Voltage unbalance is a costly and potentially damaging phenomenon. It affects both distribution network operators (DNOs) and customers. As part of a move toward greater network visibility, DNOs are increasingly motivated to monitor their networks in finer detail using an increasingly diverse range of monitoring devices. If the information from monitoring devices can be collated intelligently, the impact of unbalance and other power-quality issues can be accurately estimated throughout the network. In this paper, a new methodology is presented which utilizes distribution system state estimation (DSSE) to estimate the level, location, and impact of voltage unbalance on a real distribution network. The developed methodology is novel, pulling together and advancing existing research on DSSE and unbalance. The methodology is validated using data from a real U.K. distribution network with significant unbalance. The methodology is shown to be capable of statistically estimating the level, location, and effects of unbalance within the network, even when some areas of the network are unobservable.
Analyses of pollen, tephra, mineral input and degree of peat humification from three neighbouring raised peat profiles at Corlea, central Ireland, covering the period of the deposition of a tephra layer dated to just before 2290 cal. BC, and thought to represent Hekla-4 (2310±20 BC), are used to show the problems of relying on data from a single profile when invoking relationships between volcanic activity, climate and ecosys-tem response. While there appears to be a strong correlation between tephra deposition and flooding of the bog surface in one profile, with a short-lived increase in the rate of peat accumulation, comparison with the other two records suggests that peat had already begun a trend to a less humified condition before tephra deposition, and that evidence of local bog surface flooding was neither consistent nor synchronous.
Estimating voltage sag performance is important for distribution network operators who are keen to reduce costly interruptions, plan network investment and reduce operational expenditure. This paper proposes a robust method to locate faults and estimate the magnitude of voltage sags using information from a limited set of arbitrarily accurate monitoring devices. The developed method uses statistical analysis and impedance based fault location equations to find the most likely fault location and sag magnitude at non-monitored busbars. The method robustly handles measurement errors, and helps to eliminate some of the sensitivity present in existing impedance based fault location algorithms. The method is also shown to be effective at eliminating multiple fault location solutions caused by multiple overlapping impedance paths by synthesizing information from all monitors installed in a network. The method is validated and shown to be effective on a generic section of the UK's distribution network.
This paper presents a novel optimisation methodology, Optimal Placement of Monitors (OPM Power ), for optimal device/monitor placement in distribution networks. OPM Power is developed based on gradient search and Particle Swarm Optimisation (PSO). The proposed method integrates network topology into search process via spanning trees and uses the historical experience for search guidance. The method is particularly suited for optimal placement problems in power systems. The application is illustrated on the problem of optimal monitor placement for estimation of voltage unbalance in a section of existing UK distribution network and in a generic distribution network. It is demonstrated that the proposed methodology outperforms generic integer optimisation algorithms which are widely used for optimal placement problems in literature, e.g. Genetic Algorithms (GA).
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