Kianoush Emami

Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.

A Novel Quasi-Decentralized Functional Observer Approach to LFC of Interconnected Power Systems

This paper presents a novel functional observer based quasi-decentralized load frequency control scheme for power systems. Based on functional observers theory, quasi-decentralized functional observers are designed to implement any given state feedback controller. The designed functional observers are decoupled from each other and have a simpler structure in comparison to the state observer based schemes. The proposed functional observer scheme is applied to a complex nonlinear power system and the proposed design method is based on the entire network topology.

A functional observer based fault detection technique for dynamical systems

This paper presents a functional observer based fault detection method. The fault detection is achieved using a functional observer based fault indicator that asymptotically converges to a fault indicator that can be derived based on the nominal system. The asymptotic value of the proposed fault indicator is independent of the functional observer parameters and also the convergence rate of the fault indicator can be altered by choosing appropriate functional observer parameters. The advantage of using this new method is that the observed system is not necessarily needed to be observable; therefore, the proposed fault detection technique is also applicable for systems where state observers cannot be designed; moreover, the functional observer fault detection scheme is always of reduced order in comparison to a state observer based scheme.

State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems

This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed.

Cooperative Dispatch of BESS and Wind Power Generation Considering Carbon Emission Limitation in Australia

In this paper, an intelligent economic dispatch (ED) model integrating wind energy, carbon tax, and battery energy storage system (BESS) is developed. BESS is incorporated with wind generation to reduce fluctuation of wind energy output. To verify the suitable storage size for the Australian power grid, a sensitivity analysis is performed with different levels of BESS. Carbon tax is also considered to reduce carbon emissions in the proposed ED scheme. A hybrid computational framework based on quantum-inspired particle swarm optimization (QPSO) is proposed to achieve faster and better optimization performance, and its viability demonstrated on a simplified 14-generator model of the Australian power system using a set of case studies. The proposed dispatch model can minimize the generating cost and enhance renewable power consumption capacity.

Application of Unscented Transform in Frequency Control of a Complex Power System Using Noisy PMU Data

This paper presents a novel unscented transform (UT)-based quasi-decentralized load frequency control scheme for power systems. The designed load frequency controllers are decoupled from each other, and can cope with noisy and discrete phasor measurement unit data. The proposed UT-based scheme is applied to a complex nonlinear power system. Furthermore, the design and analysis of the proposed controllers are based on considering the entire network topology.

Dynamic State Estimation Based Control Strategy for DFIG Wind Turbine Connected to Complex Power Systems

This paper proposes a viable solution to the long-lasting issue of using flux-involved control scheme to regulate the behavior of doubly fed induction generator (DFIG) during faults. Instead of trying to design a complicated method to measure flux, which cannot be directly measured with contemporary technology, the solution utilizes unscented Kalman filter-based dynamic state estimation of DFIG connected to a complex power system to estimate the wanted variables. The decentralized estimation scheme takes into consideration the overall power system network and uses only local noisy PMU measurement data. DFIG control schemes are also investigated to a fair extent where three control methods are discussed with comparison results presented. The improved control scheme displays a better fault recovery response and system compatibility. A number of considerations are taken into account in the design of DFIG control schemes, including reactive power supports and dc-link current compensation.

Power system dynamic state estimation using particle filter

A particle filter based power system dynamic state estimation scheme is presented in this paper. The proposed method can be considered as an alternative to the other schemes which are mostly based on the Kaiman Filter. The particle filter approach can be used to estimate the states of nonlinear systems which are subjected to both Gaussian and non-Gaussian noise. Furthermore, the presented scheme has a simple algorithm that can be easily implemented numerically. The case study considered in this paper reveals that the method has considerable accuracy and provides smooth dynamic state estimation even when the noise variance differs from a known initial value.

A Functional Observer Based Dynamic State Estimation Technique for Grid Connected Solid Oxide Fuel Cells

This paper presents a functional observer based technique for estimating gaseous partial pressures in triple phase boundary of a high-order solid oxide fuel cell. Triple phase boundary is a nanoscale region in solid oxide fuel cells where direct measurement of partial pressure of individual gases is not possible. For a reliable and a safe operation those quantities must be monitored. This paper reports a novel functional observer based dynamic state estimation approach that utilizes a system decomposition algorithm to provide a functional observer with minimum order. Therefore, the proposed technique has a simpler structure than conventional state observer based schemes. Case studies of the proposed technique, implemented on a complex nonlinear power system, show accurate and smooth estimations in comparison to full-order state observer based techniques in terms of tracking of nonlinear partial pressures.

Particle filter approach to dynamic state estimation of generators in power systems

This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.