Acousto-optic integrated wind energy full-automatic detection system

Abstract

The invention discloses an acousto-optic integrated wind energy full-automatic detection system, which relates to the technical field of wind energy detection, and constructs a multi-module collaborative architecture including wind energy acquisition and processing, control processing, data acquisition, feature extraction, interference assessment, risk regulation and control and data feedback. A comprehensive feature vector is constructed based on the output voltage change rate and the control abnormity trigger frequency, and in combination with an interference level judgment and dynamic regulation strategy, real-time sensing, accurate judgment and closed-loop self-adaptive regulation of the wind energy system when facing electromagnetic interference are realized, so that the risks of false alarm, missing alarm and system failure are effectively reduced, and the system reliability is improved. The stability, the reliability and the intelligent level of the system are improved, and the system is particularly suitable for complex application scenes such as unattended operation and violent wind fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of wind energy detection, and in particular to an acoustic and optical integrated full-automatic wind energy detection system. Background Art

[0002] The acoustic and optical integrated wind energy fully automatic detection is a detection device or system that combines sound (such as a buzzer) and light (such as a warning light) prompt functions and uses wind energy to power it to achieve fully automatic operation. This technology uses wind energy to drive the device to generate electricity, does not rely on an external power supply, and integrates sound and light alarm or prompt modules. It can automatically detect specific targets (such as smoke, gas, displacement, etc.) in unmanned environments and immediately issue sound and light warnings. It is energy-saving, environmentally friendly, and intelligent, and is widely used in outdoor safety monitoring, environmental testing and other fields. However, in existing wind power generation and energy supply systems, there may be high-frequency electromagnetic interference (EMI) that affects microcontrollers and sensor systems, especially under extreme weather conditions such as drastic changes in wind speed or lightning. In addition, such interference may enter the system through power line coupling or space radiation, causing the main control chip to reset, sensors to trigger incorrectly, or communication anomalies, thereby causing false alarms, missed alarms, or even system paralysis. Summary of the Invention

[0003] The purpose of the present invention is to provide an acoustic and optical integrated wind energy fully automatic detection system to solve the shortcomings of the background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solution: an acoustic-optical integrated wind energy fully automatic detection system, comprising a wind energy collection and processing module, a control processing module, a data collection module, a feature extraction module, a wind energy interference assessment module, a wind energy risk control module, and a data feedback module;

[0005] A wind energy collection and processing module is used to collect wind energy through a vertical axis or horizontal axis wind turbine and output electrical energy, and to store the electrical energy after rectifying and stabilizing the voltage;

[0006] The control processing module is used to control the operation of the sensor module and the sound and light alarm module, and monitor the system operation status;

[0007] The data acquisition module is used to collect the output voltage, current, ambient wind speed of the wind turbine and abnormal status information of the control processing module;

[0008] a feature extraction module, configured to extract the output voltage change rate and the abnormal trigger frequency of the control processing module from the data acquired by the data acquisition module;

[0009] A wind power interference assessment module is used to determine the current electromagnetic interference risk level of the wind power supply system after analyzing the output voltage change rate and abnormal trigger frequency;

[0010] a wind energy risk control module, configured to execute different interference suppression strategies according to the risk level output by the wind energy interference assessment module;

[0011] The data feedback module is used to evaluate the execution effect of the interference suppression strategy and send it back to the control processing module for subsequent strategy optimization.

[0012] Preferably, the wind energy collection and processing module includes: collecting wind energy and outputting electrical energy through a small vertical axis or horizontal axis wind turbine, and the wind turbine uses a permanent magnet DC motor or a three-phase brushless AC motor; rectifying the AC or DC power output by the generator, using a full-wave bridge rectifier circuit; suppressing high-frequency interference through filter capacitors and fast recovery diodes; inputting the rectified electrical energy into a DC-DC voltage stabilization module for voltage stabilization; storing the stabilized electrical energy in a lithium battery pack or a supercapacitor, and managing charging and discharging through a BMS system.

[0013] Preferably, the control processing module includes: the main control chip controls the sensor module to perform regular sampling, and determines whether there is an abnormality based on the sampling results; when the sampling data exceeds the set threshold, controls the start of the sound and light alarm module to send out an alarm signal; monitors the MCU voltage, current, temperature and operating status in real time; and automatically switches to a degraded operating mode or triggers power-off protection when frequent restarts or illegal interruptions occur.

