A design method of a primary frequency modulation controller of a pumped storage unit

Abstract

The application discloses a design method of a primary frequency modulation controller of a pumped storage unit, and belongs to the technical field of power system frequency stability control. The method comprises the following steps: collecting historical data of the unit; designing a primary frequency modulation controller of an MRAC architecture, which comprises a nominal control input and an adaptive disturbance compensation input; constructing a multi-index parameter adaptive optimization model; collecting system state data in real time, correcting model parameters online, and updating the optimization model; and combining a recursive least square method to optimize controller parameters until convergence. The application combines online parameter identification and model reference adaptive control, realizes dynamic self-tuning of controller parameters, offsets the influence of unknown disturbances through an adaptive compensation mechanism of disturbance upper and lower bound estimation, and comprehensively considers multi-dimensional frequency modulation performance of a parameter optimization model. The method overcomes the limitations of a traditional fixed parameter controller, improves the frequency modulation accuracy and robustness of the pumped storage unit under variable working conditions and strong disturbance scenes, and guarantees the frequency stability of a power grid.

Description

Technical Field

[0001] This invention belongs to the field of power system frequency stability control technology, and more specifically, relates to a design method for a primary frequency control controller for pumped storage units based on online parameter identification. Background Technology

[0002] Building a new power system dominated by renewable energy generation has become a core task in power development. Pumped storage units, as a clean and flexible regulating resource, play a key role in primary frequency regulation of the power grid due to their advantages of rapid response and reliable regulation. They are an important support for ensuring grid frequency stability under the integration of a high proportion of renewable energy.

[0003] However, the intermittency and volatility of high-proportion renewable energy sources such as wind and solar power have profoundly altered the operating characteristics of the power system, leading to increasingly frequent grid frequency disturbances. This places higher demands on the frequency regulation response speed, control accuracy, and robustness of pumped storage units. Currently, primary frequency regulation of pumped storage units mostly uses traditional PID controllers, whose parameters mainly rely on design values ​​or static test tuning, which has significant shortcomings: On the one hand, the unit's speed regulation system includes multiple complex components such as governors, electro-hydraulic servo devices, hydraulic channels, pumps, and turbines, and the parameters drift with changes in operating conditions such as head and load. Fixed-parameter controllers are difficult to adapt to the dynamic characteristics under all operating conditions, easily leading to deterioration in regulation performance. On the other hand, traditional controllers do not fully consider the influence of nonlinear factors such as the "S" region characteristics of pumps and turbines and hydraulic inertia. Under complex disturbances, problems such as large overshoot, long settling time, or high-frequency oscillations may occur, restricting the full utilization of the unit's frequency regulation capability.

[0004] Scholars have conducted research on controller parameter optimization, employing methods such as particle swarm optimization and robust optimization to improve control performance. However, the time-varying nature of parameters remains unresolved, making it difficult to respond to system state changes in real time. Therefore, designing a primary frequency controller capable of accurately sensing the unit's operating status and dynamically updating control parameters, while improving frequency stability without affecting the unit's regulation capability, has become a critical technical problem urgently needing to be solved in the field of pumped storage unit control in new power systems. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and provide a design method for a primary frequency regulation controller for pumped storage units. By identifying the dynamic parameters of the unit online and adaptively updating the control parameters, the controller can ensure optimal frequency regulation performance under varying operating conditions and strong disturbance scenarios, thereby improving the accuracy and robustness of the primary frequency regulation of pumped storage units and ensuring grid frequency stability.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A design method for a primary frequency control controller of a pumped storage unit includes the following steps;

[0008] Step 1: Collect historical data of the pumped storage unit, including state variable vector X and non-state variable vector Y. The state variable vector X includes: generator rotor angle and speed, excitation system excitation voltage and current, prime mover guide vane opening, electro-hydraulic servo state, etc. The non-state variable vector Y includes: input and output control variables of various regulators inside the prime mover.

