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Jan H. Van Schuppen – One of the best experts on this subject based on the ideXlab platform.
Multi-Level Power-Imbalance Allocation Control for Secondary Frequency Control of Power SystemsIEEE Transactions on Automatic Control, 2020Co-Authors: Hai Xiang Lin, Chen Shen, Jan H. Van SchuppenAbstract:
A consensus-Control-based multi-level Control law named Multi-Level Power-Imbalance Allocation Control (MLPIAC) is presented for a large-scale power system partitioned into two or more groups. Centralized Control is implemented in each group while distributed Control is implemented at the coordination level of the groups. Besides restoring nominal frequency with a minimal Control cost, MLPIAC can improve the transient performance of the system through an accelerated convergence of the Control inputs without oscillations. At the coordination level of the Control groups, because the number of the groups is smaller than that of nodes, MLPIAC is more effective to obtain the minimized Control cost than the purely distributed Control law. At the level of the Control in each group, because the number of nodes is much smaller than the total number of nodes in the whole network, the overheads in the communications and the computations are reduced compared to the pure centralized Control. The asymptotic stability of MLPIAC is proven using the Lyapunov method and the performance is evaluated through simulations.
Transient Performance of Power Systems with Distributed Power-Imbalance Allocation Control.arXiv: Optimization and Control, 2019Co-Authors: Hai Xiang Lin, Jan H. Van SchuppenAbstract:
We investigate the sensitivity of the transient performance of power systems Controlled by Distributed Power-Imbalance Allocation Control (DPIAC) on the parameters of the Control law. We model the disturbances at power loads as Gaussian white noises and measure the transient performance of the frequency deviation and Control cost by the H_2 norm. For a power system with a communication network of the same topology as the power network, analysis shows that the transient performance of the frequency can be greatly improved by accelerating the convergence of the frequencies to the nominal value through a singe Control gain coefficient. However, the Control cost increases linearly as this Control gain coefficient increases. Hence, in the feedback Control law, DPIAC, there is a trade-off between the frequency deviation and Control cost which is determined by this Control gain coefficient. By increasing another Control gain coefficient, the Control cost can be decreased with an accelerated consensus of the marginal costs during the transient phase. Furthermore, the behavior of the state approaches that of a centralized Control law when the consensus of the marginal costs is accelerated.
ICCA – Robustness of Power-Imbalance Allocation Control for Power Systems2019 IEEE 15th International Conference on Control and Automation (ICCA), 2019Co-Authors: Hai Xiang Lin, Jan H. Van SchuppenAbstract:
We investigate the performance and robustness to noise of a centralized Control called Power-Imbalance Allocation Control (PIAC) for secondary frequency Control of a power system. The noise affects the frequency measurements and the communications. The impact of the noise on the synchronous state of the system is investigated. Analysis shows that the synchronized frequency deviation is proportional to the bias of the noise in the frequency measurements and gathering procedure of these measurements. With the noise considered as an input, the Input-to-State Stability (ISS) is proven for the closed-loop system.
Yukikazu Nakamoto – One of the best experts on this subject based on the ideXlab platform.
Adaptive Resource Allocation Control for Fair QoS ManagementIEEE Transactions on Computers, 2007Co-Authors: F. Harada, Toshimitsu Ushio, Yukikazu NakamotoAbstract:
A novel Control method for fair resource Allocation and maximization of the quality of service (QoS) levels of individual tasks is proposed. In the proposed adaptive QoS Controller, resource utilization is assigned to each task through an online search for the fair QoS level based on the errors between the current QoS levels and their average. The proposed Controller eliminates the need for precise detection of the consumption functions as in conventional feedback Control methods. The computational complexity of the proposed method is also very low compared to straightforward methods solving a nonlinear problem
Multi-Resource Allocation Control for Fair QoS Management in Real-Time SystemsProceedings of the 44th IEEE Conference on Decision and Control, 1Co-Authors: F. Harada, Toshimitsu Ushio, Yukikazu NakamotoAbstract:
In this paper, we propose an adaptive resource Control method for multiple resource real-time systems. Execution results of applications are evaluated as a QoS level and a fair QoS management is an important issue. The fair QoS level depends on the number of active applications and their characteristics so that it may change dynamically due to the current states of the real-time systems. The proposed Controller activates at discrete times and updates resource Allocations to achieve the optimal fair QoS level. Moreover, the algorithms used in the Controller solve the update in O(mn) time per its activation. We derive sufficient conditions for achievement of the fair resource Allocation. Simulation experiments show that a fair resource Allocation can be achieved under the conditions.
Georges Habchi – One of the best experts on this subject based on the ideXlab platform.
Supervisory-based capacity Allocation Control for manufacturing systemsInternational Journal of Manufacturing Technology and Management, 2010Co-Authors: Karim Tamani, Reda Boukezzoula, Georges HabchiAbstract:
This paper aims at developing supervisory Control architecture in order to improve the performance of manufacturing systems. The proposed Control architecture is hierarchical. It is composed of basic-level fuzzy logic Controllers supervised by a higher level decision-making combining local and global performance indicators to allocate the production capacity. The global performance indicator used in the supervisory level evolves in a tolerance interval defined by the normal operating conditions of the process. When a performance indicator value is outside the predefined tolerance interval, abnormal behaviour occurs. In this case, the supervisor allocates the production capacity or reduces the production throughput according to the aggregated global performance indicators. The simulation results for two applications of manufacturing systems are presented to illustrate the feasibility of the proposed approach.
FUZZ-IEEE – Fuzzy Supervisory Based Capacity Allocation Control for Manufacturing Systems2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Karim Tamani, Reda Boukezzoula, Georges HabchiAbstract:
In this paper, a fuzzy supervisory Control technique for manufacturing systems is proposed. The developed method uses a hierarchical structure consisting of a supervisor at the higher level and sub-Controllers at the lower one. Each sub-Controller regulates the production flow at each production resource in order to: reduce the difference between the cumulative production and demand, avoid overloading, and eliminate machine starvation or blocking. Based on the information about the overall system’s performance, the supervisor tunes the predesigned local Controllers by adjusting the production capacity of each resource. The feasibility of the proposed method is illustrated by a simulation example for a manufacturing system.