Dynamics and predictability of El Nino-Southern Oscillation: an Australian perspective on progress and challenges

Many scientific challenges remain for managing the risk of future ENSO impacts in countries like Australia that are strongly affected by ENSO event diversity

Transient earth voltage (TEV) based partial discharge detection and analysis

Partial discharge (PD) detection is an effective way to evaluate the insulation condition of electrical equipment in power systems. The non-intrusive TEV-detecting method which detects transient earth voltage (TEV) signals from the external surface of equipment does not require interruptions of electrical operations and is thus preferred by more and more researchers, engineers and users. However, as a new technique, TEV based PD measurement is not well developed in many aspects, for example, the measuring system and the de-noising methods. Consequently, the research and development of the TEV based PD measurement has become an interesting topic in recent decades. This thesis presents an investigation on the sensing system and the de-noising methods of TEV based PD measurement system. First of all, the mechanism, popular measuring systems, noise types and existing de-noising methods of PDs are reviewed. Secondly, based on the characteristics of TEV signals, a TEV based PD measuring system was proposed and its effectiveness has been demonstrated by an experimental test. Next, the optimal settings of a popular de-noising method for non-impulsive noise, wavelet thresholding, are selected and its processing efficiency is enhanced by using parallelism algorithm in C environment. Furthermore, the wavelet entropy is proposed to classify PD pulses from impulsive noises. Finally, a noise reduction system using Fourier transform and time-frequency entropy is proposed to reject various kinds of noises. The non-intrusive PD measuring techniques have been more and more popular in recent years. In this thesis, a TEV based PD measuring system is proposed. The major parts: non-intrusive sensor and high-pass filter are designed according to the characteristics of TEV signals. The performance of proposed system is demonstrated by an experimental test where the PD signals are collected by both TEV and HFCT sensors. By considering the features of proposed system, the detected TEV signals are well simulated. Due to the external locations of TEV sensors, the performance of TEV based PD detection is limited by noises. To remove non-impulsive noises, wavelet thresholding is often used. As the de-noised results depend on the settings of algorithm, the optimal ones are selected according to the features of TEV signal and the proposed system. Further, the processing efficiency of wavelet thresholding with optimal settings is enhanced. As wavelet transform is good at time-frequency analysis of PD signals, its capability in rejecting impulsive noises is also explored. Therefore, wavelet entropy is proposed. By comparing with the traditional energy spectrum, the wavelet entropy is more stable to represent a single pulse. With the help of a trained ANN whose parameters are selected carefully, the PD pulses can be extracted with good percentages. The impulsive noise reduction based on features of single pulses is often ineffective when PD pulse and noise occur at the same time. Thus, a de-noising system is proposed to remove both impulsive and non-impulsive noises even if they occur at the same time. In this system, Fourier transform and time frequency entropy are employed. The de-noised results of two experimental signals and one field-collected signal show that the proposed noise-rejecting system is effective in extracting real PD pulses.DOCTOR OF PHILOSOPHY (EEE

Recognition of partial discharge using wavelet entropy and neural network for TEV measurement

Partial discharge (PD) is caused by the deterioration of insulation materials. Its detection and accurate measurement are very important to prevent insulation breakdown and catastrophic failures. Detection of PDs in metal-clad apparatus via TEV method is a promising approach in non-intrusive on-line tests. However, the electrical interference from background environment is the major barrier of improving its measuring accuracy. The combination of wavelet analysis that reveals local features and entropy that measures disorder can just fulfill the requirements of PD signal analysis and is thus investigated in this paper. Then a wavelet-entropy based PD recognition method is proposed. The pulse features that are characterized by wavelet entropy are employed as the input pattern of a classifier constructed with feed-forward back-propagation neural network. Finally, some PD groups with noisy interferences are tested by trained network. The recognition rate of real PD pulses demonstrates the proposed wavelet-entropy based method is effective in PD signal de-noising

Entropy application in partial discharge analysis with non-intrusive measurement

Partial discharge (PD) occurs when insulation deterioration happens in electrical apparatus. It is often detected in order to evaluate the state of insulation. For metal-clad equipments, external sensors which are easy to install and interruption-free on operations are preferred. However, their performances are compromised by heavy noise. Although time-frequency (TF) spectrum provides much information to discriminate PDs and noises, automatic selection remains a tough issue in field application. Entropy, a measure of disorder, is applied in this paper to extract PD pulses automatically. This entropy-based algorithm is implemented and examined by two field-collected datasets. Practical results show that true PDs can be identified and extracted effectively

Transient signal identification of HVDC transmission lines based on wavelet entropy and SVM

High-voltage DC (HVDC) transmission plays an important role in power transmission projects due to its advantages of large transmission power and good control performance. As the main protection of the DC transmission line, transient protection uses the high-frequency signal generated by fault transient to detect faults, having the characteristics of fast response and high accuracy. However, the HVDC transmission line has complex conditions along the route and is vulnerable to lightning strikes and other accidents, resulting in the occurrence of a variety of transients in the line, which increases the difficulty of fault identification. Being able to reveal signal time-frequency characteristic, wavelet entropy is an effective tool of signal recognition. This study proposes a method of transient signal identification based on the wavelet entropy and support vector machine (SVM). Firstly, the transient processes of three kinds of signals, including unipolar faults, lightning strike faults, and lightning disturbances, are briefly introduced. Then the time−frequency features of three kinds of transient signals under different scenes are analysed by wavelet entropy. Finally, the training set was used to train the SVM classification model with the signal wavelet entropy being taken as the eigenvector, and the test results validate the effectiveness of the proposed method

Recognition of Traveling Surges in HVDC with Wavelet Entropy

Traveling surges are commonly adopted in protection devices of high-voltage direct current (HVDC) transmission systems. Lightning strikes also can produce large-amplitude traveling surges which lead to the malfunction of relays. To ensure the reliable operation of protection devices, recognition of traveling surges must be considered. Wavelet entropy, which can reveal time-frequency distribution features, is a potential tool for traveling surge recognition. In this paper, the effectiveness of wavelet entropy in characterizing traveling surges is demonstrated by comparing its representations of different kinds of surges and discussing its stability with the effects of propagation distance and fault resistance. A wavelet entropy-based recognition method is proposed and tested by simulated traveling surges. The results show wavelet entropy can discriminate fault traveling surges with a good recognition rate