Method for performance optimization of a power system with voltage and damping coordinated support of a phase modifier

Abstract

The application discloses a method for performance optimization of a phase modifier grid facing voltage and damping collaborative support, which comprises the following steps: determining the optimization direction of the excitation parameter of the phase modifier according to the parameter trajectory sensitivity and the parameter perturbation damping increment, and taking the excitation parameter of the phase modifier and the excitation parameter of a strongly related unit corresponding to the minimum damping ratio in a system electromechanical oscillation mode as the to-be-optimized parameters; weighting and combining the effective reactive current gain and the minimum damping ratio of the system electromechanical oscillation mode to establish a target function for collaborative support of voltage and damping, and constructing a collaborative optimization model considering voltage stability and small disturbance power angle stability; and automatically setting the to-be-optimized parameters based on the collaborative optimization model. The application improves the low-frequency oscillation suppression capability of the phase modifier without affecting the voltage support of the phase modifier to the grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a performance optimization method of a phase modifier involved in a power grid for coordinated support of voltage and damping. BACKGROUND

[0002] With the rapid development of China's ultra-high voltage direct current, large-scale development of clean energy and the concentration of large proportion of power receiving areas, the characteristics of the power grid have changed greatly, the dynamic reactive power reserve of the direct current receiving end power grid has decreased, and the voltage support has become increasingly insufficient. One of the effective ways to solve this problem is to install a phase modifier near the direct current receiving end, which has strong over-excitation capability and can quickly respond to the reactive power demand of the system and maintain voltage stability by continuously adjusting the reactive power output. However, the fast response speed will cause the stability of the phase modifier to decrease, weaken the damping of the system, and increase the risk of system oscillation. In the current technical literature, the phase modifier is mainly used as a reactive power compensation device to improve voltage stability, although it can also provide damping support to the system through some other ways, but there are few studies on small disturbance power angle stability represented by low-frequency oscillation, and there are no performance optimization measures for the phase modifier involved in the power grid for coordinated support of voltage and damping. In the current power system, the system does not have enough oscillation damping, which has the greatest impact on small disturbance power angle stability, therefore, in order to fully exert the stabilizing effect of the phase modifier on the power grid and the benefits brought by the investment in the phase modifier, the performance of the phase modifier involved in the power grid needs to be optimized to improve the ability of the phase modifier to suppress low-frequency oscillation without affecting the voltage support of the phase modifier on the power grid. SUMMARY

[0003] In view of this, the present application provides a performance optimization method of a phase modifier involved in a power grid for coordinated support of voltage and damping, which realizes effective suppression of low-frequency oscillation of the phase modifier without affecting the voltage support of the phase modifier on the power grid, or even with certain improvement, so as to be applied to the performance optimization of the phase modifier involved in the power grid with different capacities which have been put into operation and will be gradually put into operation in the future.

[0004] The present application discloses a performance optimization method of a phase modifier involved in a power grid for coordinated support of voltage and damping, which comprises:

[0005] Step 1: determining the optimization direction of the excitation parameters of the phase modifier according to the parameter trajectory sensitivity and the parameter perturbation damping increment, and taking the excitation parameters of the phase modifier and the excitation parameters of the strongly related unit corresponding to the minimum damping ratio in the electromechanical oscillation mode of the system as the parameters to be optimized;

[0006] Step 2: weighting and combining the effective reactive current gain and the minimum damping ratio of the electromechanical oscillation mode of the system to establish a target function for coordinated support of voltage and damping, and constructing a coordinated optimization model considering voltage stability and small disturbance power angle stability;

[0007] Step 3: automatically setting the parameters to be optimized based on the collaborative optimization model.

[0008] Further, the parameter trajectory sensitivity is determined by the following formula:

[0009]

[0010] wherein, is the parameter trajectory sensitivity of the parameter in continuous time, k is the candidate parameter, Q0(t) is the reactive power response curve of the original parameter of the phase modifier, Q k (t) is the reactive power response curve of the kth parameter compared with the original parameter after increasing, and Δh is the increment of the kth parameter compared with the original parameter.

