Power system inertia online evaluation method, device, equipment and medium

Abstract

The invention relates to the technical field of power system frequency stability control, in particular to a power system inertia online evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining frequency deviation data and active power data of each node, carrying out the low-pass filtering of the frequency data, and calculating the amount of active unbalance; an inertia response model is constructed based on the rotor motion equation of the synchronous generator, and inertia and damping coefficients are estimated by using a least square method; constructing a transfer function and judging the stability of the transfer function, if the transfer function is stable, inputting an active unbalance amount to obtain a frequency deviation predicted value, and calculating a Pearson correlation coefficient with an actual frequency; different time periods are selected through a sliding window to repeat the process, finally, the inertia corresponding to the maximum Pearson's correlation coefficient is selected as a system inertia evaluation result, the inertia level of the system can be accurately evaluated on line through the method, and therefore data support is provided for frequency control, and the frequency stability and safety of the system are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of power system frequency stability control technology, and in particular to a method, apparatus, equipment and medium for online evaluation of power system inertia. Background Technology

[0002] As the proportion of renewable energy power generation continues to increase, synchronous generator units, which are the main source of inertia, are being gradually replaced, leading to a continuous decrease in the overall inertia level of the power system. This poses a risk of frequency instability and major blackouts in the future. Several major blackouts abroad have occurred due to insufficient inertia support capabilities. Therefore, there is an urgent need to conduct research on online and accurate assessment technology for power system inertia, enabling real-time sensing of grid inertia levels and accurate identification of weak grid inertia scenarios. This will provide a foundation for refined frequency stability control of high-proportion renewable energy power systems.

[0003] Existing inertia assessment methods are generally divided into parameter identification methods and direct calculation methods. Direct calculation methods obtain the inertia constant by directly calculating the ratio of active power to the rate of frequency change based on the swing equation. However, this method ignores the influence of the system damping coefficient, resulting in low calculation accuracy and susceptibility to noise. Parameter identification methods establish a system inertia response model and solve for the model parameters based on measurement data using methods such as automatic regression moving average models, output error models, dynamic regression extensions, and hybrid processes, thereby obtaining the system inertia level. These methods often struggle to determine the order of the inertia and cannot determine which time period after a disturbance yields the highest accuracy in inertia assessment.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for online assessment of power system inertia, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: an online evaluation method for power system inertia, comprising the following steps:

[0007] S10: Obtain frequency deviation data and active power data of each node in the system, perform low-pass filtering on the frequency deviation data to eliminate random interference, and calculate the active power imbalance based on the characteristic that the mechanical power of the prime mover cannot change abruptly before and after the disturbance.

[0008] S20: Based on the rotor motion equation of the synchronous generator set, an inertial response mathematical model including inertia and damping coefficient is constructed; the active power imbalance and filtered frequency deviation data within a data window are selected, and the inertia and damping coefficient are calculated using the least squares method based on the inertial response mathematical model.

[0009] S30: Construct the system inertia response transfer function based on the inertia and damping coefficient, and determine whether the system corresponding to the transfer function is stable. If yes, proceed to step S40; otherwise, discard the calculation result of step S20 and reselect the data segment for parameter solving.

[0010] S40: Input the active power imbalance into the transfer function to obtain the frequency deviation prediction value, and calculate the Pearson correlation coefficient between the frequency deviation prediction value and the actual frequency data;

[0011] S50: Select data from different time periods after the disturbance occurs using a sliding window method, and repeat steps S20 to S40 to obtain the inertia, damping coefficient and Pearson correlation coefficient corresponding to multiple time periods. Select the inertia corresponding to the time period with the largest Pearson correlation coefficient as the final system inertia evaluation result.

[0012] Further, in step S10, the model for calculating the active power imbalance includes:

[0013] ΔP(k)=P(k)-P m ;

[0014] In the formula, ΔP(·) represents the active power imbalance after the disturbance; P(·) represents the active power measured by the PMU; P m This represents the active power before the disturbance; k represents the index number of the discrete-time sampling point.

[0015] Further, step S20 includes:

[0016] S21: Constructing a mathematical model of the system's inertial response based on the continuous-time rotor motion equations of a synchronous generator;

[0017] S22: Discretize the rotor motion equations to obtain a differential mathematical model of the system inertial response.

