Offshore floating wind-solar hybrid power generation method, system and device

Abstract

The application discloses a sea floating type wind-solar complementary power generation method, system and equipment, relates to the wind-solar cooperative power generation technical field, and the method comprises the following steps: a three-dimensional anchoring module fixes a sea floating type power generation equipment in a target anchoring area according to real-time anchoring information sent by a control terminal; a multi-sensor control array is activated to collect environment working condition data; the power generation environment working condition data is returned to an EMS energy management module; a power supply path decision is made, and a complementary power generation regulation and control instruction is output; during the multi-mode cooperative output process, a BMS system intelligent storage module is operated to perform real-time electric energy compensation on the complementary power generation module. The application solves the technical problems of unstable anchoring of the sea power generation equipment, low power generation efficiency, great influence of the environment on power supply, and poor stability in the prior art, achieves stable operation and high-efficiency power generation of the sea floating type power generation equipment, and improves the stability and reliability of the power supply.

Description

Technical Field

[0001] This invention relates to the field of wind-solar synergistic power generation technology, specifically to offshore floating wind-solar hybrid power generation methods, systems, and equipment. Background Technology

[0002] Offshore wind and solar energy resources are abundant and stable, giving rise to offshore floating wind-solar hybrid power generation technology. However, this technology currently faces many challenges in practical applications. Existing anchoring systems for offshore floating power generation equipment are not precise or efficient enough, making it difficult to stably fix the equipment in the optimal power generation position under complex sea conditions. This results in frequent equipment swaying and displacement, affecting power generation efficiency and increasing equipment wear and maintenance costs. Moreover, the ability of multi-sensor collaborative environmental data collection is weak, and the accuracy and timeliness of the data are difficult to guarantee. This prevents the energy management system from making accurate power supply path decisions based on real-time environmental conditions, hindering the full realization of the complementary advantages of wind and solar power generation. At the same time, the energy storage and compensation mechanisms are imperfect. When the power generation capacity does not match the load demand, timely and effective adjustments cannot be made, resulting in poor power supply stability and difficulty in meeting the needs of various power consumption scenarios.

[0003] Existing technologies suffer from technical problems such as unstable anchoring of offshore power generation equipment, low power generation efficiency, and poor stability of power supply due to significant environmental influences. Summary of the Invention

[0004] This application provides a method, system, and equipment for offshore floating wind-solar hybrid power generation, which addresses the technical problems of unstable anchoring, low power generation efficiency, and poor stability of power supply due to environmental influences in existing technologies.

[0005] In view of the above problems, this application provides a method, system and equipment for offshore floating wind-solar hybrid power generation.

[0006] A first aspect of this application provides a method for offshore floating wind-solar hybrid power generation, the method comprising:

[0007] The three-dimensional mooring module fixes the floating power generation equipment in the target mooring area based on the real-time mooring information sent by the control terminal. When the mooring control behavior of the floating power generation equipment in the target mooring area meets the preset environmental condition acquisition window, the multi-sensor control array is activated to collect environmental condition data. The multi-sensor control array collects and transmits the power generation environmental condition data back to the EMS energy management module. The EMS energy management module makes power supply path decisions based on the dynamic output voltage-ampere characteristics and the power generation environmental condition data, and outputs complementary power generation control commands. During the process of the complementary power generation module executing multi-modal collaborative output according to the complementary power generation control commands, the BMS system intelligent storage module performs real-time power compensation for the complementary power generation module.

[0008] A second aspect of this application provides an offshore floating wind-solar hybrid power generation system, the system comprising:

[0009] The system includes a real-time mooring information transmission unit for fixing the floating power generation equipment in the target mooring area based on real-time mooring information sent by the control terminal; a data acquisition unit for activating a multi-sensor control array to collect environmental condition data when the mooring control behavior of the floating power generation equipment in the target mooring area meets a preset environmental condition acquisition window; a condition data feedback unit for the multi-sensor control array to collect and transmit power generation environmental condition data back to the EMS energy management module; a control command output unit for the EMS energy management module to make power supply path decisions based on dynamic output voltage-ampere characteristics and the power generation environmental condition data, and output complementary power generation control commands; and a real-time power compensation unit for running the BMS system intelligent storage module to perform real-time power compensation for the complementary power generation module during the multi-modal collaborative output process executed by the complementary power generation module according to the complementary power generation control commands.

[0010] A third aspect of this application provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the offshore floating wind-solar hybrid power generation method provided in this application.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The three-dimensional mooring module, based on real-time mooring information sent by the control terminal, anchors the floating power generation equipment in the target mooring area; activates a multi-sensor control array to collect environmental condition data; transmits the power generation environmental condition data back to the EMS energy management module; makes power supply path decisions based on the dynamic output voltage-current characteristics and the power generation environmental condition data, and outputs complementary power generation control commands; during the execution of multi-modal collaborative output, the BMS system's intelligent storage module performs real-time power compensation for the complementary power generation module. This achieves stable operation and efficient power generation of the floating power generation equipment, improving the stability and reliability of the power supply. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1This is a schematic diagram of the equipment operation process in the offshore floating wind-solar hybrid power generation method provided in the embodiments of this application.

[0015] Figure 2 This is a schematic diagram of the offshore floating wind-solar hybrid power generation method provided in the embodiments of this application.

[0016] Figure 3 This is a schematic diagram of the structure of a floating wind-solar hybrid power generation system provided in an embodiment of this application.

[0017] Figure 4 This is a schematic diagram of the structure of an electronic device provided in this application.

[0018] Explanation of reference numerals in the attached drawings: Real-time mooring information transmission unit 10, data acquisition unit 20, working condition data feedback unit 30, control command output unit 40, real-time power compensation unit 50, processor 21, memory 22, input device 23, output device 24. Detailed Implementation

[0019] This application provides a method, system, and equipment for offshore floating wind-solar hybrid power generation, which addresses the technical problems of unstable anchoring, low power generation efficiency, and poor stability of power supply due to environmental influences in existing offshore power generation equipment.

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] Example 1, as Figure 1 and Figure 2 As shown, this application provides a method for offshore floating wind-solar hybrid power generation, the method comprising:

[0022] Step S100: The three-dimensional mooring module fixes the offshore floating power generation equipment to the target mooring area according to the real-time mooring information sent by the control terminal.

[0023] Specifically, the 3D mooring module undertakes the task of positioning and fixing. The control terminal sends real-time mooring information, which includes various constraints of the mooring area. After receiving the information, the 3D mooring module first parses the constraints of the mooring area and then activates the dual-mode positioning system to scan the 3D topographic contour of the sea area to obtain the seabed topographic contour of the mooring area. Subsequently, it identifies the hazard features of this topographic contour and selects suitable target mooring areas. Next, based on the regional distribution characteristics of the target mooring areas, a three-dimensional mooring network is constructed. Finally, according to the three-dimensional mooring network, the anchor chains are deployed in a coordinated manner. A regular 12-sided floating structure made of honeycomb composite material (outer layer of aluminum alloy with micro-arc oxidation coating to prevent seawater corrosion, inner layer of PU foam) with a diameter of 25m is used as a modular floating platform for offshore floating power generation equipment. This ensures the stable operation of the equipment at sea and lays the foundation for subsequent power generation operations.

[0024] Step S200: When the anchoring control behavior of the offshore floating power generation equipment in the target anchoring area meets the preset environmental condition acquisition window, the multi-sensor control array is activated to acquire environmental condition data.

