Cooperative control method and system based on water hammer effect of wind and light storage compensation water turbine

Abstract

The invention relates to the technical field of power system control, in particular to a cooperative control method and system based on the water hammer effect of a wind and light storage compensation water turbine. A performance index value calculated by an initial angular frequency and an ideal angular frequency is used as a weight updating criterion, and an obtained updated weight, an initial virtual inertia and a virtual damping coefficient are used as input of a neural network to dynamically update parameters so as to realize more stable water hammer effect resistant power compensation. The objective of the invention is to solve the problem of how to realize more stable water hammer effect-resistant power compensation through dynamic parameter design.

Description

Technical Field

[0001] This application relates to the field of power system control technology, and in particular to a collaborative control method and system based on the water hammer effect of wind-solar-storage compensation turbines. Background Technology

[0002] Hydropower generation typically involves two conversion stages: In the first stage, the water flow drives a turbine to convert the kinetic energy of the water into mechanical energy. In the second stage, the turbine transfers this mechanical energy to a generator, causing the generator's rotor to rotate and generate a magnetic field. This magnetic field interacts with the stationary stator coils, cutting magnetic lines of force to generate an electromotive force, thus outputting electrical energy. This process is accompanied by the generation of electromagnetic resistance in the opposite direction to maintain system equilibrium. During this process, due to the continuous water flow, the torque extracted by the turbine from the water flow counteracts the electromagnetic braking torque on the generator rotor. When the two reach equilibrium, the hydropower unit operates at a constant speed, achieving stable hydropower generation. However, during hydropower generation, the hydropower unit experiences water hammer: sudden changes in water flow velocity cause rapid pressure fluctuations within the pipe, forming pressure shock waves. This phenomenon leads to power reversal in the hydropower unit. Therefore, effectively compensating for the power reversal caused by water hammer is a problem that urgently needs to be solved.

[0003] In the relevant technical solutions, the applicant's earlier Chinese patent application, application number 202411004183.4, discloses a method for compensating for the water hammer effect of a hydropower turbine in a photovoltaic-storage system and its coordinated frequency regulation. It discloses that by adding a photovoltaic system and a hybrid energy storage system to the wind-solar-hydro-storage system, the power back-regulation when the hydropower unit experiences the water hammer effect is compensated. Based on the photovoltaic active power output of the photovoltaic system and the battery charge state in the hybrid energy storage system, a corresponding load reduction control strategy is formulated to accelerate the recovery of the system frequency and quickly smooth out power oscillations.

[0004] However, during the process of conceiving and implementing this application, the applicant discovered that the water hammer effect causes the frequency dynamics of the wind-solar-hydro storage system to exhibit significant nonlinear characteristics. In traditional control schemes, the rotational inertia and damping coefficient of the system are fixed parameter values. This design is prone to limited control effect in nonlinear environments and occasional power oscillations may still occur.

[0005] Therefore, this application aims to propose a new control method to achieve more stable power compensation against water hammer through dynamic parameter design. Summary of the Invention

[0006] The main objective of this application is to provide a collaborative control method for water hammer effect compensation turbine based on wind, solar and storage, aiming to solve the problem of how to achieve more stable power compensation against water hammer effect through dynamic parameter design.

[0007] To achieve the above objectives, this application provides a collaborative control method based on wind-solar-storage compensation for water hammer effect in hydropower turbines, applicable to a wind-solar-hydropower-storage system. The system includes a hydropower unit, a photovoltaic system, a wind turbine system, and an energy storage system. The method comprises the following steps:

[0008] Obtain the initial angular frequency, initial virtual inertia, and initial virtual damping coefficient of the wind-solar-hydro-storage system;

[0009] The performance index value is calculated based on the initial angular frequency and the preset ideal angular frequency, wherein the calculation expression for the performance index value is:

[0010]

[0011] Where, Here, k represents the performance metric value, and k is the number of neurons in the output layer of the neural network. For the ideal angular frequency, The initial angular frequency;

[0012] When the performance index value is greater than a preset threshold, the update weight is calculated based on the performance index value, and the update weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0013] When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

[0014] Optionally, the updated weights include weights and biases, and the step of calculating the updated weights based on the performance index values ​​includes:

[0015] The updated weights are calculated using the gradient descent method based on the performance index values:

[0016]

[0017] In the formula: For weight, For bias, For learning rate, Indicates performance index value The partial derivative with respect to the weight w, Indicates performance index value The partial derivative with respect to bias b.

[0018] Optionally, the step of controlling at least one of the wind turbine system, photovoltaic system, and energy storage system to implement the target water hammer effect compensation strategy based on the current remaining power value, the target virtual inertia, and / or the target virtual damping coefficient includes:

[0019] Determine the frequency difference between the frequency values ​​collected at two different times in the wind-solar-hydro-storage system;

[0020] When the frequency difference is within the first interval, a photovoltaic compensation water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0021] When the frequency difference is within the second interval, a photovoltaic-wind turbine water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0022] When the frequency difference is within the third interval, a water hammer effect compensation strategy for the photovoltaic-wind turbine-energy storage system is executed based on the current remaining power, the target virtual inertia and the target virtual inertia.

[0023] Wherein, the first interval is smaller than the second interval, which is smaller than the third interval.

