Inertia and virtual inertia estimation method, device and equipment based on global probability

Abstract

The invention relates to the technical field of power system stability analysis, in particular to a global probability-based inertia and virtual inertia estimation method, device and equipment, and the method comprises the steps: constructing a new energy power system simulation model, carrying out the transient simulation of a power system under a fault, and obtaining the active and reactive output power; introducing the active and reactive output power and the PMU measurement output power into a Gaussian unbiased function, and calculating a correction probability value by combining the initial probability values of the inertia and the virtual inertia; samples are extracted according to pseudo weight distribution of a multi-weight redistribution algorithm, and inertia and virtual inertia correction probability values serve as target distribution to calculate sample influence coefficients; and carrying out weight redistribution on the samples, obtaining correction probability distribution of the inertia and the virtual inertia by using a global probability outlining technology, and realizing estimation of the inertia and the virtual inertia according to the correction probability distribution. According to the invention, each region where the target is distributed can be better covered, and the stability and accuracy of estimation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system stability analysis, and in particular to a method, device and equipment for estimating inertia and virtual inertia based on global probability. Background Art

[0002] With the continuous development of grid-based virtual control technology, the number of grid-based virtual synchronous machines is gradually increasing. By introducing inertia and damping characteristics in its control algorithm, the grid-based virtual synchronous machine makes its output characteristics similar to those of traditional synchronous generators, thereby providing virtual inertia and virtual damping for the power system. The role of these virtual synchronous machines in the power system is becoming increasingly important, especially in the context of the rapid development of renewable energy power generation.

[0003] The virtual inertia provided by the virtual synchronous machine can help the new energy power system cope with the challenges brought by the replacement of traditional dynamic units with new energy. As more new energy sources are connected to the grid through power electronic devices, the retirement of traditional thermal power units leads to a decrease in the overall physical inertia of the system, and the stability of the power system is threatened. In this case, the introduction of virtual inertia makes up for the lack of physical inertia, making the system more capable of resisting frequency fluctuations, thereby helping to maintain the stability of the system. It is crucial to accurately understand the total inertia and inertia distribution characteristics of the system, which helps to effectively design and implement frequency control strategies. Therefore, for new power systems, accurate estimation of inertia and virtual inertia is essential.

[0004] In the prior art, methods based on Kalman filtering and unscented Kalman filtering are widely used in online estimation, and these methods have improved the accuracy of inertia estimation to a certain extent. However, this method usually relies on the Gaussian assumption, which is not always true in practical applications. This affects the accuracy of estimation in practical applications. Methods for inertia estimation based on Bayesian reasoning of Markov chain Monte Carlo sampling algorithm have also attracted attention, but such methods have low computational efficiency and slow convergence speed due to the inability to perform parallel calculations. In addition, in the prior art, when performing inertia estimation, the virtual inertia provided by the virtual synchronous generator is not fully considered, which leads to a deviation between the estimation result and the actual operating state of the power system.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, device and equipment for estimating inertia and virtual inertia based on global probability, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for estimating inertia and virtual inertia based on global probability, comprising the following steps:

[0008] Construct a simulation model of a new energy power system, and perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power;

[0009] The active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU are introduced into a Gaussian unbiased function, and the inertia and virtual inertia correction probability values ​​are calculated in combination with the initial probability values ​​of the inertia and virtual inertia;

[0010] Extract samples according to the pseudo-weight distribution of the multiple weight redistribution algorithm, and calculate the sample influence coefficient using the inertia and virtual inertia correction probability values ​​as the target distribution;

[0011] The weights of the samples are redistributed based on the sample influence coefficients, and a global probability contouring technique is used to obtain a modified probability distribution of inertia and virtual inertia, and the estimation of inertia and virtual inertia is achieved according to the modified probability distribution.

[0012] Furthermore, the new energy power system simulation model includes:

[0013] The second-order swing equation of a conventional synchronous generator is:

[0014]

[0015] In the formula, δ g is the internal voltage phase angle of the generator; ω and ω0 are the rotor angular frequency and rated angular frequency of the synchronous generator respectively; H g is the inertia time constant; D is the damping ratio; T M is the mechanical torque of the synchronous generator; P e is the active output of the synchronous generator, t is the time;

[0016] The third-order equation of the networked virtual synchronous machine:

[0017]

[0018] In the formula, δ GFM is the power angle of the network-building virtual synchronous machine; ω GFM is the angular frequency of the network-forming virtual synchronous machine; T * and T em are the reference torque and output torque respectively; J is the inertia of the networked virtual synchronous machine, and its inertia time constant H m The relationship is H m =Jω 2 / (2S base );S baseis the rated power of the network-building virtual synchronous machine; D P and D q They represent the active damping coefficient and QV droop coefficient respectively; K is the voltage integral gain; Q0 and Q em are the reference reactive power and output reactive power respectively; E and E0 are the internal voltage and reference voltage of the grid-forming virtual synchronous machine respectively.

