IEC 61850, the standard for communication in substations, has resolved the interoperability between intelligent electronic devices (IEDs) from different vendors. Thus, the IEDs' uniform communication standard contributes to the realization of more sophisticated functionality in substation automation systems. However, there are still several issues unsolved for the simulation, planning, and extension of substation communication network (SCN). This paper proposes three kinds of mathematical models for typical data flow within substations according to IEC 61850, which are cyclic data, stochastic data, and burst data. Thereby, a quantitative analysis of data flow is carried out for a typical substation based on the proposed data models. The advantage of virtual local area network (VLAN) and impacts of system faults, as well as network topologies, on a VLAN-based network are evaluated and simulated by OPNET Modeler. The data flow models are beneficial for the acquisition of more convincing results to assess network performance. Thus, the simulation results for a sample substation can be used to support power utility personnel with the planning and construction process of SCN. Index Terms-Data flow, IEC 61850, OPNET Modeler, substation communication network (SCN), virtual local area network (VLAN).

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Self-sustained oscillations in complex networks consisting of nonoscillatory nodes have attracted long-standing interest in diverse natural and social systems. We study the self-sustained periodic oscillations in random networks consisting of excitable nodes. We reveal the underlying dynamic structure by applying a dominant phase-advanced driving method. The oscillation sources and wave propagation paths can be illustrated clearly via the dynamic structure revealed. Then we are able to control the oscillations with surprisingly high efficiency based on our understanding.

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A phase change memory (PCM) model suitable for neuromorphic circuit simulations is developed. A crystallization ratio module is used to track the memory state in the SET process, and an active region radius module is developed to track the continuously varying amorphous region in the RESET process. To converge the simulations with bi-stable memory states, a predictive filament module is proposed using a previous state in iterations of nonlinear circuit matrix under a voltage-driven mode. Both DC and transient analysis are successfully converged in circuits with voltage sources. The spiking-time-dependent-plasticity (STDP) characteristics essential for synaptic PCM are successfully reproduced with SPICE simulations verifying the model's promising applications in neuromorphic circuit designs. Further on, the developed PCM model is applied to propose a neuron circuit topology with lateral inhibitions which is more bionic and capable of distinguishing fuzzy memories. Finally, unsupervised learning of handwritten digits on neuromorphic circuits is simulated to verify the integrity of models in a large-scale-integration circuits. For the first time in literature an emerging memory model is developed and applied successfully in neuromorphic circuit designs, and the model is applicable to flexible designs of neuron circuits for further performance improvements.INDEX TERMS Neuromorphic circuits, phase change memory, SPICE model, spike-time-dependent plasticity, spiking neural networks.

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