Automatic optimization method for AES multi-stage flexible self-adaptive control parameters of variable-speed pumped storage unit

Abstract

The invention discloses a multi-stage flexible self-adaptive control parameter optimization method for a variable-speed pumped storage AES (advanced encryption standard), which at least solves the problem of parameter optimization of an AES controller. The method comprises the following steps: reading a working stage signal of the variable-speed pumped storage unit, and judging the current working stage of an AES-MSC controller; an AES-MSC controller parameter optimization algorithm is called to carry out iterative optimization on the optimization objective function of the current working stage, so that the AES-MSC controller parameters are optimal; according to the current working stage of the AES-MSC controller, an offline simulation model of the current working stage of the AES-MSC controller is called based on the finite-state machine principle; when the maximum number of iterations is reached, outputting a parameter optimization result of the AES-MSC controller; substituting a parameter optimization result into the offline simulation model for verification; if the verification result does not meet the expectation, iterative optimization is carried out again; and if the verification result reaches the expectation, storing the parameter optimization result of the AES-MSC controller.

Description

Technical Field

[0001] The present invention relates to the technical field of variable speed pumped storage AC excitation system control, and more specifically to a multi-stage parameter automatic optimization method of an AC excitation system controller under a variable speed pumped storage unit power generation condition. Background Art

[0002] With the increasing penetration rate of distributed power sources, fixed-speed pumped storage units can no longer meet the needs of flexible regulation of new power systems. Flexible control of variable-speed pumped storage (VSPS) units has become an urgent need. AC excitation system (AES) is the key equipment for flexible control of variable-speed units, but there is currently no reasonable and accurate controller parameter optimization method.

[0003] AES controller parameter optimization is an important part of the design of VSPS unit excitation control system. Its purpose is to find a set of optimal or approximately optimal controller parameters so that the performance of the excitation closed-loop control system can reach the expected goals. These goals include system stability, rapidity, accuracy, robustness, etc.

[0004] In the multi-stage flexible adaptive control process of VSPS units, the physical characteristics and control objectives of VSPS units at different stages are different, which makes the control model and controller parameters of AES change with the change of unit stage. Therefore, the AES controller has the physical characteristics of adaptive control parameters. The optimization of its adaptive control parameters is an important part of designing the AES multi-stage flexible adaptive control strategy. Its rationality and accuracy are the premise of studying the transient process of VSPS units.

[0005] On the other hand, AES is a multivariable, strongly coupled, high-order nonlinear system, and its control performance is closely related to the operating parameters of the generator motor. When the generator motor runs for a long time, its own electrical parameters will change. If the AES controller parameters remain unchanged, its control performance will decrease or it will not work properly.

[0006] AES controller parameter optimization is an iterative process that requires multiple adjustments and verifications. In practical applications, the optimization process may become complicated due to factors such as nonlinearity, uncertainty, and external interference of the VSPS unit. Therefore, choosing the right optimization strategy and algorithm is crucial to achieving satisfactory control results. In addition, with the improvement of computing power and the development of optimization algorithms, the application of control parameter optimization in engineering is becoming more and more extensive and efficient.

[0007] In order to solve the above problems, the present invention proposes a method for automatically optimizing multi-stage flexible adaptive control parameters of a variable speed pumped storage unit AES. Summary of the invention

[0008] In a first aspect, the present invention proposes a method for dividing different stages of the power generation process and the pumping process of a VSPS unit.

[0009] On the second aspect, the present invention proposes a multi-stage objective optimization function of an AES controller applicable to the power generation process of a VSPS unit, which truly reflects the control objectives of the AES controller at different stages of the power generation process.

[0010] On the third aspect, the present invention proposes a mathematical model and a control model applicable to the multi-stage AES controller of the VSPS unit power generation process, which finely restores the physical characteristics of the AES controller at different stages of the power generation process.

[0011] In a fourth aspect, the AES-MSC controller parameter optimization method proposed in the present invention realizes the intelligent optimization function of the AES-MSC controller parameters.

[0012] In a fifth aspect, the present invention proposes a multi-stage flexible adaptive control parameter optimization method of an AES-MSC controller based on a finite state machine to realize the automatic optimization function of the AES-MSC controller parameters.

