MPPT Optimization Strategy Based On RBF Neural Network For Wind And Solar Complementary Unmanned Monitoring Ship

Unmanned monitoring vessels are advantageous over manned ones because of their smaller size,weight,and power demands,along with their lower energy consumption.This makes them a viable substitute for human resources in high-risk waters and provides a relatively safe and low-cost solution for conducting long-term monitoring tasks in the waters.At the same time,the unmanned monitoring vessel has good navigation and visualization functions and can perform tasks autonomously for a long time only by equipping itself with different combinations of multiple sensors.It can be used not only to collect information on buoys,weather,and vessel sources,but also to perform tasks such as monitoring weather data,identifying water pollution or mapping the marine environment and biological population data,and to collect data on poachers’ activities in protected areas to help combat illegal hunting and killing.Consequently,unmanned monitoring vessels research has gained broad attention in military and civilian sectors,both domestically and internationally.Due to the relatively remote and complex operating environment of unmanned monitoring vessels and the high requirements for concealment,it is difficult to use fossil energy that requires frequent human resupply,which is not conducive to the long-term sustainable operation of unmanned monitoring vessels.In order to reduce the number of unmanned monitoring ship power recharge,certain scholars suggest that the water surface can offer adequate wind and solar energy(WASE),and have put forward the concept of utilizing WASE as the power source for small unmanned monitoring vessels in a complementary manner.This is an effective means to achieve "one boat with multiple energy",combining WASE organically,which not only effectively avoids the difficulty of fossil energy needing frequent human resupply and saves human cost,but also improves the invisibility of the unmanned boat for long-term monitoring,and helps the unmanned boat to be sustainable and stable under various natural conditions.operation,it can also reduce the damage to the earth’s environment The use of maximum power point tracking(MPPT)is common in wind and photovoltaic power generation(WAPPG)as natural environmental conditions do not always remain constant.It is aiming to enhance the efficiency and stability of their output.Hence,the persistent enhancement and optimization of the algorithm and control strategy employed in MPPT control can facilitate the rapid and precise tracking of the MPP in WAPPG systems,this improvement can enhance the power generation system’s output stability and minimize the influence of weather conditions on the range of the unmanned monitoring vessels.It also helps to reduce the impact of weather on the range of the unmanned monitoring vessel and improve its long-term working reliability.The conventional MPPT control strategy for the complementary energy landscape system typically involves individual reconciliation of the WAPPG subsystem to attain their respective maximum power points.Nonetheless,the output voltage at the maximum power point of the two subsystems is frequently dissimilar under identical environmental circumstances.Due to the voltage equality of each branch in the parallel circuit,one of the subsystems cannot produce output at the maximum power point voltage under the present environmental conditions,thereby restricting the combined output power of the complementary energy landscape system.To prevent this issue and enhance the consolidated output power of the WAPPG system,this study suggests an RBF neural network-based approach to enhance the MPPT control strategy after evaluating the present MPPT control strategy for WAPPG.The WAPPG subsystems are linked via the RBF neural network.Inputs such as northward wind speed,meteorological data from the environment are utilized to calculate and output the duty cycle,and the output voltage of the complementary system is uniformly regulated by the Boost circuit.The RBF neural network is created with the aid of computer and trained with 160 sets of weather data selected as training and testing samples,ensuring that the network adheres to the error specifications.Then,the MPPT control performance of the optimized strategy is experimentally verified by simulation software.Additionally,the perturbation observation method is implemented to evaluate the optimized strategy’s feasibility under different circumstances,a control group utilizing distinct MPPT for WAPPG is implemented.The simulation findings demonstrate that the method’s accuracy,effectiveness,and responsiveness can be ensured.which provides the small unmanned monitoring vessel to improve the long-term This provides a way to improve the long-term endurance and stable operation capability of small unmanned monitoring vessels.