A method and system for optimizing primary frequency modulation parameters of a bulb tubular hydroelectric generating unit

Abstract

The present application belongs to the field of hydroelectric power generation, and particularly relates to a method and system for optimizing primary frequency modulation parameters of a bulb tubular hydroelectric generating set, which calculates the primary frequency modulation when a fixed frequency is disturbed under different loads, obtains a working condition corresponding to a maximum value of a primary frequency modulation performance index as a most unfavorable working condition, sets a primary frequency modulation performance index constraint and a decision variable constraint, randomly generates a decision variable group according to the primary frequency modulation performance index constraint and the decision variable constraint, obtains a primary frequency modulation performance target function and a power counter-regulation performance target function value according to the decision variable group, and performs non-dominated fast sorting and congestion evaluation, takes the most unfavorable working condition as a benchmark working condition for optimization, selects an optimal solution cluster through genetic algorithm crossover and mutation, and then selects the decision variable corresponding to the point closest to the origin in the Euclidean distance as the primary frequency modulation optimization parameter. The present application can cover all working conditions, obtain optimal frequency modulation parameters, and improve the primary frequency modulation effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of hydroelectric power generation, and particularly relates to a method and system for optimizing primary frequency modulation parameters of a bulb-turbine hydroelectric generating unit. BACKGROUND

[0002] In a new power system, the grid-connection of large-scale renewable energy brings a severe test to the frequency control of the power grid, and hydroelectric power generation is one of the renewable energies. Hydroelectric power generation is the main regulating power source in the power grid, and its primary frequency modulation characteristics are of great significance to the safety and stability of the power grid. The bulb-turbine unit has good adaptability and economy in low-head power stations and has been widely used in China. Due to low water head and large flow, the water flow inertia of the bulb-turbine unit water conveying system is generally large, which greatly deteriorates the primary frequency modulation performance of the hydroelectric generating unit, and on the other hand, creates a larger power reverse modulation ratio, which not only cannot provide effective frequency support, but also offsets the correct power change of other units, which is not conducive to the safety and stability of the power grid.

[0003] In the related art, the primary frequency modulation of the hydroelectric generating unit is mainly based on the comprehensive characteristic curve of the water turbine model or uses a local linearization model to simulate the primary frequency modulation performance of the hydroelectric generating unit. In the primary frequency modulation process, field tests are usually carried out for a certain working condition, and the frequency modulation parameters are determined according to the actual results.

[0004] In view of the above related art, the frequency modulation is for a single working condition and the frequency modulation is tested on site, which cannot cover all working conditions in all directions, and by manually changing the frequency modulation parameters, the optimal primary frequency modulation parameters cannot be obtained, so that the performance cannot be optimized. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a method and system for optimizing primary frequency modulation parameters of a bulb-turbine hydroelectric generating unit, which can cover all working conditions and obtain optimal frequency modulation parameters to improve the effect of primary frequency modulation.

[0006] A method for optimizing primary frequency modulation parameters of a bulb-turbine hydroelectric generating unit, comprising:

[0007] Between the maximum water head and the minimum water head of the hydroelectric generating unit, a plurality of characteristic water heads are taken, the primary frequency modulation is calculated when the frequency is disturbed at a fixed frequency under different loads, the primary frequency modulation performance indicators of the hydroelectric generating unit under different working conditions are obtained, and the working condition corresponding to the maximum primary frequency modulation performance indicator is obtained. The primary frequency modulation performance indicators include power reverse modulation amount, power lag time, power rise time and regulation time. The working condition corresponding to the maximum primary frequency modulation performance indicator is taken as the most unfavorable working condition, and the working condition is composed of water head, guide vane opening and paddle opening.

[0008] The primary frequency performance index constraint and the decision variable constraint are set, the decision variable being a primary frequency parameter, the primary frequency parameter including a regulator proportional link gain coefficient and a regulator integral link gain coefficient;

[0009] According to the primary frequency performance index constraint and the decision variable constraint, a decision variable group is randomly generated;

[0010] According to the decision variable group, the primary frequency performance index and a primary frequency performance objective function, a first objective function value corresponding to different decision variables is obtained;

[0011] According to the decision variable group, the primary frequency parameter and a power reverse regulation performance objective function, a second objective function value corresponding to different decision variables is obtained;

[0012] The first objective function value and the second objective function value are subjected to non-dominated fast sorting and crowding degree evaluation, the most unfavorable working condition being taken as a benchmark working condition for optimization, a parent population of multiple decision variables being obtained, and the parent population being subjected to genetic mutation to obtain a child population until the number of iterations reaches a set number, an optimal population in an iteration process being obtained;

[0013] The Euclidean distance of each individual in the optimal population from the origin is calculated, and the decision variable with the closest Euclidean distance from the origin is selected as the primary frequency optimization parameter.

