A honeycomb-like predictive controller with a reduced computational burden for three-level NPC converters

This article presents a predictive control strategy to control a photovoltaic plant based on bifacial photovoltaic (BPV) panels connected to the electrical grid through a three-phase neutral point clamped (NPC) power converter. Electricity generation plants based on nonconventional renewable energies are affected by the availability of the natural resource and grid changes. To achieve optimal use of natural resources and equipment, BPV cells must operate at the maximum power point and the currents injected into a grid must be in phase with the voltages. Due to the high number of states, one important drawback of conventional predictive control in the NPC converter is its computational burden. The proposed controller avoids the use of a cost function and can achieve the same control objectives with only 20% of the computational cost of the conventional strategy. To achieve this reduction the proposed strategy exploits the operating area of the converter, separating the possible voltages in hexagons, which results in a shape like a honeycomb. Simulation and experimental results show the feasibility of the proposed method in different operating conditions

Microgrid Power Sharing Framework for Software Defined Networking and Cybersecurity Analysis

Hierarchical control is a widely used strategy that can increase resilience and improve the reliability of the electrical network based on microgrid global variables. Large amounts of data required during transitions prompt the use of more reliable and flexible communications to achieve the control objectives. Such communications can involve potential cyber vulnerabilities and latency restrictions, which cannot be always addressed in real-time. To accurately capture the system’s overall operation, this paper proposes a co-simulation framework driven by flexible communications and a resilient control algorithm to regulate the frequency and voltage deviations in a networked microgrids. Model-based predictive control has been implemented, to avoid slow transient response associated with linear hierarchical control. Software-Defined Networking (SDN) is responsible for increasing the communication intelligence during the power-sharing process. The effects of critical communications and overall system performance are reviewed and compared for different co-simulation scenarios. Graphical Network Simulator (GNS3) is used in combination with model-based predictive control and SDN, to provide latency below 100 ms, as defined in IEC 61850. Testing of the proposed system under different cyber attack scenarios demonstrate its excellent performance. The novel control architecture presented in the paper provides a reference framework for future cloud computing-based microgrids