Evaluation of a Maximum Likelihood Estimator for the Identification of Power Systems Oscillation Modes
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Evaluation of a Maximum Likelihood Estimator for the Identification of Power Systems Oscillation Modes João C. Y. Menezes1 · Aguinaldo S. e Silva1 Received: 24 October 2017 / Revised: 6 February 2018 / Accepted: 23 July 2018 / Published online: 13 August 2018 © Brazilian Society for Automatics–SBA 2018
Abstract In this paper, a maximum likelihood (ML) estimator is applied for the estimation of power system oscillation modes. A regularization term is used in order to improve the estimation. An index is proposed to rank the modes and separate spurious from real modes. The ML estimator is compared with Prony method, and its advantages and limitations are discussed. Both methods are applied to synthetic systems and to real Phasor Measurement Unit (PMU) data acquired from the Brazilian Interconnected Power System (BIPS). The results show that the proposed maximum likelihood estimator is useful to complement and validate the results obtained by Prony analysis. Keywords Power systems · Oscillation modes · Maximum likelihood estimation · Prony method
1 Introduction Power system controllers, such as Power System Stabilizers (PSSs), keep the small-signal stability, ensuring that oscillation modes are well damped. Controllers tuning is usually performed using a model obtained by the linearization of the system equations around one or a set of operating points. Model-based controller design and evaluation are inherently limited, since only a subset of operating conditions is employed, which does not cover the wide range of topologies, load and generation dispatch in which power systems operate. The measurement-based estimation of the oscillation modes allows the evaluation of the performance of power system controllers in a wide range of operating conditions. Wide Area Monitoring System (WAMS), based on the use of synchronized measurements, provides systemwide measurements, allowing the identification of the system oscillation modes. These measurements can be obtained after large disturbances, originating transient (or ringdown) data, or in the normal system operating conditions, generating ambient data (Zhou et al. 2012; Leandro et al. 2015).
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João C. Y. Menezes [email protected] Aguinaldo S. e Silva [email protected]
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Department of Electrical Engineering, Federal University of Santa Catarina, Santa Catarina, Brazil
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Large disturbances excite several oscillation modes, making their identification easier. Therefore, the damping of the oscillations modes and the effect of PSS can be clearly evaluated. The identified oscillation modes can also be used for system-wide model validation (Decker et al. 2010). Prony method has been widely used for the identification of oscillation modes using transient data, although several estimation methods can be applied (Lu et al. 2012; Kamwa et al. 2011; Bronzini et al. 2007; Messina and Vittal 2006). However, it presents several shortcomings specially for noisy signals. Variants were proposed to improve its performance but usually the noise is dealt with by increasing the mo
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