A wind-resistant and vibration-reducing system for large-scale dish solar thermal power generation

Abstract

The present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation, which relates to the technical field of solar thermal power generation and includes: a wind load and structure monitoring module for acquiring environmental wind load data and structural response data in real time; a decision-making module for formulating wind-resistant and vibration-reducing strategies; a vibration-reduction control module for adjusting the state of the vibration-reduction structure; a wind condition prediction module for predicting wind condition change trends; a state adjustment module for outputting structural state adjustment instructions to the vibration-reduction control module; and a fault detection and emergency module for identifying extreme trends in wind condition change trends and outputting emergency protection instructions. The present invention dynamically formulates wind-resistant and vibration-reducing strategies and outputs precise vibration-reduction control instructions through the effective extraction and analysis of wind load and structural vibration characteristics, thereby improving the system's adaptability and anti-interference capabilities. By integrating the ARIMA and LSTM prediction algorithms and combining historical meteorological data with real-time wind load data, the system achieves accurate prediction of short-term wind conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of solar thermal power generation, and in particular to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation. Background Art

[0002] Large-scale dish solar thermal power generation systems span over 25 meters. Because the concentrators of these devices are typically subject to significant wind loads, they are easily damaged by strong winds. Historically, wind-induced damage to concentrators has occurred numerous times. While typical concentrator designs are required to withstand winds of force 6, dish solar systems often operate in areas subject to significant wind and sand, requiring them to withstand winds of force 8. Concentrators built in my country have also experienced numerous wind-induced overturning and structural damage incidents. Therefore, wind load is the most significant of all loads on a solar concentrator during operation, making vibration reduction devices for this equipment a core technology in this field.

[0003] The field of large-scale dish-type solar thermal power generation systems is still in its developmental stages, and vibration reduction technologies for these systems are immature, resulting in numerous wind-induced overturning and structural failures. Existing systems often rely on fixed parameters or empirical strategies for vibration control, resulting in limited effectiveness and poor adaptability to complex and variable wind environments. Vibration reduction is typically achieved through a single approach (such as fixed damper parameters), without the ability to flexibly adjust the state of the supporting and vibration-reduction structures based on structural and wind conditions. This lack of a multi-level, multi-dimensional coordinated control mechanism results in suboptimal vibration reduction and makes the equipment susceptible to excessive vibrations from strong winds.

[0004] In order to solve the above-mentioned defects in the prior art, the present technical solution proposes a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation. Summary of the Invention

[0005] The present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation, so as to solve the defects in the prior art.

[0006] In one aspect, the present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation, comprising:

[0007] Wind load and structure monitoring module, used to obtain environmental wind load data and structural response data in real time;

[0008] A decision-making module is used to formulate wind-resistant vibration reduction strategies based on wind load data and structural response data, and output vibration reduction control instructions;

[0009] A vibration reduction control module, used to adjust the state of the vibration reduction structure according to the vibration reduction control instruction;

[0010] Wind condition prediction module, used to predict wind condition change trends based on wind load data and historical meteorological data, and output wind condition prediction results;

[0011] The state adjustment module is used to output a structural state adjustment instruction to the vibration reduction control module according to the wind condition prediction result, and update the vibration reduction structure state;

[0012] The fault detection and emergency module is used to identify extreme trends in wind condition changes and output emergency protection instructions based on the extreme trends.

[0013] According to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the present invention, the wind load and structure monitoring module includes a wind load monitoring unit and a structure monitoring unit; the wind load monitoring unit is used to monitor the real-time wind speed, wind direction and change trend in the environment and output wind load data; the structure monitoring unit is used to monitor the structural vibration and deformation in real time and output the structural response data.

[0014] According to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the present invention, the decision-making module includes a data processing unit and a control strategy unit; the data processing unit is used to preprocess wind load data and structural response data, and extract effective wind load characteristics and structural vibration characteristics; the control strategy unit is used to formulate an wind-resistant and vibration-reducing strategy based on the effective wind load characteristics and structural vibration characteristics, and output vibration reduction control instructions.

[0015] According to a wind-resistant vibration reduction system for large-scale dish-type solar thermal power generation provided by the present invention, the steps of extracting effective wind load characteristics and structural vibration characteristics by a data processing unit include:

[0016] Preprocess the wind load data and output standard wind load data;

[0017] According to the standard wind load data, the effective wind load characteristics are output by calculating the turbulence intensity value, identifying the gust frequency and calculating the wind speed change;

[0018] Based on the structural response data, the structural vibration characteristics are output by identifying the natural frequencies and calculating the damping ratio.

