Multi-region power system inertia estimation method, system, equipment and medium

Abstract

The invention relates to the technical field of power system control, in particular to a multi-region power system inertia estimation method, which comprises the following steps of: dividing a power system into a plurality of voltage control regions based on an electrical distance between power grid nodes, selecting a pilot bus in each voltage control region, and acquiring voltage data of each pilot bus; processing the dynamic operation state data of the synchronous generator to identify a system power imbalance disturbance moment; intercepting transient stage system response data based on the voltage data, the processed dynamic operation state data and the disturbance moment, and identifying modal parameters in the system response data; and calculating an inertia component of each voltage control area based on the voltage data, the processed dynamic operation state data and the modal parameters, and calculating the total inertia of the system according to the inertia components. The method has the beneficial effects that panoramic, high-precision and automatic estimation of the total inertia of the system is realized, and the accuracy of stable analysis of the power grid frequency is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power system control technology, and in particular to a method, system, equipment and medium for estimating the inertia of a multi-regional power system. Background Technology

[0002] With the large-scale integration of renewable energy, the proportion of traditional synchronous generators is declining, and the inertia of the power system exhibits characteristics of "uneven distribution across multiple regions and frequent dynamic fluctuations," posing a severe challenge to frequency stability—low inertia scenarios are prone to high frequency change rates and exceeding minimum frequency limits, triggering protection trips or load shedding. Therefore, real-time and accurate multi-regional inertia estimation has become crucial for ensuring power grid security.

[0003] However, existing inertia estimation technologies suffer from drawbacks such as poor real-time performance, insufficient adaptability to dynamic characteristics in multiple regions, incomplete coverage of inertia components, high monitoring costs, and weak resistance to modal interference. As a result, they are difficult to achieve accurate, economical, and reliable full-system inertia assessment when facing complex power grids with a high proportion of new energy sources. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for estimating the inertia of a multi-regional power system, comprising: dividing the power system into multiple voltage control zones based on the electrical distance between power grid nodes; selecting a pilot bus in each voltage control zone; and acquiring voltage data for each pilot bus. The dynamic operating status data of the synchronous generator is processed to identify the timing of system power imbalance disturbances; Based on voltage data, processed dynamic operating status data, and transient phase system response data captured at the time of disturbance, modal parameters in the system response data are identified. Based on voltage data, processed dynamic operating status data, and modal parameters, the inertia component of each voltage control zone is calculated, and the total system inertia is calculated based on the inertia component.

[0005] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, the power system is divided into multiple voltage control zones based on the electrical distance between grid nodes, including: Acquire steady-state data of the system during the steady-state phase; Calculate the dV / dQ sensitivity matrix between nodes based on steady-state data; Calculate the electrical distance between nodes based on the dV / dQ sensitivity matrix; A hierarchical clustering algorithm is used to cluster nodes based on electrical distance to form a preliminary voltage control zone; The voltage control region is verified to form the final voltage control region.

[0006] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, the total system inertia is calculated based on the inertia components, including... Calculate the inertia of the synchronous machine, the load inertia, and the asynchronous inertia; Based on the synchronous machine inertia, load inertia, and asynchronous inertia of each region, the total system inertia is calculated by weighting the values ​​according to the rated power of each region.

[0007] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, the modal parameters in the system response data are identified by the matrix pencil method, and only complex conjugate oscillation modes are selected during the identification process.

[0008] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, the dV / dQ sensitivity matrix is ​​extracted by solving the inverse matrix of the Jacobian matrix calculated by the Newton-Raphson power flow method.

[0009] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, the calculation of load inertia includes, The system inertia center frequency is calculated, and combined with the voltage data of the pilot bus, the load power change is calculated using a voltage / frequency dependent load model to obtain the load inertia response.

[0010] As a preferred embodiment of the multi-regional power system inertia estimation method of the present invention, wherein: the pilot bus of each voltage control zone is the maximum load bus in the voltage control zone, and the number of pilot buses is 15% to 30% of the total number of load buses in the system.