[0009] Step 2: Establish a linearized open-loop model of the pumped storage unit based on the online parameter identification method;

[0010] Step 3: Design the primary frequency control controller for the pumped storage unit based on the linearized equivalent model;

[0011] Step 4: Construct an adaptive optimization model for the primary frequency control controller parameters. ;

[0012] Step 5: Real-time acquisition of the real-time state variable vector ΔX and non-state variable vector ΔY of the power system;

[0013] Step 6: Substitute the real-time acquired state variable vector ΔX and non-state variable vector ΔY into the linearized open-loop model of the pumped storage unit. Correct the parameters of the linearized open-loop model online, and then update the adaptive optimization model of the primary frequency controller parameters. Finally, combine the recursive least squares method to optimize the control of the primary frequency controller, so that... The parameters are gradually reduced until the algorithm converges, thus obtaining the optimal controller parameters.

[0014] As a further technical solution of the present invention: the process of establishing the linearized open-loop model of the pumped storage unit includes the following steps:

[0015] Step 2.1: Using the small-signal analysis theory of power systems, generate a system augmented matrix. Linearized power system model:

[0016] ;

[0017] in, Represents the augmented matrix of the system; State variable vector The derivative; , , , For matrix The four submatrices, It is a dynamic matrix that represents the dynamic coupling relationship between state variables. The coupling matrix represents the relationship between the input and output variables of each component of the generator and the generator's dynamics. , These are the coefficient matrix and the algebraic coupling matrix of the algebraic equation, respectively, representing the static relationship between the state variables and the input and output variables of each component of the generator;

[0018] Step 2.2: Transform the above linearized power system model using matrix linear transformation, as follows:

[0019] ;

[0020] in, Represents the state matrix of the closed-loop system;

[0021] Step 2.3: By postposing the state variables related to the pumped storage unit governor, the formula in Step 2.2 can be rewritten as follows:

[0022] ;

[0023] ;

[0024] in, This represents a vector of state variables independent of the pumped storage unit's governor. This represents the vector of state variables related to the governor of a pumped storage unit. This is the non-governor state submatrix, representing the mutual influence between non-governor states. This is the governor-non-governor coupling submatrix, representing the effect of the governor state on the non-governor state. This is the non-governor-governor coupling submatrix, representing the feedback of the non-governor state to the governor state. This is the governor state submatrix, representing the dynamic relationship of the internal states of the governor;

[0025] Step 2.4: Establish a linearized open-loop model of the pumped storage unit:

[0026] ;

[0027] in, This represents the input variable vector between the open-loop system and the governor. , The state matrix and input matrix of the open-loop system are represented as follows:

[0028] .

[0029] As a further technical solution of the present invention: the design method of the primary frequency control controller of the pumped storage unit is as follows:

[0030] Step 3.1: Taking into account the identification error, the linearized open-loop model of the pumped storage unit is further expressed as:

[0031] ;

[0032] in, To linearize the identification error;

[0033] Step 3.2: Design the adaptive control reference model:

[0034] ;

[0035] in, Indicates the expected reference model, The state matrix of the reference model, The state matrix of the reference model, This represents the reference input variable vector between the open-loop system and the governor;

[0036] Step 3.3: Set tracking error :

[0037] ;

[0038] Step 3.4: Design a primary frequency modulation controller based on the MRAC architecture. :

[0039] ;

[0040] in, For nominal control input, For adaptive disturbance compensation input, , , where are the coordination gain coefficients for the nominal control component and the adaptive disturbance compensation input, respectively;

[0041] Among them, nominal control input satisfy:

[0042]

[0043] Wherein, constant matrix and The following matching conditions must be met for a match to be valid:

[0044]

[0045] Adaptive disturbance compensation input satisfy:

[0046]

[0047] Where I is the identity matrix, for The real-time estimate of the upper bound, for The real-time estimate of the lower bound, , The first derivative of satisfies the following condition:

[0048]

[0049] Among them, superscript Indicates transpose. To satisfy the positive definite matrix of the Lyapunov equation:

[0050]

[0051] Auxiliary variables :

[0052]

[0053] in, It is a symmetric positive definite weight matrix. Let X be the dimension of the state variable vector.

[0054] As a further technical solution of the present invention: the established adaptive optimization model for the primary frequency modulation controller parameters is as follows:

[0055] ;

[0056] in, These are the controller parameters to be optimized. This represents the maximum frequency deviation. This refers to the time it takes for the frequency to recover to the allowable range. To assess the length of the time window; The output of the controller, Given a steady-state value; , , These are the weighting coefficients.

[0057] As a further technical solution of the present invention: the evaluation time window length The value is 10s, and the steady-state value is... The weighting coefficient is 1. , , .

