Yaoyu Li

A review of recent advances in wind turbine condition monitoring and fault diagnosis

The state-of-the-art advancement in wind turbine condition monitoring and fault diagnosis for the recent several years is reviewed. Since the existing surveys on wind turbine condition monitoring cover the literatures up to 2006, this review aims to report the most recent advances in the past three years, with primary focus on gearbox and bearing, rotor and blades, generator and power electronics, as well as system-wise turbine diagnosis. There are several major trends observed through the survey. Due to the variable-speed nature of wind turbine operation and the unsteady load involved, time-frequency analysis tools such as wavelets have been accepted as a key signal processing tool for such application. Acoustic emission has lately gained much more attention in order to detect incipient failures because of the low-speed operation for wind turbines. There has been an increasing trend of developing model based reasoning algorithms for fault detection and isolation as cost-effective approach for wind turbines as relatively complicated system. The impact of unsteady aerodynamic load on the robustness of diagnostic signatures has been notified. Decoupling the wind load from condition monitoring decision making will reduce the associated down-time cost.

Trip-Based Optimal Power Management of Plug-in Hybrid Electric Vehicles

The plug-in hybrid electric vehicle (PHEV), utilizing more battery power, is considered a next-generation hybrid electric vehicles with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Global optimization charge-depletion power management would be desirable. However, this has so far been hampered due the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of Intelligent Transportation Systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, gas-kinetic base trip modeling approach was used for the highway portion trip and for the local road portion the traffic light sequences throughout the trip will be synchronized with the vehicle operation. Several trip models approaches were studied for a specific case. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control. The gas-kinetic based trip model for the highway portion can describe the dynamics of the traffic flow on highway with on/off ramps which may be missed by the model which used only the main road detectors data.

Sequential ESC-Based Global MPPT Control for Photovoltaic Array With Variable Shading

The photovoltaic (PV) systems are often subject to shading in practical operation, which often results in multipeak power-voltage (P-V) characteristics. Most existing maximum power point tracking (MPPT) control strategies are local-optimum oriented, based on the gradient search or its variations, e.g., perturb-and-observation (P&O) or extremum seeking control (ESC). In order to deal with the multimodal P-V characteristics for PV array with variable shading, this study proposes a sequential ESC-based global MPPT control strategy, based on approximate modeling and analysis for the P-V characteristics under variable-shading circumstances. For the multipeak P-V characteristic curve, the bound of variation for the turning-point voltage is found, and based on which the initial voltage for the segmental search can be set. The local minimum of power for the previous segment is used as the start of the next segment, and thus the initial voltage can be set with consistent bound. Such a sequential scheme can thus significantly reduce the searching interval, i.e., the searching efficiency. Another parallel analysis was conducted for the staircase current-voltage (I-V) characteristic for variable-shading situation. It reveals that the current step size is proportional to the change of shading levels. The current profiles obtained by the sequential ESC-based global MPPT search can thus be used to indicate the shading distribution. Simulation study of partially shaded PV panels validates the modeling analysis and the proposed global MPPT.

Extremum seeking control of a tunable thermoacoustic cooler

In this paper, the performance of a prototype standing wave thermoacoustic cooler is optimized using an extremum seeking control (ESC) algorithm. A tunable Helmholtz resonator was developed for a thermoacoustic cooler to change the boundary condition of the standing wave tube. The volume of the resonator is changed by changing the location of a piston on a ball-screw assembly driven by a dc motor. Multiparameter ESC was applied to optimize the cooling power via tuning both the boundary condition (piston location) and the driving frequency. Experiments were conducted for the online optimization under both fixed and varying operating conditions. The experimental results demonstrated the effectiveness of using ESC for maintaining maximum achievable performance. The effect of changing parameters in the ESC algorithm on the transient behavior was also investigated.

Electrified Vehicles and the Smart Grid: The ITS Perspective

Vehicle electrification is envisioned to be a significant component of the forthcoming smart grid. In this paper, a smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.

Optimal power management of plug-in HEV with intelligent transportation system

Hybrid electric vehicles (HEV) have demonstrated their capability of improving the fuel economy and emission. The plug-in HEV (PHEV), utilizing more battery power, has become a more attractive upgrade of HEV. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. In the past, the trip information has been considered as future information for vehicle operation and thus unavailable a priori. This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board geographical information systems (GIS), global positioning systems (GPS) and advanced traffic flow modeling techniques. In this paper, a new approach of optimal power management of PHEV in the charge-depletion mode is proposed with driving cycle modeling based on the historic traffic information. A dynamic programming (DP) algorithm is applied to reinforce the charge-depletion control such that the SOC drops to a specific terminal value at the final time of the cycle. The vehicle model was based on a hybrid SUV. Only fuel consumption is considered for the current stage of study. Simulation results showed significant improvement in fuel economy compared with rule-based power management. Furthermore, simulations on several driving cycles using the proposed method showed much better consistency in fuel economy compared to the rule-based control.

