A novel methodology for modeling waveforms for power quality disturbance analysis

Rodriguez-Guerrero, Marco Antonio; Carranza-Lopez-Padilla, Rene D.; Osornio-Ríos, Roque Alfredo; Romero-Troncoso, René de Jesús

doi:10.1016/j.epsr.2016.09.003

Cited by 37 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The STF model must be established appropriately to detect and classify power disturbances. According to Rodriguez‐Guerrero et al, a mathematical model for PQ analysis should not be specific to a single PQD but give a comprehensive description of simultaneous PQ phenomena. By taking the DC component, harmonics, and noise into consideration, the power signal can be expressed in discrete form as follows:

y ((), k) = A_{0} ((), k) + \sum_{m = 1}^{4} A_{m} ((), k) cos ((), ω_{m} ((), k) {kT}_{s} + φ_{m} ((), k)) + e ((), k)

where y ( k ) is the measured signal; A 0 ( k ) is the DC component; m is the harmonic order index; A m ( k ), ω m ( k ), and φ m ( k ) are the AM, the angular frequency, and phase of each harmonic component; T s is the sampling period; and e ( k ) is the additive noise.…”

Section: Pattern Design Via Stf Methodsmentioning

confidence: 99%

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Chen

Xiao

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

SummaryThe classification of single and simultaneous power quality disturbances (PQDs) has become an issue of concern in the power system field. This paper proposes a novel approach based on dual strong tracking filters (STFs) and the rule-based extreme learning machine (ELM) for detecting and classifying single and simultaneous PQDs. Dual STFs are a hybrid structure of a low-order STF and high-order STF. The fading factor of the low-order STF is used to detect sudden changes in PQDs; the fundamental amplitude variation is tracked by the high-order STF. Six distinctive features extracted from the dual STFs serve as the input to the ELM classifier for PQD classification. The rule-based ELM technique, which is equipped with certain decision rules, can improve the ELM classification accuracy when the number of hidden nodes is insufficient. In consideration of special structures of matrices, the real-time computation of the proposed method can be realized. A PQD dataset is generated in MATLAB for simulation experiments; the results show that 20 types of PQDs, including single and simultaneous disturbances, can be accurately classified under the different levels of noise via the proposed method. The method is also tested on a real recorded waveform to verify its effectiveness in PQD classification. | INTRODUCTIONIn recent years, power quality (PQ) has become a critical issue for researchers and developers concerned with power system stability and the propagation of PQ-related effects in the grid. Poor PQ can lead to maloperations or failures of sensitive loads such as embedded systems or electronic devices, ultimately incurring substantial cost for end users. The increased use of unbalanced and nonlinear loads, line faults, switching devices, and power electric converters represents a wide variety of disturbances. Faults among lines to ground, unbalanced, or nonlinear loads can lead to amplitude (AM) variations of the fundamental frequency such as sags, swells, and interruptions. Flickers andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

y ((), k) = A_{0} ((), k) + \sum_{m = 1}^{4} A_{m} ((), k) cos ((), ω_{m} ((), k) {kT}_{s} + φ_{m} ((), k)) + e ((), k)

Section: Pattern Design Via Stf Methodsmentioning

confidence: 99%

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Chen

Xiao

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…From Eqs. (6) and 9, if the frequency is changed at a fixed rate, the phasor equation can be rewritten as (12,14,15)…”

Section: Power System Signal Model Of Curve Fittingmentioning

confidence: 99%

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

Lee¹,

Huang²,

Men-Shen³

2020

Sensors and Materials

View full text Add to dashboard Cite

In recent years, owing to an increase in electrical energy consumption, the quality and safety issues of power systems have drawn considerable attention. The frequency of a power system is an important indicator of its stability. The system frequency will drop when large generator sets are tripped. Therefore, it is important to measure the fundamental frequency of a power system accurately. The power system frequency can be estimated through various methods in time or frequency domains. Among these methods, curve fitting is a time domain approach that can be used to identify the parameters of a model using the input information obtained by utilizing a nonlinear regression method to fit the input curve. The physical phenomenon of a power system is described by a mathematical model. The curve fitting approach is applied to find the parameters such that the model is closer to the measured signal. These parameters are used to obtain the fundamental frequency of the power system. In this paper, the genetic algorithm (GA) is compared with the conventional regression analysis (RA) method for identifying the parameters of the model. The performance of curve fitting using different mathematical models on various power system events is discussed.

show abstract

“…A critical review has been carried out of classification methods of power quality events in [123] [224], this comparative study presents the strengths and weakness for different power qualities by various AI techniques are provided in Table 5 to classify and analyzed. While in Table 6 various classification methods for various Power quality disturbances are shown.…”

Section: Power Quality Events Comparative Cram Of Classification Techmentioning

confidence: 99%

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Ahsan¹,

Pan²,

Li³

2018

JPEE

View full text Add to dashboard Cite

Around the globe, the necessity of green supply with a dedicated standard quality thrust of consumers is increasing day by day. The advancement in technology urges the electrical power system to deliver a high-quality rated undistorted sinusoidal current, the voltage at a constant desired standard frequency to its consumers. The present paper reveals a complete and inclusive study of power quality events, such as automatic classification and signal processing via creative techniques and the noises effect on the detection and classification of power quality disturbances. It's planned to make a possible list for quick reference to obtain an extensive variety on the condition & status of available research for detection and classification for young engineers, designers and researchers who enter in the power quality field. The current extensive study is supported by a critical review of more than 200 publications on detection and classification techniques of power quality disturbances.

show abstract

A novel methodology for modeling waveforms for power quality disturbance analysis

Cited by 37 publications

References 19 publications

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

Contact Info

Product

Resources

About

A novel methodology for modeling waveforms for power quality disturbance analysis

Cited by 37 publications

References 19 publications

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Classification of power quality disturbances using dual strong tracking filters and rule-based extreme learning machine

Curve Fitting Approach for Fundamental Frequency Measurement for Power Systems

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection &amp; Classification

Contact Info

Product

Resources

About

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification