Method, system, equipment and medium for improving stability of network construction type flexible direct current system

Abstract

The invention provides a method, a system, equipment and a medium for improving the stability of a network construction type flexible direct current system, and the method comprises the steps: determining the operation state of a wind power plant based on the operation parameters of a converter station at the fan side in the wind power plant; if the wind power plant is in a conventional operation state, regulating and controlling the output power of a fan by combining a self-adaptive neural network algorithm based on the power grid frequency deviation value of a grid-side converter station in the wind power plant and fan-side alternating current power grid operation parameters, so as to support a grid-side power grid; if the wind power plant is in an isolated island operation state and in a sufficient power state, regulating and controlling the output power of a fan based on the power grid frequency deviation value to support a grid-side power grid; if the wind power plant is in an isolated island operation state but in an insufficient power state, a grid-side converter station is regulated and controlled based on fan-side converter data, and a grid-side power grid is supported; according to the method, the operation states of the wind power plant are divided, and different control strategies are adopted based on different operation states, so that the overall control flexibility and stability of the direct-flexible system are enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-voltage power transmission, and specifically relates to a method, system, equipment and medium for improving the stability of a grid-type flexible direct current system. Background Art

[0002] Compared with onshore wind power, wind power at the sending end has the advantages of abundant resources, high power generation utilization hours, no land occupation, no water consumption and suitable for large-scale development. Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) based on modular multilevel converter is a long-distance transmission technology suitable for offshore wind farms. In order to provide support for power systems that do not use phase-locked loops (PLL), various grid-type control methods for new energy power generation systems and flexible direct currents have been proposed, including droop control, power synchronization control, virtual synchronous machines / virtual synchronous generators, matching control and virtual oscillation control, etc. The dynamic power decoupling of virtual synchronous machines can enhance the stability of the system. These methods have been fully evaluated and discussed in the literature. And analysis shows that the application of wind turbines and modular multilevel flexible direct current transmission systems (MMC-HVDC) in weak power grids is more advantageous when grid-type control is used.

[0003] At present, the operating status of wind turbines connected to the wind turbine-side converter station is subject to changeable and highly uncertain weather conditions, while the operating conditions of the AC power grid are also relatively complex. The existing technology has a relatively simple control strategy for wind farms. When wind farms and power grids work together, the various uncertain factors that exist will cause frequent sudden changes in power and voltage. There is a lack of flexible and diverse control strategies. In the complex working scenarios of wind farms, there is still room for further optimization of the flexible DC system to provide stable frequency support for the grid-side power grid. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a method for improving the stability of a grid-type flexible direct current system, comprising:

[0005] Based on the operating parameters of the wind turbine-side converter station in the wind farm, determining the operating state of the wind farm; the operating state includes a normal operating state, an island operation with sufficient power state, and an island operation with insufficient power state;

[0006] If the wind farm is in the normal operation state, based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, the output power of the wind turbine in the wind farm is regulated to support the grid-side grid frequency;

[0007] If the wind farm is in island operation and has sufficient power, the output power of the wind turbine is regulated based on the grid frequency deviation to support the grid-side grid frequency;

[0008] If the wind farm is in island operation but is underpowered, the output power of the grid-side converter station is regulated based on the DC voltage and power data of the wind turbine-side converter and combined with the grid frequency deviation to support the grid-side grid frequency.

[0009] Preferably, the wind turbine side converter station operating parameters include: wind turbine side frequency data, DC voltage and power; the determining the operating state of the wind farm based on the wind turbine side converter station operating parameters in the wind farm includes:

[0010] Based on the fan side frequency data, calculating the fan side frequency change;

[0011] Based on the wind turbine side frequency change and the preset threshold, it is determined whether the wind turbine side frequency change is less than the preset threshold; if so, the wind farm is in a normal operation state; otherwise, the wind farm is in an island operation state;

[0012] When the wind farm is in an island operation state, it is determined whether the power of the wind turbine can maintain the DC voltage of the flexible DC system. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state with insufficient power.

[0013] Preferably, the output power of the wind turbines in the wind farm is regulated based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, in combination with an adaptive neural network algorithm, to support the grid-side grid frequency, including:

[0014] Based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, an adaptive neural network algorithm is used to determine an adaptive wind turbine side frequency deviation, and the adaptive wind turbine side frequency deviation is sent to the wind turbine in the wind farm;

[0015] Inputting the grid frequency deviation into a pre-built mapping model to obtain a wind turbine output power reference value, and sending the wind turbine output power reference value to the wind turbine, wherein the mapping model is built based on grid frequency deviation historical data and corresponding wind turbine output power historical data;

[0016] In the wind turbine, based on the adaptive wind turbine-side frequency deviation and the wind turbine output power reference value, the wind turbine output power is regulated to support the grid-side power grid frequency.

[0017] Preferably, the method of using an adaptive neural network algorithm to determine an adaptive wind turbine side frequency deviation based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side includes:

[0018] Based on the wind turbine side AC power grid operating parameters, an adaptive neural network algorithm is used to identify the wind turbine side AC power grid operating condition, and an adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition is determined;

[0019] In the grid-side converter station, the grid frequency deviation is low-pass filtered to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the adaptive operating condition control coefficient, a frequency-voltage conversion algorithm is used to calculate a frequency-superimposed DC voltage value, and the superimposed DC voltage value is sent to the wind turbine-side converter station;

[0020] In the wind turbine side converter station, based on the frequency superimposed DC voltage and the adaptive operating condition control coefficient, a frequency sensing algorithm is used to calculate an adaptive wind turbine side frequency deviation.

[0021] Preferably, based on the wind turbine side AC power grid operating parameters, identifying the wind turbine side AC power grid operating condition through an adaptive neural network algorithm, and determining an adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition, includes:

[0022] Based on the wind turbine side AC power grid operating parameters, the wind turbine side AC power grid operating conditions are identified through a pre-trained operating condition analysis neural network model to obtain the current wind turbine side AC power grid operating conditions, wherein the wind turbine side AC power grid operating conditions include: normal operating conditions, under-frequency operating conditions, over-frequency operating conditions, and impedance abnormality operating conditions;

[0023] The current wind turbine side AC grid operating condition and the current converter control parameters are input into the adaptive parameter generation model to obtain an adaptive operating condition control coefficient corresponding to the wind turbine side AC grid operating condition, wherein the adaptive operating condition control coefficient includes: an adaptive frequency-DC voltage adjustment coefficient and an adaptive DC voltage-frequency adjustment coefficient.

[0024] Preferably, the controlling the wind turbine output power based on the adaptive wind turbine side frequency deviation and the wind turbine output power reference value to support the grid side power grid frequency includes:

[0025] Based on the adaptive fan side frequency deviation and the current fan output power, the fan output power value is calculated according to the network control strategy algorithm;

[0026] The difference between the fan output power value and the fan output power reference value is compared with a preset threshold value. If it is less than the preset threshold value, the fan is regulated based on the fan output power value; if it is greater than the preset threshold value, the fan output power is regulated based on the fan output power reference value to achieve support for the grid-side power grid frequency.

[0027] Preferably, the DC voltage and power data of the wind turbine side converter are combined with the grid frequency deviation to adjust the output power of the grid side converter station to support the grid side grid frequency, including:

[0028] In the wind turbine side converter station, based on the output power of the wind turbine, the DC voltage of the wind turbine side converter and power data, the power compensation amount of the wind turbine side converter is calculated; based on the power compensation amount and the voltage set value, the power output of the wind turbine side converter is controlled by a proportional integral controller to maintain the DC voltage stable;

[0029] In the grid-side converter station, a DC voltage control strategy is adopted based on the power output of the wind turbine-side converter station and the grid frequency deviation, and the output of the grid-side converter is modulated by a proportional-integral controller to support the grid-side grid frequency.

[0030] Preferably, the controlling the output power of the wind turbine based on the grid frequency deviation to support the grid-side grid frequency includes:

[0031] At the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and a preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate a superimposed voltage, and the superimposed voltage is sent to the wind turbine-side converter station;

[0032] In the wind turbine side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the wind turbine side frequency deviation, and the wind turbine side frequency deviation is sent to the wind turbine;

[0033] In the wind turbine, the output power of the wind farm is regulated based on the frequency deviation on the wind turbine side to support the grid frequency on the grid side.

[0034] Based on the same inventive concept, the present invention also provides a system for improving the stability of a grid-type flexible direct current system, comprising:

[0035] Operation status judgment module: specifically used to determine the operation status of the wind farm based on the operation parameters of the wind turbine side converter station in the wind farm; the operation status includes normal operation status, island operation with sufficient power status and island operation with insufficient power status;

[0036] Composite strategy control module: specifically used for, when the wind farm is in the conventional operation state, adjusting the output power of the wind turbine in the wind farm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, to achieve support for the grid-side grid frequency;

[0037] A network strategy control module: specifically used for regulating the output power of the wind turbine based on the grid frequency deviation to support the grid-side grid frequency when the wind farm is in island operation and has sufficient power;

[0038] Power shortage strategy control module: specifically used for regulating the output power of the grid-side converter station based on the DC voltage and power data of the wind turbine-side converter and the grid frequency deviation when the wind farm is in island operation but in a power shortage state, so as to support the grid-side grid frequency.

[0039] Preferably, the operating status judgment module includes:

[0040] A frequency change calculation unit: used to calculate the frequency change amount of the fan side based on the fan side frequency data;

[0041] An island determination unit: used to determine whether the wind turbine side frequency change is less than a preset threshold based on the wind turbine side frequency change and a preset threshold; if so, the wind farm is in a normal operation state; otherwise, the wind farm is in an island operation state;

[0042] Power judgment unit: used to judge whether the power of the wind turbine can maintain the DC voltage of the flexible DC system when the wind farm is in an island operation state. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state but with insufficient power.

[0043] Preferably, the composite strategy control module includes:

[0044] Adaptive frequency deviation calculation submodule: used to determine the adaptive frequency deviation of the wind turbine side by using an adaptive neural network algorithm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, and send the adaptive frequency deviation of the wind turbine side to the wind turbine in the wind farm;

[0045] A power reference value calculation submodule: used for inputting the grid frequency deviation into a pre-built mapping model, obtaining a wind turbine output power reference value, and sending the wind turbine output power reference value to the wind turbine, wherein the mapping model is constructed based on the grid frequency deviation historical data and the corresponding wind turbine output power historical data;

[0046] The fan control submodule is used to control the fan output power in the fan based on the adaptive fan-side frequency deviation and the fan output power reference value, so as to support the grid-side power grid frequency.

