Self-adaptive compensation wind power generation system

Abstract

The invention discloses a wind power generation system with a self-adaptive compensation function. The wind power generation system comprises a wind power generator, an energy storage device, a self-adaptive compensation controller and a power grid connection module, the self-adaptive compensation controller comprises a real-time monitoring module, a prediction analysis module, a self-adaptive compensation decision module and a control execution module, and the real-time monitoring module obtains operation parameters of the wind power generator and state parameters of the energy storage device; the predictive analysis module predicts and analyzes the wind power change trend and the power grid load demand, the self-adaptive compensation decision module generates control instructions of the wind driven generator and the energy storage device, the control execution module adjusts the operation state of the wind driven generator, and meanwhile the charging and discharging process of the energy storage device is controlled. By monitoring the states of the wind power and the power grid in real time, applying an intelligent algorithm to predict and analyze, and combining with an energy storage technology, self-adaptive compensation of wind power generation fluctuation is realized, the stability and reliability of wind power generation are improved, and adverse effects on the power grid are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to a wind power generation system with adaptive compensation. Background Art

[0002] As a clean and renewable energy source, wind energy is playing an increasingly important role in the global energy structure. For a long time, humans have continuously improved the utilization of wind energy. The emergence of large-scale wind turbines has greatly improved the utilization efficiency of wind energy, and wind power generation plays an important role in daily life.

[0003] However, wind power generation has significant characteristics of random volatility. The instability of wind speed will lead to frequent changes in the output power of wind turbines. This volatility will have a great impact on the safe and economic operation of the power grid. For example, when the wind suddenly increases, the output power of the generator will rise sharply, which may exceed the carrying capacity of the power grid, causing problems such as power grid voltage fluctuations and frequency instability; when the wind suddenly decreases, the output power of the generator will drop suddenly, which may lead to insufficient power supply in the power grid and affect the normal power consumption of users. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to some extent.

[0005] To this end, an embodiment of the present invention provides a wind power generation system with adaptive compensation to solve the problems caused by the random volatility of wind power generation to the safe and economic operation of the power grid.

[0006] The wind power generation system with adaptive compensation according to the embodiment of the present invention includes a wind turbine, an energy storage device, an adaptive compensation controller, and a grid connection module. The adaptive compensation controller includes a real-time monitoring module, a prediction and analysis module, an adaptive compensation decision module, and a control execution module that are connected to each other;

[0007] The real-time monitoring module is connected to the wind turbine and the energy storage device, and is used to obtain the operation parameters of the wind turbine and the state parameters of the energy storage device, and transmit the acquired data to the prediction and analysis module;

[0008] Based on the received data, the prediction and analysis module uses algorithms to predict and analyze the wind change trend and the grid load demand, and transmits the analysis results to the adaptive compensation decision module;

[0009] According to the analysis results and combined with the real-time demand of the power grid, the adaptive compensation decision module generates control instructions for the wind turbine and the energy storage device, and transmits the instructions to the control execution module;

[0010] The control execution module is connected to the wind turbine, the energy storage device, and the grid connection module to send control instructions to the wind turbine and the energy storage device, adjust the operating state of the wind turbine, control the charging and discharging process of the energy storage device, and interact with the grid through the grid connection module.

[0011] In the adaptive compensation wind power generation system according to the embodiments of the present invention, by real-time monitoring the states of the wind and the grid, using intelligent algorithms for prediction and analysis, and combining energy storage technologies, adaptive compensation for wind power generation fluctuations is achieved, improving the stability and reliability of wind power generation and reducing the adverse effects on the grid.

[0012] In some embodiments, the real-time monitoring module transmits the collected data to the prediction and analysis module through wired or wireless communication means. The real-time monitoring module includes:

[0013] A wind sensor, which is arranged on the blades and / or the top of the nacelle of the wind turbine to real-time monitor the wind direction and wind speed;

[0014] An angle sensor, which is arranged on the blades of the wind turbine to measure the blade pitch angle;

[0015] A first power sensor and a speed sensor, the first power sensor and the speed sensor are arranged on the generator of the wind turbine, the first power sensor is used to obtain the output power of the generator, and the speed sensor is used to monitor the speed of the generator;

[0016] A monitoring circuit, a second power sensor, and a temperature sensor, the monitoring circuit, the second power sensor, and the temperature sensor are arranged on the energy storage device, and the remaining power of the energy storage device is obtained through the monitoring circuit. The second power sensor is used to measure the charging and discharging power of the energy storage device, and the temperature sensor is used to collect the temperature of the energy storage device.

[0017] In some embodiments, the prediction and analysis module and the adaptive compensation decision module perform data transmission through a data bus;

[0018] The neural network algorithm of the prediction and analysis module learns and trains historical wind speed data, generator operating parameters, and grid load data to establish a prediction model, and predicts the wind power change and grid load demand within the next 1-2 hours according to the data obtained by the real-time monitoring module;

[0019] The fuzzy control algorithm of the prediction and analysis module performs fuzzy processing and reasoning on the real-time monitoring data according to the set fuzzy rules, and outputs the prediction results of the wind power and load changes.

[0020] In some embodiments, the adaptive compensation decision module is connected to the control execution module through a control signal transmission line, and the control instructions of the adaptive compensation decision module include:

[0021] When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device has not reached the upper limit, the control instruction is to increase the blade pitch angle, and at the same time store the excess electric energy into the energy storage device, and the energy storage device is charged at a set charging power;

[0022] When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device reaches the upper limit, the control instruction is to decrease the blade pitch angle and reduce the generator output power to match the grid demand;

[0023] When the predicted wind power generation is less than the grid demand and the remaining power of the energy storage device is sufficient, the control instruction is that the energy storage device releases electric energy to the grid at a set discharge power, and at the same time adjusts the generator excitation current to increase the generator output power;

[0024] When the predicted wind power generation is less than the grid demand and the remaining power of the energy storage device is insufficient, the control instruction is to obtain part of the electric energy from the grid through the grid connection module, and at the same time adjust the generator operation parameters to increase the power generation power.