[0024] Optionally, the step of executing the photovoltaic compensation water hammer effect compensation strategy based on the target virtual inertia includes:

[0025] Determine the additional photovoltaic compensation power value of the photovoltaic system. Wherein, the photovoltaic compensation power value Satisfy the following expression:

[0026]

[0027] In the formula, K f The rated load shedding rate is given, ∆f is the grid frequency deviation, and f0 is the rated grid frequency. This represents the active power output of the photovoltaic system after load reduction. The target virtual inertia coefficient, The target virtual damping coefficient;

[0028] The photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0029] Optionally, the step of executing the photovoltaic-wind turbine water hammer effect compensation strategy based on the target virtual inertia and the target virtual damping coefficient includes:

[0030] Determine the additional wind turbine compensation power value of the wind turbine unit. The wind turbine compensation power value Satisfy the following expression:

[0031]

[0032] In the formula, β is the adaptive coefficient output by the fuzzy controller; D is the target virtual inertia coefficient. This represents the rate of change of the power grid frequency deviation.

[0033] And, determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0034] The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power output, and the control of the photovoltaic system to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0035] Optionally, the step of executing the water hammer effect compensation strategy for the photovoltaic-wind turbine-energy storage system based on the current remaining power, the target virtual inertia, and the target virtual inertia includes:

[0036] The target droop coefficient for the charging process of the energy storage system is determined based on the current remaining power value. and the target droop coefficient during the discharge process :

[0037]

[0038] Where, , These are the piecewise functions for charging and discharging, respectively. This represents the maximum value of the droop control coefficient;

[0039] in:

[0040]

[0041] In the formula, S represents the total power consumption, and its value ranges from [0,1]. This is the lower limit of battery capacity. The second lowest battery level. This is the second highest energy level. This is the maximum battery capacity.

[0042] According to the target droop coefficient of the charging process and the target droop coefficient during the discharge process Determine the compensation power value of the energy storage system :

[0043]

[0044] In the formula, f is the real-time frequency of the hydroelectric generator unit. This refers to the frequency difference of the hydroelectric generator unit.

[0045] Determine the additional wind turbine compensation power value of the wind turbine unit. And determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0046] The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power of the photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power of the energy storage system, and the control of the energy storage system to compensate the hydropower unit to meet the active power requirements of the energy storage system. The power.

[0047] Optionally, the first interval is (-0.05Hz, 0.05Hz), and the second interval is... The third interval is .

[0048] Optionally, the water hammer effect detection step includes:

[0049] Obtain the rotor angular velocity deviation and mechanical power deviation of the hydroelectric generator unit;

[0050] When the positive and negative relationships between the rotor angular velocity deviation and the mechanical power deviation of the hydropower unit are the same, it is determined that the hydropower unit is experiencing the water hammer effect.

[0051] Otherwise, it is determined that the water hammer effect does not occur in the hydroelectric generator unit.

[0052] Optionally, after the step of calculating the performance index value based on the initial angular frequency and the ideal angular frequency, the method further includes:

[0053] When the performance index value is less than or equal to a preset threshold, the current weight is obtained, and the current weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0054] When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

[0055] In addition, to achieve the above objectives, this application also provides a computer system, the computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the coordinated control method based on the wind-solar-storage compensation turbine water hammer effect as described in any of the preceding claims.

[0056] This application has at least the following beneficial effects:

[0057] 1. The performance index values ​​calculated by the initial angular frequency and the ideal angular frequency are used as the weight update criteria. The obtained updated weights, along with the initial virtual inertia and virtual damping coefficient, are used as inputs to the neural network to dynamically update the parameters, so as to achieve more stable power compensation against water hammer effect.

[0058] 2. By adding wind power, photovoltaic and energy storage systems to the wind-solar-hydro-storage system, the power back-regulation problem caused by the water hammer effect of the turbine is compensated, the frequency recovery is accelerated, the power oscillation is quickly suppressed, and the stability and reliability of the power grid supply are guaranteed. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the first embodiment of the collaborative control method for wind-solar-storage turbine water hammer effect based on the present application.

[0060] Figure 2 This is a schematic diagram of the BP neural network architecture involved in the embodiments of this application;

[0061] Figure 3 This is a schematic diagram of the simulation model architecture involved in the embodiments of this application;

[0062] Figure 4 This is a schematic diagram of the frequency difference change input, frequency difference change rate input, and adaptive coefficient output of the fuzzy controller for a wind turbine involved in an embodiment of this application;

[0063] Figure 5 This is a comparison chart of the frequency and power response curves of fixed virtual inertia and virtual damping coefficient and dynamic virtual inertia and virtual damping coefficient under VSG control in the embodiments of this application.