[0018] S23: Perform variable substitution and linear transformation on the system model in the difference form to construct a linear model suitable for solving by the least squares method;

[0019] S24: Based on the estimation results of the linear model solved by the least squares method, the equivalent inertia and damping coefficient of the system are calculated.

[0020] Further, in step S24, based on the estimation results of the linear model solved by the least squares method, the model of the system's equivalent inertia and damping coefficient is calculated, including:

[0021] J sys =b·ΔT;

[0022] D sys =ab;

[0023] In the formula, J sys D represents the equivalent inertia of the system. sys ΔT represents the equivalent damping coefficient of the system; ΔT represents the time interval of the frequency data measured by the PMU; a and b represent the parameter coefficients obtained by fitting based on the least squares method.

[0024] Further, in step S30, based on the parameter coefficients obtained by least squares fitting, a system inertia response transfer function is constructed. The model of the system inertia response transfer function includes:

[0025]

[0026] In the formula, H(z) represents the transfer function of the system's inertial response; ΔW(z) represents the z-transform of the system's angular frequency deviation; ΔQ(z) represents the z-transform of the active power imbalance; z represents the complex variable in the z-transform; and a and b represent the parameter coefficients obtained by fitting based on the least squares method.

[0027] Further, in step S30, it is determined whether the system corresponding to the transfer function is stable, based on whether the poles of the transfer function are located inside the unit circle. The determination condition is as follows:

[0028]

[0029] In the formula, a and b represent the parameter coefficients obtained based on least squares fitting;

[0030] when If the condition is met, the system response corresponding to the transfer function is stable; otherwise, discard the currently estimated inertia coefficient and damping coefficient, reselect the time period data, and repeat the parameter estimation process in step S20.

[0031] Further, in step S40, the model for obtaining the frequency deviation prediction value includes:

[0032]

[0033] In the formula, Δω p (k) represents the predicted k-th frequency deviation; Δω p(k-1) represents the predicted (k-1)th frequency deviation; a and b represent the parameter coefficients obtained based on least squares fitting; Δq(k) represents the active power imbalance at the kth time.

[0034] Further, in step S40, the model for calculating the Pearson correlation coefficient between the predicted frequency deviation and the actual frequency data includes:

[0035]

[0036] In the formula, R is the similarity between the predicted and actual frequency deviation values; ω(k) represents the k-th angular frequency value; Δω p (k) represents the predicted k-th frequency deviation; N represents the number of frequency data points within the analysis time window; Δω(k) represents the measured k-th frequency deviation.

[0037] Further, in step S50, a sliding window method is used to select data from different time periods after the disturbance occurs, and steps S20 to S40 are repeated to obtain the inertia, damping coefficient, and Pearson correlation coefficient corresponding to multiple time periods, including:

[0038] J = [J1 J2…J M ];

[0039] D = [D1 D2…D] M ];

[0040] R = [R1 R2…R] M ];

[0041] In the formula, J represents a vector composed of inertia measured from M time intervals; J1, J2, ..., J... M D represents the inertia calculated from the data of the 1st, 2nd, ..., Mth time intervals; D represents the vector composed of damping coefficients measured from the data of the M time intervals; D1, D2, ..., D M R represents the damping coefficient calculated from the data of the 1st, 2nd, ..., Mth time periods; R represents the vector composed of the Pearson correlation coefficients measured from the data of the M time periods; R1, R2, ..., R M This represents the Pearson correlation coefficient calculated from the data of the 1st, 2nd, ..., Mth time periods;

[0042] When the index of the maximum value in the vector R composed of the Pearson correlation coefficients is L, the Lth element J in the inertia vector J is selected. L and the Lth element D in the damping coefficient vector D L This serves as the final evaluation result for the system's inertia and damping coefficient.

[0043] The present invention also includes an online power system inertia assessment device, using the method described above, comprising:

[0044] The data acquisition module is used to acquire frequency deviation data and active power data of each node in the system, perform low-pass filtering on the frequency deviation data to eliminate random interference, and calculate the active power imbalance based on the characteristic that the mechanical power of the prime mover cannot change abruptly before and after the disturbance.

