Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Ai, Chunyu; He, Shan; Fan, Xiaomeng

doi:10.1109/access.2023.3243081

Cited by 5 publications

(3 citation statements)

References 33 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step4: Map the points of the chaotic sequence back to the original space according to the following equation (Ai et al, 2023):…”

Section: Chaos Initializationmentioning

confidence: 99%

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Ren,

Meng,

Liu

et al. 2024

10.5267/j.ijiec

View full text Add to dashboard Cite

This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

show abstract

“…Step4: Map the points of the chaotic sequence back to the original space according to the following equation (Ai et al, 2023):…”

Section: Chaos Initializationmentioning

confidence: 99%

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Ren,

Meng,

Liu

et al. 2024

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…The state space transition equation, a core concept in dynamic system theory, describes the evolution of system states over time, as shown in Figure 9. The design of the state transition equation in this research is based on the following mathematical representation [77][78][79]:…”

Section: State Space Transition Equationmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

View full text Add to dashboard Cite

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.

show abstract

“…In addition, X. Li et al [11] conducted a chaos analysis on the six-dimensional model of the three-bus power system, which showed that due to the increase of load, the critical chaos oscillation tends to occur when the system is closer to the stable operation limit. Meanwhile, C. Ai et al [12] generated chaotic oscillations in a six-dimensional model of the power system by mechanical input power and model order, and proposed a state transition algorithm based on the lens imaging learning strategy to estimate the parameters of the chaotic model of the power system and reproduce its chaotic behaviour. Reference [13] analyzed the chaos of the sevendimensional power system model by the input mechanical power, and showed that there is a strong correlation between the power parameters and the chaos behavior.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Cooperative Internal Mode Control of RBF Neural Network for Power System Chaos Suppression

Liu,

Zhang,

Yang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In this paper, a multivariate cooperative internal mode control method based on RBF neural network (RBF-NN) inverse system is proposed to suppress the chaotic behavior in the power system. Firstly, a seven-dimensional model of the controlled power system including energy storage (ES) and static var compensator (SVC) is constructed, and the chaotic dynamics of the system is analyzed by local bifurcation diagram, attractor phase diagram and timing diagram, and the merging crisis and coexistence of chaotic attractors are found in the power system under the action of the low-frequency power disturbance. Secondly, considering the parameter uncertainties of the power system, an RBF-NN inverse system model of the controlled power system is established based on inverse system theory and neural network theory to realize its pseudo-linearization, and a multivariable cooperative internal mode controller is designed to suppress the chaotic behavior in the power system by combining the ES and the SVC. Finally, the effectiveness and robustness of the proposed chaos suppression control strategy are verified by simulation.INDEX TERMS chaos, chaos suppression, internal mode control, power system, RBF neural network.

show abstract

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Cited by 5 publications

References 33 publications

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Multivariate Cooperative Internal Mode Control of RBF Neural Network for Power System Chaos Suppression

Contact Info

Product

Resources

About