[0014] Optionally, the power reverse regulation amount includes:

[0015] A fixed-pitch model characteristic curve of a hydroelectric generating set is obtained;

[0016] An operating parameter of the hydroelectric generating set is obtained from the fixed-pitch model characteristic curve of the hydroelectric generating set;

[0017] A flow characteristic curve and a torque characteristic curve are established according to the operating parameter, and a unit speed and a unit torque of an arbitrary working condition point are obtained according to the flow characteristic curve and the torque characteristic curve;

[0018] The active power of the hydroelectric generating set is calculated according to the unit speed and the unit torque;

[0019] The power reverse regulation amount in the primary frequency process is obtained according to the active power and a preset formula.

[0020] Optionally, the power reverse regulation amount in the primary frequency process is obtained according to the active power and a preset formula, and includes:

[0021] In the primary frequency process, if it is a power increasing process, the preset formula is:

[0022] P fpP0-P min ;

[0023] If it is a power reduction process, the preset formula is:

[0024] P fp =P max -P0;

[0025] Wherein, P0 is the active power of the unit before the primary frequency modulation action, P min is the minimum power in the primary frequency modulation action process, P max is the maximum power in the primary frequency modulation action process, and P fp is the power reverse adjustment amount.

[0026] Optionally, the flow characteristic curve and the torque characteristic curve are established according to the operating parameters, and the unit speed and the unit torque of an arbitrary working condition point are obtained according to the flow characteristic curve and the torque characteristic curve, and the method comprises the following steps:

[0027] The operating parameters comprise a unit speed, a unit flow and a water turbine efficiency;

[0028] The unit flow, the unit speed and the water turbine efficiency under different guide vane opening degrees and paddle opening degrees are obtained according to the operating parameters, and a unit flow matrix and an efficiency matrix are formed;

[0029] A unit torque matrix is obtained according to the unit flow matrix, the efficiency matrix and a torque matrix formula;

[0030] The flow characteristic curve and the torque characteristic curve are established according to the unit torque matrix, the efficiency matrix, the unit speed and the unit flow, and the unit speed and the unit torque of an arbitrary working condition point are obtained according to the flow characteristic curve and the torque characteristic curve;

[0031] The torque matrix formula is:

[0032]

[0033] Wherein, n is the number of paddle opening degrees, m is the number of guide vane opening degrees, i=1, 2, 3...n, j=1, 2, 3...m, k=1, 2, 3...p, Q 11,i,j,k is a unit flow matrix, η i,j,k is an efficiency matrix, n 11,i,j,k is a unit speed, and P is the number of points read on each guide vane opening degree line.

[0034] Optionally, the setting of the primary frequency modulation performance index constraint and the decision variable constraint comprises:

[0035] A standard parameter of the primary frequency modulation performance index is obtained;

[0036] setting the primary frequency modulation performance index to be less than or equal to the standard parameter as a primary frequency modulation performance index constraint;

[0037] obtaining a parameter magnitude of a preset decision variable, and taking the parameter magnitude as a decision variable constraint.

[0038] Optionally, comprising:

[0039] The primary frequency modulation performance target function is:

[0040]

[0041] wherein, K P is a regulator proportional link gain coefficient, K I is a regulator integral link gain coefficient, t hx is a power lag time, t 0.9 is a time from the start of the primary frequency modulation action to the time when the active power of the unit reaches 90% of the target value, t s is a time from the start of the primary frequency modulation action to the time when the absolute value of the deviation between the actual active power of the unit and the target value is less than 5%, t hx_s is a standard power lag time, t 0.9_s is a standard power rise time, t s-s is a standard regulation time;

[0042] The power reverse modulation performance target function is:

[0043]

[0044] wherein, ΔP0 is a power disturbance amount in the primary frequency modulation process, P fp is a power reverse modulation amount.

[0045] A primary frequency modulation parameter optimization system for a bulb tubular hydroelectric generating unit, comprising:

[0046] A first calculation module, configured to take a plurality of characteristic water heads between the maximum water head and the minimum water head of the hydroelectric generating unit, calculate the primary frequency modulation at a fixed frequency disturbance under different loads, obtain the primary frequency modulation performance indexes of the hydroelectric generating unit under different working conditions, and obtain a working condition corresponding to the maximum primary frequency modulation performance index, wherein the primary frequency modulation performance indexes include a power reverse modulation amount, a power lag time, a power rise time, and a regulation time, and the working condition corresponding to the maximum primary frequency modulation performance index is taken as the most unfavorable working condition, and the working condition is composed of a water head, a guide vane opening, and a paddle opening;

[0047] A setting module, configured to set a primary frequency modulation performance index constraint and a decision variable constraint, wherein the decision variable is a primary frequency modulation parameter, and the primary frequency modulation parameter includes a regulator proportional link gain coefficient and a regulator integral link gain coefficient.