[0019] According to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the present invention, the steps of formulating a wind-resistant and vibration-reducing strategy by a control strategy unit include:

[0020] Classify wind speed changes and output wind speed classification data;

[0021] Develop wind resistance strategies based on wind speed classification data;

[0022] According to the turbulence intensity value and gust frequency, the damper parameters are dynamically adjusted and the vibration reduction strategy is output.

[0023] According to a wind-resistant vibration reduction system for large-scale dish-type solar thermal power generation provided by the present invention, the vibration reduction control module includes a state adjustment unit and a balance vibration reduction unit; the state adjustment unit is used to adjust the structural state of the supporting structure, and the balance vibration reduction unit is used to adjust the vibration reduction structure state based on the structural state and according to the vibration reduction control instruction.

[0024] According to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the present invention, the wind condition prediction module includes a data acquisition unit, a prediction algorithm unit and a risk rating unit; the data acquisition unit is used to extract historical wind condition data from a historical meteorological database, the prediction algorithm unit is used to predict future short-term wind condition changes based on historical wind condition data and wind load data, and output wind condition prediction results; the risk rating unit is used to generate a wind condition risk level based on the wind condition prediction results, and mark extreme wind conditions.

[0025] According to a wind-resistant vibration reduction system for large-scale dish-type solar thermal power generation provided by the present invention, the steps of predicting future short-term wind condition changes by a prediction algorithm unit include:

[0026] Based on historical wind data, an ARIMA forecasting model is established to predict the linear characteristics of short-term wind changes and output linear prediction results;

[0027] Based on historical wind data, the LSTM prediction model is used to predict the nonlinear characteristics of short-term wind changes based on a sliding window, and the nonlinear prediction results are output;

[0028] According to the linear prediction results and nonlinear prediction results, a short-term wind speed prediction model is output through weighted fusion calculation;

[0029] Based on the prediction results of the short-term wind speed prediction model, the predicted gust frequency and Reynolds number are calculated, and combined with the wind speed change rate, a turbulence intensity prediction model is established;

[0030] Calculate the wind direction change rate based on the wind direction data in the wind load data, and predict the risk of sudden wind direction changes in the future based on the wind direction change rate;

[0031] The wind condition prediction results are output by matching the prediction results of the short-term wind speed prediction model, turbulence intensity prediction model and wind direction change rate through the time window.

[0032] According to a wind-resistant vibration reduction system for large-scale dish-type solar thermal power generation provided by the present invention, the steps of generating a wind risk level by a risk rating unit include:

[0033] According to the wind condition forecast results, the risk indicators are quantified and the quantitative risk indicators are output;

[0034] Perform weighted summation on the quantitative risk indicators and output the weighted result;

[0035] The wind condition forecast results are risk graded according to the weighted results, and the comprehensive risk level is output.

[0036] According to a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the present invention, the fault detection and emergency module includes an extreme trend identification unit, a protection strategy formulation unit, an emergency protection instruction output unit and an emergency status monitoring unit; the extreme trend identification unit is used to analyze the trend of wind condition changes and identify the extreme trends therein; the protection strategy formulation unit is used to formulate emergency protection instructions according to the type and severity of the extreme trend; the emergency protection instruction output unit is used to output the emergency protection instructions to the vibration reduction control module and execute protection measures; the emergency status monitoring unit is used to monitor in real time the changes in the state of the vibration reduction structure during the execution of the protection measures.

[0037] The present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation. By effectively extracting and analyzing wind loads and structural vibration characteristics, it is able to dynamically formulate wind-resistant and vibration-reducing strategies and output precise vibration reduction control instructions, thus realizing an intelligent closed loop from data to control and significantly improving the system's adaptability and anti-interference capabilities. Through the dual mechanisms of state adjustment and balanced vibration reduction, the state of the supporting structure and the vibration reduction structure can be flexibly adjusted according to the control instructions, realizing multi-level and multi-dimensional vibration reduction control, effectively reducing the impact of wind-induced vibration on dish-type solar thermal power generation equipment, and improving the safety and stability of equipment operation. By integrating advanced prediction algorithms such as ARIMA and LSTM through the wind condition prediction module, and combining historical meteorological data with real-time wind load data, it is possible to accurately predict short-term wind condition changes, especially key risk factors such as turbulence intensity, gust frequency, and sudden changes in wind direction, providing a scientific basis for taking protective measures in advance and enhancing the system's foresight and initiative. The fault detection and emergency response module features functions such as extreme trend identification, emergency protection instruction formulation and execution, and emergency status monitoring. It can rapidly initiate protective measures in extreme wind conditions and monitor their effectiveness in real time, ensuring the system's safe operation under abnormal circumstances and effectively preventing equipment damage and safety accidents. The risk rating unit, through quantitative and weighted analysis, achieves scientific classification of wind risk and identification of extreme trends, enabling the system to respond quickly to high-risk wind conditions and improving overall wind resistance and safety assurance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 This is a schematic structural diagram of a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation provided by the first embodiment of the present invention;

