Wind power high-voltage direct-current power transmission system

Abstract

The invention provides a wind power high-voltage direct-current power transmission system, and belongs to the technical field of power transmission. The system comprises an intelligent wind driven generator, a modular converter station, an intelligent temperature monitoring direct current transmission line, a power grid friendly intelligent inverter station, and an intelligent regulation and control system integrating real-time data analysis, dynamic control and fault self-diagnosis. Through a maximum power point tracking algorithm and a self-adaptive control strategy, the system optimizes the wind energy conversion efficiency. The intelligent regulation and control system predicts wind power output by using a deep learning model and a random forest algorithm, dynamically adjusts voltage and current, and quickly responds to power grid demand changes. In addition, the system can quickly position and isolate faults, start a standby path and ensure continuous power supply. By adopting the wind power high-voltage direct-current power transmission system, the intelligent level of wind power high-voltage direct-current power transmission is improved, and the power transmission efficiency and the system stability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission, and in particular to a wind power high voltage direct current transmission system. Background Art

[0002] With the increasing attention paid to renewable energy and the rapid development of wind power technology, wind power high-voltage direct current transmission system, as an efficient and reliable power transmission method, has gradually become a research hotspot in the field of wind power generation. The system converts wind energy into alternating current, then converts it into high-voltage direct current for long-distance transmission through a converter station, and finally converts it into alternating current at the receiving grid side through an inverter station and integrates it into the grid, thus achieving efficient utilization of wind energy and flexible scheduling of power transmission.

[0003] However, despite the many advantages of wind power HVDC transmission systems, they still face some technical challenges and shortcomings in practical applications, especially in terms of system intelligence, transmission efficiency, stability, and fault response.

[0004] (1) Insufficient intelligence: Traditional wind power HVDC transmission systems have obvious shortcomings in terms of intelligence. The system lacks an efficient intelligent control mechanism, making it difficult to achieve real-time monitoring and prediction of wind power output, and thus unable to make dynamic adjustments based on wind power fluctuations and grid demand, resulting in limited transmission efficiency and stability.

[0005] (2) Transmission efficiency and stability issues: The existing wind power high voltage direct current transmission system is difficult to further improve its transmission efficiency due to factors such as low wind energy conversion efficiency, performance limitations of power electronic converters, and losses in direct current transmission lines. At the same time, the system lacks effective response measures when facing wind power fluctuations and grid failures, resulting in insufficient stability.

[0006] (3) Weak fault response capability: Traditional systems have obvious deficiencies in fault detection, location, and recovery. Once a fault occurs, it is often difficult for the system to quickly locate the fault point and effectively isolate it, which causes the fault scope to expand and affects the normal operation of the entire system. In addition, the system lacks backup paths and rapid recovery mechanisms, and cannot quickly restore power supply after a fault occurs.

[0007] (4) Lack of comprehensive optimization strategy: Existing wind power HVDC transmission systems often lack comprehensive optimization strategies and are unable to coordinate and optimize various links such as wind farms, converter stations, DC transmission lines, and receiving grids from the perspective of the entire system. This leads to problems such as energy waste and low efficiency during system operation. Summary of the invention

[0008] The purpose of the present invention is to provide a wind power high voltage direct current transmission system, which improves the intelligence level of wind power high voltage direct current transmission and enhances the transmission efficiency and system stability.

[0009] To achieve the above object, the present invention provides a wind power high voltage direct current transmission system, comprising: The wind farm side module is equipped with an intelligent wind turbine generator, which integrates a maximum power point tracking algorithm to convert wind energy into AC power; The converter station module uses a fully controlled power electronic converter with modular design, supports four-quadrant operation, and is used to convert the AC power output by the modules on the wind farm side into high-voltage DC power; A DC transmission line module, equipped with an intelligent temperature sensor and a fault warning system, is used to transmit high-voltage DC power; The receiving-end grid-side module is equipped with an intelligent inverter station that integrates a grid-friendly control strategy to convert high-voltage direct current into alternating current and achieve connection with the existing AC grid. Intelligent control system, including real-time data analysis and prediction module, dynamic voltage and current control module, fault self-diagnosis and rapid recovery module, used to realize intelligent control of the system; The smart wind turbine also includes wind speed and direction monitoring sensors, as well as adaptive control algorithms based on wind speed and direction data to further optimize wind energy conversion efficiency.