[0058] Both the main line clamp and the main line clamp are 2T wedge clamps, or can be replaced with equivalent high-friction wedge clamps. The clamping surfaces of the clamps are provided with anti-slip teeth.

[0059] This technology proposes a design method for a primary frequency control controller for pumped storage units, which has the following advantages and benefits:

[0060] (1) By combining model reference adaptive control with online parameter identification, the problem of performance degradation of traditional fixed parameter controllers under varying operating conditions is overcome, and dynamic self-tuning of controller parameters is realized, which significantly improves the response speed and control accuracy of primary frequency regulation of pumped storage units.

[0061] (2) An adaptive compensation mechanism based on the estimation of upper and lower bounds of disturbance was designed, which can offset the influence of uncertain factors such as new energy fluctuations and water head changes in real time without the need for prior information on disturbance, thereby enhancing the robustness and frequency stability of the system under strong disturbance scenarios.

[0062] (3) The parameter adaptive optimization model comprehensively considers multi-dimensional frequency modulation performance indicators to ensure that the controller achieves the optimal balance between frequency suppression, response speed and action smoothness. Attached Figure Description

[0063] Figure 1 This is a structural diagram of a 10-machine, 39-node test system;

[0064] Figure 2 This is a flowchart of the method of the present invention;

[0065] Figure 3 This is a comparison chart of the speed variable responses of each controller under operating condition 1;

[0066] Figure 4 This is a comparison chart of the speed variable response of each controller under operating condition 2;

[0067] Figure 5 This is a comparison chart of the speed variable response of each controller under operating condition 3. Detailed Implementation

[0068] The present invention will be further described below with reference to the embodiments. It should be noted that these are merely examples and descriptions of the inventive concept. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in the claims, they should all be considered to fall within the protection scope of the present invention.

[0069] like Figure 1-5As shown, the present invention proposes a design method for a primary frequency regulation controller of a pumped storage unit based on online parameter identification. First, during the grid-connected operation of the unit, input and output signals related to primary frequency regulation are collected in real time, and a linearized equivalent model reflecting the current operating conditions of the unit is established based on the online parameter identification method. Then, a primary frequency regulation controller for the pumped storage unit is designed based on the linearized equivalent model. Finally, according to the online update results of the model parameters during unit operation, the parameters of the primary frequency regulation controller are adaptively adjusted, thereby realizing dynamic updates of the controller parameters according to changes in the unit's operating state. This ensures that the pumped storage unit can maintain good primary frequency regulation performance under different operating conditions, improving the frequency stability of the power grid.

[0070] Example

[0071] In this embodiment, Figure 1 It is a standard 10-unit 39-bus test system, which includes 10 generators and 39 buses, with the units at 9 of the buses forming pumped storage units.

[0072] Below we combine Figure 1 This invention provides a detailed description of a design method for a primary frequency control controller of a pumped storage unit based on online parameter identification, as follows: Figure 2 As shown, the specific steps include:

[0073] S1. Historical data collection;

[0074] Historical data of the pumped storage unit is collected, including state variable vector X and non-state variable vector Y. The state variable vector X includes: generator rotor angle and speed, excitation voltage and current of the excitation system, prime mover guide vane opening, electro-hydraulic servo state, and other state variables; the non-state variable vector Y includes: input and output control variables of various regulators inside the prime mover.

[0075] S2. Establish a linearized open-loop model of the pumped storage unit based on the online parameter identification method;

[0076] S2.1. Combining the small-signal analysis theory of power systems, generate a system augmented matrix. Linearized power system model:

[0077] ;

[0078] in, Represents the augmented matrix of the system; State variable vector The derivative; , , , For matrix The four submatrices, It is a dynamic matrix that represents the dynamic coupling relationship between state variables. The coupling matrix represents the relationship between the input and output variables of each component of the generator and the generator's dynamics. , These are the coefficient matrix and the algebraic coupling matrix of the algebraic equation, respectively, representing the static relationship between the state variables and the input and output variables of each component of the generator;

[0079] S2.2. The linearized power system model described above is transformed using matrix linear transformation, and is expressed as follows:

[0080] ;

[0081] in, Represents the state matrix of the closed-loop system;

[0082] S2.3. Reorder the state variables, placing the state variables related to the pumped storage unit governor at the end. Then, the formula in step S2.2 can be re-expressed as follows:

