Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances

Razo-Hernández, José R.; Valtierra-Rodríguez, Martín; Amezquita-Sanchez, Juan P.; Granados-Lieberman, David; Gómez‐Aguilar, J.F.; Rangel-Magdaleno, José

doi:10.3390/electronics7120433

Cited by 9 publications

(6 citation statements)

References 44 publications

(72 reference statements)

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“…This method adds historical information and circumstance factors on the basis of existing feature extraction methods, which expands the feature set and improves the accuracy of combination disturbances, but the calculation is complicated. In [11], a new method for classification using the information provided by a phasor measurement unit (PMU) scheme and the homogeneity index is proposed. Only if-then-else is used for classification, which greatly reduces the computational burden.…”

Section: Figure 1 Block Diagram Representation Of Traditional Pqd Det...mentioning

confidence: 99%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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This paper presents a new concise deep learning-based sequence model to detect the power quality disturbances (PQD), which only uses original signals and does not require pre-processing and complex artificial feature extraction process. A simple gated recurrent network (SGRN) with a new recurrent cell structure is developed, which consists of only two gates: forget gate and input gate, and two weight matrices. Compared with the standard Recurrent Neural Network (RNN) model, the training process of the proposed method is more stable and the prediction accuracy is higher. In addition, this special structure retains basic non-linearity and long-term memory, while enabling the simple gated recurrent network model to be superior to Long Short-Term Memory (LSTM) Network and Gated Recurrent Unit (GRU) Network in terms of the number of parameters (i.e. memory cost) and detection speed. In the light of the experimental results, the simple gated recurrent network algorithm can achieve 99.07% detection accuracy, and contains only 18,959 parameters, which indicates that our proposed method is easier to deploy in resource-constrained internet of things (IoT) micro-controllers.

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Section: Figure 1 Block Diagram Representation Of Traditional Pqd Det...mentioning

confidence: 99%

A simple gated recurrent network for detection of power quality disturbances

Wei

2020

IET Generation Trans & Dist

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“…Figure 3 presents the proposed threshold-based methodology for PQ distortions at the fundamental component. In the first three steps of algorithm each phase voltage magnitude is compared with a threshold chosen according to the IEEE Standard 1159, like it was introduced in [2]. In the last step asymmetry is calculated and compered with the chosen threshold.…”

Section: The Use Of Pmu For Detecting and Measuring Ofmentioning

confidence: 99%

“…Nowadays there is a high number of PMUs installed in transmission and also distribution systems, and further increase of PMU number is expected. New functionalities of PMU data, including PQ are now being investigated, and it is expected that PMU data will provide a new insight into PQ monitoring in distribution system [2][3].…”

Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Barczentewicz¹,

Bień²,

Duda³

2020

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Power quality (PQ) monitoring is important for both the utilities and also the users of electric power. The most widespread measurement instrument used for PQ monitoring is the PQM (Power Quality Monitor) or PQA (Power Quality Analyzer). In this paper we propose the usage of PMU data for PQ parameters monitoring. We present a new methodology of PQ parameters monitoring and classification based on PMU data. The proposed methodology is tested with real measurements performed in distribution system using dedicated PMU system.

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“…The distribution system is plagued by faults all day, every day. Furthermore, trustworthiness will improve [5][6][7]. To that end, the ability of traditional gauges and phasor measurement unit (PMU) gauges to provide reliable state estimation is investigated.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

Khosravi,

Trik,

Ansari

2024

The Journal of Engineering

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The dynamic nature of distribution networks raises fresh issues with how such electrical systems function. These networks have some characteristics that indicate the need for better monitoring and control capabilities, including dispersed generation employing renewable resources, changing load profiles, and rising reliability requirements. Phasor measurement units (PMUs) offer simultaneous voltage and current phasor measurements at various places and offer a variety of options for gauging the condition and health of the power distribution network. In this regard, a cost‐optimized PMU with some unique features for distribution systems is presented in this work. These features include a fuzzy inference system to determine the root cause of potential electrical disturbances and methods to estimate electrical parameters through measured field data, which are necessities. This study takes into account the modelling of PMUs, utilizing a process for fault detection and classification with a fuzzy inference network. The 9‐bus distribution network's dependability model is built once the components and their functions are first outlined. The proposed model is then used to calculate the availability of the presented model, which has been examined to provide an analogous reliability model for PMUs. Depending on the specific manufacturer, the PMU's design and specs will change. To extract phase and size measurement features for the proposed model adaptive neural‐fuzzy inference system network's training, two PMU structures and associated reliability models are described here. When merging input data for fuzzy neural network prediction using MATLAB software, fuzzy sets are taken into account for error classification analysis.

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Homogeneity-PMU-Based Method for Detection and Classification of Power Quality Disturbances

Cited by 9 publications

References 44 publications

A simple gated recurrent network for detection of power quality disturbances

A simple gated recurrent network for detection of power quality disturbances

International Journal of Electronics and Telecommunications

Diagnosis and classification of disturbances in the power distribution network by phasor measurement unit based on fuzzy intelligent system

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