[0040] Figure 2 This is a schematic structural diagram of a large-scale dish-type solar thermal power generation system according to a second embodiment of the invention;

[0041] Figure 3 yes Figure 2 A schematic diagram of the structure of the vibration reduction device for the concentrator part;

[0042] Figure 4 yes Figure 1 Schematic diagram of the structure of the vibration reduction device for the entire dish solar thermal power generation system.

[0043] In the figure: 1. Concentrator; 2. Concentrator connecting support frame; 3. Support frame connection; 4. Vibration reduction connecting bolt; 5. Concentrator vibration reduction system; 6. Housing; 7. Vibration reduction bolt buckle; 8. Vibration reduction spring; 9. Vibration reduction rubber; 10. Fixing bolt; 11. Connecting support frame; 12. Balance vibration reduction box; 13. Vibration reduction medium. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0045] Example 1:

[0046] The following combination Figure 1 The present invention describes a wind-resistant vibration reduction system for large-scale dish-type solar thermal power generation.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation, comprising:

[0048] The wind load and structure monitoring module is used to obtain environmental wind load data and structural response data in real time. The wind load and structure monitoring module includes a wind load monitoring unit and a structure monitoring unit. The wind load monitoring unit is used to monitor the real-time wind speed, wind direction and change trend in the environment and output wind load data. The wind load monitoring unit integrates lidar and ultrasonic anemometer, and eliminates the error of a single device through multi-sensor data fusion algorithms (such as Kalman filtering) to improve the accuracy of wind speed / direction measurements. The structure monitoring unit is used to monitor structural vibration and deformation in real time and output structural response data. The monitoring data is pre-processed by the edge computing gateway (such as outlier removal) to reduce cloud transmission delay. The structure monitoring unit deploys fiber grating sensors and inertial measurement units to synchronously monitor the three-dimensional deformation of the dish concentrator (including deflection, torsion, etc.) and the stress distribution of the supporting structure.

[0049] The decision-making module is used to formulate wind-resistance and vibration reduction strategies based on wind load and structural response data and output vibration reduction control instructions. The decision-making module includes a data processing unit and a control strategy unit. The data processing unit is used to preprocess the wind load and structural response data and extract effective wind load and structural vibration characteristics. The control strategy unit is used to formulate wind-resistance and vibration reduction strategies based on the effective wind load and structural vibration characteristics and output vibration reduction control instructions.

[0050] The steps of extracting effective wind load characteristics and structural vibration characteristics by the data processing unit include:

[0051] Preprocess wind load data and output standard wind load data. Specific steps include: Using a sliding average filter or low-pass filter to smooth instantaneous fluctuations in wind speed and direction. Using a Kalman filter (using state equations to model sensor dynamic characteristics and observation equations to fuse multi-sensor data) or wavelet noise reduction to process acceleration and displacement data. Using statistical methods to identify outliers outside the normal range, these values ​​are replaced by linear interpolation of adjacent normal data. Missing data due to communication delays or sensor failures is filled using spline interpolation or autoregressive model prediction. Time alignment algorithms are used to correct for minor time deviations between different sensors.

[0052] According to the standard wind load data, the effective wind load characteristics are output by calculating the turbulence intensity value, identifying the gust frequency and calculating the wind speed change. The calculation method of the turbulence intensity value is expressed as:

[0053]

[0054] Where I is the turbulence intensity value, which is used to evaluate the instability of the wind field. is the standard deviation of wind speed, calculated using a sliding window with a window size of 10 to 30 seconds. U is the average wind speed.

[0055] When performing gust frequency identification, the wind speed signal is subjected to fast Fourier transform to analyze the spectrum energy distribution and identify the dominant frequency (for example, the peak frequency in the range of 0.1 to 1 Hz corresponds to the gust period), thereby obtaining the dominant gust frequency and amplitude.

