Alireza Rouhani

Evaluation and performance comparison of power swing detection algorithms in presence of series compensation on transmission lines

Any sudden change in the configuration or the loading of an electrical network causes power swing between the load concentrations of the network. In order to prevent the distance protection from tripping during such conditions, a power swing blocking is utilized. This paper compares and evaluates the performance of several power swing detection algorithms in the presence of series compensation and without it. The decreasing impedance, the Vcosφ and the power derivation methods are ordinary algorithms for power swing detection. A precise model for power swing conditions has been generated by EMTDC/PSCAD software. In this paper this model is used to simulate the algorithms behavior under different operational conditions.

Linear Phasor Estimator Assisted Dynamic State Estimation

Measurements provided by the phasor measurement units (PMUs) in a power network can be highly erroneous. Furthermore, some generating units may not have a local PMU, therefore it may not be possible to obtain high accurate and reliable results based on the previously studied dynamic state estimation approaches, which rely on the raw measurements provided by the PMUs. In order to address these issues, this paper presents a robust distributed dynamic state estimation approach that not only is robust against bad data, but also makes it possible to obtain the dynamic state estimation results for the generators without a local PMU. The proposed approach also accounts for expected delays in receiving estimated measurements by using a multi-step ahead state predictor to correct for delayed inputs. This procedure can also be useful for the short-term transient stability predictions.

Tracking the machine states using a linear phasor estimator assisted dynamic state estimator

This paper is concerned about efficiently performing dynamic state estimation in very large scale power systems. Unlike the commonly used approach of approximating bus loads as constant impedances and using a reduced system model, this paper relies on a phasor based linear state estimator that uses the detailed system model to obtain the bus voltage estimates at a fast rate, thus eliminating the need to model loads. Moreover, the computational effort associated with dynamic state estimation is reduced by strategically dividing the system into small size zones and carrying out the dynamic state estimation independently for each one. As an illustration, dynamic state variables of a two-axis synchronous generator with IEEE-Type1 Exciter are estimated based on the proposed approach. The issue of possible synchronization errors among measurements is also addressed.

Improving performance of dynamic state estimators under unknown load changes

This paper investigates the use of dynamic state estimation for monitoring the transmission grid not only during pseudo steady-state but also during and following disturbances. The paper mainly considers line switching events but the developed estimator can be used under any other type of system disturbance. Unlike previous studies reported in the literature, this work takes into account changes in bus loads which are assumed to be monitored at longer intervals. Assumed variance of the system state is strategically varied to minimize the impact of this approximation on the dynamic state estimation. The well-known and documented Unscented Kalman Filter (UKF) is chosen in formulating the dynamic state estimator, due to its advantages over the commonly used Extended Kalman Filter (EKF). Simulation results will be shown to illustrate the proposed approach to handle slow load changes as well as dynamic estimation of state trajectory during line switching transients.

Distributed implementation of an augmented state dynamic estimator

Load models are commonly approximated by fixed impedance type loads when formulating dynamic simulations. This is an acceptable approximation when using dynamic simulation for a specific initial operating condition and the study runs for a brief period typically to check system stability. When implementing an on-line dynamic estimator such assumptions may not be viable due to the load dynamics which are not typically known. Another difficulty is related to the sheer size of the problem when a large scale system is considered with several generators. This paper addresses these problems by extending the state vector to include network variables, thus eliminating the need to model loads and also by implementing a distributed yet synchronized solution algorithm that facilities simultaneous solutions by multiple areas. Previously studied Unscented Kalman Filter (UKF) is used and the proposed method is tested on the New-England 37-bus system.

Real-Time Dynamic Parameter Estimation for an Exponential Dynamic Load Model

This paper is concerned about real-time modeling and identification of dynamically changing loads in power systems. An exponential dynamic load model was proposed earlier and was well accepted by several investigators who worked on this paper. This paper considers this model and identifies its parameters in real-time based on synchronously sampled measurements. An unscented Kalman filter is used to track the unknown parameters of the exponential dynamic load model. This paper first implements and tests the proposed method using simulated measurements. The method is then applied to actual recorded utility measurements to identify and track the bus load of the utility. The results are found to be promising, suggesting viability of tracking dynamic load models for online applications.

Detection of voltage sag using unscented Kalman smoother

A novel method based on Kalman algorithm is proposed in this paper to estimate the amplitude and the time duration of voltage sag in power systems. The new method uses the unscented transform and the RTS algorithm to have a smoother and optimal tracking response. A nonlinear model of the system is developed and the mathematics of the method is derived for a supposed power signal. The algorithm is simulated in MATLAB with the assumption of a measured voltage with low-order harmonics and in presence of a noise with SNR of 10. Simulation results show that the proposed method performs with a smooth, fast dynamic and tracking response in present of noise and harmonics. The accuracy of the method is also better in comparison with other Kalman based algorithms.

Observability Analysis for Dynamic State Estimation of Synchronous Machines

This paper is concerned about the observability analysis of time-varying nonlinear dynamic model of a synchronous generator with its associated control systems. A byproduct of observability study is a set of guidelines to choose the appropriate set of measurements or sensors to be used to ensure strong observability for the dynamic states. The proposed analysis is developed using a Lie derivative based observability matrix and its singular values. A two-axis synchronous generator model and an associated IEEE-Type1 exciter are used to validate the results of observability analysis with dynamic simulations of disturbance scenarios using different types of measurements. It is shown that the dynamic state estimates will converge to the true trajectory faster and smoother if appropriate measurements that provide higher level of observability based on the proposed analysis are chosen. Results can be extended to other dynamic elements such as loads or time-varying parameters of machines.

A robust dynamic state estimator against exciter failures

This paper proposes a novel approach for the dynamic state estimation of the synchronous generators. Distinguishing feature of the proposed approach is that it not only remains robust under the failure of the excitation system also detects the occurrence of the exciter failure. Unscented Kalman Filter (UKF) and Iterated Unscented Kalman Filter (IUKF) are both used in the proposed approach. An alternative dynamic state estimation approach for the synchronous generators with unknown inputs is also presented in the paper. Multiple model estimation technique is implemented in order to detect the occurrence of the exciter failure. The proposed approach is implemented on a two-axis synchronous generator with IEEE-Type 1 exciter in a medium size utility test system and the simulation results validates the effectiveness of the proposed approach.

Local detection of PMU measurement errors using dynamic state estimators

This paper proposes the use of a couple of local PMUs and associated dynamic state estimators in order to detect and remove bad data in PMU measurements. Distinguishing features of the proposed approach is that it facilitates detection of bad-data in local PMU measurements without requiring a system-wide state estimator. The proposed approach relies on a performance evaluation technique which computes the probability density function (pdf) of the residuals provided by a dynamic state estimator. In the proposed approach it is assumed that there are at least two local PMUs that provide measurements to the dynamic state estimators of a synchronous generator. The proposed approach is implemented using a two-axis model of a synchronous generator with IEEE-Type 1 exciter. The performance of the proposed approach is investigated in the presence of bad-data associated with the measurements provided by the PMUs.