A new configuration for shunt active power filters

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new power circuit configuration to be used in shunt active power filters. A new control algorithm based on the linear voltage control suitable for the proposed circuit is introduced. The system is analysed both in time and frequency domains. The practical implementation of the system proves its suitability for the proposed task. The switching frequency of the proposed circuit is much lower than that in other active filters. The switching losses are then considerably reduced, in addition to the fact that the switching devices can withstand larger values of currents being switched on and off at lower frequencies which is an advantage to this circuit. The component sizes (capacitors and inductors) in the proposed circuit are also much smaller than those in other filter configurations. In addition, the thesis presents a new method for categorising the active filter systems proposed in the surveyed literature. The survey includes a comparison of these techniques showing their respective merits and drawbacks. The thesis also includes an implementation of a reference current generator that is suitable for single-phase applications without the need for excessive computations. The technique involves a modified Fourier analysis, which is suitable for active filtering applications

Customer active power consumption prediction for the next day based on historical profile

Energy consumption prediction application is one of the most important fieldsthat is artificially controlled with Artificial Intelligence technologies to maintainaccuracy for electricity market costs reduction. This work presents a way to buildand apply a model to each costumer in residential buildings. This model is built by using Long Short Term Memory (LSTM) networks to address a demonstration of time-series prediction problem and Deep Learning to take into consideration the historical consumption of customers and hourly load profiles in order to predict future consumption. Using this model, the most probable sequence of a certain industrial customer’s consumption levels for a coming day is predicted. In the case of residential customers, determining the particular period of the prediction in terms of either a year or a month would be helpful and more accurate due to changes in consumption according to the changes in temperature and weather conditions in general. Both of them are used together in this research work to make a wide or narrow prediction window.A test data set for a set of customers is used. Consumption readings for anycustomer in the test data set applying LSTM model are varying between minimum and maximum values of active power consumption. These values are always alternating during the day according to customer consumption behavior. This consumption variation leads to leveling all readings to be determined in a finite set and deterministic values. These levels could be then used in building the prediction model. Levels of consumption’s are modeling states in the transition matrix. Twenty five readings are recorded per day on each hour and cover leap years extra ones. Emission matrix is built using twenty five values numbered from one to twenty five and represent the observations. Calculating probabilities of being in each level (node) is also covered. Logistic Regression Algorithm is used to determine the most probable nodes for the next 25 hours in case of residential or industrial customers.Index Terms—Smart Grids, Load Forecasting, Consumption Prediction, Long Short Term Memory (LSTM), Logistic Regression Algorithm, Load Profile, Electrical Consumption.</p

Studying the effect of lossy compression and image fusion on image classification

Nowadays, the remotely sensed images are of huge sizes that require the implementation of compression technique to be easily stored on the internet. In addition, image fusion which is the merging of panchromatic and multispectral images to generate a single image with high spatial and spectral resolutions is required to increase the information in the resulted image. The purpose of this paper is to study the effect of image compression and fusion techniques on the classification accuracy. In this study, two pan and mul Geo-eye images covering an area of Cape Town, South Africa were registered and fused using different fusion techniques. The fused image with the superior accuracy was then compressed with various compression ratios ranging from 1:10 to 1:100. Then, the compressed fused images were classified using Maximum Likelihood Classification and Artificial Neural Network Classification techniques. Finally, the confusion matrices of the classified images were generated and evaluated to determine the effect of compression and fusion techniques on the accuracy of the classification process. Keywords: Lossy compression, MrSid, Overall accuracy, HPF, RMS

Convolutional-neural-network-based multi-signals fault diagnosis of induction motor using single and multi-channels datasets

Using deep learning in three-phase induction motor fault diagnosis has gained increasing interest nowadays. This paper proposes a Convolutional Neural Network (CNN) model to diagnose induction motor faults during the starting period. The model is able to detect various faults (locked-rotor, overload, voltage-unbalance, overvoltage, and undervoltage) under three loading levels (Light, Normal, and Heavy loads). The proposed model has high reliability as it depends on image data from multiple motor signals (voltages, currents, torque, and speed). The transient response of the motor starting period, makes it challenging to diagnose faults depending on raw time-domain images only. Therefore, the measured signals are additionally represented as d-q, and Lissajous images to focus on the effect of using these representations on the model performance. With multi-signals data, two forms of data (single- and multi-channel input-shape) can be implemented and evaluated to figure out the form that can capture the related fault features more efficiently. Experiments are performed using simulated machine model, to investigate the best signal representation and the best input shape for the proposed fault diagnosis system. Eight distinct datasets are generated from the motor data to be used in training and testing the CNN model. For the comparison of signals representations, the best accuracy is achieved with datasets containing both voltage and current d-q signals representation. They performed better than the other representations in the range from (0.03%) to (3.57%). For the comparison of input shapes, all multi-channels datasets proved to have better performance than single-channel datasets in the range from (0.77%) to (4.06%)