[0064] Figure 6 This is a comparison chart of the frequency and power response curves of uncompensated and wind-solar-storage compensated systems involved in the embodiments of this application;

[0065] Figure 7 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0066] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0068] First embodiment

[0069] Reference Figure 1 In this embodiment, the collaborative control method based on wind-solar-storage compensation for water hammer effect of hydro turbines is applied to a wind-solar-hydro-storage system, which includes a hydropower unit, a photovoltaic system, a wind turbine system, and a hybrid energy storage system. The method includes the following steps:

[0070] Step S10: Obtain the initial angular frequency, initial virtual inertia, and initial virtual damping coefficient of the wind-solar-hydro-storage system;

[0071] Step S20: Calculate the performance index value based on the initial angular frequency and the preset ideal angular frequency, wherein the calculation expression for the performance index value is:

[0072]

[0073] Where, Here, k represents the performance metric value, and k is the number of neurons in the output layer of the neural network. For the ideal angular frequency, The initial angular frequency;

[0074] In this embodiment, the initial angular frequency and the ideal angular frequency are used as criteria for whether the weights in the neural network need to be updated. The magnitude of the performance index values ​​calculated from the initial angular frequency and the ideal angular frequency is used to determine whether the virtual inertia and virtual damping coefficient need to be updated.

[0075] It should be noted that the ideal angular frequency refers to the rated angular velocity of the power grid, which is a preset value. ,

[0076] Step S30: When the performance index value is greater than or equal to a preset threshold, calculate the update weight based on the performance index value, and input the update weight, the initial virtual inertia and the initial virtual damping coefficient into the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0077] In this step, when the performance index value is greater than or equal to a preset threshold, it means that the weights need to be updated. The updated weights are calculated based on the performance index value.

[0078] In some alternative implementations, gradient descent is used to calculate the updated weights based on the performance metric values:

[0079]

[0080] In the formula: For weight, For bias, For learning rate, Indicates performance index value The partial derivative with respect to the weight w, Indicates performance index value The partial derivative with respect to bias b.

[0081] The obtained updated weights, initial virtual inertia, and initial virtual damping coefficient are input into the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0082] It should be noted that when VSG control is applied to wind-solar-hydro-storage systems, the water hammer effect causes the frequency dynamics to exhibit significantly nonlinear characteristics. Traditional fixed J and D parameter designs have limited control effectiveness under such nonlinear conditions. By utilizing the adaptive learning characteristics of neural networks and updating network weights online through real-time error feedback, the virtual inertia J and virtual damping coefficient D can be dynamically optimized to better cope with water hammer disturbances and improve the stability of wind-solar-hydro-storage systems.

[0083] In some alternative implementations, refer to Figure 2 The diagram shown illustrates the BP neural network architecture. Based on the BP neural network, VSG parameters are adaptively optimized. The input layer contains three variables: updated weights, initial virtual inertia, and initial virtual damping. The hidden layer has m neurons, and the output layer contains two quantities: J and D. The specific calculation steps are as follows:

[0084] 1) Import input variables, where x is the i-th neuron in the input layer. i ;

[0085] 2) Calculate h of the i-th neuron in the hidden layer i input value With output value ;

[0086]

[0087]

[0088] In the formula: n is the number of neurons in the input layer; These are the weights of the hidden layer; For the hidden layer bias, (·) represents the activation function of the hidden layer, using the sigmoid function, as shown below:

[0089]

[0090] 3) Calculate the y of the i-th neuron in the output layer. i Input volume With output :

[0091]

[0092]

[0093] In the formula: m is the number of neurons in the hidden layer; These are the output layer weights; For output layer bias, (·) is the activation function for the hidden layer, which also uses the sigmoid function.

[0094] On the other hand, in some optional implementations, if the performance index value is less than or equal to a preset threshold, the weights are not updated, but the virtual inertia and virtual damping coefficient may need to be updated. The current weights, initial virtual inertia, and initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network. At this time, the target virtual inertia and target virtual damping coefficient obtained may be the same as or different from the initial values. When performing compensation control for the water hammer effect of the turbine in the future, the control is still based on the target virtual inertia and target virtual damping coefficient obtained after dynamic adjustment by the neural network.

[0095] Step S40: When water hammer effect is detected in the hydropower unit, the current remaining power value of the hybrid energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, the photovoltaic system and the hybrid energy storage system is controlled to execute the target water hammer effect compensation strategy.

[0096] After obtaining the updated target virtual inertia and target virtual damping coefficient, this step is triggered when the water hammer effect of the hydropower unit is detected.

[0097] In some optional implementations, the rotor angular velocity deviation and mechanical power deviation of the hydropower unit are obtained; when the positive and negative relationships between the rotor angular velocity deviation and the mechanical power deviation of the hydropower unit are the same, it is determined that the hydropower unit has the water hammer effect; otherwise (i.e., the positive and negative relationships between the rotor angular velocity deviation and the mechanical power deviation are opposite, or other situations), it is determined that the hydropower unit has not experienced the water hammer effect.

[0098] It should be noted that the rotor angular velocity deviation is w(t) - w(t-1), which is the rotor angular velocity at second t minus the rotor angular velocity at (t-1). The mechanical power deviation is Pm(t) - Pm(t-1), which is the mechanical power at second t minus the mechanical power at (t-1).

[0099] In some optional implementations, both rotor angular velocity deviation and mechanical power deviation can be captured using the grid frequency. The grid frequency is monitored in real time to capture frequency difference changes during grid operation. Advanced frequency measurement technology is used to accurately monitor real-time grid frequency data. This data is compared with pre-set thresholds to determine whether a compensation mechanism needs to be activated. When the detected frequency difference exceeds the set threshold, corresponding compensation measures are automatically implemented based on the level of frequency difference change to mitigate the impact of water hammer on grid stability.