[0011] Further, the parameter perturbation damping increment is determined by the following formula:

[0012]

[0013] wherein, is the minimum damping ratio in the system electromechanical oscillation mode after the parameter is perturbed compared with the initial value of the parameter, is the minimum damping ratio of the electromechanical oscillation mode of the original parameter.

[0014] Further, the effective reactive current gain is:

[0015]

[0016] wherein, K iQ is the effective reactive current gain, ΔI fd is the excitation current increment of the phase modifier, and Δu is the voltage increment of the low-voltage side bus of the step-up transformer of the phase modifier.

[0017] Further, the minimum damping ratio of the system electromechanical oscillation mode is:

[0018]

[0019] wherein, λ i is the system eigenvalue, α i is the real part of the system eigenvalue, β i is the imaginary part of the system eigenvalue, f i is the oscillation frequency, ξ is the minimum damping ratio in the system electromechanical oscillation mode of interest, and ρ i is the electromechanical loop correlation ratio.

[0020] Further, the objective function is:

[0021]

[0022] Wherein, J is the target function, η1, η2 are the weight coefficients of optimization index, p is the number of expected accidents is the effective reactive current gain under the n th expected accident, ξ (n) is the minimum damping ratio of the system electromechanical oscillation mode under the n th expected accident.

[0023] Further, the expression of the collaborative optimization model is:

[0024]

[0025] Wherein, H i is the vector composed of the parameters to be optimized in step 1, and are the set minimum value and maximum value respectively.

[0026] Further, the collaborative optimization model considering voltage stability and small disturbance power angle stability is constructed by the improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm includes: the population position is not initialized in a completely random way, the position of all populations is the original value of the parameter, and the speed of all populations is generated randomly; the random weight method and the asynchronous change learning factor are used when the population is iterated, and the calculation formula of the random weight is:

[0027]

[0028] Wherein, μ is the updated random weight average value, w is the random weight, μ max is the maximum value of the random weight average value, μ min is the minimum value of the random weight average value, N(0,1) is a random number of normal distribution, σ is the variance of the random weight, and rand is a random distribution function.

[0029] The asynchronous change learning factor is:

[0030]

[0031] Wherein, c 1,iv , c 2,iv respectively represent the initial value of the asynchronous change learning factor c1, c2, c 1,fv , c 2,fv respectively represent the final value of c1, c2, m is the current iteration number, m max is the maximum iteration number.

[0032] Further, the step 3 includes:

[0033] Step 31: based on the PSASP software interface, the power flow is calculated for the simulation project, the transient stability output information is set, the transient stability calculation and small disturbance stability calculation are carried out, and the data in the project engineering temp folder is initialized;

[0034] Step 32: the to-be-optimized parameters in the corresponding position of the DATALIB.DAT file under the project engineering temp folder are automatically modified, and then the executable file under the PSASP installation directory is automatically called to carry out the power flow, transient stability and small disturbance stability calculation;

[0035] Step 33: the to-be-optimized parameters in step 1 are set based on the intelligent optimization algorithm;

[0036] Step 34: if the convergence condition of the optimization algorithm is reached, the setting is ended, otherwise, returning to step 32.

[0037] Due to the adoption of the above technical solutions, the present application has the following advantages:

[0038] 1. The present application combines the effective reactive current gain and the minimum damping ratio of the system electromechanical oscillation mode, so that the multi-objective optimization problem of voltage and damping support is converted into a single-objective optimization problem, and the parameter setting of the phase modifier and the related generator excitation system is realized based on the intelligent optimization algorithm.

