HOS network-based classification of power quality events via regression algorithms

Palomares-Salas, José Carlos; Rosa, Juan José González de la; Fernández, José María Sierra; Agüera-Pérez, Agustín

doi:10.1186/s13634-015-0204-3

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…This does not exclude the possibility to apply higher-order statistics (HOS) and Kurtosis procedures. However, the HOS evaluation is more oriented to harmonic analysis (spectral kurtosis) [5][6][7]. Basically, the arithmetic average value per cycle and the previous average calculated during many cycles provide information about the voltage waveform integrity and the supply continuity (interruptions).…”

Section: Statistical Processing Techniquesmentioning

confidence: 99%

An Embedded Sensor Node for the Surveillance of Power Quality

et al. 2019

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The energy supply of office buildings and smart homes is a key issue in the global energy system. The growing use of microelectronics-based technology achieves new devices for a more comfortable life and wider use of electronic office equipment. On the one hand, these applications incorporate more and more sensitive electronic devices which are potentially affected by any external electrical transient. On the other hand, the existing electrical loads, which generally use electronic power systems (such as different types of battery chargers, ballasts, inverters, switching power supplies, etc.), generate different kinds of transients in their own electrical internal network. Moreover, improvements in the information of the state of the mains alternating current (AC) power line allows risk evaluation of any disturbance caused to permanently connected electronic equipment, such as computers, appliances, home security systems, phones, TVs, etc. For this reason, it is nowadays more important to introduce monitoring solutions into the electrical network to measure the level of power quality so that it can protect itself when necessary. This article describes a small and compact detector using a low-cost microcontroller and a very simple direct acquiring circuit. In addition; it analyzes different methods to implement various power quality (PQ) surveillance algorithms that can be implemented in this proposed minimum hardware platform. Hence; it is possible to achieve cheap and low-power monitoring devices that can become nodes of a wireless sensor network (WSN). The work shows that using a small computational effort; reasonable execution speed; and acceptable reliability; this solution can be used to detect a variety of large disturbance phenomena and spread the respective failure report through a 433 MHz or 2.4 GHz radio transmitter. Therefore, this work can easily be extended to the Internet of Things (IoT) paradigm. Simultaneously, a software application (PulsAC) has been developed to monitor the microcontroller’s real-time progress and detection capability. Moreover, this high-level code (C++ language), allows us to test and debug the different utilized algorithms that will be later run by the microcontroller unit. These tests have been performed with real signals introduced by a function generator and superimposed on the true AC sine wave

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Section: Statistical Processing Techniquesmentioning

confidence: 99%

An Embedded Sensor Node for the Surveillance of Power Quality

et al. 2019

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“…In [1], the authors use higher-order statistics-based feature extraction and regression algorithms implemented in artificial neural networks (ANNs) to classify power quality disturbances. The novel aspect of the method proposed is the use of a feature vector based on the combination of time and frequency domain higher-order statistic coefficients obtained from the samples of the input signal.…”

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confidence: 99%

Guest editorial introduction to the special issue on “advanced signal processing techniques and telecommunications network infrastructures for smart grid analysis, monitoring, and management”

Bracale

Barros

Cacciapuoti

et al. 2015

EURASIP J. Adv. Signal Process.

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Electrical power systems are undergoing a radical change in structure, components, and operational paradigms, and are progressively approaching the new concept of smart grids (SGs). Future power distribution systems will be characterized by the simultaneous presence of various distributed resources, such as renewable energy systems (i.e., photovoltaic power plant and wind farms), storage systems, and controllable/non-controllable loads. Control and optimization architectures will enable network-wide coordination of these grid components in order to improve system efficiency and reliability and to limit greenhouse gas emissions. In this context, the energy flows will be bidirectional from large power plants to end users and vice versa; producers and consumers will continuously interact at different voltage levels to determine in advance the requests of loads and to adapt the production and demand for electricity flexibly and efficiently also taking into account the presence of storage systems. This evolution of power distribution networks involves also an increasingly complex and performing Information and Communication Technology (ICT) infrastructure. An increasing and continuous flow of information will be exchanged among the various levels of such systems in order to ensure an optimized operation of the network and an efficient management of distributed resources. Then, SGs operate with two-way flows of both electricity and information. The necessary communication resources heavily depend on the choices related to the required signal processing for analysis, monitoring, and management of the network.Many research contributions have been published recently in the relevant literature, covering the innovative topics associated with this new concept of functional power systems. However, few papers have addressed the problems inherent to the study of the advanced signal processing techniques and telecommunications network infrastructures which are needed for the modern SGs. This is the reason for this special issue.There are many aspects where signal processing plays a crucial role for planning and operation of SGs. This special issue focuses on some of them, addressing in particular the development of new, advanced, signal processing methods for studying the characteristics of highly non-stationary waveforms involved in the analysis, monitoring and management of the electrical quantities at nodes/branches of the smart grids.Our call for papers attracted numerous submissions worldwide. In response to the call for papers for this special issue, after the review process, 12 were accepted for publication, with co-authors from Asia, Europe, South America, and the USA These papers are briefly described in the following.In [1], the authors use higher-order statistics-based feature extraction and regression algorithms implemented in artificial neural networks (ANNs) to classify power quality disturbances. The novel aspect of the method proposed is the use of a feature vector based on the combination of time and frequency d...

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