[0019] Furthermore, the transient simulation of the new energy power system under fault conditions includes the following steps:

[0020] Normalizing and reducing the parameters of all generators in the new energy power system simulation model;

[0021] Set the type of fault event, fault location and fault occurrence time, and set the system admittance matrix after the fault occurs;

[0022] Use power flow calculation to obtain the algebraic variables of all synchronous generators;

[0023] The fifth-order ordinary differential equations of synchronous generators and virtual synchronous generators are solved by fourth-order Runge-Kutta iteration;

[0024] Calculate the active and reactive power output by the generator in each iteration.

[0025] Furthermore, the active and reactive power output by the generator in each iteration is expressed as:

[0026]

[0027] Where δ is the power angle of the generator during the simulation; V and θ are the voltage amplitude and phase angle at the bus terminal, respectively; V d and V q are the d-axis and q-axis voltages of the generator during the simulation; I d and I q are the d-axis and q-axis currents of the generator during the simulation process; P and Q represent the active and reactive output powers of the generator calculated during the simulation process.

[0028] Furthermore, the active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU are introduced into a Gaussian unbiased function, comprising:

[0029]

[0030] In the formula, π Gauss is a Gaussian unbiased function; P and Q represent the active and reactive output power calculated during the simulation process; P0 and Q0 represent the active power measurement and reactive power measurement of PMU respectively; Γ noise is the Gaussian measurement noise covariance matrix;

[0031] The calculation of the inertia and virtual inertia correction probability values ​​by combining the initial probability values ​​of inertia and virtual inertia includes:

[0032]

[0033] In the formula, π re is the corrected probability value, H g is the inertia time constant of the synchronous generator; H m is the inertia time constant of the network-type virtual synchronous machine; H = [H g ;H m ] represents the inertia and virtual inertia of all generators in the whole system; H pr and Γ pr are the mean and covariance matrix of the initial distribution of inertia and virtual inertia, respectively.

[0034] Furthermore, extracting samples according to the pseudo weight distribution of the multiple weight redistribution algorithm comprises the following steps:

[0035] According to the initial values ​​of inertia and virtual inertia, the pseudo weight distribution of J multiple weight redistribution algorithms is determined, that is, the initial distribution of inertia and virtual inertia q1(H), q2(H), ..., q J (H);

[0036] Draw M from the ith pseudo-weight distribution i samples, a total of M tot Samples:

[0037]

[0038] Furthermore, the calculation of the sample influence coefficient by taking the real distribution of inertia and virtual inertia as the target distribution includes:

[0039] The complete pseudo weight distribution is calculated by weighting it according to the number of samples drawn from each pseudo weight distribution:

[0040]

[0041] In the formula, belong to For the The distribution of samples;

[0042] The real distribution of inertia and virtual inertia is used as the target distribution of the multiple weight redistribution algorithm;

[0043] Calculate the influence coefficient of the sample based on the target distribution and the complete pseudo weight distribution:

[0044]

[0045] In the formula, π re is the corrected probability value.

[0046] Furthermore, the weights of the samples are redistributed based on the sample influence coefficients, and a global probability contouring technique is used to obtain a modified probability distribution of inertia and virtual inertia, including:

[0047] Normalize the influence coefficients of all samples:

[0048]

[0049] The weights of inertia and virtual inertia samples are redistributed based on the normalized influence coefficient and the global probability contouring technique is used to obtain the modified probability distribution of inertia and virtual inertia:

[0050]

[0051] Where Δ(x) is the Dirac delta function.

[0052] Further, the estimating of inertia and virtual inertia according to the modified probability distribution includes:

[0053] Implement inertia and virtual inertia estimation from modified probability distributions:

[0054] H MAP =argmin{-logπ re (H)};

[0055] Among them, H MAP are the values ​​of the estimated inertia and virtual inertia.

[0056] The present invention also includes an inertia and virtual inertia estimation device based on global probability. Further, using the above method, the device includes:

[0057] A simulation unit is used to construct a simulation model of a new energy power system and to perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power;

[0058] A sampling unit, used for introducing the active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU into a Gaussian unbiased function, and calculating the inertia and virtual inertia correction probability value in combination with the initial probability value of the inertia and virtual inertia;

[0059] A sample unit, used for extracting samples according to the pseudo weight distribution of the multiple weight redistribution algorithm, and calculating the sample influence coefficient by taking the inertia and the virtual inertia correction probability value as the target distribution;

[0060] The estimation unit is used to redistribute the weights of the samples based on the sample influence coefficients, obtain the modified probability distribution of inertia and virtual inertia using the global probability contouring technology, and estimate the inertia and virtual inertia according to the modified probability distribution.

