This paper presents an integrated workflow for the design and deployment of Wide-Area Measurement Systems (WAMS) and demonstrates its effectiveness on a modified IEEE 39-bus New England benchmark grid with additional offshore wind farms. Unlike fragmented approaches that address modeling, sensor siting, or signal processing in isolation, the proposed pipeline couples these tasks so insights from simulation flow directly into real-time operation.

The process begins with a detailed nonlinear model encompassing synchronous machine dynamics, including turbine-governor and automatic-voltage-regulator controllers, and dynamic representations of the integrated wind farms. This hybrid model captures interactions between conventional generators and variable renewable sources.

A set of representative operating points reflects plausible load and generation patterns, including high- and low-inertia scenarios induced by wind penetration. Each point is linearized to form small-signal models, from which eigenvalues and eigenvectors are extracted. Together, these linearizations capture lightly damped local and inter-area oscillatory modes under different conditions.

These modal data drive an observability study that selects a set of Phasor Measurement Unit (PMU) locations capable of providing robust visibility of critical modes across the operating envelope. Modal observability indices are computed for candidate buses to guide the PMU's placement.

With the sensor topology fixed, an oscillation-analysis layer is anchored. In the offline tier, classical batch Prony analysis is applied to time-series snapshots from simulations to validate modal estimates and tune detection thresholds. In the online tier, an adaptive Prony algorithm processes streaming PMU measurements to continuously estimate mode frequency and damping ratio; its low computational footprint enables deployment on standard substation hardware or integration into a grid defence system.

By jointly optimizing model fidelity, operating-point coverage, sensor placement, and detector selection in a closed loop, the proposed workflow narrows the gap between simulation and field deployment. This blueprint can be extended to larger networks, hybrid sensor fleets, or machine-learning–based predictors.