Distributed wind-solar complementary control system, method and device and storage medium

Abstract

The invention discloses a distributed wind-solar complementary control system, method and device and a storage medium, and relates to the technical field of energy control. The data acquisition module adopts a sensor to acquire environment and operation data of wind power and photoelectric equipment per minute, and has a self-calibration function; the data analysis module predicts the power generation potential of wind energy and solar energy and evaluates the operation state of the equipment in combination with multiple types of information; the power distribution module dynamically distributes power through an algorithm according to an analysis result and a load demand; the communication module combines a wireless mode and a wired mode, and has a retransmission mechanism to guarantee communication stability and safety; the energy storage management module monitors parameters of the energy storage device, adopts an intelligent charging and discharging strategy, controls the charging and discharging process and ensures safe and efficient operation of the energy storage device, high-precision data collection guarantees accurate power generation prediction, power is reasonably distributed, the energy utilization rate is improved, faults are diagnosed in time, the downtime is shortened, dispatching is optimized, and the power generation efficiency is improved. Energy storage management is intelligent, and efficient utilization of renewable energy sources is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of energy control systems, and in particular to a distributed wind-solar hybrid control system, method, device and storage medium. Background Art

[0002] With the continuous growth of global energy demand and the increasing attention to environmental protection, the development and utilization of renewable energy has become a research hotspot in the world's energy field. Wind energy and solar energy, as abundant and clean renewable energy sources, have broad development prospects. Distributed wind-solar hybrid power generation system combines wind power generation and solar power generation, giving full play to the complementarity of the two in time and space, and can provide electricity more stably and efficiently. It is one of the effective ways to solve energy problems. However, the existing distributed wind-solar hybrid power generation system faces many challenges in practical applications. First, wind and solar energy are intermittent and uncertain, and their power generation will fluctuate violently with changes in weather, seasons and other factors. For example, at night or on cloudy days, solar power generation almost stops; and during periods of low wind, the output power of wind power generation will also be greatly reduced. This volatility makes it difficult for the power generation system to stably meet the load demand, which has a great impact on the reliability of power supply. Secondly, the current wind-solar hybrid control system has the problems of low accuracy and insufficient real-time performance in data acquisition. The measurement error of the sensor is large, and it is impossible to accurately obtain the operating status and environmental parameters of the wind turbine and solar panel, resulting in inaccurate subsequent power generation prediction and power allocation. Moreover, the frequency of data collection is low, which cannot reflect the dynamic changes of the power generation system in time, making it difficult for the control system to make timely adjustments. Furthermore, the fault detection and diagnosis capabilities of power generation equipment are weak. Since wind and solar power generation equipment are usually installed in remote areas or complex environments, it is difficult to monitor the operating status of the equipment in real time. When a device fails, it cannot be discovered and located in time, resulting in untimely maintenance, which increases the downtime and maintenance cost of the power generation system. In addition, the existing wind-solar complementary control system lacks an effective optimization scheduling strategy. The layout and operating parameters of power generation equipment are often fixed and cannot be dynamically adjusted according to the actual power generation situation and load demand. This makes the power generation efficiency of the power generation system low and cannot give full play to the potential of wind and solar energy. In addition, there are also some problems in the application of communication technology in wind-solar complementary control systems. The stability and security of data transmission are not guaranteed, and it is easy to be interfered by the outside world and attacked by the network, resulting in data loss or leakage, affecting the normal operation of the control system. In summary, the development of an efficient and intelligent distributed wind-solar complementary control system has important practical significance. The system can accurately collect data, precisely predict power generation potential, reasonably allocate power, detect faults in a timely manner, optimize the scheduling of power generation equipment, and at the same time ensure the stability and security of communications, thereby improving the power generation efficiency and reliability of distributed wind and solar complementary power generation systems and promoting the large-scale application of renewable energy. Summary of the invention

