Hydro-generator governor proportional integral parameter optimization method and system

Abstract

The invention belongs to the field of water-turbine generator set control, and particularly relates to a water-turbine generator speed regulator proportional integral parameter optimization method and system. Control signals are obtained according to water turbine expected generation power, water turbine initial generation power, control parameters and a controller transfer function, and the control signals are obtained according to a servomotor transfer function; the method comprises the steps of obtaining a water turbine guide vane opening degree deviation relative value, then calculating to obtain adjusted water turbine generating power, comparing the adjusted water turbine generating power with expected water turbine generating power, obtaining a control error, calculating an objective function value, and within an adjusting range, respectively iterating a P control parameter and an I control parameter to obtain a target P control parameter and a target I control parameter when the objective function value is minimum; and taking the parameters as optimization parameters. Compared with a traditional engineering setting method, the method has the advantages that optimal parameters are selected through multiple iterations, so that parameter optimization is more accurate, and compared with a heuristic algorithm, additional parameters do not need to be introduced for optimization, computing resources are saved, and the power tracking effect is improved.

Description

Technical Field

[0001] The present invention belongs to the field of control of hydro-generator sets, and particularly relates to a method and system for optimizing proportional-integral parameters of a hydro-generator governor. Background Art

[0002] With the increasing proportion of new energy installed capacity in the power system year by year, in order to ensure the stability and safety of the power grid, higher requirements are put forward for the regulation quality of hydro-generators in the power grid. At present, the PID control law is generally adopted in the speed regulation system of hydro-generators. After the unit is connected to the large power grid, a set of fixed PI control parameters are used to adjust the opening and power of the unit. However, the hydro-generator speed regulation system has characteristics such as nonlinearity, variable structure, and variable parameters, making it difficult for the traditional classical control theory to meet the higher regulation quality requirements of the speed regulation system.

[0003] In related technologies, in the setting and optimization of governor control parameters, the engineering setting method and heuristic algorithms are mainly used. The engineering setting method is based on on-site tests and empirical formulas, and the governor control parameters are optimized according to the calculated results. The heuristic algorithm is a class of computational methods used to solve complex problems, usually used when an exact algorithm cannot effectively find a solution. They rely on specific heuristic rules or empirical rules.

[0004] For the above-mentioned related technologies, although the engineering setting method is simple, the obtained optimal parameters are often not accurate enough, and the adjustment of parameters requires rich expert experience, which undoubtedly increases the burden on engineering and technical personnel. In the calculation process of the heuristic algorithm, in order to improve the calculation accuracy, new parameters are inevitably introduced, increasing the complexity of the algorithm and having high requirements for computing resources, which greatly limits the engineering application. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for optimizing proportional-integral parameters of a hydro-generator governor, which can improve the accuracy of parameter optimization, consume less computing resources, and improve the power tracking effect.

[0006] A method for optimizing proportional-integral parameters of a hydro-generator governor includes:

[0007] Obtain a hydro-turbine power generation model, a hydro-turbine constant coefficient algebraic model, and a water diversion system model;

[0008] Set the desired power generation of the hydro-turbine, the initial power generation of the hydro-turbine, and initial control parameters, where the initial control parameters include P control parameters and I control parameters;

[0009] Obtain a control signal according to the desired power generation of the hydro-turbine, the initial power generation of the hydro-turbine, the initial control parameters, and the controller transfer function;

[0010] Based on the control signal and the servomotor transfer function, obtain the relative value of the opening deviation of the turbine guide vane;

[0011] Based on the relative value of the opening deviation of the turbine guide vane, the constant coefficient algebraic model of the turbine, and the penstock system model, obtain the relative value of the turbine torque deviation;

[0012] Based on the relative value of the turbine torque deviation and the turbine power generation model, adjust the power generation power of the turbine;

[0013] Based on the adjusted power generation power of the turbine and the desired power generation power of the turbine, obtain the control error;

[0014] Set the adjustment ranges of the P control parameter and the I control parameter;

[0015] Based on the control error, construct an objective function. Within the adjustment range, set the I control parameter to 0, and with the initial control parameter as a reference, adjust the P control parameter to obtain the target P control parameter corresponding to the minimum value of the objective function. The objective function is expressed as:

[0016]

[0017] where t is the simulation time and e(t) is the control error;

[0018] Based on the control error and the preset objective function, within the adjustment range, set the P control parameter to the target P control parameter, and with the initial control parameter as a reference, adjust the I control parameter to obtain the target I control parameter corresponding to the minimum value of the objective function;

[0019] Take the target P control parameter and the target I control parameter as the optimization parameters.