[0039] 2. The present application considers the coordination and cooperation between the excitation system of the phase modifier and the excitation systems of other generators when the phase modifier is involved in the network, so as to cooperatively support the grid voltage and damping, ensuring the normal operation and performance of the entire system, and fully exerting the role of the phase modifier in improving the stability of the grid system and the investment benefit; at the same time, the present application provides a convenient example of automatic parameter setting between PSASP and other programming languages, which to some extent expands the application range of PSASP. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0041] Figure 1 The EPRI-36 node AC-DC hybrid system structure diagram of the embodiment of the present application;

[0042] Figure 2 The reactive power trajectory sensitivity curve of the excitation parameter change of the phase modifier of the embodiment of the present application;

[0043] Figure 3A flow chart for realizing automatic combined simulation of Python and PSASP for the embodiment of the present application;

[0044] Figure 4 A comparison result before and after optimization of the generator power angle oscillation curve for the embodiment of the present application;

[0045] Figure 5 A comparison result before and after optimization of the fault bus voltage level for the embodiment of the present application;

[0046] Figure 6 A flow chart of a phase modifier grid performance optimization method for voltage and damping collaborative support for the embodiment of the present application. DETAILED DESCRIPTION

[0047] The present application is further described in conjunction with the drawings and embodiments, and the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art shall belong to the scope of protection of the embodiments of the present application.

[0048] The EPRI-36 node AC-DC hybrid system in PSASP is taken as an example for illustration, and a phase modifier is added at bus 29 at the inverter side of the DC power transmission, and the grid system structure is as shown in Figure 1 . The numbers 1-50 in Figure 1 respectively represent buses, G1 to G8 respectively represent generators, and SC represents a phase modifier and its matching step-up transformer.

[0049] Referring to Figure 6 , an embodiment of a phase modifier grid performance optimization method for voltage and damping collaborative support is provided, comprising:

[0050] S1: determining the optimization direction of the excitation parameters of the phase modifier according to the parameter trajectory sensitivity and the parameter perturbation damping increment, and taking the excitation parameters of the phase modifier and the excitation parameters of the strongly related unit corresponding to the minimum damping ratio in the electromechanical oscillation mode of the system as the to-be-optimized parameters;

[0051] The parameter trajectory sensitivity is determined as follows:

[0052]

[0053] wherein, is the parameter trajectory sensitivity obtained in continuous time, k is a candidate parameter, Q0(t) is the reactive power response curve of the original parameters of the phase modifier, and Q k(t) represents the reactive response curve after the k-th parameter is increased compared to the original parameter, and Δh represents the increment of the k-th parameter compared to the original parameter. In this embodiment, the parameter is increased by 5% of the original parameter to obtain the sensitivity curve of the changed parameter trajectory.

[0054] The parameter perturbation damping increment is determined by the following formula:

[0055]

[0056] in, The minimum damping ratio in the electromechanical oscillation mode of the system after the parameter is perturbed relative to its initial value. This represents the minimum damping ratio of the electromechanical oscillation mode with the original parameters. In this embodiment, the perturbation step size is the same as the parameter trajectory sensitivity.

[0057] The strongly correlated units corresponding to the minimum damping ratio are determined based on the amplitude of the right eigenvector component of the oscillation mode. That is, the two units with the largest oscillation amplitude are defined as strongly correlated units.

[0058] It should be noted that the excitation system of the synchronous rectifier is described here using the Type 12 excitation system regulator as an example. The parameters involved in the series correction stage are: K, T1, T2, T3, T4, and the amplification stage parameter is: K. a T a Where K is the DC gain of the series compensation circuit, and T1, T2, T3, and T4 are the time constants of the series compensation circuit. a For the gain of the power amplification stage, T a The time constant of the power amplification stage; the excitation system of the strongly correlated unit is a type 2 excitation system regulator, and the relevant amplification stage parameters are: K a T a The intermediate parameters are: T1, T2, T3, T4, where K a T represents the amplification factor of the amplification stage. a T1, T2, T3, and T4 are the time constants for the amplification stage, while T1, T2, T3, and T4 are the time constants for the intermediate stages. It should be noted that in PSASP, the meanings of parameters in different excitation systems are not entirely consistent; the same variable may have slightly different meanings. Refer to the "Dynamic Component Library User Manual" for clarification.