[0025] In some embodiments, the prediction analysis module and / or the adaptive compensation decision module perform data interaction with the dispatching center of the grid through a communication network, receive the real-time demand instructions of the grid, and feedback the operation status information of the wind power generation system to the dispatching center of the grid.

[0026] In some embodiments, the control execution module includes:

[0027] A pitch angle adjustment component, which is arranged on the blade of the wind turbine to drive the blade to rotate to a specified angle;

[0028] An excitation controller, which is arranged on the generator of the wind turbine to adjust the current magnitude of the generator excitation winding;

[0029] A charge and discharge controller, which is arranged on the energy storage device to adjust the energy flow between the energy storage device and the grid.

[0030] In some embodiments, the adaptive compensation controller adopts a distributed architecture, and the real-time monitoring module, the prediction analysis module, the adaptive compensation decision module, and the control execution module are distributed in different control units, and data transmission is performed between the control units through a high-speed communication bus.

[0031] In some embodiments, the adaptive compensation controller further includes a fault diagnosis and protection module, which is connected to the real-time monitoring module and the control execution module to receive the data of the real-time monitoring module in real time, monitor the operating states of the wind turbine and the energy storage device, and automatically issue an alarm and send a protection instruction to the control execution module when an abnormal situation is detected, so as to take corresponding protection measures.

[0032] In some embodiments, the energy storage device is connected to the control execution module through a bidirectional DC-DC converter, and the bidirectional DC-DC converter adjusts the charge and discharge current and voltage of the energy storage device according to the instruction of the control execution module.

[0033] In some embodiments, the grid connection module includes a transformer, a circuit breaker and a power quality monitoring device. The transformer is used to convert the voltage of the electric energy output by the wind turbine and the energy storage device to meet the grid access requirements. The circuit breaker is used to cut off the circuit when a fault occurs in the wind power generation system or maintenance is required. The power quality monitoring device is used to monitor the quality parameters of the electric energy output by the grid and the wind power generation system in real time, and feedback the data to the adaptive compensation decision module to assist it in generating control instructions. Description of the Drawings

[0034] Figure 1 is a schematic diagram of the adaptive compensation wind power generation system according to an embodiment of the present invention.

[0035] Reference Numerals:

[0036] 1 - Wind turbine; 2 - Energy storage device; 3 - Adaptive compensation controller; 31 - Real-time monitoring module; 32 - Prediction and analysis module; 33 - Adaptive compensation decision module; 34 - Control execution module; 35 - Fault diagnosis and protection module; 4 - Grid connection module; 41 - Transformer; 42 - Circuit breaker; 43 - Power quality monitoring device; 5 - DC-DC converter. Detailed Embodiments

[0037] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0038] The adaptive compensation wind power generation system according to an embodiment of the present invention will be described below with reference to the drawings.

[0039] As Figure 1 shown, the adaptive compensation wind power generation system according to an embodiment of the present invention includes a wind turbine 1, an energy storage device 2, an adaptive compensation controller 3 and a grid connection module 4.

[0040] Installation and commissioning of the wind power generation system according to the embodiments of the present invention. The wind turbine 1 is installed in a site with rich wind resources and is fixed to the foundation and electrically connected. The energy storage device 2 is installed, including selecting a suitable installation location (considering factors such as heat dissipation and maintenance) and performing electrical wiring to ensure the correct connection of the energy storage device 2 with the wind turbine 1 and the grid connection module 4. Each module of the adaptive compensation controller 3 is installed, and each module is distributed in different control units according to the requirements of the distributed architecture and is connected and debugged through a high-speed communication bus. The transformer 41, circuit breaker 42, and power quality monitoring device 43 of the grid connection module 4 are installed to complete the access and commissioning with the grid. The entire system is comprehensively commissioned to check whether the communication between modules is normal, whether the sensor data acquisition is accurate, and whether the transmission and execution of control instructions are effective.

[0041] As Figure 1 shown, the adaptive compensation controller 3 includes a connected real-time monitoring module 31, a prediction analysis module 32, an adaptive compensation decision module 33, a control execution module 34, and a fault diagnosis and protection module 35.

[0042] The real-time monitoring module 31 is connected to the wind turbine 1 and the energy storage device 2, and is used to obtain the operating parameters of the wind turbine 1 and the state parameters of the energy storage device 2, and transmit the obtained data to the prediction analysis module 32.

[0043] Based on the received data, the prediction analysis module 32 uses an algorithm to predict and analyze the wind power change trend and the grid load demand, and transmits the analysis result to the adaptive compensation decision module 33.

[0044] According to the analysis result and combined with the real-time demand of the grid, the adaptive compensation decision module 33 generates control instructions for the wind turbine 1 and the energy storage device 2, and transmits the instructions to the control execution module 34.

[0045] The prediction analysis module 32 and the adaptive compensation decision module 33 perform data interaction with the dispatching center of the grid through a communication network, receive the real-time demand instructions of the grid, and feedback the operating status information of the wind power generation system to the dispatching center of the grid.

[0046] The control execution module 34 is connected to the wind turbine 1, the energy storage device 2, and the grid connection module 4 to send control instructions to the wind turbine 1 and the energy storage device 2, adjust the operating status of the wind turbine 1, and at the same time control the charge and discharge process of the energy storage device 2, and perform energy interaction with the grid through the grid connection module 4.

[0047] The fault diagnosis and protection module 35 is connected to the real-time monitoring module 31 and the control execution module 34 to receive the data of the real-time monitoring module 31 in real time, monitor the operating states of the wind turbine 1 and the energy storage device 2, and automatically issue an alarm and send a protection instruction to the control execution module 34 when an abnormal situation is detected, so as to take corresponding protection measures.

[0048] The operation process of the adaptive compensation wind power generation system according to the embodiment of the present invention is briefly described below.

