring. Secondly, it utilizes a combined VMD and KF algorithm to remove environmental noise and directly extracts a smoothed first-order derivative signal with a high signal-to-noise ratio. For the high impedance ratio characteristics of low-voltage lines, a fully coupled dynamic model is established. A differential observer is used to identify the equivalent resistance and reactance of the transformer area in real time, correcting fundamental deviations in the model. Finally, based on the identified impedance, the virtual frequency within the transformer area is corrected, and an inertia differential observer incorporating a dead-time function is used to calculate the equivalent inertia constant in real time. 3. This application fully considers the large R / X ratio of low-voltage distribution networks, introduces equivalent resistance parameters, solves the problem of failure of traditional pure reactance models on the low-voltage side, and significantly improves the evaluation accuracy; adopts the VMD-KF joint algorithm, effectively separates the switching noise of power electronic equipment from the dynamic response of the system, realizes the integration of "noise reduction" and "differential extraction", and solves the problem of noise amplification in differential operations; the entire evaluation process uses an integral differential tracker instead of direct algebraic division, avoids calculation divergence caused by the denominator crossing zero, and is suitable for continuous real-time evaluation. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is an overall flowchart of the equivalent inertia assessment method for low-voltage distribution radio stations in this embodiment.

[0018] Figure 2This is a schematic diagram of the algorithm principle of the equivalent inertia assessment method for low-voltage distribution radio stations in this embodiment.

[0019] Figure 3 This is a comparison chart of the equivalent inertia simulation results of the equivalent inertia evaluation method for the low-voltage distribution transformer area in this embodiment. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] In one embodiment, such as Figure 1 As shown, this application discloses a method for evaluating the equivalent inertia of a low-voltage distribution radio area, which specifically includes the following steps: S1: Obtain the distributed multidimensional feature parameters of the low-voltage distribution transformer area, perform variational mode decomposition on the distributed multidimensional feature parameters, and filter the effective mode data for superposition processing to obtain the multidimensional feature reconstruction vector.

[0025] Specifically, the bus voltage amplitude is collected using high-frequency data acquisition devices such as intelligent distribution transformer terminals in low-voltage distribution substations. Phase angle Bus real-time frequency Total active power of the line With reactive power The data is collected to obtain distributed multidimensional feature parameters. In this embodiment, the sampling frequency is set to 100Hz, and the collected data vector is defined as follows: D ( t ): (19) Specifically, step S1 includes: S11: Preprocess the distributed multidimensional feature parameters to generate several multidimensional feature modal components. The preprocessing expression is as follows: (1) in, This represents the distributed multidimensional characteristic parameters of the original signal in a low-voltage distribution substation. express Multidimensional feature modal components at time intervals , It is the number of modes in the variational mode decomposition, in this embodiment , Represents the decomposition residual amount.

[0026] S12: Quantify the correlation between the multidimensional feature modal components and the distributed multidimensional feature parameters to obtain the correlation coefficient between the multidimensional feature modal components and the distributed multidimensional feature parameters.

[0027] Specifically, to effectively separate real physical signals from noise, this embodiment focuses on using the Pearson correlation coefficient for effective mode selection. The calculation expression for the correlation coefficient is shown below: (5) in, Indicates the number of sampling points. and These represent the mean values ​​between the multidimensional feature modal components and the distributed multidimensional feature parameters, respectively. and These represent the sampling points. Multimodal feature components and distributed multidimensional feature parameters.

[0028] S13: Compare the correlation coefficient with the preset correlation threshold, select the effective multidimensional feature modal components based on the comparison results, and perform modal component superposition to obtain the multidimensional feature reconstruction vector.

[0029] Specifically, this embodiment sets a threshold. ρ th =0.15, if ρ th If the value is ≥0.15, the IMF component is determined to be an effective mode containing inertial response characteristics; ρ thIf the correlation coefficient is less than 0.15, the IMF component is determined to be high-frequency noise or a spurious component and is removed. Low-frequency modes with correlation coefficients greater than the threshold are retained, while high-frequency noise modes are discarded. The resulting multidimensional feature reconstruction vector is obtained by superimposing these components. correspond V rec ( t ), f rec ( t ), P rec ( t ), Q rec ( t ).