[0048] a generating module configured to generate a decision variable group randomly according to the primary frequency modulation performance index constraint and the decision variable constraint;

[0049] a second calculating module configured to obtain a first target function value corresponding to different decision variables according to the decision variable group, the primary frequency modulation performance index, and a primary frequency modulation performance target function;

[0050] a third calculating module configured to obtain a second target function value corresponding to different decision variables according to the decision variable group, the primary frequency modulation parameter, and a power inverse modulation performance target function;

[0051] an iteration module configured to perform non-dominated fast sorting and crowding degree evaluation on the first target function value and the second target function value, take the most unfavorable working condition as a benchmark working condition for optimization, obtain a parent population of multiple decision variables, and obtain a child population by genetic mutation of the parent population until the number of iterations reaches a set number of times to obtain an optimal population in an iteration process;

[0052] an optimization module configured to calculate the Euclidean distance of each individual in the optimal population from the origin, and select a decision variable with the closest Euclidean distance from the origin as a primary frequency modulation optimization parameter.

[0053] Optionally, the setting module comprises:

[0054] a first obtaining unit configured to obtain a standard parameter of a primary frequency modulation performance index;

[0055] a setting unit configured to set the primary frequency modulation performance index less than or equal to the standard parameter as a primary frequency modulation performance index constraint;

[0056] a second obtaining unit configured to obtain a parameter magnitude of a preset decision variable, and take the parameter magnitude as a decision variable constraint.

[0057] A terminal device comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor loads and executes the computer program, and adopts a primary frequency modulation parameter optimization method for a bulb tubular water turbine generator.

[0058] A computer readable storage medium stores a computer program, and the computer program is loaded and executed by a processor, and adopts a primary frequency modulation parameter optimization method for a bulb tubular water turbine generator.

[0059] The present application has the following advantages:

[0060] The primary frequency modulation performance index maximum value corresponding working condition is obtained as the most unfavorable working condition by calculating the primary frequency modulation under the fixed frequency disturbance at different loads between the maximum water head and the minimum water head of the hydroelectric generating set, and then the most unfavorable working condition is taken as the basic working condition for parameter optimization. First, the primary frequency modulation performance index constraint and the decision variable constraint are set, then the decision variable group is randomly generated, the primary frequency modulation performance objective function and the power counter-adjustment performance objective function value are calculated according to the decision variable group, and the non-dominated quick sorting and congestion evaluation are carried out, the iteration is continuously carried out through genetic mutation, and the optimal population is output when the iteration number is reached. The decision variable with the closest Euclidean distance from the origin is selected as the primary frequency modulation optimization parameter from the optimal population. The application has the advantages of being capable of modulating frequency for all working conditions, obtaining optimal frequency modulation parameters, and improving the primary frequency modulation effect. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is a flowchart of the primary frequency modulation parameter optimization method of the bulb tubular hydroelectric generating set of the application.

[0062] Figure 2 It is a characteristic curve diagram of the fixed-paddle model of the hydroelectric generating set of the application.

[0063] Figure 3 It is a flow characteristic curve diagram of the application.

[0064] Figure 4 It is a torque characteristic curve diagram of the application.

[0065] Figure 5 It is a speed regulating system diagram of the hydroelectric generating set of the application.

[0066] Figure 6 It is a power counter-adjustment diagram in the primary frequency modulation process of the application.

[0067] Figure 7 It is a lag time diagram in the primary frequency modulation process of the application.

[0068] Figure 8 It is a rise time diagram in the primary frequency modulation process of the application.

[0069] Figure 9 It is a regulation time diagram in the primary frequency modulation process of the application.

[0070] Figure 10 It is a leading solution set diagram obtained by the primary frequency modulation parameter optimization method of the bulb tubular hydroelectric generating set of the application. DETAILED DESCRIPTION

[0071] A primary frequency modulation parameter optimization method of a bulb tubular hydroelectric generating set, as shown in Figure 1 , comprises:

[0072] S100. Between the maximum and minimum head of the hydropower unit, take several characteristic heads and calculate the primary frequency regulation under different loads with a fixed frequency disturbance. Obtain the primary frequency regulation performance index of the hydropower unit under different operating conditions, and obtain the operating condition corresponding to the maximum value of the primary frequency regulation performance index. The primary frequency regulation performance index includes power reverse regulation, power lag time, power rise time and adjustment time. Take the operating condition corresponding to the maximum value of the primary frequency regulation performance index as the most unfavorable operating condition. The operating condition consists of head, guide vane opening and blade opening.

[0073] Specifically, in this embodiment, the primary frequency regulation during fixed frequency disturbance is a fixed frequency ±0.2Hz, and five characteristic heads are selected. Different loads are selected as 10%Pr, 30%Pr, 50%Pr, 70%Pr and 90%Pr, respectively, where Pr is the rated output of the unit, i.e., the load.

[0074] The maximum and minimum head of a hydropower unit are the highest and lowest water levels allowed during actual operation.

[0075] Primary frequency modulation performance indicators include power lag time t hx Power rise time t 0.9 Power settling time t s and power inverse regulation P fp .

[0076] During a single frequency modulation process, the higher the frequency modulation performance index, the worse the frequency modulation effect.

[0077] Power back-adjustment refers to the amount of power adjustment made by generators or other power equipment in order to maintain system frequency stability when the system faces load changes or frequency fluctuations.

