Decoupled State-Estimation in Energy Control Centres

Power system state estimation is usually formulated as a weighted least-squares problem and solved iterativeLy by the normal equations method. The normal equations solution method is well-known to exhibit a tendency to be numerically unstable on some networks. A manifestation of this numerical instability is an ill-conditioned set of linear equations that are to be solved at each iterative step. Recent papers have presented orthogonal factorization methods as remedies for this instability. Experience shows, however, that these methods tend to suffer from excessive fill-in when confronted with networks that have a high measurement redundancy. The folklore of power-system state estimation says that large numbers of bus injection measurements (as opposed to line fLow measurements) tend to aggrevate the instability; no explanation for this observation, however, has appeared in the literature. This paper presents an analysis of the sources of ill-conditioning in the power system state estimation problem and offers an alternative solution method, due to Peters and Wilkinson, that overcomes this ill-conditioning without losing matrix sparsity.

In allusion to the defect that conventional weight least squares (WLS), when applied to the power system state estimation, generally takes the same weight. It causes large state estimation error. A state estimation method combining optimal weight setting with weighted least squares is proposed. On the basis of weighted least squares state estimation models, through the theoretical analysis of different kinds of voltage measurements, power injection measurements and power flow measurements data, which impact differently on the results, different weights are set corresponding to their measurement sensitivity in order to reduce the estimation errors. Finally, the algorithm is tested on IEEE14 bus system by MATLAB. Compared with the traditional weighted least squares, this method is similar with regard to iteration time. However, the voltage and phase angle state estimation accuracy can be improved by about 0.16 and 4.66 percentage points. Therefore, the algorithm has certain advantages and application value.

A recursive measurement error estimation identification algorithm is proposed for identifying multiple interacting bad data in power system static state estimation. A set of linearized formulae are developed and used to recursively calculate normalized residuals and normalized measurement error estimates upon which the bad data identification method is based. Sparse vector and partial factor modification techniques are used in the recursive identification calculations. Neither the submatrix of the residual sensitivity matrix, W/sub ss/, nor state reestimation is needed in the whole identification process. Digital tests on various power systems, including a 171 bus real system, are done to show the validity and efficiency of the proposed bad data identification method. >

Power system state estimation is heavily subjected to measurement error, which comes from the noise of measuring instruments, communication noise, and some unclear randomness. Traditional weighted least square (WLS), as the most universal state estimation method, attempts to minimize the residual between measurements and the estimation of measured variables, but it is unable to handle the measurement error. To solve this problem, based on random matrix theory, this paper proposes a data-driven approach to clean measurement error in matrix-level. Our method significantly reduces the negative effect of measurement error, and conducts a two-stage state estimation scheme combined with WLS. In this method, a Hermitian matrix is constructed to establish an invertible relationship between the eigenvalues of measurements and their covariance matrix. Random matrix tools, combined with an optimization scheme, are used to clean measurement error by shrinking the eigenvalues of the covariance matrix. With great robustness and generality, our approach is particularly suitable for large interconnected power grids. Our method has been numerically evaluated using different testing systems, multiple models of measured noise and matrix size ratios.

Power system static state estimation based upon nodal voltages

This paper presents a review on major blackouts occurred in power grid across the world and importance for the need of state estimation (SE) solution. This work introduces the applications of phasor measurement units (PMU) to reduce the occurrence of blackouts in power system. The blackouts in power system can occur due to overload, light load conditions, heavy storms or due to line outages. The cascade failure due to line outages, i.e., measured as N-1 outages can lead to power system blackout. The necessity of power system state estimation (PSSE) solution for protection in view of blackouts is discussed in this paper. A Generalized Linear State Estimation (GLSE) method is proposed to obtain robust and accurate states of power system network. A matrix integrating phasor and conventional measurements are formulated to obtain accurate states. IEEE transmission bus network such as 14-bus network is considered as test case for proposed GLSE programming in MATLAB. The accurate states are computed and compared with standard weighted least squares (WLS) method to show its effectiveness.

One of the important applications of synchrophasor technology is in state estimation. The purpose of this chapter is to illuminate several of the contemporary estimation techniques that are either in use or will be in use across the industry in the very near term. The role of state estimation techniques play in the analytics pipeline as well as an introduction to network observability and topology processing in the context of a completely synchrophasor based measurement set are presented. This is followed by a presentation of both a linear weighted least square (WLS) and a robust linear least absolute value (LAV) power system state estimator in both a positive sequence and a three-phase formulation. Following this, several applications that employ estimation techniques and/or estimation results were presented including dynamic load modeling, exciter failure detection, and symmetrical component calculation.