[0014] Preferably, the data acquisition module includes: collecting the output voltage and current signals of the wind turbine, and inputting them into the MCU through the voltage divider circuit and the Hall current sensor; collecting the ambient wind speed through the wind speed sensor, and outputting a PWM or analog voltage signal; communicating with the control processing module to obtain the MCU abnormal event log, including watchdog restart, interrupt trigger or power-off records; recording data at set time intervals and transmitting it to the feature extraction module.

[0015] Preferably, the feature extraction module includes: analyzing the rate of change of the wind power output voltage to obtain the wind power instantaneous voltage anomaly index, and the acquisition method is:

[0016] The voltage time series data sequence is collected from the output terminal of the wind turbine: V={V1, V2,…, V n}; Perform empirical mode decomposition on the time series voltage signal V to obtain several intrinsic mode functions and a residual term, which is expressed as: Among them, the IMF i (t) represents the i-th mode function, corresponding to different frequency components, R k (t) is the residual, which represents the low-frequency trend of the signal, k is the number of decomposed voltage signals; V(t) is the time series of the original voltage output of wind power;

[0017] Select the first m high-frequency modes for analysis to form a high-frequency combined signal V HF (t), the expression is: In V HF (t) Set the sliding window w to calculate the continuous difference sequence ΔV t , the expression is: ΔV t =V HF (t)-V HF (t-1);

[0018] Calculate the average value μ for the w change rate values ​​before the current moment t and standard deviation σ t , using the current rate of change ΔV t The wind power generation instantaneous voltage anomaly index is calculated by combining the statistics in the sliding window. The expression is: Where ∈ is a constant.

[0019] Preferably, after analyzing the abnormal trigger frequency of the control processing module, the control abnormal trigger frequency interference index is obtained, and the acquisition method is:

[0020] Set the statistical time window length T, and record the number of abnormal events f that occur in the system within each window period t Based on the long-term health status log of the system, the average number of abnormal events per unit time during normal operation is counted as λ baseline , which is the value of the total number of abnormalities divided by the total number of observation time periods; according to the Poisson distribution model, the expectation of the occurrence of events per unit time is Where z is the number of events actually observed, λ is the average event rate, and e is a natural constant. t With the reference frequency λ baseline The degree of deviation between them is used as the control abnormal trigger frequency interference index CFI, which is expressed as:

[0021] Preferably, the calculated wind power generation instantaneous voltage anomaly index and the control abnormality trigger frequency interference index are fused to form a two-dimensional comprehensive feature vector for characterizing the current electromagnetic disturbance state of the wind power supply system; the comprehensive feature vector is used as input and input into a polynomial regression model, and the model outputs the electromagnetic interference risk score value of the current wind power supply system. The polynomial regression model uses each group of feature vectors in the training set as input and the corresponding actual electromagnetic interference score value label as the prediction target, and learns by minimizing the sum of prediction errors as the training target. The training process continues until the sum of prediction errors converges or is lower than the preset tolerance threshold, and the training is terminated.

[0022] Preferably, compare the electromagnetic interference risk score value R of the current wind energy power supply system with the set two-level gradient standard thresholds, where the gradient standard thresholds include: the first standard threshold T1 and the second standard threshold T2, and T1 < T2 is satisfied; if R > T2, it indicates that the current electromagnetic interference risk level is severe, and immediately enter the high-risk control mode, including disconnecting the wind energy input, switching to the standby power supply, and the controller entering the frequency reduction operation or the protection mode; if T1 ≤ R ≤ T2, it indicates that the current electromagnetic interference risk level is medium, and execute the risk buffering strategy, including starting the filtering compensation, adjusting the sensor sensitivity, and issuing a warning; if R < T1, it indicates that the current electromagnetic interference risk level is slight, and execute the normal operation mode.

[0023] Preferably, the data feedback module includes: after the strategy is executed, respectively obtain the voltage fluctuation variance and the control anomaly frequency before and after, and calculate the strategy execution effect score, and the expression is: Where: represents the voltage fluctuation variance before the interference strategy is executed; represents the voltage fluctuation variance after the interference strategy is executed; represents the number of control anomaly triggers per unit time before execution; represents the number of control anomaly triggers per unit time after execution; α and β represent the proportional weight coefficients, and α + β = 1; E s represents the strategy execution effect evaluation value; the score value E s is fed back to the control processing module.

