Virtual synchronization method and system for energy storage system and radial-flow water turbine generator set

Abstract

A virtual synchronization method for an energy storage system and a radial-flow water turbine generator set, said method comprising: analyzing characteristics of a radial-flow water turbine generator set, and designing a suitable energy storage system so as to smooth the power fluctuation of the radial-flow water turbine generator set (S1); acquiring characteristic parameters of the radial-flow water turbine generator set, on the basis of the characteristic parameters, designing a virtual rotor model, and establishing a machinery dynamics equation (S2); on the basis of the virtual rotor model, designing a virtual synchronization controller, constructing a closed-loop control system, and implementing charging and discharging control for the energy storage system (S3); establishing a simulation model for coupling the energy storage system and the radial-flow water turbine generator set, and optimizing parameters of the controller (S4); and, on a test platform of an actual radial-flow water turbine generator set, implementing a proposed virtual synchronous control policy, and carrying out experiment verification and result analysis (S5).

Description

Virtual synchronization method and system for energy storage system and run-of-river hydro-generator set Technical Field

[0001] The present invention relates to the technical field of electrical control strategy design, and in particular to a virtual synchronization method and system for an energy storage system and a radial turbine generator set. Background Art

[0002] Large power fluctuations are a natural characteristic of radial hydro-turbine generators, and energy storage system matching is a key means of smoothing these fluctuations. The core technology of this solution is the specific application of virtual synchronous machine theory to this system. By combining a high-precision mechanical dynamics model with vector closed-loop control, the virtual synchronous machine synchronizes the generator terminal voltage with the actual rotor, providing virtual mechanical inertia and enhancing system dynamic response. Compared to direct idling control, this effectively suppresses power fluctuations.

[0003] However, there are still problems when applying existing virtual synchronous machine technology to energy storage systems: first, it is difficult to calibrate the unit parameters, resulting in poor control stability; second, it has weak adaptability to variable operating conditions; and third, it is difficult to achieve the optimal controller parameters when manually adjusting them. This solution designs adaptive control, predictive control, and multi-model control modules to solve these problems. A set of globally optimal controller parameters is obtained through simulation and optimization, but the theory is complex and difficult to implement. In addition, existing literature rarely considers the joint response of the system under abnormal conditions. This solution verifies the reliability of the solution by configuring simulation signals for multiple fault scenarios, ensuring its practical value. Overall, the high system integration, sophisticated model design, and ideal optimization results are the characteristics of this solution, which is expected to further narrow the gap between theory and practice and achieve economical and efficient utilization of run-of-river hydropower generation.

[0004] Summary of the Invention

[0005] In view of the problems existing in the application of the existing virtual synchronous machine technology in energy storage systems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a virtual synchronization method of an energy storage system and a radial turbine generator set, which includes:

[0009] Analyze the characteristics of run-of-river hydro-generator sets, design appropriate energy storage systems, and smooth out power fluctuations of run-of-river hydro-generator sets;

[0010] Obtain the characteristic parameters of the radial turbine generator set, design a virtual rotor model based on the characteristic parameters, and establish the mechanical dynamics equations;

[0011] Based on the virtual rotor model, a virtual synchronous controller is designed to build a closed-loop control system to achieve charge and discharge control of the energy storage system;

[0012] Establish a simulation model for the coupling of an energy storage system and a run-of-river hydro-generator set, and optimize the controller parameters;

[0013] The proposed virtual synchronization control strategy is implemented on a test platform of an actual run-of-river hydro-generator set, and experimental verification and result analysis are carried out.

[0014] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, the design of the energy storage system includes the following steps:

[0015] Extract the detection data of the past power generation of the radial generator set;

[0016] Use Weibull distribution to get the probability density function expression;

[0017] Set constraints on the capacity to absorb power fluctuations;

[0018] Substitute the constraints into the Weibull distribution function to calculate the minimum capacity of the energy storage system;

[0019] Initialize the energy storage system capacity to the minimum capacity, cyclically calculate the number of charge and discharge times that meet the smoothing requirements, and obtain the final capacity of the energy storage system;

[0020] The probability density function expression is shown below:

[0021] Where c is the scale parameter of the Weibull distribution, k is the shape parameter of the Weibull distribution, and p is the sample data.

[0022] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, wherein: the virtual rotor model includes a turbine model and an electromagnetic torque model;

[0023] The turbine model is shown below:

[0024] Tw=k1(a)QH+k2(H)*Q 2

[0025] Where α is the guide vane opening, k1(α) and k2(H) are complex functions of the opening and water head;

[0026] Considering that the guide vane opening α affects the speed and flow of water entering the turbine:

[0027] k1(a)=Ka(1-a)sin(πa)

[0028] Where K is the coefficient related to the water head.