[0025] Specifically, once the offshore floating power generation equipment is successfully anchored in the target anchoring area using a three-dimensional anchoring module, its anchoring control behavior enters the monitoring phase. Continuous monitoring of behavioral data such as anchoring duration is conducted. Once the anchoring control duration reaches the preset environmental condition acquisition window, a response is immediately initiated. A multi-sensor control array, consisting of a panel temperature sensing array, a photovoltaic irradiance monitoring array, and an ultrasonic wind speed monitoring array, sends an activation command for condition acquisition. Once activated, the multi-sensor control array begins efficient operation. The panel temperature sensing array collects characteristic data on the surface temperature of the solar power generation module, the photovoltaic irradiance monitoring array collects characteristic data on photovoltaic irradiance, and the ultrasonic wind speed monitoring array focuses on acquiring characteristic wind speed data around the wind power generation module. This comprehensive collection of power generation environmental condition data provides crucial information for subsequent energy management and power generation strategy adjustments.

[0026] Step S300: The multi-sensor control array collects and transmits power generation environment condition data back to the EMS energy management module.

[0027] Specifically, after the offshore floating wind-solar hybrid power generation equipment completes anchoring and the multi-sensor control array is activated, the data acquisition and transmission phase begins. The multi-sensor control array consists of multiple sensor arrays deployed at different locations within the hybrid power generation module. In the solar power generation module, the panel temperature sensing array and the photovoltaic irradiance monitoring array monitor the temperature changes and light intensity data of the photovoltaic panels, respectively. In the wind power generation module, the ultrasonic wind speed monitoring array constantly captures wind speed characteristic data. These sensors each perform their specific functions, acquiring data with extremely high accuracy and frequency. The collected panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data are then integrated and stably and quickly transmitted back to the RTU (Remote Terminal Unit) in the EMS energy management module via data transmission lines. This power generation environment condition data is the core basis for the EMS energy management module to subsequently perform energy strategy analysis, power supply path decisions, and power generation mode control, playing a crucial role in achieving efficient and stable operation of the wind-solar hybrid power generation system.

[0028] Step S400: The EMS energy management module makes energy supply path decisions based on the dynamic output voltage-current characteristics and the power generation environment operating data, and outputs complementary power generation control commands.

[0029] Specifically, the EMS energy management module uses a PMU (Phasor Measurement Unit) to collect the dynamic output current-voltage characteristics of the solar and wind power generation modules, obtaining the photovoltaic (PV) IV characteristic curve and the wind power PV characteristic curve. Simultaneously, it receives power generation environment data from a multi-sensor control array, covering panel temperature characteristics, photovoltaic irradiance characteristics, and wind speed characteristics. Based on the acquired data, the EMS energy management module uses a decay analysis algorithm to fully consider the changes in power generation efficiency of the photovoltaic panel under different temperature and illumination conditions for the panel temperature and photovoltaic irradiance characteristics, thereby correcting the PV IV characteristic curve to generate a more accurate PV IV correction curve. For wind speed characteristics, the module uses a wind energy capture efficiency calculation model and the relationship between wind speed and wind energy conversion efficiency to compensate for the wind power PV characteristic curve, obtaining the wind power PV equivalent curve. Subsequently, the EMS energy management module performs spatiotemporal synchronous coupling operations on the PV IV correction curve and the wind power PV equivalent curve. In this process, multiple sets of sample-coupled photovoltaic-wind power curves with sample power generation weight ratios are first interactively acquired. Based on these curves and the sample power generation weight ratios, a wind-solar joint volt-ampere characteristic model is constructed. Next, a preset sliding segmentation window is used to divide the corrected photovoltaic curve and the equivalent wind power curve into M sets of local photovoltaic-wind power curves. These are simultaneously input into the wind-solar joint volt-ampere characteristic model for curve similarity comparison, outputting M sample power generation weight ratios. Then, a time-series weighted algorithm is used to process these M sample power generation weight ratios to obtain the target power generation weight ratio. Finally, the IED (Intelligent Electronic Device) in the EMS energy management module generates and outputs complementary power generation control commands based on the target power generation weight ratio. These commands precisely control the complementary power generation modules, determining the operating mode and power distribution of the solar and wind power generation modules, achieving efficient complementary utilization of wind and solar energy, ensuring stable and efficient operation of the power generation system under different environmental conditions, and meeting the power demand of the load.

[0030] Step S500: During the process of the complementary power generation module executing multi-mode collaborative output according to the complementary power generation control command, the BMS system intelligent storage module performs real-time power compensation for the complementary power generation module.

[0031] Specifically, when the complementary power generation module executes multi-modal coordinated output of photovoltaic power generation, wind power generation, or a combination of both, based on the complementary power generation control commands output by the EMS energy management module, the BMS system's intelligent storage module begins to play a crucial role in power regulation. The PMU (Phasor Measurement Unit) in the system continuously collects instantaneous photovoltaic current and voltage from the solar power generation module, and instantaneous wind power from the wind power generation module. This real-time data is rapidly transmitted to the computing unit, where the current instantaneous power generation is obtained through power calculation formulas. Simultaneously, the local load power demand is pre-set, and the instantaneous power generation is compared with the pre-set load power demand to calculate the instantaneous power gap. Once a power gap exists, the BMS system's intelligent storage module immediately activates. This module uses a lithium iron phosphate battery pack and is equipped with an advanced battery management system capable of accurately monitoring battery power. Based on the magnitude and sign of the instantaneous power gap, the BMS system's intelligent storage module performs intelligent charging and discharging operations on the lithium iron phosphate battery pack. If the instantaneous power generation is lower than the load power demand, resulting in a power deficit, the battery pack releases electrical energy to compensate the complementary power generation module in real time, ensuring that the power output of the power generation system can stably meet the load demand. If the instantaneous power generation is higher than the load power demand, the battery pack stores the excess electrical energy, achieving effective energy recovery and utilization. This real-time power compensation process continues until the EMS energy management module updates the complementary power generation control command, thereby dynamically maintaining the power balance of the power generation system and ensuring the stable and efficient operation of the entire offshore floating wind-solar hybrid power generation system.

[0032] In one possible implementation, step S200 further includes:

[0033] Step S210: The intelligent control unit receives the real-time anchoring information sent by the control terminal.

[0034] Step S220: After sending the real-time mooring information to the three-dimensional mooring module, the intelligent control unit starts the environmental condition acquisition window.

[0035] Step S230: After the anchoring control time of the offshore floating power generation equipment in the target anchoring area reaches the environmental condition acquisition window, the intelligent control unit sends an activation command for condition acquisition to the multi-sensor control array to activate the multi-sensor control array to acquire environmental condition data.

[0036] Specifically, the intelligent control unit plays a role in coordination and scheduling. When the control terminal sends real-time mooring information, the intelligent control unit immediately receives it. This information includes key mooring instructions and important data such as relevant sea area constraints.

[0037] The intelligent control unit rapidly forwards this real-time mooring information to the 3D mooring module, providing crucial information for the equipment to find suitable mooring areas and secure itself. After completing the information transmission, the intelligent control unit activates the environmental condition acquisition window, marking the start of the mooring duration monitoring phase, where the equipment's mooring time in the target mooring area is accurately recorded.

[0038] As the equipment anchors stably in the target area and time progresses, when the anchoring control duration reaches the preset environmental condition data acquisition window, the intelligent control unit immediately issues a condition acquisition activation command, which is precisely transmitted to the multi-sensor control array. The multi-sensor control array consists of a panel temperature sensor array and a photovoltaic irradiance monitoring array deployed on the complementary power generation module's solar power module, and an ultrasonic wind speed monitoring array on the wind power module. Upon receiving the command, the multi-sensor control array quickly activates, and the sensors begin to work collaboratively, collecting panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data respectively. This provides fundamental data support for the subsequent energy management module's power supply path decision-making, ensuring that the entire power generation system can operate efficiently and stably based on real-time environmental conditions.

