Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Consensus-Based Distributed Control for Accurate Reactive, Harmonic, and Imbalance Power Sharing in Microgrids

Abstract: This paper investigates the issue of accurate reactive, harmonic, and imbalance power sharing in a microgrid. Harmonic and imbalance droop controllers are developed to proportionally share the harmonic power and the imbalance power among distributed generation (DG) units and improve the voltage quality at the point of common coupling (PCC). Further, a distributed consensus protocol is developed to adaptively regulate the virtual impedance at fundamental frequency and selected harmonic frequencies. Additionally, a dynamic consensus based method is adopted to restore the voltage to their average voltage. With the proposed methods, the microgrid system reliability and flexibility can be enhanced and the knowledge of the line impedance is not required. And the reactive, harmonic, and imbalance power can be proportionally shared among the DG units. Moreover, the quality of the voltage at PCC can be greatly improved. Simulation and experimental results are presented to demonstrate the proposed method.

An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace

Abstract: Combined the modified AdaBoost.RT with extreme learning machine (ELM), a new hybrid artificial intelligent technique called ensemble ELM is developed for regression problem in this study. First, a new ELM algorithm is selected as ensemble predictor due to its rapid speed and good performance. Second, a modified AdaBoost.RT is proposed to overcome the limitation of original AdaBoost.RT by self-adaptively modifying the threshold value. Then, an ensemble ELM is presented by using the modified AdaBoost.RT for better accuracy of predictability than individual method. Finally, this new hybrid intelligence method is used to establish a temperature prediction model of molten steel by analyzing the metallurgic process of ladle furnace (LF). The model is examined by data of production from 300t LF in Baoshan Iron and Steel Co., Ltd. and compared with the models that established by single ELM, GA-BP (combined genetic algorithm with BP network), and original AdaBoost.RT. The experiments demonstrated that the hybrid intelligence method can improved generalization performance and boost the accuracy, and the accuracy of the temperature prediction is satisfied for the process of practical producing.

Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence Analysis

Abstract: In this paper, a novel discrete-time deterministic $ Q$ -learning algorithm is developed. In each iteration of the developed $ Q$ -learning algorithm, the iterative $ Q$ function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional $ Q$ -learning algorithm. A new convergence criterion is established to guarantee that the iterative $ Q$ function converges to the optimum, where the convergence criterion of the learning rates for traditional $ Q$ -learning algorithms is simplified. During the convergence analysis, the upper and lower bounds of the iterative $ Q$ function are analyzed to obtain the convergence criterion, instead of analyzing the iterative $ Q$ function itself. For convenience of analysis, the convergence properties for undiscounted case of the deterministic $ Q$ -learning algorithm are first developed. Then, considering the discounted factor, the convergence criterion for the discounted case is established. Neural networks are used to approximate the iterative $ Q$ function and compute the iterative control law, respectively, for facilitating the implementation of the deterministic $ Q$ -learning algorithm. Finally, simulation results and comparisons are given to illustrate the performance of the developed algorithm.

A self-learning call admission control scheme for CDMA cellular networks

Abstract: In the present paper, a call admission control scheme that can learn from the network environment and user behavior is developed for code division multiple access (CDMA) cellular networks that handle both voice and data services. The idea is built upon a novel learning control architecture with only a single module instead of two or three modules in adaptive critic designs (ACDs). The use of adaptive critic approach for call admission control in wireless cellular networks is new. The call admission controller can perform learning in real-time as well as in offline environments and the controller improves its performance as it gains more experience. Another important contribution in the present work is the choice of utility function for the present self-learning control approach which makes the present learning process much more efficient than existing learning control methods. The performance of our algorithm will be shown through computer simulation and compared with existing algorithms.