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The escalating global demand for electricity is driving a significant expansion in the size and complexity of electric power systems. Explosive growth strains power distribution, creating challenges for operators. Among these challenges, the imperative to minimise power losses is crucial. To enhance power efficiency and quality, Distributed Generation (DG) technology, particularly wind energy, is being integrated into distribution systems. However, the variable nature of wind speed poses a significant challenge to the seamless integration of wind energy into grids. To address this challenge, wind speed prediction methods are explored. In this work, a Machine Learning framework is employed for multivariate wind speed forecasting in Tamil Nadu, India $\left[{{{8.0883}^ \circ }N,{{77.5385}^ \circ }E} \right]$ 8.0883 ∘ N , 77.5385 ∘ E . Key performance metrics, such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error values (MAPE), are used to assess the accuracy of the wind speed predictions. The predicted wind speed data is then utilised to estimate wind farm power output which is seamlessly integrated into the distribution system. Load Flow analysis is conducted using the robust and straightforward current injection method, focusing on IEEE-33 and IEEE-69 bus-balanced Mesh distribution systems. The aim is to determine the impact of wind-powered Distributed Generation on power losses within these systems. [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]

This article presents implementation of a solar energy-based DG (Distribution Generation) system, which is optimally designed by taking combined effect of solar irradiation and temperature into consideration to minimize the power loss in the Radial Distribution Systems (RDS). A power gap of 35 W/panel is reduced when the solar panel is considered under ambient temperature effect. Novelty of this work lies in the implementation of the obstructed solar astronomical model to improve the accuracy in the prediction of radiation level. In this work, the considered radiation level is determined to be 14.86% of the global radiation data. Consideration of self-shadowing losses for maximum efficiency from PV panels is another significant aspect of this work. Two different test systems are designed by integrating the proposed DG system to IEEE-15 and IEEE-28 RDS. Load Impedance Matrix (LIM) method is adopted to perform load flow in above mentioned test systems due to its single step analysis with faster results unlike traditional Backward Forward Sweep (BFS) method. Grey Wolf Optimization (GWO) technique is used to obtain optimal location of DG for both the test systems for its simple evaluation, good flexibility, avoidance of local optimal point and derivation free tool, which makes this technique popular among the other heuristic methods. Following DG allocation in IEEE-15 bus RDS, it is observed that active power loss is reduced by 39.69%. Similarly, for IEEE-28 bus RDS, reduction in active power loss is found to be 41.20% after integration of a DG unit. There is also a remarkable improvement in voltage profile in both systems after integration of DG units. [ABSTRACT FROM AUTHOR]

Conventional protection scheme of power transformer maloperation during abnormalities such as inrush condition, current transformer (CT) saturation during external fault and high resistance internal fault conditions. This article presents inrush detection based on an average angle of 2nd order derivative of differential current. The magnitude and phase angle of primary and secondary currents are estimated using a Modified Full Cycle Discrete Fourier Transform (MFCDFT) algorithm. Normally, during an internal fault, the differential current is well above the restraining current; however, the same condition arises during external fault with heavy CT saturation. Hence, a phase angle comparison based scheme is combined with percentage biased differential protection. During an internal fault, the phase angle difference of primary and secondary current is <90°, whereas it differs >90° for any external fault conditions. In the proposed scheme, 315 MVA, 400/220 kV transformer is considered for validation of various fault conditions simulated in PSCAD™ software. Moreover, the proposed scheme is validated on developed laboratory prototype of 2 kVA, 230/115 V single phase transformer using ATmega328 microcontroller. The proposed algorithm is effectively validated on both, simulation and hardware to discriminate inrush, internal and external fault by considering various system and fault parameters. [ABSTRACT FROM AUTHOR]