A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of i...A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of input saturation and dead-zone.In regard to the input nonlinearities,the right inverse function block of the dead-zone is added before the input nonlinearities,which simplifies the input nonlinearities into an equivalent input saturation.To deal with the equivalent input saturation,an auxiliary error system is designed to compensate for the impact of the input saturation.Meanwhile,uncertainties in pitch stiffness,plunge stiffness,and pitch damping are all considered,and radial basis function neural networks（RBFNNs） are applied to approximate the system uncertainties.In combination with the designed auxiliary error system and the backstepping control technique,a constrained adaptive neural network controller is designed,and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method.Finally,extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust,system uncertainties,and input nonlinearities.展开更多
Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s over...Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s overall security level.This new model is called the risk-based security-constrained economic dispatch(RB-SCED).Relative to the securityconstrained economic dispatch(SCED)used in the power industry today,the RB-SCED finds more secure and economic operating conditions.It does this by obtaining solutions that achieve a better balance between post-contingency flows on individual branches and the overall system risk.The method exploits the fact that,in a SCED solution,some postcontingency branch flows which exceed their limits impose little risk while other post-contingency branch flows which are within their limits impose significant risk.The RB-SCED softens constraints for the former and hardens constraints for the latter,thus achieving simultaneous improvement in both security and economy.In this work,the basic concept and the mathematical formulation of the RB-SCED model are systematically described.Experimental results on a 9-bus system and the ISO New England actual system have demonstrated the advantages of RB-SCED over SCED.展开更多
In recent years,many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks.The deployment of the unsupervised autoencoder model is computationally expensive in ...In recent years,many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks.The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices.This study proposes quantized autoencoder(QAE)model for intrusion detection systems to detect anomalies.QAE is an optimization model derived from autoencoders that incorporate pruning,clustering,and integer quantization techniques.Quantized autoencoder uint8(QAE-u8)and quantized autoencoder float16(QAE-f16)are two variants of QAE built to deploy computationally expensive Al models into Edge devices.First,we have generated a Real-Time Internet of Things 2022 dataset for normal and attack traffic.The autoencoder model operates on normal traffic during the training phase.The same model is then used to reconstruct anomaly traffic under the assumption that the reconstruction error(RE)of the anomaly will be high,which helps to identify the attacks.Furthermore,we study the performance of the autoencoders,QAE-u8,and QAE-f16 using accuracy,precision,recall,and F1 score through an extensive experimental study.We showed that QAE-u8 outperforms all other models with a reduction of 70.01%in average memory utilization,92.23%in memory size compression,and 27.94%in peak CPU utilization.Thus,the proposed QAE-u8 model is more suitable for deployment on resource-constrained IoT edge devices.展开更多
Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of...Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.展开更多
This paper is concerned with identifying a Takagi-Sugeno（TS） fuzzy model for turbofan aero-engines working under the maximum power status（non-afterburning）. To establish the fuzzy system, theoretical contributions...This paper is concerned with identifying a Takagi-Sugeno（TS） fuzzy model for turbofan aero-engines working under the maximum power status（non-afterburning）. To establish the fuzzy system, theoretical contributions are made as follows. First, by fixing antecedent parameters, the estimation of consequent parameters in state-space representations is formulated as minimizing a quadratic cost function. Second, to avoid obtaining unstable identified models, a new theorem is proposed to transform the prior-knowledge of stability into constraints. Then based on the aforementioned work, the identification problem is synthesized as a constrained quadratic optimization.By solving the constrained optimization, a TS fuzzy system is identified with guaranteed stability.Finally, the proposed method is applied to the turbofan aero-engine using simulation data generated from an aerothermodynamics component-level model. Results show the identified fuzzy model achieves a high fitting accuracy while stabilities of the overall fuzzy system and all its local models are also guaranteed.展开更多
This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosum...This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61473307 and 61304120)the Aeronautical Science Foundation of China(No. 20155896026)
文摘A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of input saturation and dead-zone.In regard to the input nonlinearities,the right inverse function block of the dead-zone is added before the input nonlinearities,which simplifies the input nonlinearities into an equivalent input saturation.To deal with the equivalent input saturation,an auxiliary error system is designed to compensate for the impact of the input saturation.Meanwhile,uncertainties in pitch stiffness,plunge stiffness,and pitch damping are all considered,and radial basis function neural networks（RBFNNs） are applied to approximate the system uncertainties.In combination with the designed auxiliary error system and the backstepping control technique,a constrained adaptive neural network controller is designed,and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method.Finally,extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust,system uncertainties,and input nonlinearities.