[0100] Furthermore, the target water hammer effect compensation strategies include a photovoltaic compensation water hammer effect compensation strategy where the photovoltaic system compensates alone, a photovoltaic-wind turbine compensation water hammer effect compensation strategy where the photovoltaic system and wind turbine system jointly compensate, and a photovoltaic-wind turbine-energy storage system compensation water hammer effect compensation strategy where the photovoltaic system, wind turbine system, and energy storage system jointly compensate. The selection of each strategy is determined based on the frequency difference between the frequency values ​​collected at two different times in the wind, solar, hydro, and energy storage systems. This part will be further explained in subsequent embodiments and will not be repeated here.

[0101] In the technical solution provided in this embodiment, the performance index values ​​calculated by the initial angular frequency and the ideal angular frequency are used as the weight update criteria. The obtained updated weights, as well as the initial virtual inertia and virtual damping coefficient, are used as the input of the neural network to dynamically update the parameters. Based on the updated target virtual inertia and target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and hybrid energy storage system in the wind-solar-hydro-storage system is controlled to execute the target water hammer effect compensation strategy, so as to achieve more stable power compensation against water hammer effect.

[0102] Second embodiment

[0103] Based on the first embodiment, in this embodiment, step S40 includes:

[0104] Step S41: Determine the frequency difference between the frequency values ​​of the wind-solar-hydro-storage system collected at two different times;

[0105] Step S42: When the frequency difference is within the first interval, a photovoltaic compensation water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0106] Optionally, photovoltaic compensation strategies for water hammer effects include:

[0107] Step S421: Determine the additional photovoltaic compensation power value of the photovoltaic system. Wherein, the photovoltaic compensation power value Satisfy the following expression:

[0108]

[0109] In the formula, K f The rated load shedding rate is given, ∆f is the grid frequency deviation, and f0 is the rated grid frequency. This represents the active power output of the photovoltaic system after load reduction. The target virtual inertia coefficient, The target virtual damping coefficient;

[0110] Step S422: Control the photovoltaic system to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0111] In step S422, when the power system frequency fluctuation is within ±0.05Hz, the power fluctuation of the hydropower unit has a relatively minor impact on the power grid, and the power grid can maintain stability through conventional frequency regulation. At this time, the photovoltaic system can serve as the primary compensation method. Because photovoltaic power generation has a fast response speed, it can provide electricity promptly during the day to compensate for power fluctuations caused by water hammer. The compensation power is:

[0112]

[0113] In the formula: This represents the total additional force generated by the combined system when the water hammer effect occurs.

[0114] The photovoltaic compensation power value in step S421 is described below. The derivation process of the expression is illustrated by example:

[0115] The photovoltaic system operates in a load shedding mode and employs Maximum Power Point Tracking (MPPT) control. After reserving the load shedding rate, the expression for the load shedding rate of the photovoltaic power generation system is defined as follows:

[0116]

[0117] Where: K f Δf is the rated load shedding rate, f0 is the grid frequency deviation, d is the rated grid frequency, and d is the photovoltaic load shedding rate.

[0118] The active power output of the photovoltaic system after load reduction is:

[0119]

[0120] In the formula: P MPPT P represents the maximum power output of photovoltaics. DPPT This refers to the active power output of the photovoltaic system after load reduction.

[0121] The photovoltaic inertia control strategy is implemented through the dynamic characteristics of a phase-locked loop (PLL). The power system frequency under PLL control is:

[0122]

[0123] Where: U tq K represents the q-axis component of the inverter terminal voltage. P K I These are the phase-locked loop control parameters.

[0124] Inertial power component P PV1 for:

[0125]

[0126] In the formula: Let be the target virtual inertia coefficient of the photovoltaic system.

[0127] Damping power component P PV2 for:

[0128]

[0129] In the formula: This represents the target virtual damping coefficient for the photovoltaic system.

[0130] Considering photovoltaic DPPT and virtual inertial control, the additional active power that the photovoltaic system can generate when the water hammer effect occurs is:

[0131]

[0132] Step S43: When the frequency difference is within the second interval, a photovoltaic-wind turbine water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0133] Specifically, the steps of the photovoltaic-wind turbine water hammer effect compensation strategy include:

[0134] Step S431: Determine the additional wind turbine compensation power value generated by the wind turbine unit. The wind turbine compensation power value Satisfy the following expression:

[0135]

[0136] In the formula, β is the adaptive coefficient output by the fuzzy controller; D is the target virtual inertia coefficient. This represents the rate of change of the power grid frequency deviation.

[0137] And, determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0138] Step S432: Control the wind turbine to compensate the hydropower unit to meet the wind turbine compensation power value. The power output, and the control of the photovoltaic system to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0139] In step S432, when the power system frequency fluctuates between ±0.05Hz and ±0.2Hz, the power fluctuations of hydropower units are relatively large, which may put some pressure on the stability of the power grid. At this time, in addition to the photovoltaic system, wind power also participates in compensation. Photovoltaics and wind power provide rapid power compensation through coordinated control. Photovoltaics operate during the day, while wind power can flexibly provide power compensation according to wind speed changes, balancing the power instability caused by the fluctuations in hydropower units. By combining wind and photovoltaic power, a smoother response to frequency fluctuations can be achieved, alleviating the pressure on the power grid caused by the water hammer effect. The compensation power is:

[0140]

[0141] The following describes the fan compensation power value in step S431. The derivation process of the expression is illustrated by example:

[0142] The rotor of a wind turbine can store kinetic energy. When the system experiences water hammer, this kinetic energy can be released through control strategies, increasing the active power output of the wind turbine and compensating for the power deficit caused by the water hammer effect in hydroelectric turbines. Through fuzzy control strategies, the wind turbine can dynamically adjust its output power according to real-time changes in grid frequency. This control strategy not only improves the wind turbine's responsiveness to grid frequency changes but also optimizes its energy utilization.