[0010] Preferably, the adaptive control algorithm based on wind speed and wind direction data includes: S11, particle initialization; For each particle, its position ,in, represents the position of each particle; Indicates The pitch angle corresponding to each particle is Indicates The yaw angle corresponding to each particle; Each particle Speed represents the change of the control parameter, ,in, represents the change in pitch angle, Indicates the change in yaw angle; Each particle The best historical position represents the best control parameter combination found previously, ,in, represents the historical optimal value of the pitch angle, Indicates the historical optimal value of the yaw angle; Global optimal position represents the best control parameter combination found by all particles in the entire particle swarm, ,in, represents the global optimal value of the pitch angle, Represents the global optimal value of the yaw angle; S12. Define fitness function : ; S13. Update the speed and position of particles: ; ; in, Indicates the updated speed; Indicates the updated position; represents the inertia weight; , represents the acceleration constant; , Represents a random number between [0,1]; Indicates the number of iterations; Indicates The particle in The speed at the iteration; Indicates The particle in The historical best position at the iteration; Indicates The particle in The current position at the iteration; Indicates The global optimal position at the iteration; S14. Calculate the fitness for each particle's new position ; S15, update individual and global optimal; When the fitness of the current particle is greater than its historical optimal fitness, the individual optimal position is updated; When the fitness of the current particle is greater than the global optimal fitness, update the global optimal position; S16. Repeat steps S13-S15 until the maximum number of iterations is reached.

[0011] Preferably, the fully controlled power electronic converter is a voltage source converter and is equipped with insulated gate bipolar transistors.

[0012] Preferably, the real-time data analysis and prediction module in the intelligent control system monitors and predicts the wind power output power in real time to adjust the power transmission strategy in advance, including: Normalize the collected wind speed, wind direction, temperature and output power; Pearson correlation analysis was used to preliminarily screen features that were highly correlated with output power; ; in, Indicates the selected features; represents a preset correlation threshold; represents a measure of relevance; represents the normalized data; Indicates output power; Use the autoencoder as a deep learning model to filter the features Perform feature extraction to obtain more compact features ; The characteristics With features Combined to form the final feature set : ; Using Feature Sets Train a random forest model and perform real-time power prediction.

[0013] Preferably, the dynamic voltage and current control module is based on the model predictive control algorithm MPC, dynamically adjusts the DC voltage and current according to wind power fluctuations and grid demand to ensure transmission efficiency and stability, including: Define the system state vector , including DC voltage and DC current ; ; Set the control target vector , including the desired DC voltage and DC current ; ; Execute the model predictive control algorithm MPC, according to the system state vector and the control target vector , calculate the control input ; ; The control input Applied to the system to adjust DC voltage and current.

[0014] Preferably, the fault self-diagnosis and rapid recovery module integrates a fault detection and isolation algorithm to achieve rapid location and removal of the fault point, and simultaneously activates the backup path to ensure continuous power supply to the system, including: Establish a state model based on the mean vector and covariance matrix of system parameters in a healthy state : ; in, Represents the mean vector of system parameters in a healthy state; Represents the covariance matrix of system parameters in a healthy state; Real-time monitoring of system parameters , including voltage, current and temperature; Calculate the residuals between system parameters and the health state model using Mahalanobis distance as the residual metric : ; in, represents transpose; When the residual When the preset threshold is exceeded, fault detection is triggered; According to the residual Locate the faulty module and perform isolation operations; The backup path is activated to maintain system power.

