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This paper proposes a parametric, statistical model for adapting the load-frequency-control (LFC) target values that are used for a performance evaluation. These target values should be determined in such a way that the system frequency will be bounded within the target range. One way of bounding the system frequency is to bound the variations of the area-control error (ACE) by increasing the control activations. On the other hand, the frequency can also be bounded by mutual coordination of the control among the control areas, thus affecting the covariances between the ACEs. Existing models for determining the LFC target values do not consider the covariances between the ACEs. The proposed model incorporates, for each area individually, the estimated value of the covariances between the ACEs and is inherently frequency dependent. In this way, appropriate shares are obtained for the target ACE ranges among the control areas. The extensive field measurements in the discussed control area confirm the advantages of the proposed model over existing models for a monthly and online estimation of the LFC target values. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized state-estimation approach that relies on an elaborated instance of the Lagrangian relaxation decomposition technique. The proposed algorithm does not require a central coordinator but just to moderate interchanges of information among neighboring regions, and exploits the structure of the problem to achieve a fast and accurate convergence. Additionally, a decentralized bad measurement identification procedure is developed, which is efficient and robust in terms of identifying bad measurements within regions and in border tie-lines. The accuracy and efficiency of the proposed procedures are assessed by a large number of simulations, which allows drawing statistically sound conclusions. [ABSTRACT FROM PUBLISHER]

This paper proposes a technique for partitioning a large power system into a number of coherent electric areas for possible application to dynamic vulnerability assessment. The coherency concept and a fuzzy clustering algorithm grouping of buses are combined to achieve this goal. The clusters are obtained by selecting representative buses from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. The initialization problem of the conventional Fuzzy c-means algorithm, which usually leads to multiple solutions, is suitably tackled by incorporating the maximum-dissimilarity based sequential Phasor Measurement Unit (PMU) placement technique. Results of bus grouping for two test systems of three and nine areas demonstrate the potential of the approach. It is observed that such an approach to bus grouping results in a PMU configuration with minimum number of devices and fast data aggregation for a wide-area measurement system. [ABSTRACT FROM AUTHOR]

The state estimation algorithm estimates the values of the state variables based on the measurement model described as the system of equations. Prior to applying the state estimation algorithm, the existence and uniqueness of the solution of the underlying system of equations is determined through the observability analysis. If a unique solution does not exist, the observability analysis defines observable islands and further defines an additional set of equations (measurements) needed to determine a unique solution. For the first time, we utilise factor graphs and Gaussian belief propagation algorithm to define a novel observability analysis approach. The observable islands and placement of measurements to restore observability are identified by following the evolution of variances across the iterations of the Gaussian belief propagation algorithm over the factor graph. Due to sparsity of the underlying power network, the resulting method has the linear computational complexity (assuming a constant number of iterations) making it particularly suitable for solving large-scale systems. The method can be flexibly matched to distributed computational resources, allowing for determination of observable islands and observability restoration in a distributed fashion. Finally, we discuss performances of the proposed observability analysis using power systems whose size ranges between 1354 and 70 000 buses. [ABSTRACT FROM AUTHOR]