A fast algorithm for the weighted least absolute value state estimation

The weighted least absolute value minimization method is used to solve the power system state estimation problem. The problem can be shown to be equivalent to a linear programming program and hence can be solved using the well-known Simplex method. However, a direct adaptation of the Simplex method results in an inefficient code. A fast algorithm originally suggested by I. Barrodale and F.D.K. Roberts (1973) is further improved and applied to the power system state estimation problem. The use of the algorithm resulted in considerable savings in computation times, as demonstrated by the numerical examples given for various test systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper describes an approach by which the status of circuit breakers which are not directly monitored can be determined. The approach combines a previously proposed circuit breaker model with the least absolute value (LAV) state estimation method for this purpose. The ability of the LAV estimators to reject inconsistent measurements is exploited in order to decide whether a circuit breaker is closed or open. The formulation of the LAV state estimation is accordingly modified to include the circuit breaker models and associated constraints. The proposed approach is implemented and tested. Simulation results for single and multiple circuit breaker configurations are given. Robustness of the approach against analog measurement errors is also demonstrated by examples. >

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 this paper, we model the generalized power system state estimation problem as a hybrid estimation problem in which the network configuration and voltage phasor states are treated as discrete and continuous states, respectively. We propose the Multiple model Adaptive Power system State Estimator (MAPSE), which implements a multiple model adaptive estimator with a bank of Unscented Kalman Filters (UKFs) to provide a running estimate of the voltage phasor states and a probability distribution for the candidate network configurations. The MAPSE updates the network configuration probability distribution by applying a Bayesian recursion to the measurement residuals from each of the UKFs at each timestep. Simulation on a modified version of the 33 node distribution network demonstrates successful network configuration detection and accurate voltage phasor state estimation.

Nowadays the operation of electric power systems (EPS) requires that measures against cyber attacks be taken not only to protect the process equipment but also to upgrade the software involved in the power system control. State estimation (SE) is the most important function providing the EPS monitoring and control with reliable quality information. A state estimation tool acts as a barrier to the corruption of data on current operating conditions of the EPS in the control problem, including the data corruption caused by cyber attacks (CA) on data collection and processing systems. The paper presents a new decomposition approach to solve the SE problem under cyber attack: the calculation network is divided into areas that have a SCADA server and a Wide Area Measurement System (WAMS) concentrator at the center of every area. To detect the CA event and its localization, we propose to use a combination of a-priori and a-posteriori bad data detection (BDD).

This paper addresses the power system state estimation problem considering the correlations among instrument transformer signals. The correlations among processed measurements are estimated using the Unscented Transformation given the correlation and basic statistical information of internal measurements. In addition, the paper analyzes the impact of these correlations on the estimated states using the IEEE 118-bus test system under various operating conditions and measurement configurations.

The paper presents a study of the convergence characteristics and an assessment of the performance of the algorithm used to solve power system state estimation problems. The algorithm is studied to determine and compare the convergence patterns using several measurement sets consisting of line flows, bus injections, voltage magnitudes and line currents. The algorithm was tested on different IEEE test systems. Results related to the test with IEEE 14 bus bar system are presented.

Pattern analysis in power system state estimation

Constrained Power System State Estimation

A new algorithm is described to solve the static, time-invariant weighted least-square state-estimation problem for large-scale electric power systems. The solution is obtained through P-θ and Q-V decoupling and alternately iterating the active and reactive equations using fixed, simplified submatrices of the information matrix. Thus, a much faster algorithm is obtained yielding the exact solution and requiring little computer storage. The new method is compared with the basic Weighted-least-square and the Line-Only algorithms on a practical HV network.

The inclusion of HVDC links into power-system state estimation is described. A quite general, 11-state HVDC link model is chosen, and 25 possible HVDC link measurements are identified, as well as seven pseudo measurements which describe convertor-terminal conditions. It is shown that the AC terminal injections, together with the pseudo measurements, constitute a minimal measurement set for HVDC link observabiity. Performance of the AC-DC state estimator is investigated on a multi-AC-DC test system having 31 states and 59 measurements. Convergence in 4-5 iterations to a tolerance less than 0.00001 p.u. is obtained, provided AC slack (reference) bus bars are retained. Geographic partitioning of HVDC links is considered. This permits separate state estimation for each AC system and associated HVDC convertor terminal. Last, fast decoupled state-estimation schemes are developed and tested; only the AC portion of the multi-ACHVDC system can be fast decoupled. Inclusion of HVDC links in fast decoupled AC state estimation causes no increase in the number of iterations and only a small increase in computation time

This paper investigates the state estimation problem of power systems. A novel, fast and accurate state estimation algorithm is presented to solve this problem based on the one-dimensional denoising autoencoder and deep support vector machine (1D DA–DSVM). Besides, for further reducing the computation burden, a partitioning method is presented to divide the power system into several sub-networks and the proposed algorithm can be applied to each sub-network. A hybrid computing architecture of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) is employed in the overall state estimation, in which the GPU is used to estimate each sub-network and the CPU is used to integrate all the calculation results and output the state estimate. Simulation results show that the proposed method can effectively improve the accuracy and computational efficiency of the state estimation of power systems.

Decoupled State-Estimation in Energy Control Centres

Guest Editorial: Situational awareness of integrated energy systems

The proper choice of stochastic model is of great importance to global navigation satellite system (GNSS) data processing. Whereas extensive investigations into stochastic modeling are mainly based on the relative (or differential) method employing zero and/or short baselines, this work proposes an absolute method that relies upon a stand-alone receiver and works by applying the least-squares variance component estimation to the geometry-free functional model, thus facilitating the characterization of stochastic properties of multi-frequency GNSS observables at the undifferenced level. In developing the absolute method, special care has been taken of the code multipath effects by introducing ambiguity-like parameters to the code observation equations. By means of both the relative and absolute methods, we characterize the precision, cross and time correlation of the code and phase observables of two newly emerging constellations, namely the Chinese BDS and the European Galileo, collected by a variety of receivers of different types at multiple frequencies. Our first finding is that so far as the precision is concerned, the absolute method yields nearly the same numerical values as those derived by the zero-baseline-based relative method. However, the two methods give contradictory results with regard to the cross correlation, which is found (not) to occur between BDS phase observables when use has been made of the relative (absolute) method. Our explanation to this discrepancy is that the cross correlation found in the relative method originates from the parts (antenna, cable, low noise amplifier) shared by two receivers creating a zero baseline. The time correlation is only of significance when the multipath effects are present, as is the case with the short-baseline-based relative method; this correlation turns out to be largely weaker (or ideally absent) in the absolute (or zero-baseline-based relative) method. Moreover, with the absolute method, the stochastic properties determined for two receivers of the same type but subject to different multipath effects are virtually the same. We take this as a convincing evidence that the absolute method is robust against multipath effects. Hence, the absolute method proposed in the present work represents a promising complement to the relative method and appears to be particularly beneficial to GNSS positioning, navigation and timing technologies based on the undifferenced observables, typically the precise point positioning.