[0049] In the real-time monitoring stage, the real-time monitoring module 31 continuously collects the operating parameters and status parameters of the wind turbine 1 and the energy storage device 2, and transmits the data to the prediction and analysis module 32. For example, the wind speed sensor measures the wind speed in real time, and the power monitoring circuit monitors the remaining power of the energy storage device 2 in real time.

[0050] In the prediction and analysis stage, the prediction and analysis module 32 processes and analyzes the real-time monitoring data by using intelligent algorithms to predict the wind power change trend and grid load demand in the next period of time. For example, the neural network algorithm predicts the wind power situation in the next 1-2 hours according to historical data and real-time data.

[0051] In the decision-making generation stage, the adaptive compensation decision-making module 33 generates an adaptive compensation control instruction according to the prediction and analysis result and the real-time grid demand according to the preset rules. For example, when it is predicted that the wind power increases and the grid demand is small, if the remaining power of the energy storage device 2 does not reach the upper limit, an instruction to increase the blade pitch angle and charge the energy storage device 2 is generated.

[0052] In the control execution stage, the control execution module 34 sends the control instruction to the wind turbine 1 and the energy storage device 2. For the wind turbine 1, the blade pitch angle is adjusted through the pitch angle adjustment mechanism, and the excitation current is adjusted through the excitation controller; for the energy storage device 2, its charging and discharging process is controlled through the charge and discharge controller.

[0053] In the fault diagnosis and protection stage, the fault diagnosis and protection module 35 monitors the system operation state in real time. Once an abnormal situation is found, such as the generator temperature is too high or the energy storage device 2 is overcharged, an alarm is immediately issued and the control execution module 34 is controlled to take protection measures, such as cutting off the circuit.

[0054] In the grid communication stage, the adaptive compensation controller 3 maintains data interaction with the grid dispatching center, receives the real-time demand instruction of the grid, and feeds back the system operation state information to the grid dispatching center to realize the coordinated operation of wind power generation and the grid.

[0055] The adaptive compensation wind power generation system according to the embodiments of the present invention monitors the states of wind power and the power grid in real time, uses intelligent algorithms for predictive analysis, and combines energy storage technologies to achieve adaptive compensation for the fluctuations of wind power generation, improve the stability and reliability of wind power generation, and reduce the adverse effects on the power grid.

[0056] Moreover, each component of the system is regularly inspected and maintained, including the calibration of sensors, the software update of the control module, the performance detection of the energy storage device 2, etc. According to the operation data and actual effects of the system, the intelligent algorithms of the predictive analysis module 32 and the control rules of the adaptive compensation decision module 33 are continuously optimized to improve the adaptive compensation ability and operation efficiency of the system.

[0057] In some embodiments, the real-time monitoring module 31 transmits the collected data to the predictive analysis module 32 through wired or wireless communication means. The real-time monitoring module 31 includes a wind sensor, an angle sensor, a first power sensor, a rotational speed sensor, a monitoring circuit, a second power sensor, and a temperature sensor (not shown in the figure).

[0058] The wind sensor is arranged on the blades and the top of the nacelle of the wind turbine 1 to monitor the wind direction and wind speed in real time.

[0059] The blade is a key component that directly contacts the wind and converts wind energy into mechanical energy. Installing a wind sensor on the blade can most directly obtain the wind direction and wind speed information acting on the blade. The wind forces received by the blades at different positions may vary during rotation. The sensors installed on the blades can capture these subtle changes, thereby providing accurate data for precisely adjusting the blade pitch angle, keeping the blades in the best windward state at all times, and improving the capture efficiency of wind energy.

[0060] The position at the top of the nacelle is open and less affected by surrounding obstacles, and can more accurately measure the local true wind direction and wind speed. Moreover, this position is relatively fixed, which is convenient for the installation and maintenance of sensors. At the same time, the sensor data at the top of the nacelle can be used as an overall reference data, complementing and verifying the sensor data on the blades to ensure comprehensive and accurate monitoring of the wind conditions.

[0061] The wind direction and wind speed are the two most important parameters in wind power generation. The wind direction determines the orientation of the wind turbine 1. Only by aligning the wind wheel of the wind turbine 1 with the wind direction can the wind energy be captured to the greatest extent. The wind speed directly affects the output power of the wind turbine 1. By monitoring the wind speed in real time, the operating parameters of the generator can be adjusted in a timely manner according to the change of the wind speed, such as adjusting the blade pitch angle, the excitation current of the generator, etc., to ensure that the generator can operate stably and efficiently at different wind speeds.

[0062] The angle sensor is installed on the blade of the wind turbine 1 to measure the blade pitch angle. The blade pitch angle refers to the angle between the blade and the wind wheel rotation plane, which has a crucial impact on the performance of the wind turbine 1. By adjusting the blade pitch angle, the magnitude and direction of the wind force on the blade can be controlled, thereby realizing the adjustment of the generator output power.

[0063] Installing the angle sensor on the blade can measure the actual pitch angle of the blade in real time and accurately. In this way, the adaptive compensation controller 3 can accurately adjust the pitch angle adjustment mechanism according to the real-time pitch angle data and wind conditions, so that the blade pitch angle always remains at the optimal value, improving the power generation efficiency and avoiding problems such as generator overload or power instability caused by inappropriate pitch angles.

[0064] The first power sensor and the speed sensor are installed on the generator of the wind turbine 1. The first power sensor is used to obtain the output power of the generator, and the speed sensor is used to monitor the speed of the generator.

[0065] The output power of the generator is one of the key indicators to measure the performance of the wind power generation system. Installing the first power sensor on the generator can directly measure the actual electrical power output by the generator. By monitoring the output power in real time, the working state and power generation efficiency of the generator can be understood in a timely manner. When the output power shows abnormal fluctuations, the reasons can be quickly analyzed, which may be due to wind force changes, inappropriate blade pitch angles or generator malfunctions, etc., and corresponding measures can be taken for adjustment and repair to ensure the stable operation of the power generation system.