[0059] The trajectory sensitivity of the synchronous condenser excitation parameters can be obtained by solving equation (1), such as... Figure 2 As shown, the direction of parameter optimization that is beneficial to the effective reactive current gain can be determined, that is, the excitation parameters of the synchronous condenser that should be increased are: K, T1, T3, K a The excitation parameters of the synchronous condenser that should be reduced are: T2, T4, T aIt can also be understood that the positive and negative of the damping increment of formula (2) can determine the optimization direction of the excitation parameters of the phase modifier beneficial to the minimum damping ratio in the electromechanical oscillation mode of the system, that is, the excitation parameters of the phase modifier that should be increased are: K, T1, T4, K a , T a , and the excitation parameters of the phase modifier that should be reduced are: T2, T3. It can be seen that the increase of parameters K, T1, K a is beneficial to both the effective reactive current gain and the minimum damping ratio, so the original data of these parameters should be set to the lower limit and only increased. Similarly, the decrease of T2 is also beneficial to the two optimization indicators, so the original data of T2 should be set to the upper limit and only decreased. The increase of T3, T4 and T a has opposite effects on the two optimization indicators of the effective reactive current gain and the minimum damping ratio of the electromechanical oscillation mode, so the original data of these three parameters should not be set to the upper limit or the lower limit, but only a reasonable parameter range is given, and the compromise value is solved by using an intelligent optimization algorithm. At the same time, the excitation parameters of the strongly related units are also only given a range.

[0060] S2: The effective reactive current gain and the minimum damping ratio of the electromechanical oscillation mode of the system are combined by weighting, a target function for cooperatively supporting voltage and damping is established, and a cooperative optimization model considering voltage stability and small disturbance power angle stability is constructed;

[0061] The effective reactive current gain is as follows:

[0062]

[0063] Wherein, K iQ is the effective reactive current gain, ΔI fd is the excitation current increment of the phase modifier, and Δu is the voltage increment of the low-voltage side bus of the booster transformer of the phase modifier.

[0064] The minimum damping ratio of the electromechanical oscillation mode of the system is determined by the following formula:

[0065]

[0066] Wherein, λ i is the system eigenvalue, α i is the real part of the system eigenvalue, β i is the imaginary part of the system eigenvalue, f i is the oscillation frequency, generally between 0.1 Hz and 2.5 Hz, ξ is the minimum damping ratio in the electromechanical oscillation mode of the system concerned, and ρ i is the related ratio of the electromechanical loop.

[0067] The target function is as follows:

[0068]

[0069] where J is the objective function, η1, η2 are the weight coefficients of optimization indexes, and p is the number of expected accidents is the effective reactive current gain under the n th expected accident, and ξ (n) is the minimum damping ratio of the system electromechanical oscillation mode under the n th expected accident.

[0070] It should be noted that the effective reactive current gain of the phase modifier is used to represent the reactive power compensation capability of the phase modifier, and thus represents the support of the phase modifier to the grid voltage; the minimum damping ratio obtained from the small disturbance stability analysis program of PSASP and formula (4) is used to represent the damping level of the system. By maximizing the objective function composed of the two indexes, the coordinated support of the system voltage and damping can be achieved. By setting different weight coefficients in formula (5), the bias to a certain optimization index can be represented, and in the present application, the same level is optimized, i.e. η1=η2=1.

[0071] The coordinated optimization model is described as follows:

[0072]

[0073] where H i is the vector composed of the to-be-optimized parameters in step S1, and are the set minimum value and maximum value, respectively.

[0074] It should be noted that the intelligent optimization algorithm can be a particle swarm optimization algorithm, a genetic algorithm, etc., without limitation, and the present application is described by using an improved particle swarm optimization algorithm. The improvement mode is as follows: the population position is not initialized in a completely random manner, and the position of all populations is the original value of the parameter, and the speed of all populations is generated randomly; the random weight method and the asynchronous change learning factor are used in population iteration, and the random weight calculation formula is as follows:

[0075]

[0076] where μ is the updated random weight average value, w is the random weight, μ max is the maximum value of the random weight average value, μ min is the minimum value of the random weight average value, N(0,1) is a random number of normal distribution, and σ is the variance of the random weight, and rand is a random distribution function;

[0077] The asynchronous change learning factor is as follows:

[0078]

[0079] where c 1,iv , c 2,ivc1, c2 respectively represent initial values of learning factors c1, c2 changed asynchronously, c 1,fv , c 2,fv c1, c2 respectively represent final values of c1, c2, m is a current iteration number, and m max is a maximum iteration number.

