A novel power system stability control method

Abstract

The present application relates to the technical field of power system stability control, and particularly relates to a power system stability control method based on input and output data optimization control parameters. The method first collects power system input data, output data and oscillation response data; then constructs a parameterized stability controller with several undetermined coefficients, and sets the initial value of the to-be-optimized coefficients; then superimposes the controller output to the additional control channel of the controlled object, constructs an optimization function with the minimum system oscillation as the target, and sets the constraint condition that the average value of the parameters is 0, so as to ensure that the controller output is 0 after the oscillation disappears; then iteratively updates the to-be-optimized coefficients under the constraint condition by using an optimization algorithm, and obtains the target control coefficients; finally, writes the optimized target control coefficients into the parameterized controller, and forms the final stability controller. The simulation results show that the present application can effectively suppress the subsynchronous oscillation and low-frequency oscillation of the synchronous generator, reduce the oscillation amplitude, and speed up the oscillation decay process.

Description

Technical Field

[0001] This invention relates to the field of power system stability control technology, and in particular to a power system stability control method based on optimizing control parameters using input and output data. Background Technology

[0002] During operation, power systems are susceptible to low-frequency oscillations, subsynchronous oscillations, and other forms of electromechanical or electromagnetic oscillations due to factors such as sudden load changes, line fault clearing, connection and disconnection of series compensation lines, power fluctuations, weak grid operation, and the coupling effect of multiple devices. If the system damping is insufficient, the oscillations may decay slowly over a long period of time or even be amplified, thereby affecting the quality of inter-regional power exchange, equipment operation safety, and the overall stability of the system.

[0003] In existing engineering projects, system damping is typically improved by introducing additional damping controllers into the excitation system, speed control system, converter control system, or other auxiliary control channels. These controllers generally process speed deviations, power deviations, voltage deviations, or other oscillation-related signals to generate additional control quantities, which are then superimposed onto the main control channel to suppress oscillations. However, the parameters of existing additional controllers mostly rely on manual experience tuning, repeated trial and error, or offline parameter adjustment for single operating conditions. This not only results in low tuning efficiency and poor repeatability but also limited adaptability to changes in operating point, disturbance type, and system structure.

[0004] Furthermore, existing auxiliary controllers often fail to automatically return to zero after the oscillations disappear, introducing steady-state bias or ineffective auxiliary control effects, thus affecting the normal operation of the main control system. Especially in practical engineering, when system input, output, and disturbance response data are available but the optimal controller parameters are difficult to directly provide, a new stability control method is urgently needed. This method should automatically optimize controller parameters based solely on input and output data, effectively suppressing oscillations while ensuring the controller output is zero after the oscillations disappear.

[0005] Therefore, existing technologies still require a stable control method for power system oscillation suppression. This method involves setting several undetermined control coefficients and initial parameters, constructing an optimization problem based on input and output data, and using optimization algorithms to solve for the control parameters. This minimizes oscillations and ensures that the controller output is zero after the oscillations disappear. Summary of the Invention

[0006] The purpose of this invention is to provide a power system stability control method. By constructing a parameterized controller with several undetermined coefficients and optimizing the undetermined coefficients based on system input and output data, the system oscillation is minimized, while ensuring that the controller output is zero after the oscillation disappears. This solves the problems of existing additional controllers relying on manual tuning, unstable vibration suppression effect under different operating conditions, and the existence of steady-state residual output of the controller.

[0007] This invention is achieved using the following technical solution:

[0008] Step A1: Collect power system input data, output data, and oscillation response data. The input data is the controller input signal, the output data is the controlled object output signal, and the oscillation response data is a state quantity or output quantity used to characterize the degree of system oscillation.

[0009] Step A2: Construct a parameterized stable controller and set initial values ​​for the coefficients to be optimized. The discrete output expression of the parameterized stable controller is:

[0010] (1)

[0011] In the formula, u c (k) represents the controller output at sampling time k, x(k−i) represents the current and historical terms of the controller input signal, and a i Let n be the control coefficient to be optimized, and n be the number of control coefficients.

[0012] Based on the control accuracy requirements, oscillation frequency range, and engineering implementation complexity, the number of control coefficients n is set, and an initial value a is given for the control coefficient to be optimized. i (0) .

[0013] Step A3: Superimpose the output of the parameterized stable controller onto the additional control channel of the controlled object. The controller output u constructed in step A2... c (k) is superimposed on the control reference value of the controlled object to suppress system oscillation.

[0014] Step A4: Construct the objective function and constraints. To minimize system oscillations and ensure that the controller output is zero after the oscillations disappear, the following constrained optimization problem is constructed:

[0015] (2)

[0016] In the formula, J1 is the objective function, z(k) is the response signal characterizing the degree of system oscillation, N is the evaluation time domain length, and a iThe control coefficients to be optimized are defined as follows: The objective function aims to minimize the oscillation amplitude, oscillation energy, peak deviation, or oscillation duration of the system after a disturbance; the constraints ensure that the controller output returns to zero when the system oscillation disappears and the controller input tends to a steady-state constant value.

