Hesam Khazraj

A performance comparison between extended Kalman Filter and unscented Kalman Filter in power system dynamic state estimation

Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve the stability of power system. State estimation with EKF and UKF methods can be used for monitoring and estimating the dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle. This paper uses Powerfactory to solve power flow analysis of simulations, then a non-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks. This article will focus on comparing and studying the advantages and disadvantages of both methods under transient conditions. It is demonstrated that UKF is easier to implement and accurate in estimation.

An improved current controller to ensure the robust performance of grid-connected converters under weak grid Conditions

Voltage Source Converters (VSCs) operating in very weak grids with low Short Circuit Ratio (SCR) are known to meet stability challenges. This article investigates instability of a grid connected current-controlled converter under weak grid conditions, which is often attributed to the dynamic interaction between the phase-locked loop (PLL) and system impedance networks. To accomplish this object, available approaches are overviewed, and their advantages and disadvantages are briefly explained. Then a simple yet effective technique based on the joint operation of virtual impedance technique and an amplitude estimation strategy is presented to tackle their shortcomings. The effectiveness of the proposed strategy is finally evaluated using the simulation results.

Modeling of HVDC in Dynamic State Estimation using Unscented Kalman Filter method

HVDC transmission is an integral part of various power system networks. This article presents an Unscented Kalman Filter dynamic state estimator algorithm that considers the presence of HVDC links. The AC - DC power flow analysis, which is implemented as power flow solver for Dynamic State Estimation (DSE), creates an updated admittance matrix. First, a hybrid AC/DC network model is developed to combine the AC network and DC links. Then a non-linear state estimator can solve for hybrid AC/DC states by applying the unscented Kalman filter (UKF) algorithm. It is demonstrated that UKF is easy to implement and accurate in estimation. The dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle, are estimated to study transient behavior of the power system network. Finally, a dynamic state estimation model is built for a 14 bus power system network to evaluate the proposed algorithm for hybrid AC/DC networks.

Addressing single and multiple bad data in the modern PMU-based power system state estimation

Detection and analysis of bad data is an important sector of the static state estimation. This paper addresses single and multiple bad data in the modern phasor measurement unit (PMU)-based power system static state estimations. To accomplish this objective, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the state estimation. The proposed algorithms of phasor measurements utilization in state estimation can detect and identify single and multiple bad data in redundant and critical measurements. To validate simulations, IEEE 30 bus system are implemented in PowerFactory and Matlab is used to solve proposed state estimation using postprocessing of PMUs and mixed methods. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analyze of bad data are available by the result of largest normalized residual test.

Comparison between conventional anc post-processing PMU-based state estimation to deal with bad data

Detection and analysis of bad data is one of the most important sector of static state estimation. This paper focuses on the comparison between a novel method for multi bad data detection and identification in PMU-based state estimation, namely post-processing PMU-based method for state estimation and the conventional PMU-based state estimation. To accomplish this object, available approaches in the PMU-based state estimation are overviewed, and their advantages and disadvantages are briefly explained. The largest normalized residual test is used to identify bad data. Then, phasor measurements are added by post-processing step in the second level of state estimation. The proposed algorithm of phasor measurements utilization in state estimation can prove that post-processing algorithm can detect and identify multi bad data in critical measurements, which it is not detectable by conventional methods. To validate simulations, IEEE 30 bus is implemented in PowerFactory and Matlab is used to solve proposed state estimation using post-processing of PMUs. Bad data is generated manually and added in PMU and conventional measurements profile. Finally, the location and analysis of bad data are available by result of largest normalized residual test.

An analysis of delay-based and integrator-based sequence detectors for grid-connected converters

Detecting and separating positive and negative sequence components of the grid voltage or current is of vital importance in the control of grid-connected power converters, HVDC systems, etc. To this end, several techniques have been proposed in recent years. These techniques can be broadly classified into two main classes: The integrator-based techniques and Delay-based techniques. The complex-coefficient filter-based technique, dual second-order generalized integrator-based method, multiple reference frame approach are the main members of the integrator-based sequence detector and the delay-signal cancellation operators are the main members of the delay-based sequence detectors. The aim of this paper is to provide a theoretical and experimental comparative study between integrator and delay based sequence detectors. The theoretical analysis is conducted based on the small-signal modelling, and experimental study is conducted using dSpace platform.