基金This work is jointly supported by National High Technology Research and Development Program of China(863 Program)(No.2011AA05A105)a key project from Zhejiang Electric Power Corporation.
文摘Based on a new perspective in coordinating with the traditional"N-1"criteria and system risk,a real-time electricity market model is presented,in which the system risk is employed to model the system’s overall security level.This new model is called the risk-based security-constrained economic dispatch(RB-SCED).Relative to the securityconstrained economic dispatch(SCED)used in the power industry today,the RB-SCED finds more secure and economic operating conditions.It does this by obtaining solutions that achieve a better balance between post-contingency flows on individual branches and the overall system risk.The method exploits the fact that,in a SCED solution,some postcontingency branch flows which exceed their limits impose little risk while other post-contingency branch flows which are within their limits impose significant risk.The RB-SCED softens constraints for the former and hardens constraints for the latter,thus achieving simultaneous improvement in both security and economy.In this work,the basic concept and the mathematical formulation of the RB-SCED model are systematically described.Experimental results on a 9-bus system and the ISO New England actual system have demonstrated the advantages of RB-SCED over SCED.
文摘In recent years,many researchers focused on unsupervised learning for network anomaly detection in edge devices to identify attacks.The deployment of the unsupervised autoencoder model is computationally expensive in resource-constrained edge devices.This study proposes quantized autoencoder(QAE)model for intrusion detection systems to detect anomalies.QAE is an optimization model derived from autoencoders that incorporate pruning,clustering,and integer quantization techniques.Quantized autoencoder uint8(QAE-u8)and quantized autoencoder float16(QAE-f16)are two variants of QAE built to deploy computationally expensive Al models into Edge devices.First,we have generated a Real-Time Internet of Things 2022 dataset for normal and attack traffic.The autoencoder model operates on normal traffic during the training phase.The same model is then used to reconstruct anomaly traffic under the assumption that the reconstruction error(RE)of the anomaly will be high,which helps to identify the attacks.Furthermore,we study the performance of the autoencoders,QAE-u8,and QAE-f16 using accuracy,precision,recall,and F1 score through an extensive experimental study.We showed that QAE-u8 outperforms all other models with a reduction of 70.01%in average memory utilization,92.23%in memory size compression,and 27.94%in peak CPU utilization.Thus,the proposed QAE-u8 model is more suitable for deployment on resource-constrained IoT edge devices.
文摘Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.
文摘This paper is concerned with identifying a Takagi-Sugeno（TS） fuzzy model for turbofan aero-engines working under the maximum power status（non-afterburning）. To establish the fuzzy system, theoretical contributions are made as follows. First, by fixing antecedent parameters, the estimation of consequent parameters in state-space representations is formulated as minimizing a quadratic cost function. Second, to avoid obtaining unstable identified models, a new theorem is proposed to transform the prior-knowledge of stability into constraints. Then based on the aforementioned work, the identification problem is synthesized as a constrained quadratic optimization.By solving the constrained optimization, a TS fuzzy system is identified with guaranteed stability.Finally, the proposed method is applied to the turbofan aero-engine using simulation data generated from an aerothermodynamics component-level model. Results show the identified fuzzy model achieves a high fitting accuracy while stabilities of the overall fuzzy system and all its local models are also guaranteed.
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
基金supported by Australian Research Council (ARC)Discovery Project (No.160102571)。
文摘This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.
基金National Natural Science Foundation of China(No.52171331)Guangdong Provincial Basic and Applied Basic Research Fund(No.2023A1515011311)Guangzhou City School Joint Laboratory Project(No.2023A03J0120)。
