Yuzhang Lin

Strategic Use of Synchronized Phasor Measurements to Improve Network Parameter Error Detection

The Normalized Lagrange Multiplier (NLM) test is generally effective for network parameter error identification, but suffers occasional detection failures even in the presence of substantial parameter errors. This paper exploits synchronized phasor measurements to address this issue. The parameters whose errors may go undetected are identified, and an index is defined to quantify the power of the NLM test for different parameters. Additional indices to gauge the impact of different parameter errors on the state estimation solution are also derived. Finally, a strategy for using phasor measurement units (PMUs) to avoid parameter error detection failures is presented by formulating an optimization problem using these derived indices. Simulation results are provided to illustrate the benefits brought by the strategic use of PMUs, and to validate the proposed problem formulation and solution.

A New Framework for Detection and Identification of Network Parameter Errors

Normalized Lagrange multiplier test has been shown to be very effective for network parameter error identification, but its validation has so far been solely based on extensive simulations. This paper presents a new framework by which: 1) the normalized Lagrange multiplier test is re-formulated from the perspective of hypothesis testing, enabling proper handling of missing bad parameter cases; 2) formal proofs are given for the combined utilization of normalized Lagrange multiplier test and normalized residual test for simultaneous handling of measurement and parameter errors; and 3) the concepts of detectability and identifiability for measurement errors are extended to parameter errors, and a systematic approach for identifying critical parameters and critical

Highly Efficient Implementation for Parameter Error Identification Method Exploiting Sparsity

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Closure to Discussion on “A New Framework for Detection and Identification of Network Parameter Errors”

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Enhancing Network Parameter Error Detection and Correction via Multiple Measurement Scans

Accuracy of the network parameters has a strong influence on the results of power system state estimation. It has been shown earlier that normalized Lagrange multipliers can be used as a systematic way for identifying errors in network parameters. However, this approach carries a rather heavy computational burden limiting its practical utilization to small-size systems. In this paper, a computationally efficient algorithm is proposed to address this limitation. The idea is to derive and compute only the necessary subset of the gain matrix and covariance matrix, thus avoiding the computation and storage of large dense matrices. The proposed efficient procedure can be applied either to the single-scan or multiple-scan schemes with equal ease. Test results confirm that the improvements in computational speed and memory requirements brought by the proposed algorithm are quite remarkable. The proposed implementation of the normalized Lagrange multipliers method is tested using a large utility power system. The effectiveness and limitations of the single-scan scheme, and the improvements brought by incorporating multiple measurement scans, are discussed in detail.

Fast Correction of Network Parameter Errors

We appreciate the discussers’1 interest in our work.2 The concerns the discusser raised mainly involved the basic idea and formulation of the Normalized Lagrange Multiplier (NLM) test, which was first proposed in [1] , and was only briefly reviewed in this paper. In order to better clarify, the logic and procedure of this part will be presented in greater detail below.

Efficient identification of multiple bad data

Although the normalized Lagrange multiplier (NLM) method has been shown to be very effective for network parameter error identification, errors in parameters corresponding to insensitive NLMs still remain difficult to detect and correct. This paper proposes an enhanced method for detecting and correcting network parameter errors based on multiple measurement scans. The method is developed by first deriving the relationship between parameter errors and the associated Langrage multipliers in state estimation. This is then used to clarify the reason behind the sensitivity issue of NLMs and the improvements made by performing multiple scans. An approach for estimating the necessary number of scans for satisfying various detection requirements is also proposed. Moreover, a local parameter error correction procedure based on multiple scans is presented, with detailed discussion of the local network selection and the number of required measurement scans. Simulation results in a very large utility system in North America illustrate the effectiveness of the analysis and methods proposed in this paper.

Robust transformer tap estimation

In this letter, a fast network parameter error correction scheme is proposed based on recent findings on parameter error identification. Compared to the widely applied augmented state estimation approach, it is computationally very efficient and numerically stable, and requires very modest coding effort. Simulation results in the New England power system show that it produces reliable results in the presence of both strongly correlated errors and Gaussian measurement noise.

Strategic use of PMUs to improve network parameter error detection

This paper describes a computationally more efficient alternative to the bad data identification procedure that is known as the largest normalized residual (LNR) test. LNR test is a sequential procedure where measurements suspected to carry gross errors are identified and removed from the measurement set one at a time. Thus, the computational burden of the test increases proportional to the existing bad data, making it prohibitively inefficient for systems commonly containing large numbers of measurements with gross errors. In this paper, an improved version of this approach is proposed where the number of identification and correction cycles needed to process a large number of bad data points is significantly reduced. Thus efficient application of the LNR test in very large practical power systems is facilitated.

Improving network parameter error detection via multiple measurement scans

The methods currently used for transformer tap estimation are not robust against measurement errors, while the well-documented Least Absolute Value (LAV) State Estimator (SE) is not robust against transformer tap errors. This paper addresses these shortcomings by introducing the so-called Sparse Extended Least Absolute Value (SELAV) SE. By strategically modifying the formulation of LAV SE, the “sparse” nature of l1 optimization is exploited for the tap estimation problem. The transformer tap positions can be reliably estimated, while the simultaneously produced state estimates (bus voltage angles and magnitudes) remain robust against tap errors. Case studies done using IEEE 57-bus test system are provided to illustrate the effectiveness of the proposed approach.