[0024] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0025] 1. The present invention combines the wind energy self-power supply technology with the multi-dimensional electromagnetic interference evaluation mechanism, and constructs a complete closed-loop architecture including wind energy collection and processing, feature extraction, risk assessment, intelligent regulation and data feedback. By introducing the instantaneous voltage anomaly index of wind power generation and the interference index of control anomaly trigger frequency, and using the polynomial regression model for electromagnetic interference risk scoring, it effectively realizes the perception, judgment and hierarchical response of the system to external high-frequency interference, and overcomes the problems of false alarms, missed alarms and system paralysis caused by electromagnetic interference in the existing system.

[0026] 2. The present invention quantitatively evaluates the strategy execution effect through the data feedback module, realizes the feedback of the strategy effect score based on voltage fluctuation and control anomaly, and effectively supports the dynamic optimization of the system strategy and the adaptive iteration of the model. The overall system has high autonomy, environmental adaptability and intelligent regulation ability, and is especially suitable for application scenarios with high requirements for reliability and anti-interference such as remote areas and unattended in the wild, which improves the stability, safety and energy independence of the detection system. Description of the Drawings

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0028] Figure 1 This is a mind map of the system modules of the present invention. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] For examples, see Figure 1 As shown, the acoustic and optical integrated wind energy automatic detection system described in this embodiment includes a wind energy collection and processing module, a control processing module, a data acquisition module, a feature extraction module, a wind energy interference assessment module, a wind energy risk control module, and a data feedback module;

[0031] A wind energy collection and processing module is used to collect wind energy through a vertical axis or horizontal axis wind turbine and output electrical energy, and to store the electrical energy after rectifying and stabilizing the voltage;

[0032] The control processing module is used to control the operation of the sensor module and the sound and light alarm module, and monitor the system operation status;

[0033] The data acquisition module is used to collect the output voltage, current, ambient wind speed of the wind turbine and abnormal status information of the control processing module;

[0034] a feature extraction module, configured to extract the output voltage change rate and the abnormal trigger frequency of the control processing module from the data acquired by the data acquisition module;

[0035] A wind power interference assessment module is used to determine the current electromagnetic interference risk level of the wind power supply system after analyzing the output voltage change rate and abnormal trigger frequency;

[0036] a wind energy risk control module, configured to execute different interference suppression strategies according to the risk level output by the wind energy interference assessment module;

[0037] The data feedback module is used to evaluate the execution effect of the interference suppression strategy and send it back to the control processing module for subsequent strategy optimization.

[0038] This embodiment provides a wind energy collection and processing module, which mainly includes three parts: a wind power generation subsystem, an electric energy rectification and voltage stabilization subsystem, and an energy storage subsystem. It has a compact structure and is suitable for independent deployment scenarios in the wild or remote areas.

[0039] Wind power subsystem, this subsystem uses a small wind turbine, which can be a vertical axis wind turbine (such as Savonius type) or a horizontal axis wind turbine (such as a three-blade propeller type), and the configuration is selected according to the deployment environment: the wind turbine is fixedly installed on the top of the system or the top of the bracket and has a wind pressure resistance structure; the generator type is a permanent magnet DC motor or a three-phase brushless AC motor, which has a low starting torque characteristic and is suitable for operation in low wind speed areas; the generator output end is connected to the power processing module through a three-core shielded cable to reduce electromagnetic interference during transmission.

[0040] The power rectification and voltage stabilization subsystem, the generator output is a non-constant voltage, which needs to be rectified and stabilized before it can be used to power the system: Rectification part: For three-phase AC output, a full-wave bridge rectifier circuit is used; fast recovery diodes are used to improve high-frequency anti-interference performance; parallel electrolytic capacitor filtering is used to eliminate peak pulsations in the rectified waveform.

[0041] Voltage stabilization part: The rectifier output voltage is connected to a DC-DC voltage stabilization module (such as a step-down BUCK circuit); the output stable DC voltage is 5V or 12V, which can be flexibly selected according to the detection system configuration; TVS diodes and voltage clamps are introduced to prevent overvoltage damage caused by sudden changes in wind speed.