[0066] The speed of the generator is closely related to the output power. At different wind speeds, a suitable generator speed needs to be matched to achieve the best power generation efficiency. The speed sensor installed on the generator can monitor the speed of the generator in real time. The adaptive compensation controller 3 can adjust the excitation current or blade pitch angle of the generator according to the speed data and wind conditions, so that the speed of the generator is maintained within a reasonable range, avoiding affecting the power generation efficiency and equipment safety due to too high or too low speed.

[0067] The monitoring circuit, the second power sensor and the temperature sensor are installed on the energy storage device 2. The remaining power of the energy storage device 2 is obtained through the monitoring circuit. The second power sensor is used to measure the charge and discharge power of the energy storage device 2, and the temperature sensor is used to collect the temperature of the energy storage device 2.

[0068] The remaining power of the energy storage device 2 is an important parameter that determines its charge and discharge status and capacity. The monitoring circuit can accurately obtain the remaining power information of the energy storage device 2 in real time. The adaptive compensation controller 3 arranges the charge and discharge process of the energy storage device 2 reasonably according to the remaining power data, combined with the wind power generation power and the grid demand. For example, when the remaining power is low, the energy storage device 2 is preferentially charged; when the remaining power is high and the wind power generation power is greater than the grid demand, the energy storage device 2 is controlled to charge and store the excess electric energy.

[0069] The charge and discharge power of the energy storage device 2 reflects the energy exchange situation between it and the outside world (wind turbine 1 and the grid). By measuring the charge and discharge power with the second power sensor, the working state and energy flow direction of the energy storage device 2 can be understood in real time. The adaptive compensation controller 3 can adjust the charge and discharge strategy of the energy storage device 2 according to the charge and discharge power data to ensure that the energy storage device 2 operates in a safe and efficient state, while achieving effective compensation for wind power generation fluctuations.

[0070] Temperature has a significant impact on the performance and lifespan of the energy storage device 2. Excessive temperature will accelerate the chemical reactions inside the energy storage device 2, reduce the battery capacity and charge and discharge efficiency, and may even cause safety problems. The temperature sensor is installed on the energy storage device 2 to be able to collect the temperature information of the energy storage device 2 in real time. When the temperature exceeds the safe range, the adaptive compensation controller 3 can take corresponding measures, such as adjusting the charge and discharge power, starting the heat dissipation device, etc., to ensure that the energy storage device 2 works in a suitable temperature environment and extend its service life.

[0071] In some embodiments, the prediction analysis module 32 and the adaptive compensation decision module 33 perform data transmission through a data bus.

[0072] The neural network algorithm of the prediction analysis module 32 learns and trains the historical wind speed data, generator operation parameters, and grid load data to establish a prediction model. According to the data obtained by the real-time monitoring module 31, the model predicts the wind power change and grid load demand within the next 1 - 2 hours.

[0073] Among them, the historical wind speed data can obtain the local wind speed data of the past many years from the meteorological department. The data frequency can be set to once every 10 minutes, including information such as wind speed magnitude and wind direction. At the same time, high-precision wind speed sensors are installed on the site of the wind turbine 1 to record the actual wind speed data in real time, and these data will be used for the training and verification of the model.

[0074] For the generator operation parameters, various sensors installed on the wind turbine 1 are used to collect the operation parameters of the generator, such as output power, rotational speed, blade pitch angle, etc. These data are recorded at intervals of one minute to reflect the operation state of the generator under different wind conditions.

[0075] Grid load data. Past grid load data can be obtained from the local power grid company, including information such as power consumption at different time periods, peak and valley power consumption times, etc. The data frequency can be set to once every 15 minutes to analyze the variation law of grid load.

[0076] Clean the collected data to remove outliers and missing values. For example, when the wind speed data recorded by the wind speed sensor significantly exceeds the local historical wind speed range, it is regarded as an outlier and removed; for the missing generator output power data, the method of linear interpolation is used to supplement it.

[0077] Normalize the data to unify data in different ranges into the [0,1] interval to improve the training efficiency and stability of the model. For example, for wind speed data, the min-max normalization method can be used to scale the wind speed value to the [0,1] range.

[0078] Adopt a multi-layer perceptron (MLP) as the prediction model. This model has strong non-linear mapping ability and can handle complex input-output relationships well. The MLP model includes an input layer, hidden layers, and an output layer. The number of neurons in the input layer is determined according to the dimension of the input data. Here, the input data includes historical wind speed data, generator operating parameters, and grid load data. Assuming the dimension of the input data is 10, 10 neurons are set in the input layer.

[0079] Set 2 - 3 hidden layers. The number of neurons in each layer can be adjusted according to the actual situation, generally set to 1 - 2 times the number of neurons in the input layer. For example, 15 neurons are set in each layer. The output layer sets 2 neurons, which are respectively used to predict the wind force change and grid load demand within the next 1 - 2 hours.

[0080] Divide the preprocessed data into a training set, a validation set, and a test set according to the ratio of 7:2:1. Use the training set to train the MLP model, and adopt the backpropagation algorithm to adjust the weights and biases of the model to minimize the error between the prediction result and the actual value. During the training process, set appropriate learning rate and number of iterations. For example, the learning rate is set to 0.01 and the number of iterations is set to 1000 times.

[0081] During the training process, use the validation set to evaluate the performance of the model, adjust the parameters of the model according to the error of the validation set, and prevent the model from overfitting. When the error of the validation set no longer decreases, stop the training.

[0082] After the training is completed, the test set is used to conduct a final evaluation of the model's performance. The data obtained by the real-time monitoring module 31 is input into the trained MLP model, and the model outputs the prediction results of the wind power change and grid load demand within the next 1-2 hours. For example, when the real-time monitoring shows that the current wind speed is 10 m / s, the generator output power is 500 kW, and the grid load is 1000 kW, the model predicts that the wind speed will increase to 12 m / s and the grid load will increase to 1200 kW within the next 1 hour.

