State Variables Of Generators Research Articles

Two Decentralized Dynamic State Estimation Schemes for Multimachine Power Systems with Transmission Losses

This article addresses the problem of estimating the state of a multimachine power system (MPS). To work with power networks with lossy transmission lines, a variation of the classical third-order MPS is proposed by considering generators’ electrical power injected into the grid as a state variable. Based on a linear decentralized estimation model (tailored for a specific purpose), the state variables of generators (load angle, relative speed, and electrical power) together with terminal voltage magnitudes are estimated in a decentralized fashion through two new robustly convergent linear Luenberger estimators, one based on load angle measurement and the other based on relative speed measurement. The new MPS estimation model includes a set of robustly quick observable states, one per machine, which allows capturing the interaction with other generators, transmission line losses, unknown disturbances, and model errors. The result is a design superior to other related estimation techniques such as the extended Kalman filter (EKF) or Sliding Modes Perturbation Observer (SMPO) in terms of (i) a conventional-like simple pole placement-based tuning, (ii) low online computational load and disturbance rejection capability, and (iii) small gain-based convergence assessment. The performance of the proposed state estimation scheme is illustrated in a 3-machine power system under different operational conditions.

Output Adaptive Observers Design for Linear Non-Stationary Systems with Polynomial Parameters

The article deals with the problem of state observer design for a linear time-varying plant. To solve this problem, a number of realistic assumptions are considered, assuming that the model parameters are polynomial functions of time with unknown coefficients. The problem of observer design is solved in the class of identification approaches, which provide transformation of the original mathematical model of the plant to a static linear regression equation, in which, instead of unknown constant parameters, there are state variables of generators that model non-stationary parameters. To recover the unknown functions of the regression model, we use the recently well-established method of dynamic regressor extension and mixing (DREM), which allows to obtain monotone estimates, as well as to accelerate the convergence of estimates to the true values. Despite the fact that the article deals with the problem of state observer design, it is worth noting the possibility of using the proposed approach to solve an independent and actual estimation problem of unknown time-varying parameters.

Control and Disturbances Compensation for Doubly Fed Induction Generators Using the Derivative-Free Nonlinear Kalman Filter

The paper studies differential flatness properties and an input–output linearization procedure for doubly fed induction generators (DFIGs). By defining flat outputs which are associated with the rotor's turn angle and the magnetic flux of the stator, an equivalent DFIG description in the Brunovksy (canonical) form is obtained. For the linearized canonical model of the generator, a feedback controller is designed. Moreover, a comparison of the differential flatness theory-based control method against Lie algebra-based control is provided. At the second stage, a novel Kalman Filtering method (Derivative-free nonlinear Kalman Filtering) is introduced. The proposed Kalman Filter is redesigned as disturbance observer for estimating additive input disturbances to the DFIG model. These estimated disturbance terms are finally used by a feedback controller that enables the generator's state variables to track desirable setpoints. The efficiency of the proposed state estimation-based control scheme is tested through simulation experiments.

Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

In interconnected power systems, dynamic model reduction can be applied to generators outside the area of interest (i.e., study area) to reduce the computational cost associated with transient stability studies. This paper presents a method of deriving the reduced dynamic model of the external area based on dynamic response measurements. The method consists of three steps, namely dynamic-feature extraction, attribution, and reconstruction (DEAR). In this method, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal “basis” of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original system. The network model is unchanged in the DEAR method. Tests on several IEEE standard systems show that the proposed method yields better reduction ratio and response errors than the traditional coherency based reduction methods.

A Coherency Recognition Based on Structural Decomposition Procedure

The aim of this paper is to present an algorithm for coherency recognition. In the proposed decomposition model, the system structure is explored by analyzing and quantifying the strength of connection among generating units by means of suitable coupling factors. These factors take into account interconnection constraints among dynamic components of the system. In particular, coherency indicators are based on the influence that state variables of generators have on the voltages of all other generators. Test results demonstrated that the coherency recognition obtained with this procedure is independent of fault location and of size of disturbance, since it is based on the very structural characteristics of coupling factors. The practicality of the proposed procedure is demonstrated by numerical examples involving the IEEE 118-bus test system.

Stabilizing a multimachine system via non-linear voltage controller with the use of a local control strategy

A new local control strategy with non-linear optimal controllers for a multimachine system is presented. Non-linear Field Voltage Controllers (NFVC) are designed with the use of the feedback linearization approach to adjust generator state variables to external reference signals resulting from optimal power flow calculation [Pi, Qi, Vti] in the stabilized power system. A non-linear control law has been derived for the three-dimensional [δ, ω, E′q] one-axis model of a generator. The local control strategy has been verified for the New England 39 bus system. An extensive simulation study shows robustness of the non-linear controllers and better performance than PSS-based classical controllers. © 1997 John Wiley & Sons, Ltd.