[0061] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0062] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0063] The beneficial effects of the present invention are as follows: the present invention fully considers the role of virtual inertia in maintaining system stability by incorporating the grid-type virtual synchronous generator model into the dynamic simulation model of the power system. By incorporating the inertia characteristics of the virtual synchronous machine into the simulation, the dynamic behavior of the power system can be simulated more realistically, thereby providing more accurate inertia and virtual inertia estimation; the use of a multiple weight redistribution algorithm not only meets the needs of parallel computing, but also does not require iterative calculations, significantly improves the calculation efficiency, overcomes the problem of low calculation efficiency of traditional algorithms, and is suitable for online rapid estimation of inertia and virtual inertia of new power systems; by applying a multiple weight redistribution algorithm and mixing multiple pseudo-weight distributions, it can flexibly adapt to various complex target distributions, thereby improving the versatility and adaptability of the algorithm. At the same time, it can also better cover various areas of the target distribution and improve the stability and accuracy of the estimate. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 is a flow chart of the method in Example 1;

[0066] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0067] Figure 3 It is a schematic diagram of the main flow of the estimation method in Example 2;

[0068] Figure 4 It is a schematic diagram of the main implementation process of the method for estimating inertia and virtual inertia based on the global probability delineation technology in Example 2;

[0069] Figure 5 is the parameter estimation error result of Application Example 1 in Embodiment 2;

[0070] Figure 6 is the parameter estimation error result of Application Example 2 in Embodiment 2;

[0071] Figure 7 is the parameter estimation error result of Application Example 3 in Embodiment 2;

[0072] Figure 8 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0074] Embodiment 1:

[0075] like Figure 1 As shown: A method for estimating inertia and virtual inertia based on global probability, comprising the following steps:

[0076] Construct a simulation model of a new energy power system, and perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power;

[0077] The active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU are introduced into the Gaussian unbiased function, and the corrected probability values ​​of inertia and virtual inertia are calculated by combining the initial probability values ​​of inertia and virtual inertia.

[0078] Samples are extracted according to the pseudo-weight distribution of the multiple weight redistribution algorithm, and the sample influence coefficient is calculated by taking the inertia and virtual inertia correction probability values ​​as the target distribution;

[0079] The weights of samples are redistributed based on the sample influence coefficients, and the global probability contouring technique is used to obtain the corrected probability distribution of inertia and virtual inertia. The inertia and virtual inertia are estimated based on the corrected probability distribution.

[0080] By incorporating the grid-type virtual synchronous generator model into the dynamic simulation model of the power system, the role of virtual inertia in maintaining system stability is fully considered. By incorporating the inertia characteristics of the virtual synchronous machine into the simulation, the dynamic behavior of the power system can be simulated more realistically, thereby providing more accurate inertia and virtual inertia estimation; the multiple weight redistribution algorithm not only meets the needs of parallel computing, but also does not require iterative calculations, significantly improves the calculation efficiency, overcomes the problem of low calculation efficiency of traditional algorithms, and is suitable for online rapid estimation of inertia and virtual inertia of new power systems; by applying the multiple weight redistribution algorithm, by mixing multiple pseudo-weight distributions, it can flexibly adapt to various complex target distributions, improving the versatility and adaptability of the algorithm. At the same time, it can also better cover various areas of the target distribution and improve the stability and accuracy of the estimation.

[0081] In this embodiment, the new energy power system simulation model includes:

[0082] The second-order swing equation of a conventional synchronous generator is:

[0083]

[0084] In the formula, δ g is the internal voltage phase angle of the generator; ω and ω0 are the rotor angular frequency and rated angular frequency of the synchronous generator respectively; H g is the inertia time constant; D is the damping ratio; T M is the mechanical torque of the synchronous generator; P e is the active output of the synchronous generator, t is the time;

[0085] The third-order equation of the networked virtual synchronous machine:

[0086]

[0087] In the formula, δ GFM is the power angle of the network-building virtual synchronous machine; ω GFM is the angular frequency of the network-forming virtual synchronous machine; T * and T em are the reference torque and output torque respectively; J is the inertia of the networked virtual synchronous machine, and its inertia time constant H m The relationship is H m =Jω 2 / (2S base );S base is the rated power of the network-building virtual synchronous machine; D P and D q They represent the active damping coefficient and QV droop coefficient respectively; K is the voltage integral gain; Q0 and Q emare the reference reactive power and output reactive power respectively; E and E0 are the internal voltage and reference voltage of the grid-forming virtual synchronous machine respectively.

