Frequency controller design optimization method for improving system frequency stability

Abstract

The invention discloses a novel frequency controller design optimization method for improving system frequency stability, which comprises the following steps: firstly, designing a frequency controller based on a frequency division control theory in combination with a polynomial model, and then constructing a frequency controller parameter setting model according to the structural characteristics of the frequency controller; the parameter setting model is converted into a Markov decision process by combining an asynchronous dominant action evaluation algorithm based on the parameter setting model, and then iterative optimization is performed to finally obtain a controller parameter self-updating strategy, so that online self-correction of the frequency controller can be realized according to different real-time states of the system, and the accuracy of the system is improved. It is ensured that the frequency controller can still have a good frequency stability improvement effect in a variable working condition scene, and the frequency stability of the system in a complex working condition is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of power grid frequency stability control, and more specifically, relates to a frequency controller design optimization method for improving system frequency stability. Background Art

[0002] Driven by the dual carbon goals, green and efficient development will become the future direction of my country's energy system construction. Building a new power system with a high proportion of renewable energy access, promoting the optimization of the energy industry structure, and clean and low-carbon energy have become the top priorities for future power development. Hydropower will play an important role in the process of carbon reduction in the energy field. The reason is that hydropower, as my country's main clean energy, has the advantages of clean and environmental protection, priority access to the grid and low cost. However, as the proportion of hydropower in the system gradually increases, the operation characteristics of the power grid have changed significantly. In addition, hydropower has obvious seasonal characteristics, its output changes, uncertain load fluctuations, and frequent natural disasters on the transmission channel, making the transmission power, power grid operation mode and safety and stability characteristics of the high hydropower system complex and changeable, showing high-dimensional, time-varying and nonlinear characteristics, and the problem of temporary frequency is prominent. The ability of the power grid to resist power disturbances has dropped significantly, which greatly threatens the safe and stable operation of the power grid and seriously restricts the delivery of clean energy.

[0003] To this end, relevant scholars have conducted research on frequency stability enhancement strategies in areas with high hydropower share. Considering that the unreasonable setting of the primary frequency regulation parameters of the turbine is the main cause of frequency instability in systems with high hydropower share, governor optimization has become an important means to suppress ultra-low frequency oscillations, including: particle swarm optimization method, robust optimization method, and gravitational search method are all used for the optimization design of turbine governor parameters. However, suppressing ultra-low frequency oscillations based on the strategy of optimizing governor parameters will reduce the regulation characteristics of the governor, reduce the tracking performance of the system, and restrict the system's rapid regulation ability. Therefore, how to design a new frequency controller to improve the system frequency stability without affecting the system's regulation ability has become a key issue that needs to be solved urgently. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a frequency controller design optimization method for improving the frequency stability of the system. Through the parameter adaptive tuning strategy, appropriate parameter setting information can be given according to the real-time state of the system, thereby ensuring that the proposed frequency controller can fully exert its control performance under different working conditions.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a frequency controller design optimization method for improving system frequency stability, characterized by comprising the following steps:

[0006] (1) Design a frequency controller G(s) based on the frequency division control theory, which consists of a proportional resonant control unit GPR (s) and the polynomial model G M (s) tandem composition;

[0007] (2) Collect the frequency deviation curve f(t) of the generator of the power system, identify the oscillation mode of the frequency deviation curve, and obtain the main oscillation mode information: λ=α+jω, where λ is the main oscillation mode information, α and ω are the real part and imaginary part of the ultra-low frequency oscillation mode respectively, and j represents the imaginary number sign;

[0008] (3) Calculate the damping ratio ξ of the frequency controller in the ultra-low frequency oscillation mode;

[0009]

[0010] (4) Construct a parameter tuning model of the frequency controller according to the damping ratio ξ of the ultra-low frequency oscillation mode;

[0011] (5) Real-time collection of voltage signal V(t) and power signal P(t) of the power system;

[0012] (6) The voltage signal V(t) of the power system is used as the output of the frequency controller G(s), and the power signal P(t) is used as the input of the frequency controller G(s). Then, the parameter setting model of the frequency controller is transformed into a Markov decision process in combination with the deep reinforcement learning algorithm. The Markov decision process is then iterated to continuously reduce the model value until the algorithm converges, and finally the adaptive control parameters of the frequency controller are obtained.

