The new emerged operating conditions in the power sector are forcing the power-market participants to develop new tools. Among them, load profiles are a key issue in retail power markets. For various types of small consumers without quarterhourly load measurements, determination of typical load profiles (TLPs) could serve as a tool for determining of their load diagrams. Their main function is in billing of consumers who have deviated from their contracted schedules. Moreover, a simple and straightforward method for assigning a TLP to a particular eligible consumer also needs to be established. In this paper, a methodology for allocating consumers' load profiles using probabilistic neural network (PNN) is presented. It is based on the preprocessed measured load profiles (MLPs), using wavelet multiresolution analysis, clustered with a FCM clustering algorithm with an appropriate cluster-validity measure. The results demonstrate the efficiency of the formation procedure for the proposed TLPs.Index Terms-Cluster analysis, fuzzy logic, load profiles, power distribution, probabilistic neural networks, wavelet analysis.
Analytical approach to voltage collapse proximity determination is proposed for radial networks. Under corresponding assumptions, a radial network with arbitrary bus loads is transformed into a two bus equivalent. The voltage phasors at the generator bus and at the last load bus of the radial network are transformed to form the voltage phasors of the two bus equivalent. The latter are further used for assessment of voltage collapse proximity. Exact stability limit relations for a two bus system derived from Jacobian matrix can be exploited. Moreover, an analytical expression is derived for calculation of active and reactive power reserve margins for radial network equivalent. The proposed procedure has been tested for practical examples of radial networks with inductive and capacitive loads.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.