Galvanic Corrosion of High-Temperature Low-Sag (HTLS) High Voltage Conductors: New Materials - New Challenges

High-Temperature Low-Sag (HTLS) high voltage overhead conductors offer higher operating temperatures, reduced resistance and less sag than conventional designs. With up to twice the current capacity for the same diameter conductor, they may help ease the power shortage in the constantly increasing electricity demand, but there might be some concerns about their corrosion resistance. These new conductors use materials relatively new to the power industry, such as advanced carbon fiber polymer matrix composites and unique metal matrix composites/nano-composites predominantly used in aerospace industries. This study has made an initial assessment of potential galvanic corrosion problems in three very different HTLS designs: ACCC (Aluminum Conductor Composite Core), ACCR (Aluminum Conductor Composite Reinforced) and ACSS (Aluminum Conductor Steel Supported). In particular the ACCC design was evaluated for its resistance to corrosion and compared to the other designs. The study concludes that all three designs can develop galvanic corrosion under certain circumstances. While the results are not sufficient to make service life predictions of any of the tested conductors, they point out the necessity of thorough corrosion testing of all new conductor designs.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.