[0030] Furthermore, the drawback of noise amplification in difference operations is overcome by constructing discrete state-space equations based on the Constant Velocity Model (CVM). Specifically, this includes: S14: Obtain the physical quantity magnitude and rate of change of the physical quantity magnitude of the multidimensional feature reconstruction vector, and construct the state vector of the multidimensional feature reconstruction vector. The expression of the state vector is as follows: (2) in, The amplitude of the reconstructed physical quantity. This represents the rate of change of the magnitude of the physical quantity.

[0031] S15: Construct the state transition equation of the state vector, and construct the measurement equation based on the state transition equation to perform noise removal processing on the multidimensional feature reconstruction vector, so as to obtain the noise-removed multidimensional feature reconstruction vector.

[0032] The state transition equation is expressed as follows: (3) in, Here is the state transition matrix. The input is the sampling interval. Let be the process noise covariance matrix.

[0033] The measurement equation is expressed as follows: (4) in, The reconstructed signal value is the reconstructed vector of the multidimensional feature reconstruction vector. To measure the noise covariance matrix, This is the measurement matrix.

[0034] S2: Perform Kalman filtering correction on the multidimensional feature reconstruction vector, and then perform derivative processing on the multidimensional feature reconstruction vector based on the optimal state after Kalman filtering correction to obtain the derivative values ​​of multidimensional physical quantities.

[0035] Specifically, in this embodiment, Kalman filtering (KF) is used for each sampling time. k Based on the optimal estimate from the previous time step, the prior state at the current time step is calculated. and prior error covariance Step S2 includes: S21: Perform prior state and prior error covariance estimation on the multidimensional feature reconstruction vector at the current time to obtain the predicted value of the prior state and the predicted value of the prior error covariance.

[0036] The prior state prediction expression is shown below: (6) The prior error covariance prediction expression is shown below: (7) in, Indicates by The optimal state at time t is calculated The predicted value of the prior state at the sampling time. Here is the state transition matrix. Indicates by The highest priority test error covariance at time Calculated The predicted value of the prior error covariance at the sampling time. Let be the process noise covariance matrix.

[0037] S22: Obtain the real-time observation values ​​of the multidimensional feature reconstruction vector, calculate the Kalman gain coefficient, and correct the prior state prediction value based on the Kalman gain coefficient to obtain the best state estimation vector after Kalman filtering correction.

[0038] The Kalman gain coefficient is calculated as follows: (8) The correction expression for the prior state prediction is shown below: (9) The expression for the optimal state estimation vector is as follows: (10) in, Indicates the Kalman gain coefficient. For the measurement matrix, To measure the noise covariance matrix, This represents the prior state correction value. This represents the reconstructed observation at the current moment.

[0039] This embodiment also includes: The prediction bias is corrected for the prior error covariance prediction value based on the Kalman gain coefficient. The correction expression for the prior error covariance prediction value is as follows: (11) in, This represents the correction value for the predicted value of the prior error covariance. Represents the identity matrix with the same dimension as the system state.

[0040] S23: Based on the optimal state estimation vector, perform multidimensional physical quantity differentiation processing on the low-voltage distribution transformer area to obtain the multidimensional physical quantity derivative values ​​at the low-voltage distribution transformer area gate.

[0041] Specifically, extract from the corrected optimal state estimation vector Finally, the high-precision derivative values ​​of each physical quantity of the PCC point in the low-voltage distribution area are output as input variables for the fully coupled impedance identification model.

[0042] S3: Obtain the equivalent conductance and equivalent susceptance of the low-voltage distribution substation to construct a fully coupled impedance identification model. Perform dynamic impedance identification processing on the multidimensional physical quantity derivative values ​​to obtain dynamic impedance parameters.