[0003] The present invention proposes a distributed wind-solar hybrid control system, method, device and storage medium to solve the problems mentioned in the above-mentioned prior art.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a distributed wind-solar hybrid control system, method, device and storage medium, including: Data acquisition module: Through wind speed sensor, light intensity sensor, wind direction sensor, temperature sensor, real-time data collection of wind speed v, light intensity I, wind direction of wind turbines and solar panels , temperature T data, and the output power of the wind turbine generator , speed n, output voltage of solar panel , Current Running data, the sensor has a self-calibration function, the calibration formula is ,in is the calibrated data, is the original acquisition data, C is the calibration coefficient; Data analysis module: Analyze and process the data from the data acquisition module, combine historical data and weather forecast information, and predict the potential of wind and solar power generation; wind power generation potential , according to the formula Calculate, where is the air density, A is the wind turbine swept area, v is the wind speed, is the wind energy utilization factor; solar power generation potential , according to the formula Calculate, where S is the solar panel area, I is the light intensity, is the conversion efficiency of solar panels; using time series analysis and machine learning algorithms to evaluate the operating status and efficiency of power generation equipment, by calculating the equipment efficiency coefficient ,in is the actual output power, is the power generation potential; Power distribution module: Based on the results of the data analysis module and combined with load requirements , dynamically distributed through the output power of wind turbines and solar panels. Prioritize meeting local load demand, and the remaining power is stored in energy storage devices or connected to the grid; the power generation potential, load demand and energy storage device power As input variables, the power allocation ratio is determined through the rule base and reasoning mechanism and ; Wind turbine allocation ratio, Solar panels distribution ratio to meet ,and , ,in and are the power allocated to wind turbines and solar panels, and They are the actual output power of wind turbines and solar panels respectively; Communication module: realizes communication between system modules and with the remote monitoring center, adopts a combination of wireless communication technology and wired communication technology, and uses the AES-256 encryption algorithm to encrypt the transmitted data. The encryption strength is based on the formula Calculation, where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length, and S is the data sensitivity; it also has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. The retransmission interval is based on the formula Determine, where T is the retransmission interval, is the initial retransmission interval, n is the number of retransmissions; Energy storage management module: manages the energy storage device and monitors the power of the energy storage device , charge and discharge status, temperature Parameters. According to the instructions of the power distribution module, the charging and discharging process of the energy storage device is controlled, and an intelligent charging and discharging strategy is adopted; charging current According to the formula Calculate, where is the charging factor, is the maximum capacity of the energy storage device, is the charging time; the discharge current , according to the formula Calculate, where is the discharge coefficient, is the discharge time.

[0005] Furthermore, the following modules are also included: Fault diagnosis module: real-time monitoring of the operating status of power generation equipment and system modules, using fault feature extraction and pattern recognition algorithms, for wind turbines, by monitoring the speed n, output power Parameters, calculate fault characteristic indicators ,in It is a characteristic indicator of wind turbine fault. is the normal operating speed of the wind turbine, is the normal operating output power of the wind turbine; for solar panels, calculate the fault characteristic index ,in It is a characteristic indicator of solar panel failure. is the normal operating output voltage of the solar panel, is the normal operating output current of the solar panel; when or When the set threshold is exceeded, the device is judged to be faulty.

[0006] Optimization and dispatching module: Based on long-term operation data and power generation forecast results, the layout and operation parameters of wind turbines and solar panels are optimized and adjusted; genetic algorithms and simulated annealing algorithms are used to optimize the system power generation efficiency. is the objective function, where is the system power generation efficiency, is the actual total output power of the system, The total power generation potential of the system; by continuously optimizing the installation angle of wind turbines , Blade length , the tilt angle of the solar panel parameter.

[0007] Furthermore, the sensor in the data acquisition module has a self-calibration function, which is automatically calibrated once a month to ensure the accuracy of the collected data, and the measurement accuracy of the sensor is within ±0.5%. The calibration process uses the least squares method to fit the calibration curve, and the calibration coefficient C is determined based on the calibration curve, so that the calibrated data is closer to the true value.

[0008] Furthermore, the communication module uses the AES-256 encryption algorithm to encrypt the transmitted data. The system has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. , where L is the packet loss rate, is the number of lost packets, is the number of packets sent.