[0143] Frequency difference Sum of frequency difference change β serves as the input to the fuzzy controller, and β serves as its output. When a doubly-fed induction generator (DFIG) is equipped with virtual inertia adaptive control based on fuzzy control, the output power compensating for the water hammer effect of the turbine, the frequency difference variation of the power system, and the rate of frequency change can be related by a function, the formula of which is:

[0144]

[0145] Where: ∆P wind β is the output of the wind turbine to compensate for the water hammer effect of the water turbine; D is the target virtual inertia coefficient.

[0146] When water hammer occurs, the system frequency deviates from the steady-state value. β should be increased as much as possible to increase the equivalent inertia and damping of the wind turbine and prevent the frequency from deviating too quickly. When the system frequency returns to the steady-state value, β should be decreased as much as possible to reduce the equivalent inertia and damping of the wind turbine and accelerate the recovery of the system frequency.

[0147] Step S44: When the frequency difference is within the third interval, based on the current remaining power, the target virtual inertia and the target virtual, execute the photovoltaic-wind turbine-energy storage system water hammer effect compensation strategy;

[0148] In step S44, the key to mitigating power and frequency disturbances caused by water hammer effects using energy storage lies in the economical and reliable configuration of energy storage capacity. In actual operation, limited by the scale of energy storage, charging and discharging behavior must be optimized and scheduled strictly according to the State of Charge (SOC). To prevent runaway problems in the energy storage system caused by a fixed maximum droop coefficient, a control method that adaptively adjusts the droop coefficient according to changes in SOC is usually implemented.

[0149] When the State of Charge (SOC) approaches its upper and lower limits, the energy storage system operates with a variable droop coefficient to prevent overcharging and over-discharging. By dynamically adjusting the droop coefficient when the SOC approaches its upper and lower limits using a piecewise function, the energy storage system achieves smooth power output. Furthermore, this piecewise function avoids the practical difficulties caused by numerous parameter settings in complex functions, thus facilitating practical applications.

[0150] Optionally, the steps of the water hammer effect compensation strategy for photovoltaic-wind turbine-energy storage systems include:

[0151] Step S441: Determine the target droop coefficient for the charging process of the hybrid energy storage system based on the current remaining power value. and the target droop coefficient during the discharge process :

[0152]

[0153] Where, , These are the piecewise functions for charging and discharging, respectively. This represents the maximum value of the droop control coefficient;

[0154] in:

[0155]

[0156] In the formula, S represents the total power consumption, and its value ranges from [0,1]. This is the lower limit of battery capacity. The second lowest battery level. This is the second highest energy level. This is the maximum battery capacity.

[0157] The energy storage system only charges and discharges when the hydropower unit experiences water hammer. It monitors the battery's state of charge (SOC) and grid frequency in real time, considering the safety constraints of the battery's SOC. Based on the SOC value, it adjusts the battery's charge and discharge power coefficient to avoid overcharging or over-discharging. The system controls the output of the lead-carbon battery and the intensity and speed of its response to transient power deficits through a droop coefficient, optimizing system stability and balancing supply and demand in a short time. When a power deficit occurs in the system, the active power provided by the lead-carbon battery is:

[0158] Step S442, based on the target droop coefficient of the charging process and the target droop coefficient during the discharge process Determine the compensation power value of the energy storage system :

[0159]

[0160] In the formula, f is the real-time frequency of the hydroelectric generator unit. This refers to the frequency difference of the hydroelectric generator unit.

[0161] Step S443: Determine the additional wind turbine compensation power value generated by the wind turbine unit. And determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0162] Step S444: Control the wind turbine to compensate the hydropower unit to meet the wind turbine compensation power value. The power of the photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power of the energy storage system, and the control of the energy storage system to compensate the hydropower unit to meet the active power requirements of the energy storage system. The power.

[0163] In step S444, when the power system frequency fluctuation exceeds ±0.2Hz, the instantaneous power fluctuation caused by the water hammer effect is large, which may lead to prolonged instability of the grid frequency. At this time, photovoltaic, wind power, and energy storage systems all participate in power compensation. Energy storage systems can provide more powerful and stable regulation capabilities, especially under conditions of large frequency fluctuations. Energy storage devices can balance the grid frequency through rapid charging and discharging, reducing the impact of instantaneous power fluctuations caused by the water hammer effect on the grid. The compensation power is:

[0164]

[0165] Wherein, the first interval is smaller than the second interval, which is smaller than the third interval.

[0166] Furthermore, in some optional embodiments, the first interval is (-0.05Hz, 0.05Hz), and the second interval is... The third interval is .

