In this paper, a complete cross-correlation-based fault-diagnostic method is proposed for real-time digital-signalprocessor (DSP) applications that cover both the fault-monitoring and decision-making stages. In practice, a motor driven by an inverter or utility line is run at various operating points where the frequency, amplitude, and phase of the fault signatures vary unexpectedly. These changes are considered to be one of the common factors that yield erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deal with the ambiguities of line-current noise or sensor-resolution errors and operating-point-dependent threshold issues. It is theoretically and experimentally verified that a motor fault can be continuously tracked when the sensor errors are within a limited range through the adaptively determined threshold definition of noise conditions. The offline experiments are performed via Matlab using actual line-current data obtained by a data-acquisition system. These results are verified on a DSP-based motor drive in real time where drive sensors and a digital signal processor are employed both for motor-control and fault-diagnostic purposes.

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In this paper, a comprehensive cross correlation-based fault diagnostic method is proposed for real time DSP implementation. It covers both fault monitoring and decision making stages. In practice, a motor driven by an adjustable speed drive is run at various operating points where the frequency, amplitude and phase of the fault signatures varies with time. These dynamic changes are considered as one of the common factor that yields erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deals with the operating point dependent ambiguities and threshold issues. It is theoretically and experimentally verified that the motor fault can continuously be tracked when the operating point changes within a limited range.

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This paper discusses adding a spatial dimension to the design of community microgrid projects in the interest of expanding the existing discourse related to energy performance optimization measures. A multidimensional vision for designing community microgrids with higher energy performance is considered, leveraging urban form (superstructure) to understand how it impacts the performance of the system’s distributed energy resources and loads (infrastructure). This vision engages the design sector in the technical conversation of developing community microgrids, leading to energy efficient designs of microgrid-connected communities well before their construction. A new generation of computational modeling and simulation tools that address this interaction are required. In order to position the research, this paper presents a survey of existing software packages, belonging to two distinct categories of modeling, simulation, and evaluation of community microgrids: the energy infrastructure modeling and the urban superstructure energy modeling. Results of this software survey identify a lack in software tools and simulation packages that simultaneously address the necessary interaction between the superstructure and infrastructure of community microgrids, given the importance of its study. Conclusions represent how a proposed experimental software prototype may fill an existing gap in current related software packages.