[0083] ;

[0084] ;

[0085] in, This represents a vector of state variables that are independent of the pumped storage unit's governor, such as the generator rotor angle and speed, and the excitation system voltage. This represents a vector of state variables related to the governor of a pumped storage unit, such as guide vane opening and electro-hydraulic servo state. This is the non-governor state submatrix, representing the mutual influence between non-governor states. This is the governor-non-governor coupling submatrix, representing the effect of the governor state on the non-governor state. This is the non-governor-governor coupling submatrix, representing the feedback of the non-governor state to the governor state. This is the governor state submatrix, representing the dynamic relationship of the internal states of the governor;

[0086] S2.4 Establish a linearized open-loop model of the pumped storage unit:

[0087] ;

[0088] in, This represents the input variable vector between the open-loop system and the governor. , The state matrix and input matrix of the open-loop system are represented as follows:

[0089] ;

[0090] In this embodiment, , As shown in the following formula:

[0091]

[0092]

[0093]

[0094] ;

[0095]

[0096] Wherein, standard basis vectors Indicates the first A unit column vector.

[0097] S3. Design of primary frequency control controller for pumped storage unit based on linearized equivalent model;

[0098] S3.1 Based on the power system model linearization method in step 1, and taking into account the identification error, the linearized open-loop model of the pumped storage unit is further expressed as:

[0099] ;

[0100] in, To linearize the identification error;

[0101] S3.2 To adaptively handle uncertain fluctuations in the system (fluctuations in new energy sources and load output), a model reference adaptive control framework is adopted. An adaptive control reference model is designed, and the control objective is to design a controller that makes the system so that... Reference model for tracking expectations The trajectory of the reference model allows the desired frequency modulation performance to be achieved by making the actual system track the reference model; the adaptive control reference model can be expressed as:

[0102] ;

[0103] in, Indicates the expected reference model, The state matrix of the reference model, The state matrix of the reference model, This represents the reference input variable vector between the open-loop system and the governor;

[0104] In this embodiment, , As shown in the following formula:

[0105]

[0106]

[0107] The desired frequency modulation performance can be achieved by having the actual system track this reference model.

[0108] S3.3 In this embodiment, the control objective is to design a control law that makes the actual system state... Able to asymptotically track Therefore, we set the tracking error. :

[0109]

[0110] Satisfy when The convergence time is zero. In this embodiment, As shown in the following formula:

[0111]

[0112] S3.4 To cope with unknown external disturbances, this invention designs a primary frequency control controller with adaptive disturbance compensation based on a model reference adaptive control architecture. The controller consists of two parts: a nominal control input designed to achieve model matching under ideal conditions; and an adaptive compensation input designed to dynamically estimate and counteract the effects of unknown disturbances. This is the primary frequency control controller. It can be represented as:

[0113] ;

[0114] in, For nominal control input, For adaptive disturbance compensation input, , , where are the coordination gain coefficients for the nominal control component and the adaptive disturbance compensation input, respectively, used to adjust their weights in the synthesized control quantity to achieve a balance between control performance and robustness, and are the parameters to be optimized;

[0115] Among them, nominal control input satisfy:

[0116]

[0117] Wherein, constant matrix and The following matching conditions must be met for a match to be valid:

[0118]

[0119] In this embodiment, , As shown in the following formula:

[0120]

[0121]

[0122] Adaptive disturbance compensation input satisfy:

[0123]

[0124] Where I is the identity matrix, for The real-time estimate of the upper bound, for The real-time estimate of the lower bound, , The first derivative of satisfies the following condition:

[0125]

[0126] Among them, superscript Indicates transpose. To satisfy the positive definite matrix of the Lyapunov equations:

[0127]

[0128] Auxiliary variables :

[0129]

[0130] in, The weight matrix is ​​a symmetric positive definite matrix, and its dimension is related to the tracking error. Consistency is used to ensure that the Lyapunov equation has a unique positive definite solution and to adjust the convergence characteristics of the system error. In this embodiment, we take... ; Let X be the dimension of the state variable vector.

