Chun Wei

A Review on Position/Speed Sensorless Control for Permanent-Magnet Synchronous Machine-Based Wind Energy Conversion Systems

Owing to the advantages of higher efficiency, greater reliability, and better grid compatibility, the direct-drive permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs) have drawn the highest attention from both academia and industry in the last few years. Applying mechanical position/speed sensorless control to direct-drive PMSG-based WECSs will further reduce the cost and complexity, while enhancing the reliability and robustness of the WECSs. This paper reviews the state-of-the-art and highly applicable mechanical position/speed sensorless control schemes for PMSG-based variable-speed WECSs. These include wind speed sensorless control schemes, generator rotor position and speed sensorless vector control schemes, and direct torque and direct power control schemes for a variety of direct-drive PMSG-based WECSs.

An Adaptive Network-Based Reinforcement Learning Method for MPPT Control of PMSG Wind Energy Conversion Systems

This paper proposes an artificial neural network (ANN)-based reinforcement learning (RL) maximum power point tracking (MPPT) algorithm for permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs). The proposed MPPT algorithm first learns the optimal relationship between the rotor speed and electrical power of the PMSG through a combination of the ANNs and the Q-learning method. The MPPT algorithm is switched from the online RL to the optimal relation-based online MPPT when the maximum power point is learned. The proposed online learning algorithm enables the WECS to behave like an intelligent agent with memory to learn from its own experience, thus improving the learning efficiency. The online RL process can be reactivated any time when the actual optimal relationship deviates from the learned one due to the aging of the system or a change in the environment. Simulation and experimental results are provided to validate the proposed ANN-based RL MPPT control algorithm for a 5-MW PMSG-based WECS and a small emulated PMSG-based WECS, respectively.

Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems

This paper proposes an intelligent maximum power point tracking (MPPT) algorithm for variable-speed wind energy conversion systems (WECSs) based on the reinforcement learning (RL) method. The model-free Q-learning algorithm is used by the controller of the WECS to learn a map from states to optimal control actions online by updating the action values according to the received rewards. The experienced action values are stored in a Q-table, based on which the maximum power points (MPPs) are obtained after a certain period of online learning. The learned MPPs are then used to generate an optimum speed-power curve for fast MPPT control of the WECS. Since RL enables the WECS to learn by directly interacting with the environment, knowledge of wind turbine parameters or wind speed information is not required. The proposed MPPT control algorithm is validated by simulation studies for a 1.5-MW doubly-fed induction generator-based WECS and experimental results for a 200-W permanent-magnet synchronous generator-based WECS emulator.

Transient Reconfiguration and Coordinated Control for Power Converters to Enhance the LVRT of a DFIG Wind Turbine With an Energy Storage Device

This paper proposes a novel transient reconfiguration solution and coordinating control strategy for power converters to enhance the fault ride through and transient voltage support capabilities of a doubly-fed induction generator with an energy storage device (DFIG-ESD). During a grid fault, the connection of the grid-side converter is reconfigured such that it is connected to the rotor circuit in parallel with the rotor-side converter to provide an additional route for the rotor current, while the ESD is responsible for dc-link voltage regulation. A coordinated demagnetizing and reactive current control strategy is designed for the reconfigured DFIG during transient conditions. Specifically, the demagnetizing current is used to counteract the dc and negative-sequence stator flux components so that the transient electromotive force will be reduced. Simultaneously, the reactive current is added to meet the reactive power support requirement. The enhanced low-voltage ride through (LVRT) and transient voltage support capabilities obtained from the proposed design are demonstrated on the DFIG-ESD wind conversion system under different severe fault scenarios (asymmetrical and symmetrical fault). Additionally, The enhanced transient voltage support capability of the proposed design is further demonstrated by comparing with different control strategies.

Evaluation of ANN estimation-based MPPT control for a DFIG wind turbine

This paper proposes an artificial neuronal network (ANN) estimation-based wind speed sensolress MPPT algorithm for wind turbines equipped with doubly-fed induction generators (DFIG). The ANN is designed to produce the optimal control signal for the DFIG power or speed controller. The optimal parameters of the ANN are determined by using a particle swarm optimization (PSO) algorithm. A 3.6 MW DFIG wind turbine is simulated in PSCAD to evaluate and compare the proposed MPPT method with the traditional tip speed ratio (TSR) and turbine power profile-based MPPT methods in both the speed control and power control modes in variable wind speed conditions.