[0088] Among them, the transient simulation of the new energy power system under fault includes the following steps:

[0089] Normalize and reduce the parameters of all generators in the simulation model of the new energy power system;

[0090] Set the type of fault event, fault location and fault occurrence time, and set the system admittance matrix after the fault occurs;

[0091] Use power flow calculation to obtain the algebraic variables of all synchronous generators;

[0092] The fifth-order ordinary differential equations of synchronous generators and virtual synchronous generators are solved by fourth-order Runge-Kutta iteration;

[0093] Calculate the active and reactive power output by the generator in each iteration.

[0094] The active and reactive power output by the generator in each iteration is expressed as:

[0095]

[0096] Where δ is the power angle of the generator during the simulation; V and θ are the voltage amplitude and phase angle at the bus terminal, respectively; V d and V q are the d-axis and q-axis voltages of the generator during the simulation; I d and I q are the d-axis and q-axis currents of the generator during the simulation process; P and Q represent the active and reactive output powers of the generator calculated during the simulation process.

[0097] The active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU are introduced into the Gaussian unbiased function, including:

[0098]

[0099] In the formula, π Gauss is a Gaussian unbiased function; P and Q represent the active and reactive output power calculated during the simulation process; P0 and Q0 represent the active power measurement and reactive power measurement of PMU respectively; Γ noise is the Gaussian measurement noise covariance matrix;

[0100] The inertia and virtual inertia correction probability values ​​are calculated by combining the initial probability values ​​of inertia and virtual inertia, including:

[0101]

[0102] In the formula, π re is the corrected probability value, H g is the inertia time constant of the synchronous generator; H m is the inertia time constant of the network-type virtual synchronous machine; H = [H g ;H m ] represents the inertia and virtual inertia of all generators in the whole system; H pr and Γ pr are the mean and covariance matrix of the initial distribution of inertia and virtual inertia, respectively.

[0103] In this embodiment, extracting samples according to the pseudo weight distribution of the multiple weight redistribution algorithm includes the following steps:

[0104] According to the initial values ​​of inertia and virtual inertia, the pseudo weight distribution of J multiple weight redistribution algorithms is determined, that is, the initial distribution of inertia and virtual inertia q1(H), q2(H), ..., q J (H);

[0105] Draw M from the ith pseudo-weight distribution i samples, a total of M tot Samples:

[0106]

[0107] As a preferred embodiment of the above, the real distribution of inertia and virtual inertia is used as the target distribution to calculate the sample influence coefficient, including:

[0108] The complete pseudo weight distribution is calculated by weighting it according to the number of samples drawn from each pseudo weight distribution:

[0109]

[0110] In the formula, belong to For the The distribution of samples;

[0111] The real distribution of inertia and virtual inertia is used as the target distribution of the multiple weight redistribution algorithm;

[0112] Calculate the influence coefficient of the sample based on the target distribution and the complete pseudo weight distribution:

[0113]

[0114] Among them, the weights of samples are redistributed based on the sample influence coefficients, and the global probability outlining technology is used to obtain the corrected probability distribution of inertia and virtual inertia, including:

[0115] Normalize the influence coefficients of all samples:

[0116]

[0117] The weights of inertia and virtual inertia samples are redistributed based on the normalized influence coefficient and the global probability contouring technique is used to obtain the modified probability distribution of inertia and virtual inertia:

[0118]

[0119] Where Δ(x) is the Dirac delta function.

[0120] As a preferred embodiment of the above, the estimation of inertia and virtual inertia is realized according to the modified probability distribution, including:

[0121] Implement inertia and virtual inertia estimation from modified probability distributions:

[0122] H MAP =argmin{-logπ re (H)};

[0123] Among them, H MAP are the values ​​of the estimated inertia and virtual inertia.

[0124] like Figure 2 As shown, this embodiment also includes an inertia and virtual inertia estimation device based on global probability. As a preferred embodiment of the above embodiment, using the above method, the device includes:

[0125] A simulation unit is used to construct a simulation model of a new energy power system and to perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power;

[0126] The sampling unit is used to introduce the active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU into the Gaussian unbiased function, and calculate the corrected probability value of the inertia and virtual inertia in combination with the initial probability value of the inertia and virtual inertia;

[0127] A sample unit is used to extract samples according to the pseudo weight distribution of the multiple weight redistribution algorithm, and calculate the sample influence coefficient by taking the inertia and virtual inertia correction probability values ​​as the target distribution;

[0128] The estimation unit is used to redistribute the weights of samples based on the sample influence coefficients, and use the global probability contouring technology to obtain the corrected probability distribution of inertia and virtual inertia, and estimate the inertia and virtual inertia according to the corrected probability distribution.