[0080] S3: automatically setting parameters to be optimized based on a collaborative optimization model.

[0081] This step combines Python programming language to realize automatic setting of model parameters in PSASP, as shown in the following steps: Figure 3

[0082] S31: based on a PSASP software interface, calculating power flow and setting transient stability output information for a simulation project, carrying out transient stability calculation and small disturbance stability calculation, and initializing data in a project engineering temp folder;

[0083] S32: automatically modifying parameters to be optimized in a corresponding position of a DATALIB.DAT file under the project engineering temp folder, and then automatically calling an executable file under a PSASP installation directory to carry out power flow, transient stability and small disturbance stability calculation;

[0084] It should be noted that the executable file and its functions are as follows: a WMLFRTMsg.exe file realizes power flow calculation, a wmudrt.exe file realizes transient stability calculation, and Wsmpara.exe, Wsstlin.exe and Wssteig.exe files realize small disturbance stability calculation.

[0085] S33: setting parameters to be optimized in S1 based on an intelligent optimization algorithm;

[0086] S34: if a convergence condition of the optimization algorithm is reached, the setting is ended, otherwise, returning to step S32.

[0087] It should be noted that the programming language is not limited to Python language, and can also be MATLAB and the like, but the executable file of PSASP calls and writes the intelligent optimization algorithm, which should be based on the same language, so that it is more convenient for data interaction between PSASP and the programming language. It should be understood that this way is not a cracking of PSASP, and all executable files need a secret key when realizing corresponding calculation functions.

[0088] The following is an example analysis of this embodiment:

[0089] ​A single-phase short-circuit ground fault is set at bus 16 of the EPRI-36 node system, the fault occurs at the 1st second and lasts for 100 ms. Based on the above steps, the parameter range to be optimized is set as shown in Table 1, the coordinated optimization objective model is established, the automatic parameter optimization of voltage and damping coordinated support is realized based on PSASP and Python, and the parameter optimization result is shown in Table 2.

[0090] Table 1 excitation parameter range

[0091]

[0092]

[0093] Table 2 parameter optimization result

[0094]

[0095] According to the parameters before and after optimization in Table 2, the following results can be obtained Figure 4 and Figure 5 The comparison results are shown in Table 3, wherein Figure 4 is the comparison result of generator power angle oscillation curve before and after optimization, it can be seen that the optimized parameters effectively reduce the power angle oscillation between the generators; Figure 5 is the comparison result of bus voltage level at the fault before and after optimization, the gray rectangular part is a local magnification of the voltage level in the recovery process, it can be seen that the optimized parameters slightly improve the voltage recovery after system fault. Table 3 shows the optimization results of effective reactive current gain and minimum damping ratio of system electromechanical oscillation mode, compared with the original parameters, the effective reactive current gain is slightly improved, which is consistent with the result of Figure 5 , while the minimum damping ratio of system electromechanical oscillation mode is optimized from 2.97473% to 3.61317%, which is enhanced to a certain extent. In summary, the method of the present application can effectively improve the system electromechanical oscillation damping ratio without impairing the voltage support of the phase modifier, and plays a role in coordinated support of voltage and damping.

[0096] Table 3 comparison of optimization indicators

[0097]

[0098] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, any modification or equivalent replacement thereof should be covered within the protection scope of the claims of the present application.

