Andre Aboulian’s research while affiliated with Massachusetts Institute of Technology and other places

To improve the energy efficiencies of building cooling systems, manufacturers are increasingly utilizing variable speed drive (VSD) motors in system components, e.g. compressors and condensers. While these technologies can provide significant energy savings, these benefits are only realized if these components operate as intended and under proper control. Undetected faults can foil efficiency gains. As such, it’s imperative to monitor cooling system performance to both identify faulty conditions and to properly inform building or multi-building models used for predictive control and energy management. This paper presents nonintrusive load monitoring (NILM) based “mapping” techniques for tracking the performance of a building’s central air conditioning from smart electrical meter or energy monitor data. Using a multivariate linear model, a first mapping disaggregates the air conditioner’s power draw from that of the total building by exploiting the correlations between the building’s line-current harmonics and the power consumption of the air conditioner’s VSD motors. A second mapping then estimates the air conditioner’s heat rejection performance using as inputs the estimated power draw of the first mapping, the building’s zonal temperature, and the outside environmental temperature. The usefulness of these mapping techniques are demonstrated using data collected from a research facility building on the Masdar City Campus of Khalifa University. The mapping techniques combine to provide accurate estimates of the building’s air conditioning performance when operating under normal conditions. These estimates could thus be used as feedback in building energy management controllers and can provide a performance baseline for detection of air conditioner underperformance.

As crew sizes aboard maritime vessels shrink in efforts to reduce operational costs, ship operators increasingly rely on advanced monitoring systems to ensure proper operation of shipboard equipment. The nonintrusive load monitor (NILM) is an inexpensive, robust, and easy to install system useful for this task. NILMs measure power data at centralized locations in ship electric grids and disaggregate power draws of individual electric loads. This data contains information related to the health of shipboard equipment. We present a NILM-based framework for performing fault detection and isolation (FDI), with a particular emphasis on systems employing closed-loop hysteresis control. Such controllers can mask component faults, eventually leading to damaging system failure. The NILM system uses a neural network (NN) for load disaggregation and calculates operational metrics related to machinery health. We demonstrate the framework’s effectiveness using data collected from two NILMs installed aboard a U.S. Coast Guard (USCG) cutter. The NILMs accurately disaggregate loads, and the diagnostic metrics provide easy distinction of several faults in the gray water disposal system. Early detection of such faults prevents costly wear and avoids catastrophic failures.

Non-contact power monitors use electromagnetic field sensors to measure voltage and current in multiphase power lines without any ohmic contact or geometric isolation of the conductors. The complex field geometry requires these sensors to be calibrated before use. Existing work has focused on manual calibration procedures requiring a service interruption, additional loads, or other intrusive techniques. This paper introduces an autonomous calibration technique that can be used to bootstrap additional non-contact power monitors from an existing power monitor located elsewhere in the distribution network. This provides the flexibility to “zoom-in” with downstream monitors or “zoom-out” with upstream monitors and efficiently target the loads of interest. Laboratory benchmarks and installations on US Coast Guard and Naval vessels show that autonomous calibration is accurate and robust with non-contact monitors reading within 1% of commercial power meters.

Non-intrusive load monitoring (NILM) uses electrical measurements taken at a centralized point in a network to monitor many loads downstream. This paper introduces NILM Dashboard, a machine intelligence and graphical platform that uses NILM data for real-time electromechanical system diagnostics. The operation of individual loads is disaggregated using signal processing and presented as time-based load activity and statistical indicators. The software allows multiple NILM devices to be networked together to provide information about loads residing on different electrical branches at the same time. A graphical user interface provides analysis tools for energy scorekeeping, detecting fault conditions, and determining operating state. The NILM Dashboard is demonstrated on the power system data from two USCG Cutters.