[0167] In the technical solution provided in this embodiment, by adding wind power, photovoltaic and energy storage systems to the wind-solar-hydro-storage system, the power back-regulation problem caused by the water hammer effect of the turbine is compensated, the frequency recovery is accelerated, the power oscillation is quickly suppressed, and the stability and reliability of the power grid supply are guaranteed.

[0168] Third Embodiment

[0169] Based on any of the above embodiments, this embodiment provides a simulation model on the basis of the content described in the above embodiments, as follows:

[0170] Building models, such as Figure 3 As shown in the figure (S) VSG The pulse signal representing VSG, SOC initial_lc This indicates the initial state of charge (SOC) of a lead-carbon battery. lc D represents the current state of the measured SOC. lc D represents the trigger pulse of a lead-carbon battery. dppt P represents the photovoltaic load shedding control trigger pulse. wind (This refers to a wind turbine power pulse). The system includes hydropower units, wind power systems, photovoltaic systems, and energy storage systems.

[0171] The hydropower unit has a capacity of 50MW, a base voltage of 6.3kV, and is stepped up to 35kV for grid connection. The three-phase load is set at 10MW. The system rated frequency is 50Hz, and the simulation results use per-unit (pu) values ​​with a power base of 50MW. The hydropower unit parameters are taken as: K P =5,K I =2,K D =2, bp =0,T y =0.2, T w =1.5, T a =8,e n =3.

[0172] The wind turbine has an installed capacity of 20MW. The wind power system adopts fuzzy virtual inertia control and is connected to the AC bus via AC / DC and DC / AC converters. The initial virtual inertia coefficient of the wind turbine is K=5, and the fuzzy controller... Input rules The input rules and the adaptive coefficient β of the fuzzy controller output are as follows: Figure 4 As shown.

[0173] The photovoltaic system has an installed capacity of 20MW. It employs a hybrid control system that follows changes in system output and frequency deviation, and is connected to the AC bus via DC / DC and DC / AC converters. The rated load shedding rate K of the photovoltaic system is... f =0.9.

[0174] The energy storage system has a capacity of 10MW and consists of lead-carbon batteries, employing variable coefficient droop control. Figure 3 In this context, E represents a piecewise function of the charge and discharge coefficients; during charging, E becomes... During discharge, E becomes The energy is exchanged and stored through a bidirectional DC / DC converter, and then connected to the AC bus via DC / AC.

[0175] Fourth embodiment

[0176] Based on any of the above embodiments, this embodiment uses a BP neural network to dynamically optimize the dynamic parameters of virtual inertia and virtual damping coefficient to verify the improvement of system performance. It should be noted that among the water hammer effect compensation strategies executed by the wind-solar-hydro-storage system, the system frequency fluctuation corresponding to the photovoltaic-wind turbine-energy storage system water hammer effect compensation strategy is the largest and most likely to cause long-term grid frequency instability. Therefore, this section only verifies the method of compensating for the water hammer effect of the turbine in the photovoltaic-wind turbine-energy storage system combined system, as follows:

[0177] Figure 5 The test demonstrates the frequency and power response curves for fixed J, D and dynamic J, D under VSG control. In this test scenario, the wind-solar-storage system is not involved in the processing; only the performance of the fixed J, D and dynamic J, D systems under VSG control is tested, with a sudden load increase ΔP at time t=5s. W =0.1pu.

[0178] With fixed J and D control, the frequency minimum change is 0.023 pu, the overshoot is approximately 0.006 pu, the power inversion minimum is 0.075 pu, the power overshoot maximum is 0.185 pu, and the steady-state recovery time is 35 s. With dynamic J and D control, the frequency minimum change is 0.022 pu, the overshoot is approximately 0.005 pu, the power inversion minimum is 0.075 pu, the power overshoot maximum is 0.185 pu, and the steady-state recovery time is 30 s.

[0179] Table 1. Comparison of Fixed J and D with Dynamic J and D

[0180]

[0181] Table 1 shows that, compared with fixed J and D control, dynamic J and D control reduces the frequency minimum change by 4.347%, overshoot by 16.667%, power inversion minimum by 1.333%, power overshoot maximum by 3.784%, and steady-state recovery time by 14.286%.

[0182] Fifth embodiment

[0183] Based on any of the above embodiments, this implementation verifies the resistance to water hammer effect between a wind-solar-hydro-storage system that incorporates photovoltaic, wind turbine, and energy storage systems for power compensation and a traditional hydropower system without compensation.

[0184] Figure 6 The system frequency and power response curves of uncompensated and wind-solar-storage compensated systems are displayed. In this test scenario, all wind, solar, and storage systems participate in power output, and a BP neural network is used to dynamically optimize the VSG control parameters J and D. A sudden load increase ΔP occurs at time t=5s. W =0.1pu.

[0185] Without compensation, the frequency minimum change is 0.024 pu, the overshoot is approximately 0.008 pu, the power inversion minimum is 0.075 pu, the power overshoot maximum is 0.196 pu, and the steady-state recovery time is 40 s. With wind-solar-storage compensation, the frequency minimum change is 0.013 pu, the overshoot is approximately 0 p.u., the power inversion minimum is 0.024 pu, the power overshoot maximum is 0.102 pu, and the steady-state recovery time is 15 s.

