The prodigious amount of data movement among sources, data centers, or processing elements precludes the utilization of least-squares (LS) and fusion-center (FC)-based modeling and control. The LS methods are offline in nature, hence may face difficulty in real-time implementation. FC-based methods that are nonrobust due to single point failure, require large communication bandwidth and computationally fast processing unit. To curb these limitations, this article identifies distributed parameter systems by estimating the parameters of spatio-temporal Volterra model using in-network data processing. It can handle the immense volume of data by distributing the processing tasks of FC among the wireless sensor network nodes. To facilitate distributed optimization, the global objective function is reformulated as a multiple constrained separable problem which is then decomposed into augmented Lagrangian form. Then, alternating direction method of multipliers along with coordinate descent method is employed to obtain the global optimal solution collaboratively. Further, a communication-efficient algorithm is designed for the proposed approach to deploy in an ad-hoc network. Simulations are carried out on two industrial distributed parameter systems (catalytic rod and tubular reactor) to illustrate the practicality of the proposed algorithm.

Distributed estimation over wireless sensor networks (WSNs) has been used to obtain the parameters of interest with reduced resource consumption, hence gained importance in system modeling and control applications. Unlike least-squares and fusion-center based approaches, distributed signal processing is competent in real-time applications. In this article, Volterra-Laguerre model and Wiener model are identified in a distributed manner through WSNs for modeling of nonlinear systems. A block-structured Wiener model has been widely used as it is characterized by a small number of parameters, but can only model specific nonlinearities. A generalized Volterra model over Wiener model can approximate any nonlinear system to a desired precision but has increased parameter complexity. By expanding nonlinear Volterra kernels with orthogonal Laguerre functions, the parameter complexity is reduced significantly. A distributed recursive algorithm for the identification of abovementioned nonlinear models is designed by minimizing the quadratic prediction error. The algorithm reformulates model identification framework into multiple constrained separable subtasks. These subtasks are optimized using a powerful method called alternating direction method of multipliers. Simulation results for an infinite-order and a 2nd-order nonlinear systems are obtained under the influence of process noise and are compared with the results of non-cooperative estimation showing the superiority of the proposed algorithm.

Accurate prediction of wind ramp events is critical for ensuring the reliability and stability of the power systems with high penetration of wind energy. This paper proposes a classification based approach for estimating the future class of wind ramp event based on certain thresholds. A parallelized gradient boosted regression tree based technique has been proposed to accurately classify the normal as well as rare extreme wind power ramp events. The model has been validated using wind power data obtained from the National Renewable Energy Laboratory database. Performance comparison with several benchmark techniques indicates the superiority of the proposed technique in terms of superior classification accuracy.