[0020] Optionally, the turbine power generation model is expressed as:

[0021]

[0022] where P m is the mechanical power output of the turbine, P e is the electromagnetic power of the generator, P D is the damping power of the hydrogenerator, E f is the excitation electromotive force, E' q and E' q ' are the q-axis transient electromotive force and the subtransient electromotive force respectively, E' d ' is the d-axis subtransient electromotive force, T' d0 and T' d '0 are the d-axis open-circuit transient time constant and the subtransient time constant, T' q ' 0 is the q-axis open-circuit subtransient time constant, x d , x' d and x' d ' are the d-axis synchronous reactance, transient reactance and subtransient reactance, x q and x' q ' are the q-axis synchronous reactance and subtransient reactance, I d and I q are the stator current components on the d-axis and q-axis, ω 0 is the rated speed, ω is the rotor angular velocity, T a is the generator inertia time constant, is the derivative of δ, where δ is the difference between the electrical angle of the generator rotor and the phase angle of the grid voltage, is the reciprocal of ω, is the reciprocal of E' q is the reciprocal of E' q ' is the reciprocal of E' d '

[0023] Optionally, the constant coefficient algebraic model of the water turbine is expressed as:

[0024]

[0025] where, m t represents the relative value of the water turbine torque deviation, y t represents the relative value of the water turbine guide vane opening deviation, x t represents the relative value of the water turbine speed deviation, and are the derivatives of the relative torque and flow rate of the water turbine with respect to the relative speed when the guide vane opening and water head are stable; and are the relative torque and flow rate of the water turbine with respect to the relative guide vane servomotor stroke when the speed and water head are stable; and are the derivatives of the relative torque and flow rate of the water turbine with respect to the relative water head when the guide vane opening and speed are stable, q t is the relative value of the unit flow rate deviation, h t is the relative value of the unit water pressure deviation;

[0026] The water conveyance system model is expressed as:

[0027] ​​​

[0028] Among them, is the derivative of q t , h t is the relative value of the water pressure deviation of the unit, and T w represents the water flow inertia time constant of the pipeline.

[0029] Optionally, obtaining the control signal according to the desired power generation power of the water turbine, the initial power generation power of the water turbine, the initial control parameter, and the controller transfer function includes:

[0030] Obtaining a desired difference according to the desired power generation power of the water turbine and the initial power generation power of the water turbine;

[0031] Substituting the desired difference and the initial control parameter into the controller transfer function to obtain a control signal;

[0032] The controller transfer function is expressed as:

[0033]

[0034] Among them, u(s) is the control signal, e(s) is the desired difference, and K P is the P control parameter, K I is the I control parameter, and s is the Laplace operator.

[0035] Optionally, obtaining the relative value of the opening deviation of the water turbine guide vane according to the control signal and the servomotor transfer function includes:

[0036] Substituting the control signal into the servomotor transfer function to obtain the opening signal of the water turbine guide vane;

[0037] Obtaining the relative value of the opening deviation of the water turbine guide vane according to the opening signal of the water turbine guide vane;

[0038] The servomotor transfer function is expressed as:

[0039]

[0040] Among them, y(s) is the opening signal of the water turbine guide vane, u(s) is the control signal, and T y is the servomotor time constant, and S is the Laplace operator.

[0041] Optionally, constructing an objective function according to the control error, and setting the I control parameter to 0 and adjusting the P control parameter within the adjustment range according to the control error and the objective function, and adjusting the p control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum value of the objective function value includes:

[0042] Initialize the number of iterations and the population size;

[0043] According to the population size, obtain the number of values of the P control parameter;

[0044] Within the adjustment range, set the I control parameter to 0, change the value of the P control parameter, and obtain the control error;

[0045] According to the control error and the objective function, obtain the objective function value;

[0046] Obtain the objective function values corresponding to different values of the P control parameter, and the number of the objective function values is equal to the number of values;

[0047] Obtain the minimum objective function value from the objective function values corresponding to different values of the P control parameter, and use the minimum objective function value as the iteration result of one round of iteration;

[0048] According to the iteration result and the number of iterations, obtain the final iteration result, and use the final iteration result as the target P control parameter.

[0049] Optionally, the obtaining method of the target I control parameter is the same as that of the target P control parameter.

[0050] A proportional-integral parameter optimization system for a hydrogenerator governor, comprising:

[0051] An acquisition module, configured to acquire a hydroturbine power generation model, a hydroturbine constant coefficient algebraic model, and a water diversion system model;

[0052] A first setting module, configured to set a hydroturbine desired power generation, a hydroturbine initial power generation, and an initial control parameter, where the initial control parameter includes a P control parameter and an I control parameter;

[0053] A first calculation module, configured to obtain a control signal according to the hydroturbine desired power generation, the hydroturbine initial power generation, the initial control parameter, and a controller transfer function;

[0054] A second calculation module, configured to obtain a relative value of the hydroturbine guide vane opening deviation according to the control signal and a servomotor transfer function;

[0055] A third calculation module, configured to obtain a relative value of the hydroturbine torque deviation according to the relative value of the hydroturbine guide vane opening deviation, the hydroturbine constant coefficient algebraic model, and the water diversion system model;

[0056] A fourth calculation module, configured to adjust the hydroturbine power generation according to the relative value of the hydroturbine torque deviation and the hydroturbine power generation model;

[0057] A fifth calculation module, configured to obtain a control error according to the adjusted power generation power of the water turbine and the desired power generation power of the water turbine;

[0058] A second setting module, configured to set the adjustment ranges of the P control parameter and the I control parameter;

[0059] A first optimization module, configured to construct an objective function according to the control error, set the I control parameter to 0 within the adjustment range, and adjust the P control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum value of the objective function. The objective function is expressed as:

[0060]

[0061] where t is the simulation time and e(t) is the control error;

[0062] A second optimization module, configured to set the P control parameter to the target P control parameter within the adjustment range according to the control error and a preset objective function, and adjust the I control parameter based on the initial control parameter to obtain the target I control parameter corresponding to the minimum value of the objective function;

[0063] Use the target P control parameter and the target I control parameter as the optimization parameters.