[0039] In one possible implementation, step S100 further includes:

[0040] Step S110: Analyze the real-time mooring information to obtain the mooring area constraints.

[0041] Step S120: Activate the dual-mode positioning system according to the anchorage area constraints to perform a three-dimensional topographic contour scan of the sea area and obtain the anchorage seabed topographic contour.

[0042] Step S130: Identify the hazardous features of the seabed topography contour of the anchorage and select the target anchorage area.

[0043] Step S140: Construct a three-dimensional anchoring network based on the regional distribution characteristics of the target anchoring area.

[0044] Step S150: By deploying anchor chains in a coordinated manner according to the three-dimensional mooring network, the floating power generation equipment is fixed in the target mooring area.

[0045] Specifically, when parsing real-time mooring information to obtain mooring area constraints, a rule-based matching and data mining algorithm can be used. First, the real-time mooring information undergoes preliminary format parsing, breaking it down into multiple data fields, such as timestamps, sea area coordinate ranges, seabed geological types, and information on surrounding obstacles. Next, a rule-based matching algorithm is used to filter and judge each field according to a pre-defined set of rules. For example, if the rules specify that certain seabed geological conditions are unsuitable for mooring, then when this type of geological information is parsed, it is marked as a constraint. For the sea area coordinate range field, a Geographic Information System (GIS) algorithm is used to determine its boundaries and internal special areas, such as no-anchoring zones and shipping channels, and these areas are extracted as constraints. Simultaneously, data mining algorithms are used to analyze historical mooring data and real-time environmental data to uncover potential constraint factors. For example, by analyzing the ocean current speed and direction data over a period of time, it is determined whether certain areas are suitable for mooring during a specific time period, and these constraints obtained from data analysis are also included in the final set of mooring area constraints. Finally, all extracted constraints are integrated and verified, and duplicate and invalid constraints are removed to form complete and accurate anchorage area constraint information.

[0046] Based on the constraints and operational requirements imposed by the anchorage area, a dual-mode positioning system was quickly activated. This system combines the high-precision positioning capabilities of satellite positioning technology with the autonomy and continuity advantages of inertial navigation technology. Satellite positioning acquires the approximate location of the equipment in a vast sea area, while inertial navigation ensures positioning continuity even when satellite signals are blocked. Building upon this positioning, the dual-mode system begins a three-dimensional topographic contour scan of the target sea area. It emits signals at specific frequencies, which are reflected back upon encountering the seabed and surrounding objects. By receiving parameters such as the time and intensity of the reflected signals, the system accurately calculates the distance and direction of signal propagation. As the equipment moves through the sea area, this data is continuously collected, processed, and integrated in real time. After a series of data processing and analysis steps, a seabed topographic contour is finally constructed, revealing detailed information such as seabed depth variations, reef distribution, and trench orientation, providing intuitive and accurate topographical information for subsequent selection of safe anchorage areas.

[0047] A morphological analysis-based algorithm is employed to perform dilation and erosion operations on anchored seabed topographic contour data using structural elements, thereby identifying dangerous topographic features such as isolated reefs and abrupt sea peaks. If an area abnormally increases in size after dilation and is clearly separated from the surrounding terrain, it can be identified as a dangerous reef area. Next, a slope analysis algorithm is used to calculate the slope value of each point in the topography and compare it with a preset safe slope threshold. When the slope exceeds the threshold, it indicates that the area may contain steep trenches or slopes, classifying it as a dangerous area. Simultaneously, clustering algorithms from machine learning, such as the DBSCAN algorithm, are used to cluster the topographic contour data. The algorithm divides the data into different clusters based on the density between data points; clusters far from the main cluster and with sparse data point distribution are highly likely to represent isolated dangerous areas. Furthermore, a fuzzy logic algorithm is introduced to comprehensively consider multiple influencing factors, such as topographic depth variations and geological stability. Based on expert experience and historical data, fuzzy rules are established to fuzzily assess the hazard level of each area, resulting in a hazard value between 0 and 1. The closer the value is to 1, the higher the hazard level. Finally, by combining the identification results of the above algorithms, all areas judged as dangerous are excluded, and the remaining areas are the initially screened safe areas. These safe areas are then scored and evaluated according to the performance parameters and safety requirements of the mooring equipment, and the area with the highest score is the target mooring area.

[0048] The geographic information of the target anchoring area is digitized and transformed into three-dimensional point cloud data, including the latitude, longitude, and depth information of each point within the area. Next, the Delaunay triangulation algorithm is used to process this point cloud data, dividing the target anchoring area into multiple interconnected triangular meshes. This algorithm ensures that the generated triangular meshes are as close as possible to equilateral triangles, avoiding narrow or sharp triangles, thus better reflecting the geometric characteristics of the area. Then, a genetic algorithm is used to optimize the anchor point locations. A fitness function is set, which comprehensively considers multiple factors, such as the distance between anchor points, the distance from the anchor point to the area boundary, seabed geological conditions, and the stress balance of the anchor chain. By simulating a biological evolution process, an optimal set of anchor point locations is found through iterative searching, minimizing the cost while meeting the stability requirements of the entire anchoring network. After determining the anchor point locations, based on fluid dynamics principles and marine environmental data (such as the direction and intensity of waves and currents), the finite element analysis algorithm is used to calculate the stress on each anchor point. By establishing a mechanical model of the anchor chain and anchorage, the tension distribution of the anchor chain and the holding force of the anchorage under different working conditions are simulated to ensure that the force on each anchor point is within a safe range. Finally, based on the calculated force on the anchor points, graph theory algorithms are used to determine the connection relationships between the anchor points. A graph is constructed with anchor points as nodes and anchor chain connections as edges. By solving the minimum spanning tree problem, the optimal anchor chain connection scheme is determined, making the entire three-dimensional mooring network as simple as possible while ensuring stability, thus completing the construction of the three-dimensional mooring network.

[0049] After constructing the three-dimensional mooring network, the next step is the coordinated deployment of anchor chains to securely anchor the floating power generation equipment to the target mooring area. Following the pre-constructed three-dimensional mooring network plan, three main anchor chains symmetrically distributed at 120° are deployed first, forming a horizontal anti-drift system. At the end of each main anchor chain, a three-pronged holding anchor is installed; its unique three-pronged structure effectively enhances the holding force on the seabed. Simultaneously, to achieve real-time and accurate monitoring of the seabed holding force distribution, distributed fiber optic pressure sensors are integrated at the anchor chain ends. These sensors have a sampling rate of up to 100Hz, enabling rapid and continuous data acquisition and timely feedback of seabed holding force changes to the control system. While deploying the main anchor chains, four sets of 45° inclined auxiliary anchor chains are simultaneously deployed. The ends of these auxiliary anchor chains are connected to hydraulic rotary dampers, whose damping coefficients can be adjusted according to actual sea conditions within the range of 5-200 kN·s / m. When waves are generated on the sea surface, the wave frequency causes heave displacement of the equipment. The control system adjusts the damping coefficient of the hydraulic rotary damper in real time based on the wave frequency. If the wave frequency is high, the damping coefficient is increased appropriately to enhance the suppression of heave displacement; if the wave frequency is low, the damping coefficient is decreased to avoid excessive damping affecting the normal operation of the equipment. In this way, the main anchor chain and auxiliary anchor chain work together to form a three-dimensional anchoring network that provides stable anchoring support for the offshore floating power generation equipment in both horizontal and oblique dimensions. This ensures that the equipment is stably fixed in the target anchoring area, creating a stable foundation for subsequent wind-solar hybrid power generation operations.