[0047] Preferably, the adaptive frequency deviation calculation submodule includes:

[0048] An adaptive coefficient calculation unit is used to identify the working condition of the AC power grid on the wind turbine side and determine the adaptive working condition control coefficient corresponding to the working condition of the AC power grid on the wind turbine side through an adaptive neural network algorithm based on the operating parameters of the AC power grid on the wind turbine side;

[0049] Adaptive DC voltage calculation unit: used for, in the grid-side converter station, performing low-pass filtering on the grid frequency deviation to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the adaptive operating condition control coefficient, using a frequency-voltage conversion algorithm to calculate a frequency-superimposed DC voltage value, and sending the superimposed DC voltage value to the wind turbine-side converter station;

[0050] Adaptive frequency calculation unit: used in the fan side converter station, based on the frequency superimposed DC voltage and the adaptive operating condition control coefficient, using a frequency sensing algorithm to calculate an adaptive fan side frequency deviation.

[0051] Preferably, the adaptive coefficient calculation unit includes:

[0052] The working condition identification subunit is used to identify the working condition of the AC power grid on the wind turbine side based on the operating parameters of the AC power grid on the wind turbine side and obtain the current working condition of the AC power grid on the wind turbine side through a pre-trained working condition analysis neural network model. The working condition of the AC power grid on the wind turbine side includes: normal working condition, under-frequency working condition, over-frequency working condition and impedance abnormal working condition;

[0053] Adaptive operating condition control coefficient calculation subunit: used to input the current wind turbine side AC power grid operating condition and the current converter control parameter into the adaptive parameter generation model to obtain the adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition, the adaptive operating condition control coefficient includes: adaptive frequency-DC voltage adjustment coefficient and adaptive DC voltage-frequency adjustment coefficient.

[0054] Preferably, the fan control submodule includes:

[0055] Output power calculation unit: used to calculate the output power value of the fan based on the adaptive fan side frequency deviation and the current fan output power according to the network control strategy algorithm;

[0056] Wind turbine control unit: used to compare the difference between the wind turbine output power value and the wind turbine output power reference value with a preset threshold value. If the difference is less than the preset threshold value, the wind turbine is controlled based on the wind turbine output power value; if the difference is greater than the preset threshold value, the wind turbine output power is controlled based on the wind turbine output power reference value to support the grid-side power grid frequency.

[0057] Preferably, the insufficient power strategy control module includes:

[0058] A DC voltage control submodule: used to calculate the power compensation amount of the fan side converter in the fan side converter station based on the output power of the fan, the DC voltage of the fan side converter and the power data; based on the power compensation amount and the voltage set value, control the power output of the fan side converter through a proportional-integral controller to maintain the DC voltage stability;

[0059] Grid-side converter output modulation submodule: In the grid-side converter station, based on the power output of the wind turbine-side converter station and the grid frequency deviation, a DC voltage control strategy is adopted to modulate the output of the grid-side converter through a proportional-integral controller to support the grid-side grid frequency.

[0060] Preferably, the networking strategy control module includes:

[0061] The grid-side voltage calculation submodule: at the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and a preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate a superimposed voltage, and the superimposed voltage is sent to the wind turbine-side converter station;

[0062] The fan side frequency calculation submodule: in the fan side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the fan side frequency deviation, and the fan side frequency deviation is sent to the fan;

[0063] Wind turbine control submodule: In the wind turbine, the output power of the wind farm is controlled based on the frequency deviation on the wind turbine side to support the grid frequency on the grid side.

[0064] Based on the same inventive concept, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0065] The memory is used to store one or more programs;

[0066] When the one or more programs are executed by the at least one processor, a method for improving the stability of a meshed flexible direct current system as described above is implemented.

[0067] Based on the same inventive concept, the present invention also provides a readable storage medium having an execution program stored thereon. When the execution program is executed, a method for improving the stability of a meshed flexible direct current system as described above is implemented.

[0068] Compared with the closest prior art, the present invention has the following beneficial effects:

[0069] The present invention provides a method for improving the stability of a grid-type flexible direct current system, comprising: determining the operating state of the wind farm based on the operating parameters of a wind turbine-side converter station in the wind farm; the operating state includes a conventional operating state, an island operation state with sufficient power, and an island operation state with insufficient power; if the wind farm is in the conventional operating state, then based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operating parameters on the wind turbine side, combined with an adaptive neural network algorithm, regulating the output power of the wind turbine in the wind farm to support the grid-side grid frequency; if the wind farm is in an island operation state with sufficient power, then based on the grid frequency deviation , regulate the output power of the wind turbine to support the grid-side power grid frequency; if the wind farm is in island operation but in a power-deficient state, based on the DC voltage and power data of the wind turbine-side converter, combined with the grid frequency deviation, regulate the output power of the grid-side converter station to support the grid-side power grid frequency; the present invention is based on the AC power grid operating parameters and wind turbine operating status of the wind turbine side connected to the wind farm, divides the operating status of the wind farm, and adopts different control strategies for different operating status of the wind farm, thereby enhancing the flexibility and diversity of the wind farm control strategy, and further improving the overall operation stability of the wind farm when providing frequency support for the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 The present invention provides a flow chart of a method for improving the stability of a grid-type flexible direct current system;

[0071] Figure 2 A simplified topological diagram of a flexible DC converter station and AC systems on both sides provided by the present invention;

[0072] Figure 3 A control strategy flow chart of a flexible direct current grid-side converter station according to the present invention;

[0073] Figure 4 A control process strategy diagram of a flexible direct current wind turbine side converter station provided by the present invention;

[0074] Figure 5A schematic diagram of simulated control results of a type I control strategy and a type II control strategy provided by the present invention;

[0075] Figure 6 A schematic diagram of fan frequency support control under a grid-following control strategy provided by the present invention;

[0076] Figure 7 A schematic diagram of a system structure for improving the stability of a grid-forming flexible direct current system provided by the present invention;

[0077] Figure 8 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0078] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0079] Embodiment 1:

[0080] The present invention provides a method for improving the frequency stability of a wind power grid-connected flexible direct current transmission system, wherein the flow chart of the method for improving the stability is as follows: Figure 1 As shown, the specific steps include:

[0081] Step 1: Based on the operating parameters of the wind turbine-side converter station in the wind farm, determine the operating state of the wind farm; the operating state includes a normal operating state, an island operation with sufficient power state, and an island operation with insufficient power state;

[0082] Step 2: If the wind farm is in the normal operation state, based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, the output power of the wind turbine in the wind farm is regulated to support the grid-side grid frequency;

[0083] Step 3: If the wind farm is in island operation and has sufficient power, the output power of the wind turbine is regulated based on the grid frequency deviation to support the grid-side grid frequency;

[0084] Step 4: If the wind farm is in island operation but is underpowered, the output power of the grid-side converter station is regulated based on the DC voltage and power data of the wind turbine-side converter and combined with the grid frequency deviation to support the grid-side grid frequency.

[0085] Step 1 Specifically, the operating parameters of the wind power grid-type flexible direct current transmission system are obtained to determine the operating status of the wind farm and the wind turbine side AC system connected to the wind turbine side converter station. First, determine whether the wind farm is disconnected from the grid and enters an islanded operation state. There are many methods for detecting wind turbine islanding, including judgment based on active power value and DC bus voltage value, or frequency detection method and voltage detection method. In the present invention, the frequency data on the wind turbine side is used to calculate the frequency change on the wind turbine side, and then the frequency change on the wind turbine side is used to determine whether an islanded state occurs. When the wind turbine generator set is connected to the grid and operated, the frequency is basically a constant value (such as 50Hz), and when an island occurs, the frequency will change due to the imbalance of active power. Therefore, it is possible to determine whether an islanded state occurs by detecting the frequency change. In the present invention, a frequency change threshold is set to detect and determine whether the wind farm is in an islanded operation. Specifically, when the frequency change is less than a preset threshold, the wind farm is in a normal operation state; when the frequency change is greater than the preset threshold, the wind farm is in an island operation state; then it is judged whether the power of the wind turbine can maintain the DC voltage of the flexible DC system. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state but with insufficient power.

[0086] Finally, the corresponding control strategy is determined according to the three different operating states of the wind farm. In the present invention, when the wind farm is in the normal operating state, a grid-type composite control strategy is adopted; when the wind farm is in an island operation state with sufficient power, a grid-type DC voltage frequency control strategy is adopted; when the wind farm is in an island operation state with insufficient power, a frequency support control strategy is adopted.

[0087] The present invention divides the operating status of the wind farm on the wind turbine side into three situations, and performs grid-type control on the flexible DC system according to different situations to achieve stable operation based on the overall system. Compared with the prior art, the present invention takes into account various operating conditions of the wind farm, and can perform corresponding regulation of the flexible DC system according to different operating states of the wind farm, thereby enhancing the comprehensiveness and stability of the overall system regulation.

[0088] Step 2, specifically, when the wind farm is not operating in an isolated state, it is determined that the wind farm is in a normal operating state, and then the grid-type composite control strategy proposed in the present invention can be adopted. In the composite strategy, the output power of the wind turbine in the wind farm is regulated mainly based on the grid-side grid frequency deviation obtained by the grid-side converter station and the real-time operating parameters of the wind turbine-side AC grid, combined with an adaptive neural network algorithm, to achieve support for the grid-side grid frequency.

[0089] Specifically, the grid-type composite control strategy is mainly divided into three parts. The first part first calculates the adaptive frequency deviation of the wind turbine side according to the operating conditions of the AC power grid on the wind turbine side and the frequency deviation of the grid-side power grid, combined with the adaptive algorithm, according to the operating parameters of the AC power grid on the wind turbine side, and sends the adaptive frequency deviation of the wind turbine side to the wind farm control module, which is then used to calculate the output power value of the wind turbine. The second part, according to the historical data of the grid-side power grid frequency deviation and the corresponding historical data of the wind turbine output power, constructs a mapping model, inputs the grid-side power grid frequency deviation into the mapping model, obtains the reference value of the wind turbine output power based on the corresponding relationship of the historical data, and sends it to the wind turbine as the standard value for judgment. The third part, in the wind turbine, calculates the output power value of the wind turbine according to the adaptive frequency deviation of the wind turbine side, and then compares it with the reference value, and eliminates the output power value of the wind turbine that deviates too much from the reference value, so as to reduce the extreme fluctuation of the system and enhance the stability of the system.