[0045] The modeling and calculation module is used to construct an inertial response mathematical model containing inertia and damping coefficient based on the rotor motion equation of the synchronous generator set; select the active power imbalance and filtered frequency deviation data within a data window, and calculate the inertia and damping coefficient using the least squares method based on the inertial response mathematical model.

[0046] The stability judgment module is used to construct the system inertia response transfer function based on the inertia and damping coefficient, and to determine whether the system corresponding to the transfer function is stable. If it is stable, the prediction verification module is executed; if not, the calculation results of the modeling calculation module are discarded, and a new data segment is selected for parameter solving.

[0047] The prediction verification module is used to input the active power imbalance into the transfer function to obtain the frequency deviation prediction value, and to calculate the Pearson correlation coefficient between the frequency deviation prediction value and the actual frequency data.

[0048] The sliding analysis module is used to select data from different time periods after the disturbance occurs using a sliding window method, and repeatedly execute the modeling calculation module, stability judgment module, and prediction verification module to obtain the inertia, damping coefficient, and Pearson correlation coefficient for multiple time periods.

[0049] The evaluation result determination module is used to select the inertia corresponding to the time period with the largest Pearson correlation coefficient as the final system inertia evaluation result.

[0050] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0051] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0052] The beneficial effects of this invention are as follows:

[0053] By incorporating the Pearson correlation coefficient into the inertia assessment accuracy measurement mechanism and combining it with system stability criteria and a sliding time window strategy, adaptive identification of the optimal assessment time period in disturbance response data is achieved, significantly improving the accuracy and practicality of parameter identification. This method can accurately assess the system's inertia level online, thereby providing data support for frequency control and ensuring the system's frequency stability and safety. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of the online evaluation method for power system inertia;

[0056] Figure 2 This is a schematic diagram of the structure of an online power system inertia assessment device;

[0057] Figure 3 This is a schematic diagram of the structure of a computer device. Detailed Implementation

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0059] like Figure 1 As shown: A method for online evaluation of power system inertia, comprising the following steps:

[0060] S10: Obtain frequency deviation data and active power data of each node in the system, perform low-pass filtering on the frequency deviation data to eliminate random interference, and calculate the active power imbalance based on the characteristic that the mechanical power of the prime mover cannot change abruptly before and after the disturbance.

[0061] S20: Based on the rotor motion equation of the synchronous generator set, an inertial response mathematical model including inertia and damping coefficient is constructed; active power imbalance and filtered frequency deviation data within a data window are selected, and the inertia and damping coefficient are calculated using the least squares method based on the inertial response mathematical model.

[0062] S30: Construct the system inertia response transfer function based on inertia and damping coefficient, and determine whether the system corresponding to the transfer function is stable. If yes, proceed to step S40; otherwise, discard the calculation result of step S20 and reselect the data segment for parameter solving.

[0063] S40: Input the active power imbalance into the transfer function to obtain the predicted frequency deviation value, and calculate the Pearson correlation coefficient between the predicted frequency deviation value and the actual frequency data.

[0064] S50: Select data from different time periods after the disturbance occurs using a sliding window method, repeat steps S20 to S40 to obtain the inertia, damping coefficient and Pearson correlation coefficient corresponding to multiple time periods, and select the inertia corresponding to the time period with the largest Pearson correlation coefficient as the final system inertia evaluation result.

[0065] This method can be used to accurately assess the inertia level of a system online, thereby providing data support for frequency control and ensuring the frequency stability and safety of the system.

[0066] The least squares method is used to fit the frequency deviation and active power imbalance during the disturbance process. The equivalent inertia and damping coefficient of the system are accurately extracted through parameter identification, which significantly improves the accuracy of inertia assessment compared with the traditional direct ratio method. A system stability judgment mechanism is introduced in the estimation process. The pole location is analyzed by using the inertia response transfer function. Only the data segment with stable dynamic response characteristics is retained to avoid the interference of unreliable disturbance segments on the assessment results, thereby enhancing the robustness and reliability of the method.