[0013] A variable speed pumped storage AES multi-stage flexible adaptive control parameter optimization method comprises: 1) reading a variable speed pumped storage unit working stage signal to determine the current working stage of an AES-MSC controller; 2) calling an AES-MSC controller parameter optimization algorithm to iteratively optimize the optimization objective function of the current working stage, so that the AES-MSC controller parameters reach the optimum; 3) calling an offline simulation model of the current working stage of the AES-MSC controller based on the finite state machine principle according to the current working stage of the AES-MSC controller; 4) when the maximum number of iterations is reached, outputting the parameter optimization result of the AES-MSC controller; 5) substituting the parameter optimization result into the offline simulation model for verification; 6) if the verification result is not as expected, jumping to step 2) to perform secondary optimization; 7) if the verification result is as expected, saving the AES-MSC controller parameter optimization result; 8) if the working stage of the variable speed pumped storage unit changes and is not the shutdown stage, jumping to step 1) to start parameter optimization of the next working stage; 9) if the current working stage of the variable speed pumped storage unit is the shutdown stage, then exiting the AES-MSC controller parameter automatic optimization program. The AES-MSC controller selects the PI structure. The AES-MSC controller parameters include the outer and inner loop control coefficients of the d-axis and q-axis at different stages of power generation conditions.

[0014] The present invention has at least the following advantages or beneficial effects: automatic optimization of the multi-stage flexible adaptive control parameters of the AES controller in the power generation process of the VSPS unit based on a finite state machine can realize automatic optimization of the multi-stage parameters of the controller according to the VSPS unit status signal; controller parameter optimization based on an intelligent algorithm improves the speed and accuracy of AES controller parameter optimization; the dynamic and static performance of the AES multi-stage flexible control is used as the target optimization function of the intelligent algorithm to effectively ensure the dynamic and static performance of the AES controller at different stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the VSPS unit structure.

[0016] Figure 2 This is a schematic diagram of the multi-stage conversion process of the VSPS unit power generation process.

[0017] Figure 3 It is a schematic diagram of the multi-stage conversion of the pumping process of the VSPS unit.

[0018] Figure 4 It is a control model block diagram of an AES-MSC controller in a no-load pressure building stage provided by an embodiment of the present invention.

[0019] Figure 5 The control model block diagram of the AES-MSC controller in the flexible grid-connected stage provided by one embodiment of the present invention.

[0020] Figure 6 It is a control model block diagram of an AES-MSC controller provided by an embodiment of the present invention in a steady-state power generation stage.

[0021] Figure 7 It is a control model block diagram of an AES-MSC controller in the electrical braking stage provided by an embodiment of the present invention.

[0022] Figure 8 The present invention is a flowchart of a method for automatically optimizing multi-stage parameters of an AES-MSC controller based on a finite state machine according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] Figure 1 The VSPS unit structure is shown, which is extremely complex and includes a monitoring system, a coordination controller, a generator motor, an AC excitation system (AES), a speed governor and its control system, a reversible pump turbine, an upper reservoir, a lower reservoir, a water diversion system, and a surge tank, etc. The VSPS unit is a hydraulic-mechanical-electrical-control coupled system with multi-time scale, multi-stage, and strongly coupled physical characteristics.

[0024] The AC excitation system (AES) includes the machine-side converter (MSC) and its controller and the grid-side converter (GSC) and its controller. The control target of the AES-GSC controller is the DC voltage of the capacitor. The parameters of the AES-GSC controller remain unchanged in different stages; the parameters of the AES-MSC controller are also different in different stages, and have multi-stage adaptive physical characteristics. The AES-GSC controller is also called the GSC controller. The AES-MSC controller is also called the MSC controller. The AES controller includes the GSC controller and the MSC controller.

[0025] Since the parameter optimization methods of the MSC controller and the GSC controller are similar, this embodiment only introduces the automatic optimization method of the MSC controller parameters, and the optimization method of the GSC controller parameters is not described in detail.

[0026] like Figure 2 As shown in Figure 1, the power generation process of the VSPS unit includes multiple stages: shutdown, power generation startup, no-load voltage build-up, flexible grid connection, load increase, steady-state power generation, load shedding and electrical braking. The MSC does not work during the power generation startup and shutdown stages.

[0027] like Figure 3 As shown in the figure, the pumping process of the VSPS unit includes multiple stages: shutdown, pumping start, electric synchronization, steady-state pumping, speed reduction, and electrical braking. When the pumping start adopts SFC, the GSC starts to charge the DC bus, but the MSC does not work; when the pumping start adopts self-start, both the GSC and MSC are in working state; except for the shutdown and SFC start modes, the MSC is in working state in other stages.

