Jaco A. Jordaan

Feeder load balancing using neural network

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general, a combinatorial optimization problem. This paper proposes optimal reconfiguration of the phase balancing using the neural network, to switch on and off the different switches, allowing the three phases supply by the transformer to the end-users to be balanced. This paper presents the application examples of the proposed method using the real and simulated test data.

A new approach to load forecasting: using semi-parametric method and neural networks

A new approach to electrical load forecasting is investigated. The method is based on the semi-parametric spectral estimation method that is used to decompose a signal into a harmonic linear signal model and a non-linear part. A neural network is then used to predict the non-linear part. The final predicted signal is then found by adding the neural network predicted non-linear part and the linear part. The performance of the proposed method seems to be more robust than using only the raw load data.

Control algorithm of a smart grid device for optimal radial feeder load reconfiguration

Secondary distribution network, generally speaking, performs as well as the performance of its LV feeders. The main problem a feeder is experiencing is the load unbalancing due to the stochastic nature of its individual single-phase loads: bigger losses in certain phase accompanied with bed voltage regulation and voltage unbalance. The aim of this paper is to address the issue of automatic balancing as progressing from the end of the feeder towards the front using smart device based on three-ways switch selector and artificial intelligence algorithm to minimize the neutral current.

Comparison between general cross correlation and a template-matching scheme in the application of acoustic gunshot detection

This paper discusses an application of gunshot detection on game farms in South Africa. For the preservation of endangered species and commercial hunting of game, there is a need for a system that can identify when and where a gun is fired. Two methods used for gunshot detection are discussed, namely cross correlation and a template-matching scheme based on Reproducing Kernel Hilbert Spaces.

Feeder load balancing using combinatorial optimization-based heuristic method

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general a combinatorial optimization problem. Several Algorithms have been proposed and had achieved good result. However, the running time may be very high or the algorithm can not work with a complex network. This paper proposes a combinatorial optimization-based fast heuristic method to balance the phase currents. It can provide an efficient and fast alternative solution for the load balancing task.

Distribution Feeder Load Balancing Using Support Vector Machines

The electrical network should ensure that an adequate supply is available to meet the estimated load of the consumers in both the near and more distant future. This must of course, be done at minimum possible cost consistent with satisfactory reliability and quality of the supply. In order to avoid excessive voltage drop and minimise loss, it may be economical to install apparatus to balance or partially balance the loads. It is believed that the technology to achieve an automatic load balancing lends itself readily for the implementation of different types of algorithms for automatically rearranging the connection of consumers on the low voltage side of a feeder for optimal performance. In this paper the authors present a Support Vector Machines (SVM) implementation. The loads are first normalised and then sorted before applying the SVM to do the balancing.

The Use of Semi-parametric Methods for Feature Extraction in Mobile Cellular Networks

By 2006, the number of mobile subscribers in Africa outnumbered that of fixed line subscribers with nearly 200 million mobile subscribers across the continent [1][2]. By the end of 2007, it was estimated that the number of mobile subscribers would exceed 278 million subscribers [2]. Mobile Telephony has been viewed as a critical enabling technology that is capable of boosting local economies across Africa due to the ease of roll out of wireless technologies in comparison to fixed line networks. With the boom in wireless networks across Africa, a growing demand to effectively predict the rate of growth in demand for capacity in various sectors of the network has risen with cellular network operators. This paper looks at using Spectral Analysis techniques for the extraction of features from cellular network traffic data that could be linked to subscriber behavior. This could then in turn be used to determine capacity requirements within the network.

Load forecasting with support vector machines and semi-parametric method

A new approach to short-term electrical load forecasting is investigated in this paper. As electrical load data are highly non-linear in nature, in the proposed approach, we first separate out the linear and the non-linear parts, and then forecast using the non-linear part only. Semi-parametric spectral estimation method is used to decompose a load data signal into a harmonic linear signal model and a non-linear trend. A support vector machine is then used to predict the non-linear trend. The final predicted signal is then found by adding the support vector machine predicted trend and the linear signal part. The performance of the proposed method seems to be more robust than using only the raw load data. This is due to the fact that the proposed method is intended to be more focused on the non-linear part rather than a diluted mixture of the linear and the non-linear parts as done usually.

Advanced beamforming techniques for acoustic source localization

This paper tackles the issue of acoustic source localization using an array of microphones. From the conventional method to advanced beamforming, the algorithms are presented from a theoretical point of view. The advantages and drawbacks of each method are then discussed. Finally, simulation results are presented.

A fault classification and localization method in a power distribution network

This paper presents a method of fault diagnosis in a power distribution network. A segment of a 88 kV power distribution network is modelled in Digsilent Power Factory. Various types of fault cases are obtained through an Electromagnetic Transient study on the model. Discrete wavelet transform (DWT) is used to extract features from transient fault currents measured at the source terminal of the network. The method uses two cycles of transient fault current measured at the source terminal after fault inception. The extracted features are subsequently fed into a support vector machine (SVM), Naïve Bayes, support vector regression (SVR) and Gaussian process regression (GPR) schemes in order to diagnose various system faults. SVM is used to detect and classify various types of short circuit faults. The Naïve Bayes is also used to classify faults. Furthermore, SVR and GPR schemes are used to estimate fault locations. A hybrid method using DWT, SVM and GPR is thus proposed. The feasibility of the proposed technique is tested on MATLAB. The results of the proposed method show that various types of faults can be classified with accuracy of up to 98.8% and a minimum estimation error for fault location.