[0011] Secondly, the present invention provides a multi-regional power system inertia estimation system, comprising: including, Voltage control zone division module: Based on the electrical distance between grid nodes, the power system is divided into multiple voltage control zones, a pilot bus is selected in each voltage control zone, and the voltage data of each pilot bus is obtained; Disturbance Timing Identification Module: Processes the dynamic operating status data of the synchronous generator to identify the timing of system power imbalance disturbances; Modal parameter identification module: Based on voltage data, processed dynamic operating status data and transient phase system response data captured at the time of disturbance, and identify the modal parameters in the system response data; Total Inertia Synthesis Module: Based on voltage data, processed dynamic operating status data and modal parameters, it calculates the inertia component of each voltage control zone, and calculates the total inertia of the system based on the inertia component.

[0012] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0013] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention achieves a panoramic, high-precision, and automated estimation of the total inertia of the system by using power grid partitioning based on electrical distance, disturbance timing identification, modal parameter extraction, and multi-inertia component fusion calculation, effectively improving the accuracy and engineering practicality of power grid frequency stability analysis. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for estimating the inertia of a multi-regional power system.

[0017] Figure 2 A regional partitioning map serving as a validation example of a multi-regional power system inertia estimation method.

[0018] Figure 3 Frequency and electromagnetic power are used as a validation example of a method for estimating the inertia of a multi-regional power system.

[0019] Figure 4 The input and output curves of an ARMAX model serving as a validation example for inertia estimation methods in multi-regional power systems.

[0020] Figure 5 A comparison of linear model prediction and measured nonlinear dynamics of the system frequency response, serving as a verification example for a multi-regional power system inertia estimation method. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1-5 This is the first embodiment of the present invention, which provides a method for estimating the inertia of a multi-regional power system, including: S100: Based on the electrical distance between grid nodes, the power system is divided into multiple voltage control zones, a pilot bus is selected in each voltage control zone, and the voltage data of each pilot bus is acquired. S200: Processes the dynamic operating status data of the synchronous generator to identify the moment of system power imbalance disturbance; S300: Based on voltage data, processed dynamic operating status data, and transient phase system response data captured at the time of disturbance, and identifies modal parameters in the system response data; S400: Based on voltage data, processed dynamic operating status data and modal parameters, calculate the inertia component of each voltage control zone, and calculate the total system inertia based on the inertia component.

[0023] It should be noted that existing inertia estimation techniques have shortcomings such as poor real-time performance, insufficient adaptability to dynamic characteristics in multiple regions, incomplete coverage of inertia components, high monitoring costs, and weak resistance to modal interference. As a result, when facing complex power grids with a high proportion of new energy sources, it is difficult to achieve accurate, economical, and reliable full-system inertia assessment.

[0024] Therefore, in response to the aforementioned operational monitoring and health prediction issues, the steps S100-S400 enable a panoramic, high-precision, and automated estimation of the system's total inertia, effectively improving the accuracy and engineering practicality of power grid frequency stability analysis.

[0025] Example 2, refer to Figure 1-5 As an embodiment of the present invention, based on the above embodiment, a method for estimating the inertia of a multi-regional power system is provided.

[0026] In this embodiment of the application, step S100, which divides the power system into multiple voltage control zones based on the electrical distance between grid nodes, selects a pilot bus within each voltage control zone, and obtains the voltage data of each pilot bus, includes steps A1-A7: A1. Acquire steady-state data of the system during the steady-state phase. The steady-state data includes node voltage amplitude, voltage phase angle, and reactive power.

[0027] In one alternative implementation, the steady-state data in step A1 can be acquired by a PMU, which synchronously acquires global data of the system during steady-state operation at a frequency of 30-60 phasors / second.

[0028] In another alternative implementation, the steady-state data in step A1 can be collected by a smart meter. The smart meter installed on the user side can collect steady-state data at high frequency and upload it to the main station.

[0029] In one optional implementation, step A1 can determine whether the system is in a steady state based on electrical parameter thresholds. When the system frequency deviation is less than ±0.05Hz for 30 seconds and the voltage deviation of more than 95% of the nodes is less than ±2% of the nominal value for 30 seconds, the system is determined to be in a steady state.

