Abstract
In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The performance of the tolerant estimator was compared with the conventional adaptive estimator, and the tolerant estimator showed accurate estimations against model uncertainty in complicated measurement environments.
Highlights
State estimation (SE) is important for energy management to ensure the stability and quality of electrical power networks
Redundant measurements are captured by the traditional supervisory control and data acquisition (SCADA) systems with advanced synchronized phasor measurement units (PMUs)
Compared to the adaptive tolerant estimator, the conventional adaptive estimator increases the line estimation errors under the condition of model uncertainty caused by rapid parameter changes
Summary
State estimation (SE) is important for energy management to ensure the stability and quality of electrical power networks. Complicated measurement environments lead to significant time delay, which can result in the failure of measurement data to correct for fast parameter changes. This reduces the accuracy of the state estimator, which aggravates measurement errors. An adaptive tolerant estimator with model uncertainty is proposed to overcome the fast parameter changes in the power systems under complicated measurement conditions. We designed an adaptive tolerant measurement model, which comprises adjustable parameters and weights to reduce the state estimation errors caused by the fast parameter changes in complicated measurement environments. Measurement data are not obtained from unobservable states, the mapping and transformation property of SVD realizes full state estimations with model uncertainty in the complicated measurement environments.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