[0064] A terminal device includes a memory and a processor. The memory stores a computer program capable of running on the processor. When the processor loads and executes the computer program, a proportional-integral parameter optimization method for a water turbine generator speed governor is adopted.

[0065] A computer-readable storage medium stores a computer program. When the computer program is loaded and executed by a processor, a proportional-integral parameter optimization method for a water turbine generator speed governor is adopted.

[0066] The beneficial effects of the present invention are:

[0067] According to the expected power generation of the water turbine, the initial power generation of the water turbine, the control parameters, and the transfer function of the controller, the control signal and the control error are obtained. According to the control signal and the transfer function of the servomotor, the relative value of the opening deviation of the water turbine guide vane is obtained. According to the relative value of the opening deviation of the water turbine guide vane, the algebraic model of the water turbine constant coefficient, and the diversion system model, the relative value of the torque deviation of the water turbine is obtained. According to the relative value of the torque deviation of the water turbine, the power generation of the water turbine is adjusted. According to the adjusted power generation of the water turbine and the expected power generation of the water turbine, the control error is obtained. The adjustment ranges of the P control parameter and the I control parameter are set, and then a single variable is maintained. That is, the P control parameter is first iteratively optimized with the aim of minimizing the objective function value to obtain the optimal target P control parameter. Then, the target P control parameter is kept unchanged, and the I control parameter is iteratively optimized to obtain the optimal target I control parameter. Finally, the target P control parameter and the target I control parameter are used as the optimization parameters. Compared with the traditional engineering tuning method, the optimal parameters selected through multiple iterations in this application are obtained. Compared with the heuristic algorithm, more accurate optimization parameters can be obtained without introducing additional parameters for optimization. After obtaining more accurate optimization parameters, with the optimization parameters obtained through this application, the water turbine power generation system is significantly improved in terms of the adjustment time, the rise time, and the overshoot. The optimized parameter speed control is more stable, the power adjustment speed is increased, and the power fluctuation range is reduced. Under non-linear and variable parameter working conditions, the response of the system is more stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a schematic structural diagram of the water turbine power generation system of the present invention;

[0069] Figure 2 is a schematic flow chart of a proportional-integral parameter optimization method for a water turbine governor of the present invention;

[0070] Figure 3 is a comparison diagram of the power response of the speed control system before and after optimization of the present invention;

[0071] Figure 4 is a comparison diagram of the speed response before and after optimization of the present invention;

[0072] Figure 5 is a schematic diagram of the control effect after optimization of the invention;

[0073] Figure 6 is a comparison diagram of the control effects before and after the optimization of the servomotor stroke of the present invention;

[0074] Figure 7 is a control effect diagram of the spiral case water pressure before and after optimization of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] As Figure 1As shown in the figure, the hydroelectric power generation system includes a governor, a water turbine, a water diversion system, a generator and a power system, and an automatic voltage regulator. The governor is connected to the water turbine, the water turbine is connected to the water diversion system and the generator and the power system, and the generator and the power system are connected to the automatic voltage regulator and the governor.

[0076] Specifically, the governor gives a control signal to the water turbine. Under the action of the water diversion system and the water turbine itself, the water turbine outputs the per-unit value of the relative deviation of the water turbine torque, and then obtains the mechanical power output of the water turbine according to the per-unit value of the relative deviation of the water turbine torque. The mechanical power output of the water turbine is equal to the per-unit value of the relative deviation of the water turbine torque. Then, the generator power of the water turbine is obtained according to the mechanical power output of the water turbine, and then the generator power is returned to the governor for negative feedback control, so as to change the value of the control parameter of the governor, so that the generator power of the water turbine is closer to the desired power.

[0077] A method for optimizing the proportional-integral parameters of a hydrogenerator governor, as Figure 2 shown, includes:

[0078] S1. Obtain the hydroelectric power generation model, the constant coefficient algebraic model of the water turbine, and the water diversion system model.

[0079] The hydroelectric power generation model is expressed as:

[0080]

[0081] Where, P m is the mechanical power output of the water turbine, P e is the electromagnetic power of the generator, P D is the damping power of the hydrogenerator, E f is the excitation electromotive force, E' q and E' q ' are the q-axis transient electromotive force and the subtransient electromotive force respectively, E' d ' is the d-axis subtransient electromotive force, T' d0 and T' d ' 0 are the d-axis open-circuit transient time constant and the subtransient time constant respectively, T' q ' 0 is the q-axis open-circuit subtransient time constant, x d 、x' d and x' d ' are the d-axis synchronous reactance, transient reactance and subtransient reactance respectively, x q and x' q 'are the q-axis synchronous reactance and the subtransient reactance, respectively, I d and I q are the stator current components on the d-axis and q-axis, respectively, ω 0 is the rated speed, ω is the rotor angular velocity, T a is the generator inertia time constant, is the derivative of δ, where δ is the difference between the electrical angle of the generator rotor and the phase angle of the grid voltage, is the reciprocal of ω, is the reciprocal of E' q is the reciprocal of E' q ' is the reciprocal of E' d '

[0082] The algebraic model of the hydraulic turbine with constant coefficients is expressed as:

[0083]