[0056] Wind speed changes are mainly judged by the wind speed change rate, which is calculated as follows:

[0057]

[0058] Among them, ΔV represents the wind speed change rate, that is, the change in wind speed per unit time, which is used to assess the risk of sudden wind speed changes. V(t) represents the wind speed value measured at the current time t, t represents the current time, and Δt is the time interval. Indicates a time point before the current time t The measured wind speed value.

[0059] Based on the structural response data, the structural vibration characteristics are output by identifying the natural frequencies and calculating the damping ratio. The natural frequencies are identified by performing power spectral density analysis or autoregressive model spectrum analysis on the acceleration signal to identify the structural resonant frequencies (for example, the first-order bending frequency of a dish concentrator is typically 0.5-2 Hz).

[0060] The damping ratio is a dimensionless parameter that describes the vibration energy dissipation capacity of a structure. The half-power bandwidth method estimates the damping ratio by analyzing the power spectrum density curve of the structure and using the resonance peak characteristics. Its core principle is that when the structure is subjected to simple harmonic excitation, the PSD curve will have a peak at the natural frequency. Due to the existence of damping, the two sides of the peak will gradually attenuate, and the half-power point (that is, the point where the peak power drops to times) corresponds to the frequency (low frequency side) and (High frequency side). Bandwidth and damping ratio Proportional, the public expression is:

[0061]

[0062] in, is the damping ratio, is the center frequency of the resonance peak, that is, the frequency corresponding to the peak. is the low-frequency side frequency of the half-power point, The specific method is to collect the structural vibration signal (such as the acceleration of the dish concentrator support arm) through the acceleration sensor, and the sampling frequency The Nyquist theorem must be satisfied (usually , The acquisition duration T is recommended to cover multiple vibration cycles. The Welch method (segmented average periodogram method) is used to calculate the PSD and reduce random noise interference: the signal is divided into N segments (each segment length L, usually L = 210-212 points), with an overlap rate of 50%-75%. A window (such as a Hanning window) is applied to each segment to reduce spectral leakage. After calculating the periodogram, the average is taken to obtain the PSD curve. Find the peak point with the largest amplitude in the PSD curve, and its corresponding frequency is the center frequency of the resonance peak It is necessary to exclude low-frequency noise interference (such as drift below 0.1Hz) and high-frequency random noise (such as fluctuations above the highest natural frequency of the structure). Check whether the peak is significant (such as the peak amplitude is at least 3dB higher than the adjacent frequency band) to avoid misjudging the noise as a resonance peak. Peak amplitude The corresponding power is . Half power amplitude .exist On the left (low frequency side) find the first one that satisfies Frequency .exist On the right side (high frequency side), find the first one that satisfies Frequency , and finally calculate the damping ratio. If the PSD curve is or If the fluctuations nearby are large, interpolation methods (such as linear interpolation) can be used to improve the accuracy.

[0063] The steps for the control strategy unit to formulate wind vibration reduction strategies include:

[0064] Wind speed changes are graded and wind speed classification data is output, including low wind speed, medium wind speed and high wind speed. The threshold of each classification can be set as needed.

[0065] Develop wind resistance strategies based on wind speed classification data, including normal operation at low wind speeds, angle adjustment at medium wind speeds, and shutdown and retraction at high wind speeds.

[0066] Based on the turbulence intensity and gust frequency, the system dynamically adjusts damper parameters or actuator output to implement a vibration reduction strategy. For example, TMD is activated for high-frequency vortex-induced vibrations, while active control is activated for low-frequency gust responses.

[0067] The vibration reduction control module is used to adjust the state of the vibration reduction structure according to the vibration reduction control instructions. The vibration reduction control module includes a state adjustment unit and a balanced vibration reduction unit. The state adjustment unit is used to adjust the structural state of the supporting structure. It integrates a hydraulic actuator and an electric push rod to achieve rapid adjustment of the dish concentrator pitch angle within ±15°. A dual-path drive circuit and an automatic fault switching mechanism can also be set up to ensure that the system can still operate in the event of a single point failure. The balanced vibration reduction unit is used to adjust the state of the vibration reduction structure based on the structural state and the vibration reduction control instructions. An active mass damper is used to suppress low-frequency vibrations (<5Hz), and a tuned mass damper (TMD) is combined to control high-frequency vibrations (5-20Hz). A state observer (such as the Luenberger observer) is used to compensate for the impact of sensor noise on control accuracy.