[0067] By using a sliding window approach to iterate and evaluate multiple time periods after a disturbance, and evaluating the similarity between the predicted value and the actual frequency response based on the Pearson correlation coefficient, the optimal inertia evaluation period is automatically selected from multiple candidate results, solving the problem of "difficulty in determining the time window" in existing technologies. The method does not require prior modeling of the system structure and can complete online parameter identification based on actual operation measurement data. It is particularly suitable for operation scenarios with a high proportion of new energy sources and frequent changes in power system inertia, providing reliable support for power grid frequency control and scheduling decisions.

[0068] By incorporating the Pearson correlation coefficient into the inertia assessment accuracy measurement mechanism and combining it with system stability criteria and a sliding time window strategy, adaptive identification of the optimal assessment time period in disturbance response data is achieved, significantly improving the accuracy and practicality of parameter identification.

[0069] Frequency deviation data and active power data are obtained using a synchronous phasor measurement unit (PMU) or a broadband measurement unit.

[0070] Currently, PMUs and broadband measurement devices are installed in major power plants, new energy power stations, and 500kV substations in power systems, enabling real-time acquisition of system frequency and power data. Frequency measurement is sensitive to noise and affected by communication resolution, resulting in significant random fluctuations in frequency data. Therefore, low-pass filtering is necessary to eliminate interference. The order of the low-pass filter should not be too high to avoid excessively long frequency response times during disturbances, which would increase inertia assessment errors. A data window of 200ms is recommended. The filtering process can be expressed as follows:

[0071]

[0072] In the formula, h(·) represents the low-pass filter coefficient; f(·) represents the frequency measured by the PMU; f lp (·) represents the frequency after low-pass filtering; H is the order of the low-pass filter.

[0073] Inertia assessment uses frequency deviation data, therefore the frequency deviation needs to be obtained by subtracting the system's rated frequency from the frequency in the above formula:

[0074] Δf(k)=f lp (k)-50;

[0075] In the formula, Δf(·) represents the system frequency deviation.

[0076] Inertia assessment uses active power imbalance, which is the difference between the system's mechanical power and electromagnetic power. Therefore, the acquired power data needs to be processed. Considering that the mechanical power of the prime mover remains unchanged before and after the disturbance, the mechanical power before the disturbance is equal to the electromagnetic power, and the mechanical power after the disturbance is equal to the mechanical power before the disturbance. Therefore, the active power imbalance after the disturbance can be solved by subtracting the active power before the disturbance from the active power after the disturbance.

[0077] In this embodiment, step S10, calculating the model for active power imbalance, includes:

[0078] ΔP(k)=P(k)-P m ;

[0079] In the formula, ΔP(·) represents the active power imbalance after the disturbance; P(·) represents the active power measured by the PMU; P m This represents the active power before the disturbance; k represents the index number of the discrete-time sampling point.

[0080] As a preferred embodiment of the above, in step S20, based on the rotor motion equation of the synchronous generator set, an inertial response mathematical model including inertia and damping coefficient is constructed. Active power imbalance and frequency deviation data within a data window are selected, and the inertia and damping coefficient are calculated using the least squares method. The steps include:

[0081] S21: Constructing a mathematical model of the system's inertial response based on the continuous-time rotor motion equations of a synchronous generator;

[0082] S22: Discretize the rotor motion equations to obtain a system model in difference form;

[0083] S23: Perform variable substitution and linear transformation on the difference form system model to construct a linear model suitable for solving by the least squares method;

[0084] S24: Based on the estimation results of the linear model solved by the least squares method, the equivalent inertia and damping coefficient of the system are calculated.

[0085] The rotor motion equation of a synchronous generator is:

[0086]

[0087] In the formula, J sys Let ω(k) represent the equivalent inertia of the system, and let D represent the k-th angular frequency. sys Let ω(t) represent the equivalent damping coefficient of the system, and let ω(t) represent the angular frequency of the system. The relationship between ω(t) and the system frequency is ω(t) = 2πf(t); Δω(t) represents the angular frequency deviation of the system, and Δω(t) = 2πΔf(t).

[0088] Convert the above expression into a discrete expression:

[0089]

[0090] In the formula, ΔT represents the time interval of the frequency data measured by the PMU, which is generally 10ms or 20ms.