[0028] Based on the working principle of multi-stage flexible adaptive control of VSPS units, the present invention proposes a method for automatically optimizing the multi-stage flexible adaptive control parameters of MSC controller in the power generation process of VSPS units. The following is a detailed introduction to the MSC controller parameter optimization process.

[0029] (1) Optimizing the objective function

[0030] The performance index ITAE (integral time multiplied by absolute error) of the time of the selected error sum and the voltage harmonic index THD are used as indicators to measure the performance of the MSC controller. The present invention adopts the performance index ITAE (integral time multiplied by absolute error) of the time based on the error sum and the voltage harmonic index THD, and adopts the weighted method to achieve the optimization of multiple performance targets. Since the control targets of the MSC controller at different stages are different, the target optimization functions are also different. The target optimization functions of each stage of the MSC controller are introduced below.

[0031] The control goal of the MSC controller in the no-load voltage building stage is to quickly build up the stator voltage, and its optimization objective function is:

[0032]

[0033] Where e0(t) is the error between the stator no-load voltage and the stator rated voltage.

[0034] The goal of the MSC controller in the flexible grid-connected stage is to make the impact current of the generator motor close to zero at the moment of grid connection, and to minimize the impact of grid connection on the generator motor and the power grid. Therefore, the optimization objective function of the flexible grid-connected stage is:

[0035]

[0036] Wherein, e1(t) is the error value between the stator voltage and the grid voltage amplitude, e2(t) is the error value between the stator voltage and the grid voltage phase, and a and b are weight coefficients. The weight coefficients are set according to actual needs. In one implementation, a=0.5, b=0.5.

[0037] The goal of the MSC controller in the steady-state power generation (load increase, load rejection) stage is to ensure that the amplitude and waveform of the stator output current of the generator motor meet the grid requirements and can quickly track the changes in the power command to achieve the purpose of peak load regulation and frequency regulation. Therefore, the optimization objective function of the steady-state power generation stage is:

[0038]

[0039] In the formula, i sn is the current harmonic, n is the harmonic component, e is p (t) is the error value between the active power and the active power command, and a and b are weight coefficients. The weight coefficients are adjusted according to actual needs. In one implementation, a=0.5, b=0.5.

[0040] The goal of the MSC controller in the electrical braking stage is to generate enough braking torque to brake the rotor of the generator motor, so the objective optimization function of the electrical braking stage is:

[0041]

[0042] In the formula, e n (t) is the error between the rotation speed and zero.

[0043] (2) Mathematical model and control model of MSC controller:

[0044] Using the stator flux oriented vector control strategy, the rotor voltage of the generator motor can be expressed as:

[0045]

[0046] u rd is the rotor d-axis voltage component, u rq is the rotor q-axis voltage component, u′rd is the rotor d-axis voltage feedforward compensation term, Δu rd is the rotor d-axis voltage decoupling compensation term, u′ rq is the rotor q-axis voltage feedforward compensation term, Δu rq is the rotor q-axis voltage decoupling compensation term, R r Rotor resistance, i rd is the rotor d-axis current component, α is the motor leakage coefficient, l r Rotor inductance, p is the differential operator, ω s Slip angular frequency, i rq is the q-axis current component of the rotor, l m Stator-rotor mutual inductance, L s is the stator inductance, ψ sd Stator d-axis flux.

[0047] According to the generator motor rotor voltage formula, the AES-MSC control system can be designed as a double closed-loop architecture with feedforward compensation. The controller is a PI structure, and the voltage used for decoupling compensation is Δu r .

[0048] During the no-load voltage building phase, the stator d-axis and q-axis current components i sd =i sq =0, the rotor d-axis and q-axis currents can be expressed as:

[0049]

[0050] L m is the stator-rotor mutual inductance, ψ1 is the stator flux. According to the rotor current formula, the control model of the AES-MSC controller in the no-load voltage building stage is as follows: Figure 4 shown. Figure 4 middle, is the three-phase voltage of the power grid; is the rotor three-phase control voltage.

