Wavelet based energy function for symmetrical fault detection during power swing

Dubey, Rahul; Samantaray, Subhransu Ranjan; Tripathy, Ashok; Babu, B. Chitti; Ehtesham, Muhammad

doi:10.1109/sces.2012.6199019

Cited by 26 publications

(29 citation statements)

References 12 publications

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“…Two angles are calculated for each mean value in both directions with respect to each axis which gives us six angles collectively. Equations (9)- (14) are used to derive these six angles. Figure 6.…”

Section: Three Dimensional Feature Extraction For Neural Networkbasedmentioning

confidence: 99%

“…Regarding diagnostic of these open switch faults, several methods by using output currents have been proposed based on various techniques. Some of these techniques are neural network method [11,12], artificial intelligence [13], wavelet transform [14][15][16], current deviation method [17], fuzzy logic [18,19] and normalized dc current method [20,21]. In [22], induction machine faults based on time-frequency representation and feature vector size was optimized by PSO.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Asghar¹,

Talha²,

Kim³

2017

IJFIS

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In recent times, inverters are considered as the basic building block in an electrical drive system used widely in many industrial drive applications. However, the reliability of these inverters is mainly affected by the failure of power electronic switches. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduce overall efficiency. In this paper, comparative study of three different fault detection and diagnosis systems for three phase inverter is presented. The basic purpose of these fault detection and diagnosis systems is to detect single or multiple faults efficiently. These techniques rely on the neural network for fault detection and diagnosis by using Clarke transformed two-dimensional features extraction, three-dimensional features extraction and features extraction using discrete wavelet transform (DWT) with a different number of features in each technique. Several features are extracted using different mechanisms and used in the neural network as input for fault detection and diagnosis. Furthermore, a simulation study is carried out to analyze the fault detection and diagnosis response of these techniques. Also, a comparative study has been performed by considering fault detection time and accuracy. Comparison results prove the supremacy of three-dimensional feature extraction technique over other two techniques as it can detect and diagnose single, double and triple faults in a single cycle with high accuracy as compared to other two techniques multi-cycles detection.

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Section: Three Dimensional Feature Extraction For Neural Networkbasedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Asghar¹,

Talha²,

Kim³

2017

IJFIS

View full text Add to dashboard Cite

show abstract

“…Examples of these techniques are bridge circuit method [1], surface wave [2,3], Petrinets [4], wavelet transform approach [5][6][7][8][9][10], neural network approach [11][12][13], AI [14], graph methodology [15], real time [16], and statistical methodology. Singh et al presented a method for software fault prediction at the design phase.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Gomathy¹,

Selvaperumal²

2016

Journal of Power Electronics

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Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open-and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

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“…Another possible scenario occurs when a generation unit trips, the required electrical power from the remaining generators increases. Therefore, when the load on a machine is suddenly increased, the energy furnished by the rotor results in a decrease in the rotor angular velocity causing severe power swings [1,2,8,[14][15][16][17][18][19][20][21][22].…”

Section: Condition Impactmentioning

confidence: 99%

“…The authors in [9,22,43,[45][46][47] stated that during power swing, the frequency of a system varies over a range around the nominal frequency. Due to the fact that fault causes transient, WT is efficient for power system transient analysis and feature extraction.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

Progress on power swing blocking schemes and the impact of renewable energy on power swing characteristics: A review

Gunasegaran

Tan

Bakar

et al. 2015

Renewable and Sustainable Energy Reviews

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Wavelet based energy function for symmetrical fault detection during power swing

Cited by 26 publications

References 12 publications

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Progress on power swing blocking schemes and the impact of renewable energy on power swing characteristics: A review

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