[0043] Specifically, assume that the low-voltage distribution substation can be equivalently represented as a region with an internal potential within the inertial response time scale. and internal complex impedance Z eq The generalized power supply model. Let the voltage phasor measured at the transformer substation (PCC point) be... The current phasor injected into the transformer area is The equivalent complex impedance of the transformer area is expressed as: (20) The corresponding equivalent susceptance is: (twenty one) in, For the dynamic equivalent admittance of the low-voltage distribution area to the outside world, For equivalent conductance, This is the equivalent susceptance.

[0044] Specifically, step S3 includes: S31: The expression for the fully coupled impedance identification model is shown below: (12) in, To identify the inertial time constant of the differential observer based on impedance, , The equivalent conductance and equivalent susceptance are estimated at the current moment. , These are the gain coefficients corresponding to the equivalent conductance and equivalent susceptance. Indicates meritorious residual work. Indicates the residual of no work done. The normalized rate of change of voltage amplitude. This refers to the angular frequency deviation at the junction. This represents the voltage vector obtained from the measurement at the transformer substation.

[0045] The derivation process of the fully coupled impedance identification model expression in this embodiment is as follows: Complex power injected at the gate of the distribution area Defined as: (twenty two) Furthermore, complex power The first derivative with respect to time t is as follows: (twenty three) In the formula, , The total active power and reactive power of the line. For the voltage phasor at the gate node, It is the conjugate of the current phasor at the gate node.

[0046] The voltage phasor differential expansion is as follows: (twenty four) (25) (26) (27) In the formula, The normalized rate of change of voltage amplitude. This refers to the angular frequency deviation at the junction. It represents the imaginary unit used to distinguish between active and reactive power.

[0047] Assuming that the electromotive force inside the transformer region remains rigid within an extremely short timescale of the inertial response, and that current changes are primarily caused by external voltage fluctuations. Assuming the positive direction of the current is injected into the transformer region, according to the differential form of Ohm's law: (28) Furthermore, the fully coupled observation equations and fully coupled complex equations for the low-pressure area are obtained, and their expressions are shown below: (29) Simplify using the conjugate property: (30) The changes in active power and reactive power are as follows: (31) Moving the directly measurable terms in the above equation to the left side, we define them as power residuals, where the active power residuals are... and unproductive residual As shown below: (32) Written in matrix form: (33) To avoid directly solving for the matrix inverse, a fully coupled impedance identification model is constructed for real-time tracking using the following first-order inertial differential observation expression: (34) S32: Input the derivative values ​​of multidimensional physical quantities into the fully coupled impedance identification model to calculate the equivalent resistance and equivalent reactance of the transformer area, and obtain the dynamic impedance parameters.

[0048] Specifically, the expression for calculating the dynamic impedance parameter is as follows: (13) in, , These are the equivalent conductance and equivalent susceptance calculated using the fully coupled impedance identification model, respectively. This represents the dynamic equivalent resistance. This represents the dynamic equivalent reactance.

[0049] S4: Calculate the internal equivalent frequency based on the equivalent conductance and equivalent susceptance, and perform differential observation and evaluation of the inertia parameters based on the calculation to obtain the equivalent inertia evaluation data of the low-voltage distribution substation.

[0050] Specifically, considering the voltage drop across the impedance, the frequency of the equivalent electromotive force inside the transformer area differs from the frequency at the measurement point. Step S4 includes: S41: The expression for calculating the internal equivalent frequency is shown below: (14) in, This indicates the equivalent frequency within the low-voltage distribution radio area. This indicates the measurement frequency at the control point in the distribution area. This represents the dynamic equivalent resistance. Represents dynamic equivalent reactance. This indicates the residual value of the checkpoints in the Taiwan area. This indicates the reactive residual at the checkpoint in the Taiwan area. This represents the voltage vector obtained from the measurement at the transformer substation.

[0051] S42: The equivalent inertia of the low-voltage distribution substation is quantized based on the internal equivalent frequency to obtain the equivalent inertia parameters. The expression for the equivalent inertia parameters is as follows: (15) in, Let be the equivalent inertia to be determined. Equivalent frequency within the substation area rate of change, This represents the total power output of all power sources within the transformer substation area. For the electromagnetic power output to the outside, This is the equivalent damping coefficient.