[0013] The object of the invention of the present invention is achieved in this way:

[0014] The present invention discloses a frequency controller design optimization method for improving system frequency stability. First, based on the frequency division control theory combined with a polynomial model, a frequency controller is designed. Then, according to the structural characteristics of the frequency controller, a frequency controller parameter setting model is constructed. Based on the parameter setting model combined with an asynchronous dominant action evaluation algorithm, the parameter setting model is converted into a Markov decision process. Then, iterative optimization is performed to finally obtain a controller parameter self-update strategy. Then, online self-correction of the frequency controller can be realized according to different real-time states of the system, thereby ensuring that the frequency controller can still have a good frequency stability improvement effect under variable operating conditions, thereby ensuring the frequency stability of the system under complex operating conditions.

[0015] At the same time, the frequency controller design optimization method for improving system frequency stability of the present invention also has the following beneficial effects:

[0016] (1) Applying the frequency division control theory to the design of the frequency stability controller can achieve accurate extraction of a specific main oscillation mode, thereby enhancing the oscillation suppression effect of the frequency controller on the main oscillation mode and helping to further improve the frequency stability of the system.

[0017] (2) Introducing the polynomial model into the frequency controller structure has greater design freedom compared to the lead-lag link in the traditional controller. By reasonably assigning values ​​to the polynomial model, a better controller structure can be found, which can achieve better control effects.

[0018] (3) The tuning model of the frequency controller parameters is converted into a Markov decision process and solved by combining it with the deep reinforcement learning algorithm. This can obtain a controller parameter self-update strategy to ensure the frequency stability of the system under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a frequency controller design optimization method for improving system frequency stability according to the present invention;

[0020] Figure 2 It is a simplified topological diagram of a hydroelectric generator system;

[0021] Figure 3 It is the frequency deviation curve of generator set CTK-1# under fault 1 excitation;

[0022] Figure 4 The distribution of ultra-low frequency oscillation modes under different strategies: (a) without the proposed frequency controller; (b) with the traditional parameter tuning method; (c) with the proposed parameter tuning method; DETAILED DESCRIPTION

[0023] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0024] Example

[0025] In this embodiment, Figure 2 This is a simplified topology of a hydropower group system, including 53 buses, 26 lines and 17 hydropower units. The total installed capacity of the system is 1100MW. All units use a fifth-order model and are equipped with an excitation system and a turbine regulation system. The power is transmitted to the main grid through a 500kV substation, and the load in the area can be ignored.

[0026] In this embodiment, if Figure 1As shown in the figure, we describe in detail a frequency controller design optimization method for improving system frequency stability, which specifically includes the following steps:

[0027] S1. Design of frequency controller G(s) based on frequency division control theory.

[0028] The frequency controller G(s) is composed of a proportional resonant control unit G PR (s) and the polynomial model G M (s) tandem composition;

[0029] S1.1. Considering that the main reason for the frequency stability problem of the system with high hydropower ratio is the existence of a main oscillation mode with a frequency lower than 0.1Hz inside the system, based on the frequency division control theory, a proportional resonance control unit G is introduced. PR (s):

[0030]

[0031] Among them, K P K is the proportional coefficient of the proportional resonant control unit; R is the resonance coefficient of the proportional resonance control unit; ω0 is the resonance frequency of the proportional resonance control unit; ω c is the cut-off frequency of the proportional resonant control unit, s represents a complex variable;

[0032] The proportional resonant control unit can separate and extract the main oscillation mode from the system with high hydropower ratio.

[0033] S1.2. Considering that the lead-lag link structure used in the traditional controller is simple and has limited characterization ability, it is difficult to express the optimal controller structure. The polynomial model G is introduced. M (s):

[0034]

[0035] Among them, a0, a1, a2, b0, b1, b2 are the variables to be adjusted in the polynomial.

