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BCIT Citations Collection

Developing safe fall strategies for lower limb exoskeletons
Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR) QEII Centre, London, UK, July 17-20, 2017. One of the main challenges in the use of a powered lower limb exoskeleton (LLE) is to ensure that balance is maintained throughout the operation of the device. Since no control strategy has yet been implemented that prevents falls in the case of a loss of balance, head or other serious injuries may occur during independent use of LLEs in the event of a fall. These safety concerns limit LLEs in the community to supervised use only. Using the backward fall as a model, we used optimization techniques to develop safe fall control strategies in order to avoid head impact and mitigate the impact velocity of the hips. From available human biomechanics data, we first developed an optimization methodology to study falls of healthy people. The results showed similar kinematic and dynamic characteristics to findings of previous studies on real-life human falls. Second, we extended the optimization methodology to include characteristics of a hypothetical LLE and to generate optimal joint trajectories and optimal torque profiles for the fall duration. The results revealed that by applying the optimal fall strategy, the severity of a simulated fall was minimized compared to when the device fell with locked joints (i.e., how currently used exoskeletons fall): head impact was avoided and hip impact velocity was reduced by more than 50%., Conference paper, Published.
Maintenance scheduling of Volt-VAR control assets in smart distribution networks using advanced metering infrastructure
This paper investigates a novel approach for maintenance scheduling of volt-VAR control components (VVCCs) of distribution networks with the aid of new generation of volt-VAR optimization (VVO) solutions called quasi-real-time VVO. The new quasi-real-time VVO technique optimizes distribution network using advanced metering infrastructure (AMI) data of each quasi-real-time stage. As this VVO performs automatically and online, it is necessary for VVCCs to undergo maintenance without disturbing VVO performance. Moreover, the lost benefits that could be gained by online VVO have to be minimized. Hence, this paper proposes an AMI-based VVO consisting of a VVO engine and a maintenance scheduling engine (MSE) that operate in tandem to optimize distribution network and find the optimal maintenance scheduling of different VVCCs. To test the accuracy and the applicability of the proposed solution, a 33-node distribution feeder is employed. Furthermore, five different maintenance scenarios are investigated to check the proposed VVO performance. The results prove that the integration of VVO with MSE could be a reliable approach that can solve maintenance scheduling of VVCCs without interrupting and/or resetting VVO., Article, Published
Predictive algorithm for Volt/VAR optimization of distribution networks using Neural Networks
Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering (CCECE2014),May 2014, Toronto, Canada. Smart Grid functions such as Advanced Metering Infrastructure, Pervasive Control and Distribution Management Systems have brought numerous control and optimization opportunities for distribution networks through more accurate and reliable techniques. This paper presents a new predictive approach for Volt/VAr Optimization (VVO) of smart distribution systems using Neural Networks (NN) and Genetic Algorithm (GA). The proposed predictive algorithm is capable of predicting the load profile of target nodes a day ahead by employing the historical metrology data of Smart Meters, It can further perform a comprehensive VVO in order to minimize distribution network loss/operating costs and run Conservation Voltage Reduction (CVR) to conserve more energy. To test the merits of the proposed algorithm, British Columbia Institute of Technology north campus distribution grid is used as research case study., Conference paper, Published.
Real-time co-simulation platform for Smart Grid Volt-VAR Optimization using IEC 61850
This paper presents an implementation of an IEC 61850-based real-time co-simulation platform for verification of the performance of a volt-VAR optimization (VVO) engine for smart distribution networks. The proposed VVO engine is able to minimize grid loss, volt-VAR control asset operational costs, and conservation voltage reduction operational costs through its comprehensive objective functions, weighted by fuzzification using advanced metering infrastructure (AMI) data. The optimization engine receives the AMI data stream through measurement aggregators. Moreover, it sends control commands to volt-VAR control components modeled in real-time digital simulator (RTDS) through DNP.3 protocol. To check the performance and the precision of proposed VVO, a fault scenario is imposed upon the system. IEC 61850 GOOSE messages are generated and sent to change the status of specified breakers, while the VVO engine receives system reconfiguration commands via IEC61850 Manufacturing Message Specification (MMS) protocol. The results of the study on 33-node feeder showed adequate performance of proposed VVO in grid operating scenarios., Article, Published.
Real-time communication platform for Smart Grid adaptive Volt-VAR Optimization of distribution networks
Proceeding of IEEE International Conference on Smart Energy Grid Engineering (SEGE), Aug. 2015, Oshawa, ON, Canada. This paper investigates a real-time communication platform for a Smart Grid adaptive Volt-VAR Optimization (VVO) engine. Novel VVO techniques receive inputs from Advanced Metering Infrastructure (AMI) to dynamically optimize distribution networks. As communication platform design and characteristics affect Smart Grid-based VVO performance in terms of control accuracy and response time, VVO ICT studies is essential for grid planners and/or power utilities. Hence, this paper primarily introduces a real-time co-simulated environment comprised of Smart Grid adaptive VVO engine, RTDS model and system communication platform using DNP3 protocol. This platform is built to test and asses the influence of different components included in Smart Grid monitoring and control system; namely the sensors, measurement units, communication infrastructure on the operation and control of VVO. Moreover, this paper uses a real-time platform to check the robustness of the monitoring and control applications for communication network considerations such as delays and packet loss. Next, this paper investigates how such a platform could look into communication issues while taking system requirements into consideration. A 33-node distribution feeder is employed to check system performance through communication parameters such as throughput and response time., Conference paper, Published.
Treatment of douglas-fir heartwood thermomechanical pulp with laccases
Douglas-fir heartwood thermomechanical pulp was treated with laccase enzymes at 25 and 50°C with and without added oxygen. The treated pulps were cleached with hudrogen peroxide at increasing alkali charges. Laccase treatments without added oxygen increased bleached brightness by 1.5-2.5 pts ISO, and decreased hydrogen peroxide consumption by 15-20%. The enzyme treatments were not enhanced when supplemented with oxygen. When the effectiveness of four different laccase enzymes was compared for the treatment of Douglas-fir heartwood thermomechanical pulp, there were no significant differences found in the performance among the enzymes. Possible explanations for the observed results are given., Peer-reviewed article, Published.