Distributed load-side frequency regulation for power systems

This paper studies frequency control of power systems by coordinating generation-side control and load-side control with nonlinear network-preserving models. A distributed consensus-based controller is designed for each bus in the transmission network. The total power imbalance of the system is discovered periodically by a distributed consensus mechanism, and then compensated by both generators and controllable loads accordingly. It is shown in simulation studies that the proposed method can achieve frequency regulation more effectively than the traditional automatic generation control (AGC) and reduce the needed system spinning reserve significantly. The impact of renewables on the system frequency under the designed control method is also discussed systematically in this paper.postprin

Smart grids as distributed learning control

The topic of smart grids has received a lot of attention but from a scientific point of view it is a highly imprecise concept. This paper attempts to describe what could ultimately work as a control process to fulfill the aims usually stated for such grids without throwing away some important principles established by the pioneers in power system control. In modern terms, we need distributed (or multi-agent) learning control which is suggested to work with a certain consensus mechanism which appears to leave room for achieving cyber-physical security, robustness and performance goals. © 2012 IEEE.published_or_final_versio

Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack

In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack

Distributed optimisation and control of graph Laplacian eigenvalues for robust consensus via an adaptive multi-layer strategy

Functions of eigenvalues of the graph Laplacian matrix L, especially the extremal non-trivial eigenvalues, the algebraic connectivity2and the spectral radiusn, have been shown to be important in determining the performance in a host of consensus and synchronisation applications. In this paper, we focus on formulating an entirely distributed control law for the control of edge weights in an undirected graph to solve a constrained optimisation problem involving these extremal eigenvalues.As an objective for the distributed control law, edge weights must be found that minimise the spectral radius of the graph Laplacian, thereby maximising the robustness of the network to time delays under a simple linear consensus protocol. To constrain the problem, we use both local weight constraints that weights must be non-negative, and a global connectivity constraint, maintaining a designated minimum algebraic connectivity. This ensures that the network remains sufficiently well connected.The distributed control law is formulated as a multilayer strategy, using three layers of successive distributed estimation. Adequate timescale separation between the layers is of paramount importance for the proper functioning of the system, and we derive conditions under which the distributed system converges as we would expect for the centralised control or optimisation system to converge

Networks of Self-Adaptive Dynamical Systems

We discuss the adaptive behaviour of a collection of heterogeneous dynamical systems interacting via a weighted network. At each vertex, the network is endowed with a dynamical system with individual (initially different) control parameters governing the local dynamics. We then implement a class of network interactions which generates a self-adaptive behaviour, driving all local dynamics to adopt a set of consensual values for their local parameters. While for ordinary synchronization each individual dynamical system is restored to its original dynamics once network interactions are removed, here the consensual values of control parameters are definitively acquired—even if interactions are removed. For a wide class of dynamical systems, we show analytically how such a plastic and self-adaptive training of control parameters can be realized. We base our study on local dynamics characterized by dissipative ortho-gradient vector fields encompassing a vast class of attractors (in particular limit cycles). The forces generated by the coupling network are derived from a generalized potential. A set of numerical experiments enables us to observe the transient dynamics and corroborate the analytical results obtaine