Narjes Doggaz

Multiagent Framework for Smart Grids Recovery

Failure propagation in smart grids (SGs) complicates and prolongs the recovery time as the faults to be resolved increase. This paper presents the design and implementation of a framework for SGs modeling, simulation, and recovery. The proposed approach is based on a multiagent system composed of static and mobile agents to ensure local and remote resolutions. The efficiency and completeness of the power system recovery protocol are proved as all the existing solutions are analyzed. The use of fault classification and a performing communication process as well as the deployment of local distributed databases updated at run-time ensure the effectiveness of the proposed fault recovery strategy. An experimental study confirms and validates the expected results.

An efficient simulator for fault detection and recovery in smart grids FDIRSY

This research paper deals with failures and faults in power smart grids. We propose an original multi-agent approach for power system recovery based on fault classification. For that, we propose the classification of faults as dominant or equivalent ones. This classification has the advantage of optimizing the task of power system recovery. To test and validate our approach, we develop a simulator, named FDIRSY (Fault Detection, Isolation and Recovery SYstem). The experimental study showed that our approach ensures the search for the best solution from the existing ones thanks to the use of mobile agents. These agents have the advantage of evaluating all the existing alternatives while reducing the communication cost (in terms of exchanged messages). We demonstrate that our approach is gainful in terms of required times, actions to be performed as well as the faults to be resolved thanks to the proposed fault classification.

New solutions for fault detections and dynamic recoveries of flexible power smart grids

This research paper deals with fault detections and dynamic recoveries of electrical smart grids that should be flexible and automatically adapted at run-time when faults occur on lines or devices. These grids are composed of three levels of power lines: High Voltage Lines (44KV), Medium Voltage Lines (11KV), and Low Voltage Lines (380V). In order to control the complexity of detection and deduction, we propose new relations between faults. We define also a multi-agent architecture to allow dynamic recoveries where two types of agents are defined: static agents and mobile agents. Static agents have as a task the detection of local faults on power lines and the recovery of their normal behaviors by using a local knowledge-base. The mobile agents are created to dynamically move on lines and to find new solutions when no local solution is found. To validate and test our approach, we present experimental results showing the originality of the papers contribution by assuming a case study.

Using heading hierarchy for non-taxonomic relation extraction

Relation discovery is a crucial task in ontology learning process. The classical approaches for relation extraction, based on statistical, syntactical or pattern matching techniques, focus typically on the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected. We extend these approaches by taking into account the document structure which bears additional knowledge. This paper presents an automatic methodology that addresses the non-taxonomic learning process by considering section heading hierarchy. Our approach gives not only taxonomic relations, but also non-taxonomic ones with their corresponding labels. Experiments have been performed on French Wikipedia articles related to the medical field.

An Efficient Reconfiguration-Based Approach for Improving Smart Grid Performance

In this research paper, we deal with power grid modeling and reconfiguration to improve electrical network performance for which we propose a new definition in terms of automatic recovery rate. We propose a novel and original methodology for, intelligently, optimizing the use of power energy in smart grids based on Mutli-Agent System composed of static and mobile agents. Our work presents an assistance-design approach as it proposes new and efficient reconfigurations to automatically resolve the maximum of failures in the aim of improving the power system reliability and performance. To optimize the cost of the automatic actions to be taken for reducing the costs of the human interventions, we identify a pertinent short list of failures based on a proposed relations of dominance and equivalence between failures. The efficiency of the proposed strategy is showed by the experimental study.

Toward a taxonomy of concepts using web documents structure

Due to the rise of the Web and the need to have structured knowledge, an interesting line for research is the formalization of ontologies and the creation of conceptual taxonomies from Web documents. The traditional methods for ontology learning and especially those extracting domain concepts from a textual corpus often privilege the analysis of the text itself, whether they are based on a statistical or linguistic approach. In this paper, we propose an approach which differs from the traditional ones since it uses information on the document structure to extract relevant information. Our approach studies each material form in the text in order to extract the most relevant concepts constituting the ontology related to a given field. The concepts are obtained by analyzing the occurrences of the candidate terms in the titles and in the links belonging to the documents and by considering the used styles. Our approach has been experimented on a french corpus of Web documents related to the medical field. Primary results are encouraging and seem to validate our approach. We present also in this paper a new method for the extraction of the hierarchical links between the concepts of the ontology. The taxonomic links are established in three phases: a linguistic step is based on the canonical syntactic structure of the extracted concepts, the second step consists in applying lexico-syntactic patterns which convey the hyperonymy relation and the third step analyzes the hierarchy of the titles in each document to extract taxonomic relations.

Reconfiguration-based methodology for improving recovery performance of faults in smart grids

Abstract This paper aims to enhance the reliability and quality of service (QoS) of power smart grids by searching and applying reconfiguration oriented solutions. A novel definition of recovery performance is provided in terms of automatic recoverability and unavailability rates. The proposed approach is based on a hierarchical and distributed multi-agent system (MAS). It involves static and mobile agents, respectively, dedicated to search for new feasible reconfigurations and to identify the consumers which may play the role of feeders over smart grids. An automatic reconfiguration corresponds to the addition of a new pertinent line in a smart grid to allow further recoveries. This paper provides the implemented communication protocol for negotiating (CPN) the resources allocation which is performed by the deployed agents. The management and assignment of the energy resources are modeled as a combinatorial optimization multiple-knapsack problem (MKP) to reach a better configuration with a higher possible recovery performance rate. To decrease the cost of automatic actions, which increases the smart grid performance and reduces the costs of the human interventions, a short list of failures is identified based on proposed relations of dominance and equivalence between observed failures. Furthermore, this work investigates the effectiveness of the designed and implemented software tool for off-line assistance-design, simulation and modeling of reconfigurable smart grids. The efficiency of the proposed strategy is validated through an experimental study performed by the developed simulator.