15,781 research outputs found

    A Review of Fault Diagnosing Methods in Power Transmission Systems

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    Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

    Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy

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    Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind power generator.НаиболСС распространСнныС ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ построСния ΠΌΡƒΠ»ΡŒΡ‚ΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ классификации Π·Π°ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‚ΡΡ Π² ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡ€Π° Π΄Π²ΠΎΠΈΡ‡Π½Ρ‹Ρ… классификаторов ΠΈ ΠΈΡ… объСдинСнии. Π’ Π΄Π°Π½Π½ΠΎΠΉ ΡΡ‚Π°Ρ‚ΡŒΠ΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° машина ΠΎΠΏΠΎΡ€Π½Ρ‹Ρ… Π²Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ² с классификатором Π²Ρ‹Ρ…ΠΎΠ΄Π½Ρ‹Ρ… ΠΊΠΎΠ΄ΠΎΠ² исправлСния ошибок(ECOC-SVM) с Ρ†Π΅Π»ΡŒΡŽ ΠΊΠ»Π°ΡΡΠΈΡ„ΠΈΡ†ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΈ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΠΎΠ²Π°Ρ‚ΡŒ Ρ‚Π°ΠΊΠΈΠ΅ Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡ качСства элСктроэнСргии, ΠΊΠ°ΠΊ гармоничСскиС искаТСния, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ напряТСния ΠΈ скачок напряТСния, Π²ΠΊΠ»ΡŽΡ‡Π°Ρ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ Π²Π΅Ρ‚Ρ€ΠΎΠ²Ρ‹Ρ… элСктростанций Π² систСмах ΠΏΠ΅Ρ€Π΅Π΄Π°Ρ‡ΠΈ элСктроэнСргии. Π‘Π½Π°Ρ‡Π°Π»Π° выполняСтся Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡ‚ΠΎΠΊΠ° нСсиммСтричной Π½Π°Π³Ρ€ΡƒΠ·ΠΊΠΈ Ρ‚Ρ€Π΅Ρ… Ρ„Π°Π· для расчСта разностных характСристик элСктричСской сСти, ΡƒΡ€ΠΎΠ²Π½Π΅ΠΉ напряТСния, Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΈ Ρ€Π΅Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ мощности. ПослС этого дискрСтноС Π²Π΅ΠΉΠ²Π»Π΅Ρ‚-ΠΏΡ€Π΅ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡŠΠ΅Π΄ΠΈΠ½ΡΠ΅Ρ‚ΡΡ с вСроятностной модСлью ECOC-SVM для построСния классификатора. НаконСц, ECOC-SVM классифицируСт ΠΈ ΠΈΠ΄Π΅Π½Ρ‚ΠΈΡ„ΠΈΡ†ΠΈΡ€ΡƒΠ΅Ρ‚ Ρ‚ΠΈΠΏ возмущСния Π² соотвСтствии с ΠΎΡ‚ΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ энСргии дискрСтного Π²Π΅ΠΉΠ²Π»Π΅Ρ‚-прСобразования. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Ρ‹ΠΉ ΠΌΠ΅Ρ‚ΠΎΠ΄ Π΄Π°Π΅Ρ‚ ΡƒΠ΄ΠΎΠ²Π»Π΅Ρ‚Π²ΠΎΡ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΡƒΡŽ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ 99,2% ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с Ρ…ΠΎΡ€ΠΎΡˆΠΎ извСстными ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚, Ρ‡Ρ‚ΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΠ΅ качСства элСктроэнСргии ΠΈΠΌΠ΅Π΅Ρ‚ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½Ρ‹Π΅ отклонСния ΠΎΡ‚ чисто ΡΠΈΠ½ΡƒΡΠΎΠΈΠ΄Π°Π»ΡŒΠ½ΠΎΠΉ Ρ„ΠΎΡ€ΠΌΡ‹ Π²ΠΎΠ»Π½Ρ‹, Ρ‡Ρ‚ΠΎ способствуСт Ρ€Π°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡŽ ΠΈ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΡŽ Ρ‚ΠΈΠΏΠ° возмущСния, Π³Π΅Π½Π΅Ρ€ΠΈΡ€ΡƒΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅Ρ‚Ρ€ΠΎΠ²Ρ‹ΠΌ Π³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ΠΎΠΌ
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