Claims

1. A method for performance optimization of a voltage and damping coordinated support oriented exciter network, characterized in that, The application relates to a method for optimizing parameters of a phase modifier, comprising the following steps: Step 1: determining the optimization direction of the excitation parameters of the phase modifier according to parameter trajectory sensitivity and parameter perturbation damping increment, and taking the excitation parameters of the phase modifier and the excitation parameters of a strong correlation unit corresponding to the minimum damping ratio in a system electromechanical oscillation mode as to-be-optimized parameters; Step 2: weighting and combining effective reactive current gain and the minimum damping ratio in the system electromechanical oscillation mode to establish a target function for synergistically supporting voltage and damping, and constructing a synergistic optimization model considering voltage stability and small disturbance power angle stability; Step 3: automatically setting the to-be-optimized parameters based on the synergistic optimization model.

2. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The parameter trajectory sensitivity is determined by the following formula: wherein, is the parameter trajectory sensitivity of the parameter obtained in continuous time t, k is the candidate parameter, Q0(t) is the reactive response curve of the original parameter of the phase modifier, Q k (t) is the reactive response curve of the kth parameter compared to the original parameter after the increase, and Δh is the increment of the kth parameter compared to the original parameter.

3. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 2, wherein, The parameter perturbation damping increment is determined by the following formula: wherein is the minimum damping ratio in the electromechanical oscillation mode of the system perturbed with respect to its initial value of the parameter, is the minimum damping ratio of the electromechanical oscillation mode of the original parameter.

4. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The effective reactive current gain is: where K iQ is the effective reactive current gain, ΔI fd is the phase modifier field current increment, and Δu is the phase modifier voltage increment.

5. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The minimum damping ratio in the system electromechanical oscillation mode is: where λ i is the system eigenvalue, α i is the real part of the system eigenvalue, β i is the imaginary part of the system eigenvalue, f i is the oscillation frequency, ξ is the minimum damping ratio in the electromechanical oscillation mode of interest, and p i is the electromechanical loop correlation ratio.

6. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The target function is: Wherein, J is the target function, η1, η2 are the weight coefficients of optimization index, p is the number of expected accidents is the effective reactive current gain under the n th expected accident, ξ (n) is the minimum damping ratio of the system electromechanical oscillation mode under the n th expected accident.

7. The method of performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 6, wherein, The expression of the synergistic optimization model is: where H i is a vector of parameters to be optimized in step 1, and are the minimum and maximum values set respectively.

8. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The improved particle swarm optimization algorithm is used to construct the synergistic optimization model considering voltage stability and small disturbance power angle stability; the improved particle swarm optimization algorithm comprises the following steps: the population position is not initialized in a completely random manner, all population positions are original parameter values, and the speed of all populations is generated randomly; a random weight method and an asynchronously changed learning factor are used in population iteration, the calculation formula of the random weight is: wherein μ is the updated average value of random weights, w is a random weight, μ max is the maximum value of the average value of random weights, μ min is the minimum value of the average value of random weights, N(0, 1) is a random number of normal distribution, σ is the variance of random weights, and rand is a random distribution function. The asynchronously changed learning factor is: wherein c 1,iv , c 2,iv represent the initial values of the asynchronously varying learning factors c1, c2, respectively, c 1,fv , c 2,fv represent the final values of c1, c2, respectively, m is the current iteration number, and m max is the maximum iteration number.

9. The method for performance optimization of a voltage and damping coordinated supported synchronous condenser network according to claim 1, wherein, The step 3 comprises the following steps: Step 31: based on a PSASP software interface, calculating power flow and setting transient stability output information for a simulation project, carrying out transient stability calculation and small disturbance stability calculation, and initializing data in a project engineering temp folder; Step 32: automatically modifying the to-be-optimized parameters in a corresponding position of a DATALIB.DAT file under the project engineering temp folder, and then automatically calling an executable file in a PSASP installation directory to carry out power flow, transient stability and small disturbance stability calculation; Step 33: setting the to-be-optimized parameters in step 1 based on an intelligent optimization algorithm; Step 34: if the convergence condition of the optimization algorithm is reached, the setting is ended, otherwise, returning to step 32.

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