[0090] It should be noted that the grid-forming composite control strategy proposed in the present invention can, in the case of communication failure, continue to maintain system operation through a grid-forming DC voltage frequency control strategy based on the grid-side grid frequency deviation. At this time, there is no need to adjust the corresponding control coefficient according to the working conditions of the AC grid connected to the wind turbine side. The wind farm can be directly controlled according to the preset coefficient to adjust the output power to provide frequency support for the grid-side grid. Compared with the prior art, the control strategy of the present invention can still regulate the wind farm according to the grid-side grid frequency change in the case of communication interruption to achieve frequency support, with high stability and strong practicality.

[0091] Specifically, the first part of the network-based composite control strategy includes:

[0092] First, based on the real-time AC power grid operating parameters on the fan side and the classification information of the AC power grid operating conditions on the fan side, an adaptive working condition control coefficient corresponding to the AC power grid operating conditions on the fan side is obtained through an adaptive neural network algorithm. Specifically, based on the AC power grid operating parameters on the fan side, the AC power grid operating conditions on the fan side are identified through a pre-trained working condition analysis neural network model, and the current AC power grid operating conditions on the fan side are output, and the AC power grid operating conditions on the fan side include: normal working conditions, under-frequency working conditions, over-frequency working conditions and abnormal impedance working conditions; the current AC power grid operating conditions on the fan side and the current converter control parameters are input into the trained adaptive parameter generation model to obtain the adaptive working condition control coefficient corresponding to the AC power grid operating conditions on the fan side. In a disclosed embodiment of the present invention application, the adaptive working condition control coefficient includes: an adaptive frequency-DC voltage adjustment coefficient and an adaptive DC voltage-frequency adjustment coefficient.

[0093] In a specific embodiment of the present invention, the adaptive operating condition control coefficient is calculated through the following steps: Step 11: Acquire the real-time operating parameters of the AC power grid on the fan side, including voltage, frequency, impedance, etc., and use the data acquisition module to collect the voltage, frequency, impedance and other operating parameters of the AC power grid on the fan side in real time. Step 12: Perform data preprocessing on the collected operating parameters of the AC power grid on the fan side, and obtain standardized operating parameter data of the AC power grid on the fan side through data cleaning and normalization. Step 13: According to the standardized operating parameter data of the AC power grid on the fan side, use the adaptive neural network algorithm to identify and classify the operating status of the AC power grid on the fan side, and judge the current operating condition of the AC power grid on the fan side. The operating conditions of the AC power grid on the fan side include four operating conditions: normal operating conditions, under-frequency operating conditions, over-frequency operating conditions and impedance abnormality conditions. Step 14: Based on the real-time operating parameters of the AC power grid on the wind turbine side, the adaptive parameter generation model is trained online to determine the weight parameters in the adaptive parameter generation model, and the trained adaptive parameter generation model is determined according to the weight parameters; Step 15: The current operating conditions of the AC power grid and the current converter control parameters are input into the trained adaptive parameter generation model to obtain the adaptive condition control coefficient corresponding to the condition of the AC power grid on the wind turbine side, which is the optimal control strategy parameter of the current flexible system.

[0094] In an embodiment of the present invention, it also includes: sending the adaptive operating condition control coefficient obtained by optimizing the adaptive neural network algorithm to the flexible direct current control system, and dynamically adjusting the control strategy. The flexible direct current control system adjusts the flexible direct current system in real time according to the received adaptive operating condition control coefficient to ensure stable operation. During the regulation process, the operating parameter changes of the AC power grid on the wind turbine side are continuously monitored. If it is detected that the operating parameters exceed the preset threshold range, the training and optimization of the adaptive neural network algorithm are re-triggered to dynamically update the optimal control parameters. The present invention realizes real-time monitoring of the operating parameters of the AC power grid on the wind turbine side and dynamic optimization and adjustment of the control strategy through the coordinated work of the adaptive neural network algorithm and the control system, thereby improving the frequency stability and grid-connected performance of the wind power transmission system through the flexible direct current.

[0095] Afterwards, based on the adaptive operating condition control coefficient calculated by the adaptive neural network algorithm, a grid-type DC voltage and frequency control strategy is adopted for the flexible DC system to obtain an adaptive fan-side frequency deviation.

[0096] Specifically, the flexible direct current grid-side converter station detects the grid-side grid frequency deviation data, passes the grid-side grid frequency deviation data through a low-pass filter to obtain the filtered grid frequency deviation, and then calculates the frequency superimposed DC voltage value based on the adaptive working condition control coefficient and the DC voltage reference value of the grid-side converter station, and sends the superimposed DC voltage value to the wind turbine side converter station. In a specific embodiment of the present invention, the adaptive working condition control coefficient is an adaptive frequency-DC voltage adjustment coefficient, which is the optimal control coefficient of the current system calculated according to the operating condition of the AC grid on the wind turbine side, and can ensure that the frequency superimposed DC voltage value calculated according to the adaptive frequency-DC voltage adjustment coefficient at this time is the optimal value for system stability.

[0097] In the wind turbine side converter station, based on the frequency superimposed DC voltage value and the adaptive operating condition control coefficient, a frequency sensing algorithm is used to calculate a wind turbine side frequency deviation that is adaptive to the wind turbine side AC power grid operating condition, and the adaptive wind turbine side frequency deviation is sent to the wind turbine.

[0098] Specifically, the converter station on the side of the flexible direct current wind turbine responds to the frequency of the power grid system on the basis of the grid-type control, multiplies the difference between the normalized frequency superimposed direct current voltage value and the normalized frequency reference value by the adaptive direct current voltage-frequency adjustment coefficient, and then calculates the frequency deviation on the side of the flexible direct current wind turbine based on the power reference value of the converter station on the side of the wind turbine, the active power output value of the converter station on the side of the wind turbine, and the related gain of the converter station on the side of the wind turbine, and sends it to the wind farm and to the wind turbine. Among them, the adaptive direct current voltage-frequency adjustment coefficient of the converter station on the side of the wind turbine is obtained through an adaptive neural network algorithm, and the control coefficient is adaptive to the working conditions of the AC power grid on the side of the wind turbine, which can enhance the precision and stability of system control.

[0099] From the above, through the grid-type DC voltage frequency control strategy based on the adaptive neural network algorithm, the optimal fan side frequency deviation calculation amount is obtained, that is, the fan side frequency deviation amount that is adaptive to the fan side AC power grid working condition, and then the wind farm controls the output power of the wind farm according to the adaptive fan side frequency deviation amount to support the grid side power grid frequency. In the present invention, by optimizing the control parameters according to the different working conditions of the fan side AC power grid, the refinement of the system control is improved, and the stability of the overall system control is improved.

[0100] The second part of the grid-based composite control strategy includes: building a mapping model based on historical data, and calculating the reference value of the wind farm frequency output through the mapping model based on the current system operation data. The reference value is calculated based on the cleaned historical data and is a reference to the operation data under the stable state of the system. The output power reference value is sent to the wind farm control module.

[0101] Step 21: Based on the grid-side grid frequency deviation historical data and the corresponding wind turbine output power historical data, the grid frequency deviation is used as the model input, and the wind turbine output power adjustment amount is used as the model output. A fuzzy control algorithm is used to construct a mapping model. Step 22: The grid-side grid frequency deviation data is input into the mapping model constructed in step 1, and the wind turbine output power is output. The wind turbine output power calculated based on the historical data is used as the wind turbine output power reference value. Step 23: The wind turbine power adjustment reference is sent to the control system of the wind farm as a reference for stability. It participates in the process of regulating the wind farm output power, which can further improve the overall stability of the system regulation.

[0102] The third part of the grid-type composite control strategy includes: obtaining the adaptive wind turbine side frequency deviation based on the control system of the wind farm; calculating the wind turbine output power value based on the adaptive wind turbine side frequency deviation and the current wind turbine output power according to the grid-type control strategy algorithm; comparing the difference between the wind turbine output power value and the wind turbine output power reference value with a preset threshold value, and if it is less than the preset threshold value, regulating the wind turbine based on the wind turbine output power value; if it is greater than the preset threshold value, regulating the wind turbine output power based on the wind turbine output power reference value to support the grid-side power grid frequency.

[0103] Specifically, the wind turbine side converter station transmits the adaptive wind turbine side frequency deviation to the wind turbine control system, and the wind turbine control system determines whether to increase or decrease the wind turbine output power according to the adaptive wind turbine side frequency deviation. The wind turbine control system uses a network control strategy to calculate the wind turbine output power value according to the size of the adaptive wind turbine side frequency deviation.

[0104] The difference between the wind turbine output power value and the wind turbine output power reference value is compared with a preset threshold value. If it is less than the preset threshold value, the wind turbine is regulated based on the wind turbine output power value; if it is greater than the preset threshold value, the wind turbine output power is regulated based on the wind turbine output power reference value. The wind turbine output power is transmitted to the DC line through the wind turbine side converter station, and then transmitted to the receiving-end power grid through the receiving-end converter station to support the receiving-end power grid frequency. The receiving-end power grid frequency is restored under the support of the wind turbine power, the frequency deviation signal decreases, and the wind turbine output power decreases accordingly until the receiving-end power grid frequency is restored to the rated value, and the wind turbine output power is also restored to the normal level, completing the frequency support control process.

[0105] Among them, the preset threshold is the amount set in the present invention application, which is set according to the maximum control fluctuation that the system can accept. If the actual control amount deviates too much from the reference amount, the system cannot withstand it and will cause overall fluctuation and instability. The present invention application proposes to consider the stability of the current control of the system based on the wind turbine power adjustment reference, which can effectively eliminate data fluctuations and sudden changes caused by abnormal operation of the flexible direct current system itself, and further improve the stability of system operation.

[0106] Step 3: Specifically, if the wind farm is operating in an islanded state and has sufficient power, a grid-type DC voltage and frequency control strategy is adopted to regulate the output of the wind farm and support the frequency of the grid-side AC power grid.

[0107] In the prior art, various grid-forming control methods for new energy power generation systems and flexible direct current have been proposed, including droop control, power synchronization control, virtual synchronous machine / virtual synchronous generator, matching control and virtual oscillation control, etc. The dynamic power decoupling of the virtual synchronous machine can enhance the stability of the system. It is more advantageous to adopt grid-forming control for wind turbines and modular multilevel flexible direct current transmission systems (MMC-HVDC) in weak power grids. When the present invention is operating in an isolated island and the power is sufficient, it mainly adopts a grid-forming control strategy based on a modular multilevel flexible direct current transmission system.

