To achieve a free-positioning wireless power transfer (WPT) system, the output power must be regulated throughout the operation. This paper presents a novel output power control of a WPT system, based on the model predictive control (MPC). The output power is predicted by utilizing the system’s mathematical model. The optimal duty cycle for a desired output power is obtained through the minimization of the objective function, which is simple and easy to implement, with no need for gain tuning. The proposed controller is implemented on the primary side, without any measurement or communication devices on the secondary side. This reduces the cost, size, and complexity of the WPT system. The load resistance and mutual inductance identification method is also introduced. It is based on the reflected impedance knowledge, where only the information of primary current is required. Experimental results of the output power step response show better performance compared with conventional Proportional-Integral (PI) control. The proposed controller is experimentally validated on a 200 W kettle. The output power can be kept constant at 200 W while the kettle is laterally moved. With the proposed controller, the kettle can be placed freely up to 7 cm from the align position, which is 63.64% of the primary coil’s outer radius.

This research presents a new method based on a combined temporal convolutional neural network and long-short term memory neural network for the real-time assessment of short-term voltage stability to keep the electric grid in a secure state. The assessment includes both the voltage instability (stable state or unstable state) and the fault-induced delayed voltage recovery phenomenon subjected to disturbance. The trained model uses the time series post-disturbance bus voltage trajectories as the input in order to predict the stability state of the power system in a computationally efficient manner. The proposed method also utilizes a transfer learning approach that acclimates to the pre-trained model using only a few labeled samples, which assesses voltage instability under unseen network topology change conditions. Finally, the performance evaluated on the IEEE 9 Bus and New England 39 Bus test systems shows that the proposed method gives superior accuracy with higher efficacy and thus is suitable for online application.

This paper proposes a probabilistic method to obtain optimized parameter values for different power-system controllers, such as power-system stabilizers (PSSs) and battery energy-storage systems (BESSs) to improve probabilistic small-signal stability (PSSS) considering stochastic output power due to wind- and solar-power integration. The proposed tuning method is based on a combination of an analytical method that assesses the small-signal-stability margin, and an optimization technique that utilizes this statistical information to optimally tune power-system controllers. The optimization problem is solved using a metaheuristic technique known as the firefly algorithm. Power-system stabilizers, as well as sodium–sulfur (NaS)-based BESS controllers with power-oscillation dampers (termed as BESS controllers) are modeled in detail for this purpose in DIGSILENT. The results show that the sole use of PSSs and BESS controllers is insufficient to improve dynamic stability under fluctuating input power due to the integration of renewable-energy resources. However, the proposed strategy of using BESS and PSS controllers in a coordinated manner is highly successful in enhancing PSSS under renewable-energy-resource integration and under different critical conditions.

This paper proposes a computationally efficient method based on deep neural network and a meta-heuristic optimization algorithm known as bat algorithm to coordinate power oscillation damping controllers incorporated in renewable energy stations to enhance system small signal stability considering uncertainties. The proposed method consists of three main stages: database generation, supervised learning, and optimization using the created model. A database is first created of Probabilistic small-signal stability margin calculated using the combined cumulant and Gram-Charlier expansion and the parameters of damping controllers, which is then given to different supervised machine learning algorithms such as linear regression, support vector machine, random forest, and chiefly deep neural network to create a surrogate model. The surrogate model provides an approximate relationship between the probabilistic small-signal instability margin and damping controller parameters. An optimization problem is then formulated to minimize the surrogate probabilistic instability margin with damping controller parameters acting as constraints. Finally, this optimization problem is solved using the bat algorithm to obtain the optimized parameters for power oscillation damping controllers. Our study results tested on a large IEEE 16 machines, 68 bus system show that the power oscillation damping controllers optimized using the proposed method can largely improve the system low frequency oscillatory stability margin in a very low computational time (around 19 times faster than the conventional method). This study can be used by the power system operators to tune the parameters of damping controller in a fast manner.

This paper presents an inverter topology for a wireless power transfer (WPT) system that is intended to reduce the component counts and complexity of the conventional excitation circuit for multiple transmitter coils. The proposed inverter topology requires only (n+2) power switches, where “n” is the number of transmitter coils. An excitation of a proper transmitter coil pattern with regard to the receiver coil position is determined. The output voltage can be regulated through the primary-side control by adjusting the duty cycles of the inverter switches. A detection method of the receiver coil position is presented using the detection switches on the secondary side. The detection algorithm is based on the reflected impedance knowledge and requires only a current sensor on the primary side. A proper transmitter coil pattern is energized to ensure maximum transfer efficiency throughout the operation. The proposed system is experimentally validated on the created 500-watt WPT multi-coil system. After the receiver coil is placed in a designated area, the proper transmitter coil pattern can be automatically selected and energized. The output voltage can be regulated to a desired value under the typical operation conditions, including load change.

This paper presents an inverter topology for a static wireless power transfer (SWPT) system that is intended to reduce the component counts and complexity of the conventional excitation circuit for multiple transmitter coils. The proposed inverter topology requires only (n+1) power switches, where “n” is the number of transmitter coils. The currents in the transmitter coils can be independently controlled. The output voltage is regulated through the transmitter-side control by adjusting the duty cycle of the inverter switches. A detection method of the receiver coil position is developed by comparing the transmitter currents to the predetermined transmitter coil patterns. The proposed detection method helps decide the proper transmitter selection, which improves the transfer efficiency throughout the operation. An experimental study is performed on the created 500-watt WPT multi-coil system. As the receiver coil is placed in the designated area, the proper transmitter coil pattern can be automatically selected and energized. The output voltage can be regulated to the desired values throughout the operation, regardless of the load condition.