Venkatesh Chinde

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamaged and damaged states of the system. We use a spectral diffusion map approach to identify the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using a singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. To achieve this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with a relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

Comparative Evaluation of Control-Oriented Zone Temperature Prediction Modeling Strategies in Buildings

Predictive modeling of zone environment plays a critical role in developing and deploying advanced performance monitoring and control strategies for energy usage minimization in buildings while maintaining occupant comfort. The task remains extremely challenging, as buildings are fundamentally complex systems with large uncertainties stemming from weather, occupants, and building dynamics. Over the past few years, purely data-driven various control-oriented modeling techniques have been proposed to address different requirements, such as prediction accuracy, flexibility, computation and memory complexity. In this context, this paper presents a comparative evaluation among representative methods of different classes of models, such as first principles driven (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric). Apart from quantitative metrics described above, various qualitative aspects such as cost of commissioning, robustness and adaptability are discussed as well. Real data from Iowa Energy Center’s Energy Resource Station (ERS) test bed is used as the basis of evaluation presented here.Copyright

Stability Analysis of Constrained Optimization Dynamics via Passivity Techniques

In this letter, we present passivity-based convergence analysis of continuous time primal-dual gradient method for convex optimization problems. We first show that a convex optimization problem with only affine equality constraints admits a Brayton Moser formulation. This observation leads to a new passivity property derived from a Krasovskii-type storage function. Second, the inequality constraints are modeled as a state dependent switching system. Using tools from hybrid systems theory, it is shown that each switching mode is passive and the passivity of the system is preserved under arbitrary switching. Finally, the two systems: 1) one derived from the Brayton Moser formulation and 2) the state dependent switching system, are interconnected in a power conserving way. The resulting trajectories of the overall system are shown to converge asymptotically, to the optimal solution of the convex optimization problem. The proposed methodology is applied to an energy management problem in buildings and simulations are provided for corroboration.

Spectral diffusion map approach for structural health monitoring of wind turbine blades

In this paper, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the different data set corresponding to different damage cases. The application of this approach is demonstrated for damage diagnosis of wind turbine blades. Our simulation results show that the proposed diffusion map-based metric is not only able to distinguish the damage from undamage system state, but can also determine the extent and the location of the damage.

Production scheduling with differential pricing for prioritized microgrids

This paper explores the effectiveness of an energy market, modelling the interaction between a centralized power grid and a prioritized set of prosumer microgrids. The focus is on a differential pricing system for the electricity market while accounting for the physical constraints of power generation. More specifically, the model aims to optimize the scheduling of centralized power generation through a mix of integer programming and Model Predictive Control (MPC) over a fixed horizon.

Scalable supervisory control of building energy systems using generalized gossip

This paper presents a novel distributed optimization framework to achieve energy efficiency in large-scale buildings. The modular problem formulation presented in this paper decouples the supervisory optimization scheme from the data-driven micro-level modeling aspect leading to significant scalability and flexibility. Recently developed generalized gossip protocol is used as a robust distributed optimization technique. A supervisory control design problem for multi-zone temperature regulation and energy usage minimization is considered as a case study to describe the generic framework. Numerical simulation results, presented based on a physical testbed in the Iowa Energy Center, demonstrate the advantages of the distributed optimization methodology compared to a typical baseline strategy. The paper also outlines a software architecture based on the VOLTTRON platform, recently developed by the Pacific Northwest National Laboratory (PNNL), for real-life implementation of the proposed framework.

Building HVAC systems control using power shaping approach

Heating, Ventilating and Air-conditioning (HVAC) control systems play an important role in regulating indoor air temperature to provide building occupants a comfortable environment. Design of HVAC control system to provide an optimal balance between comfort and energy usage is a challenging problem. This paper presents a framework for control of building HVAC systems using a methodology based on power shaping paradigm that exploits passivity theory. The controller design uses Brayton-Moser formulation for the system dynamics wherein the mixed potential function is the power function and the power shaping technique is used to synthesize the controller by assigning a desired power function to the closed loop dynamics so as to make the equilibrium point asymptotically stable. The methodology is demonstrated using two example HVAC subsystems - a two-zone building system and a heat exchanger system.

Computation of the Lyapunov measure for almost everywhere stochastic stability

In our recent work [1], we introduced Lyapunov measure as a new tool to verify weaker set-theoretic notion of almost everywhere stability of stochastic nonlinear systems. A Linear transfer Perron-Frobenius operator for stochastic systems was used to provide an explicit formula for the Lyapunov measure, verifying almost everywhere almost sure stability of stochastic systems. The focus of this paper is on the computational aspect of the Lyapunov measure for stochastic systems. We used set-oriented numerical methods for the finite dimensional approximation of the linear operator and the Lyapunov measure. Stability results in the finite dimensional approximation space are also presented. In particular, we show the finite dimensional approximation leads to a further weaker notion of stability referred to as coarse stability.

Feedback Linearization of Single-Input and Multi-Input Control System

Abstract A new algorithm is proposed for computing locally the linearizing output of single-input and multi-input nonlinear affine system. The algorithm modifies the extended Goursat normal form to iteratively obtain the successive integrations of one dimensional distributions of control system. The algorithm takes ideas from both vector field approach of feedback linearization and exterior differential system tools, hence the name Blended Algorithm. The proposed algorithm leads to a tower like structure depending upon the number of system inputs. Within individual tower, the coordinates are reduced one by one by finding the annihilators of vector fields at each step. The process is repeated till the single vector field is obtained for exact linearizable system. The scheme exhibits reduced computational complexity over the existing methods and can be extended to address feedback linearization of various class of control systems.