A demonstrator tool to provide the network operator with microservices
based on big data and semantic web technologies

E. Bionda, F. Belloni, R. Chiumeo, D. Della Giustina, D. Pala, G. Proserpio, S. Pugliese, H. Shadmehr and L. Tenti

2017/04/25

Abstract

The paper presents a demonstrator tool, designed as an architecture of microservices, to visualize and correlate Power Quality (PQ) data with topological, cartographic and meteorological information. Starting from a single web access point, the tool allows a network operator to “cast a glance” to different kind of data, involved in the management of its network or coming from open data sets, to investigate possible correlations among them.
The adopted approach in data management make use of both the ontological and big data paradigma. This choice allows the operator to manage and correlate large volume of data of different kind (structured and unstructured data).
The adopted ontology is the IEC Common Information Model (CIM), which allows the integration of PQ information recorded by the QuEEN monitoring system, at the HV/MV stations of the DSO Unareti S.p.A., with the associated distribution network topology. Services offered regard: (i) the visualization of PQ indices in association with topological and cartographic information; (ii) a classifier, based on a Software Vector Machine (SVM) algorithm, to define the origin of voltage dips; (iii) a web application, developed in the Spark framework, to correlate in space and time QuEEN voltage dips times series with storm cells.

Published in: Renewable Energy & Power Quality Journal (RE&PQJ, Nº. 15)
Pages: 137-142 Date of Publication: 2017/04/25
ISSN: 2172-038X Date of Current Version:
REF: 250-17 Issue Date: April 2017
DOI:10.24084/repqj15.250 Publisher: EA4EPQ

Authors and affiliations

E. Bionda(1), F. Belloni(1), R. Chiumeo(1), D. Della Giustina(2), D. Pala(1), G. Proserpio(1), S. Pugliese(2), H. Shadmehr(1) and L. Tenti(1)
1. Ricerca sul Sistema Energetico – RSE S.p.A. Milano, Italy
2. Unareti S.p.A. Reti Elettriche. Milano (Italy)

Key word

Power Quality, Common Information Model, Big Data, Semantic Web, Microservices.

References

[1]. E. Bionda, D. Della Giustina, D. Pala, G. Proserpio, S. Pugliese and L. Tenti, “Integration of Power Quality information in the framework of a standard semantic model”, 26th IEEE Inter. Conf. on Environment and Electrical Engineering, 7-8 June 2016, Florence, paper 449.
[2]. R. Chiumeo, A. Porrino, L. Garbero, L. Tenti, M. de Nigris, 2009, " The Italian Power Quality Monitoring System Of The MV Network: Results Of The Measurements Of Voltage Dips After 3 Years Campaign ", 20th International Conference on Electricity Distribution, CIRED, Prague, paper 0737.
[3]. P. Marcacci, G. Stella, "STAF V.2 - Storm Track Alert and Forecast, Ver. 2”, Final Technical Report ERSE 09004789, 31st December 2009 (in Italian).
[4]. Microservices, http://www.martinfowler.com/articles/microservices.html.
[5]. Core IEC Standards, http://www.iec.ch/smartgrid/standards.
[6]. RDF, https://www.w3.org/RDF.
[7]. G. Proserpio, D. Pala, E. Bionda, 2015, Studio ed applicazione di tecnologie ICT per la realizzazione dell’interoperabilità tra i sistemi componenti la smart grid, RSE, RdS, Report 15000384 (in Italian).
[8]. AMQP, https://www.amqp.org.
[9]. RabbitMQ, https://www.rabbitmq.com.
[10]. RabbitMQ-Topics, https://www.rabbitmq.com/tutorials/tutorial-five-python.html.
[11]. SPARQL, http://www.w3.org/TR/rdf-sparql-query.
[12]. Apache Spark, http://spark.apache.org.
[13]. Databricks-Apache Spark, https://databricks.com.
[14]. R. Chiumeo, H. Shadmehr and L. Tenti, “Self-tuning Kalman filter and machine learning algorithms for voltage dips upstream or downstream origin detection”, ISSN 2172-038X Renewable Energy & Power Quality Journal, No.14, May 2016, Paper 346, pg. 409-413.
[15]. R. Chiumeo, L. Garbero, F. Malegori, L. Tenti, “Feasible methods to evaluate voltage dips origin”, ISN 2172-038X Renewable Energy & Power Quality Journal, No.13, April 2015, Paper 325, 357-361.