Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

Florencias-Oliveros, Olivia; González-de-la-Rosa, Juan-José; Agüera-Pérez, Agustín; Palomares-Salas, José Carlos

doi:10.3390/en12010055

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…In [18], the index is primarily tested during a short-term one-week campaign, which is an effective tool for voltage characterization using HOS. Two strategies are extracted: the first strategy detects the extreme values of the PQ events; and the second one consists of the continuous monitoring patterns of working and non-working days.…”

Section: A Pq Indexmentioning

confidence: 99%

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Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

Florencias-Oliveros¹,

González-de-la-Rosa²,

Sierra-Fernández³

et al. 2022

Journal of Modern Power Systems and Clean Energy

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The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers'pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.

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Section: A Pq Indexmentioning

confidence: 99%

“…This contributes to improving future prediction tools based on artificial intelligence. The authors' previous work [18] settled down the basis of the index and showed a preliminary controlled experience that has been tested and validated in the current work.…”

Section: Introductionmentioning

confidence: 97%

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

Florencias-Oliveros¹,

González-de-la-Rosa²,

Sierra-Fernández³

et al. 2022

Journal of Modern Power Systems and Clean Energy

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“…Inside the same set of former topics, the fourth paper, "Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid" [4] by O. Florencias et al, proposed a new index for both PQ and reliability assessment (depending on the considered analysis window's length) thought to be used in measurement campaigns that require deep statistical characterization. The index consists of a summation of three differential terms: variance, skewness, and kurtosis, each with respect to the ideal value of the statistic.…”

Section: A Short Review Of the Contributions In This Issuementioning

confidence: 99%

Special Issue “Analysis for Power Quality Monitoring”

de-la-Rosa

Pérez-Donsión

2020

Energies

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We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. As a consequence, numerous emerging words are relevant to this point: Internet of Things (IoT), big data, smart cities, smart grid, industry 4.0, etc. To achieve this formidable goal, systems should work more efficiently, a fact that inevitably leads to power quality (PQ) assurance. Apart from its economic losses, a bad PQ implies serious risks for machines and, consequently, for people. Many researchers are endeavouring to develop new analysis techniques, instruments, measurement methods, and new indices and norms that match and fulfil the requirements regarding the current operation of the electrical network. This book, and its associated Special Issue, offer a compilation of some of the recent advances in this field. The chapters range from computing to technological implementation, going through event detection strategies and new indices and measurement methods that contribute significantly to the advance of PQ analysis and regulation. Experiments have been developed within the frameworks of research units and projects and deal with real data from industry practice and public buildings. Human beings have an unavoidable commitment to sustainability, which implies adapting PQ monitoring techniques to our dynamic world, defining a digital and smart concept of quality for electricity.

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“…Furthermore, other research has been focused on how to identify the different type of disturbances from occurring events; for instance, in References [6][7][8][9][10][11]. Once PQ disturbances are detected, classifying them and compressing them into separate events is a challenging task and requires advanced tools; see References [12][13][14][15]. Some authors have considered fuzzy logic or neural networks to classify them [16][17][18][19], some others proceed based on clustering the data depending on its origin from offline and prescribed events [20,21], and lastly, certain studies have focused on real-time classification, which results in a successful classification; for instance, see References [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

2019

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This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.

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Reliability Monitoring Based on Higher-Order Statistics: A Scalable Proposal for the Smart Grid

Cited by 12 publications

References 24 publications

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

Special Issue “Analysis for Power Quality Monitoring”

Power Quality Disturbances Assessment during Unintentional Islanding Scenarios. A Contribution to Voltage Sag Studies

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