[0083] The fuzzy control algorithm of the prediction analysis module 32 performs fuzzy processing and reasoning on the real-time monitoring data according to the set fuzzy rules, and outputs the prediction results of the wind power and load changes.

[0084] For example, fuzzy rule 1: If the current wind speed is "low", the generator output power is "low", and the grid load is "high", then it is predicted that the future wind power will "increase" and the grid load will "increase"; Fuzzy rule 2: If the current wind speed is "high", the generator output power is "high", and the grid load is "low", then it is predicted that the future wind power will "decrease" and the grid load will "decrease". Here, "low", "high", "increase", "decrease", etc. are all fuzzy concepts and need to be defined through membership functions.

[0085] Perform fuzzy processing on the real-time monitoring data to convert the accurate numerical values into the membership degrees of fuzzy sets. For example, for wind speed data, the membership function of "low" wind speed is defined as a trapezoidal function. When the wind speed is less than 5 m / s, the membership degree is 1; when the wind speed is between 5-10 m / s, the membership degree linearly decreases from 1 to 0; when the wind speed is greater than 10 m / s, the membership degree is 0. According to this membership function, the real-time monitored wind speed value is converted into the membership degree of "low" wind speed.

[0086] According to the set fuzzy rules and the fuzzified input data, perform fuzzy reasoning. The Mamdani reasoning method is adopted to calculate the output membership degree of each rule through the premise conditions of the rules and the membership degrees of the input data. For example, for rule 1, if the membership degree of the current wind speed belonging to "low" wind speed is 0.8, the membership degree of the generator output power belonging to "low" is 0.7, and the membership degree of the grid load belonging to "high" is 0.9, then the output membership degree of this rule is 0.8×0.7×0.9 = 0.504.

[0087] Defuzzify the output membership degree obtained by fuzzy reasoning to obtain accurate prediction results. The centroid method is used for defuzzification, and the centroid of the output fuzzy set is calculated as the final prediction value. For example, for the fuzzy set predicting the future wind power change, a specific wind speed change value is calculated through the centroid method as the prediction result of the wind power change within the next 1-2 hours.

[0088] In summary, the prediction results of the neural network algorithm and the fuzzy control algorithm are comprehensively analyzed and fused. The weighted average method can be adopted to assign different weights to the two algorithms according to their prediction accuracies in different situations. For example, when the wind power changes smoothly, the prediction accuracy of the neural network algorithm is relatively high, and its weight can be set to 0.7, while the weight of the fuzzy control algorithm is set to 0.3; when the wind power changes complexly, the fuzzy control algorithm can better handle uncertainties, and its weight can be set to 0.6, while the weight of the neural network algorithm is set to 0.4. Finally, a more accurate and reliable prediction result of the wind power change and the grid load demand within the next 1-2 hours is obtained.

[0089] In some embodiments, the adaptive compensation decision module 33 is connected to the control execution module 34 through a control signal transmission line, and the control instructions of the adaptive compensation decision module 33 include:

[0090] When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device 2 has not reached the upper limit, the control instruction is to increase the blade pitch angle, and at the same time store the excess electric energy into the energy storage device 2, and the energy storage device 2 is charged at a set charging power.

[0091] Increasing the blade pitch angle can make the blades face the wind more fully and capture more wind energy. When the wind power generation is already greater than the grid demand, since the energy storage device 2 still has remaining capacity, increasing the pitch angle at this time can further improve the output power of the generator, convert more wind energy into electric energy, improve the utilization rate of wind energy, make full use of the current rich wind resources, and convert the wind energy that may not be utilized due to pitch angle limitations into electric energy to prepare for charging the energy storage device 2.

[0092] To ensure the safety and service life of the energy storage device 2, it is necessary to control its charging power within a reasonable range. The set charging power is determined according to the characteristics and design requirements of the energy storage device 2. Avoid damaging the energy storage device 2 due to excessive charging power, and at the same time ensure the efficiency of the charging process, so that the energy storage device 2 can be fully charged as soon as possible under the premise of safety.

[0093] When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device 2 reaches the upper limit, the control instruction is to decrease the blade pitch angle and reduce the generator output power to match the grid demand.

[0094] Decreasing the blade pitch angle will make the angle between the blade and the wind direction smaller, reduce the wind force on the blade, and thus reduce the output power of the generator. When the energy storage device 2 is already fully charged and cannot store more electric energy, reducing the generator power by decreasing the blade pitch angle can prevent excessive electric energy from flowing into the grid and prevent problems such as too high grid voltage and unstable frequency, ensuring the safe and stable operation of the grid.

[0095] The operation of the power grid needs to maintain the balance between power supply and demand. When the power generation power is greater than the demand, it will have an adverse impact on the power grid. Therefore, by adjusting the output power of the generator to be equal to the grid demand, the stable operation of the power grid can be maintained. Ensure the coordinated operation of the wind power generation system and the power grid, improve the power supply quality of the power grid, and avoid equipment damage and energy waste caused by power overage.

[0096] When the predicted wind power generation power is less than the grid demand and the remaining power of the energy storage device 2 is sufficient, the control instruction is that the energy storage device 2 releases electric energy to the grid at a set discharge power, and at the same time adjusts the excitation current of the generator to increase the output power of the generator.

[0097] When the wind power generation power is insufficient to meet the grid demand, the energy storage device 2 can release the previously stored electric energy to make up for the power gap in the grid. The set discharge power is determined according to the performance of the energy storage device 2 and the grid demand to ensure the safety and stability of the discharge process. Quickly make up for the power shortage in the grid, maintain the normal operation of the grid, reduce problems such as power outages or voltage instability caused by wind fluctuations, and improve the reliability of power supply.

[0098] The output power of the generator is closely related to the excitation current. By increasing the excitation current, the magnetic field strength of the generator can be enhanced, thereby increasing the output power of the generator. While the energy storage device 2 discharges, increasing the output power of the generator further makes up for the power gap, enabling the wind power generation system to better meet the grid demand and improving the power supply capacity of the entire system.