[0186] Table 2. Verification of water hammer effect in wind-solar-storage compensated hydro turbines

[0187]

[0188] Table 2 shows that under wind-solar-storage compensation, the change in the lowest frequency point is reduced by 45.833%, there is no frequency overshoot, the lowest power inversion point is reduced by 72%, the highest power overshoot point is reduced by 47.959%, ​​and the steady-state recovery time is reduced by 62.5%.

[0189] Furthermore, as an implementation scheme, Figure 7 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0190] like Figure 7 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0191] Those skilled in the art will understand that Figure 7 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0192] like Figure 7 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0193] exist Figure 7 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0194] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein:

[0195] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0196] Obtain the initial angular frequency, initial virtual inertia, and initial virtual damping coefficient of the wind-solar-hydro-storage system;

[0197] The performance index value is calculated based on the initial angular frequency and the preset ideal angular frequency, wherein the calculation expression for the performance index value is:

[0198]

[0199] Where, Here, k represents the performance metric value, and k is the number of neurons in the output layer of the neural network. For the ideal angular frequency, The initial angular frequency;

[0200] When the performance index value is greater than a preset threshold, the update weight is calculated based on the performance index value, and the update weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0201] When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

[0202] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0203] Determine the frequency difference between the frequency values ​​collected at two different times in the wind-solar-hydro-storage system;

[0204] When the frequency difference is within the first interval, a photovoltaic compensation water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0205] When the frequency difference is within the second interval, a photovoltaic-wind turbine water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient.

[0206] When the frequency difference is within the third interval, a water hammer effect compensation strategy for the photovoltaic-wind turbine-energy storage system is executed based on the current remaining power, the target virtual inertia and the target virtual inertia.

[0207] Wherein, the first interval is smaller than the second interval, which is smaller than the third interval.

[0208] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0209] Determine the additional photovoltaic compensation power value of the photovoltaic system. Wherein, the photovoltaic compensation power value Satisfy the following expression:

[0210]

[0211] In the formula, K f The rated load shedding rate is given, ∆f is the grid frequency deviation, and f0 is the rated grid frequency. This represents the active power output of the photovoltaic system after load reduction. The target virtual inertia coefficient, The target virtual damping coefficient;

[0212] The photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0213] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0214] Determine the additional wind turbine compensation power value of the wind turbine unit. The wind turbine compensation power value Satisfy the following expression:

[0215]

[0216] In the formula, β is the adaptive coefficient output by the fuzzy controller; D is the target virtual inertia coefficient. This represents the rate of change of the power grid frequency deviation.

[0217] And, determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0218] The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power output, and the control of the photovoltaic system to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

[0219] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0220] The target droop coefficient for the charging process of the energy storage system is determined based on the current remaining power value. and the target droop coefficient during the discharge process :

[0221]

[0222] Where, , These are the piecewise functions for charging and discharging, respectively. This represents the maximum value of the droop control coefficient;

[0223] in:

[0224]

[0225] In the formula, S represents the total power consumption, and its value ranges from [0,1]. This is the lower limit of battery capacity. The second lowest battery level. This is the second highest energy level. This is the maximum battery capacity.

[0226] According to the target droop coefficient of the charging process and the target droop coefficient during the discharge process Determine the compensation power value of the energy storage system :

[0227]

[0228] In the formula, f is the real-time frequency of the hydroelectric generator unit. This refers to the frequency difference of the hydroelectric generator unit.

[0229] Determine the additional wind turbine compensation power value of the wind turbine unit. And determine the additional photovoltaic compensation power value generated by the photovoltaic system. ;

[0230] The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power of the photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power of the energy storage system, and the control of the energy storage system to compensate the hydropower unit to meet the active power requirements of the energy storage system. The power.

[0231] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0232] Obtain the rotor angular velocity deviation and mechanical power deviation of the hydroelectric generator unit;

[0233] When the positive and negative relationships between the rotor angular velocity deviation and the mechanical power deviation of the hydropower unit are the same, it is determined that the hydropower unit is experiencing the water hammer effect.

[0234] Otherwise, it is determined that the water hammer effect does not occur in the hydroelectric generator unit.

[0235] When processor 1001 calls a computer program stored in memory 1005, it performs the following operations:

[0236] When the performance index value is less than or equal to a preset threshold, the current weight is obtained, and the current weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network.

[0237] When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

[0238] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0239] Therefore, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the collaborative control method based on the water hammer effect of a wind-solar-storage turbine as described in the above embodiments.

[0240] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0241] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0242] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0243] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0244] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0245] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0246] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0247] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0248] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A collaborative control method based on wind-solar-storage compensation for water hammer effect in hydroelectric turbines, characterized in that, The method, applicable to wind-solar-hydro-storage systems, which include hydropower units, photovoltaic systems, wind turbine systems, and energy storage systems, comprises the following steps: Obtain the initial angular frequency, initial virtual inertia, and initial virtual damping coefficient of the wind-solar-hydro-storage system; The performance index value is calculated based on the initial angular frequency and the preset ideal angular frequency, wherein the calculation expression for the performance index value is: ； Where, Here, k represents the performance metric value, and k is the number of neurons in the output layer of the neural network. For the ideal angular frequency, The initial angular frequency; When the performance index value is greater than a preset threshold, the update weight is calculated based on the performance index value, and the update weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network. When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

2. The method as described in claim 1, characterized in that, The updated weights include weights and biases, and the step of calculating the updated weights based on the performance index values ​​includes: The updated weights are calculated using the gradient descent method based on the performance index values: ； In the formula: For weight, For bias, For learning rate, Indicates performance index value The partial derivative with respect to the weight w, Indicates performance index value The partial derivative with respect to bias b.

