The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation h...The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.展开更多
Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-...Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data.This paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies.We provide a technical explanation of the proposed cloud architecture and its usage.In addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings.Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage.The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.展开更多
Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims ...Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.展开更多
基金supported by the National Natural Science Foundation of China(52007173 and U22B2098).
文摘The real-time AC optimal power flow(OPF)problem is a key issue in making fast and accurate decisions to ensure the safety and economy of power systems.With the rapid development of renewable energies,the fluctuation has grown more vibrant,thus a novel approach called safe deep reinforcement learning is proposed in this paper.Herein,the real-time ACOPF problem is modeled as a constrained Markov decision process,and primal-dual optimization(PDO)based proximal policy optimization(PPO)is used to learn the optimal generator outputs in the primal domain and security constraints in the dual domain,which avoids manually selecting a trade-off between penalties for constraint violations and rewards for the economy.Before training,behavior cloning clones the expert experience into the initial weights of neural networks.Moreover,multiprocessing training is utilized to accelerate the training speed.Case studies are conducted on the IEEE 118-bus system and the modified IEEE 118-bus system.Compared with other methods,the experimental results show that the proposed method can achieve security and near-optimal economic goals by fast calculating the real-time ACOPF problem.
基金supported by the Netherlands eScience Center under grant number ODISSEI.2022.023。
文摘Active learning can be used for optimizing and speeding up the screening phase of systematic reviews.Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data.This paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies.We provide a technical explanation of the proposed cloud architecture and its usage.In addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings.Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage.The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.
基金supported by the National Natural Science Foundation of China(12322210,12172351,92252202,and 12388101)the Fundamental Research Funds for the Central Universities.
文摘Rod-airfoil interaction noise becomes a major issue in some aeronautical applications.The design of four wavy leading edges(WLEs)with varying wavelengths,bioinspired by the tubercles on humpback whales’flippers,aims to mitigate far-field noise.Among these cases,a reduction in the wavelength is found to be advantageous for noise suppression,with the smallest wavelength case achieving a maximum noise reduction of 1.9 dB.Furthermore,the noise radiation induced by WLEs is suppressed mainly at medium frequencies.The theory of multiprocess aeroacoustics is applied to reveal their underlying mechanisms.The dominant factor is the source cutoff effect,which significantly decreases the source strength on hills.Additionally,spanwise decoherence with phase interference serves as another crucial mechanism,particularly for reducing mid-frequency noise.
