Smart Experts for Network State Estimation

Network state estimation using smart experts

Several core network protocols and applications adjust their operation dynamically based on current network conditions. TCP and IEEE 802.11 are notable examples, both of which periodically adapt the retransmission timeout and the contention window size depending on the average round trip time and the degree of collisions, respectively. Consequently, accurate network state estimation is critical...

Error Modeling in Distribution Network State Estimation Using RBF-Based Artificial Neural Network

State estimation is essential to access observable network models for online monitoring and analyzing of power systems. Due to the integration of distributed energy resources and new technologies, state estimation in distribution systems would be necessary. However, accurate input data are essential for an accurate estimation along with knowledge on the possible correlation between the real and...

Kalman Filters for Dynamic and Secure Smart Grid State Estimation

Combining dynamic state estimation methods such asKalmanfilterswith real-timedata generated/collected by digital meters such as phasor measurement units (PMU) can lead to advanced techniques for improving the quality of monitoring and controllability in smart grids. Classic Kalman filters achieve optimal performance with ideal systemmodels, which are usually hard to obtain in practice with unex...

channel estimation for mimo-ofdm systems

تخمین دقیق مشخصات کانال در سیستم های مخابراتی یک امر مهم محسوب می گردد. این امر به ویژه در کانال های بیسیم با ‏خاصیت فرکانس گزینی و زمان گزینی شدید، چالش بزرگی است. مقالات متعدد پر از روش های مبتکرانه ای برای طراحی و آنالیز ‏الگوریتم های تخمین کانال است که بیشتر آنها از روش های خاصی استفاده می کنند که یا دارای عملکرد خوب با پیچیدگی ‏محاسباتی بالا هستند و یا با عملکرد نه چندان خوب پیچیدگی پایینی...

Distribution System State Estimation - A 1 step towards Smart Grid