[0029] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, the electromagnetic torque model is shown as follows:

[0030] Where Te is the electromagnetic torque, which represents the torque output by the motor, I f is the excitation current, which is the current used to generate the magnetic field in the motor, and a, b, and c are constant coefficients.

[0031] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, the mechanical dynamics equation is as follows:

[0032] Where J is the moment of inertia, which represents the inertia of the system to rotational motion, θ is the angular displacement, which represents the angular position of the system, t is time, F is the damping coefficient of the system, D is the stiffness of the system, Tm is the torque of the driving system, Te is the electromagnetic torque, and Tw is the external torque.

[0033] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, the virtual synchronization controller includes an EKF recursive estimation framework, as shown in the following formula:

[0034] Pk|k-1=AkPk-1|k-1ATk+Q

[0035] Kk=Pk|k-1HT(HPK|K-1HT+R)-1

[0036] Where, is the predicted state vector at time k, Pk|k-1 is the predicted state covariance matrix at time k, Ak is the state transfer matrix, which represents the derivative of the state equation with respect to the state variable, Pk-1|k-1 is the updated state covariance matrix at time k-1, Q is the process noise covariance matrix, Kk is the Kalman gain matrix at time k, yk is the measured output vector at time k, is the measurement prediction based on the state prediction at time k, H is the measurement matrix connecting the state variables and the measurement variables, R is the measurement noise covariance matrix, is the updated state vector estimate at time k, θk|k: the updated estimate of the parameter vector at time k, and θCorrect is the vector quantity that corrects the parameter estimate using the parameter certainty index.

[0037] As a preferred solution of the virtual synchronization method of the energy storage system and the radial turbine generator set of the present invention, the optimization of the controller parameters includes the following steps:

[0038] Establish a simulation model for coupling the energy storage system with a run-of-river hydro-generator set;

[0039] Design anomalies;

[0040] Design a particle swarm optimization algorithm to calculate the optimal group solution;

[0041] The particle swarm optimization algorithm includes the following steps:

[0042] Randomly generate the positions and velocities of N particles;

[0043] Bring the position of each particle into the simulation model, run the simulation, calculate the set evaluation indicators, and obtain the FITNESS value of each particle.

[0044] If the particle FITNESS is better than the individual historical optimal value pbest, the current value is set as the new pbest;

[0045] If FITNESS is better than the optimal value gbest of all particles, it is considered that the current global optimal solution is found and set as gbest;

[0046] Using the PSO formula, combining individual thinking and group collaboration, the speed and position of each particle are updated to generate a new controller parameter solution;

[0047] If the set maximum number of iterations or FITNESS error is reached, the calculation is terminated and the optimal parameter combination is output; otherwise, the iterative search continues by returning to the FITNESS value of each particle.

[0048] In a second aspect, an embodiment of the present invention provides a virtual synchronization system for an energy storage system and a run-of-river hydro-generator set, comprising: an energy storage system design module for selecting a suitable energy storage system and determining its capacity based on power fluctuation characteristics of the run-of-river hydro-generator set;

[0049] The virtual rotor model building module is used to obtain the parameters of the radial turbine generator set, establish an accurate turbine model and high-order electrical model, and design the virtual rotor model;

[0050] A virtual synchronous controller design module is used to build a closed-loop control system to achieve charge and discharge control of the energy storage system, and improve control performance through adaptive PI parameter adjustment and speed prediction.

[0051] Joint simulation modeling and optimization module, used to establish a coupled simulation platform, configure input signals under various working conditions, conduct multi-case joint simulation, and optimize controller parameters using particle swarm optimization;

[0052] The experimental verification module is used to implement the virtual synchronous control strategy on the actual test platform, conduct no-load tests, load tests and abnormal working condition verification, and analyze the results.

[0053] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned virtual synchronization method of the energy storage system and the radial turbine generator set is implemented.

[0054] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for virtual synchronization of an energy storage system and a run-of-river hydro-turbine generator set is implemented.

[0055] The beneficial effect of the present invention is that it effectively smoothes the fluctuations in the output power of the run-of-river hydro-turbine generator set. By rationally configuring the energy storage system and coordinating the charge and discharge control of the unit and the energy storage, the grid-connected power can be kept stable, frequent start-up and shutdown and grid-off can be avoided, the power generation efficiency can be improved, and the dynamic support capacity of the grid side can be enhanced. The application of virtual synchronous machine theory provides virtual mechanical inertia for the power grid, which helps to suppress abnormal fluctuations in voltage and frequency, improve system stability and power supply reliability, and realize real-time online calibration of unit parameters and adaptive optimization and adjustment of controllers. Through independently developed prediction and multi-model control strategy modules, the robustness of the control system is greatly enhanced, adapting to variable working conditions, and enriching the modeling and analysis theory of the run-of-river hydro-turbine generator set and energy storage coupling system. Through joint simulation under various working conditions, a large amount of valuable state response data is obtained, laying the foundation for further improving the level of coordinated control of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0057] FIG1 is a flow chart of a virtual synchronization method of an energy storage system and a run-of-river hydro-generator set.