[0129] Embodiment 2:

[0130] like Figure 3As shown, this embodiment includes a method for estimating inertia and virtual inertia based on global probability profiling technology, including:

[0131] Construct a simulation model of a new energy power system, and perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power;

[0132] The output power obtained by simulation and the output power measured by PMU are introduced into the Gaussian unbiased function, and the corrected probability values ​​of inertia and virtual inertia are calculated by combining the initial probability values ​​of inertia and virtual inertia;

[0133] Samples are extracted according to the pseudo-weight distribution of the multiple weight redistribution algorithm, and the sample influence coefficient is calculated by taking the inertia and virtual inertia correction probability values ​​as the target distribution;

[0134] The weights of samples are redistributed based on the sample influence coefficients and the global probability contouring technique is used to obtain the corrected probability distribution of inertia and virtual inertia. Inertia and virtual inertia estimation are achieved based on the corrected probability distribution.

[0135] Combine the following Figure 4 The solution process of this embodiment is described.

[0136] A method for estimating inertia and virtual inertia based on global probabilistic profiling technology, the method comprising the following steps:

[0137] S1. Construct a simulation model of a new energy power system, and perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power.

[0138] S11. Construct a dynamic simulation model of the new energy power system.

[0139] S111. Establish the second-order swing equation of the traditional synchronous generator:

[0140]

[0141] Among them, δ g is the internal voltage phase angle of the generator; ω and ω0 are the rotor angular frequency and rated angular frequency of the synchronous generator respectively; H g is the inertia time constant; D is the damping ratio; T M is the mechanical torque of the synchronous generator; P e is the active output of the synchronous generator, t is the time;

[0142] S112. Establish the third-order equation of the network-building virtual synchronous machine:

[0143]

[0144] Among them, δ GFM is the power angle of the network-forming virtual synchronous machine; ωGFM is the angular frequency of the networked virtual synchronous machine; T * and T em are the reference torque and output torque respectively; J is the inertia of the networked virtual synchronous machine, and its inertia time constant H m The relationship is H m =Jω 2 / (2S base );S base is the rated power of the network-building virtual synchronous machine; D P and D q Represent the active damping coefficient and QV droop coefficient respectively; K is the voltage integral gain; Q0 and Q em are the reference reactive power and the output reactive power respectively; E and E0 are the internal voltage and the reference voltage of the grid-forming virtual synchronous machine respectively.

[0145] S12. Conduct transient simulation of the new energy power system under fault conditions.

[0146] S121, normalizing and reducing the parameters of all generators in the new energy power system simulation model;

[0147] S122, setting the type of fault event to tangent fault, the fault location to the transmission line of bus 15 and bus 16, the fault occurrence time to 0.5s after the start of simulation, and setting the system admittance matrix after the fault occurs;

[0148] S123, obtaining algebraic variables of all synchronous generators by using power flow calculation;

[0149] S124, solving the fifth-order ordinary differential equations of the synchronous generator and the virtual synchronous generator by fourth-order Runge-Kutta iteration;

[0150] S13. Calculate the active and reactive power output by the generator in each iteration.

[0151]

[0152] Where δ is the power angle of the generator during the simulation; V and θ are the bus terminal voltage amplitude and phase angle respectively; Vd and V q are the d-axis and q-axis voltages of the generator during the simulation; I d and I q are the d-axis and q-axis currents of the generator during the simulation process; P and Q represent the active and reactive output powers of the generator calculated during the simulation process;

[0153] S2. The output power obtained by simulation and the output power measured by PMU are introduced into the Gaussian unbiased function, and the corrected probability values ​​of inertia and virtual inertia are calculated by combining the initial probability values ​​of inertia and virtual inertia.

[0154] S21. Introduce the output power obtained by simulation and the output power measured by PMU into the Gaussian unbiased function:

[0155]

[0156] Among them, π Gauss is a Gaussian unbiased function; P and Q represent the active and reactive output power calculated during the simulation process; P0 and Q0 represent the active power measurement and reactive power measurement of PMU respectively; Γ noise is the Gaussian measurement noise covariance matrix.

[0157] S22. Calculate the corrected probability value of inertia and virtual inertia by combining the initial probability values ​​of inertia and virtual inertia:

[0158]

[0159] Among them, π re is the corrected probability value, H g is the inertia time constant of the synchronous generator; H m is the inertia time constant of the network-type virtual synchronous machine; H = [H g ;H m ] represents the inertia and virtual inertia of all generators in the whole system; H pr and Γ pr are the mean and covariance matrix of the initial distribution of inertia and virtual inertia, respectively.

