Wave energy power generation efficiency optimization control method and device based on artificial intelligence

Abstract

The invention discloses a wave power generation efficiency optimization control method and device based on artificial intelligence. The method comprises the steps that S1, a marine environment data set is collected; s2, forming a preprocessed marine environment data set; s3, according to the preprocessed marine environment data set and the structure parameters of the wave energy power generation system, constructing an optimization objective function; s4, performing global search on the optimization objective function by using a bitter fish optimization algorithm, initializing an optimization population, and determining a plurality of candidate optimal parameter regions; s5, adopting an adaptive jellyfish search algorithm to perform local fine search on the area where the candidate optimal parameters are located, updating and optimizing the key control parameters, and obtaining the key control parameters for further improving the electric energy conversion efficiency; and S6, the key control parameters are applied to the wave energy power generation system, and dynamic closed-loop feedback control is formed. According to the invention, the system is always in an optimal working state, so that the electric energy conversion efficiency is further improved, and the energy loss is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wave energy power generation, and in particular to an artificial intelligence-based wave energy power generation efficiency optimization control method and device. Background Art

[0002] With the increasing global demand for renewable energy, wave energy has attracted widespread attention as a green and sustainable form of energy. Current wave energy power generation control strategies mostly use fixed parameters or empirical formulas to construct optimization objective functions and constraints, failing to fully consider the dynamic changes and nonlinear characteristics of the wave environment. Static control methods have obvious limitations in practical applications: on the one hand, it is difficult for the system to achieve timely and accurate dynamic response and parameter adjustment when encountering sudden sea conditions or extreme environments; on the other hand, traditional optimization algorithms are insufficient in global search and local fine optimization and are often prone to falling into local optimal states, making it impossible to fully explore and mine the optimal operating parameters of the wave energy power generation system, resulting in energy conversion efficiency and output power stability difficult to achieve ideal levels.

[0003] However, existing technologies generally neglect the systematic processing of marine environmental monitoring data and the construction of intelligent decision-making capabilities. Most current wave energy power generation systems rely on fixed control strategies or manually set parameters, lacking a deep understanding of complex environmental factor data and the ability to dynamically respond. In fact, the availability of wave energy is closely related to the dynamic changes of multiple ocean factors such as wave height, period, wind speed, and current speed. To achieve optimal control of system performance, there is an urgent need for an intelligent control method that can receive and process multi-source environmental monitoring data in real time, analyze the impact of the environment on power generation efficiency, and dynamically generate key control parameters to adapt to rapidly changing sea conditions. Summary of the Invention

[0004] One purpose of the present invention is to propose an artificial intelligence-based wave energy power generation efficiency optimization control method and device. The present invention ensures that the system is always in the best working state, thereby further improving the power conversion efficiency and significantly reducing energy loss.

[0005] According to an embodiment of the present invention, a wave energy generation efficiency optimization control method based on artificial intelligence includes the following steps: S1. Collect marine environment datasets; S2. Preprocessing the collected marine environment data set to form a preprocessed marine environment data set; S3. Constructing an optimization objective function based on the preprocessed marine environment data set and the structural parameters of the wave energy power generation system, and determining the corresponding constraints; S4. Using the bitterfish optimization algorithm to perform a global search for the optimization objective function, initialize the optimization population, and perform a rough search for key control parameters of the wave energy power generation system based on fitness evaluation to determine several candidate optimal parameter regions; S5. Use an adaptive jellyfish search algorithm to perform a localized, refined search of the candidate optimal parameter region, update and optimize key control parameters, and obtain key control parameters that further improve power conversion efficiency. S6. Apply key control parameters to the wave energy power generation system, and combine the real-time monitored marine environmental data with the power generation system operating status to form a dynamic closed-loop feedback control to adjust the wave energy power generation system parameters in real time.

[0006] Optionally, the S1 includes the following steps: S11. Marine environment dataset defining wave energy generation systems The marine environment dataset includes significant wave height, average wave period, average wind speed, and average current speed:

[0007] in, represents the effective wave height, represents the average wave period, represents the average wind speed, represents the average ocean current speed; S12. Construct a time series set of environmental data based on the measurement requirements of the wave energy power generation system, and define the time series of the wave energy power generation system. Marine environment datasets ; S13. Use a multi-point measurement method to collect marine environmental datasets at different measurement points of the wave energy power generation system, and construct a marine environmental dataset with spatial distribution by combining the measurement points:

[0008] in, 、 、 、 Respectively indicate the measurement points The effective wave height, average wave period, average wind speed and average current speed at the location.

[0009] Optionally, the S2 includes the following steps: S21. Marine environment dataset Perform integrity checks and eliminate incomplete data records to ensure that each data record contains effective wave height, average wave period, average wind speed, and average current speed, forming a complete marine environment data set; S22. Perform outlier detection on the marine environment dataset and remove data points that exceed the range to form a marine environment dataset after outlier removal; S23. Using a noise filtering method to smooth the marine environment dataset after outliers are removed, constructing a filtered marine environment dataset; S24. Perform normalization on the filtered marine environment dataset so that all environmental parameters are normalized to the same numerical range to form a normalized marine environment dataset. ; S25. Based on time step For the normalized marine environment dataset Resampling is performed to ensure that the time intervals of the data are consistent, forming the final preprocessed marine environment dataset: .

