An identity state observer for the permanent-magnet synchronous motor is derived which reconstructs the electrical and mechanical states of the motor from current and voltage measurements. The observer operates in the rotor frame and estimates direct and quadrature stator currents, rotor velocity, and rotor position. Since the rotor position is estimated, the rotor reference frame is approximated using the latest rotor position estimate. The motor dynamics and the transformation into the estimated rotor frame are nonlinear, and thus the observer and observer error dynamics are nonlinear. Therefore, stability is analyzed using a linearized error model. Simulations including realistic measurement disturbances are used to investigate the global stability and accuracy of the observer. >

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A state observer for the permanent-magnet synchronous motor

An efficient, timely, and accurate state estimation is a prerequisite for most energy management system (EMS) applications in power system control centers. The emerging wide-area measurement systems (WAMSs) offer new opportunities for developing more effective methods to monitor power system dynamics online. Recently, alternative methods for power system state estimation have caught much attention. Due to the nonlinearity of state transition and observation equation, linearization and Jacobian matrix calculation are indispensible in the existing methods of power system state estimation. This makes WAMS' high performance compromised by burdensome calculation. In order to overcome the drawbacks, this study tries to develop an effective state estimation method without the linearization and Jacobian matrix calculation. Firstly, unscented transformation is introduced as an effective method to calculate the means and covariances of a random vector undergoing a nonlinear transformation. Secondly, by embedding the unscented transformation into the Kalman filter process, a method is developed for power system dynamic state estimation. Finally, some simulation results are presented showing accuracy and easier implementation of the new method.

An Alternative Method for Power System Dynamic State Estimation Based on Unscented Transform

This paper proposes a decentralized algorithm for real-time estimation of the dynamic states of a power system. The scheme employs phasor measurement units (PMUs) for the measurement of local signals at each generation unit, and subsequent state estimation using unscented Kalman filtering (UKF). The novelty of the scheme is that the state estimation at one generation unit is independent from the estimation at other units, and therefore the transmission of remote signals to a central estimator is not required. This in turn reduces the complexity of each distributed estimator, and makes the estimation process highly efficient, accurate and easily implementable. The applicability of the proposed algorithm has been thoroughly demonstrated on a representative model.

/pdf/decentralized-dynamic-state-estimation-in-power-systems-leqcsfmnx1.pdf

Decentralized Dynamic State Estimation in Power Systems Using Unscented Transformation

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.

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

An identity state observer for the smooth-rotor permanent-magnet synchronous motor is derived which reconstructs the electrical and mechanical states of the motor from current and voltage measurements. The observer operates in the rotor frame and estimates direct and quadrature stator currents, rotor velocity, and rotor position. Since the rotor position is estimated, the rotor reference frame is approximated using the latest rotor position estimate. The motor dynamics and the transformation into the estimated rotor frame are nonlinear and thus the observer and observer error dynamics are nonlinear. Therefore, small signal stability is analyzed from a linearized model. Simulations including realis-tic measurement disturbances are used to investigate the global stability and accuracy of the observer.

/pdf/a-state-observer-for-the-permanent-magnet-synchronous-motor-3nwljkvgtv.pdf

A State Observer For The Permanent-Magnet Synchronous Motor

This paper describes the application of the invariant imbedding non-linear dynamic estimation technique to determine the transient state of a power system. The estimator handles system and measurement non-linearities effectively and works in the presence of system input disturbance and measurement noise. Off-line computer simulations of the estimator applied to a single machine infinite busbar power system successfully estimated the states of the machine. The results indicated that successful estimation is achieved by a careful choice of the input disturbance noise covariance matrix, Q. High Q values are recommended for the stepout region and low Q values for the hunting region.

C. K. Gharban

Papers

Non-Linear Dynamic Power System State Estimation