A constrained optimization approach to dynamic state estimation for power systems including PMU measurements (original) (raw)
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IEEE Transactions on Control Systems Technology, 2015
In this paper, a hybrid filter algorithm is developed to deal with the state estimation problem for power systems by taking into account the impact from the phasor measurement units (PMU). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed state estimation problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved by using the particle swarm optimization (PSO) algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements. Simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.
Optimal utilisation of PMU measurements in power system hybrid state estimators
IET Generation, Transmission & Distribution, 2019
A state estimator provides power system states by minimising the difference between the available measurement set and expected measurements. This minimisation problem is solved using the weighted least squares method or conventional optimisation methods. Most of the optimisation-based conventional methods used for power system state estimation are based on derivative approaches. These traditional methods suffer from problems such as the complication in handling inequality constraints, slow convergence etc. Non-linear constrained optimisation problems can be efficiently solved by the sequential quadratic programming (SQP) algorithm. SQP can be applied to small as well as large-scale non-linear programming problems having a large number of constraints. In the proposed algorithm, hybrid measurement configuration, consisting of measurements from phasor measurement units as well as conventional measurement devices, is used and applied on IEEE 14 bus, IEEE 118 bus test system and Indian northern region power grid-246 bus system.
Power System State Estimation by Novel Approach of Kalman Filter
Indonesian Journal of Electrical Engineering and Computer Science, 2017
The electrical network measurements by measuring device Phasor Measurement Device (PMU) are usually sent to the control centers using data acquisition system and other communication protocols available. However, these measurements contain uncertainties due to the measurements and communication noise (errors), incomplete metering or unavailability of some of measurements. The overall aim of state estimation is to calculate the state variables of the power system by minimizing errors available at the control center. Due to generate desired quantities by optimal estimate which is given the set of measurements, Kalman filters are widely used. This paper discusses the application of an Extended Kalman Filter (EKF) algorithm, the Unscented Kalman Filter (UKF) algorithm, and New EKF+M and UKF+M estimator algorithm,those are modification of EKF and UKF for enhance accuracy and elapse time is less. The effectiveness and performance of EKF+M and UKF+M Estimator over another Filtration algorithm is shown. These state estimation techniques applied on IEEE-30 bus, 14 bus and 9 bus test system.
Particle Swarm Optimization for Power System State Estimation
Neurocomputing, 2014
The electrical network measurements are usually sent to the control centers using specific communication protocols. However, these measurements contain uncertainties due to the meters and communication errors (noise), incomplete metering or unavailability of some of these measurements. The aim of state estimation is to estimate the state variables of the power system by minimizing all measurement errors available at the control center. In the past, many traditional algorithms, based on gradient approach, have been used for this purpose. This paper discusses the application of an artificial intelligence (AI) algorithm, the particle swarm optimization (PSO), to solve the state estimation problem within a power system. Two objective functions are formulated: the weighted least square (WLS) and weighted least absolute value (WLAV). The effectiveness of PSO over another AI optimization algorithm, genetic algorithm (GA), is shown by comparing both two solutions to the true state variable values obtained using Newton-Raphson (NR) algorithm.
An Algorithm for Improving the Power System State Estimation Using PMU Measurements
— State estimation provides the platform for advanced security monitoring applications in control centers. An algorithm based on pseudo-measurements and PMU measurements was developed and implemented in MATLAB environment. The novelty proposed by this paper consists in the methodology of computing the considered pseudo-measurements. For increasing the accuracy of the state estimator algorithm, adjustment of the network parameters with the ambient temperature is considered. Furthermore, different models of the electrical lines are applied. The tests and simulations were performed on the NORDIC32 test system. The purpose is to show that state estimation using phasor measurements may improve the actual estimations based on the SCADA measurements.
IEEE Transactions on Power Systems, 2000
Availability of the synchronous machine angle and speed variables give us an accurate picture of the overall condition of power networks leading therefore to an improved situational awareness by system operators. In addition, they would be essential in developing local and global control schemes aimed at enhancing system stability and reliability. In this paper, the extended Kalman filter (EKF) technique for dynamic state estimation of a synchronous machine using phasor measurement unit (PMU) quantities is developed. The simulation results of the EKF approach show the accuracy of the resulting state estimates. However, the traditional EKF method requires that all externally observed variables, including input signals, be measured or available, which may not always be the case. In synchronous machines, for example, the exciter output voltage may not be available for measuring in all cases. As a result, the extended Kalman filter with unknown inputs, referred to as EKF-UI, is proposed for identifying and estimating the states and the unknown inputs of the synchronous machine simultaneously. Simulation results demonstrate the efficiency and accuracy of the EKF-UI method under noisy or fault conditions, compared to the classic EKF approach and confirms its great potential in cases where there is no access to the input signals of the system.
On the use of PMUs in power system state estimation
2011
Synchronized phasor measurement units (PMUs) are becoming a reality in more and more power sys- tems, mainly at the transmission level. This paper presents, in a tutorial manner, the benefits that existing and future State Estimators (SE) can achieve by incorporating these de- vices in the monitoring process. After a review of the rele- vant PMU technological aspects and the associated deploy- ment issues (observability, optimal location, etc.), the alter- native SE formulations in the presence of PMUs are revis- ited. Then, several application environments are separately addressed, regarding the enhancements potentially brought about by the use of PMUs.
This paper presents a new PMU-based Kalman Filter approach for Power System State Estimation, in which an estimator model is proposed to include the system frequency and exploit synchronized frequency measurements. The performance of this new estimator is evaluated in comparison to different state estimation methodologies, in order to assess the impact and potential benefits given by the integration of the frequency input. The improvement in the estimation accuracy achievable for the quantities of interest (node voltages and branch currents) thanks to the proposed approach is assessed under realistic operating conditions, taking into account, in particular, the possible occurrence of off-nominal frequency conditions. The main findings and results obtained by means of simulations performed on a standard IEEE network are presented and discussed.
Particle Filter in State Vector Estimation Problem for Power System
Pomiary Automatyka Robotyka, 2014
Particle Filter is a tool, which has been used more frequently over the years. Calculations with using Particle Filter methods are very versatile (in comparison to the Kalman Filter), which can be used in high complex and nonlinear problems. Example of such a problem is the power system, where Particle Filter is used to state estimation of network parameters based on measurements. Paper presents theoretical basis regarding Particle Filter and power system state estimation. Results of experiment have shown that Particle Filter usually gives better outcome comparing to the Weighted Least Squares method. In extension Multi Probability Density Function Particle Filter is proposed, which improves obtained results so that they are always better than Weighted Least Squares method.
Power System Dynamic State Estimation
2018
State estimation, the core of the Energy Management System (EMS) is a prerequisite for operation of modern power grid. It changed its emergence with the introduction of high speed Phasor Measurement Unit (PMU) based Wide-Area Measurement Systems (WAMS) featured with synchronous sampling later leading to Dynamic State Estimation (DSE) due to slow update rate of SCADA systems. This paper deals with state estimation process, based on Kalman Filtering techniques, multi-machine power systems. Comparison of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms is also done along with their verification under transient conditions. It is demonstrated that UKF is easier to implement and more accurate in estimation. Additionally, the parameter estimation for assumed ZIP load model is performed based on the Weighted Least Square (WLS) estimation method. A more accurate load modeling development and integration in DSE can be done as a future work. Keywords— Dynamic State Est...