[0017] After the system oscillation disappears, if the controller input tends to the steady-state constant value x0, then:

[0018] (3)

[0019] Because the constraint conditions are satisfied Therefore, u c (k)=0, meaning the controller output is zero after the oscillation disappears. The constraint can also be equivalently expressed as the average parameter value being 0.

[0020] Step A5: Under the stated constraints, an optimization algorithm is used to iteratively update the coefficients to be optimized. Under the satisfied constraints, the optimization algorithm is used to update the control coefficient a. i Perform iterative optimization to minimize the objective function J1, and obtain the target control coefficient a. i * The optimization algorithm may be particle swarm optimization, genetic algorithm, differential evolution algorithm, simulated annealing algorithm, gradient optimization algorithm, least squares algorithm, constrained optimization algorithm, or a combination of the above algorithms.

[0021] Step A6: Write the optimized target control coefficients into the parameterized controller to form the final stable controller. The optimized target control coefficients a i * Substituting into the parameterized controller, we obtain the final control law:

[0022] (4)

[0023] This control law is then used in the additional control channel of the actual controlled object to form a stable controller for oscillation suppression.

[0024] Compared with the prior art, the present invention has at least the following beneficial effects:

[0025] First, by constructing a parameterized controller containing several undetermined coefficients and optimizing the control parameters based on input and output data, this invention avoids the problem of traditional auxiliary controllers relying on repeated manual tuning and improves the automation and repeatability of parameter tuning.

[0026] Second, this invention takes minimizing system oscillation as the optimization objective, allowing the controller parameters to be adjusted directly around the oscillation suppression effect, which has clear engineering objectives and physical significance.

[0027] Third, by constraining the average value of parameters to 0, this invention directly transforms the requirement that the controller output should be 0 after the oscillation disappears into a control parameter constraint condition; wherein, the average value of parameters being 0 is equivalent to the sum of parameters being 0, making the steady-state characteristics of the controller clear, the structure clear, and the results easy to interpret.

[0028] Fourth, this invention is applicable not only to the additional excitation damping control of synchronous generators, but also to wind power grid connection control, grid-connected converter stability control, high-voltage DC transmission additional damping control, and other power system oscillation suppression scenarios, and has strong versatility and engineering promotion value. Attached Figure Description

[0029] Figure 1 The flowchart illustrates a novel power system stability control method provided by this invention.

[0030] Figure 2 The figure shows the simulation results of the suppression of subsynchronous oscillations in an embodiment of the present invention.

[0031] Figure 3 The figure shows the simulation results of low-frequency oscillation suppression in an embodiment of the present invention. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, various equivalent changes and substitutions can be made to the present invention without departing from the spirit and substance of the invention, and all such changes and substitutions should fall within the scope of protection of the present invention.

[0033] The core idea of ​​this invention is as follows: First, a parameterized controller with several undetermined coefficients is constructed based on the power system oscillation suppression requirements; then, based on the system input, output and disturbance response data, initial values ​​of the control coefficients are given, and iterative optimization is performed under the constraints of the objective function of "minimizing oscillation" and "the average value of the parameters is zero"; finally, the optimized control parameters are written into the controller to form a stable controller for oscillation suppression, and the waveform results and parameter results before and after optimization are output.

[0034] This embodiment uses the suppression of low-frequency oscillations or subsynchronous oscillations after a synchronous generator disturbance as an example for illustration. It should be noted that this embodiment is merely a specific application of the present invention in the scenario of additional excitation control of a synchronous generator, and does not constitute a limitation on the applicable scope of the present invention.

[0035] 1. Suppression of Subsynchronous Oscillations in Synchronous Generators

[0036] In this embodiment, the synchronous generator speed deviation signal Δω(k) is selected as the controller input signal, and the controller output signal is the additional control quantity u.c (k), and this additional control quantity is superimposed on the excitation regulator voltage reference input, that is:

[0037] (5)

[0038] In the formula, V ref (k) is the excitation regulator voltage reference input after superimposing additional control quantities, V ref0 (k) is the original voltage reference input.

[0039] In this embodiment, the parameterized controller is represented as:

[0040] (6)

[0041] Where a0, a1, ..., a7 are the control coefficients to be optimized.

[0042] First, a disturbance condition is set in the simulation platform, and the input, output, and oscillation response data of the system under disturbance are collected. The input data is the speed deviation signal Δω(k). Then, the number of control coefficients is set to n=8, and the control coefficients are optimized using a particle swarm optimization algorithm under the constraint that the average parameter value is 0. The optimized target control coefficients are: [0.71075, 0.38475, 0.05275, −0.22325, −0.34525, −0.29425, −0.18225, −0.10325]. The control coefficients satisfy:

[0043] (7)

[0044] Since the average parameter value is 0, the controller output automatically returns to zero when the oscillation disappears and the controller input approaches its steady-state constant value. The optimized control coefficients are then written into the parameterized controller to obtain the final control law.

