Considering the rapidly rising cost of primary fuel for electricity generation and the extensive concern of the international community for global warning, electricity generation with renewable sources has been actively developed all over the world. A large number of renewable energy generators, more highly sensitive electronic equipment and more electronics or microprocessor controllers are used in the power system. It has brought new challenges to supply quality, and thus the study of Power Quality (PQ) has become obviously important. Harmonic analysis plays an important role in PQ study because harmonic has great influence on the power system equipment as well as on their operation. Harmonics can lead to operation failure of electrical and electronic components, overheating of neutral wires and transformer, failure of power factor correction capacitors, loss in power generation and transmission, and interference with protection, control and communication networks as well as customer loads. Therefore, developing an advanced PQ disturbances classification system and a more accurate harmonic analysis method is the key of this thesis. It is necessary to determine the sources and causes of such disturbances to solve PQ problems. When the type of disturbance has been classified accurately, PQ engineers can define the major effects at the load and analyse the source of the disturbances. Many approaches based on Fourier Transform (FT) and neural network for the classification of PQ disturbance have been developed in the last few years. The key factor of these methods is that the correct rate for the actual event is not high enough and thus there is still space to improve accuracy. In this thesis, a fuzzy-expert system based on Wavelet Transforms (WT) to classify power supply waveforms into different groups or categories for PQ classification is proposed with the aim which is to classify the disturbance type with higher accuracy. A new approach for the evaluation of harmonic contents of power system waveforms is also proposed in [sic] thesis. The conventional harmonic analysis method is Fourier analysis. However, Fourier analysis provides signals which are mainly localised in the frequency domain and it gives limited information of the signals in the time domain. Furthermore, the FT cannot obtain accurate values of amplitude and phases from harmonics with frequencies different from that of the window function frequency. In order to overcome the limitations of Fourier analysis and obtain better results, wavelet analysis has been proposed. A novel harmonic analysis method using Discrete Wavelet Packet Transform (DWPT) filter bank decomposition and Continuous Wavelet Transform (CWT) identification has been proposed. In order to evaluate the performance and result of the proposed analysis method, another two conventional methods, i.e. Fast Fourier Transform (FFT) and the combination method of Discrete Wavelet Transform (DWT) filter bank and CWT calculation, are compared through a large number of identical applications. Based on the harmonic analysis, the harmonic penetration is considered and its effects to power networks with increasing of renewable power generations are investigated. With increasing of renewable generators in power networks, it creates PQ problems caused by harmonic injections with a large frequency range, such as integer-harmonics, inter-harmonics and sub-harmonics. Therefore, the steady state harmonic power flow in power system with discrete frequencies is calculated with Root Mean Square (RMS) values of bus current and voltage magnitudes and Total Harmonic Voltage Distortion (THDv) values. Variable of tests are designed to investigate the effects to the harmonic penetration with multiple types of harmonic sources in power networks.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:725141 |
| Date | January 2017 |
| Creators | Gu, Xinyi |
| Publisher | University of Strathclyde |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28655 |
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