[0058] FIG2 is a flow chart of optimized controller parameters for the virtual synchronization method between the energy storage system and the run-of-river hydro-generator set. DETAILED DESCRIPTION

[0059] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0062] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0063] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0064] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0065] Example 1

[0066] 1 and 2 , which illustrate a first embodiment of the present invention, provide a virtual synchronization method for an energy storage system and a radial turbine generator set, including:

[0067] S1: Analyze the characteristics of run-of-river turbine generator sets and design a suitable energy storage system to smooth the power fluctuations of the run-of-river turbine generator sets;

[0068] Analyzing the power generation characteristics of the current runoff turbine generator sets, considering that the energy storage system needs to have the function of filling the power generation fluctuations, and the runoff turbine has a large power generation capacity, a large-capacity battery needs to be selected. Taking into account the system response rate requirements and the rapid changes in the runoff turbine power fluctuations, the energy storage system composed of batteries needs to have a fast response speed. Based on the above analysis and considerations, the energy storage system composed of liquid flow batteries and lithium batteries is matched with the current application situation.

[0069] Check the operation logs of the run-of-river turbine generator set over the past period of time, extract the monitoring data of the generated power, process the extracted data, and perform preprocessing such as denoising and interpolation to obtain continuous time series data of the generated power: {P(t1), P(t2), ..., P(tn)}.

[0070] Samples {P1, P2, ..., Pn} are extracted from the time series data of power generation. The sample data is sorted to obtain the order statistical samples {P(1), P(2), ..., P(n)}. The order samples are mapped to a uniform distribution to obtain the transformed samples {F(1), F(2), ..., F(n)}. Where: F(i) = (i - 0.5) / n. The logarithm of the transformed samples is taken and linear regression is performed to determine k and c: ln[-ln(1 - F(i))] = kln(P(i)) - kln(c).

[0071] The preprocessed power generation time series data is used as a sample, imported into the probability distribution fitting toolkit, and the expression of the probability density function is obtained using the Weibull distribution:

[0072] Where c is the scale parameter of the Weibull distribution and k is the shape parameter of the Weibull distribution.

[0073] Furthermore, the mean value μ and standard deviation σ of the samples are calculated from the preprocessed power generation time series data. The ratio of the standard deviation σ to the mean value μ is the coefficient of variation Cv. The calculated coefficient of variation Cv is compared with the coefficient of variation of typical hydropower generation to determine the power stability of this unit.

[0074] Furthermore, a constraint on the capacity to absorb power fluctuations is set. Based on the integral calculation of the probability density function of the Weibull distribution, when the energy storage system capacity is greater than 2.6σ of the power fluctuation, it can absorb more than 99% of the power fluctuation. In other words, the energy storage system capacity is set to be greater than 3*σ.

[0075] Furthermore, the power fluctuation range that meets a given reliability requirement is calculated, for example, the power fluctuation is required to be less than 20% of the average power and the reliability is 99.7%.

[0076] The minimum charging capacity is: average power * 20% * (1-3 / n)

[0077] n is the sample size of the time series data, and 3 / n corresponds to a three-sigma range of 99.7% reliability. So the constraints are set as:

[0078] The minimum charge capacity of the energy storage system > average power * 20% * (1-3 / n).

[0079] Substitute the constraints into the Weibull distribution function and calculate the minimum capacity Cmin of the energy storage system that satisfies the constraints through integral calculation. Initialize the minimum capacity of the energy storage system C = Cmin.

[0080] Furthermore, the rated power Pe of the run-of-river turbine generator set is obtained to obtain its maximum power Pcmax, and its maximum allowable charging power Pcbattery_max is obtained according to the characteristics of the energy storage system. The charging power range Pc is obtained according to the power grid's restriction on the power change rate of the generator set. grid_min ~Pc grid_max . Set PC min =max(Pc grid_min ,Pcbattery_min),Pc max =min(Pc grid_max ,Pcbattery_max,Pc max ). So the range of charging power Pc is: Pc min ≤Pc≤Pc max , select the middle value as the optimal Pc value, and the same applies to the discharge power Pd.