[0078] The power inversion amount is obtained including:

[0079] Obtain the characteristic curves of the hydroelectric generator's fixed-blade model.

[0080] Specifically, the characteristic curves of the fixed-blade model of a hydroelectric generator unit are composed of the comprehensive characteristic curves of the turbine under different blade openings. These curves reflect the comprehensive characteristics of the turbine under various guide vane and blade openings. These curves express the relationships between the hydroelectric generator unit's operating efficiency, power output, speed, and flow rate through a series of parameters. The characteristic curves of the fixed-blade model of the hydroelectric generator unit are as follows: Figure 2 As shown.

[0081] The operating parameters of the hydropower unit are obtained from the characteristic curves of the fixed-blade model of the hydropower unit.

[0082] Specifically, the operating parameters include unit speed, unit flow and turbine efficiency. The unit speed, unit flow and turbine efficiency are read on the constant guide vane opening line of the bulb tubular turbine constant pitch characteristic curve.

[0083] The flow characteristic curve and the torque characteristic curve are established according to the operating parameters, and the unit speed and unit torque of any working condition point are obtained according to the flow characteristic curve and the torque characteristic curve.

[0084] Specifically, the flow characteristic curve is as shown in Figure 3 , and the torque characteristic curve is as shown in Figure 4 .

[0085] Specifically, the unit flow and turbine efficiency corresponding to different guide vane openings and blade openings are obtained to obtain a blade opening matrix, a guide vane opening matrix, a unit flow matrix and an efficiency matrix.

[0086] The active power of the hydroelectric generating set is calculated according to the unit speed and the unit torque.

[0087] Specifically, the calculation method of the active power of the turbine is as follows:

[0088]

[0089] M=M 11 D1 3 H

[0090]

[0091] Wherein, Q is the real machine flow, n is the real machine speed, n 11 is the unit speed, H is the water head, D1 is the turbine runner diameter, M is the real machine torque, M 11 is the unit torque, and P is the real machine active power.

[0092] According to the active power and a preset formula, the power counter-adjusting amount in the primary frequency modulation process is obtained.

[0093] According to the active power and a preset formula, the power counter-adjusting amount in the primary frequency modulation process is obtained.

[0094] In the primary frequency modulation process, if it is a power increasing process, the preset formula is:

[0095] P fp =P0-P min .

[0096] If it is a power decreasing process, the preset formula is:

[0097] P fp =P max -P0.

[0098] P0 is the active power of the unit before the primary frequency modulation action, P min Pmin is the minimum power during the primary frequency modulation action, P max Pmax is the maximum power during the primary frequency modulation action, P fp P is the power reverse adjustment amount.

[0099] The bulb tubular turbine is mainly used in low water head and large flow hydropower stations, and the water delivery system is simple and short in length. The rigid water hammer model can accurately simulate the water hammer characteristics in the flow passage, as shown in the following formula.

[0100]

[0101] In the formula, q and h are the Laplace transform of the flow rate and the unit value of the water turbine working water head, respectively, s is the Laplace operator; Tw is the water flow inertia time constant, L is the length of the water delivery system, V is the water flow velocity in the water delivery system under the rated flow, g is the gravity acceleration, and Hr is the rated head of the water turbine.

[0102] Due to the existence of water hammer characteristics, the power of the water turbine is not monotonous but fluctuating, so when calculating the power reverse adjustment amount, if the power is increased, the minimum power is found to obtain the power reverse adjustment amount, and if the power is decreased, the maximum power is found to obtain the power reverse adjustment amount.

[0103] The flow characteristic curve and the torque characteristic curve are established according to the operating parameters, and the unit speed and the unit torque of any working condition point are obtained according to the flow characteristic curve and the torque characteristic curve, including:

[0104] The operating parameters include the unit speed, the unit flow, and the turbine efficiency.

[0105] The unit flow, the unit speed, and the turbine efficiency under different guide vane opening and blade opening are obtained according to the operating parameters, and the unit flow matrix and the efficiency matrix are formed.

[0106] The unit torque matrix is obtained according to the unit flow matrix, the efficiency matrix, and the torque matrix formula.

[0107] The flow characteristic curve and the torque characteristic curve are established according to the unit torque matrix, the efficiency matrix, the unit speed, and the unit flow, and the unit speed and the unit torque of any working condition point are obtained according to the flow characteristic curve and the torque characteristic curve.

[0108] The torque matrix formula is:

[0109]

[0110] Wherein, n is the number of blade opening lines, m is the number of guide vane opening lines, i = 1, 2, 3...n, j = 1, 2, 3...m, k = 1, 2, 3...p, Q 11,i,j,k is the unit flow matrix, η i,j,k is the efficiency matrix, n 11,i,j,k is the unit speed, and p is the number of points read on each guide vane opening line.

[0111] S110, set the primary frequency performance index constraint and the decision variable constraint, the decision variable is the primary frequency parameter, and the primary frequency parameter includes the regulator proportional link gain coefficient and the regulator integral link gain coefficient.