Adaptive Saturation Controller-Based Direct Torque Control for Permanent-Magnet Synchronous Machines

This paper proposes a novel direct torque control (DTC) scheme for permanent-magnet synchronous motor (PMSM) drives using a relatively low sampling frequency. Unlike the conventional DTC in which a single voltage vector is selected according to the outputs of the hysteresis controllers, the proposed DTC uses nonlinear adaptive-midpoint saturation controllers to regulate the torque and flux tracking errors and determine the durations of multiple voltage vectors which are selected from a new switching table. The proposed DTC naturally inherits most intrinsic merits of the conventional DTC, e.g., fast dynamics, robust to disturbances, no coordinate transformation, etc. Meanwhile, the steady-state torque and flux ripples which afflict the conventional DTC are significantly reduced. Moreover, by adjusting the midpoints of the saturation controllers adaptively, the steady-state torque tracking error, which is a common issue in the DTC schemes particularly when the sampling frequency is relatively low, is fully eliminated. The effectiveness of the proposed adaptive saturation controller-based DTC is verified by experimental results on a 180-W PMSM drive system.

Intelligent Maximum Power Extraction Control for Wind Energy Conversion Systems Based on Online Q-learning with Function Approximation

This paper proposes an intelligent maximum power point tracking (MPPT) algorithm for variable-speed wind energy conversion systems (WECSs) based on an online Q-learning algorithm. Instead of using the conventional Q-learning that uses a lookup table to store the action values for the discretized states, artificial neural networks (ANNs) are used as function approximators to output the action values by using the electrical power and rotor speed of the generator as inputs. This eliminates the need for a large storage memory. The proposed method learns the optimal speed control strategy of the WECS by updating the connecting weights of the ANNs, which has a lower computational cost than the conventional Q-learning method. Moreover, the knowledge of wind turbine characteristics or wind speed measurement is not required in the proposed method. The proposed method is validated by simulations for a WECS equipped with a doubly-fed induction generator (DFIG) and experimental results for an emulated WECS equipped with a permanent-magnet synchronous generator (PMSG).

A soft-switched three-port single-stage inverter for photovoltaic-battery systems

This paper proposes a soft-switched three-port single-stage inverter (TPSI) for a photovoltaic (PV)-battery system. Compared to the existing soft-switched TPSIs, the proposed TPSI has the advantages of using the least number of switches and that all of the switches are capable of being turned on under the zero voltage switching (ZVS) condition. A prototype of the TPSI without using electrolytic capacitors is built to verify the proposed topology. Experimental results show that the proposed TPSI is effective in managing the power flows among the three ports while maintaining the desired sinusoidal voltage at the load port.

Stator current-based sliding mode observer for sensorless vector contorl of doubly-fed induction geneartors

This paper proposes a novel rotor position estimation algorithm for the vector control of the doubly-fed induction generators (DFIGs) in wind power applications. The proposed algorithm uses a sliding mode observer (SMO) to estimate the rotor position information from the stator currents. The estimated rotor position is then used for the position sensorless vector control of the DFIG. The proposed SMO-based rotor position estimation algorithm is not sensitive to the variations of machine parameters and, therefore, is robust to the machine parameter uncertainty. The effectiveness of the proposed sensorless vector control scheme is confirmed by simulation results for a 2-MW DFIG-based wind energy conversion system (WECS) in MATLAB/Simulink and experimental results for a 7.5-kW DFIG-based WECS simulator. The proposed sensorless control scheme will improve the robustness and reliability of the DFIG-based WECSs.

Current/voltage sensor fault detection and isolation in wind energy conversion systems based on power balance

This paper proposes a novel current/voltage sensor fault detection and isolation (FDI) method for wind energy conversion systems (WECSs) based on the power balance principle. The proposed method uses the sensor-measured signals to calculate the imbalanced power in the power converters of a WECS, which is then used as the indicator for sensor fault detection. The fault isolation process is started when a fault is detected and is achieved by generating and comparing the residuals between the estimated and measured signals, where the residuals are generated by a trigonometric function-based estimation algorithm. The proposed method does not require additional hardware or the information of generator parameters, is capable of detecting and isolating both machine-side and grid-side current/voltage sensor faults, and is not influenced by other types of faults in WECSs or power grids. The effectiveness of the proposed method is confirmed by simulation results in MATLAB/Simulink and experimental results for a 2.4-kW permanent-magnet synchronous generator-based WECS.