Yizheng Liao

SnowFort: An Open Source Wireless Sensor Network for Data Analytics in Infrastructure and Environmental Monitoring

SnowFort, an open source wireless sensor network (WSN) for data analytics, is proposed for monitoring infrastructure and environment. The wireless sensing unit is optimized to be low power for extremely long-term deployments. Several features, such as data compression and online reconfiguration, are introduced to further reduce power consumption. A low-power WSN over optimized time division multiple access scheme is designed to be scalable and reliable for a network with hundreds of sensors. Real-time data visualization and analytical tools are provided with a representational state transfer (RESTful) application programming interface. We utilize SnowFort to develop a real-time damage detection application in structural health monitoring. We develop a distributed algorithm robust to data loss and validate it in a laboratory setup.

Urban distribution grid topology reconstruction via Lasso

The growing integration of distributed energy resources (DERs) in urban areas raises various reliability issues. To ensure robust distribution grid operation, grid monitoring tools are needed, where the topology reconstruction serves as the first step. However, the topology reconstruction is hard in distribution grid. This is because 1) the branches are difficult and expensive to monitor since most of them are underground in urban areas; and 2) the assumption of radial topology in many studies is inappropriate for meshed urban grids. To address these drawbacks, we propose a new data-driven approach to reconstruct distribution grid topology by utilizing the newly available smart meter data. Specifically, a graphical model is built to model the probabilistic relationships among different voltage measurements. With proof, the topology reconstruction problem is formulated as a regularized linear regression problem (Lasso) to deal with meshed network structures. Simulation results show highly accurate estimation in IEEE standard distribution test systems with and without loops using real smart meter data.

Distribution grid topology reconstruction: An information theoretic approach

Recently, a rapidly penetration of distributed generation raises various issues. One of the key issues is frequent distribution grid re-configuration, which is hard to detect based on traditional approaches. Wrong topology information causes wrong control signal, making fast changing smart grid prone to go over stability boundaries and to collapse. To ensure system robustness, we propose a new data-driven re-configuration approach, thanks to recently progressively deployed larger sensor networks in distribution systems by utilities. Specifically, an Information Theory based algorithm, Chow-Liu algorithm, is used based on a proof for assumption and verification in distribution systems. Simulation results show highly accurate re-configuration estimation in IEEE standard distribution test systems.

Message-passing sequential detection of multiple structural damages

This paper introduces a multiple structural damage detection algorithm for structural health monitoring. We propose a sequential damage detection algorithm that uses the Bayesian inferences to diagnose multiple damages and their relationships. The proposed algorithm is purely data driven and does not require the prior knowledge of the structural properties. The sequential detectors are implemented by a computationally efficient message passing protocol that enables distributed and simultaneous damage detection. Also, the detectors achieve minimum detection delay with a desired false alarm rate. The performances of our algorithm are validated on the ASCE benchmark structure.

Distributed Energy Resources Topology Identification via Graphical Modeling

Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the grid more than ever before, which introduces tremendous changes in the distribution grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution grid. But the exact distribution system topology is difficult to obtain due to frequent distribution grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution grid topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.

Sequential damage detection based on the continuous wavelet transform

This paper presents a sequential structural damage detection algorithm that is based on a statistical model for the wavelet transform of the structural responses. The detector uses the coefficients of the wavelet model and does not require prior knowledge of the structural properties. Principal Component Analysis is applied to select and extract the most sensitive features from the wavelet coefficients as the damage sensitive features. The damage detection algorithm is validated using the simulation data collected from a four-story steel moment frame. Various features have been explored and the detection algorithm was able to identify damage. Additionally, we show that for a desired probability of false alarm, the proposed detector is asymptotically optimal on the expected delay.

Structural health monitoring approach for detecting ice accretion on bridge cable using the Haar Wavelet Transform

Ice accretion on cables of bridge structures poses serious risk to the structure as well as to vehicular traffic when the ice falls onto the road. Detection of ice formation, quantification of the amount of ice accumulated, and prediction of icefalls will increase the safety and serviceability of the structure. In this paper, an ice accretion detection algorithm is presented based on the Continuous Wavelet Transform (CWT). In the proposed algorithm, the acceleration signals obtained from bridge cables are transformed using wavelet method. The damage sensitive features (DSFs) are defined as a function of the wavelet energy at specific wavelet scales. It is found that as ice accretes on the cables, the mass of cable increases, thus changing the wavelet energies. Hence, the DSFs can be used to track the change of cables mass. To validate the proposed algorithm, we use the data collected from a laboratory experiment conducted at the Technical University of Denmark (DTU). In this experiment, a cable was placed in a wind tunnel as ice volume grew progressively. Several accelerometers were installed at various locations along the testing cable to collect vibration signals.

Angular velocity-based structural damage detection

Damage detection is an important application of structural health monitoring. With the recent development of sensing technology, additional information about structures, angular velocity, has become available. In this paper, the angular velocity signals obtained from gyroscopes are modeled as an autoregressive (AR) model. The damage sensitive features (DSFs) are defined as a function of the AR coefficients. It is found that the mean values of the DSF for the damaged and undamaged signals are different. Also, we show that the angular velocity- based AR model has a linear relationship with the acceleration-based AR model. To test the proposed damage detection method, the algorithm has been tested with the experimental data from a recent shake table test where the damage is introduced systemically. The results indicate that the change of DSF means is statistically significant, and the angular velocity-based DSFs are sensitive to damage.

Application of Acceleration-based Damage Detection Algorithms to Experimental Data from Multi-story Steel Structures

This paper presents a recent shake table experiment on two three-story steel frame structures with controllable damage. The performance and accuracy of SnowFort, a wireless infrastructure monitoring system, were tested. By analyzing the data, we show that this new system achieves the same accuracy as the wired sensing units. In addition, a structural damage detection algorithm based on Continuous Wavelet Transform was validated based on the experimental data. doi: 10.12783/SHM2015/143