[0030] In another optional implementation, step A1 can also use a composite criterion to determine whether the system is in a steady state. It is confirmed that the energy management system has not had any switch changes or protection action alarms in the last 10 minutes. The standard deviation of the system frequency within the 10-minute window is calculated to be less than 0.01Hz and its peak-to-peak value is less than 0.08Hz. When all the above conditions are met, the system is determined to be in a steady state.

[0031] A2. Denoise the reactive power and node voltage amplitude to generate real reactive power and real node voltage amplitude. In one alternative implementation, the noise reduction of reactive power and node voltage amplitude in step A2 can be achieved by using an averaging filter to take the arithmetic mean of multiple consecutive data points in the signal sequence and using this average value to replace the data point at the current moment, thereby smoothing out random noise in the data.

[0032] In another alternative implementation, the noise reduction processing of reactive power and node voltage amplitude in step A2 can also be performed by using a low-pass filter, designing a digital filter to directly filter out components higher than the system dynamic frequency.

[0033] A3. Obtain the dV / dQ matrix by inverting the power flow equation and the Jacobian matrix, specifically: A31. By combining the voltage phase angle, actual reactive power, actual node voltage magnitude, and node admittance matrix, a complete nonlinear power flow equation model describing the current system power balance relationship is formed. The power flow equation is: ; In the formula, V is the actual node voltage amplitude; θ is the node voltage phase angle; Q is the actual reactive power; Φij is the impedance angle of the node admittance Yij; and n is the total number of nodes in the system.

[0034] A32. At the current steady-state operating point, take the partial derivative of the power flow equation to obtain the Jacobian matrix at that operating point. Then, perform the inverse operation on the resulting Jacobian matrix to obtain the inverse matrix.

[0035] From the inverse matrix, extract the sub-matrix block corresponding to the relationship between voltage change and reactive power change, i.e., the dV / dQ sensitivity matrix. Each element in the dV / dQ sensitivity matrix... It accurately characterizes the impact of injecting a unit reactive power at node j on the voltage at node i.

[0036] A4. The electrical distance D between nodes i and j i,j The electrical distance D is defined as the normalized value of the absolute value of the elements of the dV / dQ matrix. i,j The formula is: ; In the formula, max(|dV|) is the maximum absolute value of all elements in the dV / dQ matrix. D i,j The closer the value is to 1, the closer the electrical distance and the tighter the coupling.

[0037] A5. Based on electrical distance, nodes with close electrical coupling are grouped together in the same region through hierarchical clustering. The specific process of hierarchical clustering is as follows: Treat each node as an independent sub-cluster, calculate the average electrical distance between all pairs of sub-clusters, find the two sub-clusters with the smallest average electrical distance from all cluster pairs, and check whether either of the following conditions is met: the minimum average electrical distance is greater than a preset clustering threshold; or the number of current sub-clusters has been reduced to a preset expected range, with the preferred range of the clustering threshold being 0.25 to 0.35; the preferred range of the preset expected range is 5 to 10. If none of the termination conditions are met, the two closest subclusters are merged into a new cluster, and the iteration continues. If any condition is met, the iteration stops immediately, and all the current subclusters constitute the initially defined voltage control region.

[0038] In an optional implementation, the average electrical distance between all pairs of all subclusters in step A5 can be calculated using the average linkage criterion. The electrical distance between all pairs of nodes in two subclusters is calculated, and then the arithmetic mean of these distances is taken as the average electrical distance.

[0039] In another alternative implementation, the average electrical distance between all pairs of subclusters in step A5 can also be calculated using the single linkage criterion, with the distance between the two nearest nodes in two subclusters representing the average electrical distance between the two subclusters.

[0040] A6. Verify the initial voltage control zone (VCZ). Verification condition one is: the average electrical distance between all node pairs within the same VCZ is >0.6. The second verification condition is: the average electrical distance between node pairs in different VCZs is <0.3.

[0041] The preliminary voltage control zone (VCZ) division is deemed valid and marked as the final voltage control zone only if both of the above conditions are met simultaneously. If any condition is not met, the clustering threshold will be automatically adjusted and clustering will be performed again until a qualified verification result is obtained, and it will be marked as the final voltage control region.

[0042] It should be noted that the first verification condition is to ensure tight internal coupling of the VCZ; the second verification condition is to ensure loose external coupling of the VCZ.