[0050] In one possible implementation, step S300 further includes:

[0051] Step S310: Deploy a panel temperature sensing array and a photovoltaic irradiance monitoring array on the solar power generation module of the complementary power generation module.

[0052] Step S320: Deploy an ultrasonic wind speed monitoring array in the wind power generation module of the complementary power generation module, wherein the panel temperature sensing array, the photovoltaic irradiance monitoring array and the ultrasonic wind speed monitoring array constitute the multi-sensor control array.

[0053] Step S330: The panel temperature sensing array, photovoltaic irradiance monitoring array, and ultrasonic wind speed monitoring array respectively collect panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data, and transmit the panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data back to the RTU of the EMS energy management module. The panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data constitute the power generation environment condition data.

[0054] Specifically, to monitor the real-time operating status and environmental conditions of the solar photovoltaic module, a panel temperature sensing array and a photovoltaic irradiance monitoring array are deployed on its surface. The panel temperature sensing array consists of multiple high-precision temperature sensors evenly distributed across the surface of the solar photovoltaic panel. These sensors can accurately measure the temperature at different locations on the photovoltaic panel, enabling timely detection of anomalies such as localized overheating. The photovoltaic irradiance monitoring array uses light sensors to accurately monitor the intensity of solar radiation reaching the photovoltaic panel, providing crucial data support for evaluating photovoltaic power generation efficiency.

[0055] On the wind power generation module of the complementary power generation module, an ultrasonic wind speed monitoring array is deployed. This array uses high-precision ultrasonic sensors to accurately measure wind speed based on the time difference generated when ultrasonic waves encounter different wind speeds during propagation in the air. It features fast response speed and high measurement accuracy, and can capture subtle changes in wind speed in real time, stably acquiring wind speed data even in complex marine environments. Simultaneously, the panel temperature sensing array and photovoltaic irradiance monitoring array deployed on the solar power generation module work in conjunction with the ultrasonic wind speed monitoring array. The panel temperature sensing array monitors the surface temperature of the photovoltaic panels, providing temperature data support for judging the operating status and power generation efficiency of the photovoltaic panels; the photovoltaic irradiance monitoring array focuses on monitoring the intensity of solar radiation reaching the photovoltaic panels, a key data source for assessing the potential of photovoltaic power generation. These three arrays together constitute a multi-sensor control array, providing comprehensive perception of the power generation environment from different dimensions. The data they collect, including panel temperature characteristics, photovoltaic irradiance characteristics, and wind speed characteristics, will serve as an important component of the power generation environment conditions data. This will provide crucial information for the EMS energy management module to make subsequent energy supply path decisions and adjust power generation strategies, thereby ensuring that the entire power generation system can operate efficiently and stably according to actual environmental conditions.

[0056] Once the equipment is stably anchored and the multi-sensor control array is activated, the panel temperature sensing array, photovoltaic irradiance monitoring array, and ultrasonic wind speed monitoring array begin to perform their respective functions. The panel temperature sensing array is attached to the surface of the photovoltaic panel of the solar power generation module, and its multiple high-precision temperature sensors continuously monitor temperature changes at different locations on the photovoltaic panel. Since the temperature of the photovoltaic panel significantly affects its power generation efficiency, these sensors can sensitively capture subtle temperature fluctuations, converting the collected temperature data into panel temperature characteristic data. The photovoltaic irradiance monitoring array uses highly sensitive light sensors to accurately measure the intensity of sunlight radiating onto the photovoltaic panel. It constantly tracks the dynamic changes in light intensity, generating accurate photovoltaic irradiance characteristic data, providing crucial information for assessing the potential and actual efficiency of photovoltaic power generation. The ultrasonic wind speed monitoring array, installed on the wind power generation module, uses ultrasonic technology to accurately calculate wind speed by measuring the time difference of ultrasonic waves traveling through the air. Whether it's low wind speeds of 0.5-3.3 m / s, medium wind speeds of 3.4-10.7 m / s, high wind speeds of 10.8-13.8 m / s, or extremely strong winds of 13.9 m / s and above, this array can respond quickly and acquire real-time wind speed characteristic data. These panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data collected by different arrays together constitute power generation environment condition data reflecting the power generation environment status. To enable the EMS energy management module to make timely decisions based on this data, each array transmits the collected data back to the EMS energy management module's RTU (Remote Terminal Unit) via data transmission lines in a high-speed and stable manner. After receiving the data, the RTU integrates and stores it for the EMS energy management module to conduct in-depth analysis. This provides comprehensive and accurate data support for the energy management system to decide on energy supply paths and output complementary power generation control commands, ensuring that the entire power generation system can operate efficiently according to actual environmental changes.

[0057] In one possible implementation, step S400 further includes:

[0058] Step S410: The EMS energy management module collects the dynamic output volt-ampere characteristics via the PMU, wherein the dynamic output volt-ampere characteristics include the photovoltaic IV characteristic curve and the wind power PV characteristic curve.

[0059] Step S420: Perform attenuation analysis on the plate surface temperature characteristic data and photovoltaic irradiance characteristic data, and correct the photovoltaic IV characteristic curve based on the analysis results to obtain the photovoltaic IV correction curve.

[0060] Step S430: Calculate the wind energy capture efficiency based on the wind speed characteristic data, and compensate the wind power PV characteristic curve according to the calculation results, and output the wind power PV equivalent curve.

[0061] Step S440: After spatiotemporally coupling the photovoltaic IV correction curve and the wind power PV equivalent curve, perform power generation weight optimization allocation and output the complementary power generation control command.

[0062] Specifically, the EMS energy management module establishes an electrical connection with the solar power generation module and the wind power generation module through the PMU (Phasor Measurement Unit). The PMU possesses high-precision measurement and real-time data acquisition capabilities, enabling comprehensive monitoring of the output of both power generation modules. For the solar power generation module, the PMU continuously tracks its current (I) and voltage (V) changes under different light and temperature conditions, integrating and analyzing this real-time data to ultimately generate a photovoltaic (PV) I-V characteristic curve. This curve visually reflects the power generation performance of the solar photovoltaic panel under various operating conditions. For example, how the output voltage and current of the photovoltaic panel coordinate to generate electricity when there is sufficient sunlight; and how its output characteristics change when the light intensity decreases or the photovoltaic panel temperature increases. For the wind power generation module, the PMU monitors the power (P) and voltage (V) data of the wind turbine generator in real time at different wind speeds. Due to the instability of wind speed, the output power and voltage of the wind turbine generator will also fluctuate. The PMU can accurately capture these changes and plot the wind power PV characteristic curve. The curve clearly illustrates the output characteristics of wind power generation in different wind speed ranges, such as the power and voltage output of the wind turbine near the starting wind speed, and the trend of power increase with increasing wind speed. By collecting the photovoltaic (PV) characteristic curve and the wind power (PV) characteristic curve, the EMS energy management module obtains key data reflecting the real-time operating status of the two power generation modules, providing a solid data foundation for subsequent energy management and power generation strategy adjustments, and helping to achieve efficient synergy in wind-solar hybrid power generation.

