Stochastic Power Grid Analysis Considering Process Variations

TL;DR

This paper introduces an efficient stochastic analysis method for power grid voltage fluctuations caused by process variations, using orthogonal polynomial expansions to improve computational speed and accuracy.

Contribution

It presents a novel analytical approach employing orthogonal polynomials for stochastic voltage response computation, implemented in the OPERA software, enabling faster analysis of industrial power grids.

Findings

Speed-up of up to 100x in analysis time

Voltage variations of approximately ±35% observed

Need for variation-aware power grid analysis confirmed

Abstract

In this paper, we investigate the impact of interconnect and device process variations on voltage fluctuations in power grids. We consider random variations in the power grid's electrical parameters as spatial stochastic processes and propose a new and efficient method to compute the stochastic voltage response of the power grid. Our approach provides an explicit analytical representation of the stochastic voltage response using orthogonal polynomials in a Hilbert space. The approach has been implemented in a prototype software called OPERA (Orthogonal Polynomial Expansions for Response Analysis). Use of OPERA on industrial power grids demonstrated speed-ups of up to two orders of magnitude. The results also show a significant variation of about $\pm$ 35% in the nominal voltage drops at various nodes of the power grids and demonstrate the need for variation-aware power grid analysis.