[0043] A7. Select the node with the highest historical average load in each final voltage control zone as the pilot bus for that zone, thereby controlling its voltage to most effectively stabilize the voltage level of the entire zone; continuously collect the real-time voltage V of the pilot bus in each final voltage control zone through the PMU. vcz .

[0044] It should be noted that the number of pilot buses is 15% to 30% of the total number of load buses in the system. The maximum load node is usually the electrical center and power hub of the area. Its voltage status can reflect the comprehensive voltage effect caused by reactive power flow of all nodes in the area to the greatest extent and has the strongest representativeness. Controlling this point is equivalent to controlling the key node of voltage stability in the voltage control area.

[0045] Preferably, step S100 quantifies the electrical distance based on the power flow equation and the inverse of the Jacobian matrix, and adopts a hierarchical clustering algorithm to realize the scientific transformation of power grid zoning from experience-dependent to data-driven, standardized, and fully automated. By setting clear verification standards and a closed-loop feedback mechanism, the zoning quality is guaranteed, laying a reliable foundation for low-cost monitoring of point-to-area control. In addition, by providing a variety of steady-state judgment, noise reduction, and clustering criteria to adapt to different engineering scenarios, and combining the strategy of using the maximum load node as a pilot bus, the engineering practicality, robustness, and control efficiency of the solution are significantly improved.

[0046] In this embodiment of the application, step S200 involves processing the dynamic operating status data of the synchronous generator to identify the moment of system power imbalance disturbance, including the following steps B1-B3: B1: The PMU continuously monitors and collects the frequency data, electromagnetic power, and mechanical power of the synchronous generator output bus at 20ms intervals, and performs noise reduction to generate the true frequency data f and the true electromagnetic power P. e and the actual mechanical power P m Refer to step A2 for noise reduction methods; In one alternative implementation, step B1 may involve collecting frequency data, electromagnetic power, and mechanical power from all synchronous generator output buses.

[0047] In another optional implementation, step B1 may also collect frequency data, electromagnetic power, and mechanical power of one or more key synchronous generator buses. The key synchronous generator refers to the unit with the largest rated capacity and closest to the electrical center of the power grid. The frequency dynamics of this unit can most effectively represent the frequency change trend of the entire system.

[0048] B2: Based on the principle of amplifying instantaneous changes in the frequency signal and suppressing background noise, the real frequency data stream is labeled as x(n). The Teager-Kaiser energy operator is applied to x(n). The discrete form of the Teager-Kaiser energy operator is as follows: ; In the formula, n is the sampling sequence number; The physical meaning of this operator is to estimate the instantaneous energy during signal generation. When the system is in steady state, the frequency changes gradually, and the output value of the Teager-Kaiser energy operator is very small. Once a power disturbance occurs, the rapid change in frequency will cause a sharp pulse in the output value of the Teager-Kaiser energy operator, thereby significantly improving the sensitivity and signal-to-noise ratio of disturbance detection.

[0049] B3: Ψ during the normal operation phase of a statistical system d [x(n)], where a preset energy threshold Ψ is determined. th The preset energy threshold is the Max(Ψ) output during normal operation. d [x(n)]) is λ times, where λ is the sensitivity coefficient, and the preferred range of λ is 1.5 to 2.0. Ψ d [x(n)] and the set energy threshold Ψ th In comparison, when Ψ d [x(n)] continuously exceeds Ψ th When the preset number of consecutive points is reached, a power imbalance disturbance is determined to have occurred, and the time corresponding to the first sampling point of the consecutive exceedance is marked as the disturbance time t0; The preset number of consecutive points is preferably 3 consecutive points.

[0050] Preferably, step S200 uses the Teager-Kaiser energy operator to process the generator's real frequency data and sets an adaptive threshold and continuous triggering mechanism based on historical statistics to achieve rapid and accurate identification of the moment of system power imbalance disturbance. This provides a reliable time reference with millisecond-level accuracy for subsequent analysis and effectively avoids misjudgment caused by noise interference.