[0068] The wind forecasting module is used to predict wind trends based on wind load data and historical meteorological data, and output wind forecast results. The module includes a data acquisition unit, a prediction algorithm unit, and a risk rating unit. The data acquisition unit extracts historical wind data from the historical meteorological database, while the prediction algorithm unit predicts future short-term wind changes based on historical wind data and wind load data, and outputs wind forecast results.

[0069] The steps for predicting future short-term wind changes include:

[0070] Based on historical wind data, an ARIMA forecasting model is established to predict the linear features in short-term wind changes and output linear forecast results. The stationarity of the sequence is judged by the ADF test (Augmented Dickey-Fuller Test). If the p-value is less than 0.05, the sequence is stationary (d=0); otherwise, first-order difference (d=1) is performed until it is stationary. The autocorrelation function (ACF) is calculated to observe the lag order at which the autocorrelation coefficient first crosses the confidence interval (95%) (for example, if the ACF significantly decays after lag 2, then p=2). The partial autocorrelation function (PACF) is calculated and q is determined in a similar way (for example, if the PACF is truncated after lag 1, then q=1). Where d is the difference order, p is the autoregressive order, and q is the moving average order. Use historical 1-hour data to train the ARIMA model and predict the wind speed for the next 1 minute (10 points, frequency 10Hz). Output linear forecast results. .

[0071] According to historical wind data, the LSTM prediction model is used to predict the nonlinear features in short-term wind changes based on a sliding window, and the nonlinear prediction results are output. Nonlinear features include wind speed mean, wind speed standard deviation, wind direction mean, turbulence intensity (real-time calculation) and wind speed change rate. The sliding window is usually 5s. The network structure of the LSTM model includes an input layer, LSTM layer one, LSTM layer two and a fully connected layer. The input layer inputs the above nonlinear features. LSTM layer one contains 128 neurons, the activation function is tanh, and Dropout=0.2 (to prevent overfitting). LSTM layer two contains 128 neurons with the same parameters. The fully connected layer is used to predict wind speed. The loss function of the LSTM model uses mean square error. The model is trained using data from the past 7 days to obtain nonlinear prediction results. .

[0072] Based on the linear and nonlinear prediction results, a weighted fusion calculation is performed to output a short-term wind speed forecast model. The root mean square error (RMSE) of ARIMA and LSTM is calculated on the validation set (data from the past month). The weighted fusion calculation formula is expressed as:

[0073]

[0074]

[0075] Among them, α is the weighting coefficient of the ARIMA model prediction result, and its value range is [0,1]. If the LSTM error is smaller, α increases. To integrate the prediction results, R ARIMA is the root mean square error between the ARIMA model prediction and the actual observed wind speed. LSTM is the root mean square error between the LSTM model prediction value and the actual observed wind speed.

[0076] According to the prediction results of the short-term wind speed prediction model, the predicted gust frequency and Reynolds number are calculated, and combined with the wind speed change rate, a turbulence intensity prediction model is established. The Reynolds number calculation method is expressed as:

[0077]

[0078] Where ρ is the air density, D is the characteristic length, such as the diameter of the support arm, μ is the air viscosity, and V is the wind speed. Calculated by sliding window. The turbulence intensity prediction model is expressed as:

[0079]

[0080] Where, It is the predicted turbulence intensity at time t+1, which measures the intensity of velocity fluctuations in the wind field and reflects the instability of the wind. represents the Reynolds number at time t, represents the dominant frequency of wind speed pulsation at time t (extracted from the wind speed signal by FFT), is the constant term of the regression model, which means that and The turbulence intensity baseline value is 0. for right The influence weight of for right The influence weight of Represents random fluctuations, such as measurement noise, which reflects the uncertainty of model prediction and obeys a normal distribution with a mean of 0.

[0081] Calculate the wind direction change rate based on wind direction data from wind load data and use this to predict the risk of future sudden wind direction changes. This prediction is typically performed using a hidden Markov model. Define states: steady state (low change rate), transition state (medium change rate), and sudden change state (high change rate). Annotate historical wind direction sudden change events.

[0082] The wind forecast results are output by matching the prediction results of the short-term wind speed prediction model, turbulence intensity prediction model, and wind direction change rate within the time window. The prediction results of the wind speed prediction model, turbulence intensity prediction model, and wind direction change rate within the same time window are combined together to form the wind forecast results within this time window.