[0160] S3. Extract samples according to the pseudo-weight distribution of the multiple weight redistribution algorithm, and calculate the sample influence coefficient by taking the real distribution of inertia and virtual inertia as the target distribution.

[0161] S31. Extract samples according to multiple valid pseudo-weight distributions of the multiple weight redistribution algorithm.

[0162] S311, determine the pseudo weight distribution of J multiple weight redistribution algorithms according to the initial values ​​of inertia and virtual inertia, that is, the initial distribution of inertia and virtual inertia q1(H), q2(H), ..., q J (H);

[0163] S312, extract M from the i-th pseudo weight distribution i samples, a total of M tot Samples:

[0164]

[0165] S32. Select the complete pseudo-weight distribution and target distribution of the multiple weight redistribution algorithm.

[0166] S321. Weighting is performed according to the number of samples drawn from each pseudo weight distribution to calculate the complete pseudo weight distribution:

[0167]

[0168] S322. Using the real distribution of inertia and virtual inertia as the target distribution of the multiple weight redistribution algorithm.

[0169] In the formula, belong to For the The distribution of samples;

[0170] S33. Calculate the influence coefficient of the sample based on the target distribution and the complete pseudo weight distribution:

[0171]

[0172] S4. Redistribute the weights of the samples based on the sample influence coefficients and use the global probability contouring technique to obtain the corrected probability distribution of inertia and virtual inertia, and estimate the inertia and virtual inertia based on the corrected probability distribution.

[0173] S41, Normalized sample influence coefficient:

[0174]

[0175] S42. Redistribute the weights of the inertia and virtual inertia samples based on the normalized influence coefficient to obtain the corrected probability distribution of the inertia and virtual inertia:

[0176]

[0177] Here, Δ(x) is the Dirac delta function.

[0178] S43. Estimation of inertia and virtual inertia from modified probability distribution:

[0179] H MAP =argmin{-logπ re (H)};

[0180] Among them, H MAP are the values ​​of the estimated inertia and virtual inertia.

[0181] In summary, this embodiment constructs an estimation method of inertia and virtual inertia based on global probability profiling technology and applies it to the field of power systems to quickly and accurately estimate the inertia results. Figures 5 to 7 The estimation error of applying the multiple weight redistribution algorithm is shown. Figures 5 to 7 In , the horizontal axis represents each generator, and the vertical axis represents the relative error between the estimated value and the true value of the inertia or virtual inertia parameter of each generator.

[0182] In this embodiment, the advantages and progress of this embodiment are verified through the following multiple application examples.

[0183] Application example 1:

[0184] The new power system inertia and virtual inertia estimation method of this application example is applied in the following power system environment: the power system is a New England 39 bus system using a classic generator model, in which the synchronous generators at bus 38 and bus 39 are replaced by grid-type virtual synchronous generators. A group of PMUs are placed at the generator terminals to provide active and reactive power measurements P0 and Q0.

[0185] In order to analyze the dynamics of the power system when it is disturbed, the transmission line between bus No. 15 and bus No. 16 is opened, the simulation duration is set to 5.5 seconds, the fault start time is 0.5 seconds, and the sampling time is 0.01 seconds. The initial distribution of the given inertia and virtual inertia parameters obeys a Gaussian distribution with a mean of [525; 29; 34; 27.5; 24.5; 36; 26.4; 24.3; 4.8; 5] and a standard deviation of 10% of the mean. The true values ​​of the inertia and virtual inertia parameters are [500.0; 30.3; 35.8; 28.6; 26.0; 34.8; 28; 25.5; 5.0224; 5.3].

[0186] According to the steps in this embodiment, the power system inertia and virtual inertia parameters are estimated, and the parameter estimation result error of the inertia and virtual inertia estimation method based on the global probability delineation technology is as follows: Figure 5 shown.

[0187] This embodiment can quickly and accurately estimate the parameter results of inertia and virtual inertia.

[0188] Application Example 2:

[0189] The power system of this application example is the New England 39-bus system using the classic generator model, in which the synchronous generators at bus 36 and bus 37 are replaced by grid-type virtual synchronous generators. Among them, a group of PMUs are placed on the generator terminals to provide active and reactive power measurements P0 and Q0. In order to analyze the dynamics of the power system when it is disturbed, the transmission line between bus 15 and bus 16 is opened, the simulation duration is set to 5.5 seconds, the fault start time is 0.5 seconds, and the sampling time is 0.01 seconds. The initial distribution of the given inertia and virtual inertia parameters follows a Gaussian distribution with a mean of [525; 29; 34; 27.5; 24.5; 36; 4.8; 5; 36; 40] and a standard deviation of 10% of the mean. The true values ​​of the inertia and virtual inertia parameters are [500.0; 30.3; 35.8; 28.6; 5.0224; 5.3; 34.5; 42].