Maximizing Wind Turbine Energy Capture using Multivariable Extremum Seeking Control

Maximizing energy capture has become an important issue as more turbines are installed in low wind areas. This paper investigates the application of extremum seeking control (ESC) to maximizing the energy capture of variable speed wind turbines. The optimal control torque and pitch angle are searched via ESC based on the measurement of the rotor power. The advantage of this method is the independency from accurate turbine modelling and wind measurement. Simulation was conducted on FAST for a wind turbine dynamic model, under smooth, turbulent and field recorded wind profiles. The simulation results demonstrated significant improvement in energy capture compared to the standard control with fixed reference. An anti-windup ESC was applied to overcome the integral windup due to actuator saturation which would otherwise disable the ESC process. Finally, the integrator and high-pass filter resetting schemes were applied to improve the transient under the abrupt changes of wind.

Power management of plug-in hybrid electric vehicles using neural network based trip modeling

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The Gas-kinetic based model was used for the trip modeling in our previous study. The complicated partial deferential equation based model with several parameters needs to be calibrated had for implementation. In this paper, a neural network based trip model was developed for the highway portion, using the given data from WisTransPortal. The real test data was used for the training and validation of the network. The simulation results show that the obtained trip model using neural network can greatly improve the trip modeling accuracy, and thus improve the fuel economy. The potential of the advantages were indicated by the fuel economy comparison.

Optimal Energy Management of Wind-Battery Hybrid Power System With Two-Scale Dynamic Programming

This study is concerned with the optimal energy management for a wind-battery hybrid power system (WBHPS) with local load and grid connection, by including the current and future information on generation, demand, and real-time utility price. When applying typical dynamic optimization schemes to such a problem with a single time scale, the following dilemma usually presents: it is more beneficial to plan the (battery) storage setpoint trajectory for the longer horizon, while prediction of renewable generation, utility price, and load demand is more accurate for the shorter term. To relieve such conflict, a two-scale dynamic programming (DP) scheme is applied based on multiscale predictions of wind power generation, utility price, and load. A macro-scale dynamic programming (MASDP) is first performed for the whole operational period, based on long-term ahead prediction of electricity price and wind energy. The resultant battery state-of-charge (SOC) is thus obtained as the macro-scale reference trajectory. As the operation proceeds, the micro-scale dynamic programming (MISDP) is applied to the short-term interval based on short-term three-hour ahead predictions. The MASDP battery SOC trajectory is used as the terminal condition for the MISDP. Simulation results show that the proposed method can significantly decrease the energy cost compared with the single scale DP method.

Trip Based Power Management of Plug-in Hybrid Electric Vehicle with Two-Scale Dynamic Programming

The plug-in HEV (PHEV), utilizing more battery power, has become the next-generation HEV with great promise of higher fuel economy. The charge-depletion mode is more appropriate for the power management of PHEV, i.e. the state of charge (SOC) is expected to drop to a low threshold when the vehicle reaches the destination of the trip. Globally optimized charge-depletion power management would be desirable. However, this has so far been hampered due to the a priori nature of the trip information and the prohibitive computational cost of global optimization techniques such as dynamic programming (DP). This situation can be changed by the current advancement of intelligent transportation systems (ITS) based on the use of on-board GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling techniques. In this paper, charge-depletion control of PHEV is nearly globally optimized with a two-scale dynamic programming approach based on trip modeling with real-time and historical traffic data. For DP based charge-depletion control of PHEV, the SOC is desired to drop to a specific terminal value at the end of the trip. By specifying the origin and destination of a trip, the trip model, i.e. the driving cycle, is first obtained with the average of the historic traffic data, and the globally optimized SOC profile can be obtained by solving the overall or the macro-scale DP problem. The actual power management can be adapted during real-time vehicle operation with a micro-scale DP framework. The whole trip is divided into a number of segments, and for each segment, a smaller DP will be solved using the on-line traffic data transmitted to the vehicle from the traffic flow sensors within the segment. The SOC obtained in the macro-scale DP solution at the terminal location is reinforced to be the final value. Simulation study has been performed on a hybrid SUV model from ADVISOR, and a defined trip in the greater Milwaukee area. The simulation results demonstrated significant improvement in fuel economy using DP based charge-depletion control compared to rule based control, and also the benefit of adaptation using the two-scale DP method.