[0083] The risk rating unit is used to generate a wind risk level based on the wind forecast results and mark extreme wind conditions. The steps for the risk rating unit to generate a wind risk level include:

[0084] According to the wind forecast results, the risk index is quantified and the quantitative risk index is output. The specific expression is:

[0085]

[0086]

[0087]

[0088] in, Indicates wind speed risk, represents the turbulence risk, Indicates wind direction risk, Indicates the baseline threshold for wind speed risk, exceeding which wind mitigation measures (such as retracting reflective mirrors) must be initiated. The maximum permissible wind speed for which a wind farm or structure is designed (exceeding this value may cause structural failure). It indicates the safety critical value of turbulence intensity. If this value is exceeded, wind resistance and vibration reduction measures need to be strengthened. It represents the limit value of turbulence intensity (corresponding to extreme turbulence conditions), exceeding which may lead to structural fatigue failure. It represents the turbulence intensity predicted by the model for the next 1 minute. Indicates the probability of a sudden change in wind direction (>30° / min) within the next minute, output by a hidden Markov model (HMM) or support vector machine (SVM).

[0089] Perform weighted summation on the quantitative risk indicators and output the weighted result, which is expressed as:

[0090]

[0091] in, 、 and is the weight value of each indicator, is the weighted result.

[0092] The wind condition forecast results are risk graded according to the weighted results, and the comprehensive risk level is output, including low risk or no risk, medium risk and high risk.

[0093] The state adjustment module is used to output a structural state adjustment instruction to the vibration reduction control module according to the wind condition prediction result, and update the vibration reduction structure state.

[0094] The fault detection and emergency module is used to identify extreme trends in wind condition changes and output emergency protection instructions based on the extreme trends. The fault detection and emergency module includes an extreme trend identification unit, a protection strategy formulation unit, an emergency protection instruction output unit, and an emergency status monitoring unit. The extreme trend identification unit is used to analyze the wind condition change trend and identify the extreme trends therein. The protection strategy formulation unit is used to formulate emergency protection instructions based on the type and severity of the extreme trend. The emergency protection instruction output unit is used to output the emergency protection instructions to the vibration reduction control module and execute protection measures. The emergency status monitoring unit is used to monitor in real time the changes in the state of the vibration reduction structure during the execution of the protection measures.

[0095] In summary, the present invention provides a wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation. By effectively extracting and analyzing wind loads and structural vibration characteristics, it is able to dynamically formulate wind-resistant and vibration-reducing strategies and output precise vibration reduction control instructions, thus realizing an intelligent closed loop from data to control and significantly improving the system's adaptability and anti-interference capabilities. Through the dual mechanisms of state adjustment and balanced vibration reduction, the state of the supporting structure and the vibration reduction structure can be flexibly adjusted according to the control instructions, realizing multi-level and multi-dimensional vibration reduction control, effectively reducing the impact of wind-induced vibration on dish-type solar thermal power generation equipment, and improving the safety and stability of equipment operation. By integrating advanced prediction algorithms such as ARIMA and LSTM through the wind condition prediction module, and combining historical meteorological data with real-time wind load data, it is possible to accurately predict short-term wind condition changes, especially key risk factors such as turbulence intensity, gust frequency, and sudden changes in wind direction, providing a scientific basis for taking protective measures in advance and enhancing the system's foresight and initiative. The fault detection and emergency response module features functions such as extreme trend identification, emergency protection instruction formulation and execution, and emergency status monitoring. It can rapidly initiate protective measures in extreme wind conditions and monitor their effectiveness in real time, ensuring the system's safe operation under abnormal circumstances and effectively preventing equipment damage and safety accidents. The risk rating unit, through quantitative and weighted analysis, achieves scientific classification of wind risk and identification of extreme trends, enabling the system to respond quickly to high-risk wind conditions and improving overall wind resistance and safety assurance.

[0096] Example 2:

[0097] like Figure 2-Figure 4 As shown, for the overall vibration reduction of a large-scale dish-type solar thermal power generation system, this solution also provides a vibration reduction balancing device.

[0098] The device consists of a connecting frame and a balancing and vibration reduction system. The connecting frame is connected to the dish solar system support frame, and a vibration reduction and balancing device is installed at the rear end. The vibration reduction and balancing device includes a balancing tank and a vibration reduction medium 13, which is water, effectively reducing costs. When addressing the overall vibration of the dish solar thermal power generation system, the device uses the dynamic lateral force generated by the sloshing of the liquid in the fixed balancing tank to provide vibration reduction. It has the advantages of simple structure, easy installation, good automatic activation performance, and no need for a starting device.

