Analysis for Power Quality Monitoring - Second Edition: Power Quality Measurement Systems and Big Data Analytics in the Smart Grid and the Industry 4.0

Dear Colleagues,

The Guest Editors are welcoming submissions to this Special Issue of Energies (MDPI) on the subject area of “Analysis for Power Quality Monitoring—Second Edition: Power Quality Measurement Systems and Big Data Analytics in the Smart Grid and the Industry 4.0".

Power quality (PQ) refers to a set of characteristics related to the electricity transport, and their delivery to the end-consumers assuring both the balance between network performance and customer satisfaction. Two main normative frames have been traditionally adopted by technicians and researchers. Firstly, the UNE-EN 50160 standard defines the characteristics from which the service of a common power system could be considered secure, continuous, and constant. Secondly, the IEC 61000-4-30 standard summarises the methodologies that must be incorporated within the measurement equipment, regarding their technology and class of precision to ensure that previously established requirements or measurement characteristics are met.

However, it is widely known that electricity networks and market are currently changing and adapting to new technologies and concepts of energy usage that are emerging within two incipient frames: the smart grid (SG) and the indutrial digital revolution (Industry 4.0). This conception is based on the new capabilities of system production by non-conventional means (e.g., structural issues) with numerous distributed energy resources and loads, whose highly fluctuating demands alter the ideal power delivery conditions. Thus, modern instrumentation and computational intelligence should inform energy behaviour and its dynamics almost in real time, and specifically in the field of PQ, smart instruments should track the continuity and reliability of supply, i.e., perform a continuous and permanent monitoring, including short-term forecasting capabilities. The aim is to provide customers and industrial managers with new tools in order to be capable of interpreting measurements more accurately and flexibly according to the smart grid framework demands.

As a direct consequence of the introduction of new technologies, the massive operational data (Big Data), generated by the measurement equipment deployed during the monitoring campaigns, are usually difficult or tricky to interpret and manage due to different causes, such as complex hardware structures and communication protocols that hinder accessibility to storage units, and the limited possibilities of monitoring equipment, based on dare-to-say obsolete regulations that do not reflect the current real-life operation. Consequently, a new conception of data handling is required to be based on time, frequency, and space domains compression techniques, with the goal of offering more robust measurement solutions under real conditions.

With all of these precedents, this second edition of the Special Issue aims to gather research and review manuscripts dealing with the last advances in PQ analysis and measurement solutions. This issue also pays special attention to the human, technological, and financial consequences of a bad PQ. Topics of interest for publication include but are not limited to:

• Power quality and reliability;
• Statistical signal processing applied to PQ;
• Intelligent methods for PQ analysis;
• PQ indices and thresholds;
• Soft computing for PQ;
• Information theory and PQ;
• Customized PQ for utilities, customers, and specific areas;
• Big data in the smart grid: format, compression, and temporal and spatial scalability;
• Modeling and forecasting of PQ time-series;
• PQ monitoring systems: architectures and communications;
• Distributed measurement systems;
• New tendencies in smart instruments for PQ;
• Sensors networks for PQ monitoring;
• Graphical visualization of PQ: new displays and hand-held instruments;
• PQ loss assessment and mitigation;
• Economic impact of bad PQ losses;
• PQ maintenance strategies in networks;
• Industry research benchmark reports on PQ metrics;
• Prospective introduction of new PQ monitoring norms and standards.

Dr. Juan-José González de la Rosa
Dr. Olivia Florencias-Oliveros
Prof. Dr. Sara Sulis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Power quality (PQ) and reliability monitoring systems
• Statistical signal processing
• Intelligent methods for PQ analysis
• PQ indices and thresholds
• Customized PQ for utilities and customers
• Big data in the smart grid: temporal and space compression and scalability
• Graphical PQ
• PQ mitigation
• PQ loss assessment
• Economic Impact of bad PQ losses
• PQ maintenance strategies in networks
• New tendencies in smart instruments for PQ
• PQ norms