16 research outputs found

    Comparison of two partial discharge classification methods

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    Two signal classification methods have been examined to discover their suitability for the task of partial discharge (PD) identification. An experiment has been designed to artificially mimic signals produced by a range of PD sources that are known to occur within high voltage (HV) items of plant. The bushing tap point of a large Auto-transformer has been highlighted as a possible point on which to attach PD sensing equipment and is utilized in this experiment. Artificial PD signals are injected into the HV electrode of the bushing itself and a high frequency current transformer (HFCT) is used to monitor the current between the tap-point and earth. The experimentally produced data was analyzed using two different signal processing algorithms and their classification performance compared. The signals produced by four different artificial PD sources (surface discharge in air, corona discharge in air, floating discharge in oil and internal discharge in oil) have been processed, then classified using two machine learning techniques, namely the support vector machine (SVM) and probabilistic neural network (PNN). The feature extraction algorithms involve performing wavelet packet analysis on the PD signals recorded over a single power cycle. The dimensionality of the data has been reduced by finding the first four moments of the probability density function (Mean, Standard deviation, Skew and Kurtosis) of the wavelet packet coefficients to produce a suitable feature vector. Initial results indicate that very high identification rates are possible with the SVM able to classify PD signals with a slightly higher accuracy than a PNN

    Classification of partial discharge signals using probabilistic neural network

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    Partial Discharge (PD) classification in power cables and high voltage equipment is essential in evaluating the severity of the damage in the insulation. In this paper, the Probabilistic Neural Network (PNN) method is used to classify the PDs. After the algorithm has been trained it uses the input vector, which contains the features that would be used for classification, to calculate the probability density function (pdf) of each class and together with the assignment of a cost for a misclassification the decision that minimizes the expected risk is taken. The maximum likelihood training is employed here. The success of this particular method for classification is asserted. This method has the advantage over Multilayer Neural Network that it gives rapid training speed, guaranteed convergence to a Bayes classifier if enough training examples are provided (i.e. it approaches Bayes optimality), incremental training which is fast (i.e. additionally provided training examples can be incorporated without difficulties) and robustness to noisy examples. The results obtained here (99.3%, 84.3% and 85.5% for the corona, the floating in oil and the internal discharges respectively) are very encouraging for the use of PNN in PD classification

    Performance of the support vector machine partial discharge classification method to noise contamination using phase synchronous measurements

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    The Support Vector Machine (SVM) method has been used with success in classifying Partial Discharge (PD) data of different sources. In this work it was investigated whether the previous success of the Support Vector Machine (SVM) could be extended to the case where a PD measurement was corrupted by Additive White Gaussian Noise (AWGN). Data was collected from experiments using PDs of different sources under controlled laboratory conditions at the Tony Davies High Voltage Laboratory, University of Southampton. Artificial PD signals were injected into the HV electrode of a bushing and a high frequency current transformer (HFCT) was used to monitor the current between the tap-point and earth. The signals produced by four different artificial PD sources (corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air) were acquired using the peak detection mode of the oscilloscope and were processed to extract the feature that was used by each algorithm. The feature extraction algorithm involved the use of the Wavelet Packet Transform (WPT) on phase synchronous measurements corrupted by artificial AWGN. Once the SVM was trained using part of the data acquired in the laboratory then the remaining data was corrupted by noise of two different amplitudes, giving SNRs of 7 dB and 3dB. These noisy data were classified using the SVM and the classification results were recorded. This procedure validated the SVM as an effective classification method that can be trained using laboratory noise free PD signals which can subsequently be used to classify field on-line measurements that have been corrupted with noise

    Photoluminescence analysis of coupling effects The impact of shunt resistance and temperature

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    In multi junction devices, due to the series connection of junctions, recombination current from the top junctions can be directed to the bottom ones affecting their electrical characteristics. Recently, luminescence coupling effects during External Quantum Efficiency EQE measurements at very intense light bias conditions indicated high recombination current flowing towards the bottom junctions of the cells. In an attempt to find the magnitude of coupling current as well as the factors affecting the optical interactions between junctions, excitation and voltage dependent Photoluminescence PL measurements of GaInP GaInAs Ge have been carried out. An investigation using junctions with different shunt resistances has been conducted to identify the impact of shunts on the coupling current. Furthermore the impact of temperature on the coupling current has been considered. Our results show that a maximum of 2.3 of the recombination current of the top junction is converted to coupling current in the middle junction depending on the devices under examination. The coupling efficiency depends on the shunt resistance of the top junctions as well as on the temperature. Furthermore a physical model of the current limiting junction was built taking into consideration the impact of local ohmic shunts in the solar cell device and used to validate the experimental data take
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