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The increasing development of the smart grid demands reliable monitoring of the power quality at different levels, introducing more and more measurement points. In this framework, the advanced metering infrastructure must deal with this large amount of data, storage capabilities, improving visualization, and introducing customer-oriented interfaces. This work proposes a method that optimizes the smart grid data, monitoring the real voltage supplied based on higher order statistics. The method proposes monitoring the network from a scalable point of view and offers a two-fold perspective based on the duality utility-prosumer as a function of the measurement time. A global PQ index and 2D graphs are introduced in order to compress the time domain information and quantify the deviations of the waveform shape by means of three parameters. Time-scalability allows two extra features: long-term supply reliability and power quality in the short term. As a case study, the work illustrates a real-life monitoring in a building connection point, offering 2D diagrams, which show time and space compression capabilities, as well.Keywords: signal waveform compression; higher-order statistics (HOS); power quality (PQ); computational solutions for advanced metering infrastructure (AMI); smart grid (SG) applications

In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network.

This paper presents a higher-order statistics-based approach of detecting transients that uses the fourth-order discrete spectrogram to monitor the power supply in a node of the domestic smart grid. Taking advantage of the mixed time-frequency domain information, the method allows for the transient detection and the subsequent identification of the potential area in which the fault takes place. The proposed method is evaluated through real power-line signals from the Spanish electrical grid. Thanks to the peakedness enhancement capability of the higher-order spectra, the results show that the procedure is able to detect low-level transients, which are likely ignored by the traditional detection procedures, where the concern pertains to power reliability (not oriented to micro grids), and this, by analyzing the duration and frequency content of the electrical perturbation, may indicate prospective faulty states of elements in a grid. Easy to implement in a hand-held instrument, the computational strategy has a 5 Hz resolution in the range 0-500 Hz and a 50 Hz resolution in the range of 0-5 kHz, and could be consequently used by technicians in order to allocate new types of transients originated by the distributed energy resources. Four real-life case-studies illustrate the performance.

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