Power system state estimation is classically formulated as a weighted least squares (WLS) optimization problem over the real domain, with the state variables being the voltage magnitudes and angles or the voltage real and imaginary parts. Although the voltages in ac networks are complex variables, a solution in the complex domain has not been previously reported; this is because a real function of complex variables is nonanalytic in its arguments, i.e., the Taylor series expansion in its arguments alone does not exist. By making use of the Wirtinger calculus, this paper presents a new implementation of the WLS state estimation problem in complex variables. The partial derivatives employed in the method give rise to a structure that is very simple to implement. The method can straightforwardly handle both legacy and phasor measurements; particularly, the processing of phasor current measurements does not require any special provisions, unlike previously reported hybrid state estimator implementations over the real domain. Numerical results are reported on networks with up to 9241 nodes, and they demonstrate that the accuracy of the complex variable implementation is competitive with that of the real variable constrained hybrid state estimator, while being significantly faster.

This chapter deals with the power flow problem. The power flow algorithms include the Newton-Raphson method in both polar and rectangular forms, the Gauss-Seidel method, the DC power flow method, and all kinds of decoupled power flow methods such as fast decoupled power flow, simplified BX and XB methods, as well as decoupled power flow without major approximation. Power system state estimation derives a real-time model through the received data from a redundant measurement set. Different kinds of methods about state estimation are introduced in the chapter. Among them, the weighted least squares (WLS) state estimation methods are widely used. There are three most commonly used measurement types in power system state estimation. They are the bus power injections, the line power flows, and the bus voltage magnitudes.

The amount of data available has grown rapidly in recently years, not least in the context of Industrialization 4.0 and the advent of Cyber Physical Systems (CPS) and IoT devices. Thus, Machine Learning and Big Data analysis are being taken seriously as promising solutions to cope with the challenges of exponentially growing data. A widely known and widely used approach to quantify the relationship between a dependent variable and multiple numerical predictors is the Generalized Linear Model (GLM). In this paper, we introduce an approach to accelerate the GLM's fitting algorithm. Our approach is involved in two steps. First, reimplement GLM fitting algorithm which applies Normal Equation [1] method when solving the Linear Least Squares Equation. Then, we port the implemented GLM fitting to be executable with GPGPU. When Normal Equation method is applied to tall-skinny data, which is typically found in log data of CPS, solving the post Linear Least Squares equation becomes trivial, and the computational burden is transferred from solving that equation to matrix multiplication which can be parallelized in a straightforward manner. In an experiment employing actual user log access data, the Normal Equation method was executed 1.9 times faster when being executed in CPU, combined with 16.8-fold acceleration by GPGPU, leading to being 31.3 times faster overall.

Guest Editorial: Situational awareness of integrated energy systems

State estimation in power systems is utilized to provide an ongoing data set for controlling and observing power systems' performance. There are a few traditional methods like Weighted Least Square (WLS), Weighted Least Average Value (WLAV), and settled utilizing iterative techniques, for example, Gauss-Newton methods. However, these techniques are sensitive to the operating conditions of power systems and uncertainties associated with Renewable Energy Systems (RESs). These techniques yield poor results in the presence of missing data arising out of unreliable communication networks and false data injection, as in the case of a cyber-attack. This paper proposes Bi-directional LSTM (BiLSTM) based Machine Learning method to forecast the state values of power systems. The BiLSTM model utilizes forward and backward layers, having bidirectional memory, making it capable of estimating both past and future hidden layers data. This paper investigates the performance of the proposed method studied on different test system datasets. The efficacy of the proposed method is investigated by comparing the obtained results with other Machine Learning techniques results in the literature. Further, the BiLSTM model's robustness for state estimation is analyzed in the presence of Gaussian noise and missing data points.

State estimation of power systems containing facts controllers

For analysis, operation and planning of a power system, state estimation and power flow analysis are important tools. Accurate state estimation enables active monitoring of power systems. The state estimation with minimum data is important for reducing the complexity in the state estimation process. In this paper, a state estimation based on the Weighted Least Squares (WLS) method with minimum data (12 measurement) is considered. The state estimation is done with different measurement data that is 62 measurement data, 18 measurement data and 12 measurement data for 6 bus test system. A comparison between these different measurements data is also analyzed in terms of the state estimated.

This paper deals with a simple technique for measurement placement method of power system state estimation. The minimum condition number of the measurement matrix is used as the criteria in conjunction with sequential elimination to generalize the measurement placement. The singular value decomposition (SVD) approach will be used to solve the state estimation. The simulation study is performed on the IEEE 14-bus test system by linear weighted least square (WLS). It is found that, this algorithm can give a solution of measurement placement of injection current and voltage that make the power system observable.