[0099] When the predicted wind power generation power is less than the grid demand and the remaining power of the energy storage device 2 is insufficient, the control instruction is to obtain part of the electric energy from the grid through the grid connection module 4, and at the same time adjust the generator operation parameters to increase the power generation power.

[0100] To ensure the normal power consumption of the load, it is necessary to obtain part of the electric energy from the grid. The grid connection module 4 plays the role of a bridge connecting the wind power generation system and the grid and can realize the bidirectional transmission of electric energy. Ensure that the load can continuously obtain power supply, avoid equipment failures and production stagnation caused by power shortages, and ensure the normal power consumption of users.

[0101] In addition to adjusting the excitation current, the output power of the generator can also be increased by adjusting other operation parameters, such as the blade pitch angle, the speed of the generator, etc. Under the condition that the wind conditions permit, the power generation capacity of the generator is increased as much as possible. Reduce the dependence on grid electric energy, improve the power supply capacity of the wind power generation system itself, and at the same time contribute to quickly restoring independent power supply when the wind conditions improve and reducing the operation cost.

[0102] In some embodiments, the control execution module 34 includes a pitch angle adjustment component, an excitation controller, and a charge and discharge controller.

[0103] The pitch angle adjustment component is provided on the blade of the wind turbine 1 to drive the blade to rotate to a specified angle.

[0104] The core function of the pitch angle adjustment component is to precisely control the pitch angle of the blade of the wind turbine 1, that is, the angle between the blade and the rotating plane of the wind wheel. Its operation is based on the coordinated action of mechanical transmission and control signals. The control execution module 34 transmits specific control signals to the pitch angle adjustment component according to the instructions issued by the adaptive compensation decision module 33. After receiving the signal, this component uses built-in driving devices (such as motors, hydraulic systems, etc.) to generate power, and transmits the power to the blade through mechanical transmission mechanisms (such as gears, chains, etc.), driving the blade to rotate around its axis, thereby changing the pitch angle of the blade. The purpose of this is to enable the blade to be in the best windward state under different wind conditions, so as to achieve efficient capture of wind energy and effective control of the output power of the generator.

[0105] For example, the pitch angle adjustment component mainly consists of a servo motor, a reducer, and transmission gears. When the adaptive compensation decision module 33 determines that it is necessary to increase the pitch angle of the blade to capture more wind energy, it will send a control signal to increase the angle to the pitch angle adjustment component. After receiving the signal, the servo motor starts to rotate, reduces the speed and increases the torque through the reducer, and then transmits the power to the rotating mechanism at the root of the blade through the transmission gears, causing the blade to rotate according to the set angle. For example, when the wind speed is low, the adaptive compensation decision module 33 may issue an instruction to increase the pitch angle of the blade from 10° to 15°. After receiving the instruction, the pitch angle adjustment component accurately drives the blade to rotate to the 15° position, so that the blade can better capture wind energy and increase the output power of the generator.

[0106] The excitation controller is provided on the generator of the wind turbine 1 to adjust the magnitude of the current in the excitation winding of the generator.

[0107] The main function of the excitation controller is to adjust the magnitude of the current in the excitation winding of the wind turbine 1, and then control the magnetic field strength of the generator, and finally achieve the adjustment of the output voltage and power of the generator. Its working principle is based on the law of electromagnetic induction. The output voltage and power of the generator are closely related to the magnetic field strength generated by the excitation winding. The excitation controller detects the output parameters of the generator (such as voltage, current, power, etc.), compares them with the preset target values, and then automatically adjusts the current in the excitation winding according to the comparison results. When the output voltage or power of the generator is lower than the target value, the excitation controller will increase the excitation current and enhance the magnetic field strength, thereby increasing the output of the generator; conversely, when the output is higher than the target value, it will decrease the excitation current and reduce the magnetic field strength.

[0108] For example, the power grid requires the generator to output a voltage of 690V and a frequency of 50Hz. When the wind speed suddenly increases and the output power of the generator rises, resulting in the output voltage increasing to 720V, after the excitation controller detects this change, it reduces the current in the excitation winding by adjusting the conduction angle of the thyristor or other means, weakens the magnetic field intensity, thereby reducing the output voltage of the generator and gradually restoring it to the target value of 690V. Conversely, when the wind speed decreases and the output voltage drops, the excitation controller will increase the excitation current to raise the output voltage of the generator and ensure that the generator stably transmits electrical energy meeting the requirements to the power grid.

[0109] The charge-discharge controller is provided on the energy storage device 2 to regulate the energy flow between the energy storage device 2 and the power grid.

[0110] The charge-discharge controller is installed on the energy storage device 2. Its main task is to precisely regulate the energy flow between the energy storage device 2 and the power grid, ensuring that the energy storage device 2 conducts charge and discharge operations in a safe and efficient state. The controller controls the charge and discharge process according to the preset control strategy by real-time monitoring of the state parameters of the energy storage device 2 (such as remaining battery level, temperature, charge and discharge current, etc.) and the requirements and status of the power grid. During charging, the controller will reasonably adjust the charging current and voltage according to the remaining battery level and charging capacity of the energy storage device 2 to avoid overcharging; during discharging, it will control the discharge power according to the requirements of the power grid and the remaining battery level of the energy storage device 2 to prevent over-discharging.

[0111] For example, when it is predicted that the wind power generation power is greater than the power grid demand and the remaining battery level of the lithium battery has not reached the upper limit, the charge-discharge controller will control the energy storage device 2 to start charging. At this time, the remaining battery level of the lithium battery is 30%, and the maximum charging current allowed by the charging capacity is 100A. The charge-discharge controller will set the charging current to 80A according to these parameters and charge the lithium battery with a suitable charging voltage.

