Power Quality

Dear Colleagues,

Power quality (PQ) refers to a set of characteristics related to electricity transport and delivery to end consumers, assuring 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 of a common power system service that could be considered secure, continuous and constant. Secondly, the IEC 61000-4-30 standard summarizes 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, electricity networks and the market are continuously changing and adapting to new technologies and concepts of energy usage emerging within two incipient frames: the smart grid (SG) and the industrial digital revolution (the 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 behavior 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 continuous and permanent monitoring, including short-term forecasting. The aim is to provide customers and industrial managers with new tools 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, massive operational data (big data) generated by the measurement equipment deployed during monitoring campaigns are usually difficult or tricky to interpret and manage due to, among other factors, their complex hardware structures and communication protocols, which hinder accessibility to storage units, and the limited possibilities of monitoring equipment, based on dare-to-say obsolete regulations that do not reflect current real-life operation. Consequently, a new conception of data handling is required based on time, frequency and space domain compression techniques, with the goal of offering more robust measurement solutions under real conditions.

Furthermore, PQ problems have serious economic, human and technological consequences. More and more works demand customer-oriented PQ assessment solutions and measurement equipment. Differences in load sensitivity make necessary several specific contractual conditions, introducing thresholds that allow for claims against eventual contractual breaches. Indeed, in the industrial sector, companies request ad hoc PQ assessment, which in fact reflects the real situation, or the “PQ mapping”, of the company facilities and manufacturer process. Industry research benchmarking reports would allow performance comparison of PQ metrics. Hence, quantifying losses and the proposal of compensation strategies are key factors.

Moreover, domestic easy-to-handle instruments should incorporate elements of indication and visualization that do not require extensive technical knowledge and aid in end customers’ understanding of PQ reports (smart indicators).

It can be concluded that keeping PQ in the grid assures human safety and equipment life. This can be achieved by implementing flexible monitoring solutions that can accomplish data management while accounting for temporal and spatial scalability.

Considering this introduction, it is of high interest to classify research literature on PQ into the following flourishing topics or branches that are directly and transversely addressed in multidisciplinary work teams:

• Statistical Signal Processing (SSP) and intelligent methods for PQ analysis.
  • Statistical planning and characterization in PQ campaigns,
  • Higher-Order Statistics (HOS) for PQ characterization,
  • Intelligent methods for PQ analysis,
  • New estimators for PQ monitoring

• Power Quality and Reliability characterization.
• PQ indices and thresholds,
• Customized PQ for utilities, customers and specific geographical areas,
• Industry research benchmark reports on PQ metrics,
• New types of electrical perturbations
• Management of PQ Big Data in the Smart Grid.
• Spatial and temporal compression of measurements,
• Spatial and temporal scalability of measurements,
• Modelling and forecasting of PQ time-series,
• Graphical visualization of PQ: plots, diagrams and trajectories,
• PQ and Information Theory
• PQ monitoring systems: architectures and communications.
• New tendencies in smart instruments for PQ,
• Uncertainty in PQ instruments,
• Sensors networks for PQ monitoring,
• Non-intrusive load monitoring,
• PQ for renewable energy systems,
• Low-cost measurement equipment.
• PQ losses and mitigation assessment.
• Energy efficiency and PQ,
• Economic impact and losses due to poor PQ,
• PQ maintenance strategies in networks,
• PQ mitigation.
• New PQ monitoring norms and standards.
• PQ indices,
• PQ norms,
• PQ standardized measurements for PMUs
• PQ monitoring in the industry 4.0.

With all of these precedents, all in all, this second edition of the Special Issue in Analysis for Power Quality Monitoring aims to gather research and review manuscripts dealing with the last advances in PQ analysis and measurement solutions, comprising ad hoc signal processing techniques, artificial intelligence and soft computing, big data analytics and cloud computing for the smart grid, development of new PQ indices, monitoring with newly PQ graphical representations, and their practical implementation in distributed measurement equipment. As a novelty, this issue also pays special attention to the human, technological and financial consequences of a bad PQ, welcoming economic and techno-economic works focusing on losses and the financial effects of PQ mitigation plans. 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,
• Modelling 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 losses 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
Dr. Sara Sulis
Topic Editors