[0081] Furthermore, a program is written to cyclically calculate the number of charge and discharge cycles required to meet power smoothing requirements under a set energy storage system capacity. The set energy storage system capacity C, charging power Pc, and discharging power Pd are input, and the generator set power time series data P(t) is read. A loop counter count = 0 is initialized and the power time series data is traversed: if P(t) > C, the counter is incremented; if P(t) < 0, the counter is incremented, and the energy storage system charge is updated: SOC = SOC + Pc * Δt (for charging); SOC = SOC - Pd * Δt (for discharging).

[0082] At the end of the cycle, the output count is the total number of charge and discharge times. Change the energy storage system capacity C and repeat the above steps to obtain the number of charge and discharge times under different capacities. Draw a curve showing the relationship between different capacities and the number of charge and discharge times. Based on the characteristics of the curve, select the capacity with the least number of charge and discharge times as the optimal value. Perform simulation verification and field testing on this capacity. If the verification passes, it is determined as the final capacity.

[0083] S2: Obtain the characteristic parameters of the radial turbine generator set, design a virtual rotor model based on the characteristic parameters, and establish the mechanical dynamics equation

[0084] Check the technical data of the radial turbine generator set and record the key parameters, including rated power Pn, rated speed Nn, rated torque Tn, flow rate Q and head H during turbine operation, and moment of inertia J of the turbine and generator.

[0085] Furthermore, a no-load test is performed to record the no-load voltage U0 and no-load current I0, a short-circuit test is performed to record the short-circuit current Ik and short-circuit resistance Rk, and then a load test is performed to obtain the power P, speed n, current I, voltage U, and power factor cosφ at each load point.

[0086] Furthermore, based on the relevant parameters of the radial turbine generator set, an accurate turbine model is established by considering the influence of the guide vane opening and the water head. The turbine torque expression is:

[0087] Tw=k1(a)QH+k2(H)*Q 2

[0088] Where α is the guide vane opening, k1(α) and k2(H) are complex functions of the opening and water head.

[0089] Among them, considering that the guide vane opening α will affect the speed and flow of water entering the turbine, according to the fluid dynamics theory, it can be obtained:

[0090] k1(a)=Ka(1-a)sin(πa)

[0091] Where K is a coefficient related to the hydraulic head. This function reflects the nonlinear effect of the opening α. When α is close to 0, k1 is approximately 0; when α is about 0.5, k1 reaches its maximum; and when α is close to 1, k1 approaches 0 again.

[0092] On the other hand, the head H determines the kinetic energy of the water flow. According to turbine theory, the relationship between torque and head is approximately a square relationship: k2(H)=K'*H^2.

[0093] Where K' is a coefficient related to the guide vane opening, which reflects the quadratic relationship of water head. The higher the water head, the greater the kinetic energy of the water flow and the greater the torque generated by the turbine.

[0094] Furthermore, a high-order electrical model is established, and the electromagnetic torque formula is a third-order expression:

[0095]

[0096] Where Te is the electromagnetic torque, which represents the torque output by the motor, I f is the excitation current, which is the current used to generate the magnetic field in the motor, and a, b, and c are constant coefficients.

[0097] Furthermore, a virtual rotor model is designed based on the characteristic parameters, and the mechanical dynamics equation is established:

[0098] Where J is the moment of inertia, which represents the inertia of the system to rotational motion, θ is the angular displacement, which represents the angular position of the system, t is time, F is the damping coefficient of the system, D is the stiffness of the system, Tm is the torque of the driving system, Te is the electromagnetic torque, and Tw is the external torque.

[0099] S3: Based on the virtual rotor model, a virtual synchronous controller is designed to build a closed-loop control system to realize the charging and discharging control of the energy storage system.

[0100] A PI parameter online adaptive adjustment module based on fuzzy logic is designed to monitor the speed deviation and deviation change rate in real time, and automatically adjust KP and KI according to different working conditions to optimize the dynamic response of the closed-loop control system.

[0101] Furthermore, a speed prediction model module is added to the front end of the PI controller. This module uses historical data and a state estimation algorithm to predict the speed trend over a certain period of time in the future. This a priori prediction value is combined with the actual sampled speed data as the input signal of the PI controller, improving the controller's adaptability to random disturbances and unexpected events.

[0102] Furthermore, multiple linearized virtual rotor models were established, and an automatic controller switching module based on multi-model and model identifiability theory was designed. When a significant change in the system's operating state is detected, the system quickly switches to the most appropriate candidate controller, ensuring closed-loop stability and fast tracking.

[0103] The EKF recursive estimation framework is used to estimate the virtual rotor parameters and coordinate tuning with the PI controller, including state prediction, covariance prediction, Kalman gain calculation, state update and other steps, to calibrate the virtual rotor parameters online.