[0084] where m t represents the relative value of the hydraulic turbine torque deviation, y t represents the relative value of the deviation of the hydraulic turbine guide vane opening, x t represents the relative value of the deviation of the hydraulic turbine speed, and are the derivatives of the relative torque and flow rate of the hydraulic turbine with respect to the relative speed when the guide vane opening and the water head are stable, respectively; and are the relative torque and flow rate of the hydraulic turbine with respect to the relative guide vane servomotor stroke when the speed and the water head are stable, respectively; and are the derivatives of the relative torque and flow rate of the hydraulic turbine with respect to the relative water head when the guide vane opening and the speed are stable, respectively, q t is the relative value of the deviation of the unit flow rate, h t is the relative value of the deviation of the unit water pressure;

[0085] The penstock system model is expressed as:

[0086]

[0087] where, is the derivative of q t h t is the relative value of the deviation of the unit water pressure, T w represents the water flow inertia time constant of the pipeline.

[0088] S2. Set the desired power generation of the hydraulic turbine, the initial power generation of the hydraulic turbine, and the initial control parameters. The initial control parameters include the P control parameter and the I control parameter.​​​

[0089] Specifically, the expected power generation of the water turbine is the theoretically set power generation of the water turbine. The initial power generation of the water turbine can be set manually, which describes the power generation at the start of the water turbine, or the power generation of the water turbine at a certain moment can be obtained as the initial power generation of the water turbine.

[0090] The power generation of the water turbine is adjusted by a PI controller. The initial control parameters are the values of the P control parameter and the I control parameter, which can be set manually or randomly selected within the range of the initial control parameters as the initial control parameters.

[0091] S3. Obtain a control signal based on the expected power generation of the water turbine, the initial power generation of the water turbine, the initial control parameters, and the controller transfer function.

[0092] Obtaining a control signal based on the expected power generation of the water turbine, the initial power generation of the water turbine, the control parameters, and the controller transfer function includes:

[0093] Obtain an expected difference based on the expected power generation of the water turbine and the initial power generation of the water turbine;

[0094] Specifically, the expected difference is the deviation value between the expected power generation of the water turbine and the actual power generation at the initial control moment.

[0095] Substitute the expected difference and the control parameters into the controller transfer function to obtain a control signal;

[0096] Specifically, according to the expected difference, adjust the PI control parameters, thereby changing the actual power generation of the water turbine. In this process, adjust the PI control parameters according to the expected difference, so that the actual power generation of the water turbine changes. The purpose is to make the actual power generation of the water turbine closer to the expected power generation of the water turbine.

[0097] After the unit is connected to the grid, to avoid the differential link of the controller from amplifying the system noise, the PI regulation law is adopted for the controller in the governor. At this time, the corresponding controller transfer function is expressed as:

[0098]

[0099] Among them, u(s) is the control signal, e(s) is the expected difference, K P is the P control parameter, K I is the I control parameter, and S is the Laplace operator.

[0100] S4. Obtain the relative value of the deviation of the water turbine guide vane opening according to the control signal and the servomotor transfer function.

[0101] Based on the control signal and the servomotor transfer function, obtaining the relative value of the opening deviation of the turbine guide vane includes:

[0102] Substitute the control signal into the servomotor transfer function to obtain the opening signal of the turbine guide vane;

[0103] Based on the opening signal of the turbine guide vane, obtain the relative value of the opening deviation of the turbine guide vane;

[0104] Specifically, throughout the control process, signals are transmitted in the form of transfer functions, but the turbine finally outputs the actual relative value of the opening deviation of the turbine guide vane according to the received signal.

[0105] The electro-hydraulic servo system consists of components such as the main servomotor, the pressure regulating valve, and the electro-hydraulic converter. The transfer function of the servomotor of this system is expressed as:

[0106]

[0107] where y(s) is the opening signal of the turbine guide vane, u(s) is the control signal, T y is the servomotor time constant, and S is the Laplace operator.

[0108] S5. Based on the relative value of the opening deviation of the turbine guide vane, the constant coefficient algebraic model of the turbine, and the water diversion system model, obtain the relative value of the turbine torque deviation.

[0109] Specifically, after calculating the relative value of the opening deviation of the turbine guide vane and substituting it into the constant coefficient algebraic model of the turbine and considering the relationship between the relative value of the flow deviation and the relative value of the water pressure deviation in the water diversion system model, the relative value of the turbine torque deviation can be calculated. There is a conversion relationship between the opening signal of the turbine guide vane and the relative value of the opening deviation of the turbine guide vane.

[0110] S6. Based on the relative value of the turbine torque deviation and the turbine power generation model, adjust the power generation power of the turbine.

[0111] Specifically, there is a certain conversion relationship between the relative value of the turbine torque deviation and the power generation power of the adjusted turbine. The torque of the turbine refers to the torsional torque received on its output shaft, while the power generation power is the energy transmitted by the turbine to the generator through the shaft power. The relationship between the two can be related by the angular velocity, that is, the power generation power of the turbine is equal to the product of the turbine torque and the angular velocity of the rotating shaft.

[0112] S7. Based on the adjusted power generation power of the turbine and the desired power generation power of the turbine, obtain the control error.

[0113] Specifically, the control error at this time is the error between the adjusted power generation power of the water turbine and the expected power generation power of the water turbine obtained after the initial control parameter adjustment under the initial parameters, that is, the control error.

