In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing ...In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.展开更多
Early detection of faults in photovoltaic(PV)arrays has always been the center of attention to maintain system efficiency and reliability.However,conventional protection devices have shown various deficiencies,especia...Early detection of faults in photovoltaic(PV)arrays has always been the center of attention to maintain system efficiency and reliability.However,conventional protection devices have shown various deficiencies,especially when dealing with less severe faults.Hence,artificial intelligence(AI)models,specifically machine learning(ML)have complemented the conventional protection devices to compensate for their limitations.Despite their obvious advantages,ML models have also shown several shortcomings,such as(i)most of them relied on a massive amount of training dataset to provide a fairly satisfying accuracy,(ii)not many of them were able to detect less severe faults,and(iii)those which were able to detect less severe faults could not produce high accuracy.To this end,the present paper proposes a state-of-the-art deep reinforcement learning(DRL)model based on deep Q-network(DQN)to overcome all the existing challenges in previous ML models for PV arrays fault detection and diagnosis.The model carries out a two-stage process employing two DQN-based agents which is not only able to accurately detect and classify(first stage)various faults in PV arrays,but it is also able to assess the severity of line-to-line(LL)and line-to-ground(LG)faults(second stage)in PV arrays using only a small training dataset.The training and testing datasets include several voltage and current values on PV array current-voltage(I-V)characteristic curve which is extracted using the variable load technique for PV array I-V curve extraction.The model has been implemented on an experimental standalone PV array and the results show outstanding accuracies of 98.61%and 100%when it is verified by testing datasets in the first and the second stage,respectively.展开更多
文摘In the current era of intelligent technologies,comprehensive and precise regional coverage path planning is critical for tasks such as environmental monitoring,emergency rescue,and agricultural plant protection.Owing to their exceptional flexibility and rapid deployment capabilities,unmanned aerial vehicles(UAVs)have emerged as the ideal platforms for accomplishing these tasks.This study proposes a swarm A^(*)-guided Deep Q-Network(SADQN)algorithm to address the coverage path planning(CPP)problem for UAV swarms in complex environments.Firstly,to overcome the dependency of traditional modeling methods on regular terrain environments,this study proposes an improved cellular decomposition method for map discretization.Simultaneously,a distributed UAV swarm system architecture is adopted,which,through the integration of multi-scale maps,addresses the issues of redundant operations and flight conflicts inmulti-UAV cooperative coverage.Secondly,the heuristic mechanism of the A^(*)algorithmis combinedwith full-coverage path planning,and this approach is incorporated at the initial stage ofDeep Q-Network(DQN)algorithm training to provide effective guidance in action selection,thereby accelerating convergence.Additionally,a prioritized experience replay mechanism is introduced to further enhance the coverage performance of the algorithm.To evaluate the efficacy of the proposed algorithm,simulation experiments were conducted in several irregular environments and compared with several popular algorithms.Simulation results show that the SADQNalgorithmoutperforms othermethods,achieving performance comparable to that of the baseline prior algorithm,with an average coverage efficiency exceeding 2.6 and fewer turning maneuvers.In addition,the algorithm demonstrates excellent generalization ability,enabling it to adapt to different environments.
文摘Early detection of faults in photovoltaic(PV)arrays has always been the center of attention to maintain system efficiency and reliability.However,conventional protection devices have shown various deficiencies,especially when dealing with less severe faults.Hence,artificial intelligence(AI)models,specifically machine learning(ML)have complemented the conventional protection devices to compensate for their limitations.Despite their obvious advantages,ML models have also shown several shortcomings,such as(i)most of them relied on a massive amount of training dataset to provide a fairly satisfying accuracy,(ii)not many of them were able to detect less severe faults,and(iii)those which were able to detect less severe faults could not produce high accuracy.To this end,the present paper proposes a state-of-the-art deep reinforcement learning(DRL)model based on deep Q-network(DQN)to overcome all the existing challenges in previous ML models for PV arrays fault detection and diagnosis.The model carries out a two-stage process employing two DQN-based agents which is not only able to accurately detect and classify(first stage)various faults in PV arrays,but it is also able to assess the severity of line-to-line(LL)and line-to-ground(LG)faults(second stage)in PV arrays using only a small training dataset.The training and testing datasets include several voltage and current values on PV array current-voltage(I-V)characteristic curve which is extracted using the variable load technique for PV array I-V curve extraction.The model has been implemented on an experimental standalone PV array and the results show outstanding accuracies of 98.61%and 100%when it is verified by testing datasets in the first and the second stage,respectively.
