The publication is devoted to the development of a model and research of the hydroelectric unit of a power plant with a Kaplan turbine. Matlab/Simulink environment was used for modeling. The main problems, features an...
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In this paper, optimal controlproblems with constraints on summation of auxiliary utility function are called constrained cost optimal controlproblems and a constrained cost policy iteration adaptive dynamic program...
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ISBN:
(纸本)9780738133669
In this paper, optimal controlproblems with constraints on summation of auxiliary utility function are called constrained cost optimal controlproblems and a constrained cost policy iteration adaptive dynamic programming (ADP) algorithm is developed to solve constrained cost optimal controlproblems for discrete-time nonlinear systems. A convergence analysis is developed to guarantee that the iterative value functions non-increasingly convergent to the approximate optimal value function. It is also proven that any of the iterative control policy is feasible and can stabilize the nonlinear systems. Finally, a simulation example is given to illustrate the performance of the developed constrained cost policy iteration algorithm.
Aiming at the problems of low control precision and complexcontrol process in electrical engineering automation control, this paper proposes a kind of intelligent electrical engineering automation control technology ...
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We live in a time that is complex and difficult to predict, even for events shortly. Along with the uniform development of modern technologies, wars, and artificial and natural disasters arise sporadically, which cont...
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ISBN:
(数字)9798350350821
ISBN:
(纸本)9798350350838
We live in a time that is complex and difficult to predict, even for events shortly. Along with the uniform development of modern technologies, wars, and artificial and natural disasters arise sporadically, which contemporary society has not yet learned to cope with effectively. Various world-class programs and projects exist to overcome the consequences of such phenomena, but these efforts are not enough to prevent the conditions for cataclysms. There is a need to consider the construction and development of new technologies as a particular component of a complex equivalent in the form of a system of concepts: “nature - man - society - state.” Considering the above circumstances, solving the problem of building effective bioenergy agricultural production in these conditions is becoming a promising area of scientific research in the coming decades. The efforts of scientists in many countries aim to find optimal solutions to ensure the effective operation of agricultural technologies associated with the production and distribution of energy resources available in the state. The work analyzes some conditions and theoretical prerequisites for creating new generation models using a virtual ecosystem model at the pace of technological energy production and consumption processes in bioenergy ecosystems. The shortcomings of existing approaches and technologies for modelingcomplex heterogeneous ecosystems are discussed. New innovative technology elements for constructing models and modeling the functioning modes of bioenergy agricultural technologies using artificial intelligence methods are proposed.
Recent advances in reinforcement learning techniques have shown promising results in solving complexcontrolproblems with high dimensional state and action spaces. Inspired by the success, we show that two advanced r...
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ISBN:
(纸本)9781728191270
Recent advances in reinforcement learning techniques have shown promising results in solving complexcontrolproblems with high dimensional state and action spaces. Inspired by the success, we show that two advanced reinforcement learning algorithms, Q-Learning and Q-Learning with function approximation, can predict the traffic signal timing plan for an isolated intersection. To deal with the large state cardinality of the input traffic state, we propose a discrete state representation and a finite set of actions for a sample case. The proposed algorithm helps to converge the reward function, average queue in early episodes. Our methods show promising results for an intersection traffic control simulated using SUMO micro simulator.
In this article, the authors consider the possibility of using combined machine learning methods in metallurgy for analysis, selection of materials, and prediction of properties. The authors analyze various approaches...
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ISBN:
(数字)9798331532178
ISBN:
(纸本)9798331532185
In this article, the authors consider the possibility of using combined machine learning methods in metallurgy for analysis, selection of materials, and prediction of properties. The authors analyze various approaches to using machine learning in modern metallographic analysis, establish empirical dependencies, predict properties, and select materials. We will discuss the problems associated with using these methods, offer possible solutions, and analyze key aspects of their implementation. We will also examine the advantages and disadvantages of machine learning use in metallurgy, as well as discuss prospects for its development. This article is a valuable resource for metallurgists who want to incorporate modern technologies into their research and development.
In recent years, the large-scale integration of new energy sources, such as wind and photovoltaic power into power grid, has necessitated higher standards for the safe, stable, and flexible control of power systems. T...
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ISBN:
(数字)9798331532390
ISBN:
(纸本)9798331532406
In recent years, the large-scale integration of new energy sources, such as wind and photovoltaic power into power grid, has necessitated higher standards for the safe, stable, and flexible control of power systems. The Security and Stability control System (SSCS) is proposed to monitor the operation of the grid. To address the cost and scale limitations, simulation has been emerging as a crucial method for analyzing safety and stability controlsystems. However, as the grid’s complexity increases, the precision and computation workload of the SSCS associated to the grid simulation model have significantly grown. Real-time task scheduling in multi-core systems is important for detecting abnormal events of complex stability control simulation models in time. This paper introduces a new scheduling algorithm specifically designed for the simulation model in power system. It also proposes an interference analysis method to address delays caused by contention for shared hardware resources. We conduct extensive experiments to analyze the proposed algorithm. Compared with existing scheduling algorithms, the scheduling algorithm of the paper achieves superior performance and effectiveness.
This article studies the $H$ ∞ controlproblems of uncertain stochastic singular Markov jump systems with time-varying delays. By constructing a new Lyapunov functional, using the free weight matrix scheme and line...
This article studies the $H$ ∞ controlproblems of uncertain stochastic singular Markov jump systems with time-varying delays. By constructing a new Lyapunov functional, using the free weight matrix scheme and linear matrix inequalities technique, the conditions of stochastic admissibility in the mean-square sense for uncertain stochastic singular Markov jump systems with time-varying delays are obtained. The $H$ ∞ controller is designed based on dynamic event-triggered mechanism so that the corresponding closed-loop systems stochastically admissible in the mean-square sense and satisfies the given $H$ ∞ performance index γ. In the end, the usefulness of the intended scheme is described by a numerical example.
The stability of the power system is important for ensuring the power grid's reliable operation. Traditional stability analysis and stabilization methods have limitations in handling the complexity and nonlinearit...
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ISBN:
(数字)9798350374131
ISBN:
(纸本)9798350374148
The stability of the power system is important for ensuring the power grid's reliable operation. Traditional stability analysis and stabilization methods have limitations in handling the complexity and nonlinearity of modern power systems. The combination of artificial neural network (ANN)based approaches holds particular promise for power system stability and addressing stabilization challenges. This paper aims to investigate the use of neural methods to improve the analysis and stabilization of complex power systems. Research will focus on neural network architectures and algorithms that can accurately predict and ensure the stability of power systems under various operating conditions. By using neural networks, the research aims to overcome the limitations of traditional methods and provide intelligent solutions to ensure the stability and the robustness of power systems in the face of challenges in the ongoing process. This comprehensive review provides in-depth insights into the applications, techniques, and advancements of ANN in power system stability analysis and control. The study includes ANN basic principles and appropriateness to model complex nonlinear relationships in dynamical systems and evaluate them, including feedforward. This study looks into how ANNs can be used to improve system stability and response to disturbances in areas like fault detection, dynamic modeling, control strategies, and stability assessment.
DC microgrids have the advantage of increased reliability and controllability, as well as the absence of synchronisation and the stability problems that come with synchronisation. Bipolar DC microgrids have significan...
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