[0104] The EKF prediction and update equations are shown below:

[0105] Pk|k-1=AkPk-1|k-1ATk+Q

[0106] Kk=Pk|k-1HT(HPK|K-1HT+R)-1

[0107] Where, is the predicted state vector at time k, Pk|k-1 is the predicted state covariance matrix at time k, Ak is the state transfer matrix, which represents the derivative of the state equation with respect to the state variable, Pk-1|k-1 is the updated state covariance matrix at time k-1, Q is the process noise covariance matrix, Kk is the Kalman gain matrix at time k, yk is the measured output vector at time k, is the measurement prediction based on the state prediction at time k, H is the measurement matrix connecting the state variables and the measurement variables, R is the measurement noise covariance matrix, is the updated state vector estimate at time k, θk|k: the updated estimate of the parameter vector at time k, and θCorrect is the vector quantity that corrects the parameter estimate using the parameter certainty index.

[0108] Furthermore, an empirical tuning model is established between each virtual rotor parameter and the PI controller parameters (KP, KI):

[0109] Kp=fkp(J); Ki=fki(B)

[0110] Monitor the parameter certainty index estimated by EKF and automatically correct the PI controller parameters based on the tuning model to achieve coordinated optimization of the controller and rotor parameter calibration to improve robustness.

[0111] S4: Establish a simulation model for the coupling of the energy storage system and the run-of-river turbine generator set and optimize the controller parameters.

[0112] Based on the actual turbine parameters, a hydraulic model for the turbine is established; models for the generator and excitation system are established; and combined with the head and flow data, a simulation model is constructed. Based on the battery type and corresponding capacity parameters of the energy storage system determined in S1, a charge and discharge model for the energy storage system, such as battery / supercapacitor / flywheel, is established.

[0113] In Simulink, the run-of-river generator model and the energy storage system charge and discharge control model were connected to form a coupled simulation platform. Controller parameters were set, and a previously designed virtual synchronous closed-loop control system model, including speed, voltage, and power PI controllers, was added to the joint model.

[0114] Set simulation analysis indicators such as transient stability and harmonic distortion, and then further determine the performance evaluation indicators of the controller, including bandwidth, static regulation accuracy, etc.

[0115] Furthermore, the primary input variables for a runoff turbine are head and flow. These two variables vary depending on actual river conditions. Based on historical operating data, head and flow curves are configured for different flood discharge / pumping conditions, and these curves must include a certain degree of disturbance. Abnormal conditions such as turbine shutdown and transient load disconnection are also considered.

[0116] To simulate a turbine shutdown, simply reduce the head flow input to zero. A curve with decreasing head flow is constructed to simulate a more realistic shutdown process. For transient load disconnection, a circuit breaker module is added to the co-simulation model and controlled by a signal to trigger a load disconnection. Different disconnections are set to create a transient process. For a generator short circuit, a controllable circuit breaker is also added to the co-simulation model, connecting the generator to a low-impedance load to create short-circuit faults of varying severity. Multiple short-circuit actions are set using the signal. For a DC bus fault, select any battery cell or supercapacitor cell in the energy storage system model and simulate DC bus faults by adding open-circuit or short-circuit modules. For inverter anomalies, the duty cycle and frequency of the PWM signal are varied to simulate overcurrent and overvoltage faults in the IGBT switching element. This causes the inverter to trip protection or degrade performance. For excitation faults, an anomaly module can be added to the excitation power supply model to cause a power loss in the excitation system or a short-circuit in the filter capacitor, thereby creating an excitation fault in the model.

[0117] Furthermore, input signals such as head and flow rate were automatically loaded and switched according to pre-set curves. A random disturbance term was added. The co-simulation model was launched multiple times, with input signals under different operating conditions loaded one by one, observing the response curves of each system variable. Key system parameter curves were also recorded, including generator speed, power, turbine torque, head, flow rate, and the charge and discharge status of the energy storage system. The output data was processed using Matlab software to determine the dynamic response characteristics under different operating conditions, providing a basis for subsequent controller parameter optimization.

[0118] Furthermore, the controller optimization objective is primarily to improve the system's dynamic response speed and stability. Specifically, this is achieved by setting indicators such as maximum overshoot and settling time. Optimization variables, or controller parameters, are selected, primarily the proportional and integral parameters of the PI controller. Constraints are also determined, such as limits on the control signal amplitude and minimum settling time requirements. These conditions must be met simultaneously.

[0119] Furthermore, a particle swarm optimization algorithm is designed. First, the particle swarm is initialized, and the positions (controller parameter combinations) and speeds of N particles are randomly generated. Each particle position is brought into the simulation model, the simulation is run, and the set evaluation index is calculated to obtain the FITNESS value of each particle. If the particle FITNESS is better than the individual historical optimal value pbest, the current value is set as the new pbest. If the FITNESS is better than the optimal value gbest of all particles, it is considered that the current global optimal solution has been found and is set as gbest. Using the PSO formula, combining individual thinking and group collaboration, the speed and position of each particle are updated to generate a new controller parameter solution. If the set maximum number of iterations or FITNESS error is reached, the calculation is terminated and the optimal parameter combination is output. Otherwise, return to step 2 to continue the iterative search.

