Adaptive Kalman filter and neural network based high impedance fault detection in power distribution networks

Samantaray, Subhransu Ranjan; Dash, P.K.; Upadhyay, S.

doi:10.1016/j.ijepes.2009.01.001

Cited by 62 publications

(28 citation statements)

References 17 publications

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“…Many practical dynamical systems, such as rotating machinery [30], power systems [31], ECG systems [32], and so on can exhibit such kind of trajectories or oscillations. For example, the currents of power distribution systems are oscillating in normal situation, whereas when high impedance faults occur, the currents will be distorted but still remain lower than the overcurrent thresholds [33]. Such faults are very difficult to be detected and isolated by using conventional diagnosis approaches.…”

Section: Remarkmentioning

confidence: 99%

Rapid isolation of small oscillation faults via deterministic learning

Chen

2012

Adaptive Control & Signal

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In this paper, we investigate the small fault isolation problem for a class of nonlinear uncertain systems. First, by utilizing the learned knowledge obtained through a recently proposed deterministic learning (DL) approach, a bank of estimators is constructed to represent the training normal mode and oscillation faults. Second, two isolation schemes based on the norms of the residuals are provided. The occurrence of a fault can be isolated if all the norms of the residuals associated with the matched fault estimator become smaller than the ones of the residuals associated with the other estimators in a finite time. Rigorous analysis of the performance of the both isolation schemes is also given, which includes the fault isolability condition and isolation time. The attraction of the paper lies in that an approach for fault isolation is proposed, in which the knowledge of modeling uncertainty and nonlinear faults obtained through DL is utilized to enhance the sensitivity of the isolation scheme. Simulation studies are included to demonstrate the effectiveness of the approach.

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Section: Remarkmentioning

confidence: 99%

Rapid isolation of small oscillation faults via deterministic learning

Chen

2012

Adaptive Control & Signal

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“…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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“…Such as adaptive fading Kalman filter based on innovation covariance [4] , adaptive Kalman filter based on neural network and based on fuzzy logic [5] [6] , adaptive algorithm for adjusting observation noises based on double-Kalman filter [7] , methods of adaptive filter to integrated navigation system of autonomous underwater vehicle [8] , etc.In the mentioned literature [8] , Sage -Husa adaptive Kalman filter is most widely applied. But, when the state of motion mutates, the model takes a long time to reach a steady state, and the accuracy of model is low.…”

Section: Introductionmentioning

confidence: 99%

Target Tracking Based on Amendatory Sage-Husa Adaptive Kalman Filtering

Luo¹,

Ren²

2015

Advances in Computer Science Research

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-Aiming at the boundedness of target tracking inaccuracy, even the lost of tracking caused by the error accumulation and state mutation in the aspect of target tracking using Kalman filter. The paper proposes a novel algorithm which is based on the modified Sage-Husa adaptive Kalman filter. This algorithm adjusts the predicted value of Sage-Husa adaptive Kalman filter in time by setting judgment and amendment rules, which can inhibit accumulation of error on target tracking and improve the filter precision in steady state and strengthen the stability and adaptability of filter. The experimental results show that the effectiveness of the novel algorithm.

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Adaptive Kalman filter and neural network based high impedance fault detection in power distribution networks

Cited by 62 publications

References 17 publications

Rapid isolation of small oscillation faults via deterministic learning

Rapid isolation of small oscillation faults via deterministic learning

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

Target Tracking Based on Amendatory Sage-Husa Adaptive Kalman Filtering

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