[0051] In this embodiment of the application, step S300, based on voltage data, processed dynamic operating state data, and transient phase system response data captured at the time of disturbance, and identifying modal parameters in the system response data, includes the following steps C1-C7: C1: The system automatically extracts data from the transient phase time window. The transient phase time window starts at t0 and is preferably 3 to 5 seconds long. This time window length is sufficient to cover the main inertial response and electromechanical oscillation process of the system after the disturbance, while avoiding interference from subsequent secondary frequency modulation. The extracted data includes: the real frequency data dynamic response sequence, the real electromagnetic power dynamic response sequence, the real mechanical power dynamic response sequence, and the real-time voltage dynamic response sequence of the pilot bus. C2: Calculate the center frequency of inertia f based on the actual frequency data f. COI Inertia center frequency f COI The calculation formula is: ; In the formula, m is the total number of synchronous generators in the system; H i S is the inertial time constant of the i-th generator; Ni f is the rated apparent power of the i-th generator. i The real-time frequency measured by the i-th generator; H i S Ni All data are obtained from the database.

[0052] Based on the inertia center frequency f of the extracted transient phase time window COI Generate a dynamic response sequence of the center frequency of inertia.

[0053] C3: Modality identification is performed using the matrix pencil method, specifically... The captured system response data is constructed into a Hankel matrix H with L rows. The optimal value of L is 1 / 3 to 1 / 2 of the total number of data points to achieve a balance between computational complexity and accuracy.

[0054] Singular value decomposition of the Hankel matrix H separates the signal from the noise, yielding: ; In the formula, A is an L×L left singular matrix, D is an M×M right singular matrix; Σ is a singular matrix; , ; The first p significant singular values ​​are retained, typically taking the portion of singular values ​​greater than 1% of the largest singular value, and the remaining singular values ​​corresponding to noise are truncated, thereby separating the signal subspace and achieving data denoising. The truncated matrix is ​​as follows: ; In the formula, A p Let A be the first p columns. Let D be a diagonal matrix consisting of the first p singular values. p The first p columns of D.

[0055] C4: The model order p is verified through iterative convergence, and the steps are as follows: Initialize a range of orders; for example, let the minimum order p0 = 1 and the maximum order p max =20; Iterative process: For orders p = p0, p0+1, ..., p max The solution steps are executed separately. When the change in modal parameters identified by adjacent orders is less than a preset convergence threshold, the current order is determined to be the optimal model order. The preset convergence threshold is preferably 10. -6 .

[0056] C5: The right singular matrix V obtained from singular value decomposition. p Extract two sub-matrices from: V1: by V p The first N-1 rows constitute the structure; V2: by V p The last N-1 rows constitute the structure.

[0057] Then, the problem of finding the generalized eigenvalues ​​of the pencil matrix consisting of V1 and V2 is solved: V2=λ i V1; The eigenvalue λ is obtained i (i=1,2,...,p),λ i That is, the base of the complex exponent.

[0058] C6: From the complex eigenvalue λ i Derive the discrete complex frequencies of the system: ; In the formula, For the real part, This is the imaginary part.

[0059] Damping ratio is used to calculate the natural angular frequency of a mode. ; The damping ratio is: ; C7: After solving for all mathematical modes (s) i , ζ i Afterwards, a physical screening process is required, using only the true modal outputs that conform to the dynamic characteristics of the power system for subsequent calculations. The screening criteria are as follows: Only complex conjugate oscillation modes and only damping ratio ζ are selected. iThe modalities range from 0.5% to 30%.

[0060] The system automatically discards modes that do not meet the above conditions, ensuring that (s i , ζ i The accuracy and reliability of ( ).

[0061] Preferably, step S300 extracts system response data by setting an optimized transient phase, constructs a Hankel matrix based on the inertia center frequency, and uses a matrix pencil method that includes singular value decomposition for noise reduction and iterative determination of the model order to accurately identify modal parameters. Finally, the reliability of the results is ensured by screening through physical characteristics. This achieves high-precision and interference-resistant extraction of the dominant oscillation mode frequency and damping ratio of the system, providing key and reliable dynamic characteristic parameters for subsequent inertia estimation.

