Fast Parallel Stochastic Subspace Algorithms for Large-Scale Ambient Oscillation Monitoring

Parallel stochastic programming for energy storage management in smart grid with probabilistic renewable generation and load models

“…On the other hand, to accelerate solution procedure, parallel computing [29][30][31] and decomposition algorithm [32,33] are widely used to solve large-scale problems. Using parallel computing technique, we can first decompose the optimisation problem into multiple sub-problems or scenarios, and then implement computing so that each core (thread) solves one subproblem or one scenario to speed up in the execution process.…”

“…, D n, t , which can be modelled as a dynamic programme with T stages. Therefore, for each period t, the cost function can be calculated as the current expenditure and future period t + Δt possible expenditure, given by the equation below: (see (31)) .…”

“…To further demonstrate the efficiency of the proposed method, we compare it against the scenario-based parallel processing method [29][30][31] in this simulation. This process can also be accelerated by computing each scenario parallelly in one period, but the acceleration is only valid in each time block and cannot run across time horizon and use the results from the previous iteration as a warm start, which differs from the proposed parallel nested decomposition algorithm.…”

Stochastic subspace identification based data‐driven approach for monitoring electromechanical dynamics from phasor measurement units

“…Both methods offered a great potential for estimating oscillation modes from ringdown and ambient data, but they were unable to deal with the multiple measurement channels. To overcome these problems, the numerical algorithm for subspace state space system identification (N4SID) [11], multi‐order N4SID (MN4SID) [12, 13], fast N4SID (FN4SID) [14], parallel N4SID [15] and generalised inverse and mode assurance criterion‐based N4SID (GN4SID) [16] were proposed.…”

Spatial‐temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in power systems

“…In [21], a subspace method was applied to estimate the modes and mode shapes from the ambient data. In [22], the existing ambient mode shape estimation approaches including transfer function, spectral, frequency domain decomposition, channel matching and subspace methods [23, 24] were comparatively reviewed. These mode and mode shape estimation approaches are based solely on measurement data without sophisticated power system model and thus complement the oscillation analysis to monitor the dynamic stability of a power grid in real time or near real time.…”