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Puskar Neupane

Publications and source records attributed to Puskar Neupane.

3 recordsLinked to original sources

A Cycle-Based Solvability Condition for Real Power Flow Equations

Certifying power flow solvability is important for reliable power system operations under volatile operating conditions, but solving power flow equations repeatedly can be costly and may encounter convergence issues. In this paper, we develop an explicit cycle-based solvability condition for the lossless real power flow equations on meshed networks. We decompose every feasible nodal balance solution into a particular flow plus a cycle flow correction vector. The power flow problem is then reduced to enforcing edge-wise feasibility and cycle consistency. We show that the cycle consistency function is strongly monotone and is the gradient of a strongly convex energy function. By exploiting these properties, we derive an explicit condition for the existence and uniqueness of a power flow solution with bounded angle difference. The resulting condition is invariant under the choice of cycle basis and can be verified through simple algebraic computations. Numerical results on standard test systems show that the proposed condition is significantly less conservative than existing sufficient conditions and closely approximates true loading limits.

math.OC

An Artificial Neural Network based approach for Harmonic Component Prediction in a Distribution Line

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.

eess.SY

Stability in Isolated Grids: Implementation and Analysis of the Dead-Zone Virtual Oscillator Control in Simulink and Typhoon HIL

This paper explores the analysis and implementation of the Virtual Oscillator Control (VOC) strategy for inverters aiming to enhance stability amidst the ever-increasing generation of renewable energy sources like solar PV. Key objectives include implementation and analysis of a Dead-Zone VOC (DZVOC) three-phase battery-inverter system with an additional voltage control loop, study of its stability and performance in an isolated micro-grid and exploration of their use alongside widely used grid following PV-inverter system. By modeling independent microgrids under various cases with scenarios: VOC inverters of varying capacities and VOC inverters in conjunction with PV inverters, this research addresses critical aspects of power-sharing, compatibility, response times, and fault ride-through potential, as well as improving the voltage droop profile of a general DZVOC control. The simulation is executed in MATLAB SIMULINK and validated with real-time simulation using the Typhoon-HIL 404.

eess.SY