[0036] S1.3, the proportional resonance control unit G PR (s) and the polynomial model G M (s) are connected in series to obtain the frequency controller structure G(s):

[0037]

[0038] S2, collect the frequency deviation curve f(t) of the CTHC-2# generator in the system, identify the oscillation mode of the curve, and obtain the main oscillation mode information;

[0039] λ=α+jω

[0040] Where: α and ω are the real and imaginary parts of the ultra-low frequency oscillation mode, respectively, and j represents the imaginary number sign. ξ is the damping ratio of the ultra-low frequency oscillation mode. In this embodiment, the identification results show that: α = -0.012, ω = 0.57, which is ξ = 2.1%

[0041] S3, calculating the damping ratio ξ of the frequency controller in the ultra-low frequency oscillation mode;

[0042]

[0043] S4, constructing a parameter tuning model of the frequency controller G(s);

[0044] S4.1, bandwidth B of given frequency controller G(s) = 0.1;

[0045] S4.2, in order to enable the frequency controller to accurately filter out the main oscillation mode in the system with high hydropower share, the resonant frequency of the frequency controller can be set to: ω0 = ω = 0.57, thereby completing the setting of ω0;

[0046] S4.3. Considering that the modal information identified in step S2 is easily affected by noise and has a certain error, the proportional resonance unit G of the frequency controller is used. PR (s)K needs to be set reasonably P and ω c Determine so that the frequency controller has a certain bandwidth B.

[0047] Therefore, we can calculate the cutoff frequency ω of the proportional resonant control unit based on the bandwidth B of the frequency controller G(s): c =Bπ; In this embodiment, the bandwidth B is set to 0.1, so ω can be obtained c =0.314. That is, the frequency controller ω is completed. c Setting of

[0048] For K P and K R In order to amplify or reduce the input signal V(t) when passing through the frequency controller, the signal is distorted. The gain of the frequency controller can be set to 1, that is, G PR (jω) = 1. Substitute it into the proportional resonant control unit G PR (s), we can get the relationship: K P +K R =1, so we can adjust K P The value of K R Setting.

[0049] S4.4, the remaining parameter set of the final frequency controller to be adjusted is: θ = (K P,a0,a1,a2,b0,b1,b2);

[0050] S4.5. For the remaining parameters to be adjusted, the parameter optimization model r can be constructed as follows:

[0051]

[0052] Among them, ξ set is the pre-designed damping lower limit value, which is set to 0.06 in this embodiment; θ max and θ min is the upper and lower limits of the parameters to be adjusted. In this embodiment, it is set to θ max =(100,10,10,10,10,10,10); θ min =(5,0.01,0.01,0.01,0.01,0.01,0.01)

[0053] S5, real-time acquisition of the voltage signal V(t) and power signal P(t) of the CTHC-2# generator in a hydropower group system;

[0054] S6. Use the voltage signal V(t) of the power system as the output of the frequency controller G(s) and the power signal P(t) as the input of the frequency controller G(s). Then, combine the deep reinforcement learning algorithm to transform the parameter setting model of the frequency controller into a Markov decision process. Then, iterate the Markov decision process to continuously reduce its model value until the algorithm converges, and finally obtain the adaptive control parameters of the frequency controller.

[0055] Figure 3 The frequency oscillation suppression effects of three strategies, namely, optimizing speed regulator parameters, configuring traditional controllers, and the proposed frequency controller, are given when fault 1 excitation occurs (a two-phase short circuit fault occurs on the external line of the CJL-220KV substation starting from 2 seconds and lasting for 100 milliseconds). It can be seen that compared with optimizing speed regulator parameters and configuring traditional controllers, the proposed frequency controller has a better oscillation suppression effect on the frequency oscillation curve, which can make the system oscillation suppression faster.