[0010] Optionally, S3 includes the following steps: S31. Based on the preprocessed marine environment dataset Extract characteristic variables related to the operation of wave energy power generation system and construct environmental parameter vector , based on the pre-processed marine environment data set and the structural parameters of the wave energy power generation system, a mapping relationship between environmental parameters and key performance variables of the system is established:

[0011] in, is the mapping function, Represents the set of optimization variables of the wave energy power generation system, which is used to optimize the control of power conversion efficiency, output power stability and energy loss:

[0012] in, is the active power output of the wave energy generation system, is the conversion efficiency of the wave energy power generation system, is the movement angle of the wave energy conversion device, is the natural frequency of the wave energy device, is the damping coefficient of the wave energy power generation device; S32. Based on the optimization objectives of the wave energy power generation system, combined with the pre-processed marine environment dataset and optimized variable sets , establish the optimization objective function:

[0013] in, is the weight coefficient used to balance power conversion efficiency, output power stability and energy loss during the optimization process. is the power conversion efficiency, which indicates the ratio of wave energy effectively converted into electrical energy:

[0014] in, is the wave input power, which is determined by the effective wave height and the average wave period:

[0015] in, is the acceleration due to gravity, is the average wave period; Output power stability, indicating the stability of the generated power:

[0016] in, is the average output power of the wave energy generation system within the preset time, is the total number of time steps; is the energy loss, which indicates the energy loss during system operation:

[0017] in, is the resistance loss of the power generation system, is the generator current, is the mechanical energy loss, which depends on the average wave period and the movement angle of the wave energy conversion device ; S33. According to the operation constraints of the wave energy power generation system, set the optimization variable set Constraints:

[0018]

[0019] ;

[0020]

[0021] in, Represent the minimum and maximum output power respectively, represent the minimum and maximum conversion efficiencies, respectively. Represent the minimum and maximum movement angles, denote the minimum and maximum natural frequencies, respectively. denote the minimum and maximum damping coefficients respectively; Set environmental adaptability constraints to ensure that the wave energy generation system remains stable in complex marine environments:

[0022] in, Indicates the maximum power change rate allowed by the system; S34. Based on the construction of the optimization objective function and the setting of the constraints, the final optimization problem is formed:

[0023] in, represents the space of all feasible solutions that satisfy the constraints.

[0024] Optionally, the S4 includes the following steps: S41. Based on the optimization objective function and optimized variable sets Define the optimized population of environmentally adaptive bitterlings, set the parameter vectors of the optimized individuals, and adjust the population distribution according to the environmental dynamic characteristics of the wave energy generation system:

[0025] in, Optimizing the population size for bitter fish, Indicates the The optimization variable vector for each individual:

[0026] in, For the Active power output of each individual wave energy generation system, For the The conversion efficiency of individual wave energy power generation systems, For the The movement angle of each individual wave energy conversion device, For the The natural frequency of each individual wave energy device, For the The damping coefficient of each individual wave energy power generation device is calculated; the population is dynamically initialized and optimized based on the environmental characteristics of the wave energy power generation system, and the environmental impact factor is constructed based on the marine environmental data set. :

[0027] Through environmental factors Adjust the initial population distribution to adapt the initial population to the optimization calculation under different sea conditions:

[0028] in, for A random number between and are the minimum and maximum allowed values ​​of the optimization variables, respectively; S42. Optimizing the population Each individual in , combined with the environmental adaptability of the wave energy power generation system, an environmental constraint adaptive fitness function is constructed:

[0029] in, To adaptively adjust the weight of environmental constraints, reduce the weight when the sea conditions are stable and increase the weight when the sea conditions change drastically. is the environmental constraint penalty factor, which represents the fitness deviation of individuals in different marine environments:

[0030] in, It is the optimal solution under historical circumstances. Number of samples stored for the environment; S43. Based on the environment-adaptive bitterfish optimization strategy, a dynamic step-size adjustment mechanism is introduced during the search process to update the optimization variables:

[0031] in, For the Individuals in The set of optimized variables for each generation, is the current optimal individual, is the step size factor, is a random number between [0,1], is the adaptive disturbance amplitude; S44. Adopt dynamic aggregation strategy to enhance the optimization ability of bitter fish group, introduce dynamic aggregation strategy to improve the convergence speed of bitter fish group, and define the attraction between bitter fish individuals , simulating the behavior of bitter fish groups:

[0032] in, Avoid zero denominator, The larger the Tend to be individual ; When updating individual locations, consider group aggregation behavior:

[0033] in, Controlling the strength of interactions between individuals; S45. Iterate the S42-S44 process multiple times to finally determine the candidate optimal parameter region :

[0034] in, is the fitness threshold, and only the candidate optimal solutions that meet the target performance are retained.