[0042] Output control: Configure MOS switches and overcurrent detection modules to automatically disconnect when the output exceeds the set upper limit; set soft start logic to avoid impact on downstream devices at the moment of power-on.

[0043] The energy storage subsystem stores rectified and stabilized electrical energy in an energy storage module to ensure continuous power supply to the system: a lithium battery pack (such as three 18650 cells in series) or a supercapacitor module is preferred, connected through a battery management system (BMS); the energy storage unit and the regulated output form a bidirectional energy flow structure, and when the wind speed is insufficient, the energy storage device supplies power; the energy storage module integrates a temperature sensor and an overcharge and over-discharge protection unit to ensure safe operation of the battery; a status indicator module (such as an LED or data upload) is set to monitor the current power and power supply status.

[0044] When the wind speed meets the power generation conditions, the wind drives the wind turbine blades to rotate, driving the generator to produce a variable voltage output, which is converted into pulsating DC through the rectifier bridge. After smoothing through the filtering and voltage stabilization circuit, it charges the energy storage module and provides a stable operating voltage for the system.

[0045] Through the above-mentioned structural design, the wind energy collection and processing module of this embodiment realizes: stable conversion and storage of intermittent wind energy; voltage stabilization protection under high-frequency interference; ensuring continuous operation of the system without external power supply; and is suitable for self-powered detection applications in various environments (such as uninhabited areas, plateaus, forests, etc.).

[0046] This embodiment provides a control processing module, which mainly realizes the operation management, data processing and system operation status monitoring of the sensor module and the sound and light alarm module, and has the capabilities of automatic control, abnormality identification and strategy execution.

[0047] The control processing module includes but is not limited to: a main control chip (MCU), which can adopt a low-power, high-performance microcontroller (such as STM32, ESP32, ATmega328, etc.); providing multiple GPIO interfaces for connecting signal ports such as sensor input, alarm output, and power status detection;

[0048] Integrated ADC module for reading analog signals (such as gas concentration, voltage, current); supports PWM output, serial communication, I 2 C and SPI bus protocols to achieve multi-module communication control.

[0049] The operation status monitoring unit collects system voltage, current, temperature, battery power, interference detection signal and other operating parameters in real time; a threshold judgment mechanism can be set to immediately trigger control instructions or alarms once the range is exceeded.

[0050] The timing and logic control circuit controls the sensor sampling period, alarm duration, and reset logic according to user settings or preset logic; it supports soft timing, delayed start, and power consumption optimization modes.

[0051] After the system is powered on, the MCU initializes the configuration of peripherals, including various sensor modules, power detection modules, alarm ports, etc., and enters the standby monitoring state.

[0052] The control processing module periodically activates the sensor modules (such as smoke sensors, gas sensors, temperature and humidity modules, infrared pyroelectric sensors, etc.) to collect environmental data; if it is a digital signal, the status is directly read; if it is an analog signal, the voltage is read through the ADC channel and normalized.

[0053] The collected data is analyzed in real time and compared with the preset threshold. When the detection value exceeds the set threshold, abnormal state recognition is triggered. At the same time, the system operating voltage and current are checked to see if they are within the normal power supply range.

[0054] Once an abnormal state is identified, the MCU controls the activation of the sound and light alarm module; the sound and light alarm module consists of a high-brightness LED (such as a red warning light) and a buzzer, and the operating current is controlled by the MCU PWM. The alarm duration is configurable (such as 10 seconds / 30 seconds / continuous); if the detection status returns to normal, the system automatically turns off the alarm module and records the event information.

[0055] Real-time monitoring of the control system's operating status, including MCU temperature, voltage, and power supply interference (combined with EMI monitoring results). If any abnormal operation occurs (such as frequent resets or excessive voltage fluctuations), the system enters "degraded operation mode" or "protection mode." In high-risk environments, the system can trigger a wind power input disconnect command to avoid damage to the system.

[0056] The control processing module is connected to the sensor module and the sound and light alarm module using modular pins; an optional wireless communication unit (such as LoRa, NB-IoT, Wi-Fi module) is used for remote data upload or remote control; and the system bus is shared with the wind energy interference assessment module and the risk control module, or data exchange and control signal transmission are completed through serial port communication.

[0057] It realizes fully automatic operation control of the detection system, and has the characteristics of intelligent judgment, timely response, stability and reliability. The module has a compact structure and low power consumption, and is suitable for intermittent energy scenarios such as wind power supply. It has self-status diagnosis and system protection mechanisms, which improves the robustness and stability of the system in extreme environments.