[0108] The frequency signal response path of the overall system of the wind farm operating in an isolated island is as follows: Figure 2 As shown, where U DC is the DC voltage, ω on is the frequency detected by the flexible DC receiving station, ω o ff is the frequency detected by the flexible DC sending end converter station, and the AC voltage on the grid side of the sending end converter station is U soff . It should be noted that in the flexible DC system, the receiving-end converter station is also called the grid-side converter station, and the sending-end converter station is also called the wind turbine-side converter station. The control idea of ​​the grid-type DC voltage and frequency control strategy proposed in the present invention is: the detected frequency change is superimposed on the DC voltage by the grid-side converter station and transmitted to the wind turbine-side converter station. The wind turbine-side converter station calculates the frequency change of the wind turbine-side converter station based on the DC voltage with frequency superposition through the frequency sensing corresponding algorithm. The wind farm adjusts the output power according to the frequency change of the wind turbine-side converter station to achieve frequency support for the grid-side AC power grid. During the entire control process, no communication connection is required. The grid-side frequency change signal is simply superimposed on the DC voltage and frequency information and transmitted to the wind farm. In the case of an unreliable communication environment, the system control stability can be effectively improved.

[0109] The flexible DC grid-side converter station responds to the system frequency based on the grid configuration control. Figure 3 The control strategy shown in the figure reflects the change of system frequency in the DC voltage UDC Deviation. At the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate the superimposed voltage, and the superimposed voltage is sent to the wind turbine side converter station. Specifically, in a specific embodiment of the present invention, at the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate the superimposed voltage value, and the superimposed voltage value is sent to the wind turbine side converter station. In addition, Figure 3 The meanings of the parameters are as follows: is the preset DC voltage control coefficient, Δω represents the frequency change; T d is the time constant of the low-pass filter to filter out the noise at the system frequency; s is the relevant parameter of the low-pass filter; U dcref1 P is the DC voltage reference value of the grid-side converter station; out1 is the measured power value of the grid-side converter station, P ref1 is the power reference value of the grid-side converter station, and PI is applied dc That is, the proportional-integral controller converts the deviation of the DC voltage into a change in the active reference value. ref1 is the frequency reference value of the grid-side converter station, is the relevant gain of the grid-side converter station. The expression of the frequency-voltage conversion algorithm is as follows:

[0110]

[0111] U dc2 is the frequency superimposed voltage value; is the preset DC voltage control coefficient; U dc is the grid-side DC voltage; U dcref1

[0112] in:

[0113] is the grid-side DC voltage reference value; T d is the time constant of the low-pass filter; s is the relevant parameter of the low-pass filter; Δω is the grid frequency deviation.

[0114] like Figure 3 The relevant control expressions in the strategy control flow chart shown are as follows:

[0115] P ref1 =PI dc (U dc -U dc2 )

[0116]

[0117] Among them, P ref1 is the power reference value of the grid-side converter station, PI dc The proportional-integral controller converts the deviation of DC voltage into the change of active reference value; U dc is the grid-side DC voltage; U dc2 is the frequency superimposed voltage value; P out1 is the measured power value of the grid-side converter station, ω ref1 is the frequency reference value of the grid-side converter station, is the relevant gain of the grid-side converter station, and ω1 is the frequency of the grid-side converter station.

[0118] In the wind turbine side converter station, the flexible DC wind turbine side converter station responds to the system frequency based on the grid-type control. Figure 4 In the wind turbine side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the wind turbine side frequency deviation, and the wind turbine side frequency deviation is sent to the wind turbine. Specifically, in a specific embodiment of the present invention, the superimposed voltage value superimposed with the frequency deviation is normalized to obtain The frequency reference value of the converter station on the wind turbine side ω ref2 After normalization The difference, based on The preset frequency control coefficient adopts the frequency sensing algorithm as the system frequency deviation of the flexible direct current sending end converter station, also known as the frequency deviation of the wind turbine side, which is transmitted to the wind turbine. Then, the wind turbine based on the grid control will adjust the output active power according to the frequency deviation and add it to the power reference value P of the wind turbine side converter station. ref2 . Figure 4 The explanations of the parameters are as follows: is the normalized frequency superimposed voltage value; The normalized frequency reference value of the wind turbine side converter station; is the preset frequency control coefficient; P ref2 is the power reference value of the converter station on the wind turbine side; P out2 is the actual measured power value of the converter station on the wind turbine side; is the input-output related gain of the wind turbine side converter station; ω ref2 Frequency reference value of the converter station on the wind turbine side; Δω is the grid frequency deviation.

[0119] like Figure 4 The calculation formula of the frequency sensing algorithm in the strategy control flow chart shown is as follows:

[0120]

[0121] in, is the normalized frequency superimposed voltage value; The normalized frequency reference value of the wind turbine side converter station; is the preset frequency control coefficient; P ref2 is the power reference value of the converter station on the wind turbine side; P* is the power superposition value of the converter station on the wind turbine side; P out2 is the actual measured power value of the converter station on the wind turbine side; is the input-output related gain of the wind turbine side converter station; ω ref2 Frequency reference value of converter station on wind turbine side; ω2 is frequency deviation on wind turbine side.

[0122] From the above, through the steps of the grid-side converter station and the wind turbine side converter station, the wind turbine side frequency deviation containing the grid-side power grid frequency deviation information is obtained, and the wind turbine side frequency deviation is sent to the wind turbine. The control system of the wind turbine adjusts the wind turbine output power according to the wind turbine side frequency deviation to achieve frequency support for the grid-side power grid. Specifically, two control methods can be adopted for the grid-type wind turbine and the grid-type flexible direct current combined transmission system. The first control strategy is that the flexible direct current wind turbine side converter station controls the active power, and the flexible direct current grid-side converter station controls the DC voltage; the second control strategy is that the flexible direct current wind turbine side converter station controls the DC voltage, and the flexible direct current grid-side converter station controls the active power. Based on the first control strategy, additional Figure 3 and Figure 4 The improved frequency sensing strategy is referred to as the type I control strategy. Based on the second control strategy, Figure 3 The improved frequency sensing strategy is added to the converter station on the flexible DC wind turbine side. Figure 4 The improved frequency sensing strategy is attached to the converter station on the flexible direct current grid side, which is referred to as the Type II control strategy. For the scenario where the wind turbine at the sending end is connected to a weak AC grid, either the Type I control strategy or the Type II control strategy can be used; for the scenario where the wind turbine at the sending end is not connected to the AC grid in parallel, that is, the wind turbine side is an isolated renewable energy scenario (no parallel AC grid), because the wind turbine side may not be able to provide enough power to maintain the DC voltage, only the Type I control strategy can be used in this scenario.

[0123] In the prior art, the coordination strategies proposed for receiving system support based on modular multilevel flexible direct current transmission system (MMC-HVDC) and wind turbines can be divided into communication-based strategies and non-communication strategies. In the communication-based strategy, frequency information is sent to the wind turbine through communication to provide necessary support. The control strategy without communication is more advantageous because it does not rely on communication. The core issue is how to transmit the frequency of the receiving power grid to the wind turbine side at the sending end. In addition, traditionally, both the grid-side modular multilevel converter station (MMC) and wind turbines use phase-locked loop (PLL)-based grid-following control, while the wind turbine-side converter station uses voltage-frequency (VF) control. Due to the stability requirements of the phase-locked loop (PLL), the grid-following control may suffer from instability and slow response.

[0124] The grid-type DC voltage and frequency control strategy proposed in the present invention has particularly outstanding advantages in working scenarios without communication or with poor communication environment, and can solve the problems in the above-mentioned prior art. It can obtain the variables of the grid-side grid frequency through the grid-side converter station, and based on the grid-type DC voltage and frequency control strategy, realize the regulation of the wind farm output. The grid-type control strategy can replace the support capacity of the power grid, improve the adaptability and flexibility of the system operation, and enable the grid-type wind turbine to adapt to different geographical environments and climatic conditions, whether it is plains, mountains or seaside, it can operate normally. In addition, the grid-type wind turbine also has the ability to operate in an isolated island, which is particularly important in situations where the power grid fails or local power supply is required. In addition, compared with the communication-based regulation in the prior art, the control strategy of the present invention has stronger practicality. In working scenarios with poor communication environments, it can greatly improve the stability of the system and eliminate errors caused by communication noise.

[0125] Step 4 specifically includes: if the wind turbine side is in island operation and the wind turbine power is insufficient to maintain the DC voltage, switch to a frequency support control strategy. Specifically: in the wind turbine side converter station, based on the output power of the wind turbine, the DC voltage and power data of the wind turbine side converter, calculate the power compensation amount of the wind turbine side converter; based on the power compensation amount and the voltage set value, control the power output of the wind turbine side converter through a proportional-integral controller to maintain the DC voltage stability; in the grid side converter station, based on the power output of the wind turbine side converter station and the grid frequency deviation, adopt a DC voltage control strategy, modulate the output of the grid side converter through a proportional-integral controller, and support the grid side grid frequency.

[0126] In a specific embodiment provided by the present invention application, the frequency support control strategy includes: Step 31: Obtain the DC voltage, power data and output power of the wind turbine at the wind turbine side converter station, and compare with the preset threshold value to determine whether the wind turbine side is in island operation and the wind turbine power is insufficient to maintain the DC voltage. Step 32: If the judgment result is yes, switch to the frequency support control strategy, otherwise maintain the original control strategy unchanged. Step 33: After switching to the frequency support control strategy, calculate the power compensation amount required to be provided by the sending-end converter station according to the DC voltage and power data of the wind turbine side converter station. Step 34: Use the calculated power compensation amount as the power control instruction of the sending-end converter station, and maintain the DC voltage stability by controlling the active power output of the sending-end converter station. Step 35: At the same time, switch the DC voltage control link to the receiving-end converter station (MMC), and adjust the modulation ratio of the receiving-end converter station (MMC) through the proportional-integral controller to track the DC voltage set value and maintain the DC voltage stability. It also includes continuously monitoring the DC voltage and power data of the converter station on the wind turbine side. When the wind turbine side resumes grid-connected operation and the wind turbine power recovers to the rated value, the control strategy is switched back to the original mode. During the control strategy switching process, a smooth transition algorithm is used to avoid sudden changes in voltage and power and ensure the smoothness of the switching process. The present invention provides a frequency support control strategy that can support the grid-side frequency through a converter station when the wind turbine power cannot support the grid-side frequency.

