Abstract
Accurate knowledge of static parameters forms the basis for nearly all applications in the energy management system. This paper proposes an efficient method for the simultaneous identification and correction of multiple network parameter errors based on a linear mixed-effects (LME) model and the generalized least squares (GLS) method. An LME model for parameter error identification is formulated using the residual equations of multiple-snapshot state estimation with equality constraints. The parameter errors are considered as the fixed effects and the measurement errors are considered as the random effects. Then, using the measurement error variances estimated by the LME model, the GLS method is used to estimate the parameter errors along with a hypothesis testing to infer whether each parameter error is zero. The semi-supervised adversarial autoencoder is used for bad data detection in the presence of erroneous parameters and limited labels such that only measurement snapshots that are free of any bad data are used. The proposed methodology is efficient in that the LME model is only used to estimate the variances of the measurement errors using a small number of measurement snapshots, therefore the huge computation burden needed to solve a large-scale LME model is avoided. In addition, the GLS only involves inversion of low-dimension matrices, which is very efficient even a large number of measurement snapshots are used. Thorough tests of the proposed methodology on a large number of scenarios are provided to show the effectiveness of the proposed methodology with promising results.
Highlights
Correct static parameters of transmission lines and transformers are essential for many applications in the energy management systems of all electric utilities, such as state estimation (SE), power flow, security assessment, etc
Using the measurement error variances (MEVs) estimated by solving the linear mixed-effects (LME) model, the generalized least squares (GLS) method is adopted to estimate the parameter errors along with a hypothesis testing to infer whether each parameter error is zero
The measurements are divided into five groups: real power flow measurements (PF), real power injection measurements (PI), reactive power flow measurements (QF), reactive power injection measurements (QI), voltage magnitude measurements (VM), and different levels of measurement noise are used for each group
Summary
Correct static parameters of transmission lines and transformers are essential for many applications in the energy management systems of all electric utilities, such as state estimation (SE), power flow, security assessment, etc. It has been reported by the federal energy regulatory commission that finding a good solution for AC optimal power flow (OPF) could potentially save tens of billions of dollars annually (Cain et al, 2012). The OPF results may be hugely distorted and may even be infeasible due to potential errors in network parameters. It is very important to identify and correct erroneous parameters in power networks
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