[0120] After calculating a set of optimal parameters using the particle swarm optimization algorithm, we solve it based on multiple situations and optimize it for different disturbance inputs and different working conditions to obtain a set of frequency-domain stable parameter combinations.

[0121] For different head and flow conditions, the head and flow signals under the dry season, flood season and medium water flow conditions are configured respectively, and the controller parameter optimization is repeated to obtain the optimal parameter solutions corresponding to these three conditions.

[0122] Different load disturbances are considered. Step load changes and linear load increases and decreases are added to each operating condition to optimize parameters. Harmonic disturbances of varying frequencies and amplitudes are also considered in the load parameters.

[0123] Different abnormal conditions are combined. On the basis of each operating condition signal, additional abnormal conditions such as turbine shutdown and load disconnection are combined to perform parameter optimization and solve, so as to obtain a more comprehensive optimal parameter combination.

[0124] The capacity of the energy storage system changes, and system models are established under three conditions of small, medium and large energy storage battery capacities. Based on the optimization of each working condition signal, the best matching of parameter selection under different energy storage capacities is compared.

[0125] The generator parameter distribution changes, and the random disturbance of the generator stator resistance and rotor reactance parameters is considered to observe the impact on the frequency domain performance of the controller parameters.

[0126] Finally, random noise input is added, the system time domain waveform is observed, and various evaluation indicators are analyzed to prove that the optimization meets the set target requirements.

[0127] S5: Implement the proposed virtual synchronization control strategy on the test platform of an actual run-of-river hydro-turbine generator set, and conduct experimental verification and result analysis.

[0128] Control system hardware construction: Install the industrial computer, data acquisition card, and information distribution module on the test bench, complete the electrical connections, and assemble the hydro-generator unit and energy storage device to ensure the proper functioning of the hardware system. Software function code development: Using tools such as Simulink and PLCOpen, convert the designed virtual synchronous closed-loop control model into unit control execution code and download it to the industrial computer. Parameter setting and debugging: Set the turbine load conditions, configure controller parameters, including PI coefficients and virtual rotor inertia, and carefully debug the control code logic.

[0129] Next, a no-load test was conducted, starting the turbine at no load to verify the generator magnetic field establishment and rotor acceleration process, and to observe whether the controller can accurately track the set speed-voltage curve. Grid-connected operation was then carried out. Load measurements were then conducted, connecting loads of varying sizes to the generator to test the control system's tracking speed for load transients and the turbine flow stability. Waveform analysis was used to determine control performance.

[0130] Finally, to verify the abnormal operating conditions, artificially create abnormal conditions such as intermittent water head and load disconnection, record the recovery process of the control system, analyze the dynamic response curves of each state quantity, and verify the stability and dynamic characteristics of the system.

[0131] Furthermore, this embodiment also provides a virtual synchronization system of the energy storage system and the radial turbine generator set, including:

[0132] The energy storage system design module is used to select a suitable energy storage system and determine its capacity based on the power fluctuation characteristics of the run-of-river turbine generator set;

[0133] The virtual rotor model building module is used to obtain the parameters of the radial turbine generator set, establish an accurate turbine model and high-order electrical model, and design a virtual rotor model;

[0134] A virtual synchronous controller design module is used to build a closed-loop control system to achieve charge and discharge control of the energy storage system, and improve control performance through adaptive PI parameter adjustment and speed prediction.

[0135] Joint simulation modeling and optimization module, used to establish a coupled simulation platform, configure input signals under various working conditions, conduct multi-case joint simulation, and optimize controller parameters using particle swarm optimization;

[0136] The experimental verification module is used to implement the virtual synchronous control strategy on the actual test platform, conduct no-load tests, load tests and abnormal working condition verification, and analyze the results.

[0137] This embodiment also provides a computer device suitable for the virtual synchronization method of an energy storage system and a run-of-river hydro-turbine generator set, comprising a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the virtual synchronization method of the energy storage system and the run-of-river hydro-turbine generator set proposed in the above embodiment.

[0138] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0139] This embodiment further provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the method for realizing virtual synchronization between an energy storage system and a radial turbine generator set as proposed in the above embodiment is implemented.

[0140] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0141] Example 2

[0142] 2 , which shows a second embodiment of the present invention, this embodiment provides a virtual synchronization method for an energy storage system and a run-of-river hydro-generator set. To verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments.