[0114] S8. Set the adjustment ranges of the P control parameter and the I control parameter.

[0115] Specifically, both the P control parameter and the I control parameter have their respective value ranges, and this value range can be set by oneself or obtained from the manufacturer.

[0116] S9. According to the control error, construct an objective function. In the adjustment range, set the I control parameter to 0, and adjust the P control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum value of the objective function. The objective function is expressed as:

[0117]

[0118] where t is the simulation time and e(t) is the control error.

[0119] Specifically, the reference to the initial control parameter here refers to the initial parameter of the P control parameter.

[0120] According to the control error, construct an objective function. In the adjustment range, set the I control parameter to 0, and adjust the P control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum value of the objective function, including:

[0121] Initialize the number of iterations and the population size.

[0122] According to the population size, obtain the number of values of the P control parameter.

[0123] Specifically, the population size is the number of P control parameters selected in one iteration process.

[0124] In the adjustment range, set the I control parameter to 0, change the value of the P control parameter, and obtain the control error;

[0125] According to the control error and the objective function, obtain the value of the objective function;

[0126] Obtain the values of the objective function corresponding to different values of the P control parameter. The number of values of the objective function is equal to the number of values;

[0127] Obtain the minimum value of the objective function from the values of the objective function corresponding to different values of the P control parameter, and use the minimum value of the objective function as the iteration result of one round of iteration;

[0128] Specifically, during the iteration process, according to different selected P control parameter values, the corresponding objective function values are calculated, and then the P control parameter corresponding to the minimum objective function value is selected as the iteration result of one round of iteration.

[0129] The entire iteration process needs to be carried out for N rounds (i.e., the number of iteration rounds), and the optimal P control parameter is selected from the N rounds.

[0130] Based on the iteration result and the number of iterations, the final iteration result is obtained, and the final iteration result is used as the target P control parameter.

[0131] Specifically, the entire parameter optimization process is divided into two parts. In the first round, the proportional gain of the governor is set to zero (I control parameter), and only the integral parameter is optimized. Through the local reinforcement optimizer, each "learner" in the initial population is simulated and fitness evaluated. After several generations of iterative calculations, the optimal solution of the integral time constant is obtained and the ITAE value is minimized.

[0132] Keeping the integral parameter obtained in the first round of optimization unchanged, the proportional gain of the governor is further optimized. The proportional parameter is optimized using the local reinforcement optimizer, and the optimal proportional gain is obtained after several generations of evolution.

[0133] Through two rounds of optimization, the optimal proportional-integral control parameters of the governor are finally obtained, which significantly improves the dynamic response quality of the unit under various working conditions.

[0134] The local reinforcement optimizer introduces the rules and concepts in the local reinforcement theory and models the local reinforcement theory as an optimization algorithm. First, consider the following assumptions:

[0135] Learner - Learner: The behavior of the learner needs to be trained through the local reinforcement theory. It is modeled as a solution in the optimization problem.

[0136] Behavior - Behavior: The behavior of the learner is regarded as a solution of the decision variable and is regarded as a solution (learner) which is a vector of decision variables (behavior).

[0137] Population - Population: In the local reinforcement optimizer, a group of learners constitutes a population. Each row in the population represents a solution (Learner), and each element represents a decision variable (Behavior).

[0138] Objective function - Objectivefunction: The behavior fitness of each learner X i is evaluated through a user-defined objective function is calculated, and this objective function acts on the decision variables.

[0139] Time - Interval: The number of iterations between two stimulation, evaluation, or reinforcement phases, which is considered as the time interval for specifying the learner's decision variables (Behavior) in the search process. In the proposed algorithm, a scoring mechanism is used so that behaviors with higher scores have a higher chance of being reinforced in the next iteration.

[0140] Response: The main goal is to obtain more responses, and a response is defined as a successful improvement in the objective function value.

[0141] Schedule: The concept of a schedule is how and when a behavior needs to be reinforced and is modeled as a data structure during different intervals.

[0142] Each learner has a specific Schedule. Since each scalar represents the score / priority of a specific learner behavior, behaviors with higher scores / priorities have a higher chance of being selected in the next iteration. Additionally, the variable - interval scheduling scheme is modeled as a dynamic mechanism for stochastic analysis using an operational approach. How and when a behavior needs to be reinforced is a variable factor and is continuously updated during the search process.

[0143] Therefore, in each iteration, the subset of behaviors with the highest priority from Schedule i is selected for learner i. In this way, first, the priorities in the schedule are sorted in descending order, and then the first λ items are selected as candidate rows, with the operational approach being:

[0144]

[0145] where τ is the time factor, FEs is the number of function evaluations, MaxFEs is the maximum number of function evaluations. Additionally, SR is the selection rate, μ is the subset of behaviors selected according to the schedule, λ is the size of the selected subset; N is the total number of decision variables. Schedule * represents the schedule sorted by priority, and Schedule *,λ is the λ - th item in Schedule * .

[0146] Stimulation: Any operation that attempts to stimulate the learner's behavior to elicit a response is modeled by changing the decision variables of the proposed solution. It should be noted that any operation can be applied to the stimulation to change the learner's behavior, that is, to change the decision variables. In the PRO algorithm, the following operations are used to generate new solutions, namely:

[0147]

[0148] Among them, SF i is a stimulating factor, is a new behavior, is the decision variable of learner i, is the optimal decision variable, is the decision variable of learner j, is the stimulation vector, and rand is a random number.