[0052] In this embodiment, assuming the damping is known, formula (15) can be transformed into: (35) S43: Microscopic observation of the equivalent inertia parameter is performed, and the observation expression is as follows: (16) in, Dead zone function to prevent numerical drift, The time constant for inertial observation. The rate of change of the equivalent inertia constant. The rate of change of the equivalent frequency within the transformer substation area. This represents the power residual.

[0053] The dead-time function value expression in this embodiment is as follows: (36) in, This is the dead zone threshold; in low-voltage areas, a value range of 0.02~0.05 rad / s is recommended. 2 .

[0054] S44: Based on the microscopic observation results, the equivalent inertia parameter is converted into an inertial time constant for equivalent inertia evaluation, and the equivalent inertia evaluation data of the low-voltage distribution substation is obtained.

[0055] The conversion expression for the inertial time constant is as follows: (17) in, This is the system's reference angular frequency.

[0056] The inertia assessment principle diagram of this embodiment is as follows: Figure 2 As shown in the figure, in order to facilitate a more intuitive assessment of the frequency support duration of the low-voltage substation in engineering, the equivalent inertia parameter is converted into an inertia time constant for inertia assessment.

[0057] This embodiment also includes: The power residual expression is as follows: (18) in, for Steady-state value before disturbance This represents the power at the sudden change threshold.

[0058] To avoid division by zero, a first-order inertial element is constructed: (37) In one simulation embodiment, in order to verify the effectiveness and superiority of the method of the present invention in the inertia assessment of low-voltage distribution transformer areas, a simulation model of a low-voltage distribution transformer area with high penetration distributed photovoltaic was built. A typical 0.4kV radial low-voltage distribution network model was adopted, which includes one distribution transformer and 20 user nodes.

[0059] The main parameter settings are shown in Table 1 below: Table 1 In this embodiment, the distribution transformer has a capacity of 400kVA and a transformation ratio of 10 / 0.4kV. The low-voltage line adopts a high-impedance cable model with a resistance per unit length R=0.64Ω / km and a reactance X=0.12Ω / km. The line R / X ratio is approximately 5.3, significantly different from the inductive characteristics of the transmission network. The total capacity of the photovoltaic power generation system connected to the distribution area is 160kW (40% penetration rate), and a 50kW distributed energy storage system is configured with inertia support provided by virtual synchronous generator control, with a virtual inertia constant H=5s.

[0060] The simulation conditions were set as follows: at t=1.0s, the load in the transformer area experienced a step increase of 40kW. Simultaneously, to simulate a realistic low-voltage measurement environment, Gaussian white noise with an SNR of 30dB was superimposed on the acquired voltage, frequency, and power signals. The simulation results are as follows. Figure 3 As shown, the experimental results demonstrate that the method of the present invention can evaluate the equivalent inertia more accurately than the traditional method.

[0061] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0062] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0063] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0064] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0065] If the integrated unit is implemented as 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for evaluating the equivalent inertia of a low-voltage distribution radio station area, characterized in that, The method includes: The distributed multidimensional feature parameters of the low-voltage distribution transformer area are obtained, and the distributed multidimensional feature parameters are subjected to variational mode decomposition and the effective mode data are selected and superimposed to obtain the multidimensional feature reconstruction vector. The multidimensional feature reconstruction vector is corrected by Kalman filtering, and the derivative of the multidimensional feature reconstruction vector is calculated based on the optimal state after Kalman filtering correction to obtain the derivative value of the multidimensional physical quantity. The equivalent conductance and equivalent susceptance of the low-voltage distribution substation are obtained to construct a fully coupled impedance identification model. The multidimensional physical quantity conductance values ​​are then subjected to dynamic impedance identification processing to obtain dynamic impedance parameters. Based on the equivalent conductance and equivalent susceptance, the internal equivalent frequency is calculated, and the inertia parameter differential observation and evaluation processing is performed based on the calculation to obtain the equivalent inertia evaluation data of the low-voltage distribution substation.

2. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 1, characterized in that, The process of obtaining distributed multidimensional feature parameters of low-voltage distribution substations, performing variational mode decomposition on the distributed multidimensional feature parameters, filtering effective mode data, and superimposing the data to obtain a multidimensional feature reconstruction vector specifically includes: The distributed multidimensional feature parameters are preprocessed to generate several multidimensional feature mode components. The preprocessing expression is as follows: （1） in, This represents the distributed multidimensional characteristic parameters of the original signal in a low-voltage distribution substation. express Multidimensional feature modal components at time step, It is the number of modes in the variational mode decomposition. Represents the decomposition residue; The correlation between the multidimensional feature modal components and the distributed multidimensional feature parameters is quantified to obtain the correlation coefficient between the multidimensional feature modal components and the distributed multidimensional feature parameters. The correlation coefficient is compared with a preset correlation threshold. Based on the comparison results, effective multidimensional feature modal components are selected and modal component superposition is performed to obtain a multidimensional feature reconstruction vector.

3. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 2, characterized in that, The step of comparing the correlation coefficient with a preset correlation threshold, filtering valid multidimensional feature modal components based on the comparison results, and performing modal component superposition processing to obtain a multidimensional feature reconstruction vector further includes: Obtain the physical quantity magnitude and rate of change of the physical quantity magnitude of the multidimensional feature reconstruction vector, and construct the state vector of the multidimensional feature reconstruction vector. The expression of the state vector is as follows: （2） in, The amplitude of the reconstructed physical quantity. The rate of change of the magnitude of the physical quantity; Construct the state transition equation of the state vector, and construct the measurement equation based on the state transition equation to perform noise removal processing on the multidimensional feature reconstruction vector to obtain the noise-removed multidimensional feature reconstruction vector. The state transition equation is expressed as follows: （3） in, Here is the state transition matrix. The input is the sampling interval. The process noise covariance matrix; The measurement equation is expressed as follows: （4） in, The reconstructed signal value is the reconstructed vector of the multidimensional feature reconstruction vector. To measure the noise covariance matrix, This is the measurement matrix.

4. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 2, characterized in that, The step of quantifying the correlation between the multidimensional feature modal components and the distributed multidimensional feature parameters to obtain the correlation coefficient between the multidimensional feature modal components and the distributed multidimensional feature parameters specifically includes: The formula for calculating the correlation coefficient is as follows: （5） in, Indicates the number of sampling points. and These represent the mean values ​​between the multidimensional feature modal components and the distributed multidimensional feature parameters, respectively. and These represent the sampling points. Multimodal feature components and distributed multidimensional feature parameters.

5. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 1, characterized in that, The process of applying Kalman filtering correction to the multidimensional feature reconstruction vector, and then differentiating the multidimensional feature reconstruction vector based on the optimal state after Kalman filtering correction to obtain the derivative values ​​of the multidimensional physical quantities, specifically includes: The prior state and prior error covariance are calculated for the multidimensional feature reconstruction vector at the current time to obtain the predicted value of the prior state and the predicted value of the prior error covariance. The prior state prediction expression is as follows: （6） The prior error covariance prediction expression is shown below: （7） in, Indicates by The optimal state at time t is calculated The predicted value of the prior state at the sampling time. Here is the state transition matrix. Indicates by The highest priority test error covariance at time Calculated The predicted value of the prior error covariance at the sampling time. The process noise covariance matrix; The Kalman gain coefficient is calculated by obtaining the real-time observation value of the multidimensional feature reconstruction vector. Based on the Kalman gain coefficient, the prior state prediction value is corrected to obtain the best state estimation vector after Kalman filtering correction. The Kalman gain coefficient is calculated as follows: （8） The correction expression for the prior state prediction is shown below: （9） The expression for the optimal state estimation vector is as follows: （10） in, Indicates the Kalman gain coefficient. For the measurement matrix, To measure the noise covariance matrix, This represents the prior state correction value. This represents the reconstructed observation at the current moment; Based on the optimal state estimation vector, the derivatives of the multidimensional physical quantities of the low-voltage distribution transformer area are calculated to obtain the derivative values ​​of the multidimensional physical quantities at the junction of the low-voltage distribution transformer area.

6. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 5, characterized in that, The step of performing Kalman filtering correction on the multidimensional feature reconstruction vector, and then taking the derivative of the multidimensional feature reconstruction vector based on the optimal state after Kalman filtering correction to obtain the derivative value of the multidimensional physical quantity, further includes: The prediction bias is corrected based on the Kalman gain coefficient for the predicted prior error covariance. The correction expression for the predicted prior error covariance is as follows: （11） in, This represents the correction value for the predicted value of the prior error covariance. Represents the identity matrix with the same dimension as the system state.

7. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 1, characterized in that, The process of obtaining the equivalent conductance and equivalent susceptance of the low-voltage distribution substation to construct a fully coupled impedance identification model, and performing dynamic impedance identification processing on the multidimensional physical quantity derivative values ​​to obtain dynamic impedance parameters, specifically includes: The expression for the fully coupled impedance identification model is as follows: （12） in, To identify the inertial time constant of the differential observer based on impedance, , The equivalent conductance and equivalent susceptance are estimated at the current moment. , These are the gain coefficients corresponding to the equivalent conductance and equivalent susceptance. Indicates meritorious residual work. Indicates the residual of no work done. The normalized rate of change of voltage amplitude. This refers to the angular frequency deviation at the junction. This represents the voltage vector obtained from the measurement at the transformer substation. The derivative values ​​of the multidimensional physical quantities are input into the fully coupled impedance identification model to calculate the equivalent resistance and equivalent reactance of the transformer area, thereby obtaining the dynamic impedance parameters.

8. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 7, characterized in that, The step of inputting the derivative values ​​of the multidimensional physical quantities into the fully coupled impedance identification model to calculate the equivalent resistance and equivalent reactance of the transformer area, thereby obtaining dynamic impedance parameters, specifically includes: The calculation expression for the dynamic impedance parameter is as follows: （13） in, , These are the equivalent conductance and equivalent susceptance calculated using the fully coupled impedance identification model, respectively. This represents the dynamic equivalent resistance. This represents the dynamic equivalent reactance.

9. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 1, characterized in that, The process of calculating the internal equivalent frequency based on the equivalent conductance and equivalent susceptance, and performing differential observation and evaluation of inertia parameters based on the calculation to obtain the equivalent inertia evaluation data of the low-voltage distribution substation area specifically includes: The expression for calculating the internal equivalent frequency is as follows: （14） in, This indicates the equivalent frequency within the low-voltage distribution radio area. This indicates the measurement frequency at the control point in the distribution area. This represents the dynamic equivalent resistance. Represents dynamic equivalent reactance. This indicates the residual value of the checkpoints in the Taiwan area. This indicates the reactive residual at the checkpoint in the Taiwan area. This represents the voltage vector obtained from the measurement at the transformer substation. The equivalent inertia of the low-voltage distribution substation is quantized based on the internal equivalent frequency to obtain the equivalent inertia parameter. The expression for the equivalent inertia parameter is as follows: （15） in, Let the equivalent inertia be the value to be determined. Equivalent frequency within the substation area rate of change, This represents the total power output of all power sources within the transformer substation area. For the electromagnetic power output to the outside, This is the equivalent damping coefficient; The equivalent inertia parameter is observed at a microscopic level, and the observation expression is as follows: （16） in, Dead zone function to prevent numerical drift, The time constant for inertial observation. The rate of change of the equivalent inertia constant. The rate of change of the equivalent frequency within the transformer substation area. Indicates power residual; Based on the microscopic observation results, the equivalent inertia parameter is converted into an inertial time constant for equivalent inertia evaluation, and the equivalent inertia evaluation data of the low-voltage distribution substation is obtained. The conversion expression for the inertial time constant is as follows: （17） in, This is the system's reference angular frequency.

10. The method for evaluating the equivalent inertia of a low-voltage distribution transformer area according to claim 9, characterized in that, The observation process for performing microscopic observation of the equivalent inertia parameter also includes: The power residual expression is as follows: （18） in, for Steady-state value before disturbance This represents the power at the sudden change threshold.

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