[0131] In this embodiment, adaptive disturbance compensation input Designed to suppress the effects of unknown external disturbances. To handle the unknown magnitude and direction of the disturbance, an adaptive compensation mechanism based on estimated upper and lower bounds is employed, which can self-adjust in real time without any prior knowledge; matrix As shown in the following formula:

[0132]

[0133]

[0134]

[0135]

[0136] S4. Construct an adaptive optimization model for the primary frequency controller parameters:

[0137] In this embodiment, aiming at optimizing the overall performance of the pumped storage unit participating in primary frequency regulation, a parameter adaptive optimization model is established, incorporating key indicators, using a weighted comprehensive performance index based on frequency deviation, regulation time, and control action amount. The expression for this model is:

[0138] ;

[0139] in, These are the controller parameters to be optimized. This represents the maximum frequency deviation. This refers to the time it takes for the frequency to recover to the allowable range. To evaluate the length of the time window, it is necessary to cover the main adjustment process. In this embodiment, we take... ; The output of the controller, which varies with time. As the steady-state value, we take 1. The total change reflects the smoothness of the movement; , , The weighting coefficients are, in this embodiment, determined through simulation tuning. , , .

[0140] S5. Real-time acquisition of the real-time state variable vector ΔX and non-state variable vector ΔY of the power system;

[0141] S6. Substitute the real-time acquired state variable vector ΔX and non-state variable vector ΔY into the linearized open-loop model of the pumped storage unit. Correct the parameters of the linearized open-loop model online, and then update the adaptive optimization model of the primary frequency controller parameters. Finally, combine the recursive least squares method to optimize the control of the primary frequency controller, so that... The parameters are gradually reduced until the algorithm converges, thus obtaining the optimal controller parameters. In this embodiment, the optimized parameters are obtained. Nominal control input Adaptive disturbance compensation input The following formulas are shown respectively:

[0142]

[0143] Figure 3 , Figure 4 and Figure 5The dynamic response of the system frequency under different control strategies is compared under three typical operating conditions. Specifically, the comparisons are made for three scenarios: no controller (open loop), a traditional linear feedback controller, and the proposed primary frequency control controller based on online parameter identification.

[0144] from Figure 3 As can be seen, the system frequency curve exhibits significant fluctuations after the disturbance in operating condition 1. Under open-loop conditions, the overshoot of the frequency deviation curve is large, and the oscillation amplitude decays slowly, resulting in a long recovery time. After configuring a traditional linear feedback controller, the overshoot of the frequency deviation curve is reduced, but a relatively obvious oscillation process still exists. In contrast, the proposed control strategy can effectively reduce the overshoot of the frequency deviation curve and effectively suppress the frequency oscillation amplitude, enabling the system frequency to recover to steady state in a shorter time.

[0145] Figure 4 The frequency response comparison results of various control strategies under operating condition 2 are presented. It can be seen that under this operating condition, although the traditional linear feedback control can improve the system's frequency dynamic performance to a certain extent, there are still large transient fluctuations in the early stage of disturbance; while the proposed control strategy shows a faster response speed and stronger damped frequency oscillation characteristics after the disturbance occurs, with more rapid decay and effectively shortened system recovery time.

[0146] Figure 5 The dynamic frequency response of the system under operating condition 3 is presented. The results show that under complex disturbance conditions, the system frequency oscillation duration is long and the stability is poor without a controller. Although traditional control strategies can improve the system frequency response performance, they still have shortcomings in frequency suppression and steady-state recovery. In contrast, the proposed control strategy maintains a small frequency overshoot and a fast recovery speed under different operating conditions, demonstrating good robustness and adaptability.

[0147] comprehensive Figures 3 to 5 The comparison results show that the proposed primary frequency controller based on online parameter identification can effectively reduce the overshoot of the system frequency change curve, accelerate the frequency oscillation decay speed, and shorten the time for the frequency to recover to steady state under three typical operating conditions, demonstrating good dynamic performance.

[0148] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A design method for a primary frequency control controller of a pumped storage unit, characterized in that, Includes the following steps; Step 1: Collect historical data of the pumped storage unit, including state variable vector X and non-state variable vector Y; Step 2: Establish a linearized open-loop model of the pumped storage unit based on the online parameter identification method; Step 3: Design the primary frequency control controller for the pumped storage unit based on the linearized equivalent model; Step 4: Construct an adaptive optimization model for the primary frequency control controller parameters. ; Step 5: Real-time acquisition of the real-time state variable vector ΔX and non-state variable vector ΔY of the power system; Step 6: Substitute the real-time acquired state variable vector ΔX and non-state variable vector ΔY into the linearized open-loop model of the pumped storage unit. Correct the parameters of the linearized open-loop model online, update the adaptive optimization model of the primary frequency controller parameters, and optimize the control of the primary frequency controller using the recursive least squares method. Gradually decrease the parameters until the algorithm converges, obtaining the optimal controller parameters.

