The chapter starts with an introduction of power quality. Different aspects are then discussed to define electric power quality. Different sub-branches in power quality study are discussed. After this, disturbances normally occurred in power system are discussed. Short definitions of these power system disturbances are presented. Power quality related problems are summarized. Different guidelines given by IEC, IEEE, etc. are presented in tabular form.

Electric Power Quality

Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

A wavelet transform approach using a Morlet basis function is proposed to supervise power system disturbances in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring various disturbances as time varies. The method has been tested on the detection of various simulated disturbances including voltage sag, voltage swell, momentary interruption and oscillatory transients and on the harmonic analysis of the arc furnace from the field test data. Testing results demonstrated the practicality and advantages of the proposed method for the applications.

Application of Morlet wavelets to supervise power system disturbances

The increasing trend towards renewable energy sources requires higher power quality (PQ) at the generation, transmission and distribution systems. The PQ disturbances are produced due to the nonlinear loads, power electronic converters, system faults and switching events. The utilities and consumers of electric power are expected to acquire ideal voltage and current waveforms at rated power frequency. The development of new techniques for the automatic classification of PQ events is at present a major concern. This paper presents a comprehensive literature review on the applications of digital signal processing, artificial intelligence and optimization techniques in the classification of PQ disturbances. Various signal processing techniques used for the feature extraction such as Fourier transform, wavelet transform, S-transform, Hilbert transform, Gabor transform and their hybrids have been reviewed. The artificial intelligent techniques used for the pattern recognition such as artificial neural network, fuzzy logic, support vector machine are reviewed in detail. The optimization techniques used for the optimal feature selection such as genetic algorithm, particle swarm optimization and ant colony optimization are also reviewed. A comparison of various classification systems is presented in tabular form which highlights the important techniques used in the field of PQ disturbance monitoring. The comparison of research works carried out on the classification of PQ disturbances points out that many researchers have focussed on the feature extraction and classification techniques. Only few authors have used the feature selection techniques for selecting the best suitable features. This review may be considered a valuable source for researchers as a reference point to explore the opportunities for further improvement in the field of PQ classification.

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

A fast harmonic analysis method designed for three- phase radial distribution systems is proposed in this paper. The proposed method is developed based on the exact three-phase component models, frequency-scan formulation, and a commonly used forward/backward sweep technique. The backward sweep is used to build the relationship between branch currents and bus current injections of a distribution system, and then the forward sweep can use the aforementioned relationship to calculate bus voltages. The forward/backward sweep technique is a powerful method and has been widely used in distribution system analysis; however, it cannot be used for harmonic analysis directly. Therefore, the proposed method designs some specific data formats, integrates them into the commonly used forward/backward sweep technique, and derives useful formulas to calculate the effects of system status induced by harmonic pollution. The proposed method is more efficient than the conventional harmonic analysis algorithms since the time-consuming procedure of the admittance matrix inverse is not necessary in the solution procedure. Test results demonstrate the accuracy and efficiency of the proposed method.

Backward/Forward Sweep-Based Harmonic Analysis Method for Distribution Systems

Several important research areas in electric power quality are described. Aspects of electric power quality are categorized as fundamental concepts, modeling and analysis, measurement and instrumentation, sources, solutions, effects, and educational issues. The goals are to identify the high-priority research areas in electric power quality and to stimulate interest in this topic. >

S. West

Papers

Directions of research on electric power quality