[0035] Optionally, the S5 includes the following steps: S51. Based on the determined candidate optimal parameter region , construct the local search population of the adaptive jellyfish search algorithm and set the parameter vector of the local search individual:

[0036] in, Indicates the candidate area The key control parameter vector of each individual; S52. For local search population Each individual in , use the adaptive jellyfish search algorithm to perform local fine search and update individual parameters:

[0037] in, Indicates the Individuals in The parameter vector of the generation, is the individual parameter vector with the highest current fitness in the local search population, is the local search step factor, is the local perturbation weight, The local disturbance vector generated by the jellyfish movement mechanism reflects the jellyfish's exploration ability in the local area; S53. According to the change of individual fitness during the local search process, the step factor Make adaptive adjustments:

[0038] in, is the step size attenuation control coefficient, which is used to adjust the step size update rate; S54. Iterate steps S52 to S53 until the local search reaches the preset convergence condition or the maximum number of iterations are satisfied, and finally the key control parameters for further improving the power conversion efficiency are obtained:

[0039] in, Indicates when the local search ends The optimal key control parameters for each individual.

[0040] A wave energy power generation efficiency optimization control device based on artificial intelligence comprises a processor, a memory, and a wave energy power generation efficiency optimization control program stored in the memory and executable by the processor. When the wave energy power generation efficiency optimization control program is executed by the processor, an artificial intelligence-based wave energy power generation efficiency optimization control method is implemented.

[0041] The beneficial effects of the present invention are: (1) The present invention adopts a hybrid optimization strategy that combines the bitter fish optimization algorithm with the adaptive jellyfish search algorithm. Traditional optimization methods have the problems of low global search efficiency or local optimality. The present invention first uses the bitter fish optimization algorithm to perform a global search on the entire parameter space, and then optimizes the population distribution by adaptively adjusting the environment to quickly locate the candidate area of ​​the key parameters; then the adaptive jellyfish search algorithm is used to perform a local fine search on the candidate area, realizing precise search and local refinement in the parameter space, ensuring the accuracy of the control parameters and a significant improvement in the energy conversion efficiency of the system.

[0042] (2) The present invention introduces an environmental adaptability factor to perform real-time correction on the initialization and dynamic adjustment of the optimized population, so that the system can quickly respond to sudden changes in sea conditions and adjust the control parameters in a timely manner, thereby maintaining high output power stability and low energy loss in a complex and unstable marine environment, overcoming the defects of traditional methods that are highly dependent on fixed parameters and lack dynamic adaptability.

[0043] (3) This invention designs a closed-loop feedback control mechanism based on real-time marine environmental data and the operating status of the power generation system. During the optimization process, the system continuously collects the latest environmental and equipment data, dynamically updates key control parameters, and forms a feedback closed loop, achieving adaptive adjustment. The closed-loop mechanism can effectively cope with the variable interference factors in the marine environment, ensuring that the system is always in optimal working condition, thereby further improving power conversion efficiency and significantly reducing energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of an artificial intelligence-based wave energy generation efficiency optimization control method and device proposed by the present invention. DETAILED DESCRIPTION

[0045] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0046] refer to Figure 1 , a wave energy generation efficiency optimization control method based on artificial intelligence, comprising the following steps: S1. Collect marine environment datasets; Specifically, marine environmental data sets are collected in the target sea area, including wave data, tidal change data, wind speed data, and current speed data; and an optimization model for the clustered layout of wave energy devices is constructed based on the collected data. The optimization model takes maximizing the energy capture capacity per unit area, minimizing interference between devices, and minimizing the total tension and drift risk of the anchoring system as optimization goals, and constructs a multi-objective optimization expression.

[0047] S2. preprocessing the collected marine environment dataset to form a preprocessed marine environment dataset; S3. Construct an optimization objective function based on the preprocessed ocean environment dataset and the structural parameters of the wave energy power generation system, and determine the corresponding constraints; S4. Use the bitterfish optimization algorithm to perform a global search for the optimization objective function, initialize the optimization population, and perform a rough search for the key control parameters of the wave energy power generation system based on fitness evaluation to determine several candidate optimal parameter regions; S5. Use an adaptive jellyfish search algorithm to perform a localized, refined search of the candidate optimal parameter region, update and optimize key control parameters, and obtain key control parameters that further improve power conversion efficiency. S6. Apply key control parameters to the wave energy generation system and combine them with real-time monitored ocean environmental data and the system's operating status to form a dynamic closed-loop feedback control system. This allows for real-time adjustment of the wave energy generation system parameters. These parameters include the control device's response frequency and attitude to waves; controlling the damping and electromagnetic load characteristics of the power generation module; adjusting the output power's stable slope and fluctuation range; and maintaining the device's safe operation under unexpected sea conditions. Through dynamic, real-time adjustment of the wave energy generation system's parameters, the system can maintain optimal operating conditions in complex and rapidly changing ocean environments, achieving both improved energy conversion efficiency and stability.

[0048] In this embodiment, during system operation, the marine environmental data acquisition module records key elements such as effective wave height, period, and average wind speed at a high frequency (sampling once every 10 seconds). However, the parameter optimization calculation is not performed independently for each sampling point. Instead, to ensure the representativeness and statistical stability of the environmental data, the system sets a dynamic sliding time window ΔT, usually 2-5 minutes, to construct a short-term characteristic sequence of the marine environment. Within this time window, the system will perform the following processing on multiple sets of data at each measurement point: Short-term mean and variance calculation: used to measure the average state and volatility of factors such as effective wave height and period; Trend analysis: Determine whether key environmental variables have a trend of continuous increase or sudden change; Event triggering mechanism: A sudden increase in average wind speed exceeding the dynamic threshold and a rapid shortening of the average wave period will trigger an immediate optimization response.