[0099] like Figure 2 As shown, the concentrator 1 of the dish-type solar thermal power generation system has a large area and is subject to a large wind load. Figure 3The vibration damping device for the concentrator 1 shown is connected to the concentrator connecting support frame 2, and is fastened to the inner layer of the concentrator 1 by the vibration damping connecting bolt 4 of the vibration damping device. The vibration damping connecting bolt 4 is connected to the concentrator vibration damping system 5. The concentrator vibration damping system 5 includes an outer shell 6, a vibration damping bolt buckle 7, a vibration damping spring 8, and a vibration damping rubber 9. The vibration damping bolt buckle 7 is connected to the vibration damping spring 8. The bottom end of the vibration damping spring 8 is fixed on the fixing bolt 10. The vibration damping rubber 9 is fixed inside the outer shell 6. The fixing bolt 10 is fastened to the concentrator connecting support frame 2.

[0100] like Figure 4 As shown, the connection support frame 11 of the vibration reduction device of the dish type solar thermal power generation system is connected to Figure 3 The support frame connection 3 is connected, the support frame 11 is connected to the balance vibration reduction box 12, and a certain amount of vibration reduction medium 13 is injected into the balance vibration reduction box 12.

[0101] The working principle is:

[0102] Combine Figure 2-Figure 4 When the wind load acts on the block concentrator 1, the concentrator 1 will vibrate. Figure 3 The vibration reduction system in the vibration reduction device is designed to adjust the vibration frequency to near the main structural frequency under the action of the vibration reduction spring 8 and the vibration reduction rubber 9, thereby changing the structural resonance characteristics to achieve vibration reduction. Under the control of the housing 6 and the vibration reduction bolt clip 7, the amplitude is controlled to a level that does not affect the focusing of the concentrator 1. Each concentrator 1 can reduce vibration to varying degrees, thereby reducing the overall vibration.

[0103] In order to further reduce the overall vibration, such as Figure 4 The vibration reduction system shown not only balances the dish-type solar thermal power generation system, but also transmits the vibration to the balancing vibration reduction box 12 when the dish-type solar thermal power generation system is vibrated by the wind, and uses the dynamic side force generated by the liquid in the box during the shaking process to provide vibration reduction. It has the advantages of simple structure, easy installation, good automatic activation performance, and no need for a starting device.

[0104] Figure 3 The vibration-damping spring 8 and the vibration-damping rubber 9 in the vibration-damping system shown can effectively reduce the vibration of the concentrator 1 in all directions and control the influence of wind-induced vibration on the heat collection efficiency of the concentrator 1 . Figure 4 The balanced vibration damping box 12 shown can effectively reduce the vibration of the entire dish-type solar thermal power generation system, and is easy to install, has low maintenance costs, and does not require a starting device.

[0105] In the technical background that dish-type solar thermal power generation systems lack wind-resistant and vibration-reducing devices, the present invention can effectively reduce the wind-induced vibration of the dish-type solar thermal power generation system, thereby extending the service life of the dish-type solar thermal power generation system, and has the advantages of low cost, simple installation, and convenient maintenance.

[0106] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation, characterized in that: include: Wind load and structure monitoring module, used to obtain environmental wind load data and structural response data in real time; a decision-making module, configured to formulate a wind-resistant vibration reduction strategy based on the wind load data and the structural response data, and output a vibration reduction control instruction; A vibration reduction control module, configured to adjust the state of the vibration reduction structure according to the vibration reduction control instruction; A wind condition prediction module is configured to predict the wind condition change trend based on the wind load data and historical meteorological data, and output a wind condition prediction result; the wind condition prediction module includes a data acquisition unit, a prediction algorithm unit, and a risk rating unit; the data acquisition unit is configured to extract historical wind condition data from a historical meteorological database; the prediction algorithm unit is configured to predict future short-term wind condition changes based on the historical wind condition data and the wind load data, and output a wind condition prediction result; the risk rating unit is configured to generate a wind condition risk level based on the wind condition prediction result, and mark extreme wind conditions; The step of predicting the future short-term wind condition changes by the prediction algorithm unit includes: Based on the historical wind condition data, an ARIMA prediction model is established to predict the linear characteristics of the short-term wind condition changes and output a linear prediction result; According to the historical wind condition data, using the LSTM prediction model, based on a sliding window, predict the nonlinear characteristics in the short-term wind condition changes and output a nonlinear prediction result; Outputting a short-term wind speed prediction model through weighted fusion calculation based on the linear prediction result and the nonlinear prediction result; Calculating the predicted gust frequency and Reynolds number based on the prediction results of the short-term wind speed prediction model, and establishing a turbulence intensity prediction model in combination with the wind speed change rate; Calculating a wind direction change rate based on wind direction data in the wind load data, and predicting a future risk of sudden wind direction changes based on the wind direction change rate; Matching the prediction results of the short-term wind speed prediction model, the turbulence intensity prediction model and the wind direction change rate through the time window, and outputting the wind condition prediction result; a state adjustment module, configured to output a structural state adjustment instruction to the vibration reduction control module according to the wind condition prediction result, so as to update the state of the vibration reduction structure; The fault detection and emergency module is used to identify the extreme trend in the wind condition change trend and output emergency protection instructions based on the extreme trend.

