A comprehensive numerical model was developed to address the performance of a permanent magnet direct current (PMDC) motor which is employed as a small-scale three-bladed horizontal axis ocean current turbine. This numerical model development is presented along with a comparison to experimental data to quantify the motor performance. The proposed experimental design is discussed in detail. Due to the nature of the ocean current turbine, it is required to run it first by applying input power, subsequently to be governed by hydrokinetic energy. Thus, a detailed performance of the PMDC motor is essential when it runs as a motor and generator. Based on our preliminary work, the angular speed of the small-scale turbine is less than 500 rpm. Thus, a combination of the PMDC motor and a planetary gearhead is used to fulfill this low-speed requirement. The gearhead is driven in reverse when operating as a generator which leads to poor efficiency. This efficiency is experimentally derived to be 47.8% at maximum speed of 479.4 rpm at 12V.
A comprehensive artificial intelligence-based motor drive was developed to control the performance of a permanent magnet direct current (PMDC) motor employed as a small-scale three-bladed horizontal axis ocean current turbine. Although the conventional controller performs reasonably in a lab environment where non-linear load is absented; however, for towing tank experiments with noisy and potentially non-linear input, it is crucial to run the small-scale turbine in a robust mode. A mathematical model of a PMDC motor dynamic system is derived incorporating a fuzzy logic controller. In addition, this drive control was validated experimentally. The experimental design is discussed in detail. The system performance was tested experimentally over a wide range of operating condition to validate the fuzzy logic control robustness and effectiveness. Also, it is shown that the speed of the PMDC motor was controlled by using this fuzzy logic controller. The speed tracking shows good agreement with the reference speed regardless of the load condition.
more » « less- Award ID(s):
- 1809182
- PAR ID:
- 10475751
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8667-0
- Format(s):
- Medium: X
- Location:
- Columbus, Ohio, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
null (Ed.)Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.more » « less
-
In this paper we demonstrate two applications of a low-cost remote supervisory control and data acquisition system in two models. The first model is demonstrated with a Profibus-DP protocol based system in which a master Programmable Logic Controller (PLC) unit with control inputs and display outputs controls the speed and monitors the overload condition of a DC motor that is connected to a slave PLC in real time. In the upgraded model, a Profinet protocol is used to connect PLCs, and a power-line communication link is used to remotely connect the control HMI to the network. In both models, remote Supervisory control is achieved using user-defined control functions that act altogether as a block-oriented function library or toolbox. High levels of performance are achieved in real time control and data acquisition in both models.more » « less
-
more » « less
Abstract This paper proposes a linear parameter varying (LPV) control design for a Clipper Liberty C96 2.5 MW wind turbine to operate in all wind conditions. A standard approach is to use multiple single‐input, single‐output loops for control objectives at different wind speeds. This LPV controller instead takes these objectives into a uniformed multi‐input, multi‐output design framework. The key difference is that the LPV controller is specifically designed to smoothly transition from Region 2 to Region 3 operation. Reducing structural loads is another major concern in the design. Synthesis of the controller relies on a gridded‐based LPV model of the turbine, which is constructed by interpolation of linearized turbine models at different wind speeds. To overcome the conservativeness in the design, parameter varying rates will be considered based on turbulent wind conditions. The performance of the LPV controller is evaluated using a high fidelity FAST model provided by Clipper. The LPV controller is directly compared with the baseline controller currently operating on the turbine. Simulations and analysis show that the LPV controller meets all performance objectives and has better load reduction performance.
-
Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power productionWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.more » « less