[0049] Based on the above data processing flow, the shortest cycle of system optimization response is 1 minute and the longest is no more than 5 minutes to ensure: On the one hand, it can respond quickly to sudden sea conditions without causing energy loss or system instability due to long delays; on the other hand, it can avoid misjudgments or frequent parameter adjustments when environmental fluctuations have not yet formed a trend, ensuring the stability of system operation and the convergence of the controller.

[0050] In the specific implementation, the present invention also introduces a response delay scheduler module, which is based on the execution timestamp of the previous round of control parameter update. , combined with the current environmental data change rate, dynamically adjust the next round of optimization trigger time , to ensure that the response frequency is reasonable. For example: If the current sea conditions are stable, the delay scheduler will automatically extend the response interval to 5 minutes; If a synchronous fluctuation trend is detected at three consecutive measurement points, the response cycle will be proactively shortened to within 1 minute.

[0051] In addition, in order to avoid mechanical fatigue of the equipment caused by too frequent parameter updates, the present invention sets a minimum control parameter stabilization period ,During this period, unless the sudden sea condition exceeds the system warning threshold, ,the control instructions will not be actively updated.

[0052] In summary, this embodiment introduces a sliding time window mechanism, an event trigger mechanism, and a response delay scheduling strategy during the real-time adjustment process, which not only ensures the timeliness of the control response, but also fully considers the statistical characteristics of the marine environmental data and the engineering feasibility of the system operation, thereby achieving an organic unity between dynamic closed-loop and stable parameter adjustment.

[0053] In this embodiment, the monitoring device of the wave energy power generation system is usually integrated with the wave energy device body, but in special circumstances (such as when the distance between devices is too large), a split design may be adopted to increase the amount of data.

[0054] In this embodiment, S1 includes the following steps: S11. Marine environment dataset defining wave energy generation systems , the marine environment dataset includes significant wave height, average wave period, average wind speed, and average current speed:

[0055] in, represents the effective wave height, represents the average wave period, represents the average wind speed, represents the average ocean current speed; S12. Construct a time series set of environmental data based on the measurement requirements of the wave energy power generation system, and define the time series of the wave energy power generation system. Marine environment datasets ; S13. Use a multi-point measurement method to collect marine environmental datasets at different measurement points of the wave energy power generation system, and construct a marine environmental dataset with spatial distribution by combining the measurement points:

[0056] in, 、 、 、 Respectively indicate the measurement points The effective wave height, average wave period, average wind speed and average current speed at the location.

[0057] In this embodiment, S2 includes the following steps: S21. Marine environment dataset Perform integrity checks and eliminate incomplete data records to ensure that each data record contains effective wave height, average wave period, average wind speed, and average current speed, forming a complete marine environment data set; S22. Perform outlier detection on the marine environment dataset and remove data points that exceed the range to form a marine environment dataset after outlier removal; S23. Using a noise filtering method to smooth the marine environment dataset after outliers are removed, constructing a filtered marine environment dataset; S24. Perform normalization on the filtered marine environment dataset so that all environmental parameters are normalized to the same numerical range to form a normalized marine environment dataset. ; S25. Based on time step For the normalized marine environment dataset Resampling is performed to ensure that the time intervals of the data are consistent, forming the final preprocessed marine environment dataset: .

[0058] In this embodiment, after forming the final pre-processed marine environment data set, an environmental parameter mapping model based on machine learning is constructed. and structural parameters of wave energy generation system , construct the environment parameter vector Key performance variables of the system The mapping relationship between them: , using machine learning methods to optimize the mapping model: using neural networks and regression analysis to train the mapping relationship. Using hyperparameter optimization methods to dynamically adjust model parameters to improve prediction accuracy: in, is the set of model hyperparameters, is the model loss function. Combined with historical operation data, the incremental learning mechanism is used to continuously optimize the prediction accuracy: in, is the learning rate, is the model parameter gradient.

[0059] In this embodiment, S3 includes the following steps: S31. Based on the preprocessed marine environment dataset Extract characteristic variables related to the operation of wave energy power generation system and construct environmental parameter vector , based on the pre-processed marine environment data set and the structural parameters of the wave energy power generation system, a mapping relationship between environmental parameters and key performance variables of the system is established:

[0060] in, is the mapping function, Represents the set of optimization variables of the wave energy power generation system, which is used to optimize the control of power conversion efficiency, output power stability and energy loss:

[0061] in, is the active power output of the wave energy generation system, is the conversion efficiency of the wave energy power generation system, is the movement angle of the wave energy conversion device, is the natural frequency of the wave energy device, is the damping coefficient of the wave energy power generation device; S31. Introduce machine learning model for feature mapping based on machine learning model , mapping environment parameters To optimize variables , ensuring the adaptability of the objective function construction: , optimize the dynamic update mechanism of the objective function: combine the operating data and adjust the objective function weight to make it conform to the dynamic characteristics of the wave energy power generation system: in, is the adjustment coefficient, The weight update amount calculated based on historical data.

