Method for suppressing power oscillation of network-forming type energy storage converter

Abstract

The invention discloses a method for suppressing power oscillation of a network construction type energy storage converter. The method comprises the following steps: building a network construction type energy storage converter simulation model; measuring and recording the output power waveform and the given power of the network-forming type energy storage converter; changing the control parameters of the network-forming energy storage converter to suppress the fluctuation of the output power, and recording the control parameters at the moment; taking the output power waveform, the given power and the control parameter as the input of the neural network; deploying the trained neural network in a first controller, and transmitting the real-time power to the neural network in the first controller by a second controller to obtain a control parameter; the converter control strategy is operated in the second controller, the first controller transmits the obtained control parameters to the second controller, and the control parameters are adjusted on line. According to the method, the change of the power grid environment is monitored in real time, and the parameters of the network-forming type energy storage system are dynamically adjusted according to the change, so that the system can stably run and the power oscillation is effectively inhibited.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage converter control, and in particular to a method for suppressing power oscillation of a grid-type energy storage converter. Background Art

[0002] With the transformation of the global energy structure and the rapid development of new energy technologies, the proportion of renewable energy such as wind power and photovoltaic power generation in the power system is increasing. However, these new energy power generation technologies have characteristics such as intermittency and volatility, which have brought unprecedented challenges to the stability of the power grid. With the continuous growth of new energy installed capacity, the operation mode of the power system is undergoing profound changes. Especially in areas where the proportion of new energy is high and the power grid foundation is relatively weak, such as Tibet and Xinjiang, the power system faces problems such as low short-circuit ratio of multiple stations, broadband oscillation, insufficient inertia level, and poor voltage and power angle stability. These problems pose a serious threat to the stable operation of the power system.

[0003] As a storage system that can build and maintain output voltage and frequency, operate with voltage source characteristics, and actively support the power grid when the power grid fails, the grid-building energy storage converter provides a new idea for solving the above problems. It builds a voltage source that supports the stable operation of the large power grid through the energy storage inverter, which can play a role in rapid frequency and voltage regulation, increase inertia and short-circuit capacity support, and suppress broadband oscillations, thereby enhancing the stability of the power system. In addition, the grid-building energy storage converter also has the advantages of strong overload capacity and flexible control, which can better meet the needs of new energy power systems.

[0004] Grid-connected energy storage converters usually operate in grid-connected mode. When the grid environment changes, the grid-connected energy storage system often finds it difficult to adapt to such changes due to its fixed parameter configuration, which leads to power oscillation problems. Summary of the invention

[0005] The purpose of the present application is to provide a method for suppressing power oscillation of a grid-connected energy storage converter to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, this application provides the following technical solutions:

[0007] A method for suppressing power oscillation of a grid-type energy storage converter comprises the following steps:

[0008] S1. Build a grid-type energy storage converter simulation model and set system simulation parameters;

[0009] S2. Measure and record the output power waveform and power setting of the grid-type energy storage converter at different operating points;

[0010] S3. Change the control parameters of the grid-type energy storage converter: moment of inertia and damping coefficient. By adjusting the control parameters, the output power fluctuation is suppressed and the control parameters are recorded at this time.

[0011] S4. Taking the output power waveform, power setting and control parameters as input of the neural network;

[0012] S5. Construction and training of neural network, setting the dimensions of input layer and output layer, setting the initial values ​​of the number of hidden layers and neurons, initializing the weights and biases of the neural network;

[0013] S6. The trained neural network is deployed in the first controller, and the second controller transmits the real-time power to the neural network in the first controller to obtain control parameters;

[0014] S7. The grid-type energy storage converter control strategy runs in the second controller, and the first controller transmits the obtained control parameters to the second controller to adjust the control parameters online.

[0015] Optionally, the online adjustment of the control parameters in step S7 specifically includes:

[0016] S71. The second controller detects the grid voltage and current signals in real time and calculates the power, and then transmits the power signal to the first controller;

[0017] S72. The first controller determines whether the power is oscillating at this time according to the input power signal, and if so, transmits the oscillating power to the neural network; if not, no operation is performed;

[0018] S73. The neural network obtains the control parameters that need to be adjusted according to the power waveform, and the control parameters include the moment of inertia and the damping coefficient;

[0019] S74. Determine whether the moment of inertia and the damping coefficient are within a reasonable range. If so, pass it to the second controller. If not, do not execute it.