[0091] because Therefore, the above expression can be transformed into:

[0092]

[0093] In the formula, Δω(k) represents the k-th angular frequency deviation of the system.

[0094] Combining like terms in the above equation, we get:

[0095]

[0096] make To represent the unbalanced torque, the values ​​of a and b can be obtained using the least squares method:

[0097] C = (W T W) -1 W T Q;

[0098] In the formula, C represents the coefficient vector to be solved, W represents the matrix composed of frequencies, and Q is the matrix composed of unbalanced torques. The specific expression is as follows:

[0099]

[0100] In the formula, N represents the number of frequency data points within the analysis time window.

[0101] Once C is obtained, the moment of inertia and damping coefficient can be calculated.

[0102] In step S24, based on the estimation results of the linear model solved by the least squares method, the model of the system's equivalent inertia and damping coefficient is calculated, including:

[0103] J sys =b·ΔT;

[0104] D sys =ab;

[0105] In the formula, J sys D represents the equivalent inertia of the system. sys ΔT represents the equivalent damping coefficient of the system; ΔT represents the time interval of the frequency data measured by the PMU, which is generally 10ms or 20ms; a and b represent the parameter coefficients obtained by fitting based on the least squares method, which are obtained by solving the least squares method.

[0106] Let the z-transform of Δω(k) be ΔW(z) and the z-transform of Δq(k) be ΔQ(z). Based on the characteristics of the inertial response, the z-transform expression of the rotor motion equation can be obtained:

[0107]

[0108] By transforming the above equation, we can obtain the transfer function of the inertial response;

[0109] As a preferred embodiment of the above, in step S30, the system inertia response transfer function is constructed based on the parameter coefficients obtained by least squares fitting. The model of the system inertia response transfer function includes:

[0110]

[0111] In the formula, H(z) represents the transfer function of the system's inertial response; ΔW(z) represents the z-transform of the system's angular frequency deviation; ΔQ(z) represents the z-transform of the active power imbalance; z represents the complex variable in the z-transform; and a and b represent the parameter coefficients obtained by fitting based on the least squares method.

[0112] The parameters a and b in the transfer function are fitted from the least squares method, and the model H(z) links these parameters to the dynamic behavior of the system, thereby giving the parameter estimation results practical dynamic meaning and engineering interpretability, and enhancing the credibility of the evaluation results.

[0113] In this embodiment, in step S30, it is determined whether the system corresponding to the transfer function is stable, based on whether the poles of the transfer function are located inside the unit circle. The determination condition is as follows:

[0114]

[0115] In the formula, a and b represent the parameter coefficients obtained based on least squares fitting;

[0116] when If the condition is met, the system response corresponding to the transfer function is stable; otherwise, discard the currently estimated inertia coefficient and damping coefficient, reselect the time period data, and repeat the parameter estimation process in step S20.

[0117] The mechanism of automatically "reselecting a time period and re-estimating" when the stability condition is not met enables the method to have self-correction and self-optimization capabilities. It can continuously slide and analyze different time periods to find the optimal evaluation window. Performing frequency prediction and correlation calculation only under a stable transfer function model can significantly improve the matching degree between predicted and actual values, improve the effectiveness of Pearson correlation coefficient, and indirectly enhance the accuracy of inertia identification.

[0118] To verify whether the evaluated parameters can characterize the response behavior of the actual system, the active power imbalance data in step S20 is used as the input to the transfer function in step S30 to obtain the frequency deviation prediction value.

[0119] In step S40, the model for obtaining the frequency deviation prediction value includes:

[0120]

[0121] In the formula, Δω p (k) represents the predicted k-th frequency deviation; Δω p (k-1) represents the predicted (k-1)th frequency deviation; a and b represent the parameter coefficients obtained based on least squares fitting; Δq(k) represents the active power imbalance at the kth time.

[0122] Based on the estimated inertia and damping coefficient, the frequency dynamics of the system after the disturbance can be predicted iteratively step by step, thus more realistically restoring the response process of the power system.

[0123] If the predicted value and the actual value are very similar, the evaluated parameter is considered accurate. This patent uses the Pearson correlation coefficient to measure the similarity between the two.

