Performance Verification of Power Quality Signals Classification System

Nikolovski

et al. 2020

IJECE

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This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system.

Section: The Signal Modelmentioning

confidence: 99%

An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal

Manap

Nikolovski

et al. 2020

IJECE

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“…The harmonic sources had increased and may lead to the potential risk to the existing of the electrical power system [9][10][11]. Accordingly, the harmonic current flows also lead to extra overheating, losses and overloading to the power system equipment [12,13]. Accordingly, it is imperative to distinguish the most reasonable methodologies to recognize and diagnose the kind of the harmonic sources in the power system [14,15].…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

Jopri

Ghani

et al. 2020

IJEECS

The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources. The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.

“…The identification and classification of harmonic loads based on power system measurements undeniably a difficult task [6]. The frequently changing nature of harmonic and the uncertainty associated with the harmonic characterization of even commonly used loads, make the use of conventional methods outdated [7], [8]. Reducing and controlling such harmonics have been a major concern of power engineers for many years.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Load Diagnostic Techniques and Methodologies: A Review

Hussin

Jopri

et al. 2018

IJEECS

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This paper will review on the existing techniques and methodologies of harmonic load diagnostic system. The increasingly amount of harmonic producing load used in power system are the main contribution in quantifying each harmonic disturbance effects of the multiple harmonic producing loads and it became very important. Literature proposes two different techniques and methods on the harmonic source identification under the soft computing technique classification. The advantages and disadvantages of harmonic load identification techniques and methods are discussed in this paper. In the proposed method, the issue on the harmonic contribution is determine and transformed to a data correlation analysis. Several techniques to identify the sources of harmonic signals in electric power systems are described and reviewed based on previous paper. Comparative studies of the methods are also done to evaluate the performance of each techniques. However, without sufficient information in this inconsistent environment on the property of the power system, accurate harmonic producing load diagnosis methods are important and further investigations in this regard assumes great implication.