[0127] The present invention divides the operation of the wind farm into a normal operation state, an island operation state with sufficient power, and an island operation state with insufficient power, and adopts different control strategies according to different operation states of the wind farm to regulate the output of the wind turbine, thereby realizing high-stability frequency support for the grid-side power grid. Among them, the grid-forming composite control strategy adopted in the normal operation state can calculate the optimal system control coefficient according to the operating conditions of the AC power grid on the wind turbine side, and perform a more refined regulation on the entire system to support the frequency on the grid side; the grid-forming DC voltage frequency control strategy adopted in the island operation and sufficient power state has the advantage that it does not need to rely on communication to achieve the regulation of the wind turbine output and thus support the frequency of the grid-side AC power grid. In areas with poor communication conditions, such as mountainous areas and seas, the grid-forming DC voltage frequency control strategy proposed by the present invention has the advantages of strong practicality and good adaptability. Finally, when the output power of the wind turbine is insufficient, the frequency support control strategy adopted by the present invention can support the grid-side frequency through the converter station, which ultimately ensures that the wind farm can achieve frequency support for the grid-side AC power grid under various conditions.

[0128] Embodiment 2:

[0129] The present invention provides a specific embodiment, in which a grid-type DC voltage and frequency control strategy is adopted when the wind farm is in an island operation and the power is sufficient. The parameters of the engineering application in the specific embodiment are shown in Table 1, and the technical solution proposed by the present invention is simulated, calculated and analyzed based on the parameter values ​​in Table 1.

[0130]

[0131]

[0132] Table 1

[0133] Figure 5 It shows the DC voltage U under the type I control strategy (grid-forming DC voltage frequency control strategy) and the type II control strategy (grid-forming frequency DC voltage control strategy). dc (kV) and power P off (MW) versus time t(s). As can be seen from the figure, the response of the wind power to the frequency change of the receiving grid side through the modular multilevel converter high-voltage direct current transmission (MMC-HVDC) system. After t=5 seconds, the frequency dropped to 49.85Hz. After t=7s, the system frequency dropped further to 49.7Hz. Both the Type I control strategy and the Type II control strategy can respond well to grid frequency deviations. However, to a certain extent, the grid-type DC voltage-frequency control strategy will cause a DC voltage deviation of less than 5%, which is proportional to the grid frequency deviation, which is usually within 1%. The characteristic of the grid-type frequency DC voltage control strategy is that there is no DC voltage deviation within the power capacity of the wind farm. If it exceeds the power capacity of the wind turbine, it will cause voltage collapse.

[0134] The coordination with the traditional phase-locked loop (PLL)-based grid-following control strategy is further compared. For simplicity, the modular multilevel converter high-voltage direct current transmission (MMC-HVDC) still adopts the grid-following control strategy of controlling power at the sending end and DC voltage at the receiving end, while the wind turbine can adopt the traditional grid-following control, and the frequency support additional control process is as follows Figure 6 As shown, where T dw is the filter time constant, s is the filter related parameter, ΔP is the power change, P0 is the measured power value, P ref is the power reference value, ω is the frequency value, ω ref is the frequency reference value, K pf is the frequency power conversion parameter. By comparing the results of the two control strategies, it can be seen that the overall performance of the network control strategy proposed by the present invention is better.

[0135] Embodiment 3:

[0136] Based on the same inventive concept, the present invention also provides a system for improving the stability of a grid-type flexible direct current system. The specific system structure is as follows: Figure 7 As shown, including:

[0137] Operation status judgment module: specifically used to determine the operation status of the wind farm based on the operation parameters of the wind turbine side converter station in the wind farm; the operation status includes normal operation status, island operation with sufficient power status and island operation with insufficient power status;

[0138] Composite strategy control module: specifically used for, when the wind farm is in the conventional operation state, adjusting the output power of the wind turbine in the wind farm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, to achieve support for the grid-side grid frequency;

[0139] A network strategy control module: specifically used for regulating the output power of the wind turbine based on the grid frequency deviation to support the grid-side grid frequency when the wind farm is in island operation and has sufficient power;

[0140] Power shortage strategy control module: specifically used for regulating the output power of the grid-side converter station based on the DC voltage and power data of the wind turbine-side converter and the grid frequency deviation when the wind farm is in island operation but in a power shortage state, so as to support the grid-side grid frequency.

[0141] Preferably, the operating status judgment module includes:

[0142] A frequency change calculation unit: used to calculate the frequency change amount of the fan side based on the fan side frequency data;

[0143] An island determination unit: used to determine whether the wind turbine side frequency change is less than a preset threshold based on the wind turbine side frequency change and a preset threshold; if so, the wind farm is in a normal operation state; otherwise, the wind farm is in an island operation state;

[0144] Power judgment unit: used to judge whether the power of the wind turbine can maintain the DC voltage of the flexible DC system when the wind farm is in an island operation state. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state but with insufficient power.

[0145] Preferably, the composite strategy control module includes:

[0146] Adaptive frequency deviation calculation submodule: used to determine the adaptive frequency deviation of the wind turbine side by using an adaptive neural network algorithm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, and send the adaptive frequency deviation of the wind turbine side to the wind turbine in the wind farm;

[0147] A power reference value calculation submodule: used for inputting the grid frequency deviation into a pre-built mapping model, obtaining a wind turbine output power reference value, and sending the wind turbine output power reference value to the wind turbine, wherein the mapping model is constructed based on the grid frequency deviation historical data and the corresponding wind turbine output power historical data;

[0148] The fan control submodule is used to control the fan output power in the fan based on the adaptive fan-side frequency deviation and the fan output power reference value, so as to support the grid-side power grid frequency.

[0149] Preferably, the adaptive frequency deviation calculation submodule includes:

[0150] An adaptive coefficient calculation unit is used to identify the working condition of the AC power grid on the wind turbine side and determine the adaptive working condition control coefficient corresponding to the working condition of the AC power grid on the wind turbine side through an adaptive neural network algorithm based on the operating parameters of the AC power grid on the wind turbine side;

[0151] Adaptive DC voltage calculation unit: used for, in the grid-side converter station, performing low-pass filtering on the grid frequency deviation to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the adaptive operating condition control coefficient, using a frequency-voltage conversion algorithm to calculate a frequency-superimposed DC voltage value, and sending the superimposed DC voltage value to the wind turbine-side converter station;

[0152] Adaptive frequency calculation unit: used in the fan side converter station, based on the frequency superimposed DC voltage and the adaptive operating condition control coefficient, using a frequency sensing algorithm to calculate an adaptive fan side frequency deviation.

[0153] Preferably, the adaptive coefficient calculation unit includes:

[0154] The working condition identification subunit is used to identify the working condition of the AC power grid on the wind turbine side based on the operating parameters of the AC power grid on the wind turbine side and obtain the current working condition of the AC power grid on the wind turbine side through a pre-trained working condition analysis neural network model. The working condition of the AC power grid on the wind turbine side includes: normal working condition, under-frequency working condition, over-frequency working condition and impedance abnormal working condition;

[0155] Adaptive operating condition control coefficient calculation subunit: used to input the current wind turbine side AC power grid operating condition and the current converter control parameter into the adaptive parameter generation model to obtain the adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition, the adaptive operating condition control coefficient includes: adaptive frequency-DC voltage adjustment coefficient and adaptive DC voltage-frequency adjustment coefficient.

[0156] Preferably, the fan control submodule includes:

[0157] Output power calculation unit: used to calculate the output power value of the fan based on the adaptive fan side frequency deviation and the current fan output power according to the network control strategy algorithm;

[0158] Wind turbine control unit: used to compare the difference between the wind turbine output power value and the wind turbine output power reference value with a preset threshold value. If the difference is less than the preset threshold value, the wind turbine is controlled based on the wind turbine output power value; if the difference is greater than the preset threshold value, the wind turbine output power is controlled based on the wind turbine output power reference value to support the grid-side power grid frequency.

[0159] Preferably, the insufficient power strategy control module includes:

[0160] A DC voltage control submodule: used to calculate the power compensation amount of the fan side converter in the fan side converter station based on the output power of the fan, the DC voltage of the fan side converter and the power data; based on the power compensation amount and the voltage set value, control the power output of the fan side converter through a proportional-integral controller to maintain the DC voltage stability;

[0161] Grid-side converter output modulation submodule: In the grid-side converter station, based on the power output of the wind turbine-side converter station and the grid frequency deviation, a DC voltage control strategy is adopted to modulate the output of the grid-side converter through a proportional-integral controller to support the grid-side grid frequency.

[0162] Preferably, the networking strategy control module includes:

[0163] The grid-side voltage calculation submodule: at the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and a preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate a superimposed voltage, and the superimposed voltage is sent to the wind turbine-side converter station;

[0164] The fan side frequency calculation submodule: in the fan side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the fan side frequency deviation, and the fan side frequency deviation is sent to the fan;

[0165] Wind turbine control submodule: In the wind turbine, the output power of the wind farm is controlled based on the frequency deviation on the wind turbine side to support the grid frequency on the grid side.

[0166] The present invention provides a control system for improving the frequency stability of a wind power grid-forming flexible direct current transmission system. Through an operation status judgment module, a composite strategy control module, a grid-forming strategy control module and a power shortage strategy control module, the present invention realizes that different control strategies are used to regulate wind turbines according to different operation states of the wind farm, so as to achieve high support for the frequency stability of the grid-side AC power grid, cope with different operation conditions of the wind farm, and improve the overall control stability and practicality of the system.

[0167] Embodiment 4:

[0168] Furthermore, in the present application, an energy buffer device is also provided in the DC transmission system. Based on the current operating parameters of the AC power grid on the wind turbine side and the frequency deviation on the wind turbine side, a model predictive control algorithm is adopted to predict the change trend of the DC power and voltage in the flexible DC system in the next control cycle; according to the change trend of the DC power and voltage, the charging and discharging strategy of the energy buffer device in the flexible DC system in the next control cycle is controlled to balance the grid-connected fluctuation caused by the adjustment of the wind turbine output power.

