Deepak Tiwari

The European Union, along with its ally the US have been arming Ukraine for the whole one year since Russia did the special military operation.

One of the penchant advocates of the Iraq war as a Director of the “Project for the New American Century”, John Bolton has been known for his outspoken personality.

For the last couple of days UN Secretary-General Antonio Guterres has been quite a busy man condemning Russia on one or the other platforms for the Ukraine war.

Instead of accepting Chinese proposal for peace on the Ukraine crisis, America and the EU have joined hands to impose sanctions on the Chinese firms.

Military operations from the Russian troops in Ukraine has spooked China’s neighbouring nations as they see a pattern in it.

Indo-German defence relations are going deeper, especially after the visit from German Chancellor Olaf Scholz to India on February 25-26.

To remain relevant, retain clients and improve growth, small businesses should fully adopt cloud technology.

I am the CEO of Sagenext Infotech LLC. I lead the company's sales, support and technology front.

Most power systems' approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as forward-backward sweep (FBS) and Newton-Raphson require a high number of iterations to solve non-linear PF equations making them computationally very intensive. PF is the most important study performed by utility, required in all stages of the power system, especially in operations and planning. This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids. Three deep neural networks (DNNs); Radial Basis Function Network (RBFnet), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN), are proposed in this paper to predict PF solutions. The strength of the proposed DNN models over the traditional iterative-based PF solvers is that these models can capture the nonlinear relationships in PF calculations to accurately predict the solutions. The PF problem is formulated as a multi-output regression model where two or more output values are predicted based on the inputs. The training and testing data are generated through the OpenDSS-MATLAB COM interface. These methods are completely data-driven where the training relies on reducing the mismatch at each node without the need for the knowledge of the system. The novelty of the proposed methodology is that the models can accurately predict the PF solutions for the unbalanced distribution grids with mutual coupling and are robust to different R/X ratios, topology changes as well as generation and load variability introduced by the integration of distributed energy resources (DERs) and electric vehicles (EVs). To test the efficacy of the DNN models, they are applied to IEEE 4-node and 123-node test cases, and the American Electric Power (AEP) feeder model. The PF results for RBFnet, MLP, and CNN models are discu...

is a Digital Process Automation leader at EY Tech Consulting. Digital process automation (DPA) has become a cornerstone for businesses