[0112] Setting the primary frequency performance index constraint and the decision variable constraint includes:

[0113] Obtain the standard parameter of the primary frequency performance index.

[0114] Specifically, the standard parameter of the primary frequency performance index is:

[0115] In the embodiment, the standard parameter adopted is that the power lag time is 8s, the power rise time is 15s, and the power stable time is 30s. Therefore, in the actual frequency modulation process, it is necessary to ensure that the actual primary frequency performance index is less than or equal to the corresponding standard parameter.

[0116] Set the primary frequency performance index less than or equal to the standard parameter as the primary frequency performance index constraint.

[0117] Specifically:

[0118]

[0119] Wherein, t hx-s is the standard power lag time, t 0.9_s is the standard power rise time, and t s_s is the standard power stable time.

[0120] Obtain the parameter magnitude of the preset decision variable, and take the parameter magnitude as the decision variable constraint.

[0121] Specifically, for the hydropower generating set that has been put into production, the inherent characteristics of the guide vane actuator are fixed values and cannot be adjusted, and only by changing the governor controller parameters can the primary frequency performance be optimized, so the governor control parameters K p and K I are selected as the decision variable.

[0122] The model of the governing system of a hydroelectric generating unit includes a regulator and a hydraulic actuator. The regulator is mainly a PID control model. Compared with a Francis turbine and a fixed-blade turbine, the blades of a variable-blade turbine can be adjusted, and the hydraulic actuator is more complex. The model of the blade actuator needs to be considered on the basis of the model of the guide vane actuator, and the model of the entire governing system is as shown in FIG. Figure 5 .

[0123] Figure 5 In the formula, K p is a proportional link gain coefficient of the regulator, K D is a differential link gain coefficient of the regulator, T lv is a differential link time constant of the regulator, K I is an integral link gain coefficient of the regulator, b p is a permanent state slip coefficient; K PE1 is a proportional link gain coefficient of the guide vane actuator, K DE1 is a differential link gain coefficient of the guide vane actuator, K IE1 is an integral link gain coefficient of the guide vane actuator, V max1 is a maximum opening rate limit value of the guide vane, V min1 is a maximum closing rate limit value of the guide vane, T y1 is a reaction time constant of the guide vane actuator, τ1 is a guide vane opening output delay, T R1 is a guide vane opening measurement delay, K pE2 is a proportional link gain coefficient of the blade actuator, K DE2 is a differential link gain coefficient of the blade actuator, K IE2 is an integral link gain coefficient of the blade actuator, V max2 is a maximum opening rate limit value of the blade, V min2 is a maximum closing rate limit value of the blade, T y2 is a reaction time constant of the blade actuator, τ2 is a blade opening output delay, T R2 is a blade opening measurement delay.

[0124] The control parameters K p and K I of the governor have upper and lower limits, in which K p is 0.5-20, and K I is 0.05-10.

[0125] In actual operation of a hydroelectric generating unit, the precision of the control parameters is set to two significant figures after the decimal point, and the magnitude constraint condition of the decision variable is as shown in the formula:

[0126] M[K P ,K I ] = 0.01;

[0127] Wherein, M[] represents the magnitude of the decision variable.

[0128] S120, randomly generating a decision variable group according to the primary frequency performance index constraint and the decision variable constraint.

[0129] Specifically, according to the calculated primary frequency performance index corresponding to the most unfavorable working condition as the reference working condition of optimization, the power inverse regulation diagram, the lag time diagram, the rise time diagram and the regulation time diagram corresponding to the most unfavorable working condition in the primary frequency process are respectively as shown in Figures 6 to 9

[0130] S130, obtaining the first target function value corresponding to different decision variables according to the decision variable group, the primary frequency performance index and the primary frequency performance target function.

[0131] The primary frequency performance target function is:

[0132]

[0133] Wherein, K P is the proportional link gain coefficient of the regulator, K I is the integral link gain coefficient of the regulator, t hx is the power lag time, t 0.9 is the time from the start of the primary frequency action to the active power of the unit reaching 90% of the target value, t s is the time from the start of the primary frequency action to the absolute value of the deviation between the actual active power of the unit and the target value being less than 5%, t hx-s is the standard power lag time, t 0.9-s is the standard power rise time, t s-s is the standard regulation time.

[0134] The power inverse regulation performance target function is:

[0135]

[0136] Wherein, ΔP0 is the power disturbance in the primary frequency process, P fp is the power inverse regulation amount.

[0137] S140, obtaining the second target function value corresponding to different decision variables according to the decision variable group, the primary frequency parameter and the power inverse regulation performance target function.