[0169] Specifically, step 1: according to the real-time operating parameters of the AC power grid on the wind turbine side and the frequency deviation of the receiving power grid, the current power and voltage state of the system is obtained; the model predictive control algorithm is used to establish a dynamic model of the DC transmission system and the energy buffer device; step 2: the current state of the system obtained in step 1 is used as the initial condition of the model to predict the power and voltage change trend in the future; step 3: according to the prediction result of step 2, the optimal charging and discharging strategy of the energy buffer device is determined. If it is predicted that the power or voltage will change suddenly in the future, the charging and discharging strategy of the energy buffer device is adjusted in advance to smooth the changes in power and voltage; if it is predicted that the power and voltage will change smoothly in the future, the current charging and discharging state of the energy buffer device is maintained; the charging and discharging strategy of the energy buffer device determined in step 3 is converted into a control instruction and sent to the control system of the energy buffer device; the control system of the energy buffer device adjusts its charging and discharging state according to the received control instruction to achieve smooth control of power and voltage; the operating parameters of the AC power grid on the wind turbine side and the changes in the frequency deviation of the receiving power grid are continuously monitored, and the next round of predictive control is returned to step 1 to ensure the stable operation of the system.

[0170] The energy buffer device added to the flexible DC system proposed in the present invention can predict the power and voltage change trends in the future period according to the current operating parameters of the AC power grid on the wind turbine side and the frequency deviation of the receiving-end power grid, adjust the charging and discharging strategy of the energy buffer device in advance, smooth out the sudden changes in power and voltage, reduce the grid-connected fluctuations caused by the wind turbine output power adjustment, and improve the overall stability of the system.

[0171] In a disclosed embodiment of the present application, when the wind farm is in the conventional operation state, the control strategy further includes:

[0172] Adopting adaptive dynamic programming algorithm, according to the real-time operating parameters of AC power grid on wind turbine side and the frequency deviation of receiving grid, the coordinated control strategy between DC voltage, power regulation and AC power grid frequency support is automatically optimized, the priority between various control objectives is dynamically balanced, and the comprehensive control performance of the system is ensured.

[0173] Specifically: Step 1: Obtain the current operating parameters of the AC power grid on the wind turbine side and the frequency deviation data of the receiving power grid as the input of the adaptive dynamic programming algorithm. Step 2: According to the data obtained in step 1, an adaptive dynamic programming algorithm is used to establish an optimization model between DC voltage, power regulation and AC power grid frequency support, and the optimization model includes the stability optimal objective function and constraints. Step 3: By solving the optimization model established in step 2, the optimal coordinated control strategy between DC voltage, power regulation and AC power grid frequency support is obtained. Step 4: Determine whether the optimal coordinated control strategy obtained in step 3 meets the comprehensive control performance requirements of the flexible direct current system. If so, execute step 5; otherwise, return to step 2 to adjust the constraints of the optimization model and the stability optimal objective function. Step 5: According to the optimal coordinated control strategy obtained in step 3, determine the frequency DC voltage droop control parameters of the grid-side converter station and the DC voltage frequency or AC voltage amplitude droop control parameters of the wind turbine side converter station. Step 6: Using the control parameters determined in step 5, implement frequency sensing improvement control on the grid-side converter station and the wind turbine-side converter station respectively to achieve a dynamic balance between DC voltage, power regulation and AC grid frequency support. Step 7: During the control process of step 6, continuously monitor the comprehensive control performance indicators of the system, including frequency stability, DC voltage deviation, etc. Step 8: If step 7 detects that the comprehensive control performance indicators of the system have deteriorated, the re-optimization of the adaptive dynamic programming algorithm is triggered and returns to step 2; otherwise, continue to execute the control strategy of step 6. Step 9: According to the changes in the actual operating conditions of the system, the state transition probability matrix and reward function of the optimization model in step 2 are adaptively adjusted to achieve online optimization and adaptive update of the control strategy, and continuously ensure the comprehensive control performance of the system.

[0174] In a disclosed embodiment of the present application, when the wind farm is in the conventional operation state, the control strategy further includes:

[0175] A voltage and frequency feedforward compensation link is introduced into the converter control. According to the real-time operating parameters of the AC power grid on the wind turbine side, the voltage and frequency are compensated and controlled in advance to reduce the fluctuation amplitude of voltage and frequency and improve the system's anti-disturbance capability.

[0176] Specifically, step 1: obtain the real-time operating parameters of the AC power grid on the wind turbine side, including voltage amplitude, frequency and other information, collect and transmit them to the converter control system through sensors. Step 2: calculate the voltage and frequency deviation values, that is, the difference between the actual value and the rated value, based on the real-time operating parameters obtained. Step 3: determine whether the voltage and frequency deviation exceeds the preset threshold value. If it exceeds the threshold value, start the feedforward compensation control; if it does not exceed the threshold value, maintain the normal control mode. Step 4: when the feedforward compensation control is started, calculate the voltage and frequency values ​​to be compensated according to the voltage and frequency deviation values. Step 5: input the calculated compensation voltage and frequency values ​​as feedforward signals into the voltage and frequency control link of the converter. Step 6: in the converter control system, superimpose the feedforward compensation signal with the normal voltage and frequency command signals to obtain the corrected voltage and frequency control commands. Step 7: the converter adjusts its output voltage and frequency according to the corrected control command to dynamically compensate the voltage and frequency of the AC power grid on the wind turbine side. Step 8: Through feedforward compensation control, the fluctuation amplitude of the wind turbine side grid voltage and frequency is reduced, and the system's ability to resist disturbances is improved. Step 9: During the feedforward compensation control process, the voltage and frequency parameters of the wind turbine side grid are monitored in real time, and the parameters of the feedforward compensation are adaptively adjusted according to the monitoring data to ensure the optimization of the compensation effect.

[0177] In a disclosed embodiment of the present invention, it also includes: adopting a hierarchical distributed control architecture, setting an autonomous control unit locally in each converter, and autonomously adjusting the control strategy according to the real-time operating parameters of the AC power grid on the wind turbine side to achieve rapid response and decision-making, thereby improving the real-time control performance of the system; setting a coordination control unit at the system level to coordinate the control strategies of each converter and optimize the overall performance of the system.

[0178] Specifically, step 1: according to the real-time operating parameters of the AC power grid on the wind turbine side, the control strategy of the local autonomous control unit of each converter is obtained; step 2: adopting a hierarchical distributed control architecture, and setting an autonomous control unit locally in each converter; step 3: through the autonomous control unit, according to the obtained control strategy, autonomously adjust the control parameters of each converter to achieve rapid response and decision-making; step 4: obtain the adjusted control parameters of each converter to determine whether the requirements of the overall performance optimization of the system are met; step 5: if the requirements are not met, the control parameters of each converter are passed to the coordination control unit at the system level; step 6: the coordination control unit calculates a new control strategy through an optimization algorithm based on the received control parameters; step 7: the optimized control strategy is sent to the autonomous control unit of each converter; step 8: the autonomous control unit again autonomously adjusts the control parameters of each converter based on the received control strategy; step 9: steps 4-8 are repeated continuously until the requirements of the overall performance optimization of the system are met, and the coordinated optimization process of the converter control strategy is completed.

[0179] In a disclosed embodiment of the present invention, it also includes: online identification and prediction of the operating parameters of the AC power grid on the wind turbine side through a machine learning algorithm, and adjusting the control strategy of the converter in advance according to the prediction results to achieve smooth switching of the control strategy, avoid sudden changes in power and voltage, and improve the stability of the system.

[0180] Specifically, step 1: use a machine learning algorithm to train the historical operation data of the AC power grid on the wind turbine side, and establish a prediction model for the operation parameters of the AC power grid on the wind turbine side. Step 2: According to the established prediction model, obtain the predicted value of the operation parameters of the AC power grid on the wind turbine side in the future. Step 3: By analyzing the predicted value, determine whether the operation parameters of the AC power grid on the wind turbine side will change significantly. Step 4: If it is predicted that the operation parameters of the AC power grid on the wind turbine side will change significantly, determine the converter control strategy parameters that need to be adjusted according to the predicted value. Step 5: Use a smooth transition algorithm to obtain a smooth transition curve from the current control strategy parameters to the target control strategy parameters. Step 6: According to the smooth transition curve, gradually adjust the control strategy parameters of the converter until the target value is reached. Step 7: During the adjustment process of the converter control strategy parameters, obtain the feedback value of the operation parameters of the AC power grid on the wind turbine side in real time. Step 8: By comparing the predicted value with the feedback value, dynamically correct the smooth transition curve to ensure a smooth control strategy switching process. Step 9: After the converter control strategy parameter adjustment is completed, continue to monitor the AC grid operating parameters on the wind turbine side, and repeat steps 2 to 8 as needed to achieve adaptive optimization adjustment of the control strategy.

[0181] In a disclosed embodiment of the present invention, it also includes: obtaining operating parameters such as voltage, frequency and impedance of the AC power grid on the wind turbine side, and using an adaptive neural fuzzy inference system to predict the operating parameters such as voltage, frequency and impedance of the AC power grid on the wind turbine side according to changes in wind speed and wind turbine output, so as to obtain the changing trend of the operating parameters of the AC power grid on the wind turbine side in the future.

[0182] Specifically, step 1: obtain the voltage, frequency, impedance and other operating parameters of the AC power grid on the wind turbine side, and transmit the obtained data to the adaptive neural fuzzy inference system. Step 2: use the adaptive neural fuzzy inference system to train the obtained AC power grid operating parameters on the wind turbine side, and establish a prediction model for the AC power grid operating parameters on the wind turbine side. Step 3: according to the changes in wind speed and wind turbine output, input the wind speed and wind turbine output data into the established prediction model. Step 4: calculate the change trend of the operating parameters such as voltage, frequency and impedance of the AC power grid on the wind turbine side in the future period through the prediction model. Step 5: determine whether the predicted change trend of the AC power grid operating parameters on the wind turbine side exceeds the set threshold range. If it exceeds the threshold range, the improved control strategy of the converter station on the wind turbine side is triggered to optimize and adjust the control parameters of the converter station on the wind turbine side. Step 6: according to the optimized and adjusted control parameters of the converter station on the wind turbine side, control the operation of the converter station on the wind turbine side to adapt to the change trend of the operating parameters of the AC power grid on the wind turbine side and maintain the stable operation of the AC power grid on the wind turbine side. Step 7: Obtain the optimized and adjusted wind turbine side converter station operation data, transmit it to the adaptive neural fuzzy inference system, and perform online update and optimization on the wind turbine side AC power grid operation parameter prediction model. Step 8: Based on the online updated and optimized prediction model, continuously predict the change trend of the wind turbine side AC power grid operation parameters, and dynamically optimize the control strategy of the wind turbine side converter station according to the prediction results, so as to realize the adaptive stable control of the wind turbine side AC power grid. Step 9: Real-time display of the wind turbine side AC power grid operation parameter prediction results and the wind turbine side converter station control strategy optimization results through the human-computer interaction interface, providing auxiliary decision support for power grid dispatching.