[0009] Furthermore, the energy storage management module adopts an intelligent charging and discharging strategy to , Charge and discharge efficiency and the output power of the power generation equipment, dynamically adjust the charging and discharging current and voltage; when charging, Indicates low battery threshold, fast charging mode is adopted; when Indicates high battery threshold, using trickle charge mode.

[0010] A method for applying the distributed wind-solar hybrid control system comprises the following steps: Data collection steps: Use sensors to collect real-time environmental data from wind turbines and solar panels, including wind speed v, light intensity I, wind direction , temperature T and operating data, including wind turbine output power , speed n, solar panel output voltage , Current , the collected data is calibrated using the formula ,in is the calibrated data, is the original acquisition data, C is the calibration coefficient; Data analysis steps: Analyze and process the collected data, combine historical data and weather forecast information, and use the formula ,in is the potential for wind power generation, is the air density, A is the wind turbine swept area, v is the wind speed, is the wind energy utilization coefficient, and ,in is the solar power generation potential, S is the solar panel area, I is the light intensity, The solar panel conversion efficiency predicts the wind and solar power generation potential by calculating the equipment efficiency coefficient ,in is the equipment efficiency coefficient, is the actual output power, It is the power generation potential and evaluates the operating status of power generation equipment.

[0011] Power allocation steps: Based on analysis results and load requirements , a control algorithm is used to determine the power distribution ratio of wind turbines and solar panels to meet .

[0012] Communication steps: Use a combination of wireless and wired communication to achieve communication between system modules and with the remote monitoring center. Use the AES-256 encryption algorithm to encrypt data, according to the formula Calculate the encryption strength, where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length, S is the data sensitivity, and has a data retransmission mechanism, according to the formula , where T is the retransmission interval, is the initial retransmission interval, n is the number of retransmissions; Energy storage management steps: monitoring the power of energy storage devices , charge and discharge status, temperature Parameters, according to the power allocation instruction, according to the charging and discharging strategy, through the formula Control the charging and discharging process, where is the charging current, is the charging factor; is the maximum capacity of the energy storage device, is the charging time, and ,in is the discharge current, is the discharge coefficient; is the discharge time.

[0013] Furthermore, the method further comprises the following steps: Fault diagnosis steps: Real-time monitoring of equipment and system operating status, using fault feature extraction and pattern recognition algorithms to calculate wind turbine fault feature indicators ,in is the fault characteristic index of the wind turbine, n is the current speed of the wind turbine, is the normal operating speed of the wind turbine, is the current output power of the wind turbine, It is the normal operation output power of the wind turbine and the fault characteristic index of the solar panel ,in It is a characteristic indicator of solar panel failure. is the current output voltage of the solar panel, is the normal operating output voltage of the solar panel, is the current output current of the solar panel, It is the normal output current of the solar panel. When the index exceeds the set threshold, the equipment is judged to be faulty. Optimization scheduling steps: Based on the operation data and power generation forecast results, the system power generation efficiency formula is the objective function, where is the system power generation efficiency, is the actual total output power of the system, The total power generation potential of the system is optimized through algorithms, including the installation angle of wind turbines. , Blade length , solar panel tilt angle .