[0020] S75. The second controller adjusts the parameter moment of inertia and damping coefficient of the actual controller according to the input control parameters moment of inertia and damping coefficient.

[0021] Optionally, the grid-type energy storage converter simulation model consists of an active power-frequency control module and a reactive power-voltage control module.

[0022] Optionally, the first controller adopts an ARM controller, and the ARM controller is used to implement a neural network and adjust control parameters using a power waveform.

[0023] Optionally, the second controller adopts a DSP controller, and the DSP controller is used to realize the control of the grid-type energy storage inverter.

[0024] In summary, the technical effects and advantages of the present invention are as follows:

[0025] 1. The present invention adopts a DSP controller and an ARM controller architecture. The DSP controller is used to implement the control algorithm of the grid-type energy storage converter, and the RAM controller is used to implement power oscillation detection and parameter adjustment strategy. The two controllers exchange data through communication, which solves the problem of insufficient computing power of the existing DSP controller.

[0026] 2. Using neural networks to identify power oscillations can improve recognition accuracy and speed. At the same time, the control parameters can be adjusted according to the power oscillations to achieve adaptive adjustment of control parameters to suppress power oscillations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A topological diagram of a controller in one embodiment of the present invention;

[0029] Figure 2 A power oscillation suppression flow chart in one embodiment of the present invention;

[0030] Figure 3 Schematic diagram of grid-type energy storage control strategy in one embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0033] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0034] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] In order to solve this problem, the present invention proposes a power oscillation suppression method, which monitors the changes in the power grid environment in real time and dynamically adjusts the parameters of the grid-type energy storage system accordingly to ensure that the system can operate stably and effectively suppress power oscillations. By implementing this method, the adaptability of the grid-type energy storage system to changes in the power grid environment can be significantly improved, ensuring the stable operation of the power grid.

[0036] This embodiment provides a method for suppressing power oscillation of a grid-type energy storage converter. Figure 1-Figure 3 As shown, the following steps are included:

[0037] S1. Build a grid-type energy storage converter simulation model and set the system simulation parameters. The simulation step is 1e-6s and the simulation mode is discrete solver. Figure 3 As shown, it is composed of an active power-frequency control module and a reactive power-voltage control module; Figure 3 The upper middle part is the active power-frequency control module. The active power-frequency control module is given by the rated frequency ω0, and the feedback is the actual frequency ω. The error is proportional to the link k ω Output, P ref is the power given, P is the actual power, the power error passes through the integration link 1 / Js, and the superimposed output is multiplied by the damping coefficient D to obtain the frequency error, and finally the rated frequency ω0 is added to obtain the actual frequency ω. Figure 3The middle and lower part is the reactive power-voltage control module, which is given by the reactive power Q ref , the feedback is the actual reactive power Q, and the error passes through the proportional link k E Output, and finally add the rated voltage E0, which is the actual voltage E.

[0038] S2. Under different operating points, measure and record the output power waveform and power setting of the grid-type energy storage converter, where the power waveform is stored in a time data format;

[0039] S3. Change the control parameters of the grid-type energy storage converter, the control parameters include the moment of inertia J and the damping coefficient D, and suppress the output power fluctuation by adjusting the moment of inertia J and the damping coefficient D, and record the control parameters at this time;

[0040] S4. The output power waveform, power setting and control parameters are used as inputs of the neural network. In order to improve the training efficiency of the neural network, the input data may be normalized;

[0041] S5. Construction and training of neural network, setting the dimensions of input layer and output layer, setting the initial values ​​of the number of hidden layers and neurons, initializing the weights and biases of the neural network;

[0042] S6. The trained neural network is deployed in the first controller, the first controller adopts an ARM controller, the ARM controller is used to implement the neural network, and the control parameters are adjusted using the power waveform; the second controller transmits the real-time power to the neural network in the first controller to obtain the control parameters: moment of inertia J and damping coefficient D; the second controller adopts a DSP controller, the DSP controller is used to realize the control of the grid-type energy storage converter;

[0043] S7. The grid-type energy storage converter control strategy runs in the second controller, and the first controller transmits the obtained control parameters to the second controller to adjust the control parameters online.