[0045] (8)

[0046] After integrating the control law into the additional control channel of the excitation regulator, disturbance simulations were performed on the system. Simulation results show that without additional control, the generator speed drops from approximately 57.292 rpm to approximately 57.248 rpm after a disturbance, with a maximum speed drop of approximately 0.044 rpm. With the optimized controller, the generator speed reaches a minimum of approximately 57.274 rpm, with a maximum speed drop of approximately 0.018 rpm. Compared to the uncontrolled system, the maximum speed drop is reduced by approximately 59.1%, indicating that the designed parametric stabilization controller effectively suppresses subsynchronous oscillations and improves the speed recovery level after disturbances. The oscillation response waveforms before and after optimization are shown below. Figure 2 As shown.

[0047] 2. Suppression of low-frequency oscillations in synchronous generators

[0048] In this embodiment, the synchronous generator speed deviation signal Δω(k) is selected as the controller input signal, and the controller output signal is the additional control quantity u. c (k), and this additional control quantity is superimposed on the excitation regulator voltage reference input, that is:

[0049] (9)

[0050] In this embodiment, the parameterized controller is represented as:

[0051] (10)

[0052] In the formula, a0, a1, ..., a4 are the control coefficients to be optimized.

[0053] First, a low-frequency oscillation disturbance condition is set in the simulation platform, and the input, output, and oscillation response data of the system under the disturbance are collected. The input data is the speed deviation signal Δω(k). Then, the number of control coefficients is set to n=5, and the control coefficients are optimized using a particle swarm optimization algorithm under the constraint that the average parameter value is 0. The optimized target control coefficients are: [−1.21720, 0.58280, 0.31780, 0.22480, 0.09180]. The control coefficients satisfy:

[0054] (11)

[0055] Since the average parameter value is 0, the controller output automatically returns to zero when the oscillation disappears and the controller input approaches its steady-state constant value. The optimized control coefficients are then written into the parameterized controller to obtain the final control law.

[0056] (12)

[0057] After integrating the control law into the additional control channel of the excitation regulator, disturbance simulations were performed on the system. Simulation results show that, with the optimized parametric stabilizer, the amplitude of the low-frequency power oscillation of the synchronous generator decreases, and the oscillation decay process accelerates, indicating that the designed parametric stabilizer has a good suppression effect on low-frequency oscillations. The low-frequency oscillation response waveforms before and after optimization are shown below. Figure 3 As shown.

[0058] Finally, it should be noted that the above examples of the present invention are merely illustrative and not intended to limit the implementation of the invention. Although the applicant has described the present invention in detail with reference to preferred embodiments, those skilled in the art can make other variations and modifications based on the above description. It is impossible to exhaustively list all possible implementations here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A novel power system stability control method, characterized in that, Includes the following steps: S1: Collect power system input data, output data, and oscillation response data. The input data is the controller input signal, the output data is the controlled object output signal, and the oscillation response data is a state quantity or output quantity used to characterize the degree of system oscillation. S2: Construct a parameterized stable controller, set the number of control parameters and their initial values, and ensure that the discrete output of the parameterized stable controller satisfies: (1) S3: The output of the parameterized stable controller is superimposed onto the additional control channel of the controlled object, and the control parameters are iteratively updated based on the optimization algorithm with the minimum system oscillation as the optimization objective function and the average value of the control parameters being 0 as the constraint condition, to obtain the target control parameters; S4: Substitute the target control parameters into the parameterized stable controller to form the final stable controller, thereby achieving power system oscillation suppression.

2. The novel power system stability control method according to claim 1, characterized in that, In step S3, the objective function is to minimize system oscillation, and the constraint is that the average value of the control parameters is 0. The control parameters are iteratively updated based on the optimization algorithm. The specific strategy is as follows: S31: Let the response signal characterizing the degree of system oscillation be z(k), and construct the optimization objective function within the preset evaluation time domain length N: (2) S32: Simultaneously set the control parameter constraints as follows: (3) When the system oscillation disappears and the controller input tends to the steady-state constant value x0, we have: (4) This ensures that the controller output is 0 after the oscillation disappears. Based on the aforementioned objective function and constraints, the target control parameters of the parameterized stable controller are obtained through iterative optimization algorithms. These target control parameters are then substituted into the parameterized stable controller to suppress power system oscillations.

3. The novel power system stability control method according to claim 1, characterized in that, The controller input signal in step S1 includes one or more of the following: speed deviation, power deviation, voltage deviation, current deviation, frequency deviation, and phase angle deviation.

4. The power system stability control method according to claim 1, characterized in that, The optimization algorithm mentioned in step S3 includes one or more of the following: particle swarm optimization, genetic algorithm, differential evolution algorithm, simulated annealing algorithm, gradient optimization algorithm, least squares algorithm, and constrained optimization algorithm.

5. The power system stability control method according to claim 1, characterized in that, The controlled object is a synchronous generator excitation system, and the output u of the parameterized stability controller is... c (k) is superimposed on the voltage reference input of the excitation regulator, satisfying the following formula: (5)。 6. The power system stability control method according to claim 5, characterized in that, The controller input signal in step S1 is the synchronous generator speed deviation signal Δω(k), and the parameterized stabilization controller is used to suppress low-frequency oscillations or subsynchronous oscillations of the synchronous generator.

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