2. The design method for the primary frequency control controller of a pumped storage unit according to claim 1, characterized in that, The process of establishing the linearized open-loop model of the pumped storage unit includes the following steps: Step 2.1: Using the small-signal analysis theory of power systems, generate a system augmented matrix. Linearized power system model: ； in, Represents the augmented matrix of the system; State variable vector The derivative; , , , For matrix The four submatrices, It is a dynamic matrix that represents the dynamic coupling relationship between state variables. The coupling matrix represents the relationship between the input and output variables of each component of the generator and the generator's dynamics. , These are the coefficient matrix and the algebraic coupling matrix of the algebraic equation, respectively, representing the static relationship between the state variables and the input and output variables of each component of the generator; Step 2.2: Transform the above linearized power system model using matrix linear transformation, as follows: ； in, Represents the state matrix of the closed-loop system; Step 2.3: By postposing the state variables related to the pumped storage unit governor, the formula in Step 2.2 can be rewritten as follows: ； ； in, This represents a vector of state variables independent of the pumped storage unit's governor. This represents the vector of state variables related to the governor of a pumped storage unit. This is the non-governor state submatrix, representing the mutual influence between non-governor states. This is the governor-non-governor coupling submatrix, representing the effect of the governor state on the non-governor state. This is the non-governor-governor coupling submatrix, representing the feedback of the non-governor state to the governor state. This is the governor state submatrix, representing the dynamic relationship of the internal states of the governor; Step 2.4: Establish a linearized open-loop model of the pumped storage unit: ； in, This represents the input variable vector between the open-loop system and the governor. , The state matrix and input matrix of the open-loop system are represented as follows: 。 3. The design method for the primary frequency control controller of a pumped storage unit according to claim 1, characterized in that, The design method for the primary frequency control controller of the pumped storage unit is as follows: Step 3.1: Taking into account the identification error, the linearized open-loop model of the pumped storage unit is further expressed as: ； in, To linearize the identification error; Step 3.2: Design the adaptive control reference model: ； in, Indicates the expected reference model, The state matrix of the reference model, The state matrix of the reference model, This represents the reference input variable vector between the open-loop system and the governor; Step 3.3: Set tracking error : ； Step 3.4: Design a primary frequency modulation controller based on the MRAC architecture. : ； in, For nominal control input, For adaptive disturbance compensation input, , These are the coordination gain coefficients for the nominal control component and the adaptive disturbance compensation input, respectively. Among them, nominal control input satisfy: Wherein, constant matrix and The following matching conditions must be met for a match to be valid: Adaptive disturbance compensation input satisfy: Where I is the identity matrix, for The real-time estimate of the upper bound, for The real-time estimate of the lower bound, , The first derivative of satisfies the following condition: Among them, superscript Indicates transpose. To satisfy the positive definite matrix of the Lyapunov equation: Auxiliary variables : in, It is a symmetric positive definite weight matrix. Let X be the dimension of the state variable vector.

4. The design method for the primary frequency control controller of a pumped storage unit according to claim 1, characterized in that, The established adaptive optimization model for the primary frequency control controller parameters is as follows: ； in, These are the controller parameters to be optimized. This represents the maximum frequency deviation. This refers to the time it takes for the frequency to recover to the allowable range. To assess the length of the time window; The output of the controller. Given a steady-state value; , , These are the weighting coefficients.

5. The design method for the primary frequency control controller of a pumped storage unit according to claim 4, characterized in that, The length of the evaluation time window The value is 10s, and the steady-state value is... The weighting coefficient is 1. , , .

6. The design method for the primary frequency control controller of a pumped storage unit according to claim 1, characterized in that, The state variable vector X includes: generator rotor angle and speed, excitation voltage and current of the excitation system, prime mover guide vane opening and electro-hydraulic follow-up state variables; the non-state variable vector Y includes: input and output control variables of various regulators inside the prime mover.

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