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1. Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

   Hyang-Won Lee, Jianan Zhang (October 2018, IEEE SmartGridComm)

   Identifying the location of a disturbance and its magnitude is an important component for stable operation of power systems.We study the problem of localizing and estimating a disturbance in the interconnected power system. We take a model-free approach to this problem by using frequency data from generators. Specifically, we develop a logistic regression based method for localization and a linear regression based method for estimation of the magnitude of disturbance. Our model-free approach does not require the knowledge of system parameters such as inertia constants and topology, and is shown to achieve highly accurate localization and estimation performance even in the presence of measurement noise and missing data. 

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2. A Derivative-Free Observability Analysis Method of Stochastic Power Systems

   https://doi.org/10.1109/PESGM46819.2021.9637959  

   Zheng, Zongsheng; Xu, Yijun; Mili, Lamine; Liu, Zhigang; Korkali, Mert; Wang, Yuhong (July 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   The observability analysis of a time-varying nonlinear dynamic model has recently attracted the attention of power engineers due to its vital role in power system dynamic state estimation. Generally speaking, due to the nonlinearity of the power system dynamic model, the traditional derivative-based observability analysis approaches either rely on the linear approximation to simplify the problem or require a complicated derivation procedure that ignores the uncertainties of the dynamic system model and of the observations represented by stochastic noises. Facing this challenge, we propose a novel polynomial-chaos-based derivative-free observability analysis approach that not only brings a low complexity, but also enables us to quantify the degree of observability by considering the stochastic nature of the dynamic systems. The excellent performances of the proposed method is demonstrated using simulations of a decentralized dynamic state estimation performed on a power system using a synchronous generator model with IEEE-DC1A exciter and a TGOV1 turbine-governor. 

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3. Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and Its Statistical Optimality

   Wang, Miaoyan; Li, Lexin (July 2020, Journal of machine learning research)

   We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based estimation method, and obtain the theoretical accuracy guarantees. In contrast to continuous-valued problems, the binary tensor problem exhibits an interesting phase transition phenomenon according to the signal-to-noise ratio. The error bound for the parameter tensor estimation is established, and we show that the obtained rate is minimax optimal under the considered model. Furthermore, we develop an alternating optimization algorithm with convergence guarantees. The efficacy of our approach is demonstrated through both simulations and analyses of multiple data sets on the tasks of tensor completion and clustering. 

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4. Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements

   https://doi.org/10.1115/DSCC2020-3305  

   Panetta, Chandler J.; Ennasr, Osama N.; Tan, Xiaobo (October 2020, Proceedings of ASME 2020 Dynamic Systems and Control Conference)

   null (Ed.)

   Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach. 

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5. Simultaneous identification of dynamic model and occupant-induced disturbance for commercial buildings

   Coffman, Austin; Barooah, Prabir (January 2018, Building and environment)

   A model of a building’s thermal dynamics is needed for prediction-based control. The task of identifying a thermal dynamic model is made challenging by the presence of large unmeasured disturbances, especially the heat gain due to the occupants. In fact, identification of this “occupant-induced load” is also valuable for predictive control—especially in commercial buildings. We propose a method to identify both a model (of resistance-capacitance network type) and the unmeasured disturbances from measured input-output data. The method is based on the insight that the main contributor to the unmeasured disturbance, the occupant-induced load, is piecewise constant, especially in commercial buildings. This can be used to construct an augmented dynamic model so that disturbance estimation is converted to a state estimation problem. An outer-loop optimization identifies the best-fit parameter values. The effectiveness of the method is evaluated using data from a simulation model (under both open and closed-loop operations) and a real building. 

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