[0143] This solution is applied to a 100MW run-of-river hydroelectric power station located on a stretch of the Yangtze River. This station has a large installed capacity, and its output power varies significantly with seasonal fluctuations in water flow. To smooth power fluctuations and improve the grid's capacity, this solution proposes deploying a large-capacity energy storage system at the station, coordinating control with the turbines to deliver stable power to the grid.

[0144] The solution first analyzes historical statistical power generation time series data. The Weibull distribution is used to fit the probability distribution of power fluctuations. Based on the given power fluctuation constraints, the minimum capacity of the required energy storage system is determined to be 60MWh. To balance response speed, this solution uses a combination of 80MWh flow batteries and 40MWh lithium batteries. The details are shown in Table 1.

[0145] Table 1 Battery combination

[0146] After obtaining the rated parameters of the generator set, an accurate physical model and high-order electrical model of the turbine were constructed. A virtual rotor system was designed and the corresponding mechanical dynamics equations were established to achieve matching of electrical and mechanical characteristics. On this basis, a virtual synchronous closed-loop control system was designed and developed to adjust the inverter output in real time and achieve coordinated and optimized control of the charging and discharging process of the energy storage system. The specific virtual rotor model parameters are shown in Table 2.

[0147] Table 2 Virtual rotor model parameters

[0148] In the test experiment, we set the turbine flow rate to 500m3 / s and the water head to 20m. At this time, the shaft power is about 82MW, and the load power output to the grid is 58MW. For different scenarios, through multiple simulation calculations, a set of controller parameters were optimized, such as KP=2.1 and KI=0.35 in the dry season. The closed-loop control system includes a speed prediction module, a fuzzy adaptive PI module, a multi-model switching module, and a Kalman filter rotor parameter collaborative optimization module. After simulation tests and particle swarm optimization calculations under various typical working conditions, a set of globally optimal controller parameter combinations were obtained. Finally, it was verified on the actual test platform of the power station. The results show that this design scheme can effectively suppress power fluctuations, enhance the system's floating response and fault self-recovery capabilities, and achieve the design goals. As shown in Table 3

[0149] Table 3 Parameter optimization table

[0150] This experiment selected medium head and flow conditions under typical load conditions. The turbine shaft power was set at approximately 80% of the rated power and the flow rate was set at 75% of the rated flow rate to ensure stable operation with high turbine efficiency. Offline calculations were performed to determine the optimal power point at the turbine shaft under these head and flow conditions. The generator power was set at 80% of the optimal power to ensure a stable excess power margin and facilitate verification of the controller's power tracking capabilities. Before the experimental verification began, the energy storage system was precharged with an external DC power supply. The initial state of charge (SOC) of the energy storage system was set to 50% to ensure sufficient headroom for subsequent charge and discharge adjustments and to mitigate the impact of high and low limit constraints during testing. A high-precision power analyzer was used to measure generator voltage and current data. The turbine shaft speed, as well as the energy storage system's SOC, current, and voltage, were also stored. All signals were sampled at a frequency of at least 1 kHz to fully capture dynamic evolution. Finally, verification confirmed that this solution complied with the previously specified virtual synchronization strategy.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A virtual synchronization method for an energy storage system and a runoff hydro-generating unit, characterized in that: including Analyze the characteristics of a radial flow hydroelectric generating unit, design a suitable energy storage system, and smooth the power fluctuations of the radial flow hydroelectric generating unit; Obtain the characteristic parameters of the radial flow hydroelectric generating unit, design a virtual rotor model based on the characteristic parameters, and establish a mechanical dynamics equation; Based on the virtual rotor model, design a virtual synchronous controller, construct a closed-loop control system, and realize the charge and discharge control of the energy storage system; Establish a simulation model of the coupling between the energy storage system and the radial flow hydroelectric generating unit, and optimize the parameters of the controller; On the test platform of the actual radial flow hydroelectric generating unit, implement the proposed virtual synchronous control strategy, and conduct experimental verification and result analysis.

2. The virtual synchronization method of the energy storage system and the radial flow hydroelectric generating unit according to claim 1, characterized in that: The design of the suitable energy storage system includes the following steps: Extract the detection data of the past power generation of the radial flow generating unit; Use the Weibull distribution to obtain the expression of the probability density function; Set the constraint conditions for the capacity to absorb power fluctuations; Substitute the constraint conditions into the Weibull distribution function to calculate the minimum capacity of the energy storage system; Initialize the capacity of the energy storage system to the minimum capacity, and cyclically calculate the charge and discharge times that meet the smoothing requirements to obtain the final capacity of the energy storage system; The expression of the probability density function is as follows: In the formula, c is the scale parameter of the Weibull distribution, k is the shape parameter of the Weibull distribution, and p is the sample data.