[0015] Therefore, the present invention adopts the above-mentioned wind power high voltage direct current transmission system, and the beneficial technical effects are as follows: (1) Efficient wind energy conversion and transmission: The present invention configures an intelligent wind turbine with an integrated maximum power point tracking algorithm in the wind farm side module, which can adjust the pitch angle and yaw angle in real time according to wind speed and wind direction data to maximize wind energy conversion efficiency. Through the adaptive control algorithm (particle swarm optimization algorithm), the system can intelligently find the best control parameter combination to further improve power generation efficiency.

[0016] The converter station module adopts a modularly designed fully controlled power electronic converter (such as a voltage source converter configured with an insulated gate bipolar transistor), supports four-quadrant operation, and can efficiently convert AC power into high-voltage DC power, reduce energy losses, and improve transmission efficiency.

[0017] (2) Intelligent system control and prediction: The intelligent control system integrates real-time data analysis and prediction modules, which can monitor wind power output in real time and use deep learning models (such as autoencoders combined with random forests) to make high-precision predictions of wind power output. This helps the system adjust the transmission strategy in advance, optimize resource allocation, and ensure the stable operation of the power grid.

[0018] The dynamic voltage and current control module is based on the model predictive control algorithm, which can dynamically adjust the DC voltage and current according to wind power fluctuations and grid demand to ensure transmission efficiency and stability. This flexible regulation method helps reduce grid fluctuations and improve power supply quality.

[0019] (3) Rapid fault diagnosis and recovery: The fault self-diagnosis and rapid recovery module integrates advanced fault detection and isolation algorithms, which can monitor system parameters (such as voltage, current and temperature) in real time and quickly locate the fault point through residual measurement methods such as Mahalanobis distance. Once a fault is detected, the system can immediately perform isolation operations and start the backup path to maintain power supply, thereby greatly improving the system's reliability and continuous power supply capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a working flow chart of a wind power high voltage direct current transmission system. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0022] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0023] Embodiment 1 like Figure 1 As shown, the present invention provides a wind power high voltage direct current transmission system, comprising: The wind farm side module is equipped with an intelligent wind turbine generator, which is integrated with a maximum power point tracking algorithm to convert wind energy into alternating current.

[0024] The converter station module adopts a fully controlled power electronic converter with a modular design, supports four-quadrant operation, and is used to convert the AC power output by the wind farm side module into high-voltage DC power; the fully controlled power electronic converter is a voltage source converter and is equipped with insulated gate bipolar transistors.

[0025] The DC transmission line module is equipped with an intelligent temperature sensor and a fault warning system for transmitting high-voltage DC power.

[0026] The receiving grid-side module is equipped with an intelligent inverter station, which is integrated with a grid-friendly control strategy to convert high-voltage direct current into alternating current and achieve docking with the existing AC grid.

[0027] The intelligent control system includes a real-time data analysis and prediction module, a dynamic voltage and current control module, and a fault self-diagnosis and rapid recovery module, which are used to realize the intelligent control of the system.

[0028] The smart wind turbine also includes wind speed and direction monitoring sensors, as well as adaptive control algorithms based on wind speed and direction data to further optimize wind energy conversion efficiency.

[0029] Adaptive control algorithms based on wind speed and direction data, including: S11, particle initialization; For each particle, its position ,in, represents the position of each particle; Indicates The pitch angle corresponding to each particle is Indicates The yaw angle corresponding to each particle; Each particle Speed represents the change of the control parameter, ,in, represents the change in pitch angle, Indicates the change in yaw angle; Each particle The best historical position represents the best control parameter combination found previously, ,in, represents the historical optimal value of the pitch angle, Indicates the historical optimal value of the yaw angle; Global optimal position represents the best control parameter combination found by all particles in the entire particle swarm, ,in, represents the global optimal value of the pitch angle, Represents the global optimal value of the yaw angle; S12. Define fitness function : ; Specifically, in this embodiment: ; in, , Represents the weight coefficient, which is used to balance the contribution of wind energy conversion efficiency and power generation in the fitness function; It represents the wind energy conversion efficiency; Indicates the power generated; S13. Update the speed and position of particles: ; ; in, Indicates the updated speed; Indicates the updated position; represents the inertia weight; , represents the acceleration constant; , Represents a random number between [0,1]; Indicates the number of iterations; Indicates The particle in The speed at the iteration; Indicates The particle in The historical best position at the iteration; Indicates The particle in The current position at the iteration; Indicates The global optimal position at the iteration; S14. Calculate the fitness for each particle's new position ; S15, update individual and global optimal; When the fitness of the current particle is greater than its historical optimal fitness, the individual optimal position is updated; When the fitness of the current particle is greater than the global optimal fitness, update the global optimal position; S16. Repeat steps S13-S15 until the maximum number of iterations is reached.

