Proposal of a fuzzy-based PMU for detection and classification of disturbances in power distribution networks

Sobrinho, André Sanches Fonseca; Flauzino, Rogério Andrade; Liboni, Luisa Helena Bartocci; Costa, Éder Silva

doi:10.1016/j.ijepes.2017.06.023

Cited by 28 publications

(14 citation statements)

References 19 publications

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“…The literature review unveils the exertion of minimum PMU placement with maximum redundancy, PMU outages, contingency analysis and calibration of the PMUs. In (Sobrinho et al, 2018) PMU placement is performed using a cost effective model where the features are limited in size for all power network operations which includes fuzzy modelling. All integrated techniques will be used for assessing necessary parameters that are used for identifying several disturbances that occurs frequently in power industries.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All integrated techniques will be used for assessing necessary parameters that are used for identifying several disturbances that occurs frequently in power industries. This paper (Sobrinho et al, 2018) focussed only on cost optimized PMU for discerns the fault location but it may fails to cover the observability of the power network after post fault conditions. In (Adewole et al, 2016) the authors have focused on combining two different methods which are indicated as discrete wavelet transform and neural networks to identify various fault segments in entire network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Conversely the major disadvantage of the Fourier transformation (Mohanta et al, 2015) is the inherent compromise that exists between frequency and time resolution. By considering the aforementioned issues (Sobrinho et al, 2018;Adewole et al, 2016;Pignati et al, 2017;Prasad and Vinod Kumar, 2018;Della Giustina et al, 2014;Pal et al, 2017;Wu et al, 2015;Saha Roy et al, 2017;Saber et al, 2018;Jiang et al, 2012;Das et al, 2017;Chen and Liu, 2003;Li et al, 2016;Wu et al, 2018;Barman and Roy, 2018;Appasani and Mohanta, 2018;Carvalho et al, 2018;Cui et al, 2019;Mohanta et al, 2015), a new-fangled technique is introduced in power system line fault finding and localisation method for power grids, to precisely recognize and assess the location of the fault taking place anywhere in the network using PMU measurements.…”

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confidence: 99%

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Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

Manoharan¹,

Teekaraman

Kuppusamy³

et al. 2022

Energy Exploration & Exploitation

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This research study examines the integration of wireless system and solar cells for detection and location of faults in power grids by implementing minimum number of Phasor Measurement Units (PMUs) after considering zero injection buses. A fault in the grid networks indicates a failure in the entire network that interfaces the normal flow of current which can be measured using wireless systems. The fault location in power grid is problematic because of high existence in several lateral tap-offs. In addition, the identification of exact location with presence of faults in the power system and severity of the faults are challenging tasks due to bulk and complex nature of the electrical power system state estimation. The major requirement of PMU placement approach is to find the minimum number of PMUs and their location with maximum wireless coverage under fault conditions with a prerequisite for the projected algorithm. The integrated algorithm is tested and validated for different fault conditions where the results are compared with different conventional methods using different optimization models such as heuristics, decision rules and mathematical programming where the outcomes proves to be much effective in terms of number of PMU installation for proper placements and detection, location in number of fault systems thus achieving 82% reliability using Ant Lion Optimizer (ALO).

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

Manoharan¹,

Teekaraman

Kuppusamy³

et al. 2022

Energy Exploration & Exploitation

View full text Add to dashboard Cite

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“…Other techniques such as sparse signal decomposition (SSD) [16], radial basis function neural network [17], and probabilistic neural network techniques in [18,19] have been used to identify and detect PQ problems. In [20], the phasor measurement units based on the fuzzy technique have been used to categorise the PQ disturbances. Although all existing signal-based PQ detection techniques have good results, there are some shortages such as: (i) Almost all techniques depend heavily on the experience of PQ problems detection and signal processing.…”

Section: Introductionmentioning

confidence: 99%

Categorisation of power quality problems using long short‐term memory networks

Abdelsalam

Hassanin

Hasanien

2021

IET Generation Trans & Dist

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Recognition of power quality (PQ) troubles is a critical task in the electrical power system. All previous works solve this problem using two‐step methodology: Feature extraction and classification steps with each step using its own techniques, and this consumes a computation time. The purpose of this study is to utilise a novel artificial intelligence (AI) technique for recognition of PQ events. The proposed AI technique is the long short‐term memory (LSTM) network, which detects and classifies the PQ events in one step. This technique extracts amplitude, disturbance duration and total harmonic distortion from the captured waveform, and the LSTM uses its own rules to classify the PQ events. Many simple PQ events such as interruption, sag, flicker, swell and surge or complex PQ events such as sag plus harmonics and swell plus harmonics are generated using MATLAB programming environment to evaluate the performance of LSTM. Also, real‐time measurements are collected from an industrial substation and are used to ensure the effectiveness of the proposed LSTM technique. A comparison with other techniques is conducted and the results verify the good performance of LSTM in classifying the PQ problems.

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“…A fuzzy-based cost optimised phasor measurement technique to estimate the PQ of a distributed network is well discussed in [16]. Lee and Shen [17] presented an optimal feature selection approach in which probabilistic neural network-based feature selection (PFS) combines a global optimisation algorithm with an adaptive probabilistic neural network (APNN) to gradually remove redundant and irrelevant features in noisy environments for powerquality disturbance classification.…”

Section: Introductionmentioning

confidence: 99%

Real‐time cross‐correlation‐based technique for detection and classification of power quality disturbances

De¹,

Debnath

2017

IET Generation, Transmission & Distribution

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Proposal of a fuzzy-based PMU for detection and classification of disturbances in power distribution networks

Cited by 28 publications

References 19 publications

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

Categorisation of power quality problems using long short‐term memory networks

Real‐time cross‐correlation‐based technique for detection and classification of power quality disturbances

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