[0058] This embodiment provides a data acquisition module for real-time collection of various operating parameters of the wind power generation system and control system, including the output voltage, current, ambient wind speed of the wind turbine, and abnormal status information of the control processing module, to ensure that the system has a comprehensive data foundation to support risk assessment and intelligent control functions.

[0059] The data acquisition module includes but is not limited to the following subunits:

[0060] The voltage acquisition unit is connected to the output of the wind turbine and converts the high-voltage signal into a low voltage (such as 0-3.3V) suitable for microcontroller processing through a voltage divider circuit. It is input to the main control chip through the ADC (analog-to-digital converter) channel to achieve real-time sampling of the wind output voltage. It has filtering and overvoltage protection functions to prevent instantaneous voltage fluctuations from interfering with the sampling accuracy.

[0061] The current acquisition unit uses a Hall current sensor or a low-impedance sampling resistor + operational amplifier circuit; it collects current changes at the wind turbine load end to reflect the system power consumption and power generation status; and can be combined with voltage data to analyze power change trends.

[0062] The wind speed acquisition unit integrates a wind speed sensor, which can be a mechanical cup type or a non-contact thermal anemometer. It represents wind speed through PWM frequency, pulse count or analog voltage output. It is connected to the input channel of the control processing module and is used to evaluate the causes of wind power generation changes and determine extreme weather conditions.

[0063] The system status information acquisition unit is connected to the control processing module and is used to collect the MCU operation status, including: abnormal interrupt flag; watchdog restart count; power failure record; it can also be connected to the system log unit to record the system's historical operation status.

[0064] When the wind turbine is running, the data acquisition module starts real-time sampling; the voltage and current signals are sampled once every preset period (such as 500ms) and stored in the cache area; the wind speed data is read through interruption mode to improve the response speed; the control processing module periodically reads the system status information and associates it with the electrical data; all collected data is packaged and uploaded to the wind energy interference assessment module for risk analysis.

[0065] The data acquisition module is connected to the 2 C, SPI, UART or analog signal channels communicate with the control processing module; all sensors and signal collectors are designed with electromagnetic compatibility to prevent external interference from affecting data accuracy; optional cache chips or ring cache mechanisms can be equipped to improve the system's anti-fluctuation ability and historical record availability.

[0066] This embodiment provides a feature extraction module, which is used to deeply process the original operating parameters provided by the data acquisition module to extract representative key characteristic parameters, including the rate of change of the wind power output voltage and the abnormal trigger frequency of the control processing module, for subsequent electromagnetic interference risk level assessment and regulatory response.

[0067] The feature extraction module includes the following units:

[0068] Voltage change rate calculation unit (ΔV / Δt calculator): connected to the voltage data stream obtained by the data acquisition module; analyzes the rate of change of the wind power output voltage and obtains the wind power instantaneous voltage anomaly index. The acquisition method is:

[0069] The voltage time series data sequence is collected from the output terminal of the wind turbine: V={V1, V2,…, V n}; Perform empirical mode decomposition (EMD) on the time series voltage signal V to obtain several intrinsic mode functions (IMFs) and a residual term, which is expressed as: Among them, the IMF i (t) represents the i-th mode function, corresponding to different frequency components, R k(t) is the residual, which represents the low-frequency trend of the signal, k is the number of decomposed voltage signals; V(t) is the time series of the original voltage output of wind power;

[0070] Select the first m high-frequency modes (usually m = 2 to 3) for analysis to form a high-frequency combined signal V HF (t), the expression is: This signal mainly reflects voltage mutations, spikes and unstable disturbances.

[0071] In V HF (t) Set a sliding window (e.g., size w = 10) to calculate the continuous difference (rate of change) sequence ΔV t , the expression is: ΔV t =V HF (t)-V HF (t-1);

[0072] Calculate the average value μ for the w change rate values ​​before the current moment t and standard deviation σ t , using the current rate of change ΔV t The wind power generation instantaneous voltage anomaly index is calculated by combining the statistics in the sliding window. The expression is: Where, ∈ is a small constant to avoid division by 0; EVI is the wind power generation instantaneous voltage anomaly index, and a larger value indicates a more serious anomaly.

