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The main task of the state estimator in power systems is the provision of the power system state accurately. In order to achieve this, the measurement set has to be validated by identifying erroneous measurements. This is usually achieved by using either the Chi-square or the Largest Normalized Residual test. In both tests, the estimated states that are provided by the state estimator and the measurement...
Recently, many aspects of power system dynamic analysis, including oscillation identification, load model identification and system state estimation, are conducted based on the measurement data from phasor measurement unit (PMU). However, the impact of PMU measurement error, especially in power system dynamic process, has an assignable impact on these analysis results and thus cannot be neglected...
This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster.
Vulnerability analysis of the State Estimation module have come under renewed interest in the control centres of a SCADA connected energy system. Existing researches show that the state estimation module can be compromised by a class of data integrity attacks known as ‘False Data Injection (FDI)’. The stealthy FDI attack construction strategy requires the knowledge of the power system topology and...
In Power systems state estimation, in general, measurements of very large and very small magnitudes exist at same time. This variety of measurement magnitudes brings significant numerical difficulties for the state estimation. This picture is even worse when the weights are attributed to those measurements. To solve that problem, in this paper a kind of per unit (p.u.) formulation for state estimation...
This paper provides a detailed overview of the techniques and applications related to distribution system state estimation (DSSE), together with the classification of various types of state estimation. The paper also provides the state-of-art techniques applied in DSSE including forecasted-aided state estimation, close-loop DSSE methods, the application of computation intelligence in DSSE and the...
In this paper, we compare two parameter estimation methods for distribution systems: 1) residual sensitivity analysis and 2) state-vector augmentation with a Kalman filter. These two methods were originally proposed for transmission systems, and are still the most commonly used methods for parameter estimation. Distribution systems have much lower measurement redundancy than transmission systems;...
In practical applications like power system, the distribution of the measurement noise is unknown or frequently deviates from the assumed Gaussian model, often being characterized by heavy tails and sometimes generating impulse noise, named outliers. Under these conditions, the performances of the conventional state estimation (SE) methods that assume known and Gaussian noise, will be greatly degraded...
This paper presents a novel approach to distribution network observability. The observability analysis is a part of the state estimation process and traditionally determines whether the state estimation can be performed. This works well in transmission networks, where generally numerous measurements are available. However, in distribution networks only a limited number of measurements are usually...
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