[0063] A large amount of historical data was collected, including panel temperature characteristics, photovoltaic irradiance characteristics, and corresponding standard photovoltaic IV characteristic curves at different times. This data was preprocessed, with temperature and irradiance data normalized and IV characteristic curve data converted into a format suitable for neural network input, for example, using current and voltage values ​​at discrete points on the curve as features. Next, a deep neural network model was constructed. The number of neurons in the input layer was determined based on the dimensions of the temperature and irradiance data, and multiple hidden layers were set, each containing a certain number of neurons. Activation functions such as ReLU were used to increase the model's nonlinear expressive power. The number of neurons in the output layer corresponded to the number of discrete points on the corrected IV characteristic curve. The preprocessed data was then divided into training, validation, and test sets. The neural network model was trained using the training set, with mean squared error as the loss function to measure the difference between the model's predicted corrected IV curve and the actual corrected curve. The weights and biases of the neural network were continuously adjusted using the backpropagation algorithm to minimize the loss function. During training, the validation set was used to monitor the model's generalization ability and prevent overfitting. After training, the model is evaluated using a test set to ensure it performs well on unknown data. Once the model achieves satisfactory performance, the currently collected panel temperature and photovoltaic irradiance data are input into the trained neural network model. The model output is the corrected discrete point data of the IV curve. Finally, an interpolation algorithm (such as spline interpolation) is used to fit these discrete points into a continuous curve, thus obtaining the photovoltaic IV correction curve. This curve more accurately reflects the actual output characteristics of the photovoltaic cells under the current environment.

[0064] After acquiring wind speed characteristic data, the EMS energy management module first performs data preprocessing to remove potential outliers and noise interference, ensuring data accuracy. Next, it calculates the wind energy capture efficiency (VEE) based on the wind turbine's technical parameters and physical principles. For example, according to Betz theory, VEE is related to factors such as turbine blade design, air density, and wind speed. Using the turbine's wind energy utilization coefficient, swept area, and current wind speed, the proportion of wind energy actually captured by the turbine at that wind speed to the theoretically available wind energy is calculated—this is the VEE. Subsequently, the calculated VEE is used to compensate the wind power PV characteristic curve. The original PV characteristic curve is obtained under standard or ideal conditions, but changes in actual wind speed affect the turbine's power generation efficiency. When the wind speed is lower than the rated wind speed, the power value at the corresponding voltage on the curve is reduced proportionally based on the VEE; when the wind speed is higher than the rated wind speed, the curve is adjusted accordingly, considering the turbine's power limitations and actual efficiency, to make the power value more consistent with actual power generation. After this compensation process, a new curve is generated—the wind power PV equivalent curve. This equivalent curve can more accurately reflect the actual power generation performance of the wind turbine under the current wind speed conditions, providing precise data support for the subsequent EMS energy management module to optimize the allocation of power generation weights and achieve efficient wind-solar hybrid power generation.

[0065] To eliminate the inconsistency in output characteristics between photovoltaic (PV) and wind power due to temporal and spatial differences, it is necessary to perform spatiotemporal synchronous coupling of the PV IV correction curve and the wind power PV equivalent curve. By analyzing irradiance and wind speed data at different times and geographical locations, the two curves are adjusted to a unified spatiotemporal dimension, enabling them to more intuitively reflect the current power generation status. Subsequently, interaction with historical databases and regional energy data centers is conducted to obtain multiple sets of sample coupled PV-wind power curves. These curves cover power generation data under various combinations of irradiance and wind speed, and each curve is marked with a sample power generation weight ratio. Based on this data, a wind-solar joint volt-ampere characteristic model is constructed using a neural network algorithm. This model can uncover the complex relationship between environmental factors such as irradiance and wind speed and the output power and power generation weights of PV and wind power. Next, a sliding segmentation window is preset, with the window size and sliding step determined based on the temporal accuracy of the system data and the frequency of curve changes. By sliding the window back and forth on the coupled curves, the PV IV correction curve and the wind power PV equivalent curve are segmented into M sets of local PV-wind power curves. Each set of local curves represents the power generation characteristics under specific time periods and operating conditions, providing a more detailed view of power generation trends. Subsequently, the M sets of local photovoltaic-wind power curves are synchronously input into the wind-solar combined volt-ampere characteristic model. The model uses dynamic time warping (DTW) algorithms or cosine similarity methods to calculate the similarity between each set of local curves and the sample coupled photovoltaic-wind power curves, identifying the most similar sample curves and outputting the corresponding sample power generation weight ratios, resulting in M ​​sample power generation weight ratios. Considering the varying degrees of influence of power generation data from different time periods on current power generation decisions, a time-series weighted method is used to process these M sample power generation weight ratios. Recent samples have higher weights, while earlier samples have lower weights; a target power generation weight ratio is calculated through weighted calculation. This target power generation weight ratio comprehensively considers current and recent wind and solar power generation characteristics, enabling more accurate adaptation to real-time power generation conditions. Finally, the intelligent electronic device (IED) acquires the target power generation weight ratio, converts it into specific complementary power generation control commands, such as adjusting the operating parameters of the photovoltaic inverter and controlling the pitch angle of the wind turbine, and outputs them to the complementary power generation module. The complementary power generation module adjusts the power output of photovoltaic and wind power generation in real time according to instructions, so as to achieve efficient synergy between the two and ensure that they can operate in the best state under different light and wind speed conditions, meet the power demand, and improve power generation efficiency and stability.

[0066] In one possible implementation, step S410 further includes:

[0067] Step S411: The EMS energy management module integrates RTU, IED and PMU, and the PMU is connected to the solar power generation module and the wind power generation module respectively.

[0068] Specifically, the EMS energy management module integrates three key components: RTU (Remote Terminal Unit), IED (Intelligent Electronic Device), and PMU (Synchronous Phasor Measurement Unit). The RTU is primarily responsible for collecting various real-time data from the solar and wind power generation modules, such as operating parameters like voltage, current, and temperature, and uploading this data to the EMS energy management module. It can also receive commands from the EMS energy management module to remotely control the power generation equipment. The IED possesses intelligent processing capabilities; it can analyze and process the collected data to achieve protection, monitoring, and control functions for the power generation equipment. For example, it can quickly take protective measures when an abnormality is detected. The PMU is connected to both the solar and wind power generation modules. Through high-precision clock synchronization technology, the PMU can accurately measure the electrical phasor information of the two power generation modules in real time, including voltage and current phasors. Based on this data, the EMS energy management module accurately assesses the operating status of the solar power generation module and the wind power generation module, enabling real-time synchronous monitoring and coordinated control of the two power generation modules, thereby improving the stability, reliability and power generation efficiency of the entire wind-solar hybrid power generation system.

[0069] In one possible implementation, step S440 further includes:

[0070] Step S441: Interact to obtain multiple sets of sample coupled photovoltaic-wind power curves, wherein the multiple sets of sample coupled photovoltaic-wind power curves have multiple sample power generation weight ratio identifiers.

[0071] Step S442: Construct a wind-solar combined volt-ampere characteristic model based on the coupled photovoltaic-wind power curves of the multiple sets of samples and the power generation weight ratio of the multiple samples.

[0072] Step S443: Set a sliding segmentation window and use the sliding segmentation window to divide the photovoltaic IV correction curve and the wind power PV equivalent curve into M groups of local photovoltaic-wind power curves.

[0073] Step S444: Synchronize the M groups of local photovoltaic-wind power curves to the wind-solar joint volt-ampere characteristic model for curve similarity comparison, and output the power generation weight ratio of M samples.

[0074] Step S445: Time-weight the power generation weight ratios of the M samples and output the target power generation weight ratio.

[0075] Step S446: The IED outputs the target power generation weight ratio as the complementary power generation control command to the complementary power generation module.

