5. Conclusion

In the papers presented in [42, 53–61], their main theme coincides and is to classify the faults that can be obtained from the analysis of the voltage wave through WT. Broadly speaking, it can be highlighted that they obtain the range of frequency, amplitude, time of action, waveform, etc. Although their methods differ in the application of the WT, the essence of the study is the same. One step beyond performing fault classification studies is to achieve a practical application for the inservice power network. In [62], it implements a DSP for the continuous study of energy. Ref. [63] uses an FPGA for continuous analysis of system disturbances. In [42], through a wavelet neural network and a self-organizing arrangement system, it obtains automatic equipment for the detection and suppression of multiple faults

Other areas of power quality are studied using the WT characteristic. In [64, 65], the power quality events are characterized and classified using wavelet transformation. The power quality disturbance detection in grid-connected wind energy system is development with wavelet and S-transform. In [66] is presented a comparative analysis of power quality event using wavelet for real time implementation and [67] tested measurement system to electric energy quality together digital

The voltage variation is a usual problem in electric system which affects the quality power. Ref. [68] addresses this problem from distributed energy resources.

This analysis is the key to a correct distribution of energy in the electricity grid. Basically, a prediction is made of how the loads will behave in short- or mediumterm horizons. In this sense, the behavior of the load can refer to disconnections of large equipment, circuits that have transients in start, devices with low power factor, etc. This analysis can be done through WT and some complement to make

We have cases like [69] that use a linear correlation for the load forecast. This is a more conventional method compared to the other two. In recent years, there is much talk of NN and machine learning to such a point that [70–79] use machine learning to train a WT-based neural network. The load forecasting in the short-term used WT theory is presented in [80, 81]. Thus, it can be observed that this applica-

From another point of view, we have a prediction system based on fuzzy logic in [82], and finally, in the current year, a neural network with fuzzy logic based on WT is launched for the short-term study, which may be the foot to what is coming

Mainly, the WT is used in power systems and RMS measurements, both voltage and current. As for the measurement systems of active, reactive, or apparent power, a decomposition and classification of those waves present in the fundamental are achieved. In [83], it analyzes how the variations of the loads influence the tension, as much as it falls as in descents or in blinking. Then in [23], it approximates the RMS value of the harmonics present in the voltage and current waves by means of statistics; with this purpose, the improvement of harmonic detection and measurement systems is sought. In [84], a method for determination and correction of

in the network.

Wavelet Transform and Complexity

signal processor.

4.5 Load forecasting

the prediction.

to the future.

86

tion is widely studied.

4.6 Power system measurements

measurement anomaly based in WT is proposed.

Wavelet transform is a powerful signal processing tool that transforms a timedomain waveform into time-frequency domain and estimates the signal in the time and frequency domains simultaneously. So, it is mostly used in electric power systems analysis.

This chapter carries out an approach on the WT application in EPS in order to facilitate the search for information in this area. Therefore, a classification of the different fields of EPS applications was made. A summary description for each WT application area is presented with the main objective of showing the applications of this tool in the resolution of typical problems of the energy system.

The works analyzed show that the Daubechies family was used in most of the applications in power systems analysis, especially in protection area. However, the type of mother wavelet and the decomposition level number may be changed and therefore may not be generalized to all the cases.

The use of WT together with artificial intelligence tools (neural networks, fuzzy logic, genetic algorithms, etc.) was presented as a promising methodology to diagnose faults in electrical systems.

Most of the works analyzed results of computational simulations. It is expected that in the short term, the functionality of WT it is will comprobed in real situations.