[0138] S150, performing non-dominated fast sorting and crowding degree evaluation on the first target function value and the second target function value, taking the most unfavorable working condition as the reference working condition of optimization, obtaining the parent population of multiple decision variables, and obtaining the child population through genetic mutation of the parent population until the iteration number reaches the set number, obtaining the optimal population in the iteration process. ​

[0139] Specifically, first, algorithm initialization is performed, and related parameters of the multi-objective genetic algorithm are set, including: population size N, maximum evolution number Gnum, crossover algorithm distribution index δ1, mutation algorithm distribution index δ2, and objective function dimension V m , decision variable set size N j ; initial operating condition parameters of the unit are set, including: initial water head H0, initial flow rate Q0, initial load P0, initial guide vane opening Y0, and initial blade opening Z0; the frequency disturbance is set to ±0.2 Hz, guide vane and blade actuator parameters are set according to the measured results of the governor; related parameters are set according to the turbine parameters and unit design data, including: rated water head H r , rated flow rate Q r , rated output Pr, rated speed n r , water flow inertia time parameter T w of the water conveyance system; upper and lower bounds of the decision variables are set to [K P _min, K P _max] and [K I _min, K I _max]; the current evolution generation k is set to 1.

[0140] According to the constraint conditions, initial decision variables X i _0=(KP,KI), i=1, 2, …, N are randomly generated.

[0141] Primary frequency modulation simulation of the bulb tubular unit is performed, the active power change process of the unit is obtained, power reverse modulation value Pfp, power lag time thx, power rise time t0.9, and regulation time ts are calculated, and two objective function index values Obj1 and Obj2 are obtained.

[0142] According to the two objective function values of the individuals, non-dominated fast sorting and congestion evaluation are performed, and the kth generation population G k =(X1,X2,X3,…,X n ), X i =(K P ,K I ), i=1, 2, …, N is obtained.

[0143] The last generation population is taken as the parent population, a new generation population G k ’ is generated through genetic operation, and the new generation population is taken as the child population of the parent population.

[0144] It is detected whether the current evolution generation k reaches the maximum evolution generation G num , if k=G num , the optimal population is taken as the optimal solution set and output, and if kG numThe parent population G k and the offspring population G k are merged to generate the next generation population G k+1 as initial decision variables.

[0145] In the optimization process

[0146] The non-dominated solution set (Pareto) distribution obtained by multi-objective optimization based on NSGA-II is shown in the accompanying Figure 10 The best compatible solution selection method based on ideal equilibrium point is adopted, and the point with smaller Euclidean distance from the origin is selected as the actual parameter point, as shown by the dashed line in the figure. Figure 10

[0147] S160, calculate the Euclidean distance of each individual in the optimal population from the origin, and select the decision variable with the closest Euclidean distance from the origin as the primary frequency modulation optimization parameter.

[0148] Specifically, different working conditions correspond to different primary frequency modulation optimization parameters.

[0149] A primary frequency modulation parameter optimization system for a bulb tubular hydroelectric generating set, comprising:

[0150] A first calculation module is configured to take a plurality of characteristic water heads between the maximum water head and the minimum water head of the hydroelectric generating set, calculate the primary frequency modulation when the fixed frequency is disturbed under different loads, obtain the primary frequency modulation performance indicators of the hydroelectric generating set under different working conditions, and obtain the working condition corresponding to the maximum value of the primary frequency modulation performance indicators. The primary frequency modulation performance indicators include power reverse modulation, power lag time, power rise time, and regulation time. The working condition corresponding to the maximum value of the primary frequency modulation performance indicators is taken as the most unfavorable working condition. The working condition is composed of water head, guide vane opening, and paddle opening.

[0151] A setting module is configured to set the primary frequency modulation performance indicator constraints and the decision variable constraints. The decision variable is the primary frequency modulation parameter, and the primary frequency modulation parameter includes the regulator proportional link gain coefficient and the regulator integral link gain coefficient.

[0152] A generation module is configured to randomly generate a decision variable group according to the primary frequency modulation performance indicator constraints and the decision variable constraints.

[0153] A second calculation module is configured to obtain the first objective function value corresponding to different decision variables according to the decision variable group, the primary frequency modulation performance indicators, and the primary frequency modulation performance objective function.

[0154] A third calculation module is configured to obtain the second objective function value corresponding to different decision variables according to the decision variable group, the primary frequency modulation parameter, and the power reverse modulation performance objective function.

[0155] ​The iteration module is configured to perform non-dominated fast sorting and crowdedness evaluation on the first objective function value and the second objective function value, take the worst case as a benchmark working condition of optimization, obtain a parent population of the plurality of decision variables, and obtain a child population by genetic mutation of the parent population until a number of iterations reaches a set number, to obtain an optimal population in the iteration process;

[0156] The optimization module is configured to calculate a Euclidean distance of each individual in the optimal population from an origin, and select a decision variable with the closest Euclidean distance from the origin as a primary frequency modulation optimization parameter.

[0157] The embodiment of the application further discloses a terminal device including a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor loads and executes the computer program, and a bulb tubular hydraulic turbine generator primary frequency modulation parameter optimization method is adopted.

[0158] The terminal device can be a computer device such as a desktop computer, a notebook 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 can further include an input / output device, a network access device and a bus.