3. The virtual synchronization method of the energy storage system and the radial flow hydro-generator set according to claim 2, characterized in that: The virtual rotor model includes a turbine model and an electromagnetic torque model; the turbine model is shown as follows: Tw = k1(a)QH + k2(H)*Q 2 In the formula, α is the guide vane opening, and k1(α) and k2(H) are complex functions of the opening and the water head; Consider that the guide vane opening α will affect the velocity and flow rate of the water flow into the turbine: k1(a) = Ka(1 - a)sin(πa) In the formula, K is the coefficient related to the water head.

4. The virtual synchronization method of the energy storage system and the radial flow hydro-generator set according to claim 3, characterized in that: The electromagnetic torque model is shown as follows: where Te is the electromagnetic torque, representing the torque output by the motor, and I f is the excitation current, which is the current used to generate the magnetic field in the motor, and a, b, and c are constant coefficients.

5. The virtual synchronization method of the energy storage system and the runoff-type hydro-generating unit according to claim 4, characterized in that: The mechanical dynamics equation is as follows: In the formula, J is the moment of inertia, indicating the inertia of the system to rotational motion, θ is the angular displacement, indicating the angular position of the system, t is the time, F is the damping coefficient of the system, D is the stiffness of the system, Tm is the torque driving the system, Te is the electromagnetic torque, and Tw is the external torque.

6. The virtual synchronization method of the energy storage system and the radial flow hydro-generator set according to claim 5, characterized in that: The virtual synchronous controller includes an EKF recursive estimation framework, as shown in the following formula: Pk|k-1=AkPk-1|k-1ATk+Q Kk=Pk|k-1HT(HPK|K-1HT+R)-1 In the formula, is the predicted state vector at time k, Pk|k - 1 is the predicted state covariance matrix at time k, Ak is the state transition matrix, indicating the derivative of the state equation with respect to the state variable, Pk - 1|k - 1 is the updated state covariance matrix at time k - 1, Q is the process noise covariance matrix, and Kk is the Kalman at time k Gain matrix, where yk is the measurement output vector at time k, The measurement prediction based on state prediction at time k, where H is the measurement matrix that connects the state variables and the measurement variables, and R is the measurement noise covariance matrix. is the updated state vector estimate at time k, θk|k: the updated estimate of the parameter vector at time k, and θCorrect is the vector quantity for correcting the parameter estimate using the parameter certainty index.

7. The virtual synchronization method of the energy storage system and the radial flow hydro-generating unit according to claim 6, characterized in that: The optimization of the controller parameters includes the following steps: Establish a simulation model of the coupling between the energy storage system and the radial flow hydroelectric generating unit; Design abnormal situations; Design a particle swarm optimization algorithm to calculate the optimal group solution; The particle swarm optimization algorithm includes the following steps: Randomly generate the positions and velocities of N particles; Substitute the position of each particle into the simulation model, run the simulation, calculate the set evaluation index, and obtain the FITNESS value of each particle; If the FITNESS of the particle is better than the individual historical optimal value pbest, then set the current value as the new pbest; If the FITNESS is better than the optimal value gbest of all particles, then it is considered that the current global optimal solution is found and set as gbest; Using the PSO formula, combining individual thinking and group collaboration, update the velocity and position of each particle to generate a new solution for the controller parameters; If the set maximum number of iterations or FITNESS error is reached, terminate the calculation and output the optimal parameter combination; otherwise, return to find the FITNESS value of each particle and continue the iterative search.

8. A virtual synchronization system for an energy storage system and a runoff hydro-generating unit, based on the virtual synchronization method for the energy storage system and the runoff hydro-generating unit according to any one of claims 1 to 7, characterized in that: Including, An energy storage system design module for selecting a suitable energy storage system and determining the capacity according to the power fluctuation characteristics of the runoff hydroelectric generating unit; A virtual rotor model establishment module for obtaining the parameters of the runoff hydroelectric generating unit and establishing an accurate Turbine model and high-order electrical model to design a virtual rotor model; A virtual synchronous controller design module for constructing a closed-loop control system to achieve charge and discharge control of the energy storage system, and improving the control performance through methods such as adaptive PI parameter adjustment and speed prediction; A co-simulation modeling and optimization module for establishing a coupled simulation platform, configuring input signals under various working conditions, performing multi-case co-simulation, and optimizing the controller parameters using the particle swarm algorithm, etc.; An experimental verification module for implementing the virtual synchronous control strategy on an actual test platform, conducting no-load tests, load tests, and abnormal condition verifications, and analyzing the results.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the virtual synchronization method of the energy storage system and the runoff hydroelectric generating unit according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the virtual synchronization method of the energy storage system and the runoff hydroelectric generating unit according to any one of claims 1-7.

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