[0112] When the power grid demand increases and the remaining battery level of the lithium battery is sufficient, the charge-discharge controller will control the lithium battery to release electrical energy to the power grid at the set discharge power. For example, when the power grid needs to supplement 50kW of power, the charge-discharge controller will adjust the discharge power to 50kW according to the remaining battery level and discharge capacity of the lithium battery to ensure stable power supply to the power grid. At the same time, during the charge and discharge process, if it is detected that the temperature of the lithium battery is too high, the charge-discharge controller will automatically adjust the charge and discharge current to ensure the safe operation of the lithium battery.

[0113] In some embodiments, the energy storage device 2 is connected to the control execution module 34 through a bidirectional DC-DC converter 5. The bidirectional DC-DC converter 5 adjusts the charge and discharge current and voltage of the energy storage device 2 according to the instructions of the control execution module 34.

[0114] It can be understood that by adjusting the charge and discharge current and voltage according to the instructions of the control execution module 34, the bidirectional DC-DC converter 5 can achieve precise control of the charge and discharge process of the energy storage device 2. This can not only protect the energy storage device 2 from damage caused by overcharging or over-discharging, extend its service life, but also flexibly adjust the charge and discharge power according to the real-time needs of the system, improving the energy utilization efficiency.

[0115] Taking the energy storage device 2 of lithium batteries as an example, lithium batteries have strict requirements for the charge and discharge current and voltage. During the charging process, according to the instructions of the control execution module 34, the bidirectional DC-DC converter 5 uses a larger charging current (such as 1C) to quickly charge when the battery power of the lithium battery is low, and gradually reduces the charging current (such as 0.2C) when the power is close to the full charge state to avoid damage to the battery caused by overcharging. During the discharging process, if the grid load suddenly increases, the control execution module 34 will instruct the bidirectional DC-DC converter 5 to increase the discharge current so that the energy storage device 2 can quickly supplement the required power for the grid.

[0116] In some embodiments, the grid connection module 4 includes a transformer 41, a circuit breaker 42, and a power quality monitoring device 43.

[0117] It can be understood that the voltage levels of the electric energy output by the wind turbine 1 and the energy storage device 2 often do not match the grid access requirements. The transformer 41 is used to transform the voltage of the electric energy output by the wind turbine 1 and the energy storage device 2 to adjust to the voltage level that meets the grid access standard.

[0118] For example, the wind turbine 1 may output low-voltage direct current or alternating current of several hundred volts. After being converted to alternating current by an inverter, it may still not be directly connected to the high-voltage grid. At this time, the transformer 41 can raise the voltage to an appropriate level (such as 10 kV, 35 kV or even higher) so that the electric energy can be smoothly incorporated into the grid. This ensures that the wind power generation system can be compatible with the existing grid infrastructure and realizes the effective transmission and distribution of electric energy.

[0119] The circuit breaker 42 is used to cut off the circuit when the wind power generation system fails or needs to be overhauled. The wind power generation system may encounter various faults during operation, such as short circuits, overloads, etc. When these faults occur, the current in the circuit will increase sharply, which may cause serious damage to the equipment and even lead to safety accidents. The circuit breaker 42 has the function of quickly cutting off the circuit. When it detects that the fault current exceeds the set threshold, the circuit breaker 42 will act quickly to cut off the connection between the wind power generation system and the grid, preventing the further expansion of the fault, protecting equipment such as the wind turbine 1, the energy storage device 2, and the transformer 41 from damage, and ensuring the safe operation of the entire system.

[0120] When overhauling, maintaining or debugging a wind power generation system, it is necessary to isolate the system from the power grid to ensure the safety of the staff. The circuit breaker 42 can conveniently implement the opening and closing operations of the circuit. During the overhaul, the operator can manually operate the circuit breaker 42 to cut off the circuit, separating the wind power generation system from the power grid and avoiding safety risks such as electric shock. After the overhaul is completed, the circuit is reconnected through the circuit breaker 42 to restore the normal operation of the system.

[0121] The power quality monitoring device 43 is used to monitor the quality parameters of the electric energy output by the power grid and the wind power generation system in real time, and feedback the data to the adaptive compensation decision module 33 to assist it in generating control instructions.

[0122] For example, if it is detected that the harmonic content in the electric energy output by the wind power generation system is too high, the adaptive compensation decision module 33 can adjust the operating parameters of the wind turbine 1 or control the charge and discharge strategy of the energy storage device 2 to reduce the impact of harmonics and improve the power quality. At the same time, when there are problems with the power quality of the power grid, the output of the wind power generation system can also be adjusted to compensate the power grid to a certain extent and ensure the stable operation of the power grid.

[0123] In summary, the adaptive compensation wind power generation system according to the embodiment of the present invention can timely adjust the operating state of the wind turbine 1 and the charge and discharge process of the energy storage device 2 through real-time monitoring and intelligent prediction, effectively compensate for the random fluctuations of wind power generation, make the output power more stable, and reduce the impact on the power grid.

[0124] It makes adaptive adjustments according to the real-time demand of the power grid, increases the power generation power when the power grid load is large, and reduces the power generation power when the load is small, improving the compatibility between wind power generation and the power grid and ensuring the safe and economic operation of the power grid.

[0125] The energy storage device 2 is reasonably utilized to store the excess wind energy and release it when needed, avoiding the waste of energy and improving the utilization efficiency of wind energy.

[0126] Adopting a distributed architecture, each module is distributed in different control units and works together through a high-speed communication bus, reducing the impact of a single fault on the entire system and facilitating the expansion and upgrade of the system.

[0127] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0128] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0129] In the present invention, unless otherwise clearly specified and defined, the terms "mounted", "connected", "coupled", "fixed", etc. shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection, an electrical connection, or communicable with each other; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0130] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0131] In the present invention, the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0132] Although the above embodiments have been shown and described, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Variations, modifications, substitutions, and alterations made by those of ordinary skill in the art to the above embodiments are within the scope of the present invention.