[0073] The Abnormal Trigger Frequency Identification Unit communicates with the control processing module to acquire system abnormal event markers in real time, including watchdog reset records, program abnormal interrupts, and manual restart times. It counts abnormal events and calculates their frequency (number of times per unit time) to generate abnormal trigger frequency parameters. Control abnormal events include, but are not limited to, MCU watchdog resets, illegal interrupt triggers, program deadloop detection, peripheral abnormal disconnections, and manual or automatic system restart logs.

[0074] After analyzing the abnormal trigger frequency of the control processing module, the control abnormal trigger frequency interference index is obtained. The acquisition method is:

[0075] Set the statistical time window length T (e.g. 5 minutes, 10 minutes) to observe the number of abnormal events per unit time. In each window period, record the number of abnormal events f that occurred in the system. t Based on the long-term health status log of the system, the average number of abnormal events per unit time during normal operation is counted as λ baseline , which is the value of the total number of abnormalities divided by the total number of observation time periods; according to the Poisson distribution model, the expectation of the occurrence of events per unit time is Where z is the number of events actually observed, λ is the average event rate (expected), and e is a natural constant (approximately equal to 2.718);

[0076] In this scenario, P(k) is not used as the final result, but the actual frequency f t With the reference frequency λ baseline The degree of deviation between them is used as the control abnormal trigger frequency interference index CFI, which is expressed as:

[0077] CFI<1: The control system abnormality is within the normal range; 1≤CFI<2: There are signs of mild interference, and an early warning is recommended; CFI≥≥2: Moderate to severe interference, and it is recommended to implement a protection or switching strategy; CFI≥3: Severe interference, which may trigger emergency measures (such as system frequency reduction, reset, power off, etc.).

[0078] In this method, the calculated wind power generation instantaneous voltage anomaly index and the control abnormality trigger frequency interference index are firstly integrated to form a two-dimensional comprehensive feature vector, which is used to characterize the current electromagnetic disturbance state of the wind power supply system.

[0079] The comprehensive feature vector is used as input to a polynomial regression model, and the model outputs the electromagnetic interference risk score of the current wind power supply system, which is used as the basis for determining the system risk level.

[0080] The polynomial regression model uses each set of feature vectors in the training set as input and the corresponding actual electromagnetic interference score labels as prediction targets. Learning is performed by minimizing the sum of prediction errors (e.g., mean squared error (MSE)) as the training objective. Training continues until the sum of the prediction errors converges or falls below a preset tolerance threshold, at which point training terminates and a stable prediction model is formed.

[0081] After the prediction is completed, the electromagnetic interference risk score R of the current wind power supply system is compared with the set two-level gradient standard thresholds. The gradient standard thresholds include:

[0082] The first standard threshold T1 indicates the starting point of slight interference;

[0083] The second standard threshold T2 represents the severe interference threshold and satisfies T1 <T2。

[0084] Based on the comparison results, the following response strategies are executed:

[0085] If R>T2, it means that the current electromagnetic interference risk level is serious, and the system immediately enters high-risk control mode, including: disconnecting wind input or limiting its power; starting the backup energy storage power supply (such as supercapacitor); starting the electromagnetic shielding device or overvoltage protection hardware; reducing the frequency of operation or suspending some non-core detection tasks to reduce system sensitivity.

[0086] If T1 ≤ R ≤ T2, it indicates that the current electromagnetic interference risk level is medium. Execute the risk buffer strategy, including: enabling medium-level filtering and signal protection; enabling the logging and alarm module to remind the maintenance personnel; judging whether the interference continues to escalate based on the historical trend and predicting the interference evolution trend.

[0087] If R < T1, it indicates that the current electromagnetic interference risk level is slight. Execute the normal operation mode, including: normal operation of the acquisition and detection functions; maintaining the input of wind power generation, and the system is in the low-power optimization state; continuing to collect data as subsequent training samples to optimize the self-learning ability of the model.

[0088] This embodiment provides a data feedback module, which is used to perform real-time quantitative evaluation on the execution results of various interference suppression strategies (such as voltage filtering, power supply switching, operation frequency reduction, etc.) during the operation of the wind power supply system, and feedback the evaluation results to the control processing module as the basis for subsequent strategy parameter optimization and model correction.