[0051] In the flexible grid-connected stage, if the rotor d-axis current is controlled so that In theory, the amplitude of the stator open-circuit voltage can be guaranteed to be consistent with the grid voltage. In the actual grid-connected process, due to the influence of voltage and current sampling errors, motor parameter changes and converter response characteristics, the machine-end voltage amplitude cannot accurately track the grid voltage, and there is still a certain amplitude difference between the two. In order to solve the impact of parameter changes and enhance the robustness of the system, the rotor d-axis current i rd Add a compensation term Δi rd :

[0052]

[0053] Rotor d-axis current i in the flexible grid-connected stage rdIt consists of two parts: generalized excitation component and compensation amount:

[0054]

[0055] U g is the grid voltage, U s is the stator voltage, ω g is the grid voltage angular frequency, k p1 is the proportionality coefficient, s is the differential operator, k i1 is the integral coefficient. According to the above rotor current formula, the control model of the AES-MSC controller in the flexible grid-connected stage is as follows: Figure 5 shown. Figure 5 middle, is the three-phase voltage of the power grid, is the stator three-phase voltage, is the rotor three-phase control voltage, the rotor d-axis current i rd The compensation term Δi rd Also used as a reference value for the rotor d-axis current is the rotor q-axis reference current.

[0056] In the steady-state power generation stage, the VSPS unit can automatically participate in the system frequency regulation, be controlled by the AGC or the upper dispatch system, and directly and decouple the unit's active and reactive power. The unit operates in this stage most of the time, and the output power of the generator motor is:

[0057]

[0058] P and Q are active power and reactive power respectively, u sq is the q-axis voltage component of the stator voltage, i sq is the q-axis current component of the stator current, i rq is the q-axis current component of the rotor current, i sd is the d-axis current component of the stator current, U g is the grid voltage, L m is the stator-rotor mutual inductance, L s is the stator inductance, ω g is the grid voltage angular frequency. According to the generator motor output power formula, the control model of the AES-MSC controller in the steady-state power generation stage is as follows: Figure 6 shown. Figure 6 In, P * , Q * They are the set values / command values ​​for active power and reactive power respectively.

[0059] During the electrical braking stage, the stator winding of the generator motor is short-circuited. Under the action of the excitation voltage (current), the generator motor will generate a braking torque. Due to the short circuit of the stator winding during electrical braking, the stator d-axis and q-axis voltage components u sd=u sq =0, according to the stator flux equation, the stator current is expressed by the rotor current as:

[0060]

[0061] ψ sd Stator d-axis flux, L m is the stator-rotor mutual inductance, L s is the stator inductance, i rq is the q-axis current component of the rotor current, i rd is the d-axis current component of the rotor current.

[0062] Substituting the stator current equation into the stator voltage equation and torque equation, we can obtain:

[0063]

[0064] T s is the stator winding time constant, T s =L s / R s , r S is the stator resistance, T e Electromagnetic torque, p is the differential operator, n p is the number of motor pole pairs, ψ sd Stator d-axis flux, L m is the stator-rotor mutual inductance, L s is the stator inductance, i rd is the d-axis current component of the rotor current, i rq is the q-axis current component of the rotor current.

[0065] Control the rotor d-axis excitation current i rd The stator flux can be controlled to set the d-axis excitation current reference value Setting it to a constant of zero can keep the stator flux constant. At this time, controlling the q-axis excitation current can control the electromagnetic torque and achieve rapid electrical braking of the unit. The control model of the AES-MSC controller in the electrical braking stage is as follows: Figure 7 shown.

[0066] (3) Selection of controller structure: According to the control model, system characteristics and design requirements of the AES-MSC controller, the control system is designed as a double closed-loop control architecture with feedforward, and the controller selects a PI structure.

[0067] (4) Parameterize the AES-MSC controller to prepare for the subsequent parameter optimization process. Taking the power generation process as an example, Figures 4 to 7 As shown in the table below, the control parameter optimization objects of the AES-MSC controller at different stages under power generation conditions are as follows:

[0068]

[0069] (5) Selection of AES-MSC controller parameter optimization algorithm: The control parameters can be optimized and adjusted using the Grey Wolf Algorithm (GWO), Particle Swarm Optimization Algorithm, Fruit Fly Algorithm, etc.

[0070] The steps of the multi-stage flexible adaptive parameter automatic optimization process of the AES-MSC controller based on the finite state machine are as follows:

[0071] 1) Initialize the system.

[0072] 2) Read the generator motor parameters, including the resistance, inductance, mutual inductance, etc. of the motor stator and rotor.

[0073] 3) Read the working stage signal of the VSPS unit to determine the current working stage of the AES-MSC.

[0074] 4) Call the AES-MSC controller parameter optimization algorithm.

[0075] 5) According to the current working stage of AES-MSC, the offline simulation model of the current working stage of AES-MSC controller is called based on the finite state machine principle. The offline simulation model is a simulation model of the VSPS unit. The method of the present invention does not need to be verified on the real object, but can be verified on the simulation model. The simulation model adopts the existing technology, and the present invention will not be introduced in detail.