Claims

1. An adaptive compensation wind power generation system, characterized in that, It includes a wind turbine, an energy storage device, an adaptive compensation controller, and a grid connection module. The adaptive compensation controller includes a connected real-time monitoring module, a prediction and analysis module, an adaptive compensation decision-making module, and a control execution module; The real-time monitoring module is connected to the wind turbine and the energy storage device, and is used to obtain the operating parameters of the wind turbine and the state parameters of the energy storage device, and transmit the acquired data to the prediction and analysis module; Based on the received data, the prediction and analysis module uses an algorithm to predict and analyze the wind power change trend and the grid load demand, and transmits the analysis result to the adaptive compensation decision-making module; According to the analysis result and combined with the real-time demand of the grid, the adaptive compensation decision-making module generates control instructions for the wind turbine and the energy storage device, and transmits the instructions to the control execution module; The control execution module is connected to the wind turbine, the energy storage device, and the grid connection module to send control instructions to the wind turbine and the energy storage device, adjust the operating state of the wind turbine, and at the same time control the charging and discharging process of the energy storage device, and perform energy interaction with the grid through the grid connection module.

2. The adaptive compensation wind power generation system according to claim 1, characterized in that The real-time monitoring module transmits the collected data to the prediction and analysis module through wired or wireless communication means. The real-time monitoring module includes: A wind sensor, which is arranged on the blade and / or the top of the nacelle of the wind turbine to monitor the wind direction and wind speed in real time; An angle sensor, which is arranged on the blade of the wind turbine to measure the blade pitch angle; A first power sensor and a speed sensor, the first power sensor and the speed sensor are arranged on the generator of the wind turbine. The first power sensor is used to obtain the output power of the generator, and the speed sensor is used to monitor the speed of the generator; A monitoring circuit, a second power sensor, and a temperature sensor. The monitoring circuit, the second power sensor, and the temperature sensor are arranged on the energy storage device. The remaining power of the energy storage device is obtained through the monitoring circuit. The second power sensor is used to measure the charging and discharging power of the energy storage device, and the temperature sensor is used to collect the temperature of the energy storage device.

3. The adaptive compensation wind power generation system according to claim 2, wherein The prediction and analysis module and the adaptive compensation decision-making module perform data transmission through a data bus; The neural network algorithm of the prediction and analysis module learns and trains historical wind speed data, generator operating parameters, and grid load data to establish a prediction model, and predicts the wind power change and grid load demand within the next 1-2 hours according to the data obtained by the real-time monitoring module; The fuzzy control algorithm of the prediction and analysis module performs fuzzy processing and reasoning on the real-time monitoring data according to the set fuzzy rules, and outputs the prediction results of the wind power and load changes.

4. The adaptive compensation wind power generation system according to claim 3, wherein The adaptive compensation decision-making module and the control execution module are connected through a control signal transmission line. The control instructions of the adaptive compensation decision-making module include: When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device does not reach the upper limit, the control instruction is to increase the blade pitch angle, and at the same time store the excess electric energy into the energy storage device, and the energy storage device is charged at a set charging power; When the predicted wind power generation is greater than the grid demand and the remaining power of the energy storage device reaches the upper limit, the control instruction is to decrease the blade pitch angle and reduce the generator output power to match the grid demand; When the predicted wind power generation is less than the grid demand and the remaining power of the energy storage device is sufficient, the control instruction is that the energy storage device releases electric energy to the grid at a set discharge power, and at the same time adjusts the generator excitation current to increase the generator output power; When the predicted wind power generation is less than the grid demand and the remaining power of the energy storage device is insufficient, the control instruction is to obtain part of the electric energy from the grid through the grid connection module, and at the same time adjust the generator operation parameters to increase the power generation; 5. The adaptive compensation wind power generation system according to claim 4, wherein The prediction analysis module and / or the adaptive compensation decision module perform data interaction with the dispatching center of the grid through a communication network, receive the real-time demand instruction of the grid, and feedback the operation state information of the wind power generation system to the dispatching center of the grid.

6. The adaptive compensation wind power generation system according to claim 4, characterized in that, The control execution module includes: A pitch angle adjustment component, which is arranged on the blade of the wind turbine to drive the blade to rotate to a specified angle; An excitation controller, which is arranged on the generator of the wind turbine to adjust the current magnitude of the generator excitation winding; A charge and discharge controller, which is arranged on the energy storage device to adjust the energy flow between the energy storage device and the grid.

7. The adaptive compensation wind power generation system according to claim 6, wherein The adaptive compensation controller adopts a distributed architecture. The real-time monitoring module, the prediction analysis module, the adaptive compensation decision module, and the control execution module are distributed in different control units, and data transmission is carried out between the control units through a high-speed communication bus.

8. The adaptive compensation wind power generation system according to any one of claims 1-7, characterized in that, The adaptive compensation controller further includes a fault diagnosis and protection module, which is connected to the real-time monitoring module and the control execution module to receive the data of the real-time monitoring module in real time, monitor the operation states of the wind turbine and the energy storage device, and automatically issue an alarm and send a protection instruction to the control execution module when an abnormal situation is detected to take corresponding protection measures.

9. The adaptive compensation wind power generation system according to claim 1, characterized in that, The energy storage device is connected to the control execution module through a bidirectional DC-DC converter, and the bidirectional DC-DC converter adjusts the charge and discharge current and voltage of the energy storage device according to the instruction of the control execution module.

10. The adaptive compensation wind power generation system according to claim 1, characterized in that, The grid connection module includes a transformer, a circuit breaker, and a power quality monitoring device. The transformer is used to transform the voltage of the electric energy output by the wind turbine and the energy storage device to meet the grid access requirements. The circuit breaker is used to cut off the circuit when the wind power generation system fails or needs maintenance. The power quality monitoring device is used to monitor the quality parameters of the electric energy output by the grid and the wind power generation system in real time, and feedback the data to the adaptive compensation decision module to assist it in generating control instructions.

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