[0190] According to the steps in this embodiment, the power system inertia and virtual inertia parameters are estimated, and the estimation result error of the inertia and virtual inertia estimation method based on the global probability delineation technology is as follows: Figure 6 shown.

[0191] It can be seen that although the bus position of the virtual synchronous generator has changed from bus 38 and 39 in application example 1 to bus 36 and 37 in application example 2, the accuracy of the inertia and virtual inertia estimation method based on the global probability delineation technology has not decreased, and the results of inertia and virtual inertia parameters can also be quickly and accurately estimated. The method of this embodiment can be applied to generators of different combinations.

[0192] Application Example 3

[0193] The power system of this application example is the New England 39 bus system using the classic generator model, in which the synchronous generators at bus 38 and bus 39 are replaced by grid-type virtual synchronous generators. Among them, a group of PMUs are placed on the generator terminals to provide active and reactive power measurements P0 and Q0. In order to analyze the dynamics of the power system when it is disturbed, the transmission line between bus 12 and bus 13 is opened, the simulation duration is set to 5.5 seconds, the fault start time is 0.5 seconds, and the sampling time is 0.01 seconds. The initial distribution of the given inertia and virtual inertia parameters obeys a Gaussian distribution with a mean of [525; 29; 34; 27.5; 24.5; 36; 26.4; 24.3 4.8; 5] and a standard deviation of 10% of the mean. The true values ​​of the inertia and virtual inertia parameters are [500.0; 30.3; 35.8; 28.6; 26.0; 34.8; 28; 25.5; 5.0224; 5.3].

[0194] According to the steps of this embodiment, the power system inertia and virtual inertia parameters are estimated, and the estimation result error of the inertia and virtual inertia estimation method based on the global probability delineation technology is as follows: Figure 7 shown.

[0195] It can be seen that although the position of the fault point has changed, that is, the tangent position has changed from bus 15 and 16 in application example 1 to bus 12 and 13 in application example 3, the accuracy of the inertia and virtual inertia estimation method based on the global probability delineation technology has not decreased, and the results of inertia and virtual inertia parameters can still be estimated quickly and accurately. This method can be applied to different types of fault points.

[0196] See also Figure 8A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0197] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0198] Among them, 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, disk or optical disk.

[0199] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0200] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0201] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations 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 one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0202] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0203] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0204] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0205] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0206] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for estimating inertia and virtual inertia based on global probability, characterized in that: The steps include: Construct a simulation model of a new energy power system and conduct transient simulation of the new energy power system under fault conditions to obtain active and reactive output power; The active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU are introduced into a Gaussian unbiased function, and the inertia and virtual inertia correction probability values ​​are calculated in combination with the initial probability values ​​of the inertia and virtual inertia; Extract samples according to the pseudo-weight distribution of the multiple weight redistribution algorithm, and calculate the sample influence coefficient using the inertia and virtual inertia correction probability values ​​as the target distribution; The weights of the samples are redistributed based on the sample influence coefficients, and a global probability contouring technique is used to obtain a modified probability distribution of inertia and virtual inertia, and the estimation of inertia and virtual inertia is achieved according to the modified probability distribution.

2. The method for estimating inertia and virtual inertia based on global probability according to claim 1, characterized in that: The new energy power system simulation model includes: The second-order swing equation of a conventional synchronous generator is: In the formula, δ g is the internal voltage phase angle of the generator; ω and ω0 are the rotor angular frequency and rated angular frequency of the synchronous generator respectively; H g is the inertia time constant; D is the damping ratio; T M is the mechanical torque of the synchronous generator; P e is the active output of the synchronous generator, t is the time; The third-order equation of the networked virtual synchronous machine: In the formula, δ GFM is the power angle of the network-building virtual synchronous machine; ω GFM is the angular frequency of the network-forming virtual synchronous machine; T * and T em are the reference torque and output torque respectively; J is the inertia of the networked virtual synchronous machine, and its inertia time constant H m The relationship is H m =Jω 2 / (2S base );S base is the rated power of the network-building virtual synchronous machine; D P and D q They represent the active damping coefficient and QV droop coefficient respectively; K is the voltage integral gain; Q0 and Q em are the reference reactive power and output reactive power respectively; E and E0 are the internal voltage and reference voltage of the grid-forming virtual synchronous machine respectively.

