22 research outputs found
Dynamic state estimation and prediction for real-time control and operation
Real-time control and operation are crucial to deal with increasing complexity of modern power systems. To effectively enable those functions, it is required a Dynamic State Estimation (DSE) function to provide accurate network state variables at the right moment and predict their trends ahead. This paper addresses the important role of DSE over the conventional static State Estimation in such new context of smart grids. DSE approaches normally based on Extended Kalman Filter (EKF) need to collect recursively time-historic data, to update covariance vectors, and to treat heavy computation matrices. Computation burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks although DSE was introduced several decades ago. In this paper, an improvement of DSE by using Unscented Kalman Filter (UKF) to alleviate computation burden will be discussed. The UKF-based approach avoids using linearization procedure thus outperforms the EKF-based approach to cope with non-linear models. Performance of the method is investigated with a simulation on a 18-bus test network. Preliminary results have been gained through a case study that motivate further research on this approach
Dynamic state estimation for distribution networks with renewable energy integration
The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can have accurate insight into a network’s status at the right moment and predict its future trends. Though state estimation is crucial for this purpose to trigger control functions, it has been used mainly for steady-state analysis. The need for dynamic state estimation (DSE), however, is increasing for real-time control and operation. This paper addresses the important role of DSE over conventional static-state estimation in this new distribution network context. Computational burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks, although DSE was introduced several decades ago. This paper the unscented Kalman filter (UKF) to alleviate computational burden with DSE. The UKF-based approach does not use a linearization procedure and thus outperforms the conventional Extended Kalman Filter based approach to cope with non-linear models. The performance of the UKF method is investigated with a simulation of an 18-bus distribution network on the real-time digital simulator (RTDS) platform. A distribution network with considerable integration of renewable energy production is used to evaluate the UKF-based DSE approach under different types of events
Missing-Sensor-Fault-Tolerant Control for SSSC FACTS Device With Real-Time Implementation
Grid operations with high penetration of photovoltaic systems
Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.Integrating variable generation sources such as utility-scale
photovoltaic (PV) plants into the electric grid is desirable with
the increasing quest for cleaner sources of electric power
generation and reducing cost of utility-scale PV. As a result,
solar market in the United States has more than doubled over
the past two to three years, but looking ahead, systemic
challenges to growth loom both in the near term. Real-time grid
operators are especially concerned about large-scale PV
systems operating under cloudy conditions and large
disturbances. This paper provides an overview of the
computational and optimization research carried out at the
Real-Time Power and Intelligent Systems Laboratory to
address some of the grid operational concerns with high levels
of PV penetrations.dc201
Two separate continually online-trained neurocontrollers for excitation and turbine control of a turbogenerator
Two Separate Continually Online-Trained Neurocontrollers for a Unified Power Flow Controller
Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator
Dynamic state estimation and prediction for real-time control and operation
Real-time control and operation are crucial to deal with increasing complexity of modern power systems. To effectively enable those functions, it is required a Dynamic State Estimation (DSE) function to provide accurate network state variables at the right moment and predict their trends ahead. This paper addresses the important role of DSE over the conventional static State Estimation in such new context of smart grids. DSE approaches normally based on Extended Kalman Filter (EKF) need to collect recursively time-historic data, to update covariance vectors, and to treat heavy computation matrices. Computation burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks although DSE was introduced several decades ago. In this paper, an improvement of DSE by using Unscented Kalman Filter (UKF) to alleviate computation burden will be discussed. The UKF-based approach avoids using linearization procedure thus outperforms the EKF-based approach to cope with non-linear models. Performance of the method is investigated with a simulation on a 18-bus test network. Preliminary results have been gained through a case study that motivate further research on this approach