[0062] S32. Based on the optimization objectives of the wave energy power generation system, combined with the pre-processed marine environment dataset and optimized variable sets , establish the optimization objective function:

[0063] in, is the weight coefficient used to balance power conversion efficiency, output power stability and energy loss during the optimization process. is the power conversion efficiency, which indicates the ratio of wave energy effectively converted into electrical energy:

[0064] in, is the wave input power, which is determined by the effective wave height and the average wave period:

[0065] in, is the acceleration due to gravity, is the average wave period; Output power stability, indicating the stability of the generated power:

[0066] in, is the average output power of the wave energy generation system within the preset time, is the total number of time steps; is the energy loss, which indicates the energy loss during system operation:

[0067] in, is the resistance loss of the power generation system, is the generator current, is the mechanical energy loss, which depends on the average wave period and the movement angle of the wave energy conversion device ; S33. According to the operation constraints of the wave energy power generation system, set the optimization variable set Constraints:

[0068]

[0069] ;

[0070]

[0071] in, Represent the minimum and maximum output power respectively, represent the minimum and maximum conversion efficiencies, respectively. Represent the minimum and maximum movement angles, denote the minimum and maximum natural frequencies, respectively. denote the minimum and maximum damping coefficients respectively; Set environmental adaptability constraints to ensure that the wave energy generation system remains stable in complex marine environments:

[0072] in, Indicates the maximum power change rate allowed by the system; S34. Based on the construction of the optimization objective function and the setting of the constraints, the final optimization problem is formed:

[0073] in, represents the space of all feasible solutions that satisfy the constraints.

[0074] In this embodiment, S4 includes the following steps: S41. Based on the optimization objective function and optimized variable sets Define the optimized population of environmentally adaptive bitterlings, set the parameter vectors of the optimized individuals, and adjust the population distribution according to the environmental dynamic characteristics of the wave energy generation system:

[0075] in, Optimizing the population size for bitter fish, Indicates the The optimization variable vector for each individual:

[0076] in, For the Active power output of each individual wave energy generation system, For the The conversion efficiency of individual wave energy power generation systems, For the The movement angle of each individual wave energy conversion device, For the The natural frequency of each individual wave energy device, For the The damping coefficient of each individual wave energy power generation device is calculated; the population is dynamically initialized and optimized based on the environmental characteristics of the wave energy power generation system, and the environmental impact factor is constructed based on the marine environmental data set. :

[0077] Through environmental factors Adjust the initial population distribution to adapt the initial population to the optimization calculation under different sea conditions:

[0078] in, for A random number between and are the minimum and maximum allowed values ​​of the optimization variables, respectively; S42. Optimizing the population Each individual in , combined with the environmental adaptability of the wave energy power generation system, an environmental constraint adaptive fitness function is constructed:

[0079] in, To adaptively adjust the weight of environmental constraints, reduce the weight when the sea conditions are stable and increase the weight when the sea conditions change drastically. is the environmental constraint penalty factor, which represents the fitness deviation of individuals in different marine environments:

[0080] in, It is the optimal solution under historical circumstances. Number of samples stored for the environment; S43. Based on the environment-adaptive bitterfish optimization strategy, a dynamic step-size adjustment mechanism is introduced during the search process to update the optimization variables:

[0081] in, For the Individuals in The set of optimized variables for each generation, is the current optimal individual, is the step size factor, is a random number between [0,1], is the adaptive disturbance amplitude; S44. Adopt dynamic aggregation strategy to enhance the optimization ability of bitter fish group, introduce dynamic aggregation strategy to improve the convergence speed of bitter fish group, and define the attraction between bitter fish individuals , simulating the behavior of bitter fish groups:

[0082] in, Avoid zero denominator, The larger the Tend to be individual ; When updating individual locations, consider group aggregation behavior:

[0083] in, Controlling the strength of interactions between individuals; S45. Iterate the S42-S44 process multiple times to finally determine the candidate optimal parameter region :

[0084] in, is the fitness threshold, and only the candidate optimal solutions that meet the target performance are retained.

[0085] In this embodiment, S5 includes the following steps: S51. Based on the determined candidate optimal parameter region , construct the local search population of the adaptive jellyfish search algorithm and set the parameter vector of the local search individual:

[0086] in, Indicates the candidate area The key control parameter vector of each individual; S52. For local search population Each individual in , use the adaptive jellyfish search algorithm to perform local fine search and update individual parameters:

[0087] in, Indicates the Individuals in The parameter vector of the generation, is the individual parameter vector with the highest current fitness in the local search population, is the local search step factor, is the local perturbation weight, The local disturbance vector generated by the jellyfish movement mechanism reflects the jellyfish's exploration ability in the local area; S53. According to the change of individual fitness during the local search process, the step factor Make adaptive adjustments:

[0088] in, is the step size attenuation control coefficient, which is used to adjust the step size update rate; S54. Iterate steps S52 to S53 until the local search reaches the preset convergence condition or the maximum number of iterations are satisfied, and finally the key control parameters for further improving the power conversion efficiency are obtained:

[0089] in, Indicates when the local search ends The optimal key control parameters for each individual.

