2,971 research outputs found
Intelligent power system operation in an uncertain environment
This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv
Optimal excitation controllers, and location and sizing of energy storage for all-electric ship power system
The Navy\u27s future all-electric ship power system is based on the integrated power system (IPS) architecture consisting of power generation, propulsion systems, hydrodynamics, and DC zonal electric distribution system (DC-ZEDS). To improve the power quality, optimal excitation systems, and optimal location and sizing of energy storage modules (ESMs) are studied. In this dissertation, clonal selection algorithm (CSA) based controller design is firstly introduced. CSA based controller design shows better exploitation ability with relatively long search time when compared to a particle swarm optimization (PSO) based design. Furthermore, \u27optimal\u27 small population PSO (SPPSO) based excitation controller is introduced. Parameter sensitivity analysis shows that the parameters of SPPSO for regeneration can be fined tuned to achieve fast optimal controller design, and thus exploiting SPPSO features for problem of particles get trapped in local minima and long search time. Furthermore, artificial immune system based concepts are used to develop adaptive and coordinated excitation controllers for generators on ship IPS. The computational approaches for excitation controller designs have been implemented on digital signal processors interfaced to an actual laboratory synchronous machine, and to multimachine electric ship power systems simulated on a real-time digital simulator. Finally, an approach to evaluate ESM location and sizing is proposed using three metrics: quality of service, survivability and cost. Multiple objective particle swarm optimization (MOPSO) is used to optimize these metrics and provide Pareto fronts for optimal ESM location and sizing --Abstract, page iv
Fast Damage Recovery in Robotics with the T-Resilience Algorithm
Damage recovery is critical for autonomous robots that need to operate for a
long time without assistance. Most current methods are complex and costly
because they require anticipating each potential damage in order to have a
contingency plan ready. As an alternative, we introduce the T-resilience
algorithm, a new algorithm that allows robots to quickly and autonomously
discover compensatory behaviors in unanticipated situations. This algorithm
equips the robot with a self-model and discovers new behaviors by learning to
avoid those that perform differently in the self-model and in reality. Our
algorithm thus does not identify the damaged parts but it implicitly searches
for efficient behaviors that do not use them. We evaluate the T-Resilience
algorithm on a hexapod robot that needs to adapt to leg removal, broken legs
and motor failures; we compare it to stochastic local search, policy gradient
and the self-modeling algorithm proposed by Bongard et al. The behavior of the
robot is assessed on-board thanks to a RGB-D sensor and a SLAM algorithm. Using
only 25 tests on the robot and an overall running time of 20 minutes,
T-Resilience consistently leads to substantially better results than the other
approaches
A GPU-based multi-criteria optimization algorithm for HDR brachytherapy
Currently in HDR brachytherapy planning, a manual fine-tuning of an objective
function is necessary to obtain case-specific valid plans. This study intends
to facilitate this process by proposing a patient-specific inverse planning
algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization
(gMCO).
Two GPU-based optimization engines including simulated annealing (gSA) and a
quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in
parallel. After evaluating the equivalence and the computation performance of
these two optimization engines, one preferred optimization engine was selected
for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR
cases were divided into validation set (100) and test set (462). In the
validation set, the number of Pareto optimal plans to achieve the best plan
quality was determined for the gMCO algorithm. In the test set, gMCO plans were
compared with the physician-approved clinical plans.
Over 462 cases, the number of clinically valid plans was 428 (92.6%) for
clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with
target V100 coverage greater than 95% was 288 (62.3%) for clinical plans and
414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO
algorithm to generate 1000 Pareto optimal plans.
In conclusion, gL-BFGS is able to compute thousands of SA equivalent
treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast
and robust multi-criteria optimization algorithm was implemented for HDR
prostate brachytherapy. A large-scale comparison against physician approved
clinical plans showed that treatment plan quality could be improved and
planning time could be significantly reduced with the proposed gMCO algorithm.Comment: 18 pages, 7 figure
- âŠ