[0014] Compared with the prior art, the present invention has the following beneficial effects: In terms of data collection, high-precision sensors and self-calibration functions ensure the accuracy and real-time nature of the collected data. Through monthly automatic calibration and strict precision control, the operating status and environmental parameters of wind turbines and solar panels can be accurately obtained, providing a reliable basis for subsequent power generation forecasts and power allocation. In terms of power generation forecasts, advanced formulas and algorithms are used to accurately predict the power generation potential of wind and solar energy, combining historical data and weather forecast information. This enables the system to prepare for power distribution and energy storage management in advance, improving the stability and reliability of power supply. The power distribution module uses a fuzzy control algorithm to dynamically allocate power according to power generation potential, load demand and energy storage device power. Prioritize meeting local load demand and reasonably use the remaining power, improve energy utilization efficiency and reduce energy waste. The fault diagnosis module can monitor the operating status of power generation equipment and system modules in real time, and timely detect and accurately locate faults by calculating fault characteristic indicators. This helps to carry out repairs in a timely manner, reduce the downtime of the power generation system, and reduce maintenance costs. The optimization scheduling module takes the system power generation efficiency as the objective function, and uses genetic algorithms and simulated annealing algorithms to optimize the layout and operating parameters of power generation equipment. It can be dynamically adjusted according to actual conditions, giving full play to the potential of wind and solar energy, and improving the power generation efficiency and stability of the entire system. The communication module adopts a combination of wireless and wired methods, as well as advanced encryption algorithms and data retransmission mechanisms to ensure the stability and security of data transmission. Prevent data from being stolen or tampered with during transmission to ensure the normal operation of the control system. The intelligent charging and discharging strategy of the energy storage management module dynamically adjusts the charging and discharging process according to the status of the energy storage device and the output power of the power generation equipment, which extends the service life of the energy storage device and improves the energy storage efficiency. In summary, the technical solution of this patent can effectively improve the performance and reliability of distributed wind-solar complementary power generation systems, promote the efficient use of renewable energy, and has significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic block diagram of a distributed wind-solar hybrid control system proposed by the present invention; Figure 2 This is a schematic block diagram of a distributed wind-solar complementary control method proposed by the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0018] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0019] Reference Figure 1-2 :A distributed wind-solar hybrid control system, method, device and storage medium, including: Data acquisition module: Through high-precision wind speed sensor (based on Pitot tube principle, measuring range 0-60m / s, accuracy ±0.3m / s), light intensity sensor (using photoelectric effect principle, measuring range 0-200000lux, accuracy ±100lux), wind direction sensor (using three-cup structure, measuring range 0-360°, accuracy ±5°), temperature sensor (based on thermocouple principle, measuring range -40-120°C, accuracy ±0.5°C), etc., real-time collection of wind speed v (unit: m / s), light intensity I (unit: lux), wind direction of the environment where the wind turbine and solar panel are located (unit: °), temperature T (unit: °C) and other data, while collecting the output power of the wind turbine (unit: W), speed n (unit: r / min), output voltage of solar panel (Unit: V), current (Unit: A) and other operating data, the collection frequency is once per minute. To ensure data accuracy, the sensor has a self-calibration function, the calibration formula is: ,in is the calibrated data, is the original collected data, C is the calibration factor, and it is automatically calibrated once a month, and the measurement accuracy of the sensor is within ±0.5%.

[0020] Data analysis module: Analyze and process the data collected by the data acquisition module, combine historical data and weather forecast information, and predict the wind and solar power generation potential in the future. (Unit: W) According to the formula Calculate, where is the air density (unit: kg / m³), A is the wind turbine swept area (unit: m²), v is the wind speed (unit: m / s), is the wind energy utilization factor; solar power generation potential (Unit: W) According to the formula Calculate, where S is the solar panel area (unit: m²), I is the light intensity (unit: lux), is the conversion efficiency of solar panels. Use time series analysis and machine learning algorithms (such as support vector machines) to evaluate the operating status and efficiency of power generation equipment, and calculate the equipment efficiency coefficient ,in is the actual output power (unit: W), is the power generation potential (unit: W).

[0021] Power distribution module: Based on the results of the data analysis module and combined with load requirements (Unit: W), dynamically distribute the output power of wind turbines and solar panels. Prioritize meeting local load demand, and the remaining power can be stored in energy storage devices or connected to the grid. Fuzzy control algorithm is used to calculate the power generation potential, load demand and energy storage device power. (Unit: Ah) is the input variable, and the power allocation ratio is determined by the fuzzy rule base and fuzzy inference engine. (Wind turbine allocation ratio) and (Solar panel allocation ratio), meet ,and , ,in and are the power allocated to the wind turbine and solar panels (unit: W), and They are the actual output power of wind turbines and solar panels (unit: W).