[0090] In this implementation, an automated control system is introduced to adjust parameters, using a PID controller or model predictive control to optimize key control parameters of the wave energy generation system: PID control: in, is the error between the expected value and the current value, are PID parameters.

[0091] MPC Control: in, For the prediction time domain, is the target parameter, For control input.

[0092] Set the safety threshold for parameter adjustment and the safe operating range to prevent excessive parameter adjustment from affecting device stability: If the threshold conditions are violated, the system triggers the safety protection mechanism and adjusts the parameters to return to a safe range.

[0093] Added new optimization parameters for power generation devices and expanded optimization variables , including the distributed layout of wave energy generation devices and electromagnetic parameter adjustment

[0094] Optimize the distributed layout of equipment, using bitterfish optimization + jellyfish search to optimize the distribution of power generation equipment in the ocean in, is the energy loss between power generation devices, Arrange parameters for the device.

[0095] A wave energy power generation efficiency optimization control device based on artificial intelligence includes a processor, a memory, and a wave energy power generation efficiency optimization control program stored in the memory and executable by the processor. When the wave energy power generation efficiency optimization control program is executed by the processor, an artificial intelligence-based wave energy power generation efficiency optimization control method is implemented.

[0096] Example: In this test scenario, the wave energy power generation system adopted a multi-point deployment strategy, with three subsystems deployed. These subsystems were located in three sub-areas around the main control platform P0 (coordinates 37.5003°N, 120.3003°E): Area A, Area B, and Area C. These three subsystems formed a triangular network with a radius of approximately 800 meters. Each subsystem was equipped with a wave energy conversion device, a monitoring sensor module, and an edge computing unit for local data collection and preprocessing.

[0097] Measuring point A is located approximately 100 meters west of the power generation device in Area A. It is mainly used to collect local wave characteristics in the sea area where the subsystem is located, which is used to guide the P0 central control system to adjust the parameters of the power generation equipment in Area A. Measurement point B is located 70 meters south-east of the power generation device in Area B. It provides independent environmental input for Area B and also serves as a boundary point for sharing data with Area C. Measuring point C is close to the northeast edge of area C, about 780 meters away from the main control platform. It is mainly used as a remote emergency sea condition monitoring point to detect the overall sea condition change trend and rapid warning.

[0098] At 2:30 a.m. on July 15, 2023, the monitoring system of a wave energy power station near the coast of a certain province received the latest ocean environmental data from measuring point A (latitude and longitude coordinates are 37.5001°N, 120.3005°E). The data showed that the current effective wave height was 1.8 meters, the average wave period was 7.2 seconds, the average wind speed was 5.8 meters per second, and the average current speed was 1.2 meters per second. At this time, the real-time power output of the power generation system was 470kW, the energy conversion efficiency was only 41.3%, and the power fluctuation was large, with the maximum fluctuation range reaching ±55kW.

[0099] The system's control strategy adopts a central coordination + local autonomy architecture. When the data changes at monitoring points A and B are small and the equipment is running stably, the local edge nodes can autonomously complete parameter adjustments. When multiple measuring points undergo drastic changes at the same time, the main control platform P0 will coordinate the control strategies of the three areas and achieve rapid response across the entire region.

[0100] At the same time, monitoring point B (approximately 700 meters from point A) recorded different sea conditions: an effective wave height of 2.0 meters, an average wave period of 7.5 seconds, an average wind speed of 6.1 meters per second, and an average current speed of 1.4 meters per second. However, the power generation system was still operating according to the traditional fixed parameter mode and failed to adjust to the environmental data of measurement point B. This caused the equipment power output to fluctuate further, and at 2:45 a.m. it dropped to 430kW. The system energy loss accumulated to 21kWh within 15 minutes.

[0101] To verify the optimization control method of the present invention, the system started the optimization calculation process based on the bitter fish optimization algorithm and the adaptive jellyfish search algorithm at 2:50. First, the system initialized the optimization population based on the real-time collected marine environmental data and conducted a global search. At 2:53, it determined five candidate optimal parameter areas and entered the local fine search stage. At 2:55, the system finally determined the new key control parameters: the damping coefficient was adjusted to 0.78, the motion angle was optimized to 35.2 degrees, and the natural frequency of the power generation system was adjusted to 0.92Hz, which were immediately applied to the wave energy power generation system.

[0102] At 2:57 after the optimized parameters were applied, the system's output power quickly rebounded to 490kW, and the power fluctuation range was narrowed to within ±10kW. By 3:00 in the morning, the energy conversion efficiency increased to 47.5%, an increase of 6.2 percentage points compared to 41.3% at 2:30. The system analyzed the latest sea condition data at 3:05 and found that the average wind speed began to gradually weaken to 5.4 meters per second, and the average wave period extended to 7.8 seconds. The power generation system was optimized and adjusted again based on the dynamic closed-loop feedback control mechanism, and finally stabilized the power at around 500kW. The energy loss during this period was reduced to 9.5kWh.