[0089] The data feedback module mainly realizes the following functions:

[0090] Suppression effect evaluation: After the strategy is executed, compare the changes in key operation parameters before and after the strategy; Feedback index calculation: Output a standardized score value to quantify the effectiveness of the interference strategy; Historical data upload: Write the relevant feedback data into the system log and use it for model training; Control optimization feedback: Send the feedback result to the control processing module for subsequent dynamic adjustment of the strategy.

[0091] Set the interference suppression strategy effect score E s , which consists of two main dimensions: the voltage stability improvement rate and the control anomaly frequency reduction rate. The comprehensive scoring formula is: Where: represents the voltage fluctuation variance before the execution of the interference strategy; represents the voltage fluctuation variance after the execution of the interference strategy; represents the number of control anomaly triggers per unit time before execution; represents the number of control anomaly triggers per unit time after execution; α, β represent the proportional weight coefficients (dimensionless), satisfying α + β = 1. E s represents the strategy execution effect evaluation value (score value) [0, 1], and the closer it is to 1, the better the effect.

[0092] The two proportions represent the improvement rate, and the results are both normalized to the [0, 1] interval; α, β can be adjusted according to the system design preference (for example, if more attention is paid to control anomalies, take α = 0.4, β = 0.6). The comparison of each score dimension is a ratio of the same unit, and the final result is dimensionless.

[0093] After the system completes each strategy adjustment, the data feedback module will s The data is returned to the control processing module for: evaluating the adaptability of the current strategy; dynamically adjusting the interference response parameters; updating the wind energy interference assessment model training data; and automatically switching to the alternative strategy set when the continuous score is low.

[0094] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0095] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0096] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. The acoustic and optical integrated wind energy automatic detection system is characterized by: It includes wind energy collection and processing module, control processing module, data acquisition module, feature extraction module, wind energy interference assessment module, wind energy risk control module and data feedback module; A wind energy collection and processing module is used to collect wind energy through a vertical axis or horizontal axis wind turbine and output electrical energy, and to store the electrical energy after rectifying and stabilizing the voltage; The control processing module is used to control the operation of the sensor module and the sound and light alarm module, and monitor the system operation status; The data acquisition module is used to collect the output voltage, current, ambient wind speed of the wind turbine and abnormal status information of the control processing module; a feature extraction module, configured to extract the output voltage change rate and the abnormal trigger frequency of the control processing module from the data acquired by the data acquisition module; A wind power interference assessment module is used to determine the current electromagnetic interference risk level of the wind power supply system after analyzing the output voltage change rate and abnormal trigger frequency; a wind energy risk control module, configured to execute different interference suppression strategies according to the risk level output by the wind energy interference assessment module; The data feedback module is used to evaluate the execution effect of the interference suppression strategy and send it back to the control processing module for subsequent strategy optimization.

2. The acoustic and optical integrated wind energy automatic detection system according to claim 1 is characterized in that: The wind energy collection and processing module includes: collecting wind energy and outputting electrical energy through a small vertical or horizontal axis wind turbine, which uses a permanent magnet DC motor or a three-phase brushless AC motor; rectifying the AC or DC power output by the generator using a full-wave bridge rectifier circuit; suppressing high-frequency interference through a filter capacitor and a fast recovery diode; inputting the rectified electrical energy into a DC-DC voltage regulator module for voltage stabilization; storing the stabilized electrical energy in a lithium battery pack or a supercapacitor, and managing charging and discharging through a BMS system.

3. The acoustic and optical integrated wind energy automatic detection system according to claim 1 is characterized in that: The control processing module includes: a main control chip controls the sensor module to perform regular sampling and determines whether there is an abnormality based on the sampling results; when the sampling data exceeds the set threshold, controls the activation of the sound and light alarm module to send an alarm signal; monitors the MCU voltage, current, temperature and operating status in real time; and automatically switches to a degraded operating mode or triggers power-off protection when frequent restarts or illegal interruptions occur.

4. The acoustic and optical integrated wind energy automatic detection system according to claim 1 is characterized in that: The data acquisition module includes: collecting the output voltage and current signals of the wind turbine, and inputting them into the MCU through the voltage divider circuit and the Hall current sensor; collecting the ambient wind speed through the wind speed sensor, and outputting a PWM or analog voltage signal; communicating with the control processing module to obtain the MCU abnormal event log, including watchdog restart, interrupt trigger or power failure records; recording data at set time intervals and transmitting it to the feature extraction module.