[0030] The real-time data analysis and prediction module in the intelligent control system monitors and predicts wind power output in real time to adjust the transmission strategy in advance, including: Normalize the collected wind speed, wind direction, temperature and output power; Pearson correlation analysis was used to preliminarily screen features that were highly correlated with output power; ; in, Indicates the selected features; represents a preset correlation threshold; represents a measure of relevance; represents the normalized data; Indicates output power; Use the autoencoder as a deep learning model to filter the features Perform feature extraction to obtain more compact features ; The characteristics With features Combined to form the final feature set : ; Using Feature Sets Train a random forest model and perform real-time power prediction.

[0031] The dynamic voltage and current control module is based on the model predictive control algorithm MPC. It dynamically adjusts the DC voltage and current according to wind power fluctuations and grid demand to ensure transmission efficiency and stability, including: Define the system state vector , including DC voltage and DC current ; ; Set the control target vector , including the desired DC voltage and DC current ; ; Execute the model predictive control algorithm MPC, according to the system state vector and the control target vector , calculate the control input ; ; The control input Applied to the system to adjust DC voltage and current.

[0032] The fault self-diagnosis and rapid recovery module integrates a fault detection and isolation algorithm to quickly locate and remove the fault point, while starting the backup path to ensure continuous power supply to the system, including: Establish a state model based on the mean vector and covariance matrix of system parameters in a healthy state : ; in, Represents the mean vector of system parameters in a healthy state; Represents the covariance matrix of system parameters in a healthy state; Real-time monitoring of system parameters , including voltage, current and temperature; Calculate the residuals between system parameters and the health state model using Mahalanobis distance as the residual metric : ; in, represents transpose; When the residual When the preset threshold is exceeded, fault detection is triggered; According to the residual Locate the faulty module and perform isolation operations; The backup path is activated to maintain system power.

[0033] The above-mentioned intelligent control method of the wind power high voltage direct current transmission system comprises the following steps: Step A: Use the real-time data analysis and prediction module to monitor and predict the wind power output in real time; Step B: According to the prediction results, the dynamic voltage and current control module adjusts the DC voltage and current based on the model predictive control algorithm; Step C: The fault self-diagnosis and rapid recovery module monitors the system status in real time. Once a fault is found, it will immediately locate, isolate and recover the fault. Step D: The intelligent operation and maintenance management module remotely monitors the system to achieve preventive maintenance and fault warning.

[0034] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0035] Therefore, the present invention adopts the above-mentioned wind power high voltage direct current transmission system, improves the intelligence level of wind power high voltage direct current transmission, and enhances the transmission efficiency and system stability.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A wind power high voltage direct current transmission system, characterized in that: include: The wind farm side module is equipped with an intelligent wind turbine generator, which integrates a maximum power point tracking algorithm to convert wind energy into AC power; The converter station module uses a fully controlled power electronic converter with modular design, supports four-quadrant operation, and is used to convert the AC power output by the modules on the wind farm side into high-voltage DC power; A DC transmission line module, equipped with an intelligent temperature sensor and a fault warning system, is used to transmit high-voltage DC power; The receiving-end grid-side module is equipped with an intelligent inverter station that integrates a grid-friendly control strategy to convert high-voltage direct current into alternating current and achieve connection with the existing AC grid. Intelligent control system, including real-time data analysis and prediction module, dynamic voltage and current control module, fault self-diagnosis and rapid recovery module, used to realize intelligent control of the system; The smart wind turbine also includes wind speed and direction monitoring sensors, as well as adaptive control algorithms based on wind speed and direction data to further optimize wind energy conversion efficiency.