[0149] Reinforcement: To conceptualize the reinforcement process, the following mechanism is used to update the schedule. Then positive reinforcement is applied to increase the score of a specific behavior. After the improvement in the stimulation phase, the objective function of the learner is used as the response, that is:

[0150]

[0151] Among them, RR is the reinforcement rate, represents the priority of the decision variable (behavior) selected by the i-th solution (learner).

[0152] On the other hand, negative reinforcement is applied when there is no response. In this case, the objective function of the learner decreases after the stimulation phase, resulting in a reduction in the score of a specific behavior. In the next iteration, the decision variable (behavior) with a higher score will be selected for stimulation and reinforcement, that is:

[0153]

[0154] Rescheduling: This concept refers to the process of applying a new schedule to the learner during the training process. When the learner continuously receives negative reinforcement for all behaviors, rescheduling is carried out. In this case, the PRO algorithm uses the standard deviation of the schedule as a metric to determine when it is necessary to reschedule the learner. This mechanism is implemented as:

[0155]

[0156] Among them, Std(Schedule i ) is the standard deviation of the schedule of the i-th learner, L B and U B are the lower limit and the upper limit respectively, and U(0,1) and U(L B ,U B ) represent random values uniformly distributed within the intervals (0,1) and (L B ,U B ).

[0157] In summary, before the search process of the local reinforcement optimizer begins, the population and hyperparameters are initialized. Then, an iterative process is executed to optimize the objective function. Specifically, first, the candidate decision variable (Behavior) - X of the i-th solution (Learner) is selected according to the corresponding scheduler. i .

[0158] Then, a random operation is selected to stimulate the selected decision variable of the i-th solution X i to generate a new solution X i,new . The new solution is discarded to keep the decision variable within the feasible range. The new solution X i,new is evaluated based on its objective function. Then, a comparison process of positive or negative reinforcement is carried out according to the response. The best solution X best is enabled after being updated and passing through the rescheduling process. After the iteration limit condition is completed, the best solution X best will be returned as the result.

[0159] S10. According to the control error and the preset objective function, within the adjustment range, set the P control parameter to the target P control parameter, and adjust the I control parameter based on the initial control parameter to obtain the target I control parameter corresponding to the minimum value of the objective function.

[0160] Specifically, the reference to the initial control parameter here refers to the initial parameter of the I control parameter.

[0161] S11. Use the target P control parameter and the target I control parameter as the optimization parameters.

[0162] Example 1:

[0163] During the optimization process of a certain power station, the main parameters of the unit are: the turbine transfer coefficient e y = 1, e h = 1.5, e x = 0, e qy = 1.0, e qh = 0.5, e qx = 0; the unit inertia time constant T a = 9.24; the water flow inertia time constant T w = 2.2374; the servomotor response time constant T y = 0.2; the parameters of the generator x d = 0.932, x' d = 0.278, x″ d = 0.241, x q = 0.658, x″ q = 0.236, T' d0 = 3.35, T″ d0 = 0.124, T″q0 = 0.26.

[0164] When the governor is in the power regulation mode, the optimal control parameters obtained in the first round are: the proportional parameter Kp is 0; the integral parameter Ki is 0.1712; the optimal control parameters obtained in the second round are: the proportional parameter Kp is 0.1; the integral parameter Ki is 0.1712; the Kp and Ki before optimization are 0.2 and 0.11 respectively. The experimental results are as Figure 3 shown, which shows the comparison of the power response of the speed regulation system before and after optimization. After optimization, the power adjustment speed of the system is significantly improved, and the fluctuation range is reduced.

[0165] As Figure 4 shown, which shows the comparison of the speed response before and after optimization. After optimization, the rise time and overshoot of the speed are effectively controlled. As Figure 5 shown, it further demonstrates the control effect after optimization. Under different load changes, the optimized parameters make the speed control more stable. As Figure 6 shown, which shows the comparison of the control effect of the servomotor stroke before and after optimization. The stroke control after optimization is more accurate. As Figure 7 shown, which shows the control effect of the spiral case water pressure before and after optimization. After optimization, the water pressure fluctuation is significantly reduced. From Figures 3 to 7 the comparison results, it can be seen that the optimized control parameters have been significantly improved in terms of adjustment time, rise time, overshoot, etc. Generally speaking, the control quality of the optimized system has been effectively improved. Especially under the conditions of non-linear and variable parameters, the response of the system is more stable.

[0166] A proportional-integral parameter optimization system for a hydrogenerator governor, comprising:

[0167] An acquisition module for acquiring a hydroturbine power generation model, a hydroturbine constant coefficient algebraic model, and a water diversion system model;

[0168] A first setting module for setting the desired power generation of the hydroturbine, the initial power generation of the hydroturbine, and the initial control parameters, where the initial control parameters include a P control parameter and an I control parameter;

[0169] A first calculation module for obtaining a control signal and a control error according to the desired power generation of the hydroturbine, the initial power generation of the hydroturbine, the initial control parameters, and the controller transfer function;

[0170] A second calculation module for obtaining the relative value of the deviation of the hydroturbine guide vane opening according to the control signal and the servomotor transfer function;