[0159] The processor can be a central processing unit (CPU), and of course, according to actual use, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), ready-to-program gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. can also be used, and the general-purpose processor can be a microprocessor or any conventional processor, etc. The application does not limit this.

[0160] The memory can be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash card (FC) equipped on the terminal device, and 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, and the memory can also be used to temporarily store data that has been output or will be output. The application does not limit this.

[0161] The terminal device stores the bulb tubular hydraulic turbine generator primary frequency modulation parameter optimization method in the memory of the terminal device, and the method is loaded and executed on the processor of the terminal device, which is convenient to use.

[0162] The embodiment of the application further discloses a computer readable storage medium, and the computer readable storage medium stores a computer program.

[0163] The computer program can be stored in the computer readable medium, and the computer program includes computer program code. The computer program code can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium includes any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the computer readable medium includes but is not limited to the above components.

[0164] The computer readable storage medium stores the bulb tubular turbine generator primary frequency modulation parameter optimization method in the computer readable storage medium, and is loaded and executed on the processor, so that the storage and application of the method are facilitated.

[0165] It should be understood by those skilled in the art that the above discussion of any embodiment is only exemplary and is not intended to limit the protection scope of the application to these examples; under the idea of the application, the above embodiments or technical features in different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of one or more embodiments of the application as described above. In order to be brief, they are not provided in details.

[0166] One or more embodiments of the application are intended to cover all such alternatives, modifications and variations falling within the broad scope of the application. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principles of one or more embodiments of the application should be included in the protection scope of the application.

Claims

1. A method for optimizing primary frequency modulation parameters of a bulb-tube hydroelectric generating unit, characterized in that, The method comprises the following steps: between the maximum water head and the minimum water head of a hydroelectric generating set, a plurality of characteristic water heads are taken, primary frequency modulation when perturbed at a fixed frequency under different loads is calculated, primary frequency modulation performance indexes of the hydroelectric generating set under different working conditions are obtained, a working condition corresponding to a maximum value of the primary frequency modulation performance indexes is obtained, the primary frequency modulation performance indexes include power reverse modulation, power lag time, power rise time and regulation time, the working condition corresponding to the maximum value of the primary frequency modulation performance indexes is taken as a most unfavorable working condition, and the working condition is composed of a water head, a guide vane opening degree and a paddle opening degree; primary frequency modulation performance index constraints and decision variable constraints are set, the decision variables are primary frequency modulation parameters, and the primary frequency modulation parameters include a regulator proportional link gain coefficient and a regulator integral link gain coefficient; decision variable groups are randomly generated according to the primary frequency modulation performance index constraints and the decision variable constraints; first target function values corresponding to different decision variables are obtained according to the decision variable groups, the primary frequency modulation performance indexes and a primary frequency modulation performance target function; second target function values corresponding to different decision variables are obtained according to the decision variable groups, the primary frequency modulation parameters and a power reverse modulation performance target function; non-dominated fast sorting and crowding degree evaluation are performed on the first target function values and the second target function values, the most unfavorable working condition is taken as a benchmark working condition for optimization, a parent population of a plurality of decision variables is obtained, and the parent population is subjected to genetic mutation to obtain a child population until the number of iterations reaches a set number, and an optimal population in an iteration process is obtained; a Euclidean distance of each individual in the optimal population from an origin is calculated, and a decision variable with the closest Euclidean distance from the origin is selected as a primary frequency modulation optimization parameter.

2. The method of claim 1, wherein the method further comprises: The method for obtaining the power reverse modulation comprises the following steps: a fixed-paddle model characteristic curve of the hydroelectric generating set is obtained; operation parameters of the hydroelectric generating set are obtained from the fixed-paddle model characteristic curve of the hydroelectric generating set; a flow characteristic curve and a torque characteristic curve are established according to the operation parameters, and unit speed and unit torque of an arbitrary working condition point are obtained according to the flow characteristic curve and the torque characteristic curve; active power of the hydroelectric generating set is calculated according to the unit speed and the unit torque; a power reverse modulation amount in a primary frequency modulation process is obtained according to the active power and a preset formula.

3. The method of claim 2, wherein the method further comprises: The method for obtaining the power reverse modulation amount in the primary frequency modulation process according to the active power and the preset formula comprises the following steps: in the primary frequency modulation process, if it is a power increase process, the preset formula is: P fp = P0- P min ; if it is a power decrease process, the preset formula is: P fp = P max - P0; Wherein, P0 is the active power of the unit before the primary frequency modulation action, P min is the minimum power in the primary frequency modulation action process max is the maximum power in the primary frequency modulation action process fp is the power counter-regulation amount.

