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State estimation (SE) has become an important aspect of real-time management of electrical distribution systems (EDS). However, it is a challenging problem, mainly due to the network model complexity and the lack of a reliable measurement infrastructure capable to cope with a variety of network configurations. This work presents an Extended Optimal Power Flow (E-OPF) for SE in EDS that considers different...
Real-time data redundancy is the main determinant of the state estimation (SE) performance, assessed considering the quantity, type, and location of measurements. Redundancy is a generic term used to given a rough idea of the strengths and weaknesses of measurement sets for SE. Data validation routines have a propensity to fail, when spurious measurements take place and inadequate redundancy levels...
Power system state estimation (SE) performance is very much influenced by the availability of redundant, varied, well-located measurements. Insufficient supplies of qualified measurements can compromise network observability, making the SE process incompetent to detect/identify spurious data. Individual or group of measurements whose unavailability renders unobservability are known as critical for...
This work presents a methodology for selecting the most relevant measurements for real-time power system monitoring. A genetic algorithm is employed to find the meter plan, composed of relevant, real-time measurements and pseudo-measurements that present the best compromise between investment costs and state estimation performance. This is achieved by minimizing both the number of real-time measurements...
Measurement redundancy is the fulcrum of the state estimation process. During the real-time operation of power networks, data redundancy may suffer reductions of different degrees, owing to: loss of communication channels; rejection of spurious measurements; unavailability of metering devices; etc. Criticality analysis consists in verifying if a measurement set is deficient in redundancy, for the...
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