[0022] Communication module: realizes the communication between the modules of the system and with the remote monitoring center, and adopts the combination of wireless communication technology (such as ZigBee, LoRa, etc.) and wired communication technology (such as Ethernet) to ensure the stability and reliability of communication. The communication rate is not less than 1Mbps. The AES-256 encryption algorithm is used to encrypt the transmitted data. The encryption strength is based on the formula Calculation, where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length (unit: bit), and S is the data sensitivity. At the same time, it has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. The retransmission interval is based on the formula Determine, where T is the retransmission interval (unit: s), is the initial retransmission interval (unit: s), and n is the number of retransmissions.

[0023] Energy storage management module: manages energy storage devices (such as lead-acid batteries) and monitors the power of energy storage devices (Unit: Ah), charge and discharge status, temperature (Unit: °C) and other parameters. According to the instructions of the power distribution module, the charging and discharging process of the energy storage device is controlled, and an intelligent charging and discharging strategy is adopted. Charging current (Unit: A) According to the formula Calculate, where is the charging factor, is the maximum capacity of the energy storage device (unit: Ah), is the charging time (unit: h); the discharge current (Unit: A) According to the formula Calculate, where is the discharge coefficient, is the discharge time (unit: h), ensuring the safe and efficient operation of the energy storage device.

[0024] The present invention also includes the following modules: Fault diagnosis module: Real-time monitoring of the operating status of power generation equipment and system modules, using fault feature extraction and pattern recognition algorithms (such as wavelet transform to extract fault features, neural network for pattern recognition). For wind turbines, by monitoring the speed n (unit: r / min), output power (Unit: W) and other parameters to calculate fault characteristic indicators ,in It is a characteristic indicator of wind turbine fault. is the normal operating speed of the wind turbine (unit: r / min), is the normal operating output power of the wind turbine (unit: W); for solar panels, calculate the fault characteristic index ,in It is a characteristic indicator of solar panel failure. is the normal operating output voltage of the solar panel (unit: V), is the normal operating output current of the solar panel (unit: A). or When the set threshold is exceeded, the device is judged to be faulty. Equipment failures and system abnormalities are discovered in a timely manner. When a fault is detected, the fault location is automatically located and an alarm message is sent to the remote monitoring center and on-site maintenance personnel.

[0025] Optimization and dispatching module: Based on long-term operation data and power generation forecast results, the layout and operation parameters of wind turbines and solar panels are optimized and adjusted. Genetic algorithms and simulated annealing algorithms are used to optimize the system power generation efficiency. is the objective function, where is the system power generation efficiency, is the actual total output power of the system (unit: W), is the total power generation potential of the system (unit: W). By continuously iterating and optimizing the installation angle of the wind turbine (Unit: °), Blade length (Unit: m), the tilt angle of the solar panel (Unit: °) and other parameters to improve the power generation efficiency and stability of the entire system.

[0026] In the present invention, the sensor in the data acquisition module has a self-calibration function, which is automatically calibrated once a month to ensure the accuracy of the collected data, and the measurement accuracy of the sensor is within ±0.5%. The calibration process uses the least squares method to fit the calibration curve, and the calibration coefficient C is determined according to the calibration curve, so that the calibrated data is closer to the true value. The sensor in the data acquisition module has a self-calibration function, which is a key feature to ensure data accuracy. During long-term operation, the sensor may cause the measurement accuracy to decrease due to environmental factors (such as temperature and humidity changes), component aging, etc. The self-calibration function enables the sensor to automatically adjust and correct its own measurement accuracy regularly or when necessary, and the calibration process uses the least squares method to fit the calibration curve. The least squares method is a mathematical optimization technique, and its core idea is to find the best function match for the data by minimizing the sum of squares of errors. In sensor calibration, a series of standard sample data with known accurate values ​​are collected (for example, a high-precision anemometer is used as a standard to obtain accurate wind speed values, which are compared with the measured values ​​of the calibrated sensor).