[0183] In a disclosed embodiment of the present invention, it also includes: based on the prediction results of the operating parameters of the AC power grid on the wind turbine side and in combination with the frequency deviation of the receiving-end power grid, a multi-objective optimization algorithm is used to determine the optimal coordination strategy for wind turbine output power regulation and receiving-end power grid frequency control, while ensuring that the voltage and frequency deviations at the grid connection point are within the allowable range, minimizing the impact of grid connection point voltage fluctuations and reactive power compensation caused by wind turbine power regulation on the frequency control effect.

[0184] Specifically, step 1: obtain the operating parameters of the AC power grid on the wind turbine side, including voltage, frequency, active power and reactive power, and predict these parameters to obtain the predicted values ​​for a period of time in the future. Step 2: According to the frequency deviation of the receiving power grid, determine the output power and reactive compensation amount of the wind turbine that need to be adjusted so that the frequency deviation is within the allowable range. Step 3: Adopt a multi-objective optimization algorithm, minimize the influence of the voltage fluctuation and reactive compensation at the grid connection point caused by the wind turbine power regulation on the frequency control effect as the optimization goal, and use the wind turbine output power and reactive compensation as the optimization variables to establish an optimization model. Step 4: In the optimization model, the predicted value of the operating parameters of the AC power grid on the wind turbine side is used as a constraint to ensure that the voltage and frequency deviation at the grid connection point are within the allowable range. Step 5: Solve the optimization model to obtain the optimal value of the wind turbine output power regulation and reactive compensation. Step 6: Convert the optimal value into the control instruction of the wind turbine side converter station, and adjust the wind turbine output power and reactive power by changing the modulation ratio and phase difference of the converter station (MMC). Step 7: At the same time, the optimal value is converted into the frequency control instruction of the grid-side converter station, and the DC voltage and reactive power of the converter station (MMC) are adjusted to affect the receiving-end grid frequency. Step 8: Real-time monitoring of the grid connection point voltage and the receiving-end grid frequency deviation to determine whether it exceeds the allowable range. If it exceeds, return to step 2 to recalculate the optimal control strategy. Step 9: According to the changes in the operating parameters of the AC grid on the wind turbine side, the control strategy is dynamically adjusted to ensure long-term stable operation.

[0185] In a disclosed embodiment of the present invention, it also includes: according to the optimal coordination strategy of wind turbine output power regulation and receiving-end power grid frequency control, combined with factors such as the charging and discharging efficiency, battery health status and remaining life of the energy buffer device in the DC transmission system, a deep reinforcement learning algorithm is used to obtain the optimal charging and discharging strategy of the energy buffer device, so as to maximize the utilization efficiency and service life of the energy buffer device while meeting the DC voltage and power fluctuation requirements.

[0186] Specifically, step 1: according to the output power of the wind turbine and the frequency deviation of the receiving power grid, a deep reinforcement learning algorithm is used to obtain the optimal coordinated control strategy of the wind turbine and the grid-side converter station, and determine the wind turbine output power adjustment amount and the grid-side converter station frequency control parameters. Step 2: According to the wind turbine output power adjustment amount obtained in step 1, combined with the DC voltage-frequency droop control characteristics of the wind turbine side converter station, the DC voltage reference value of the wind turbine side converter station is calculated. Step 3: According to the grid-side converter station frequency control parameters obtained in step 1, combined with the frequency-DC voltage droop control characteristics of the grid-side converter station, the DC voltage reference value of the grid-side converter station is calculated. Step 4: Obtain parameters such as the real-time charging and discharging power, battery health status and remaining life of the energy buffer device in the DC transmission system. Step 5: According to the DC voltage reference values ​​of the wind turbine side and grid-side converter stations obtained in steps 2 and 3 and the energy buffer device parameters obtained in step 4, a deep reinforcement learning algorithm is used to obtain the optimal charging and discharging strategy of the energy buffer device. Step 6: According to the optimal charging and discharging strategy of the energy buffer device obtained in step 5, combined with the real-time operating parameters of the DC transmission system, the charging and discharging power instructions of the energy buffer device are calculated. Step 7: The charging and discharging power instructions of the energy buffer device obtained in step 6 are sent to the energy buffer device control system to adjust the charging and discharging power of the energy buffer device. Step 8: According to the actual charging and discharging power of the energy buffer device, combined with the real-time operating parameters of the DC transmission system, the DC voltage reference values ​​of the wind turbine side and the grid side converter station are dynamically adjusted to ensure the stability of the DC voltage. Step 9: According to the adjusted DC voltage reference values ​​of the wind turbine side and the grid side converter station, the wind turbine output power and the grid side converter station frequency control parameters are dynamically optimized to achieve the optimal coordination of wind turbine output power regulation and receiving-end grid frequency control, while maximizing the utilization efficiency and service life of the energy buffer device.

[0187] In a disclosed embodiment of the present invention, it also includes: comprehensively optimizing multiple control objectives such as wind turbine output power regulation, receiving-end power grid frequency control, and energy buffer device charging and discharging, and adopting a distributed model predictive control method to obtain the optimal coordinated control strategy between the various control objectives, so as to achieve coordinated optimization of multiple control objectives such as wind turbine power regulation, AC power grid frequency support, and DC voltage control while ensuring stable system operation.

[0188] Specifically, step 1: According to multiple control targets such as wind turbine output power, receiving-end grid frequency and DC voltage, a comprehensive optimization control model of the wind power grid-type flexible direct current transmission system is established. Step 2: The distributed model predictive control method is used to decompose the comprehensive optimization control model into multiple sub-problems, each of which corresponds to a control target. Step 3: For the wind turbine output power adjustment sub-problem, the frequency deviation of the wind turbine side converter station is calculated through the DC voltage deviation to adjust the wind turbine output power. Step 4: For the receiving-end grid frequency control sub-problem, the receiving-end grid side converter station adopts frequency-DC voltage droop control to convert the frequency deviation into a DC voltage deviation. Step 5: For the energy buffer device charge and discharge control sub-problem, if the DC bus voltage exceeds the rated range, the energy buffer device is controlled to absorb or release energy to maintain the DC bus voltage stable. Step 6: Based on the optimization control of each sub-problem, the various control targets are coordinated through iterative calculation to obtain the optimal control strategy of the wind power grid-type flexible direct current transmission system. Step 7: According to the real-time operating conditions of the system, the control parameters in the optimal control strategy are dynamically adjusted to adapt to changes in system conditions. Step 8: Determine whether the system meets the stable operation conditions. If so, execute the current optimal control strategy; otherwise, return to step 6 to recalculate the optimal control strategy. Step 9: According to the optimal control strategy, coordinate the control of wind turbines, modular multilevel converter high-voltage direct current transmission (MMC-HVDC) and energy buffer devices to achieve comprehensive optimization of multiple control objectives such as wind turbine power regulation, AC grid frequency support and DC voltage control to ensure stable operation of the system.

[0189] The technical solution of the present invention also includes: in view of the characteristics of fast change and strong uncertainty of the AC power grid operating parameters on the wind turbine side, an adaptive dynamic programming algorithm is used to optimize and dynamically adjust the control strategy in real time, and the optimal control strategy is adaptively generated according to the real-time change of the AC power grid operating parameters on the wind turbine side, and the control strategy is smoothly switched to avoid frequent sudden changes in power and voltage. In addition, during the execution of the control strategy, key indicators such as the AC power grid operating parameters on the wind turbine side, the frequency deviation of the receiving power grid, the DC voltage and power fluctuation are monitored in real time. If the key indicators are monitored to exceed the preset threshold, the re-optimization of the control strategy is triggered, and the control strategy is adjusted in time according to the latest system operation status to ensure the stable operation of the system. Finally, according to the changes in factors such as the charging and discharging efficiency, battery health status and remaining life of the energy buffer device, the optimal charging and discharging strategy of the energy buffer device is regularly retrained and optimized. Through continuous learning and improvement, the utilization efficiency and service life of the energy buffer device are continuously improved, and the stability and reliability of the system are further enhanced.

[0190] Example 5

[0191] like Figure 8As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0192] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement corresponding method processes or corresponding functions, so as to realize a method for improving the stability of a meshed flexible direct current system in the above-mentioned embodiment.

[0193] Example 6

[0194] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can realize a method for improving the stability of a meshed flexible direct current system in the above embodiment.

[0195] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0196] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0197] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A method for improving the stability of a grid-type flexible direct current system, characterized in that: include: Determining the operating state of the wind farm based on operating parameters of a wind turbine-side converter station in the wind farm; The operating state includes a normal operating state, an island operating state with sufficient power, and an island operating state with insufficient power; If the wind farm is in the normal operation state, based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, the output power of the wind turbine in the wind farm is regulated to support the grid-side grid frequency; If the wind farm is in island operation and has sufficient power, the output power of the wind turbine is regulated based on the grid frequency deviation to support the grid-side grid frequency; If the wind farm is in island operation but is underpowered, the output power of the grid-side converter station is regulated based on the DC voltage and power data of the wind turbine-side converter and combined with the grid frequency deviation to support the grid-side grid frequency.

2. The method according to claim 1, characterized in that The wind turbine side converter station operating parameters include: wind turbine side frequency data, DC voltage and power; the determining the operating state of the wind farm based on the wind turbine side converter station operating parameters in the wind farm includes: Based on the fan side frequency data, calculating the fan side frequency change; Based on the wind turbine side frequency change and the preset threshold, it is determined whether the wind turbine side frequency change is less than the preset threshold; if so, the wind farm is in a normal operation state; otherwise, the wind farm is in an island operation state; When the wind farm is in an island operation state, it is determined whether the power of the wind turbine can maintain the DC voltage of the flexible DC system. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state with insufficient power.