[0044] Optionally, adjusting the control parameters online specifically includes:

[0045] S71. The second controller detects the grid voltage and current signals in real time and calculates the power, and then transmits the power signal to the first controller;

[0046] S72. The first controller determines whether the power is oscillating at this time according to the input power signal, and if so, transmits the oscillating power to the neural network; if not, no operation is performed;

[0047] S73. The neural network obtains the control parameters that need to be adjusted according to the power waveform: moment of inertia J and damping coefficient D;

[0048] S74. Determine whether the moment of inertia J and the damping coefficient D are within a reasonable range. If they are satisfied, they are transmitted to the second controller. If not, they are not executed.

[0049] S75. The second controller adjusts the actual controller parameters according to the input control parameters moment of inertia J and damping coefficient D.

[0050] In summary, this embodiment adopts the DSP controller and ARM controller architecture. The DSP controller is used to implement the control algorithm of the grid-type energy storage converter, and the RAM controller is used to implement the power oscillation detection and parameter adjustment strategy. The two controllers exchange data through communication, which solves the problem of insufficient computing power of the existing DSP controller. The use of neural networks to identify power oscillations can improve the recognition accuracy and speed, and at the same time, the control parameters can be adjusted according to the power oscillations to achieve adaptive adjustment of the control parameters to suppress the power oscillations. This embodiment proposes a method for suppressing power oscillations of a grid-type energy storage converter. The power waveform under different parameter conditions is obtained through an offline grid-type energy storage converter simulation model. The relationship between power and parameters is trained using a neural network model. When applied online, the real-time power of the converter is input into the neural network model to obtain optimized control parameters, thereby achieving power oscillation suppression. This method can automatically adjust the control parameters according to the characteristics of the power grid and improve the grid adaptability of the grid-type energy storage converter.

[0051] Finally, it should be noted that the above are only preferred embodiments 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 aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for suppressing power oscillation of a grid-type energy storage converter, characterized in that: The following steps are involved: S1. Build a grid-type energy storage converter simulation model and set system simulation parameters; S2. Measure and record the output power waveform and power setting of the grid-type energy storage converter at different operating points; S3. Change the control parameters of the grid-type energy storage converter: moment of inertia and damping coefficient, suppress the output power fluctuation by controlling the parameters, and record the control parameters at this time; S4. Taking the output power waveform, power setting and control parameters as input of the neural network; S5. Construction and training of neural network, setting the dimensions of input layer and output layer, setting the initial values ​​of the number of hidden layers and neurons, initializing the weights and biases of the neural network; S6. The trained neural network is deployed in the first controller, and the second controller transmits the real-time power to the neural network in the first controller to obtain control parameters; S7. The grid-type energy storage converter control strategy runs in the second controller, and the first controller transmits the obtained control parameters to the second controller to adjust the control parameters online.

2. The method for suppressing power oscillation of a grid-type energy storage converter according to claim 1, characterized in that: The online adjustment of the control parameters in step S7 specifically includes: S71. The second controller detects the grid voltage and current signals in real time and calculates the power, and then transmits the power signal to the first controller; S72. The first controller determines whether the power is oscillating at this time according to the input power signal, and if so, transmits the oscillating power to the neural network; if not, no operation is performed; S73. The neural network obtains the control parameters that need to be adjusted according to the power waveform, and the control parameters include the moment of inertia and the damping coefficient; S74. Determine whether the moment of inertia and the damping coefficient are within a reasonable range. If so, pass it to the second controller. If not, do not execute it. S75. The second controller adjusts the parameter moment of inertia and damping coefficient of the actual controller according to the input control parameters moment of inertia and damping coefficient.

3. The method for suppressing power oscillation of a grid-type energy storage converter according to claim 1, characterized in that: The grid-type energy storage converter simulation model consists of an active power-frequency control module and a reactive power-voltage control module.

4. The method for suppressing power oscillation of a grid-type energy storage converter according to claim 1, characterized in that: The first controller adopts an ARM controller, and the ARM controller is used to implement a neural network and adjust control parameters using a power waveform.

5. The method for suppressing power oscillation of a grid-type energy storage converter according to claim 1, characterized in that: The second controller adopts a DSP controller, and the DSP controller is used to realize the control of the grid-type energy storage converter.

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