[0027] In the present invention, the communication module uses the AES-256 encryption algorithm to encrypt the transmitted data to prevent the data from being stolen or tampered with during the transmission process; at the same time, the system has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. Data transmission packet loss rate , where L is the packet loss rate, is the number of lost packets, It is the number of data packets sent. When the packet loss rate exceeds 5%, the transmission parameters are automatically adjusted, such as increasing the transmission power.

[0028] In the present invention, the energy storage management module adopts an intelligent charging and discharging strategy, according to the remaining power of the energy storage device. (Unit: Ah), charge and discharge efficiency and the output power of the power generation equipment, dynamically adjusting the charging and discharging current and voltage. (low power threshold, unit: Ah), using fast charging mode, the charging current is large; when (High power threshold, unit: Ah), use trickle charging mode to prevent overcharging. When discharging, reasonably adjust the discharge current according to the load demand and the state of the energy storage device to extend the service life of the energy storage device.

[0029] The present invention also discloses a method for a distributed wind-solar hybrid control system, comprising the following steps: Data collection steps: Use various high-precision sensors to collect real-time environmental data of wind turbines and solar panels (wind speed v (unit: m / s), light intensity I (unit: lux), wind direction (unit: °), temperature T (unit: °C), etc.) and operating data (wind turbine output power (unit: W), speed n (unit: r / min), solar panel output voltage (Unit: V), current (Unit: A), etc.), the acquisition frequency is once per minute. The collected data is calibrated using the formula ,in is the calibrated data, is the original collected data, and C is the calibration coefficient to ensure data accuracy.

[0030] Data analysis steps: Analyze and process the collected data, combine historical data and weather forecast information, and use the formula (in is the wind power generation potential, unit: W; is the air density, unit: kg / m³; A is the wind turbine swept area, unit: m²; v is the wind speed, unit: m / s; is the wind energy utilization coefficient) and (in is the solar power generation potential, unit: W; S is the solar panel area, unit: m²; I is the light intensity, unit: lux; is the solar panel conversion efficiency) predicts wind and solar power generation potential by calculating the equipment efficiency coefficient (in is the equipment efficiency coefficient, is the actual output power, unit: W; is the power generation potential, unit: W) to evaluate the operating status of power generation equipment.

[0031] Power allocation steps: Based on analysis results and load requirements (Unit: W), the fuzzy control algorithm is used to determine the power allocation ratio (wind turbine allocation ratio) and (solar panel allocation ratio) to meet , dynamically allocates the output power of wind turbines and solar panels to prioritize local load needs.

[0032] Communication steps: Use a combination of wireless and wired communication methods to achieve communication between system modules and with the remote monitoring center, with a communication rate of no less than 1Mbps. Use the AES-256 encryption algorithm to encrypt data, according to the formula (Where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length, unit: bit; S is the data sensitivity) Calculate the encryption strength and have a data retransmission mechanism according to the formula (Where T is the retransmission interval, unit: s; is the initial retransmission interval, unit: s; n is the number of retransmissions) to determine the retransmission interval.

[0033] Energy storage management steps: monitoring the power of energy storage devices (Unit: Ah), charge and discharge status, temperature (Unit: °C) and other parameters, according to the power allocation instruction, according to the intelligent charging and discharging strategy, through the formula (in is the charging current, unit: A; is the charging factor; is the maximum capacity of the energy storage device, unit: Ah; is the charging time, unit: h) and (in is the discharge current, unit: A; is the discharge coefficient; is the discharge time, unit: h) to control its charging and discharging process.

[0034] The present invention also includes the following steps: Fault diagnosis steps: Real-time monitoring of equipment and system operating status, using fault feature extraction and pattern recognition algorithms to calculate wind turbine fault feature indicators (in is the fault characteristic index of the wind turbine, n is the current speed of the wind turbine, unit: r / min; is the normal operating speed of the wind turbine, unit: r / min; is the current output power of the wind turbine, unit: W; is the normal operating output power of the wind turbine, unit: W) and the fault characteristic index of the solar panel (in It is a characteristic indicator of solar panel failure. is the current output voltage of the solar panel, unit: V; is the normal operating output voltage of the solar panel, unit: V; is the current output current of the solar panel, unit: A; It is the normal output current of the solar panel, unit: A). When the indicator exceeds the set threshold, the equipment is judged to be faulty, the fault is discovered and located in time, and an alarm is issued.

