Snapshot Semantics for Temporal Multiset Relations (Extended Version)

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations and significantly outperforms such implementations on queries that involve aggregation.Comment: extended version of PVLDB pape

An extension of Newton–Raphson power flow problem

This paper explores an idea to extend Newton–Raphson power flow problem to handle power system transmission line flow limits, by means of generation redispatch and phase shifters. We extend and reformulate the power flow so that it includes a variety of flow limits (thermal, small-signal stability, voltage difference), generation redispatch, and phase shifters. The novelty of the approach is three step procedure (in case any limit violations exist in the system): run ordinary power flow (and identify flow limits violated), solve a set of linear equations using extended power flow Jacobian by adding a new column and a new raw that characterize particular limit, and resolve ordinary power flow with initial solution obtained after the correction made by solution of linear equations. The use of ordinary power flow Jacobian and minimal extensions to it in the case of limits identified makes this approach an attractive alternative for practical use. A simple numerical example and the examples using an approximate model of real-life European Interconnected Power System are included in the paper to illustrate the concept

A Component-Based Power System Model-Driven Architecture

This letter describes an approach of applying the model-driven development in power systems. A component-based model-driven architecture,that gives full flexibility of the automation in source code generation,is introduced. A design pattern to code generation is described

New developments in the application of automatic learning to power system control

peer reviewedIn this paper we present the basic principles of supervised learning and reinforcement learning as two complementary frameworks to design control laws or decision policies within the context of power system control. We also review recent developments in the realm of automatic learning methods and discuss their applicability to power system decision and control problems. Simulation results illustrating the potentials of the recently introduced fitted Q iteration learning algorithm in controlling a TCSC device aimed to damp electro-mechanical oscillations in a synthetic 4-machine system, are included in the paper

A reinforcement learning based discrete supplementary control for power system transient stability enhancement

peer reviewedThis paper proposes an application of a Reinforcement Learning (RL) method to the control of a dynamic brake aimed to enhance power system transient stability. The control law of the resistive brake is in the form of switching strategies. In particular, the paper focuses on the application of a model based RL method, known as prioritized sweeping, a method proven to be suitable in applications in which computation is considered to be cheap. The curse of dimensionality problem is resolved by the system state dimensionality reduction based on the One Machine Infinite Bus (OMIB) transformation. Results obtained by using a synthetic four-machine power system are given to illustrate the performances of the proposed methodology

Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations

This paper considers a trajectory-based approach to determine control signals superimposed to those of existing controllers so as to enhance the damping of electromechanical oscillations. This approach is framed as a discrete-time, multi-step optimization problem which can be solved by model-based and/or by learning-based methods. This paper proposes to apply a model-free tree-based batch mode Reinforcement Learning (RL) algorithm to perform such a supplementary damping control based only on information collected from observed trajectories of the power system. This RL-based supplementary damping control scheme is first implemented on a single generator and then several possibilities are investigated for extending it to multiple generators. Simulations are carried out on a 16-generators medium size power system model, where also possible benefits of combining this RL-based control with Model Predictive Control (MPC) are assessed