[0076] 6) When the maximum number of iterations is reached, the AES-MSC controller parameter optimization results are output.

[0077] 7) Substitute the parameter optimization results into the offline simulation model for verification.

[0078] 8) If the simulation result is not as expected, jump to step 4) for secondary optimization.

[0079] 9) The simulation results meet expectations, and the AES-MSC physical controller parameter optimization results are saved.

[0080] 10) If the unit's operating phase changes and it is not a shutdown phase, jump to step 3) to start parameter optimization for the next operating phase.

[0081] 11) If the unit is currently operating in the shutdown phase, the AES-MSC controller parameter automatic optimization program will be exited.

Claims

1. A variable speed pumped storage AES multi-stage flexible adaptive control parameter optimization method, characterized in that: include: 1) Read the working stage signal of the variable speed pumped storage unit to determine the current working stage of the AES-MSC controller; 2) Calling the AES-MSC controller parameter optimization algorithm to iteratively optimize the optimization objective function of the current working stage, so that the AES-MSC controller parameters reach the optimal value; 3) According to the current working stage of the AES-MSC controller, the offline simulation model of the current working stage of the AES-MSC controller is called based on the finite state machine principle; 4) When the maximum number of iterations is reached, the parameter optimization results of the AES-MSC controller are output; 5) Substitute the parameter optimization results into the offline simulation model for verification; 6) If the verification result is not as expected, jump to step 2) for secondary optimization; 7) Verify that the results meet expectations and save the AES-MSC controller parameter optimization results; 8) When the working phase of the variable speed pumped storage unit changes and it is not the shutdown phase, jump to step 1) and start parameter optimization for the next working phase; 9) If the current working stage of the variable speed pumped storage unit is the shutdown stage, the AES-MSC controller parameter automatic optimization program will be exited.

2. The method according to claim 1, characterized in that The power generation process of the variable-speed pumped storage unit includes: shutdown, power generation start-up, no-load pressure building, flexible grid connection, load increase, steady-state power generation, load shedding and electrical braking. The MSC does not work during the power generation start-up and shutdown stages.

3. The method according to claim 1, characterized in that: The pumping process of the variable-speed pumped storage unit includes: shutdown, pumping start, electric synchronization, steady-state pumping, speed reduction and electrical braking. When SFC is used for pumping start, GSC starts to charge the DC bus, but MSC does not work; when self-start is used for pumping start, both GSC and MSC are in working state; MSC is in working state in all stages except shutdown and SFC start mode.

4. The method according to claim 1, characterized in that: The error-and-time-based performance index ITAE and voltage harmonic index THD are adopted, and the weighted method is used to achieve the optimization of multiple performance objectives.

5. The method according to claim 4, characterized in that The control goal of the AES-MSC controller in the no-load voltage building stage is to quickly build up the stator voltage, and its optimization objective function is: Where e0(t) is the error between the stator no-load voltage and the stator rated voltage.

6. The method according to claim 4, characterized in that The goal of the AES-MSC controller in the flexible grid-connected stage is to make the impact current of the generator motor zero at the moment of grid connection, and reduce the impact of grid connection on the generator motor and the power grid. The optimization objective function in the flexible grid-connected stage is: Where e1(t) is the error between the stator voltage and the grid voltage amplitude, e2(t) is the error between the stator voltage and the grid voltage phase, and a and b are weight coefficients.

7. The method according to claim 4, characterized in that The goal of the AES-MSC controller in the steady-state power generation stage is to ensure that the amplitude and waveform of the stator output current of the generator motor meet the grid requirements and can quickly track the changes in the power command to achieve the purpose of peak load regulation and frequency regulation. The optimization objective function in the steady-state power generation stage is: In the formula, i sn is the current harmonic, n is the harmonic component, e is p (t) is the error value between active power and active power command, and a and b are weight coefficients.

8. The method according to claim 4, characterized in that The goal of the AES-MSC controller in the electrical braking stage is to generate braking torque to brake the generator motor rotor. The objective optimization function of the electrical braking stage is: In the formula, e n (t) is the error between the rotation speed and zero.

9. The method according to claim 1, characterized in that: The AES-MSC controller selects the PI structure.

10. The method according to claim 9, characterized in that The AES-MSC controller parameters include the outer and inner loop control coefficients of the d-axis and q-axis at different stages under power generation conditions.

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