[0103] In order to further verify the adaptability of the present invention under extreme environmental changes, the testing team conducted a sea condition sudden change scenario test at 10:00 am on July 16, 2023. At 9:55 am that day, the sea conditions were stable, the effective wave height was 2.2 meters, the average wave period was 7.9 seconds, the average wind speed was 5.9 meters / second, and the power output of the power generation system was stable at 510kW. However, at 10:02, the monitoring point C (about 800 meters away from point B) detected that the average wind speed suddenly rose to 7.5 meters / second, the average current speed increased to 1.7 meters / second, and the average wave period shortened to 7.1 seconds. This sudden change made it impossible for the traditional control method to respond quickly in a short period of time, causing the system power to drop sharply to 450kW at 10:05, and the energy loss reached 8.3kWh within 3 minutes.

[0104] The method of the present invention initiated emergency optimization and adjustment at 10:06. The system automatically detected a sudden change in the environment and immediately triggered an adaptive jellyfish search algorithm to perform a local fine search. At 10:08, the optimization control system adjusted key control parameters, optimized the motion angle of the wave energy conversion device to 37.5 degrees, adjusted the damping coefficient of the generator to 0.81, and completed the parameter update at 10:09, causing the output power to quickly recover to 495kW. By 10:12, the power was stabilized at around 510kW, and the fluctuation range was controlled within ±8kW, which was significantly reduced compared to the ±50kW fluctuation range of the traditional method, and the energy conversion efficiency was improved to 48.2%.

[0105] To evaluate the long-term optimization effect of the present invention, the testing team conducted a 7-day comparative experiment between the traditional method and the method of the present invention from July 10, 2023 to July 17, 2023. The results are as follows: The traditional method has an average power fluctuation of ±45kW over the entire test period, with energy conversion efficiency remaining stable at around 42.1% and total energy loss reaching 280kWh. The average power fluctuation range of the method of the present invention within 7 days is only ±12kW, the average energy conversion efficiency is 48.7%, and the total energy loss is controlled within 140kWh, which is 50% lower than that of traditional methods.

[0106] In addition, the test team selected 6 sets of sea condition data for detailed comparison: Table 1 Detailed comparison of 6 sets of sea condition data between the present invention and the traditional invention

[0107] It can be seen from the above data that under the same sea conditions, the power output of the method of the present invention is about 25kW higher than that of the traditional method on average, and the energy conversion efficiency is improved by about 6 percentage points. At the same time, the method of the present invention has stronger adaptability under complex environmental changes. When the sea conditions change drastically, it can quickly optimize parameters to ensure the stable operation of the power generation system and avoid power loss caused by sudden environmental changes.

[0108] The test data fully demonstrates that the optimization control method of the present invention can significantly improve the energy conversion efficiency of wave energy power generation systems, reduce energy loss, and provide more stable power output under drastic changes in the marine environment in practical applications. This enables the wave energy power generation system to have stronger environmental adaptability and higher operational stability, providing a more reliable and efficient technical solution for future large-scale ocean wave energy development.

[0109] The present invention adopts a hybrid optimization strategy that combines the bitter fish optimization algorithm with the adaptive jellyfish search algorithm. Traditional optimization methods have the problems of low global search efficiency or local optimality. The present invention first uses the bitter fish optimization algorithm to perform a global search of the entire parameter space, and quickly locates the candidate area of ​​key parameters by optimizing the population distribution through environmental adaptive adjustment; then uses the adaptive jellyfish search algorithm to perform a local fine search of the candidate area, realizing precise search and local refinement in the parameter space, ensuring the accuracy of control parameters and a significant improvement in the system energy conversion efficiency.

[0110] The present invention introduces an environmental adaptability factor to perform real-time corrections on the initialization and dynamic adjustment of the optimized population, enabling the system to quickly respond and adjust control parameters in a timely manner when faced with sudden changes in sea conditions, thereby maintaining high output power stability and low energy loss in complex and unstable marine environments, overcoming the defects of traditional methods that rely heavily on fixed parameters and lack dynamic adaptability.

[0111] The present invention designs a closed-loop feedback control mechanism based on real-time marine environmental data and the operating status of the power generation system. During the optimization process, the system continuously collects the latest environmental and equipment data, dynamically updates key control parameters, forms a feedback closed loop, and realizes adaptive adjustment. The closed-loop mechanism can effectively cope with the variable interference factors in the marine environment, ensuring that the system is always in the best working state, thereby further improving the power conversion efficiency and significantly reducing energy loss.

[0112] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A wave energy generation efficiency optimization control method based on artificial intelligence, characterized in that: The steps include: S1. Collect marine environment data sets; S2. Preprocessing the collected marine environment data set to form a preprocessed marine environment data set; S3. Constructing an optimization objective function based on the preprocessed marine environment data set and the structural parameters of the wave energy power generation system, and determining the corresponding constraints; S4. Using the bitterfish optimization algorithm to perform a global search for the optimization objective function, initialize the optimization population, and perform a rough search for key control parameters of the wave energy power generation system based on fitness evaluation to determine several candidate optimal parameter regions; S5. Use an adaptive jellyfish search algorithm to perform a localized, refined search of the candidate optimal parameter region, update and optimize key control parameters, and obtain key control parameters that further improve power conversion efficiency. S6. Apply key control parameters to the wave energy power generation system, and combine the real-time monitored marine environmental data with the power generation system operating status to form a dynamic closed-loop feedback control to adjust the wave energy power generation system parameters in real time.