[0076] Specifically, the EMS energy management module acquires multiple sets of sample coupled photovoltaic-wind power curves through interaction with various data sources. On one hand, it retrieves data from a local historical database. This data records the output of photovoltaic and wind power generation under different environmental conditions, including varying combinations of light intensity, wind speed, and temperature, presenting the power change relationship between photovoltaic and wind power as a curve – the coupled photovoltaic-wind power curve. On the other hand, if the equipment is connected to a regional energy data sharing network, the EMS energy management module also obtains relevant curve data from other similar offshore floating power generation equipment under different scenarios, thus enriching the diversity of the samples. Each set of sample coupled photovoltaic-wind power curves corresponds to a specific sample power generation weight ratio identifier. These identifiers are determined based on the proportion of photovoltaic and wind power generation in the total power generation at the time of data acquisition. For example, in a certain set of data, when sunlight is sufficient and wind speed is moderate, photovoltaic power generation accounts for 60% of the total power generation, and wind power generation accounts for 40%. Therefore, the sample power generation weight ratio identifier for this set of curves is PV 60: Wind 40. By acquiring a large number of coupling curves with different sample power generation weight ratios, we have obtained rich data support for the subsequent construction of a model that can accurately reflect the relationship between wind and solar power generation characteristics and weights, which helps to achieve a more reasonable power generation weight optimization allocation.

[0077] The EMS energy management module preprocesses this data to remove outliers and noise, ensuring accuracy and reliability. Next, based on the fundamental principles of power systems, the basic structure of the model is determined. Since the characteristics of wind and solar power generation are influenced by various factors such as sunlight intensity, wind speed, and temperature, the model needs to reflect the complex relationships between these factors and the output power and voltage of photovoltaic and wind power. A neural network algorithm from machine learning is employed, which has powerful nonlinear mapping capabilities and is suitable for handling such complex multivariate relationships. The processed samples coupled with photovoltaic-wind power curve data are used as input features, and the sample power generation weight ratio is used as the target output to train the neural network model. During training, the model parameters, such as the connection weights between neurons, are continuously adjusted to make the model's output as close as possible to the actual sample power generation weight ratio. Through multiple iterations of training, the model learns the inherent laws between environmental factors such as sunlight and wind speed and the output characteristics and power generation weights of photovoltaic and wind power. Simultaneously, to improve the model's generalization ability and avoid overfitting, methods such as cross-validation are used to evaluate and optimize the model. After repeated training and optimization, a combined wind and solar volt-ampere characteristic model was finally constructed, which can accurately describe the characteristics of combined wind and solar power generation. This model can predict the optimal weight allocation of photovoltaic and wind power generation under different operating conditions based on real-time environmental parameters such as light intensity and wind speed, providing a scientific basis for subsequent power generation regulation.

[0078] The EMS energy management module first presets a sliding window of appropriate size and step size based on the temporal resolution of the power generation system data, the frequency of curve changes, and the actual control precision requirements. For example, if the data acquisition frequency is high and the curve fluctuates significantly, a smaller window size may be set to capture subtle changes in the curve; conversely, the window size is appropriately increased. After setting the sliding window, it starts from the curve's starting point and slides sequentially along the photovoltaic IV correction curve and the wind power PV equivalent curve at predetermined step sizes. During each slide, the curve segment covered by the window is extracted, forming a set of local photovoltaic curves and a set of local wind power curves, which together constitute a set of local photovoltaic-wind power curves. This process is repeated until the sliding window has traversed the entire curve, ultimately dividing the photovoltaic IV correction curve and the wind power PV equivalent curve into M sets of local photovoltaic-wind power curves.

[0079] The EMS energy management module simultaneously feeds these M sets of local photovoltaic-wind power curves into the wind-solar combined volt-ampere characteristic model. This model stores a large number of sample coupled photovoltaic-wind power curves under historical operating conditions and their corresponding sample power generation weight ratios. Using the Dynamic Time Warping (DTW) algorithm, a detailed similarity comparison is performed between each set of local curves and the sample curves in the model. The DTW algorithm effectively handles curve scaling and offset on the time axis, accurately measuring the similarity between curves. The model calculates the DTW distance between each set of local curves and all sample curves, finding the sample curve with the smallest distance, i.e., the most similar sample to the current local curve. Finally, the corresponding sample power generation weight ratios are extracted from these most similar sample curves, resulting in M ​​sample power generation weight ratios, providing a crucial basis for subsequently determining reasonable power generation weights.

[0080] After obtaining the power generation weight ratios of M samples, the target power generation weight ratio is determined. Considering the varying importance of power generation data at different times to current power generation decisions, the EMS energy management module uses a time-series weighted method to process these M sample power generation weight ratios. Each sample power generation weight ratio is assigned a corresponding weight according to its chronological order; more recent sample power generation weight ratios better reflect the current power generation situation and are therefore assigned higher weights, while earlier sample power generation weight ratios are assigned lower weights. Then, each sample power generation weight ratio is multiplied by its corresponding weight, and all products are summed. This weighted summation yields a comprehensive value, which is the target power generation weight ratio. This target power generation weight ratio comprehensively considers the changes in wind and solar power generation characteristics at different times, enabling more accurate adaptation to real-time power generation conditions and providing a scientific and reasonable basis for subsequent power generation regulation.

[0081] Intelligent Electronic Devices (IEDs), as a crucial component of the EMS energy management module, possess powerful communication and control capabilities. They retrieve the calculated target power generation weight ratio from the data storage area. This ratio accurately reflects the ideal proportion of photovoltaic (PV) and wind power generation in the total power generation under current environmental conditions. The IED then translates the target power generation weight ratio into complementary power generation control commands. These commands contain specific control information for PV and wind power generation. For example, for the PV module, commands adjust the tracking angle of the PV panels to capture more sunlight; for the wind module, commands adjust the angle of the wind turbine blades to optimize wind energy capture efficiency. Through communication lines, the IED quickly and accurately outputs these complementary power generation control commands to the complementary power generation modules. Upon receiving the commands, the complementary power generation modules immediately adjust their internal PV and wind power components accordingly. All components work collaboratively, generating power according to the proportion specified by the target power generation weight ratio. This ensures the efficient and stable operation of the entire offshore floating wind-solar hybrid power generation system, maximizing the satisfaction of electricity demand and improving energy utilization efficiency under varying sunlight and wind speed conditions.

[0082] In one possible implementation, step S500 further includes:

[0083] Step S510: The PMU collects instantaneous photovoltaic current and instantaneous photovoltaic voltage from the solar power generation module.

[0084] Step S520: The PMU collects instantaneous wind power from the wind power generation module.

[0085] Step S530: Calculate the instantaneous power generation based on the instantaneous photovoltaic current, instantaneous photovoltaic voltage, and instantaneous wind power.

[0086] Step S540: Calculate the instantaneous power gap based on the local preset load power demand and the instantaneous power generation.

[0087] Step S550: Based on the instantaneous power gap, the BMS system intelligent storage module performs instantaneous power compensation for the complementary power generation module until the complementary power generation control command is updated.

[0088] Specifically, the PMU (Phasor Measurement Unit) begins to play its role in precise measurement. Closely connected to the solar power module, it quickly and accurately acquires the photovoltaic current and voltage data of the solar power module at a given instant. These instantaneous photovoltaic current and voltage data are crucial for assessing the real-time status of solar power generation, providing a foundation for subsequent power calculations.

[0089] The PMU further extends its monitoring scope to the wind power generation module, also collecting the instantaneous wind power generated by the wind power generation module with high accuracy. This data reflects the actual output capacity of wind power generation at the current moment, and together with the relevant data from the solar power generation module, it forms the real-time power generation data foundation of the entire power generation system.

[0090] The Power Management Unit (PMU) collects instantaneous photovoltaic (PV) current and voltage from the solar power generation module and instantaneous wind power from the wind power generation module. Based on basic electrical power calculation formulas, for the solar power generation section, multiplying the collected instantaneous PV current and voltage yields the instantaneous power output of the solar power generation module. Then, adding the instantaneous power output of the solar power generation module to the instantaneous wind power output of the wind power generation module gives the total instantaneous power output of the entire wind-solar hybrid power generation system at that moment. This calculation result reflects the system's actual power generation capacity in real time, providing crucial foundational data for subsequent assessments of the system's power supply status and determining the need for power compensation, thus facilitating precise control and stable operation of the power generation system.