2. The wind-resistant and vibration-reducing system for large-scale dish solar thermal power generation according to claim 1, characterized in that: The wind load and structure monitoring module includes a wind load monitoring unit and a structure monitoring unit; the wind load monitoring unit is used to monitor the real-time wind speed, wind direction and change trend in the environment and output wind load data; the structure monitoring unit is used to monitor the structural vibration and deformation in real time and output the structural response data.

3. The wind-resistant and vibration-reducing system for large-scale dish solar thermal power generation according to claim 1, characterized in that: The decision-making module includes a data processing unit and a control strategy unit; the data processing unit is used to preprocess the wind load data and the structural response data, and extract the effective wind load characteristics and structural vibration characteristics; the control strategy unit is used to formulate the anti-wind vibration reduction strategy based on the effective wind load characteristics and structural vibration characteristics, and output the vibration reduction control instructions.

4. The wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation according to claim 3, characterized in that: The step of extracting effective wind load characteristics and structural vibration characteristics by the data processing unit includes: Preprocessing the wind load data to output standard wind load data includes: smoothing instantaneous fluctuations in wind speed and direction using a sliding average filter or a low-pass filter; processing acceleration and displacement data using a Kalman filter; identifying outliers outside the normal range based on statistical methods and replacing them with linear interpolation of adjacent normal data; filling missing data due to communication delays with spline interpolation; and correcting slight time deviations between different sensors using a time alignment algorithm. Outputting effective wind load characteristics by calculating turbulence intensity values, identifying gust frequencies, and calculating wind speed variations based on the standard wind load data; According to the structural response data, the structural vibration characteristics are output by identifying the natural frequencies and calculating the damping ratios.

5. The wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation according to claim 4, characterized in that: The step of the control strategy unit formulating the wind resistance and vibration reduction strategy includes: Classifying the wind speed change and outputting wind speed classification data; Formulate wind resistance strategies based on the wind speed classification data; According to the turbulence intensity value and the gust frequency, the damper parameters are dynamically adjusted and a vibration reduction strategy is output.

6. The wind-resistant and vibration-reducing system for large-scale dish solar thermal power generation according to claim 1, characterized in that: The vibration reduction control module includes a state adjustment unit and a balance vibration reduction unit; the state adjustment unit is used to adjust the structural state of the support structure, and the balance vibration reduction unit is used to adjust the vibration reduction structural state based on the structural state and according to the vibration reduction control instruction.

7. The wind-resistant and vibration-reducing system for large-scale dish-type solar thermal power generation according to claim 1, characterized in that: The step of generating the wind risk level by the risk rating unit includes: quantifying the risk index according to the wind condition prediction result and outputting the quantitative risk index; Performing weighted summation on the quantitative risk indicators and outputting a weighted result; The wind condition prediction result is risk graded according to the weighted result, and a comprehensive risk grade is output.

8. The wind-resistant and vibration-reducing system for large-scale dish solar thermal power generation according to claim 1, characterized in that: The fault detection and emergency module includes an extreme trend identification unit, a protection strategy formulation unit, an emergency protection instruction output unit and an emergency status monitoring unit; the extreme trend identification unit is used to analyze the wind condition change trend and identify the extreme trend therein; the protection strategy formulation unit is used to formulate the emergency protection instruction according to the type and severity of the extreme trend; the emergency protection instruction output unit is used to output the emergency protection instruction to the vibration reduction control module and execute protection measures; the emergency status monitoring unit is used to monitor in real time the changes in the state of the vibration reduction structure during the execution of the protection measures.

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