STATE ESTIMATION OF POWER SYSTEMS

State Estimation of Power System has same function as Power Flow Analysis in determining system status which is very important in operating the power systems securely. Detail understanding on the differences between State Estimation and Power Flow Analysis has to be taken into consideration. The focus will be on testing the Power System State Estimation either by using existing software or developed software. The chosen option is to produce software using MA TLAB program that can test the State Estimation in Power System network and in obtaining consistent system states which are magnitude, lVI, and phase angles, tl, of bus voltages. Weight Least Squares Method is used to calculate estimates of the state variables (unknown data) by using measured data. When bad measurements are detected, the estimated states are not reliable anymore. So, the data have to be identified and discarded by statistical tests. The presence of bad data is assumed due to improper connection with reversed positive and negative leads at the meter equipment. The limitations that Power Flow Analysis has can be removed by state estimation based on the weighted least squares method and also the method of detecting and eliminating the bad data

pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandapower includes a Newton-Raphson power flow solver formerly based on PYPOWER, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes and a connectivity check. The pandapower network model is based on electric elements, such as lines, two and three-winding transformers or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive, publicly available case-study demonstrates a possible application of pandapower in an automated time series calculation

STATE ESTIMATION OF POWER SYSTEMS

State Estimation of Power System has same function as Power Flow Analysis in determining system status which is very important in operating the power systems securely. Detail understanding on the differences between State Estimation and Power Flow Analysis has to be taken into consideration. The focus will be on testing the Power System State Estimation either by using existing software or developed software. The chosen option is to produce software using MA TLAB program that can test the State Estimation in Power System network and in obtaining consistent system states which are magnitude, lVI, and phase angles, tl, of bus voltages. Weight Least Squares Method is used to calculate estimates of the state variables (unknown data) by using measured data. When bad measurements are detected, the estimated states are not reliable anymore. So, the data have to be identified and discarded by statistical tests. The presence of bad data is assumed due to improper connection with reversed positive and negative leads at the meter equipment. The limitations that Power Flow Analysis has can be removed by state estimation based on the weighted least squares method and also the method of detecting and eliminating the bad data

The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems

The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes

PHASOR MEASUREMENT UNIT DEPLOYMENT APPROACH FOR MAXIMUM OBSERVABILITY CONSIDERING VULNERABILITY ANALYSIS

In recent years, there has been growing interest of synchrophasor measurements like Phasor Measurement Units (PMUs) Power systems are now being gradually populated by PMU since they provide significant phasor information for the protection and control of power systems during normal and abnormal situations. There are several applications of PMUs, out of which state estimation is a widely used. To improve the robustness of state estimation, different approaches for placement of PMUs have been studied. This thesis introduces an approach for deployment the PMUs considering its vulnerability. Two different analysis have been considered to solve the problem of locating PMUs in the systems. The first analysis shows that using a very limited number of PMUs, maximum bus observability can be obtained when considering the potential loss of PMUs. This analysis have been done considering with and without conventional measurements like zero injections and branch flow measurements. The second analysis is based on selection of critical buses with PMUs. The algorithm in latter is specifically used for the system which has existing PMUs and the scenario where new locations for new PMUs has to be planned. The need for implementing this study is highlighted based on attack threads on PMUs to minimize the system observability. Both the analysis are carried out using Binary Integer Programming (BIP). Detail procedure has been explained using flow charts and effectiveness of the proposed method is testified on several IEEE test systems