[0062] In this embodiment of the application, step S400 calculates the inertia component of each voltage control zone based on voltage data, processed dynamic operating state data, and modal parameters, and calculates the total system inertia based on the inertia components, including the following steps D1-D4: D1: Calculate the synchronous inertia, specifically: D11: Obtain the true electromagnetic power dynamic response sequence P for each final voltage control region. m (t), Real mechanical power dynamic response sequence P e (t) and the dynamic response sequence f of the real frequency data i (t), with the power deviation ΔP of the synchronous generator as the metric. i Using the synchronous generator frequency deviation Δf(t) as the input sequence u[k] and the synchronous generator frequency deviation Δf(t) as the output sequence y[k], an autoregressive moving average exogenous input model is constructed, where: ; ; The standard form of an ARMAX model is: ; In the formula, n a n b n c These are the orders of autoregression, exogenous input, and moving average, respectively.

[0063] As a preferred implementation scheme, the model order is set to n. a =2,n b =1, n c =1; D12: Estimate the parameter vector θ=[a1, a2, b0, b1, c1] of the ARMAX model using the least squares method. T Equivalent inertial time constant Hsg,i As a preferred implementation scheme, the inertial constant can be calculated from the model parameters using the following simplified formula: The inversion is achieved by comparing and inverting the estimated discrete-time ARMAX model transfer function with the continuous-time physical model transfer function of the generator rotor motion. H sg,i =a² / 2b¹; The total inertia H of the regional synchronous machine is obtained by weighting the rated power of the region. sg,z : ; In the formula, the numerator is proportional to the total kinetic energy of all generator rotors in the region, and the denominator is the total rated power of the region. Therefore, H sg,z This indicates the number of seconds that the region as a whole has enough total kinetic energy to sustain operation at its rated power.

[0064] D2: Calculate the load inertia, specifically: D21: The obtained dynamic response sequence f of the center frequency of inertia COI (t), calculate its deviation Δf relative to the rated frequency. COI (t); D22: Based on the real-time voltage dynamic response sequence of the pilot bus, the change in load power ΔP is calculated using a voltage / frequency dependent load model. L (t): ; In the formula, V vcz (t) represents the pilot bus voltage of the final voltage control area, V vcz0 The voltage before the disturbance is given, and k1, k2, and k3 represent the percentages of constant impedance, constant current, and constant power loads, respectively.

[0065] D23: Regarding ΔP L (t) Integrate over the time interval where the frequency changes significantly to calculate the inertial energy E released or absorbed by the load. load and inertial energy E load Equivalent to the inertial time constant H L,z H L,z This refers to the inertial energy of the load. In one alternative implementation, the inertial energy E load Equivalent to the inertial time constant H L,z The following formula can be used: ; In the formula, S z,base This refers to the rated power of the voltage-controlled zone.

[0066] D3: Calculate the asynchronous inertia, specifically: D31: Through the disturbance power imbalance Pdist Subtract the synchronous machine response ΔP sg With load response ΔP L The asynchronous inertial response ΔP of the new energy inverter was obtained. non,si Specifically: ; ; It should be noted that, for determining the total power imbalance P caused by the disturbance event... dist For events such as generator tripping, this value can be obtained directly from the SCADA / EMS event log; for unknown disturbances, it can be estimated by the sudden change in total power generation or critical tie-line power before and after the disturbance.

[0067] D32: For ΔP non,si (t) Integrating over the frequency variation period yields the supporting energy E of the nonsynchronous inertia. non,si E non,si Equivalent to the inertial time constant H non,si,z H non,si,z This refers to asynchronous inertia, E non,si Equivalent to the inertial time constant H non,si,z Refer to step D23 for the method.

[0068] D4: Calculate the total inertia of the system: D41: Summing the three types of inertia components in each final voltage control region yields the total equivalent inertial time constant H for that region. z,tot Specifically: ; D42: The total equivalent inertial time constant H of all final voltage control regions z,tot According to the regional rated power S z,base The weighted average is then applied to the weights to obtain the total inertia H of the system. sys Specifically: ; Preferably, step S400 achieves for the first time the collaborative deconstruction and quantification of synchronous machines, loads and asynchronous inertia. Through refined models and algorithms, it provides a panoramic assessment of system inertia, significantly improving estimation accuracy and providing key data support for power grid dispatching departments to grasp the true frequency stability level.

