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Applying the Herman-Beta probabilistic method to MV feeders
Includes bibliographical references.
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2017
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| _version_ | 1867613301452570625 |
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| access_status_str | Open Access |
| author | Chihota, Munyaradzi Justice |
| author2 | Gaunt, C Trevor |
| author_browse | Chihota, Munyaradzi Justice Gaunt, C Trevor |
| author_facet | Gaunt, C Trevor Chihota, Munyaradzi Justice |
| author_sort | Chihota, Munyaradzi Justice |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/24292 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:57.504Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/24292 Applying the Herman-Beta probabilistic method to MV feeders Chihota, Munyaradzi Justice Gaunt, C Trevor Herman, Ronald Electrical Engineering Includes bibliographical references. The assessment of voltage drop in radial feeders is an important element in the process of network design and planning. This task is however not straight forward as the operation of modern power systems is highly influenced by a variety of uncertain and random variables such as stochasticity in load demand and power generation from renewable energy resources. Classic deterministic methods which model load demand and generation with fixed mean values consequently turn out to be inadequate and inaccurate tools for the analysis of power flow in the uncertainty-filled system. Statistically based methods become more suitable for such a task as they account for input variable uncertainties in their calculation of load flow. In the South African context, the Herman Beta algorithm, a probabilistic load flow tool developed by Herman et al. was adopted as the method for voltage assessment in Low Voltage (LV) network. The method was shown to have significant advantages compared with many other probabilistic methods for LV feeders, as investigated by Sellick and Gaunt. Its performance with regards to speed and accuracy is superior to deterministic, numeric probabilistic and other analytical probabilistic methods. The evolving connections of smaller generators, referred to as Distributed Generators (DGs), to the utility grid inspired the extension of the HB algorithm to active LV distribution networks. The HB algorithm was however formulated specifically for LV feeders. The assumptions of purely resistive feeders and unity power factor loads make it unsuitable for the Medium Voltage (MV) distribution network. In South Africa, deterministic methods are still being used for network design in MV distribution networks. This means that the drawbacks of such methods, for example inaccuracy and computational burden with large systems, are characteristic of the quality of network design in MV feeders. The performance of the HB algorithm together with the advantages and superiority of load modelling using the Beta probability density function (Beta pdf) suggested that modifying the input parameters could allow the HB algorithm to be used for voltage calculations on MV networks. This work therefore involves the adaptation of the way the HB algorithm is used, to make it suitable for voltage calculations on MV feeders. The HB algorithm for LV feeders is firstly analysed, coded into MATLAB, tested and then validated. Following this, the input parameters for feeder impedance and load current are modified to include the effects of reactance and non-unity power factor loads, using approximate modelling techniques. For reactance, the modulus or absolute value of the complex impedance is used in place of the resistance, to compensate for the line reactance. The load current is adjusted by inflating it by the power factor. The results of calculations with the HB algorithm are tested against a Monte-Carlo Simulation (MCS) solution of the feeder with an accurate model (full representation of feeder impedance and load power factor). The approach is extended to include shunt capacitor connections and DG in voltage calculations using the HB algorithm and testing the results with MCS. The outcomes of this research are that the approach of adjusting the input parameters of line resistance and load current significantly improves the accuracy of calculations using the HB algorithm for MV feeders. Comparison with the results of MC simulations indicates that the error of voltage calculations on MV feeders will be less than 2% of the 'accurate probabilistic value'. However, it is not possible to predict the error for a particular application. 2017-05-16T07:36:47Z 2017-05-16T07:36:47Z 2015 Master Thesis Masters MSc (Eng) http://hdl.handle.net/11427/24292 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering Chihota, Munyaradzi Justice Applying the Herman-Beta probabilistic method to MV feeders |
| thesis_degree_str | Master's |
| title | Applying the Herman-Beta probabilistic method to MV feeders |
| title_full | Applying the Herman-Beta probabilistic method to MV feeders |
| title_fullStr | Applying the Herman-Beta probabilistic method to MV feeders |
| title_full_unstemmed | Applying the Herman-Beta probabilistic method to MV feeders |
| title_short | Applying the Herman-Beta probabilistic method to MV feeders |
| title_sort | applying the herman beta probabilistic method to mv feeders |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/24292 |
| work_keys_str_mv | AT chihotamunyaradzijustice applyingthehermanbetaprobabilisticmethodtomvfeeders |