3. The method for estimating inertia and virtual inertia based on global probability according to claim 1, characterized in that: The transient simulation of the new energy power system under fault conditions includes the following steps: Normalizing and reducing the parameters of all generators in the new energy power system simulation model; Set the type of fault event, fault location and fault occurrence time, and set the system admittance matrix after the fault occurs; Use power flow calculation to obtain the algebraic variables of all synchronous generators; The fifth-order ordinary differential equations of synchronous generators and virtual synchronous generators are solved by fourth-order Runge-Kutta iteration; Calculate the active and reactive power output by the generator in each iteration.

4. The method for estimating inertia and virtual inertia based on global probability according to claim 3, characterized in that: The active and reactive power output by the generator in each iteration is expressed as: Where δ is the power angle of the generator during the simulation; V and θ are the bus terminal voltage amplitude and phase angle respectively; V d and V q are the d-axis and q-axis voltages of the generator during the simulation; I d and I q are the d-axis and q-axis currents of the generator during the simulation process; P and Q represent the active and reactive output powers of the generator calculated during the simulation process.

5. The method for estimating inertia and virtual inertia based on global probability according to claim 1, characterized in that: The step of introducing the active and reactive output power and the output power measured by a power grid synchronous phasor measurement unit PMU into a Gaussian unbiased function comprises: In the formula, π Gauss is a Gaussian unbiased function; P and Q represent the active and reactive output power calculated during the simulation process; P0 and Q0 represent the active power measurement and reactive power measurement of PMU respectively; Γ noise is the Gaussian measurement noise covariance matrix; The calculation of the inertia and virtual inertia correction probability values ​​by combining the initial probability values ​​of inertia and virtual inertia includes: In the formula, π re is the corrected probability value, H g is the inertia time constant of the synchronous generator; H m is the inertia time constant of the network-type virtual synchronous machine; H = [H g ;H m ] represents the inertia and virtual inertia of all generators in the whole system; H pr and Γ pr are the mean and covariance matrix of the initial distribution of inertia and virtual inertia, respectively.

6. The method for estimating inertia and virtual inertia based on global probability according to claim 1, characterized in that: The method of extracting samples according to the pseudo weight distribution of the multiple weight redistribution algorithm comprises the following steps: According to the initial values ​​of inertia and virtual inertia, the pseudo weight distribution of J multiple weight redistribution algorithms is determined, that is, the initial distribution of inertia and virtual inertia q1(H), q2(H), ..., q J (H); Draw M from the ith pseudo-weight distribution i samples, a total of M tot Samples:

7. The method for estimating inertia and virtual inertia based on global probability according to claim 6, characterized in that: The method of calculating the sample influence coefficient by taking the real distribution of inertia and virtual inertia as the target distribution includes: Calculate the complete pseudo weight distribution by weighting it according to the number of samples drawn from each pseudo weight distribution In the formula, belong to For the The distribution of samples; The real distribution of inertia and virtual inertia is used as the target distribution of the multiple weight redistribution algorithm; Calculate the influence coefficient of each sample based on the target distribution and the complete pseudo weight distribution In the formula, π re is the corrected probability value.

8. The method for estimating inertia and virtual inertia based on global probability according to claim 7, characterized in that: The weight redistribution of samples based on the sample influence coefficients and the use of a global probability outlining technique to obtain a modified probability distribution of inertia and virtual inertia include: Normalize the influence coefficients of all samples: The weights of inertia and virtual inertia samples are redistributed based on the normalized influence coefficient and the global probability contouring technique is used to obtain the modified probability distribution of inertia and virtual inertia: Where Δ(x) is the Dirac delta function.

9. The method for estimating inertia and virtual inertia based on global probability according to claim 8, characterized in that: The estimating of inertia and virtual inertia according to the modified probability distribution includes: Implement inertia and virtual inertia estimation from modified probability distributions: H MAP =arg min{-logπ re (H)}; Among them, H MAP are the values ​​of the estimated inertia and virtual inertia.

10. An inertia and virtual inertia estimation device based on global probability, characterized in that: Using the method according to any one of claims 1 to 9, the device comprises: A simulation unit is used to construct a simulation model of a new energy power system and to perform transient simulation of the new energy power system under fault conditions to obtain simulated active and reactive output power; A sampling unit, used for introducing the active and reactive output power and the output power measured by the power grid synchronous phasor measurement unit PMU into a Gaussian unbiased function, and calculating the inertia and virtual inertia correction probability value in combination with the initial probability value of the inertia and virtual inertia; A sample unit, used for extracting samples according to the pseudo weight distribution of the multiple weight redistribution algorithm, and calculating the sample influence coefficient by taking the inertia and the virtual inertia correction probability value as the target distribution; The estimation unit is used to redistribute the weights of the samples based on the sample influence coefficients, obtain the modified probability distribution of inertia and virtual inertia using the global probability contouring technology, and estimate the inertia and virtual inertia according to the modified probability distribution.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 9 is implemented.

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

Images