Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable e...Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems.展开更多
To address the shortcomings of existing fault diagnosis models for aero-engine gas path components,such as weak feature extraction capabilities and low diagnostic accuracy,this study proposes a fault diagnosis model u...To address the shortcomings of existing fault diagnosis models for aero-engine gas path components,such as weak feature extraction capabilities and low diagnostic accuracy,this study proposes a fault diagnosis model underpinned by the Fungal Growth Optimization(FGO)algorithm(FGO-1 DCNN-LSTM).This model integrates a 1D convolutional neural network(1 DCNN)and a long short-term memory network(LSTM).LSTM's strength in extracting temporal features compensates for the limitations of 1 DCNN in processing timeseries data.A split-path convolutional fusion module is introduced into the 1 DCNN,enabling parallel input of sensor data,thus enhancing both the network's extraction capabilities and efficiency.Simultaneously,the FGO algorithm is incorporated to tackle the issue of hyperparameter optimization.To validate the model's performance,training and validation experiments were conducted using the N-CMAPSS dataset,which covers fault data under three typical operating conditions:low-altitude low-velocity,higher-altitude and highervelocity,and high-altitude high-velocity.Experimental results indicate that the FGO-1 DCNNLSTM model attains fault diagnosis accuracies of 90.74%,91.67%,and 94.44%under three operating condition.In comparison with the unoptimized 1 DCNN-LSTM model,its diagnostic accuracy is improved by 2.78%,10.19%,and 3.7%,respectively.The results provide evidence that the FGO-1 DCNN-LSTM model can effectively achieve accurate identification and diagnosis of faults in aero-engine gas path components.展开更多
Shor’s algorithm outperforms its classical counterpart in efficient prime factorization. We explore the coherence and entanglement dynamics of the evolved states within Shor’s algorithm, showing that the coherence i...Shor’s algorithm outperforms its classical counterpart in efficient prime factorization. We explore the coherence and entanglement dynamics of the evolved states within Shor’s algorithm, showing that the coherence in each step relies on the dimension of register or the order, and discuss the relations between geometric coherence and geometric entanglement. We investigate how unitary operators induce variations in coherence and entanglement, and analyze the variations of coherence and entanglement within the entire algorithm, demonstrating that the overall effect of Shor’s algorithm tends to deplete coherence and produce entanglement. Our research not only deepens the understanding of this algorithm but also provides methodological references for studying resource dynamics in other quantum algorithms.展开更多
The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. ...The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.展开更多
摘要Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems.
基金supported by the National Natural Science Foundation of China under Grant nos.62301341,62371315 and 62531017the Liaoning Provincial Department of Science and Technology(Applied Basic Research)under Grant nos.2025JH2/101300013the Youth Project of Liaoning Provincial Department of Education under Grant nos.200080762/070。
摘要To address the shortcomings of existing fault diagnosis models for aero-engine gas path components,such as weak feature extraction capabilities and low diagnostic accuracy,this study proposes a fault diagnosis model underpinned by the Fungal Growth Optimization(FGO)algorithm(FGO-1 DCNN-LSTM).This model integrates a 1D convolutional neural network(1 DCNN)and a long short-term memory network(LSTM).LSTM's strength in extracting temporal features compensates for the limitations of 1 DCNN in processing timeseries data.A split-path convolutional fusion module is introduced into the 1 DCNN,enabling parallel input of sensor data,thus enhancing both the network's extraction capabilities and efficiency.Simultaneously,the FGO algorithm is incorporated to tackle the issue of hyperparameter optimization.To validate the model's performance,training and validation experiments were conducted using the N-CMAPSS dataset,which covers fault data under three typical operating conditions:low-altitude low-velocity,higher-altitude and highervelocity,and high-altitude high-velocity.Experimental results indicate that the FGO-1 DCNNLSTM model attains fault diagnosis accuracies of 90.74%,91.67%,and 94.44%under three operating condition.In comparison with the unoptimized 1 DCNN-LSTM model,its diagnostic accuracy is improved by 2.78%,10.19%,and 3.7%,respectively.The results provide evidence that the FGO-1 DCNN-LSTM model can effectively achieve accurate identification and diagnosis of faults in aero-engine gas path components.
基金supported by National Natural Science Foundation of China(Grant Nos.12161056,12075159,12171044)Natural Science Foundation of Jiangxi Province(Grant No.20232ACB211003)+1 种基金Beijing Natural Science Foundation(Grant No.Z190005)the specific research fund of the Innovation Platform for Academicians of Hainan Province.
摘要Shor’s algorithm outperforms its classical counterpart in efficient prime factorization. We explore the coherence and entanglement dynamics of the evolved states within Shor’s algorithm, showing that the coherence in each step relies on the dimension of register or the order, and discuss the relations between geometric coherence and geometric entanglement. We investigate how unitary operators induce variations in coherence and entanglement, and analyze the variations of coherence and entanglement within the entire algorithm, demonstrating that the overall effect of Shor’s algorithm tends to deplete coherence and produce entanglement. Our research not only deepens the understanding of this algorithm but also provides methodological references for studying resource dynamics in other quantum algorithms.
基金Under the auspices of Opening Funding of State Key Laboratory for Remote Sensing ScienceNational High-tech Research and Development Program (863 Program) (No. 2007AA120205, 2007AA120306)
摘要The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.