Ankush Tondan

Bio: Ankush Tondan is an academic researcher. The author has contributed to research in topics: Alpha beta filter & Unscented transform. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Power system state estimation comparison of Kalman filters with a new approach

Abstract: The Synchronous machine angle and speed variables availability give us an exact picture of the overall condition of power system networks leading therefore to an improved situational cognizance by system operators. As the enlargement of power systems continue, the challenge of real time monitoring and its control gets bigger. All significant network parameters are measured at various points on the system and that collected data transferred to the control center, where the data is used for Estimation Process through various Energy Management System (EMS) functions. Accuracy is directly dependent upon the data collected through Phasor Measurement Unit (PMU) with their underlying high veracity and the unique ability to evaluate the voltage angles, offers an advantage in improving the overall accuracy. This paper compares the performance of four filtering approaches in estimating dynamic states of a power system network using PMU data. The five methods are extended Kalman filter (EKF), unscented Kalman filter (UKF), Particle Filter (PF), and Enhanced unscented Kalman filter. The statistical performance of each algorithm is compared using IEEE 30 and IEEE 14 bus test system. Under the statistical framework, Efficiency of Interpolation, Sensitivity of the missing data with computation time are evaluated and compared for each approach. According to the comparison, this paper makes some recommendations for the proper use of the methods.

A Review and Performance Comparison of Power System State Estimation Techniques

Abstract: State estimation is an important area required for enabling proper operation and control of the power system and its real-time monitoring. Correct knowledge of the states (like rotor angle and speed deviations) enables us to develop control schemes to enhance the system stability and reliability. Earlier the power system loads were almost constant and it was a static system as the load models became complex and the power system changed to quasi-static or sometimes dynamic it was not computationally possible to use older methods to achieve real-time monitoring. Dynamic State Estimation (DSE) techniques use the Dynamic model of the system in conjunction with the measurement model which can be used to accurately predict the states in advance. In this paper, various algorithms of traditional state estimation are presented, based on the available literature

Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Abstract: Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

Power system dynamic state estimation considering correlation of measurement error from PMU and SCADA

Abstract: It is well known that measurements from phasor measurement unit (PMU) or supervisory control and data acquisition (SCADA) are not generally independent. Since the correlation of measurement error is a very representative feature of the actual measurement system, traditional assumptions on error independency are not adequate. In this paper, taking the correlation of measurement error of both PMU and SCADA measurements into consideration, a novel correlated extended Kalman filter (CEKF) is proposed. The actual measurement configurations are analyzed with the consideration of measurement error transfer characteristics. Then, the modified measurement error covariance matrix is calculated by using the point estimation method, which will replace the traditional diagonal variance matrix. At last, IEEE 14‐bus system and 57‐bus system are provided to illustrate the effectiveness and superiority of the method, respectively.

Comparative Analysis of State and Parameter Estimation Techniques for Power System Frequency Dynamics

Abstract: Dynamic state and parameter estimation in current and future power systems are critical for advanced monitoring, control, and protection. There are numerous methods to perform dynamic state and parameter estimation; this paper compares the accuracy and computational time of four methods (i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and moving horizon estimation (MHE)) designed to estimate the states and parameters for frequency dynamics of a power system. A simulation study was conducted using Matlab/Simulink by introducing Gaussian and non-Gaussian noise in the measurements. Results under Gaussian noise showed similar accuracy performance for all filters. EKF and UKF presented convergence or numerical instability issues due to incorrect initial guesses of parameters. MHE did not present convergence issues, however, required comparatively higher computation time. Nonetheless, the MHE could still be implemented in real-time for state and parameter estimation of power system. The impact of non-Gaussian noise on the methods was inconclusive and will require further study.

Optimization-Based Estimation of Microgrid Equivalent Parameters for Voltage and Frequency Dynamics

Abstract: Microgrid parameter estimation is essential to enable optimal voltage and frequency control using distributed energy resources (DER). Microgrid parameters vary through time, e.g., when generation is re-dispatched/committed, during microgrid reconfiguration. Furthermore, sensor measurements are noisy and preservation of the fast dynamics measurements is required, which is difficult to achieve with a lowpass filter. In this paper, a moving horizon estimation (MHE) approach is applied to estimate microgrid parameters for voltage and frequency support. The proposed approach estimates the states i.e., frequency, rate of change of frequency, grid voltage and current, and system parameters i.e., inertia, damping, and equivalent impedance. The MHE is formulated as an optimization problem using data over a fixed past horizon and solved online such that the sum of the square of measurement noise and process noise is minimized. Results showed that the proposed approach was able to estimate microgrid states, parameters, and disturbances within 5% for most values, which is sufficient to use in microgrid voltage and frequency control.