4. The method of claim 2, wherein the method further comprises: The method for establishing the flow characteristic curve and the torque characteristic curve according to the operation parameters, and obtaining the unit speed and the unit torque of the arbitrary working condition point according to the flow characteristic curve and the torque characteristic curve comprises the following steps: the operation parameters include unit speed, unit flow and turbine efficiency; unit flow, unit speed and turbine efficiency under different guide vane opening degrees and paddle opening degrees are obtained according to the operation parameters, and a unit flow matrix and an efficiency matrix are formed; a unit torque matrix is obtained according to the unit flow matrix, the efficiency matrix and a torque matrix formula. The torque characteristic curve and the flow characteristic curve are established according to the unit torque matrix, the efficiency matrix, the unit rotation speed and the unit flow, and the unit rotation speed and the unit torque of any working condition point are obtained according to the flow characteristic curve and the torque characteristic curve; The torque matrix formula is: where n is the number of blade opening lines, m is the number of guide vane opening lines, i = 1, 2, 3...n, j = 1, 2, 3...m, k = 1, 2, 3...p, Q 11,i,j,k is the unit flow matrix, η i,j,k is the efficiency matrix, n 11,i,j,k is the unit speed, p is the number of points read on each guide vane opening line.

5. The method of claim 1, wherein the method further comprises: determining the frequency of the power grid; and determining the frequency of the power grid at the time of the power grid frequency change. The setting of the primary frequency modulation performance index constraint and the decision variable constraint comprises: A standard parameter of the primary frequency modulation performance index is obtained; The primary frequency modulation performance index is set to be less than or equal to the standard parameter as the primary frequency modulation performance index constraint; A parameter order of a preset decision variable is obtained, and the parameter order is taken as the decision variable constraint.

6. The primary frequency modulation parameter optimization method of the bulb tubular hydroelectric generating set, characterized in that comprising: The primary frequency modulation performance objective function is: K P is a regulator proportional link gain coefficient, K I is a regulator integral link gain coefficient, t hx is a power lag time, t 0.9 is a time from the start of primary frequency regulation action to the time when the unit active power reaches 90% of the target value, t s is a time from the start of primary frequency regulation action to the time when the absolute value of the deviation between the unit actual active power and the target value is less than 5%, t hx-s is a standard power lag time, t 0.9-s is a standard power rise time, t s-s is a standard regulation time; The power reverse modulation performance objective function is: Wherein, ΔP0 is the power disturbance quantity in the first frequency modulation process, P fp is the power counter-adjustment quantity.

7. A primary frequency modulation parameter optimization system of a bulb tubular hydroelectric generating set, characterized in that comprising: A first calculation module is configured to calculate the primary frequency modulation of the hydroelectric generating set at different loads at a fixed frequency disturbance between the maximum water head and the minimum water head of the hydroelectric generating set, to obtain the primary frequency modulation performance indexes of the hydroelectric generating set at different working conditions, and to obtain a working condition corresponding to a maximum value of the primary frequency modulation performance indexes, wherein the primary frequency modulation performance indexes comprise a power reverse modulation amount, a power lag time, a power rise time and a regulation time, and the working condition corresponding to the maximum value of the primary frequency modulation performance indexes is taken as an adverse working condition, and the working condition comprises a water head, a guide vane opening degree and a paddle opening degree; A setting module is configured to set a primary frequency modulation performance index constraint and a decision variable constraint, and a decision variable is a primary frequency modulation parameter, and the primary frequency modulation parameter comprises a regulator proportional link gain coefficient and a regulator integral link gain coefficient; A generation module is configured to randomly generate a decision variable group according to the primary frequency modulation performance index constraint and the decision variable constraint; A second calculation module is configured to obtain first objective function values corresponding to different decision variables according to the decision variable group, the primary frequency modulation performance indexes and a primary frequency modulation performance objective function; A third calculation module is configured to obtain second objective function values corresponding to different decision variables according to the decision variable group, the primary frequency modulation parameter and a power reverse modulation performance objective function; An iteration module is configured to perform non-dominated fast sorting and crowding degree evaluation on the first objective function values and the second objective function values, to take the adverse working condition as a benchmark working condition of optimization, to obtain a parent population of the multiple decision variables, and to obtain a child population by genetic mutation of the parent population until an iteration number reaches a set number of times, to obtain an optimal population in an iteration process; An optimization module is configured to calculate a Euclidean distance of each individual in the optimal population from an origin, and to select a decision variable with the closest Euclidean distance from the origin as a primary frequency modulation optimization parameter.

8. The system of claim 7, wherein the system is characterized by: The setting module comprises: A first acquisition unit is configured to obtain a standard parameter of a primary frequency modulation performance index; A setting unit is configured to set the primary frequency modulation performance index to be less than or equal to the standard parameter as a primary frequency modulation performance index constraint. A second obtaining unit is configured to obtain a parameter magnitude of a preset decision variable, and use the parameter magnitude as a decision variable constraint. 9.A terminal device, comprising a memory and a processor, characterized in that, The memory stores a computer program capable of running on the processor, and the processor loads and executes the computer program, and adopts the optimization method according to any one of claims 1 to 6.

10. A computer-readable storage medium having stored therein a computer program, characterized in that, The computer program is loaded and executed by the processor, and the optimization method according to any one of claims 1 to 6 is adopted.

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