[0035] Optimization scheduling steps: Based on long-term operation data and power generation forecast results, the system power generation efficiency (in is the system power generation efficiency, is the actual total output power of the system, unit: W; is the total power generation potential of the system, unit: W) as the objective function, and the genetic algorithm and simulated annealing algorithm are used to optimize the layout of power generation equipment (such as the installation angle of wind turbines (Unit: °), Blade length (Unit: m), solar panel tilt angle (Unit: °), etc.) and operating parameters to improve the power generation efficiency and stability of the system.

[0036] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A distributed wind-solar hybrid control system, characterized in that: include: Data acquisition module: Through wind speed sensor, light intensity sensor, wind direction sensor, temperature sensor, real-time data collection of wind speed v, light intensity I, wind direction of wind turbines and solar panels , temperature T data, and the output power of the wind turbine generator , speed n, output voltage of solar panel , Current Running data, the sensor has a self-calibration function, the calibration formula is ,in is the calibrated data, is the original acquisition data, C is the calibration coefficient; Data analysis module: Analyze and process the data from the data acquisition module, combine historical data and weather forecast information, and predict the potential of wind and solar power generation; wind power generation potential , according to the formula Calculate, where is the air density, A is the wind turbine swept area, v is the wind speed, is the wind energy utilization factor; solar power generation potential , according to the formula Calculate, where S is the solar panel area, I is the light intensity, is the conversion efficiency of solar panels; using time series analysis and machine learning algorithms, evaluate the operating status and efficiency of power generation equipment, and calculate the equipment efficiency coefficient formula: ,in is the actual output power, is the power generation potential; Power distribution module: Based on the results of the data analysis module and combined with load requirements , dynamically distribute the output power of wind turbines and solar panels; prioritize local load demand, and store the remaining power in energy storage devices or connect to the grid; the power generation potential, load demand and energy storage device power As input variables, the power allocation ratio is determined through the rule base and reasoning mechanism and ; Wind turbine allocation ratio, Solar panels distribution ratio to meet ,and , ,in and are the power allocated to wind turbines and solar panels, and They are the actual output power of wind turbines and solar panels respectively; Communication module: realizes communication between system modules and with the remote monitoring center, adopts a combination of wireless communication technology and wired communication technology, and uses the AES-256 encryption algorithm to encrypt the transmitted data. The encryption strength is based on the formula Calculation, where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length, and S is the data sensitivity; it also has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. The retransmission interval is based on the formula Determine, where T is the retransmission interval, is the initial retransmission interval, n is the number of retransmissions; Energy storage management module: manages and monitors the power of energy storage devices , charge and discharge status, temperature Parameters; according to the instructions of the power distribution module, control the charging and discharging process of the energy storage device and optimize the charging and discharging strategy; charging current According to the formula Calculate, where is the charging factor, is the maximum capacity of the energy storage device, is the charging time; the discharge current , according to the formula Calculate, where is the discharge coefficient, is the discharge time.

2. A distributed wind-solar hybrid control system according to claim 1, characterized in that: Also includes: Fault diagnosis module: real-time monitoring of the operating status of power generation equipment and system modules, using fault feature extraction and pattern recognition algorithms, for wind turbines, by monitoring the speed n, output power Parameters, calculate fault characteristic indicators ,in It is a characteristic indicator of wind turbine fault. is the normal operating speed of the wind turbine, is the normal operating output power of the wind turbine; for solar panels, calculate the fault characteristic index ,in It is a characteristic indicator of solar panel failure. is the normal operating output voltage of the solar panel, is the normal operating output current of the solar panel; when or When the set threshold is exceeded, the device is judged to be faulty.