5. The acoustic and optical integrated wind energy automatic detection system according to claim 1 is characterized in that: The feature extraction module includes: analyzing the rate of change of the wind power output voltage to obtain the wind power instantaneous voltage anomaly index, and the acquisition method is: The voltage time series data sequence is collected from the output terminal of the wind turbine: V={V1, V2,…, V n }; Perform empirical mode decomposition on the time series voltage signal V to obtain several intrinsic mode functions and a residual term, which is expressed as: Among them, the IMF i (t) represents the i-th mode function, corresponding to different frequency components, R k (t) is the residual, which represents the low-frequency trend of the signal, k is the number of decomposed voltage signals; V(t) is the time series of the original voltage output of wind power; Select the first m high-frequency modes for analysis to form a high-frequency combined signal V HF (t), the expression is: In V HF (t) Set the sliding window w to calculate the continuous difference sequence ΔV t , the expression is: ΔV t =V HF (t)-V HF (t-1); Calculate the average value μ for the w change rate values ​​before the current moment t and standard deviation σ t , using the current rate of change ΔV t The wind power generation instantaneous voltage anomaly index is calculated by combining the statistics in the sliding window. The expression is: Where ∈ is a constant.

6. The acoustic and optical integrated wind energy automatic detection system according to claim 5 is characterized in that: After analyzing the abnormal trigger frequency of the control processing module, the control abnormal trigger frequency interference index is obtained. The acquisition method is: Set the statistical time window length T, and record the number of abnormal events f that occur in the system within each window period t Based on the long-term health status log of the system, the average number of abnormal events per unit time during normal operation is counted as λ baseline , that is, the value of the total number of abnormalities divided by the total number of observation time periods; According to the Poisson distribution model, the expectation of an event occurring per unit time is Where z is the number of events actually observed, λ is the average event rate, and e is a natural constant. t With the reference frequency λ baseline The degree of deviation between them is used as the control abnormal trigger frequency interference index CFI, which is expressed as:

7. The acoustic and optical integrated wind energy automatic detection system according to claim 6 is characterized in that: Fuse the calculated instantaneous wind power generation voltage anomaly index and the control anomaly trigger frequency interference index to form a two-dimensional comprehensive feature vector for characterizing the current electromagnetic disturbance state of the wind energy power supply system; take the comprehensive feature vector as the input and input it into a polynomial regression model. The model outputs the electromagnetic interference risk score value of the current wind energy power supply system. The polynomial regression model takes each group of feature vectors in the training set as the input, takes its corresponding actual electromagnetic interference score value label as the prediction target, and learns with minimizing the total prediction error as the training target. The training process continues until the sum of the prediction errors converges or is lower than the preset tolerance threshold, and then the training is terminated.

8. The acoustic and optical integrated wind energy automatic detection system according to claim 7 is characterized in that: Compare the electromagnetic interference risk score value R of the current wind energy power supply system with the set two-level gradient standard threshold. The gradient standard threshold includes: the first standard threshold T1 and the second standard threshold T2, and T1 < T2 is satisfied; if R > T2, it means that the current electromagnetic interference risk level is serious, and immediately enter the high-risk control mode, including disconnecting the wind energy input, switching to the standby power supply, and the controller entering the frequency reduction operation or protection mode; if T1 ≤ R ≤ T2, it means that the current electromagnetic interference risk level is medium, and execute the risk buffering strategy, including starting filtering compensation, adjusting the sensor sensitivity, and issuing a warning; if R < T1, it means that the current electromagnetic interference risk level is slight, and execute the normal operation mode.

9. The acoustic and optical integrated wind energy fully automatic detection system according to claim 8, characterized in that: The data feedback module includes: after the strategy is executed, obtaining the voltage fluctuation variance and the control abnormality frequency before and after, and calculating the strategy execution effect score, the expression is: in: represents the voltage fluctuation variance before the interference strategy is executed; represents the voltage fluctuation variance after the interference strategy is executed; Indicates the number of control exception triggers per unit time before execution; Indicates the number of control exception triggers per unit time after execution; α and β represent proportional weight coefficients, satisfying α+β=1; E s Indicates the evaluation value of the strategy execution effect; the score value E s Feedback to the control processing module.

Images