In the power distribution system, the Power Quality (PQ) is disturbed by the voltage sag and swells. The Dynamic Voltage Restorer (DVR) is used to enhance various PQ problems like voltage sag, swells, and Harmonics. The previous Intrinsic Space Vector Transformation (ISVT) control techniques with DVR system, to compensate the power quality issue. It produced a steady-state error, low efficiency, and high THD. The SMES based DVR has provided excellent results in overcoming these issues. The energy is stored by DVR through a storage element, which takes alternate energy from a Solar PV cell. The Maximum Power Point Tracking (MPPT) based P&O algorithm is implemented to equalize the solar power. The Voltage Source Inverter (VSI) generates the reactive power, which has to be compensated with the help of Pulse Width Modulation (PWM) and the feedback control loop are essential to enhance the injection of reactive power to the line. Due to this reason, a proposed Predictive Space Vector Transformation (PSVT) control-based DVR is implemented. It analyzes the variation of power on the distribution side and generates the proper feedback control to the inverter systems. In the instant of voltage injection to the line with the help of DVR, the phase angle mismatch happens which is not synchronized reactive power to the grid. Due to the non-synchronization of reactive power, more harmonic distortion is generated. A Proportional Resonant (PR) controller is introduced, which is Proportional Resonant (PR) current controller. The current injected by the inverter into the grid in phase with the grid voltage maintain constant with unity power factor. The PR controller design is cascaded with a harmonic compensator to mitigate low order odd harmonic components present in the output current of VSI and minimize the total harmonic distortion (THD). The performance of the proposed is evaluated using MATLAB 2017b software.

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( 2 ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.INDEX TERMS Solar photovoltaic system, grey box neural network, prediction error based power forecasting scheme, real case study, renewable energy.

A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-toone mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification (PSO) based methods, where simulation and experimental results demonstrate significant performance improvement with the proposed method.

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