2. A wind power high voltage direct current transmission system according to claim 1, characterized in that: Adaptive control algorithms based on wind speed and direction data, including: S11, particle initialization; For each particle, its position ,in, represents the position of each particle; Indicates The pitch angle corresponding to each particle is Indicates The yaw angle corresponding to each particle; Each particle Speed represents the change of the control parameter, ,in, represents the change in pitch angle, Indicates the change in yaw angle; Each particle The best historical position represents the best control parameter combination found previously, ,in, represents the historical optimal value of the pitch angle, Indicates the historical optimal value of the yaw angle; Global optimal position represents the best control parameter combination found by all particles in the entire particle swarm, ,in, represents the global optimal value of the pitch angle, Represents the global optimal value of the yaw angle; S12. Define fitness function : ； S13. Update the speed and position of particles: ； ； in, Indicates the updated speed; Indicates the updated position; represents the inertia weight; , represents the acceleration constant; , Represents a random number between [0,1]; Indicates the number of iterations; Indicates The particle in The speed at the iteration; Indicates The particle in The historical best position at the iteration; Indicates The particle in The current position at the iteration; Indicates The global optimal position at the iteration; S14. Calculate the fitness for each particle's new position ; S15, update individual and global optimal; When the fitness of the current particle is greater than its historical optimal fitness, the individual optimal position is updated; When the fitness of the current particle is greater than the global optimal fitness, update the global optimal position; S16. Repeat steps S13-S15 until the maximum number of iterations is reached.

3. A wind power high voltage direct current transmission system according to claim 2, characterized in that: The fully controlled power electronic converter is a voltage source inverter and is equipped with insulated gate bipolar transistors.

4. A wind power high voltage direct current transmission system according to claim 3, characterized in that: The real-time data analysis and prediction module in the intelligent control system monitors and predicts wind power output in real time to adjust the transmission strategy in advance, including: Normalize the collected wind speed, wind direction, temperature and output power; Pearson correlation analysis was used to preliminarily screen features that were highly correlated with output power; ； in, Indicates the selected features; represents a preset correlation threshold; represents a measure of relevance; represents the normalized data; Indicates output power; Use the autoencoder as a deep learning model to filter the features Perform feature extraction to obtain more compact features ; The characteristics With features Combined to form the final feature set : ； Using Feature Sets Train a random forest model and perform real-time power prediction.

5. A wind power high voltage direct current transmission system according to claim 4, characterized in that: The dynamic voltage and current control module is based on the model predictive control algorithm MPC. It dynamically adjusts the DC voltage and current according to wind power fluctuations and grid demand to ensure transmission efficiency and stability, including: Define the system state vector , including DC voltage and DC current ; ； Set the control target vector , including the desired DC voltage and DC current ; ； Execute the model predictive control algorithm MPC, according to the system state vector and the control target vector , calculate the control input ; ； The control input Applied to the system to adjust DC voltage and current.

6. A wind power high voltage direct current transmission system according to claim 5, characterized in that: The fault self-diagnosis and rapid recovery module integrates a fault detection and isolation algorithm to quickly locate and remove the fault point, while starting the backup path to ensure continuous power supply to the system, including: Establish a state model based on the mean vector and covariance matrix of system parameters in a healthy state : ； in, Represents the mean vector of system parameters in a healthy state; Represents the covariance matrix of system parameters in a healthy state; Real-time monitoring of system parameters , including voltage, current and temperature; Calculate the residuals between system parameters and the health state model using Mahalanobis distance as the residual metric : ； in, represents transpose; When the residual When the preset threshold is exceeded, fault detection is triggered; According to the residual Locate the faulty module and perform isolation operations; The backup path is activated to maintain system power.

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