Automated Magnetic Field Scanning System

One of Jefferson Laboratory’s research areas is in Superconducting Radio Frequency (SRF) science and technology. SRF cavities are tested in the Vertical Testing Area (VTA) at Jefferson Laboratory, within a series of large cylindrical dewars. The measured quality factor (Q factor) of the SRF cavity is directly influenced by any existing magnetic field. Because the VTA previously housed a cyclotron, all the rebars within the building have residual magnetic fields emanating from them. This magnetic field effect of the building renders the measurements of Q factor on the devices inaccurate and the testing data unreliable. A magnetic field scanning system must be employed to accurately map the magnetic field within the testing dewar so that an existing set of current-carrying coils installed around the dewar can be used properly for cancellation of the residual magnetic fields. This process will ensure the initial testing conditions are free of any unwanted magnetic fields that could cause unreliable testing data. The proposed system will scan the residual magnetic field inside vertical dewars of varying sizes (16”- 34” diameter by 72”- 132” depth) in three dimensions and log data for later use, as well as display a visual mapping of the data to the operator through LabView. A sensor with a sensitivity of at least 0.1 milligauss will be attached to the bottom of a long pole that will be lowered into the dewar. Translation in the z direction, on the dewar’s central axis, will be achieved by using a pair of stepper motors controlling a rack and pinion set up on the center pole. To achieve incremental mapping in the x-y plane, an arm will be attached to the bottom of the pole that will house additional sensors. The platform holding the stepper motors will turn on a dial with degree measurements, allowing for rotational movement of the entire center pole and arm. By calculating the x-y values for each sensor on the arm at that set degree amount, mapping of set increments in the x-y plane can be achieved.https://scholarscompass.vcu.edu/capstone/1036/thumbnail.jp

AC-feasible Local Flexibility Market with Continuous Trading

This paper proposes a novel continuous Local Flexibility Market where active power flexibility located in the distribution system can be traded. The market design engages the Market Operator, the Distribution System Operator and Market Participants with dispatchable assets. The proposed market operates in a single distribution system and considers network constraints via AC network sensitivities, calculated at an initial network operating point. Trading is possible when AC network constraints are respected and when anticipated network violations are alleviated or resolved. The implementation allows for partial bid matching and is computationally light, therefore, suitable for continuous trading applications. The proposed design is thoroughly described and is demonstrated in a test distribution system. It is shown that active power trading in the proposed market design can lead to resolution of line overloads.Comment: In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canad

Sensor Placement for Online Fault Diagnosis

Fault diagnosis is the problem of determining a set of faulty system components that explain discrepancies between observed and expected behavior. Due to the intrinsic relation between observations and sensors placed on a system, sensors' fault diagnosis and placement are mutually dependent. Consequently, it is imperative to solve the fault diagnosis and sensor placement problems jointly. One approach to modeling systems for fault diagnosis uses answer set programming (ASP). We present a model-based approach to sensor placement for active diagnosis using ASP, where the secondary objective is to reduce the number of sensors used. The proposed method finds locations for system sensors with around 500 components in a few minutes. To address larger systems, we propose a notion of modularity such that it is possible to treat each module as a separate system and solve the sensor placement problem for each module independently. Additionally, we provide a fixpoint algorithm for determining the modules of a system