3. The method as described in claim 1, characterized in that, The step of controlling at least one of the wind turbine system, photovoltaic system, and energy storage system to implement the target water hammer effect compensation strategy based on the current remaining power value, the target virtual inertia, and / or the target virtual damping coefficient includes: Determine the frequency difference between the frequency values ​​collected at two different times in the wind-solar-hydro-storage system; When the frequency difference is within the first interval, a photovoltaic compensation water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient. When the frequency difference is within the second interval, a photovoltaic-wind turbine water hammer effect compensation strategy is executed based on the target virtual inertia and the target virtual damping coefficient. When the frequency difference is within the third interval, a water hammer effect compensation strategy for the photovoltaic-wind turbine-energy storage system is executed based on the current remaining power, the target virtual inertia and the target virtual inertia. Wherein, the first interval is smaller than the second interval, which is smaller than the third interval.

4. The method as described in claim 3, characterized in that, The steps of executing the photovoltaic compensation water hammer effect compensation strategy based on the target virtual inertia include: Determine the additional photovoltaic compensation power value of the photovoltaic system. Wherein, the photovoltaic compensation power value Satisfy the following expression: ； In the formula, K f The rated load shedding rate is given, ∆f is the grid frequency deviation, and f0 is the rated grid frequency. This represents the active power output of the photovoltaic system after load reduction. The target virtual inertia coefficient, The target virtual damping coefficient; The photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

5. The method as described in claim 3 or 4, characterized in that, The steps for implementing the photovoltaic-wind turbine water hammer effect compensation strategy based on the target virtual inertia and the target virtual damping coefficient include: Determine the additional wind turbine compensation power value. The wind turbine compensation power value Satisfy the following expression: ； In the formula, β is the adaptive coefficient output by the fuzzy controller; D is the target virtual inertia coefficient. This represents the rate of change of the power grid frequency deviation. And, determine the additional photovoltaic compensation power value generated by the photovoltaic system. ; The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power output, and the control of the photovoltaic system to compensate the hydropower unit to meet the photovoltaic compensation power value. The power.

6. The method as described in claim 5, characterized in that, The steps for implementing the water hammer effect compensation strategy for the photovoltaic-wind turbine-energy storage system based on the current remaining power, the target virtual inertia, and the target virtual inertia include: The target droop coefficient for the charging process of the energy storage system is determined based on the current remaining power value. and the target droop coefficient during the discharge process : ； Where, , These are the piecewise functions for charging and discharging, respectively. This represents the maximum value of the droop control coefficient; in: ； ； In the formula, S represents the total power consumption, and its value ranges from [0,1]. This is the lower limit of battery capacity. The second lowest battery level. This is the second highest energy level. This is the maximum battery capacity. According to the target droop coefficient of the charging process and the target droop coefficient during the discharge process Determine the compensation power value of the energy storage system : ； In the formula, f is the real-time frequency of the hydroelectric generator unit. This refers to the frequency difference of the hydroelectric generator unit. Determine the additional wind turbine compensation power value of the wind turbine unit. And determine the additional photovoltaic compensation power value generated by the photovoltaic system. ; The wind turbine generator is controlled to compensate the hydro turbine generator to meet the wind turbine compensation power value. The power of the photovoltaic system is controlled to compensate the hydropower unit to meet the photovoltaic compensation power value. The power output, and the control of the energy storage system to compensate the hydropower unit to meet the compensation power value of the energy storage system. The power.

7. The method as described in claim 3, characterized in that, The first interval is (-0.05Hz, 0.05Hz), and the second interval is... The third interval is .

8. The method as described in claim 1, characterized in that, The detection steps for the water hammer effect include: Obtain the rotor angular velocity deviation and mechanical power deviation of the hydroelectric generator unit; When the positive and negative relationships between the rotor angular velocity deviation and the mechanical power deviation of the hydropower unit are the same, it is determined that the hydropower unit is experiencing the water hammer effect. Otherwise, it is determined that the water hammer effect does not occur in the hydroelectric generator unit.

9. The method as described in claim 1, characterized in that, After the step of calculating the performance index value based on the initial angular frequency and the ideal angular frequency, the method further includes: When the performance index value is less than or equal to a preset threshold, the current weight is obtained, and the current weight, the initial virtual inertia and the initial virtual damping coefficient are input to the input layer of the neural network to obtain the target virtual inertia and target virtual damping coefficient output by the neural network. When water hammer effect is detected in the hydropower unit, the current remaining power value of the energy storage system is obtained. Based on the current remaining power value, the target virtual inertia and / or the target virtual damping coefficient, at least one of the wind turbine system, photovoltaic system and energy storage system is controlled to execute the target water hammer effect compensation strategy.

10. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the coordinated control method based on the water hammer effect of a wind-solar-storage turbine as described in any one of claims 1 to 9.

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