3. The method according to claim 1, characterized in that The method of adjusting the output power of the wind turbines in the wind farm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, in combination with an adaptive neural network algorithm, to support the grid-side grid frequency includes: Based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, an adaptive neural network algorithm is used to determine an adaptive wind turbine side frequency deviation, and the adaptive wind turbine side frequency deviation is sent to the wind turbine in the wind farm; Inputting the grid frequency deviation into a pre-built mapping model to obtain a wind turbine output power reference value, and sending the wind turbine output power reference value to the wind turbine, wherein the mapping model is built based on grid frequency deviation historical data and corresponding wind turbine output power historical data; In the wind turbine, based on the adaptive wind turbine-side frequency deviation and the wind turbine output power reference value, the wind turbine output power is regulated to support the grid-side power grid frequency.

4. The method according to claim 3, characterized in that The method of using an adaptive neural network algorithm to determine an adaptive wind turbine side frequency deviation based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side includes: Based on the wind turbine side AC power grid operating parameters, an adaptive neural network algorithm is used to identify the wind turbine side AC power grid operating condition, and an adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition is determined; In the grid-side converter station, the grid frequency deviation is low-pass filtered to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the adaptive operating condition control coefficient, a frequency-voltage conversion algorithm is used to calculate a frequency-superimposed DC voltage value, and the frequency-superimposed DC voltage value is sent to the wind turbine-side converter station; In the wind turbine side converter station, based on the frequency superimposed DC voltage and the adaptive operating condition control coefficient, a frequency sensing algorithm is used to calculate an adaptive wind turbine side frequency deviation.

5. The method according to claim 4, characterized in that The method of identifying the AC power grid operating condition on the wind turbine side and determining the adaptive operating condition control coefficient corresponding to the AC power grid operating condition on the wind turbine side by using an adaptive neural network algorithm based on the AC power grid operating parameters on the wind turbine side includes: Based on the wind turbine side AC power grid operating parameters, the wind turbine side AC power grid operating conditions are identified through a pre-trained operating condition analysis neural network model to obtain the current wind turbine side AC power grid operating conditions, wherein the wind turbine side AC power grid operating conditions include: normal operating conditions, under-frequency operating conditions, over-frequency operating conditions, and impedance abnormality operating conditions; The current wind turbine side AC grid operating condition and the current converter control parameters are input into the adaptive parameter generation model to obtain an adaptive operating condition control coefficient corresponding to the wind turbine side AC grid operating condition, wherein the adaptive operating condition control coefficient includes: an adaptive frequency-DC voltage adjustment coefficient and an adaptive DC voltage-frequency adjustment coefficient.

6. The method according to claim 5, characterized in that The controlling of the wind turbine output power based on the adaptive wind turbine side frequency deviation and the wind turbine output power reference value to support the grid side power grid frequency includes: Based on the adaptive fan side frequency deviation and the current fan output power, the fan output power value is calculated according to the network control strategy algorithm; The difference between the fan output power value and the fan output power reference value is compared with a preset threshold value. If it is less than the preset threshold value, the fan is regulated based on the fan output power value; if it is greater than the preset threshold value, the fan output power is regulated based on the fan output power reference value to achieve support for the grid-side power grid frequency.

7. The method according to claim 1, characterized in that The DC voltage and power data of the wind turbine side converter are combined with the grid frequency deviation to adjust the output power of the grid side converter station to support the grid side grid frequency, including: In the wind turbine side converter station, based on the output power of the wind turbine, the DC voltage of the wind turbine side converter and power data, the power compensation amount of the wind turbine side converter is calculated; based on the power compensation amount and the voltage set value, the power output of the wind turbine side converter is controlled by a proportional integral controller to maintain the DC voltage stable; In the grid-side converter station, a DC voltage control strategy is adopted based on the power output of the wind turbine-side converter station and the grid frequency deviation, and the output of the grid-side converter is modulated by a proportional-integral controller to support the grid-side grid frequency.

8. The method according to claim 1, characterized in that: The step of regulating the output power of the wind turbine based on the grid frequency deviation to support the grid-side grid frequency includes: At the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and a preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate a superimposed voltage, and the superimposed voltage is sent to the wind turbine-side converter station; In the wind turbine side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the wind turbine side frequency deviation, and the wind turbine side frequency deviation is sent to the wind turbine; In the wind turbine, the output power of the wind farm is regulated based on the frequency deviation on the wind turbine side to support the grid frequency on the grid side.

9. A system for improving the stability of a meshed flexible direct current system, characterized in that: include: Operation status judgment module: specifically used to determine the operation status of the wind farm based on the operation parameters of the wind turbine side converter station in the wind farm; the operation status includes normal operation status, island operation with sufficient power status and island operation with insufficient power status; Composite strategy control module: specifically used for, when the wind farm is in the conventional operation state, adjusting the output power of the wind turbine in the wind farm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, combined with an adaptive neural network algorithm, to achieve support for the grid-side grid frequency; A network strategy control module: specifically used for regulating the output power of the wind turbine based on the grid frequency deviation to support the grid-side grid frequency when the wind farm is in island operation and has sufficient power; Power shortage strategy control module: specifically used for regulating the output power of the grid-side converter station based on the DC voltage and power data of the wind turbine-side converter and the grid frequency deviation when the wind farm is in island operation but in a power shortage state, so as to support the grid-side grid frequency.

10. The system according to claim 9, characterized in that The operating status judgment module includes: A frequency change calculation unit: used to calculate the frequency change amount of the fan side based on the fan side frequency data; An island determination unit: used to determine whether the wind turbine side frequency change is less than a preset threshold based on the wind turbine side frequency change and a preset threshold; if so, the wind farm is in a normal operation state; otherwise, the wind farm is in an island operation state; Power judgment unit: used to judge whether the power of the wind turbine can maintain the DC voltage of the flexible DC system when the wind farm is in an island operation state. If it can be maintained, the wind farm is in an island operation state with sufficient power; otherwise, the wind farm is in an island operation state but with insufficient power.

11. The system according to claim 9, characterized in that The composite strategy control module includes: Adaptive frequency deviation calculation submodule: used to determine the adaptive frequency deviation of the wind turbine side by using an adaptive neural network algorithm based on the grid frequency deviation of the grid-side converter station in the wind farm and the AC grid operation parameters on the wind turbine side, and send the adaptive frequency deviation of the wind turbine side to the wind turbine in the wind farm; A power reference value calculation submodule: used for inputting the grid frequency deviation into a pre-built mapping model, obtaining a wind turbine output power reference value, and sending the wind turbine output power reference value to the wind turbine, wherein the mapping model is constructed based on the grid frequency deviation historical data and the corresponding wind turbine output power historical data; The fan control submodule is used to control the fan output power in the fan based on the adaptive fan-side frequency deviation and the fan output power reference value, so as to support the grid-side power grid frequency.

12. The system according to claim 11, characterized in that The adaptive frequency deviation calculation submodule includes: An adaptive coefficient calculation unit is used to identify the working condition of the AC power grid on the wind turbine side and determine the adaptive working condition control coefficient corresponding to the working condition of the AC power grid on the wind turbine side through an adaptive neural network algorithm based on the operating parameters of the AC power grid on the wind turbine side; Adaptive DC voltage calculation unit: used for, in the grid-side converter station, performing low-pass filtering on the grid frequency deviation to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and the adaptive operating condition control coefficient, using a frequency-voltage conversion algorithm to calculate a frequency-superimposed DC voltage value, and sending the superimposed DC voltage value to the wind turbine-side converter station; Adaptive frequency calculation unit: used in the fan side converter station, based on the frequency superimposed DC voltage and the adaptive operating condition control coefficient, using a frequency sensing algorithm to calculate an adaptive fan side frequency deviation.

13. The system according to claim 12, characterized in that The adaptive coefficient calculation unit comprises: The working condition identification subunit is used to identify the working condition of the AC power grid on the wind turbine side based on the operating parameters of the AC power grid on the wind turbine side and obtain the current working condition of the AC power grid on the wind turbine side through a pre-trained working condition analysis neural network model. The working condition of the AC power grid on the wind turbine side includes: normal working condition, under-frequency working condition, over-frequency working condition and impedance abnormal working condition; Adaptive operating condition control coefficient calculation subunit: used to input the current wind turbine side AC power grid operating condition and the current converter control parameter into the adaptive parameter generation model to obtain the adaptive operating condition control coefficient corresponding to the wind turbine side AC power grid operating condition, the adaptive operating condition control coefficient includes: adaptive frequency-DC voltage adjustment coefficient and adaptive DC voltage-frequency adjustment coefficient.

14. The system according to claim 13, characterized in that The fan control submodule includes: Output power calculation unit: used to calculate the output power value of the fan based on the adaptive fan side frequency deviation and the current fan output power according to the network control strategy algorithm; Wind turbine control unit: used to compare the difference between the wind turbine output power value and the wind turbine output power reference value with a preset threshold value. If the difference is less than the preset threshold value, the wind turbine is controlled based on the wind turbine output power value; if the difference is greater than the preset threshold value, the wind turbine output power is controlled based on the wind turbine output power reference value to support the grid-side power grid frequency.

15. The system according to claim 9, characterized in that The insufficient power strategy control module includes: A DC voltage control submodule: used to calculate the power compensation amount of the fan side converter in the fan side converter station based on the output power of the fan, the DC voltage of the fan side converter and the power data; based on the power compensation amount and the voltage set value, control the power output of the fan side converter through a proportional-integral controller to maintain the DC voltage stability; Grid-side converter output modulation submodule: In the grid-side converter station, based on the power output of the wind turbine-side converter station and the grid frequency deviation, a DC voltage control strategy is adopted to modulate the output of the grid-side converter through a proportional-integral controller to support the grid-side grid frequency.

16. The system according to claim 9, characterized in that The networking strategy control module includes: The grid-side voltage calculation submodule: at the grid-side converter station, the grid frequency deviation is subjected to a low-pass filter to obtain a filtered grid frequency deviation; based on the filtered grid frequency deviation and a preset DC voltage control coefficient, a frequency-voltage conversion algorithm is used to calculate a superimposed voltage, and the superimposed voltage is sent to the wind turbine-side converter station; The fan side frequency calculation submodule: in the fan side converter station, based on the superimposed voltage value and the preset frequency control coefficient, a frequency sensing algorithm is used to calculate the fan side frequency deviation, and the fan side frequency deviation is sent to the fan; Wind turbine control submodule: In the wind turbine, the output power of the wind farm is controlled based on the frequency deviation on the wind turbine side to support the grid frequency on the grid side.

17. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for improving the stability of a meshed flexible direct current system as described in any one of claims 1 to 8 is implemented.

18. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a method for improving the stability of a meshed flexible direct current system as described in any one of claims 1 to 8 is implemented.

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