[0069] To verify the effectiveness of the method proposed in this invention, verification examples are provided, such as... Figure 2-5 As shown, taking a 39-node system as the simulation object, the system is divided into 3 regions, containing 10 synchronous generators, with a new energy penetration rate of 30%, and the rated power of each region is 2000 MVA.

[0070] First, the PMU collects the frequency, electrical power, and mechanical power of 10 synchronous machines, as well as the voltage of 5 VCZ pilot buses, and applies a 200ms moving average filter. Figure 3 As shown, this is the real-time change curve of the frequency and electromagnetic power of a key synchronous generator bus in Region 2, collected by the PMU before and after the disturbance occurred. The entire system is divided into 5 voltage control zones, with the pilot buses being Bus12 (Zone 1), Bus23 (Zone 2), Bus32 (Zone 3), Bus18 (Tie Line), and Bus28 (Load Center). New energy configuration: Areas 2 and 3 are connected to wind farms and photovoltaic power stations; Disturbance setting: A 540MW generator trip occurs in area 2 Bus37 (t=5s).

[0071] The PMU collects the frequency, electrical power, and mechanical power of 10 synchronous machines, as well as the bus voltage of 5 voltage control zones, and performs a 200ms moving average filtering.

[0072] The Teager-Kaiser energy operator processes the frequency data of the synchronizer in region 2. At t=5.02s, Ψ d [x(n)] = 1.2 × 10⁻⁷ Hz², the normal threshold is 0.7 × 10⁻⁷ Hz², Ψ d A value greater than the normal threshold is considered a disturbance moment.

[0073] Data from t=5.02~8.02s was extracted, and a Hankel matrix with 300 rows was constructed. Singular value decomposition yielded two sets of complex modes: Mode 1: Frequency 1.2Hz / Damping ratio 0.05; Mode 2: Frequency 0.8Hz / Damping ratio 0.03; These parameters accurately reflect the electromechanical oscillation characteristics of the system after disturbance.

[0074] Synchronous machine inertia calculation: ARMAX model estimation region 2 Synchronous machine H sg =3.8s; Load inertia calculation: inertial center frequency Δf COI =-0.2Hz, VCZ pilot voltage V vcz =0.96pu; Calculate ΔP L =130MW; Non-synchronous inertia: P dist =540MW, ΔP sg =320MW, therefore ΔP non,si =90MW, equivalent inertial time constant is approximately 2.1s; Finally, the synchronous machine inertia, load inertia, and asynchronous inertia of the three regions are summed to obtain the total equivalent inertial time constant of each region. Then, a weighted average is performed with the rated power of each region as the weight, and finally the total inertial constant of the entire system, i.e., the total inertia of the system, is obtained.

[0075] It should be noted that, Figure 3 The graph shows the changes in frequency and electromagnetic power before and after the disturbance. The sudden change in frequency visually confirms that the Teager-Kaiser energy operator in step S200 can capture this moment.

[0076] Figure 4 To verify the input and output curves of the ARMAX model in the example.

[0077] Figure 5 The graph shows a comparison between the linear model prediction and the measured nonlinear dynamics of the system frequency response. The linear curve is the system frequency response predicted based on the ARMAX model, while the nonlinear curve is the actual measured system frequency response. It intuitively demonstrates that the ARMAX model used in this invention can approximate the real system dynamics to a certain extent.

[0078] In summary, the above methods, through grid partitioning based on electrical distance, disturbance timing identification, modal parameter extraction, and multi-inertia component fusion calculation, achieve a panoramic, high-precision, and automated estimation of the system's total inertia, effectively improving the accuracy and engineering practicality of grid frequency stability analysis.

[0079] Example 3 illustrates a schematic scheme for a multi-regional power system inertia estimation method. It should be noted that the technical solution of this multi-regional power system inertia estimation system is based on the same concept as the technical solution of the aforementioned multi-regional power system inertia estimation method. Details not described in detail in this example can be found in the description of the technical solution of the aforementioned multi-regional power system inertia estimation method.