[0091] The local preset load power demand is set in advance based on factors such as the type, quantity, operating power of local electrical equipment, and historical electricity consumption patterns. This value represents the total power consumption that the system needs to meet at the current moment. Instantaneous power generation is calculated based on instantaneous photovoltaic current, instantaneous photovoltaic voltage, and instantaneous wind power, reflecting the system's current actual power generation capacity. When calculating the instantaneous power gap, the local preset load power demand is compared with the instantaneous power generation. If the instantaneous power generation is less than the local preset load power demand, the difference is the instantaneous power gap. For example, if the local preset load power demand is 100 kW, but the calculated instantaneous power generation is only 80 kW, then the instantaneous power gap is 20 kW. Obtaining this power gap value provides an accurate basis for the system to take corresponding measures to balance power supply and consumption, ensuring that the system can adjust its power generation strategy or perform power compensation in a timely manner according to actual conditions, thereby maintaining the stability of the power supply.

[0092] Upon receiving an instantaneous power shortage, the energy compensation mechanism is activated to ensure the stability of the system's power supply. The BMS system's intelligent energy storage module responds quickly upon receiving the power shortage signal. It precisely controls the energy storage device's release of power based on the magnitude of the instantaneous power shortage. If the power shortage is small, the energy storage device will supplement it with lower power output; if the power shortage is large, it will increase the power output to compensate for the insufficient generation power as quickly as possible. During this process, the BMS system's intelligent energy storage module monitors the power output in real time to ensure that the output power quality meets requirements and does not adversely affect the complementary generation module or the entire power supply system. The power output from the energy storage device is directly delivered to the complementary generation module, merging with the power from the currently generating solar and wind power modules to jointly meet the local preset load power demand. This instantaneous energy compensation operation continues until a new complementary generation control command is received. Because environmental factors such as sunlight intensity and wind speed change over time, leading to changes in generation power and load demand, the EMS energy management module continuously adjusts the complementary generation control commands. Once an update instruction is received, the BMS system's intelligent energy storage module will readjust the operating status of the energy storage device according to the requirements of the new instruction. This may involve suspending power compensation or changing the compensation power level, ensuring that the system can always achieve efficient and stable power generation and supply based on actual conditions.

[0093] Example 2, based on the same inventive concept as the offshore floating wind-solar hybrid power generation method in the foregoing examples, such as... Figure 3 As shown, this application provides an offshore floating wind-solar hybrid power generation system. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0094] The real-time mooring information sending unit 10 is used by the three-dimensional mooring module to fix the offshore floating power generation equipment to the target mooring area based on the real-time mooring information sent by the control terminal.

[0095] The data acquisition unit 20 is used to activate the multi-sensor control array to collect environmental condition data when the anchoring control behavior of the offshore floating power generation equipment in the target anchoring area meets the preset environmental condition acquisition window.

[0096] The operating condition data feedback unit 30 is used for the multi-sensor control array to collect and transmit power generation environment operating condition data to the EMS energy management module.

[0097] The control command output unit 40 is used by the EMS energy management module to make energy supply path decisions based on the dynamic output voltage-current characteristics and the power generation environment operating data, and output complementary power generation control commands.

[0098] The real-time power compensation unit 50 is used to run the BMS system intelligent storage module to perform real-time power compensation for the complementary power generation module during the process of the complementary power generation module executing multi-mode collaborative output according to the complementary power generation control command.

[0099] Furthermore, the system is also used to implement the following functions:

[0100] The intelligent control unit receives the real-time mooring information sent by the control terminal; after sending the real-time mooring information to the three-dimensional mooring module, the intelligent control unit activates the environmental condition acquisition window; after the mooring control time of the offshore floating power generation equipment in the target mooring area reaches the environmental condition acquisition window, the intelligent control unit sends an activation command for condition acquisition to the multi-sensor control array to activate the multi-sensor control array to acquire environmental condition data.

[0101] Furthermore, the system is also used to implement the following functions:

[0102] The real-time mooring information is analyzed to obtain the mooring area constraints; a dual-mode positioning system is activated based on the mooring area constraints to perform a three-dimensional topographic contour scan of the sea area, obtaining the seabed topographic contour of the mooring area; hazard features are identified on the seabed topographic contour of the mooring area to filter out the target mooring area; a three-dimensional mooring network is constructed based on the regional distribution characteristics of the target mooring area; and the floating power generation equipment is fixed to the target mooring area by coordinating the deployment of anchor chains based on the three-dimensional mooring network.

[0103] Furthermore, the system is also used to implement the following functions:

[0104] A panel temperature sensing array and a photovoltaic irradiance monitoring array are deployed in the solar power generation module of the complementary power generation module; an ultrasonic wind speed monitoring array is deployed in the wind power generation module of the complementary power generation module. The panel temperature sensing array, photovoltaic irradiance monitoring array, and ultrasonic wind speed monitoring array constitute the multi-sensor control array. The panel temperature sensing array, photovoltaic irradiance monitoring array, and ultrasonic wind speed monitoring array respectively collect panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data, and transmit these data back to the RTU of the EMS energy management module. The panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data constitute the power generation environment condition data.

[0105] Furthermore, the system is also used to implement the following functions:

[0106] The EMS energy management module acquires the dynamic output volt-ampere characteristics via the PMU, wherein the dynamic output volt-ampere characteristics include the photovoltaic (PV) characteristic curve and the wind power (PV) characteristic curve; it performs attenuation analysis on the panel temperature characteristic data and photovoltaic irradiance characteristic data, and corrects the PV characteristic curve based on the analysis results to obtain the PV correction curve; it calculates the wind energy capture efficiency based on the wind speed characteristic data, and compensates the wind power (PV) characteristic curve according to the calculation results to output the wind power (PV) equivalent curve; after spatiotemporally coupling the PV correction curve and the wind power (PV) equivalent curve, it performs power generation weight optimization allocation and outputs the complementary power generation control command.

[0107] Furthermore, the system is also used to implement the following functions:

[0108] The EMS energy management module integrates an RTU, an IED, and a PMU, with the PMU connected to the solar power generation module and the wind power generation module, respectively.

[0109] Furthermore, the system is also used to implement the following functions:

[0110] Multiple sets of sample-coupled photovoltaic-wind power curves are obtained interactively, wherein the multiple sets of sample-coupled photovoltaic-wind power curves have multiple sample power generation weight ratio identifiers; a wind-solar joint volt-ampere characteristic model is constructed based on the multiple sets of sample-coupled photovoltaic-wind power curves and multiple sample power generation weight ratios; a sliding segmentation window is preset, and the photovoltaic IV correction curve and the wind power PV equivalent curve are segmented into M sets of local photovoltaic-wind power curves using the sliding segmentation window; the M sets of local photovoltaic-wind power curves are synchronized to the wind-solar joint volt-ampere characteristic model for curve similarity comparison, and M sample power generation weight ratios are output; the M sample power generation weight ratios are time-weighted to output a target power generation weight ratio; the IED outputs the target power generation weight ratio as the complementary power generation control command to the complementary power generation module.