[0171] A third calculation module for obtaining the relative value of the deviation of the hydroturbine torque according to the relative value of the deviation of the hydroturbine guide vane opening, the hydroturbine constant coefficient algebraic model, and the water diversion system model;

[0172] A fourth calculation module, configured to obtain the regulated hydroturbine power generation according to the relative value of the hydroturbine torque deviation and the hydroturbine power generation model;

[0173] A fifth calculation module, configured to obtain a control error according to the regulated hydroturbine power generation and the desired hydroturbine power generation;

[0174] A second setting module, configured to set the adjustment ranges of the P control parameter and the I control parameter;

[0175] A first optimization module, configured to construct an objective function according to the control error, and set the I control parameter to 0 within the adjustment range according to the control error and the objective function, and adjust the P control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum objective function value, where the objective function is expressed as:

[0176]

[0177] where t is the simulation time and e(t) is the control error;

[0178] A second optimization module, configured to set the P control parameter to the target P control parameter within the adjustment range according to the control error and a preset objective function, and adjust the I control parameter based on the initial control parameter to obtain the target I control parameter corresponding to the minimum objective function value;

[0179] Use the target P control parameter and the target I control parameter as the optimization parameters.

[0180] An embodiment of the present application also discloses a terminal device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, a proportional-integral parameter optimization method for a hydrogenerator speed governor is adopted.

[0181] Wherein, the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may further include input / output devices, network access devices, and a bus, etc.

[0182] Wherein, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc. The present application places no restrictions thereon.

[0183] Among them, the memory can be an internal storage unit of the terminal device, for example, the hard disk or memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), or a flash card (FC) equipped on the terminal device, etc. Moreover, the memory can also be a combination of the internal storage unit and the external storage device of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0184] Among them, through this terminal device, a method for optimizing the proportional-integral parameters of a hydrogenerator governor in the above embodiment is stored in the memory of the terminal device, and is loaded and executed on the processor of the terminal device for convenient use.

[0185] The embodiment of the present application also discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, a method for optimizing the proportional-integral parameters of a hydrogenerator governor in the above embodiment is adopted.

[0186] Among them, the computer program can be stored in a computer-readable medium. The computer program includes computer program code. The computer program code can be in the form of source code, object code, executable file, or some middleware form, etc. The computer-readable medium includes any entity or device, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above components.

[0187] Among them, through this computer-readable storage medium, a method for optimizing the proportional-integral parameters of a hydrogenerator governor in the above embodiment is stored in the computer-readable storage medium, and is loaded and executed on the processor to facilitate the storage and application of the above method.

[0188] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the protection scope of the present application is limited to these examples; under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of one or more embodiments of the present application as described above. For the sake of brevity, they are not provided in detail.

[0189] One or more embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the present application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A method for optimizing proportional integral parameters of a hydro-generator speed governor, characterized in that: include: Obtaining a turbine power generation model, a turbine constant coefficient algebraic model, and a water diversion system model; Setting the expected power generation of the turbine, the initial power generation of the turbine and the initial control parameters, wherein the initial control parameters include the P control parameter and the I control parameter; Obtaining a control signal according to the expected power generation of the turbine, the initial power generation of the turbine, the initial control parameters and the controller transfer function; According to the control signal and the servomotor transfer function, a relative value of the guide vane opening deviation of the turbine is obtained; According to the relative value of turbine guide vane opening deviation, turbine constant coefficient algebraic model and water diversion system model, the relative value of turbine torque deviation is obtained; According to the relative value of the turbine torque deviation and the turbine power generation model, the turbine power generation power is adjusted; Obtaining a control error according to the adjustment of the turbine power generation and the turbine expected power generation; Set the adjustment range of P control parameters and I control parameters; According to the control error, the objective function is constructed. Within the adjustment range, the I control parameter is set to 0, and the P control parameter is adjusted based on the initial control parameter to obtain the target P control parameter corresponding to the minimum objective function value. The objective function is expressed as: Where t is the simulation time, e(t) is the control error; According to the control error and the preset objective function, within the adjustment range, the P control parameter is set as the target P control parameter, and the I control parameter is adjusted based on the initial control parameter to obtain the target I control parameter corresponding to the minimum objective function value; The target P control parameter and the target I control parameter are used as optimization parameters.

2. The method for optimizing proportional-integral parameters of a hydro-generator speed governor according to claim 1, characterized in that it comprises: The turbine power generation model is expressed as: Among them, P m is the mechanical power output of the turbine, P e is the electromagnetic power of the generator, P D is the damping power of the turbine generator, E f is the excitation electromotive force, E' q and E' q ' are the q-axis transient potential and subtransient potential, E' d ' is the d-axis transient potential, T' d0 and T' d ' 0 are the d-axis open-circuit transient time constant and subtransient time constant, T' q ' 0 is the q-axis open circuit subtransient time constant, x d 、x' d and x' d ' are the d-axis synchronous reactance, transient reactance and subtransient reactance, respectively, and x q and x' q ' are the q-axis synchronous reactance and subtransient reactance, I d and I q are the stator current components of the d-axis and q-axis respectively, ω0 is the rated speed, ω is the rotor angular velocity, T a is the generator inertia time constant, is the derivative of δ, δ is the difference between the generator rotor electrical angle and the grid voltage phase angle, is the reciprocal of ω, For E' q The reciprocal of For E' q ' The reciprocal of For E' d ' The reciprocal of .