[0080] This embodiment also provides a multi-regional power system inertia estimation system, including: Voltage control zone division module: Based on the electrical distance between grid nodes, the power system is divided into multiple voltage control zones, a pilot bus is selected in each voltage control zone, and the voltage data of each pilot bus is obtained; Disturbance Timing Identification Module: Processes the dynamic operating status data of the synchronous generator to identify the timing of system power imbalance disturbances; Modal parameter identification module: Based on voltage data, processed dynamic operating status data and transient phase system response data captured at the time of disturbance, and identify the modal parameters in the system response data; Total Inertia Synthesis Module: Based on voltage data, processed dynamic operating status data and modal parameters, it calculates the inertia component of each voltage control zone, and calculates the total inertia of the system based on the inertia component.

[0081] This embodiment also provides an electronic device suitable for inertia estimation of multi-regional power systems, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the inertia estimation method for multi-regional power systems proposed in the above embodiments.

[0082] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for estimating the inertia of a multi-regional power system as proposed in the above embodiments.

[0083] The storage medium proposed in this embodiment and the method for estimating the inertia of a multi-regional power system proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0084] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating the inertia of a multi-regional power system, characterized in that: include, Based on the electrical distance between power grid nodes, the power system is divided into multiple voltage control zones. A pilot bus is selected in each voltage control zone, and the voltage data of each pilot bus is acquired. The dynamic operating status data of the synchronous generator is processed to identify the timing of system power imbalance disturbances; Based on the voltage data, the processed dynamic operating status data, and the transient stage system response data captured at the disturbance time, the modal parameters in the system response data are identified. Based on the voltage data, the processed dynamic operating status data, and the modal parameters, the inertia component of each voltage control zone is calculated, and the total system inertia is calculated based on the inertia component.

2. The method for estimating the inertia of a multi-regional power system as described in claim 1, characterized in that, The power system is divided into multiple voltage control zones based on the electrical distance between grid nodes, including: Acquire steady-state data of the system during the steady-state phase; Calculate the dV / dQ sensitivity matrix between nodes based on the steady-state data; Calculate the electrical distance between nodes based on the dV / dQ sensitivity matrix; A hierarchical clustering algorithm is used to cluster the nodes based on the electrical distance to form a preliminary voltage control area; The voltage control region is verified to form the final voltage control region.

3. The method for estimating the inertia of a multi-regional power system as described in claim 1, characterized in that, The total inertia of the system is calculated based on the inertia components, including... Calculate the inertia of the synchronous machine, the load inertia, and the asynchronous inertia; Based on the synchronous machine inertia, the load inertia, and the asynchronous inertia of each region, the total system inertia is obtained by weighted calculation according to the rated power of each region.

4. The method for estimating the inertia of a multi-regional power system as described in claim 1, characterized in that, The modal parameters in the system response data are identified using the matrix pencil method, and only complex conjugate oscillation modes are selected during the identification process.

5. The method for estimating the inertia of a multi-regional power system as described in claim 2, characterized in that, The dV / dQ sensitivity matrix is ​​extracted by solving the inverse of the Jacobian matrix calculated by the Newton-Raphson power flow method.

6. The method for estimating the inertia of a multi-regional power system as described in claim 3, characterized in that, The calculation of the load inertia includes, The system inertia center frequency is calculated, and combined with the voltage data of the pilot bus, the load power change is calculated using a voltage / frequency dependent load model to obtain the load inertia response.

7. A method for estimating the inertia of a multi-regional power system as described in claim 1 or 2, characterized in that, The pilot bus for each voltage control zone is the bus with the highest load within that voltage control zone, and the number of pilot buses is 15% to 30% of the total number of load buses in the system.

8. A multi-regional power system inertia estimation system, using the method described in any one of claims 1-7, characterized in that, include, Voltage control zone division module: Based on the electrical distance between power grid nodes, the power system is divided into multiple voltage control zones, a pilot bus is selected in each voltage control zone, and the voltage data of each pilot bus is acquired; Disturbance Timing Identification Module: Processes the dynamic operating status data of the synchronous generator to identify the timing of system power imbalance disturbances; Modal parameter identification module: Based on the voltage data, the processed dynamic operating status data and the system response data during the transient phase at the time of the disturbance, the module identifies the modal parameters in the system response data. Total inertia synthesis module: Based on the voltage data, the processed dynamic operating status data and the modal parameters, calculate the inertia component of each voltage control zone, and calculate the total system inertia based on the inertia component.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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