[0111] Furthermore, the system is also used to implement the following functions:

[0112] The PMU collects instantaneous photovoltaic current and instantaneous photovoltaic voltage from the solar power generation module; the PMU collects instantaneous wind power from the wind power generation module; calculates instantaneous power generation based on the instantaneous photovoltaic current, instantaneous photovoltaic voltage, and instantaneous wind power; calculates instantaneous power deficit based on the local preset load power demand and the instantaneous power generation; and runs the BMS system's intelligent energy storage module to perform instantaneous power compensation for the complementary power generation module based on the instantaneous power deficit, until the complementary power generation control command is updated.

[0113] Example 3, Figure 4This is a schematic diagram of the electronic device provided for the offshore floating wind-solar hybrid power generation method of the present invention, showing an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 4 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 4 Taking a processor 21 as an example, the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0114] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0115] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0116] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for offshore floating wind-solar hybrid power generation, characterized in that, The method includes: The three-dimensional mooring module fixes the offshore floating power generation equipment to the target mooring area based on the real-time mooring information sent by the control terminal. When the anchoring control behavior of the offshore floating power generation equipment in the target anchoring area meets the preset environmental condition acquisition window, the multi-sensor control array is activated to acquire environmental condition data. The multi-sensor control array collects and transmits power generation environmental condition data back to the EMS energy management module. The EMS energy management module makes energy supply path decisions based on the dynamic output voltage-current characteristics and the power generation environment operating data, and outputs complementary power generation control commands. During the process of the complementary power generation module executing multi-mode coordinated output according to the complementary power generation control command, the BMS system intelligent storage module performs real-time power compensation for the complementary power generation module. The EMS energy management module makes power supply path decisions based on the dynamic output voltage-current characteristics and the power generation environment operating data, and outputs complementary power generation control commands. The method includes: The EMS energy management module collects the dynamic output voltage-current characteristics via the PMU, wherein the dynamic output voltage-current characteristics include the photovoltaic IV characteristic curve and the wind power PV characteristic curve; Attenuation analysis was performed on the panel temperature characteristic data and photovoltaic irradiance characteristic data, and the photovoltaic IV characteristic curve was corrected based on the analysis results to obtain the photovoltaic IV corrected curve; Wind energy capture efficiency is calculated based on wind speed characteristic data, and the wind power PV characteristic curve is compensated according to the calculation results to output the wind power PV equivalent curve. After the photovoltaic IV correction curve and the wind power PV equivalent curve are spatiotemporally coupled, the power generation weight is optimized and allocated, and the complementary power generation control command is output. The EMS energy management module integrates RTU, IED and PMU, and the PMU is connected to the solar power generation module and the wind power generation module respectively. After spatiotemporally coupling the photovoltaic IV correction curve and the wind power PV equivalent curve, the power generation weight is optimized and allocated, and the complementary power generation control command is output. The method includes: Multiple sets of sample coupled photovoltaic-wind power curves are obtained interactively, wherein the multiple sets of sample coupled photovoltaic-wind power curves have multiple sample power generation weight ratio labels; A combined solar-wind power characteristic model is constructed based on the coupled photovoltaic-wind power curves of the multiple sets of samples and the power generation weight ratios of multiple samples. A preset sliding segmentation window is used to divide the photovoltaic IV correction curve and the wind power PV equivalent curve into M groups of local photovoltaic-wind power curves. The M local photovoltaic-wind power curves are synchronized to the wind-solar joint volt-ampere characteristic model for curve similarity comparison, and the power generation weight ratios of M samples are output. The time-weighted power generation weight ratio of the M samples is used to output the target power generation weight ratio; The IED outputs the target power generation weight ratio as the complementary power generation control command to the complementary power generation module; During the process of the complementary power generation module executing multi-mode coordinated output according to the complementary power generation control command, the BMS system intelligent storage module performs real-time power compensation for the complementary power generation module, the method including: The PMU collects instantaneous photovoltaic current and instantaneous photovoltaic voltage from the solar power module; The PMU collects instantaneous wind power from the wind power generation module; Instantaneous power generation is calculated based on the instantaneous photovoltaic current, instantaneous photovoltaic voltage, and instantaneous wind power. Calculate the instantaneous power gap based on the local preset load power demand and the instantaneous power generation; Based on the instantaneous power gap, the BMS system's intelligent energy storage module performs instantaneous power compensation for the complementary power generation module until the complementary power generation control command is updated.

2. The offshore floating wind-solar hybrid power generation method as described in claim 1, characterized in that, When the anchoring control behavior of the offshore floating power generation equipment in the target anchoring area meets the preset environmental condition acquisition window, the multi-sensor control array is activated to acquire environmental condition data. The method includes: The intelligent control unit receives the real-time anchoring information sent by the control terminal; After sending the real-time mooring information to the three-dimensional mooring module, the intelligent control unit activates the environmental condition acquisition window. After the anchorage control time of the offshore floating power generation equipment in the target anchorage area reaches the environmental condition acquisition window, the intelligent control unit sends an operation condition acquisition activation command to the multi-sensor control array to activate the multi-sensor control array to acquire environmental condition data.

3. The offshore floating wind-solar hybrid power generation method as described in claim 2, characterized in that, The three-dimensional mooring module, based on real-time mooring information sent by the control terminal, fixes the offshore floating power generation equipment to the target mooring area. The method includes: The anchorage information is analyzed to obtain the anchorage area constraints; Based on the constraints of the anchorage area, the dual-mode positioning system is activated to perform a three-dimensional topographic contour scan of the sea area and obtain the topographic contour of the anchorage seabed. The target anchorage area is obtained by identifying hazardous features of the seabed topographic contour of the anchorage and filtering them. A three-dimensional anchorage network is constructed based on the regional distribution characteristics of the target anchorage area; By deploying anchor chains in a coordinated manner according to the three-dimensional anchoring network, the floating power generation equipment is fixed in the target anchoring area.

4. The offshore floating wind-solar hybrid power generation method as described in claim 1, characterized in that, The multi-sensor control array collects and transmits power generation environmental condition data back to the EMS energy management module, and the method includes: A panel temperature sensing array and a photovoltaic irradiance monitoring array are deployed on the solar power generation module of the complementary power generation module; An ultrasonic wind speed monitoring array is deployed in the wind power generation module of the complementary power generation module, wherein the panel temperature sensing array, the photovoltaic irradiance monitoring array and the ultrasonic wind speed monitoring array constitute the multi-sensor control array; The panel temperature sensing array, photovoltaic irradiance monitoring array, and ultrasonic wind speed monitoring array respectively collect panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data, and transmit the panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data back to the RTU of the EMS energy management module. The panel temperature characteristic data, photovoltaic irradiance characteristic data, and wind speed characteristic data constitute the power generation environment condition data.

5. A floating offshore wind-solar hybrid power generation system, characterized in that, The system is used to implement the offshore floating wind-solar hybrid power generation method according to any one of claims 1-4, the system comprising: The real-time mooring information transmission unit is used by the three-dimensional mooring module to fix the offshore floating power generation equipment to the target mooring area based on the real-time mooring information sent by the control terminal. The data acquisition unit is used to activate the multi-sensor control array to collect environmental condition data when the anchoring control behavior of the offshore floating power generation equipment in the target anchoring area meets the preset environmental condition acquisition window. The operating condition data feedback unit is used by the multi-sensor control array to collect and transmit power generation environment operating condition data back to the EMS energy management module. The control command output unit is used by the EMS energy management module to make energy supply path decisions based on the dynamic output voltage-ampere characteristics and the power generation environment operating data, and output complementary power generation control commands. The real-time power compensation unit is used to run the BMS system intelligent storage module to perform real-time power compensation for the complementary power generation module during the process of the complementary power generation module executing multi-mode collaborative output according to the complementary power generation control command.

6. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is used to execute the offshore floating wind-solar hybrid power generation method according to any one of claims 1 to 4.

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