3D imaging technologies -- Multi-dimensional signal processing and deep learning mathematical approaches and applications

3D imaging technologies -- multidimensional signal processing and deep learning methods, algorithms and applications. Volume ii

计算机网络英文课件：lecture-18-Ethernet technologies.ppt

As the main building block of the smart grid systems, microgrid (MG) integrates a number of local distributed generation units, energy storage systems, and local loads to form a small-scale, low- and medium-voltage level power system. In gen- eral, an MG can operate in two modes, i.e., the grid-connected and islanded mode. Recently, in order to standardize its operation and functionality, hierarchical con- trol for islanded MG systems has been proposed. It divides the control structure into three layers, namely, primary, secondary, and tertiary control. The primary control is based on each local distributed generation (DG) controller and is realized in a de- centralized way. In the secondary layer, the frequency and voltage restoration control as well as the power quality enhancement is usually carried out. In the tertiary con- trol, economic dispatch and power flow optimization issues are considered. However, conventionally both the secondary and tertiary control are realized in a centralized way. There are certain drawbacks to such centralized control, such as high compu- tation and communication cost, poor fault tolerance ability, lack of plug-and-play properties, and so on. In order to overcome the above drawbacks, distributed control is proposed in the secondary control and tertiary optimization in this book. In the secondary control, restorations for both voltage and frequency in the droop- controlled inverter-based islanded MG are addressed. A distributed finite-time con- trol approach is used in the voltage restoration which enables the voltages at all the DGs to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed fre- quency control is proposed for frequency restoration, subject to certain control input constraints. The proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. Then the distributed control strategy is also employed in the secondary voltage unbalance compensation to replace the conventional centralized controller. The con- cept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each xvii xviii Distributed Control and Optimization Technologies in Smart Grid Systems local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. In the tertiary layer, a distributed economic dispatch (ED) strategy based on pro- jected gradient and finite-time average consensus algorithms is proposed. By de- composing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution for the optimization problem in a distributed manner with limited communication among neighbors. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Besides, two distributed multi-cluster optimization meth- ods are proposed for a large-scale multi-area power system. We first propose to divide all the generator agents into clusters (groups) and each cluster has a leader to com- municate with the leaders of its neighboring clusters. Then two different schemes are proposed for each agent to iteratively estimate a solution of the optimization prob- lem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. In addition, a novel hierarchical decentralized optimization architecture is proposed to solve the ED problem. Similar to distributed algorithms, each local generator only solves its own problem based on its own cost function and generation constraint. An extra co- ordinator agent is employed to coordinate all the local generator agents. Besides, it also takes the responsibility for handling the global demand supply constraint. In this way, different from existing distributed algorithms, the global demand supply con- straint and local generation constraints are handled separately, which would greatly reduce the computational complexity. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. A distributed optimal energy scheduling strategy is also proposed in the tertiary layer, which is based on a newly proposed pricing strategy named PD pricing. Con- ventional real-time pricing strategies only depend on the current total energy con- sumption. In contrast to this, our proposed pricing strategy also takes the incremen- tal energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. Two different dis- tributed optimization algorithms with different communication strategies are pro- posed to solve the problem.

传统的电能质量信号采样基于奈奎斯特采样理论。 由于存在电源而存在干扰信号，因此它需要比原始信号的采样频率高两倍的信号，从而导致许多问题，例如硬件成本较高。 压缩感知算法摒弃了香农定理的特点，采用更低的采样频率和更少的信号重构信号，采用损耗压缩的方法，可以有效地解决这一问题。 北京化工大学的一个团队对此方向进行了深入研究，提出了总变化梯度减小算法，该算法对减小效果很好。 但是该算法运行速度较慢，并且需要较高的信号采样量。 因此，本文提出了一种基于Nesta算法的改进算法，以减少电能质量信号的采样数据量，提高算法的复杂度，提高算法的速度。 改进后的算法在实际应用中具有非常重要的价值。 本文在matlab上进行了仿真，仿真结果表明该方法是正确的并且可以应用。

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无线通信中很有名的技术标准，是英文文档．