3. A distributed wind-solar hybrid control system according to claim 1, characterized in that: Also includes: Optimization and scheduling module: optimizes the layout and operating parameters of wind turbines and solar panels based on operating data and power generation forecast results; Through system optimization algorithm, system power generation efficiency is the objective function, where is the system power generation efficiency, is the actual total output power of the system, The total power generation potential of the system; by continuously optimizing the installation angle of wind turbines , Blade length , the tilt angle of the solar panel parameter.

4. A distributed wind-solar hybrid control system according to claim 1, characterized in that: The communication module uses the AES-256 encryption algorithm to encrypt the transmitted data. The system has a data retransmission mechanism. When data transmission fails, it can be retransmitted up to 3 times. The packet loss rate of data transmission is , where L is the packet loss rate, is the number of lost packets, is the number of packets sent.

5. A distributed wind-solar hybrid control system according to claim 1, characterized in that: The energy storage management module adopts intelligent charging and discharging strategy, according to the remaining power of the energy storage device , Charge and discharge efficiency and the output power of the power generation equipment, dynamically adjust the charging and discharging current and voltage; when charging, Indicates low battery threshold, fast charging mode is adopted; when Indicates high battery threshold, using trickle charge mode.

6. A method for applying the distributed wind-solar hybrid control system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Data collection steps: Use sensors to collect real-time environmental data from wind turbines and solar panels, including wind speed v, light intensity I, wind direction , temperature T and operating data, including wind turbine output power , speed n, solar panel output voltage , Current , the collected data is calibrated using the formula ,in is the calibrated data, is the original acquisition data, C is the calibration coefficient; Data analysis steps: Analyze and process the collected data, combine historical data and weather forecast information, and use the formula ,in is the potential for wind power generation, is the air density, A is the wind turbine swept area, v is the wind speed, is the wind energy utilization coefficient, and ,in is the solar power generation potential, S is the solar panel area, I is the light intensity, The conversion efficiency of solar panels is used to predict the potential of wind and solar power generation and calculate the equipment efficiency coefficient formula: Evaluate the operating status of power generation equipment, including is the equipment efficiency coefficient, is the actual output power, is the power generation potential; Power allocation steps: Based on analysis results and load requirements , a control algorithm is used to determine the power distribution ratio of wind turbines and solar panels to meet ; Communication steps: Use a combination of wireless and wired communication to achieve communication between system modules and with the remote monitoring center. Use the AES-256 encryption algorithm to encrypt data, according to the formula Calculate the encryption strength, where E is the encryption strength, k is the encryption algorithm coefficient, L is the key length, S is the data sensitivity, and has a data retransmission mechanism, according to the formula , where T is the retransmission interval, is the initial retransmission interval, n is the number of retransmissions; Energy storage management steps: monitoring the power of energy storage devices , charge and discharge status, temperature Parameters, according to the power allocation instruction, according to the charging and discharging strategy, through the formula Control the charging and discharging process, where is the charging current, is the charging factor; is the maximum capacity of the energy storage device, is the charging time, and ,in is the discharge current, is the discharge coefficient; is the discharge time.

7. The method for distributed wind-solar hybrid control according to claim 6, characterized in that: Also includes: Fault diagnosis steps: Real-time monitoring of equipment and system operating status, using fault feature extraction and pattern recognition algorithms to calculate wind turbine fault feature indicators ,in is the fault characteristic index of the wind turbine, n is the current speed of the wind turbine, is the normal operating speed of the wind turbine, is the current output power of the wind turbine, It is the normal operation output power of the wind turbine and the fault characteristic index of the solar panel ,in It is a characteristic indicator of solar panel failure. is the current output voltage of the solar panel, is the normal operating output voltage of the solar panel, is the current output current of the solar panel, It is the normal output current of the solar panel. When the index exceeds the set threshold, the equipment is judged to be faulty. Optimization scheduling steps: Based on the operation data and power generation forecast results, the system power generation efficiency formula is the objective function, where is the system power generation efficiency, is the actual total output power of the system, The total power generation potential of the system is optimized through algorithms, including the installation angle of wind turbines. , Blade length , solar panel tilt angle .

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