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This paper presents a clustering technique to rank the dominant modes of low frequency oscillations in power systems under model order uncertainty of the signal. The rotational invariance property of the signal subspace is utilized to obtain the modes of oscillations. Rotational invariance of the signal under multiple shifts is considered to obtain the preliminary estimates of the modes. Subsequently, the modes are clustered to obtain the dominant modes that belong to the signal subspace. A signal subspace participation index (SSPI) is used to rank the modes according to their dominance in signal subspace. Thereafter, the convex combination of the preliminary modes, estimated from the multiple shifts of the signal subspace, is used to obtain the final mode estimates. The simulation results are presented for signal generated using the modes obtained from a two-area system. Additionally, the methodology is also tested on field data obtained from the PMU installed at a generating station in India. [ABSTRACT FROM AUTHOR]

Increase penetration of intermittent renewable sources causes reduction and time variation in system inertia, forcing dynamic changes in system coherency status. Besides increased penetration levels, other factors like intermittent generation and change in distribution of renewables also influence the system coherency that needs to be addressed. This paper examines the impact of variability of renewables on system coherency and proposes a coherency identification method based on spectrum similarity approach. The proposed method uses discrete cosine Stockwell transform to derive multiple time-frequency (TF) features that captures pair-wise similarity between synchrophasor frequency signals. Thereafter, mean shift spectral clustering is used to cluster the buses with most homogeneous TF spectrum. The proposed method is validated with renewable generation in the IEEE-39 bus system and a real transmission system of India grid. [ABSTRACT FROM AUTHOR]

Common information model (CIM) in vogue represents transmission and distribution components, and market operations in the perspective of electrical system, as unified modeling language (UML) objects. UML is object centric. CIM can offer interoperability and facilitate model exchange for large systems and is being adopted by many utilities. However, dealing effectively with multiple business processes is the backbone of the restructured power systems. Since CIM lacks chronological process sense, this has to be superimposed on CIM models, which is not easy due to inherent limitations of UML. To overcome this, this paper proposes to utilize business process modeling notation (BPMN) standards as a kind of pre-processor. BPMN is used to represent process centric interoperability requirements as business objects. UML object representation in CIM model is derived subsequently from it. The proposal is illustrated with an application use case—deriving CIM model of day-ahead market (DAM) transaction of distribution company, from its business process representation. Semantic model of DAM transaction using CIM IEC 62325 (Markets) and extensions required are presented with a sample Indian system. DAM models in BPMN and CIM can be utilized to build an efficient software application. [ABSTRACT FROM PUBLISHER]