2. The method for optimizing and controlling wave energy generation efficiency based on artificial intelligence according to claim 1, characterized in that: Said S1 comprises the following steps: S11. Marine environment dataset defining wave energy generation systems , the marine environment data set includes significant wave height, average wave period, average wind speed, and average current speed; S12. Construct a time series set of environmental data based on the measurement requirements of the wave energy power generation system, and define the time series of the wave energy power generation system. Marine environment datasets ; S13. Use a multi-point measurement method to collect marine environmental datasets at different measurement points of the wave energy power generation system, and construct a marine environmental dataset with spatial distribution by combining the measurement points.

3. The method for optimizing and controlling wave energy generation efficiency based on artificial intelligence according to claim 1, characterized in that: The S2 comprises the following steps: S21. Marine environment dataset Perform integrity checks and eliminate incomplete data records to ensure that each data record contains effective wave height, average wave period, average wind speed, and average current speed, forming a complete marine environment data set; S22. Perform outlier detection on the marine environment dataset and remove data points that exceed the range to form a marine environment dataset after outlier removal; S23. Using a noise filtering method to smooth the marine environment dataset after outliers are removed, constructing a filtered marine environment dataset; S24. Perform normalization on the filtered marine environment dataset so that all environmental parameters are normalized to the same numerical range to form a normalized marine environment dataset. ; S25. Based on time step For the normalized marine environment dataset Resampling is performed to ensure that the time intervals of the data are consistent, forming the final preprocessed marine environment dataset.

4. The method for optimizing and controlling wave energy generation efficiency based on artificial intelligence according to claim 1, characterized in that: The S3 includes the following steps: S31. Based on the preprocessed marine environment dataset Extract characteristic variables related to the operation of wave energy power generation system and construct environmental parameter vector , based on the pre-processed marine environment data set and the structural parameters of the wave energy power generation system, establish the mapping relationship between environmental parameters and key performance variables of the system ; S32. Based on the optimization objectives of the wave energy power generation system, combined with the pre-processed marine environment dataset and optimized variable sets , establish the optimization objective function; S33. Set the optimization variable set according to the operating constraints of the wave energy power generation system Constraints; Set environmental adaptability constraints to ensure that the wave energy generation system remains stable in complex marine environments; S34. Based on the construction of the optimization objective function and the setting of the constraints, the final optimization solution is formed.

5. The method for optimizing and controlling wave energy generation efficiency based on artificial intelligence according to claim 1, characterized in that: The S4 comprises the following steps: S41. Based on the optimization objective function and optimized variable sets Define the optimized population of environmentally adaptive bitterlings, set the parameter vectors of the optimized individuals, and adjust the population distribution according to the environmental dynamic characteristics of the wave energy generation system: ； in, Optimizing the population size for bitter fish, Indicates the The optimization variable vector for each individual: ； in, For the Active power output of each individual wave energy generation system, For the The conversion efficiency of individual wave energy power generation systems, For the The movement angle of each individual wave energy conversion device, For the The natural frequency of each individual wave energy device, For the The damping coefficient of each individual wave energy power generation device is calculated; the population is dynamically initialized and optimized based on the environmental characteristics of the wave energy power generation system, and the environmental impact factor is constructed based on the marine environmental data set. , through environmental factors Adjust the initial population distribution to adapt the initial population to the optimization calculation under different sea conditions; S42. Optimizing the population Each individual in ,Combined with the environmental adaptability of wave energy power generation system, an environmental constraint adaptive fitness function is constructed; S43. Based on the environment-adaptive bitterfish optimization strategy, a dynamic step size adjustment mechanism is introduced during the search process to update the optimization variables; S44. Adopt dynamic aggregation strategy to enhance the optimization ability of bitter fish group, introduce dynamic aggregation strategy to improve the convergence speed of bitter fish group, and define the attraction between bitter fish individuals ; S45. Iterate the S42-S44 process multiple times to finally determine the candidate optimal parameter region .

6. The method for optimizing and controlling wave energy generation efficiency based on artificial intelligence according to claim 5, characterized in that: The S5 comprises the following steps: S51. Based on the determined candidate optimal parameter region , construct the local search population of the adaptive jellyfish search algorithm and set the parameter vector of the local search individual; S52. For local search population Each individual in ,Adopt the adaptive jellyfish search algorithm to perform local fine search and update individual parameters; S53. According to the change of individual fitness during the local search process, the step factor Make adaptive adjustments; S54. Iterate steps S52 to S53 until the local search reaches the preset convergence condition or the maximum number of iterations is satisfied, and finally the key control parameters for further improving the power conversion efficiency are obtained.

7. An artificial intelligence-based wave energy generation efficiency optimization control device, characterized in that: The invention comprises a processor, a memory, and a wave energy power generation efficiency optimization control program stored in the memory and executable by the processor, wherein when the wave energy power generation efficiency optimization control program is executed by the processor, an artificial intelligence-based wave energy power generation efficiency optimization control method according to any one of claims 1 to 6 is implemented.

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