[0124] As a preferred embodiment of the above, step S40, which calculates the Pearson correlation coefficient between the predicted frequency deviation and the actual frequency data, includes:

[0125]

[0126] In the formula, R is the similarity between the predicted and actual frequency deviation values; ω(k) represents the k-th angular frequency value; Δω p (k) represents the predicted k-th frequency deviation; N represents the number of frequency data points within the analysis time window; Δω(k) represents the measured k-th frequency deviation.

[0127] The Pearson correlation coefficient R effectively reflects the degree of linear correlation between predicted and actual values. This model provides a clear numerical indicator to measure whether the parameters estimated by the least squares method truly reflect the system's frequency response characteristics, avoiding reliance on subjective judgment or empirical evaluation.

[0128] After selecting the data window of segment M after the disturbance, each data window can obtain a set of inertia and damping coefficients, as well as the Pearson correlation coefficient corresponding to the frequency deviation within the data window;

[0129] In this embodiment, in step S50, a sliding window method is used to select data from different time periods after the disturbance occurs, and steps S20 to S40 are repeated to obtain the inertia, damping coefficient, and Pearson correlation coefficient corresponding to multiple time periods, including:

[0130] J = [J1 J2…J M ];

[0131] D = [D1 D2…D] M ];

[0132] R = [R1 R2…R] M ];

[0133] In the formula, J represents a vector composed of inertia measured from M time intervals; J1, J2, ..., J... M D represents the inertia calculated from the data of the 1st, 2nd, ..., Mth time intervals; D represents the vector composed of damping coefficients measured from the data of the M time intervals; D1, D2, ..., D M R represents the damping coefficient calculated from the data of the 1st, 2nd, ..., Mth time periods; R represents the vector composed of the Pearson correlation coefficients measured from the data of the M time periods; R1, R2, ..., R MThis represents the Pearson correlation coefficient calculated from the data of the 1st, 2nd, ..., Mth time periods.

[0134] The aforementioned series of inertia and damping coefficients are obtained from the least squares formulas described above. The equivalent inertia and damping coefficients mentioned in the formulas obtained by the least squares method are intermediate parameter values ​​estimated by the system within a single data window. The parameters calculated in the multiple time periods mentioned above are intermediate result sets formed by repeating the formulas obtained by the least squares method. Finally, a set is selected based on the maximum Pearson correlation coefficient criterion as the final equivalent inertia and damping coefficient evaluation values ​​of the system.

[0135] By applying a sliding window after the disturbance, the inertia identification process is repeated at different time periods, avoiding the errors or anomalies that may be caused by relying on data from a single time window, and effectively reducing the interference of occasional noise or local disturbances on the results.

[0136] When the index of the maximum value in the vector R composed of Pearson correlation coefficients is L, the Lth element J in the inertia vector J is selected. L and the Lth element D in the damping coefficient vector D L This serves as the final evaluation result for the system's inertia and damping coefficient.

[0137] By selecting the set of results with the largest Pearson correlation coefficient from multiple time periods as the output, it can be ensured that the selected inertia and damping parameters are closest to the actual frequency response of the system and have the best fitting effect, which greatly improves the credibility and engineering applicability of the evaluation. This mechanism is equivalent to selecting the optimal solution from all candidate results, avoiding reliance on human experience to judge the window, and constructing an algorithm flow based on quantitative indicators to automatically judge the best, thereby improving the intelligence level of the entire method.

[0138] This method first predicts the frequency deviation, then calculates the Pearson correlation coefficient with the actual value, and finally selects the parameter result with the highest correlation. It constructs a closed-loop process of modeling, prediction, verification, and screening, which helps to achieve self-verification and adaptive optimization of the model.

[0139] The present invention also includes an online power system inertia assessment device, using the method described above, such as... Figure 2 As shown, it includes:

[0140] The data acquisition module is used to acquire frequency deviation data and active power data of each node in the system, perform low-pass filtering on the frequency deviation data to eliminate random interference, and calculate the active power imbalance based on the characteristic that the mechanical power of the prime mover cannot change abruptly before and after the disturbance.

