scholarly journals The Improvement of Weighted Least Square State Estimation Accuracy Using Optimal PMU Placement

2020 ◽  
Vol 15 ◽  
Keyword(s):  
State Estimation ◽  
System Analysis ◽  
Test System ◽  
The State ◽  
Least Square ◽  
Optimal Placement ◽  
Estimation Accuracy ◽  
Estimation Performance ◽  
Measurement Units ◽  
A New Technique

This paper proposed a new technique to improve the state estimation performance by optimal placement of phasor measurement units (OPP), the proposed technique is based on Simulating Annealing (SA) algorithm for OPP by comparing between the SA solution sets and choosing the optimal location of PMUs to enhance the state estimation performance. The proposed technique has been tested through IEEE 24 bus test system using power system analysis toolbox in MATLAB program. In this paper the root mean squared deviation (RMSD) has been used to determine the state estimation performance, the simulation result demonstrates that the proposed method proved its effectiveness to be one of the best methods used to improve the state estimation performance

scholarly journals Optimal PMU Placement with Uncertainty Using Pareto Method

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
A. Ketabi ◽  
S. M. Nosratabadi ◽  
M. R. Sheibani
Keyword(s):  
State Estimation ◽  
Test System ◽  
Optimal Placement ◽  
Pareto Optimum ◽  
De Algorithm ◽  
Suggested Strategy ◽  
Measurement Units ◽  
Pmu Placement ◽  
Pareto Method ◽  
Value Decomposition

This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs) in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD). For the normal condition, Differential Evolution (DE) algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum method has been proposed here. The suggested strategy is applied on the IEEE 30-bus test system in several case studies to evaluate the optimal PMUs placement.

Distribution System State Estimation with Phasor Measurement Units

2014 ◽  
Vol 668-669 ◽  
pp. 687-690
Author(s):  
Min Liu
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Phasor Measurement Units ◽  
Least Square ◽  
Estimation Accuracy ◽  
System State ◽  
State Estimator ◽  
Phasor Measurement ◽  
Measurement Units ◽  
Simulation Results

With phasor measurement units (PMU) become available in the distribution system; the estimation accuracy of the distribution system state estimation (DSSE) is expected to be improved. Based on the weighted least square (WLS) approach, this paper proposed a new state estimator which takes into account the PMU measurements including voltage magnitude and phasor angle, and load current magnitude and phasor angle. Simulation results indicate that the estimation accuracy is obvious improve by adding PMU measurements to the DSSE. Furthermore, the estimation accuracy changes with the installation site of PMU, and can be maximized by choosing the installation site appropriately.

scholarly journals Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2301
Author(s):  
Yun-Sung Cho ◽  
Yun-Hyuk Choi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
State Estimation ◽  
Measurement Errors ◽  
Large Scale ◽  
Test System ◽  
The State ◽  
Inner Function ◽  
Outer Function ◽  
Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

scholarly journals A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2251 ◽  
Author(s):  
Jikai Liu ◽  
Pengfei Wang ◽  
Fusheng Zha ◽  
Wei Guo ◽  
Zhenyu Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Quadruped Robot ◽  
The State ◽  
Estimation Accuracy ◽  
Robot System ◽  
Calculation Time ◽  
Motion State ◽  
Cubature Kalman Filter

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

scholarly journals Smart Grid State Estimation with PMUs Time Synchronization Errors

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5148
Author(s):  
Marco Todescato ◽  
Ruggero Carli ◽  
Luca Schenato ◽  
Grazia Barchi
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Time Synchronization ◽  
The State ◽  
Estimation Accuracy ◽  
Real Field ◽  
State Variables ◽  
Smart Power ◽  
Actual Performance ◽  
Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.

Prediction of Undervoltage Load Shedding Using Quantum-Inspired Evolutionary Programming-Support Vector Machine

2015 ◽  
Vol 785 ◽  
pp. 43-47
Author(s):  
Zuhaila Mat Yasin ◽  
Zuhaina Zakaria ◽  
Titik Khawa Abdul Rahman
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Evolutionary Programming ◽  
Test System ◽  
Least Square ◽  
Support Vector ◽  
Rbf Kernel ◽  
Validation Procedure ◽  
Programming Support ◽  
A New Technique

This paper presents a new technique to predict the optimal amount of load to be shed at various loading conditions using Quantum-Inspired Evolutionary Programming–Support Vector Machine (QIEP-SVM). QIEP is utilised to optimise the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimisation is to minimise the mean square error (MSE). The performance of QIEP-SVM technique was compared with those obtained from LS-SVM technique with prediction accuracy through a 10-fold cross-validation procedure. All simulations in this study were carried out using IEEE 69-bus distribution test system. QIEP-SVM model had shown better prediction performance as compared to LS-SVM. The results also indicate that the proposed approach outperforms the most recently reported technique in terms of accuracy and fast computation time.

scholarly journals Behavior of Attractors During Surge in Centrifugal Compression System

1998 ◽  
Author(s):  
A. Mutou ◽  
S. Mizuki ◽  
Y. Komatsubara ◽  
H. Tsujita
Keyword(s):  
Time Series ◽  
State Space ◽  
System Analysis ◽  
Spline Interpolation ◽  
Finite Difference Methods ◽  
The State ◽  
Least Square ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Compression System

A dynamical system analysis method is presented, that permits the characterization of unsteady phenomena in a centrifugal compression system. The method maps one experimental time series of data into a state space in which behaviors of the compression system should be represented, and reconstructs an attractor that geometrically characterizes a state of the compression system. The time series of data were obtained by using a high response pressure transducer and an analog to digital converter at surge condition. For the reconstruction of attractors, a noise free differentiation method in time was employed. The differentiation was made by high order finite difference methods. To remove the influence of noise, the data were passed through a filter using a third order spline interpolation. In this study, the dimension of the state space was restricted to three. The measured data itself and their first and second derivatives in time are used to represent an attractor in the state space. The modeling of the system behavior from the time series of data by second order ordinary differential equations was attempted. It is assumed that the data and their derivatives satisfy the equations at each time. Then, appropriate coefficients are determined by a least square method. The reconstructed attractor showed complex cyclic trajectories at a first glance. However, by applying a band pass filter to the original signal, it was found that the attractor consisted of three independent wave forms and formed an attractor with torus-like behavior. In contrast, the solution by the modeled equations showed a type of limit cycle.

scholarly journals OPTIMAL PLACEMENT OF PHASOR MEASUREMENT UNITS FOR POWER SYSTEM OBSERVABILITY

2013 ◽  
pp. 266-269
Author(s):  
RAJPAL SAINI ◽  
MANJU MAM ◽  
MANISH KR. SAINI
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
State Estimation ◽  
Phasor Measurement Units ◽  
Optimal Placement ◽  
Linear Algorithm ◽  
Normal Operating ◽  
Phasor Measurement ◽  
Minimum Number ◽  
Measurement Units

This paper present a method to find minimum number of phasor measurement units (PMUs) for complete observability of power system network for normal operating conditions.A linear algorithm is used to determine the minimum number of PMUs needed to make the system observable. For state estimation and fault diagnosis in power system synchronized snapshot of whole system must be necessary. The proposed method is used to benchmark the optimal PMUs placement solution for the IEEE 14-bus, IEEE 18- bus, IEEE 30-bus and IEEE 57-bus test systems.

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