3. The method for optimizing proportional-integral parameters of a hydro-generator speed governor according to claim 1, characterized in that it comprises: The constant coefficient algebraic model of the turbine is expressed as: Among them, m t Indicates the relative value of turbine torque deviation, y t Indicates the relative value of the turbine guide vane opening deviation, x t Indicates the relative value of turbine speed deviation, and are the derivatives of the relative torque and flow rate of the turbine with respect to the relative speed when the guide vane opening and water head are stable, and When the speed and head are stable, the relative torque and flow of the turbine are related to the relative guide vane relay stroke. and are the derivatives of the relative torque and flow rate of the turbine with respect to the relative head when the guide vane opening and speed are stable, q t is the relative value of the unit flow deviation, h t is the relative value of the unit water pressure deviation; The water diversion system model is expressed as: in, Q t The derivative of h t is the relative value of the unit water pressure deviation, T w Represents the inertia time constant of water flow in the pipe.

4. The method for optimizing proportional-integral parameters of a hydro-generator speed governor according to claim 1, characterized in that: The step of obtaining a control signal according to the expected power generation of the turbine, the initial power generation of the turbine, the initial control parameters and the controller transfer function comprises: Obtaining an expected difference according to the expected power generation of the water turbine and the initial power generation of the water turbine; Substituting the expected difference and the initial control parameter into the controller transfer function to obtain a control signal; The controller transfer function is expressed as: Among them, u(s) is the control signal, e(s) is the expected difference, K P is the P control parameter, K I is the I control parameter and S is the Laplace operator.

5. The method for optimizing proportional-integral parameters of a hydro-generator speed governor according to claim 1, characterized in that: The step of obtaining the relative value of the guide vane opening deviation of the turbine according to the control signal and the servomotor transfer function comprises: Substituting the control signal into the servomotor transfer function to obtain a turbine guide vane opening signal; According to the turbine guide vane opening signal, a relative value of the turbine guide vane opening deviation is obtained; The servomotor transfer function is expressed as: Among them, y(s) is the turbine guide vane opening signal, u(s) is the control signal, T y is the relay time constant, and S is the Laplace operator.

6. The method for optimizing proportional-integral parameters of a hydro-generator speed governor according to claim 1, characterized in that: The objective function is constructed according to the control error, the I control parameter is set to 0 within the adjustment range, and the P control parameter is adjusted based on the initial control parameter to obtain the target P control parameter corresponding to the minimum objective function value. Initialize the number of iterations and the population size; According to the population size, the number of values ​​of the P control parameter is obtained; Within the adjustment range, the I control parameter is set to 0, and the value of the P control parameter is changed to obtain the control error; Obtaining an objective function value according to the control error and the objective function; Obtain objective function values ​​corresponding to different values ​​of the P control parameter, the number of the objective function values ​​being equal to the number of the values; Obtaining a minimum objective function value from the objective function values ​​corresponding to different P control parameter values, and taking the minimum objective function value as an iteration result of a round of iteration; According to the iteration result and the number of iterations, a final iteration result is obtained, and the final iteration result is used as the target P control parameter.

7. The method for optimizing proportional-integral parameters of a hydro-generator speed governor as claimed in claim 6, characterized in that: The method for obtaining the target I control parameter is the same as the method for obtaining the target P control parameter.

8. A proportional-integral parameter optimization system for a hydro-generator speed governor, characterized in that: include: An acquisition module, used to acquire a turbine power generation model, a turbine constant coefficient algebraic model and a water diversion system model; A first setting module is used to set the expected power generation of the turbine, the initial power generation of the turbine and the initial control parameters, wherein the initial control parameters include a P control parameter and an I control parameter; A first calculation module is used to obtain a control signal according to the expected power generation of the turbine, the initial power generation of the turbine, the initial control parameters and the controller transfer function; A second calculation module is used to obtain a relative value of a guide vane opening deviation of a turbine according to the control signal and a servomotor transfer function; The third calculation module is used to obtain the relative value of the turbine torque deviation according to the relative value of the turbine guide vane opening deviation, the turbine constant coefficient algebraic model, and the water diversion system model; A fourth calculation module, used for adjusting the turbine power generation according to the relative value of the turbine torque deviation and the turbine power generation model; A fifth calculation module, used for obtaining a control error according to the adjusted power generation of the hydraulic turbine and the expected power generation of the hydraulic turbine; The second setting module is used to set the adjustment range of the P control parameter and the I control parameter; The first optimization module is used to construct an objective function according to the control error, set the I control parameter to 0 within the adjustment range, and adjust the P control parameter based on the initial control parameter to obtain the target P control parameter corresponding to the minimum objective function value. The objective function is expressed as: Where t is the simulation time, e(t) is the control error; The second optimization module is used to set the P control parameter as the target P control parameter within the adjustment range according to the control error and the preset objective function, and adjust the I control parameter based on the initial control parameter to obtain the target I control parameter corresponding to the minimum objective function value; The target P control parameter and the target I control parameter are used as optimization parameters.

9. A terminal device, comprising a memory and a processor, characterized in that: The memory stores a computer program that can be run on the processor, and when the processor loads and executes the computer program, the optimization method according to any one of claims 1 to 7 is adopted.

10. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by a processor, the optimization method according to any one of claims 1 to 7 is adopted.

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