[0056] Figure 4 (a) The distribution and probability density function of the main oscillation mode of the system in 2187 working conditions are statistically analyzed when the proposed frequency controller is not configured; Figure 4 (b) The distribution and probability density function of the main oscillation mode of the system in 2187 working conditions are statistically analyzed when the system is configured with the proposed frequency controller and the controller is parameterized based on the traditional method; Figure 4(c) The distribution and probability density function of the ultra-low frequency oscillation mode of the system in 2187 operating conditions are statistically analyzed when the system is configured with the proposed frequency controller and the controller parameters are tuned based on the proposed method. It can be seen that when the proposed frequency controller is not configured, the ultra-low frequency oscillation mode is located in the right plane of the real axis under certain operating conditions, which means that the system has the risk of ultra-low frequency oscillation. The results of analyzing the ultra-low frequency oscillation mode under the frequency controller designed based on the traditional parameter tuning method and the proposed parameter tuning method show that the ultra-low frequency oscillation mode is located in the left plane of the real axis under all operating conditions. In addition, compared with the traditional tuning method, the proposed parameter tuning method makes the ultra-low frequency oscillation mode move further to the left, and the system has a larger stability margin.

[0057] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A frequency controller design optimization method for improving power grid frequency stability, characterized in that: The following steps are involved: (1) Design a frequency controller G(s) based on the frequency division control theory, which consists of a proportional resonant control unit G PR (s) and the polynomial model G M (s) tandem composition; (2) Collect the frequency deviation curve f(t) of the generator of the power system, identify the oscillation mode of the frequency deviation curve, and obtain the main oscillation mode information: λ=α+jω, where λ is the main oscillation mode information, α and ω are the real part and imaginary part of the ultra-low frequency oscillation mode respectively, and j represents the imaginary number sign; (3) Calculate the damping ratio ξ of the frequency controller in the ultra-low frequency oscillation mode; (4) Construct a parameter tuning model of the frequency controller according to the damping ratio ξ of the ultra-low frequency oscillation mode; (5) Real-time collection of voltage signal V(t) and power signal P(t) of the power system; (6) The voltage signal V(t) of the power system is used as the output of the frequency controller G(s), and the power signal P(t) is used as the input of the frequency controller G(s). Then, the parameter setting model of the frequency controller is transformed into a Markov decision process in combination with the deep reinforcement learning algorithm. The Markov decision process is then iterated to continuously reduce the model value until the algorithm converges, and finally the adaptive control parameters of the frequency controller are obtained.

2. A frequency controller design optimization method for improving power grid frequency stability according to claim 1, characterized in that: The method for designing the frequency controller G(s) based on the frequency division control theory in step (1) is: (2.1) Based on the frequency division control theory, define the proportional resonant control unit G PR (s): Among them, K P K is the proportional coefficient of the proportional resonant control unit; R is the resonance coefficient of the proportional resonance control unit; ω0 is the resonance frequency of the proportional resonance control unit; ω c is the cut-off frequency of the proportional resonant control unit, s represents a complex variable; (2.2), define the polynomial model G M (s): Among them, a0, a1, a2, b0, b1, b2 are the variables to be adjusted in the polynomial; (2.3) The proportional resonance control unit G PR (s) and the polynomial model G M (s) are connected in series to obtain the frequency controller structure G(s):

3. A frequency controller design optimization method for improving power grid frequency stability according to claim 1, characterized in that: The parameter setting model of the frequency controller G(s) is: (3.1), bandwidth B of given frequency controller G(s); (3.2) Set the resonant frequency of the proportional resonance control unit to ω0 = ω, and complete the setting of ω0; (3.3) Calculate the cutoff frequency ω of the proportional resonance control unit according to the bandwidth B of the frequency controller G(s) c =Bπ, complete ω c Setting of (3.4), due to the proportional coefficient K of the proportional resonance control unit P and resonance coefficient K R Satisfaction: K P +K R =1, so by setting K P The value of K R Setting of Finally, the remaining parameter set of the frequency controller to be adjusted is: θ = (K P ,a0,a1,a2,b0,b1,b2); (3.5) According to the damping ratio ξ of the ultra-low frequency oscillation mode, the parameter tuning optimization model r of the frequency controller is constructed: Among them, ξ set is the pre-designed damping lower limit, θ max and θ min The upper and lower limits of the parameters to be adjusted.

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