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A machine learning based generalized neural network estimator (GRNNE) and Takagi‐Sugeno (T‐S) fuzzy control system is implemented to accelerate the functional performance indices of dynamic voltage restorer (DVR). The GRNNE predictive model is recommended for the fast estimation of the reference load voltage under the distorted power supply. The fruit fly optimization learning strategy is employed to optimize the weights and smoothing parameters for the extraction of the reference voltage signals as well as unit vectors, resulting in the required sinusoidal load component. The dynamics in the DC‐link voltage are optimized by the metaheuristic‐based gray wolf optimization algorithm. The coefficients of the adaptive controller are updated automatically to achieve the best fitted adaptive neuro‐fuzzy inference system predictive model with the least tracking voltage deviation error even in the presence of voltage disturbances. In comparison to classical techniques, the recommended neural‐based approach offers a faster convergence speed with fewer parameters to tune, shorter training time, and lower risks of local entrapment. The performance metrics such as mean square error, root mean square error, mean absolute error, mean absolute percent error, coefficients of correlational and determination (R and R2) are used to evaluate the efficacy of the proposed controller and hence enhance the DVR performance. Finally, the simulation results of the hybrid approach confirm that the NN‐based DVR estimator has proven its ability to alleviate voltage sensible issues at critical loads and outperform others in reducing power quality issues. [ABSTRACT FROM AUTHOR]

In this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME, SD and R were evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform. [ABSTRACT FROM AUTHOR]

Environmental parameter consideration has always prompted wind power to be used as renewable energy. However, the biggest challenge lies in wind energy integration to the power grid due to wind intermittency. Wind speed or wind power forecasting is one of the approach to manage this intermittency. Numerous prediction methods have been reported in previous literatures over few years. In this work, multivariate wind speed forecasting using Machine Learning framework in a python environment is executed. Several statistical models and neural network models are examined to best predict the wind speed of Surat, India [22.2587° N, 71.1924° E]. The model efficiency is tested in terms of measurements of correlation factors and Mean Absolute Error values. The predicted wind speed value is further considered for the power generation from the wind farm and integrated to the distribution system. Load Impedance Matrix method is implemented for Distribution System Load Flow analysis for being robust and simple with single-step computation. IEEE-19 bus and IEEE-25 bus-unbalanced radial distribution systems are considered for finding the power losses in the branches with wind power as Distributed Generation. An efficient and effective optimization technique, Teaching Learning-Based Optimization, is used to obtain the optimal location and capacity of Distributed Generation to minimize the power loss in the distribution lines. [ABSTRACT FROM AUTHOR]