[0141] The modeling and calculation module is used to construct an inertial response mathematical model containing inertia and damping coefficients based on the rotor motion equation of the synchronous generator set; select active power imbalance and filtered frequency deviation data within a data window, and calculate the inertia and damping coefficients using the least squares method based on the inertial response mathematical model.

[0142] The stability assessment module is used to construct the system's inertia response transfer function based on inertia and damping coefficient, and to determine whether the system corresponding to the transfer function is stable. If it is, the prediction verification module is executed; if not, the calculation results of the modeling calculation module are discarded, and a new data segment is selected for parameter solving.

[0143] The prediction verification module is used to input the active power imbalance into the transfer function, obtain the predicted frequency deviation value, and calculate the Pearson correlation coefficient between the predicted frequency deviation value and the actual frequency data.

[0144] The sliding analysis module is used to select data from different time periods after the disturbance occurs using a sliding window method, and repeatedly execute the modeling calculation module, stability judgment module, and prediction verification module to obtain the inertia, damping coefficient, and Pearson correlation coefficient for multiple time periods.

[0145] The evaluation result determination module is used to select the inertia corresponding to the time period with the largest Pearson correlation coefficient as the final system inertia evaluation result.

[0146] Please see Figure 3 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0147] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0148] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0149] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0150] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0151] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0152] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0154] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0155] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0156] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for online evaluation of inertia of a power system, characterized in that, The method comprises the following steps: S10: acquiring frequency deviation data and active power data of each node of the system, low-pass filtering the frequency deviation data to eliminate random interference, and calculating an active imbalance based on the characteristic that the mechanical power of the prime mover is not suddenly changed before and after the disturbance; S20: constructing an inertia response mathematical model containing inertia and damping coefficients based on a rotor motion equation of the synchronous generator set; selecting the active imbalance and the filtered frequency deviation data in a data window, and calculating the inertia and the damping coefficients based on the inertia response mathematical model by using the least square method; S30: constructing a system inertia response transfer function based on the inertia and the damping coefficients, and judging whether the system corresponding to the transfer function is stable, if yes, executing step S40; if not, discarding the calculation result of step S20, and reselecting a data segment to solve the parameters; S40: inputting the active imbalance into the transfer function to obtain a frequency deviation prediction value, and calculating a Pearson correlation coefficient of the frequency deviation prediction value and actual frequency data; S50: selecting data of different time periods after the disturbance occurs in a sliding window manner, repeatedly executing steps S20 to S40 to obtain inertia, damping coefficients and Pearson correlation coefficients corresponding to multiple time periods, and selecting the inertia corresponding to the time period with the maximum Pearson correlation coefficient as a final system inertia evaluation result.

2. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S10, the model for calculating the active imbalance comprises: ΔP(k) = P(k) - P m ; where ΔP(·) represents the post-disturbance active power imbalance; P(·) represents the active power measured by the PMU; P m represents the pre-disturbance active power; and k represents the index number of the discrete time sampling point.

3. The method of online evaluation of inertia of power system according to claim 1, characterized in that, Step S20 comprises: S21: constructing a system inertia response mathematical model based on a continuous-time rotor motion equation of the synchronous generator; S22: discretizing the rotor motion equation to obtain a differential form of the system inertia response mathematical model; S23: performing variable replacement and linear transformation on the differential form of the system model to construct a linear model suitable for least square method solving; S24: calculating the equivalent inertia and the damping coefficient of the system according to the estimation result of the linear model solved by the least square method.

4. The method of online evaluation of inertia of power system according to claim 3, characterized in that, In step S24, the model for calculating the equivalent inertia and the damping coefficient of the system according to the estimation result of the linear model solved by the least square method comprises: J sys = b · ΔT; D sys = a - b; where J sys represents the equivalent inertia of the system; D sys represents the equivalent damping coefficient of the system; AT represents the time interval of the frequency data measured by the PMU; a and b represent the parameter coefficients obtained based on the least square fitting.

5. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S30, the system inertia response transfer function is constructed based on the parameter coefficients obtained by fitting with the least square method, and the model of the system inertia response transfer function comprises: In the formula, H(z) represents the transfer function of the system inertia response; ΔW(z) represents the z transform of the system angular frequency deviation; ΔQ(z) represents the z transform of the active imbalance; z represents a complex variable in the z transform; and a and b represent the parameter coefficients obtained by fitting with the least square method.

6. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S30, whether the system corresponding to the transfer function is stable is judged, and whether the poles of the transfer function are located within a unit circle is taken as the stability basis, and the judgment condition is: In the formula, a and b represent the parameter coefficients obtained by fitting with the least square method; When If the condition is satisfied, then the system response corresponding to the transfer function is stable; otherwise, the inertia coefficient and the damping coefficient obtained by the current estimation are discarded, the time period data is reselected, and the parameter estimation process in step S20 is repeated.

7. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S40, the model for obtaining the frequency deviation prediction value comprises: where Δω p (k) represents the predicted kth frequency deviation; Δω p (k-1) represents the predicted (k-1)th frequency deviation; a and b represent parameter coefficients obtained based on least square fitting; and Δq(k) represents the active imbalance at the kth time.

8. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S40, the model for calculating the Pearson correlation coefficient of the frequency deviation prediction value and the actual frequency data comprises: where R is the similarity between the predicted and actual values of the frequency deviation; ω(k) represents the kth angular frequency value; Δω p (k) represents the predicted kth frequency deviation; N represents the number of frequency data within the analysis time window, and Δω(k) represents the measured kth frequency deviation.

9. The method of online evaluation of inertia of power system according to claim 1, characterized in that, In step S50, data of different time periods after the disturbance occurs is selected in a sliding window manner, and steps S20 to S40 are repeatedly executed to obtain inertia and damping coefficients and Pearson correlation coefficients corresponding to multiple time periods, including: J = [J1 J2...J M ]; D = [D1 D2...D M ]; R = [R1 R2...R M ]; wherein J represents a vector composed of inertias measured from M time period data; J1, J2,..., J M representing inertias calculated from the 1st, 2nd,..., Mth time period data; D represents a vector composed of damping coefficients measured from M time period data; D1, D2,..., D M representing damping coefficients calculated from the 1st, 2nd,..., Mth time period data; R represents a vector composed of Pearson correlation coefficients measured from M time period data; R1, R2,..., R M representing Pearson correlation coefficients calculated from the 1st, 2nd,..., Mth time period data. When the maximum value in the vector R composed of the Pearson correlation coefficients corresponds to the sequence number L, the Lth element J in the inertia vector J is selected L and the Lth element D in the damping coefficient vector D L as the final inertia and damping coefficient evaluation results of the system.

10. An on-line inertia assessment device for power systems, characterized by The method comprises the steps of: The data acquisition module is configured to acquire frequency deviation data and active power data of each node of the system, perform low-pass filtering on the frequency deviation data to eliminate random interference, and calculate an active imbalance based on the characteristic that the mechanical power of the prime mover is not suddenly changed before and after the disturbance; The modeling calculation module is configured to construct an inertia response mathematical model including inertia and damping coefficients based on a rotor motion equation of the synchronous generator unit, select the active imbalance and the filtered frequency deviation data in a data window, and calculate the inertia and the damping coefficients based on the inertia response mathematical model by using a least square method; The stability judgment module is configured to construct a system inertia response transfer function based on the inertia and the damping coefficients, and determine whether the system corresponding to the transfer function is stable. If yes, the prediction verification module is executed. If no, the calculation result of the modeling calculation module is discarded, and a data segment is reselected for parameter solving. The prediction verification module is configured to input the active imbalance into the transfer function to obtain a frequency deviation prediction value, and calculate a Pearson correlation coefficient of the frequency deviation prediction value and actual frequency data. The sliding analysis module is configured to select data of different time periods after the disturbance occurs in a sliding window manner, repeatedly execute the modeling calculation module, the stability judgment module, and the prediction verification module, and obtain inertia, damping coefficients, and Pearson correlation coefficients corresponding to multiple time periods. The evaluation result determination module is configured to select inertia corresponding to a time period with the largest Pearson correlation coefficient as a final system inertia evaluation result.

11. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-9.

12. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the method of any one of claims 1-9.

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