Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning

This paper proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneous settings where the sensor data have different underlying distributions. Our anonymization model, dubbed Blinder, is based on a variational autoencoder and one or multiple discriminator networks trained in an adversarial fashion. We use the model-agnostic meta-learning framework to adapt the anonymization model trained via federated learning to each user's data distribution. We evaluate Blinder under different settings and show that it provides end-to-end privacy protection on two IMU datasets at the cost of increasing privacy loss by up to 4.00% and decreasing data utility by up to 4.24%, compared to the state-of-the-art anonymization model trained on centralized data. We also showcase Blinder's ability to anonymize the radio frequency sensing modality. Our experiments confirm that Blinder can obscure multiple private attributes at once, and has sufficiently low power consumption and computational overhead for it to be deployed on edge devices and smartphones to perform real-time anonymization of sensor data

Resource Provisioning in the Electrical Grid

Transformers and storage systems in the electrical grid must be provisioned or sized just as routers and buffers must be sized in the Internet. We prove the formal equivalence between these two systems and use this insight to apply teletraffic theory to sizing the electrical grid, obtaining the capacity region corresponding to a given transformer and storage size. We conduct a fine-grained measurement study of household electrical load. These measurements are essential for two reasons. First, we use them to construct reference models for home loads; these models are used to find the capacity region using the teletraffic theory. Second, these measurements are used in numerical simulations that are done to validate our analysis. More specifically, we compare results of numerical simulations with the results from teletraffic theory. We show not only that teletraffic theory agrees well with numerical simulations but also that it closely matches with the heuristics used in current practice. Moreover, our analysis permits us to develop sizing rules for battery storage electrical grid, advancing the state of the art

On the Control of Active End-nodes in the Smart Grid

The electrical grid has substantially changed in recent years due to the integration of several disruptive load and generation technologies into low-voltage distribution networks, which are meant to smarten it and improve its efficiency. These technologies have subjected the grid to unprecedented amounts of variability and uncertainty that threaten its reliability and could reduce its efficiency. Even a low penetration of these disruptive technologies may cause equipment overloads, voltage deviations beyond permissible operating thresholds, and bidirectional power flows in distribution networks. The smart grid will comprise a vast number of active end-nodes, including electric vehicle chargers, solar inverters, storage systems, and other elastic loads, that can be quickly controlled to adjust their real and reactive power contributions. Given the availability of inexpensive measurement devices and a broadband communication network that connects measurement devices to controllers, it is possible to incorporate potentially disruptive technologies into distribution networks while maintaining service reliability, using some novel control mechanisms, which are the focus of this thesis. In this thesis, we propose a new paradigm for the control of active end-nodes at scale. This control paradigm relies on real-time measurements of the states of the distribution network and the end-nodes rather than long-term predictions. We use an optimal control framework to design mechanisms that balance a set of system-level and user-level objectives. We study control of active end-nodes in two different contexts: a radial distribution system and a grid-connected public electric vehicle charging station powered by on-site solar generation. We develop both a feedback controller and an open-loop controller, and propose centralized and distributed algorithms for solving optimal control problems. We implement and validate these control mechanisms using extensive numerical simulations and power flow analysis on a standard test system

Distributed control of electric vehicle charging

Electric vehicles (EVs) are expected to soon become widespread in the distribution network. The large magnitude of EV charging load and unpredictable mobility of EVs make them a challenge for the distribution network. Leveraging fast-timescale measurements and low-latency broadband com-munications enabled by the smart grid, we propose a dis-tributed control algorithm that adapts the charging rate of EVs to the available capacity of the network ensuring that network resources are used efficiently and each EV charger receives a fair share of these resources. We obtain sufficient conditions for stability of this control algorithm in a static network, and demonstrate through simulation in a test dis-tribution network that our algorithm quickly converges to the optimal rate allocation

Distributed control of electric vehicle charging

ABSTRACT Electric vehicles (EVs) are expected to soon become widespread in the distribution network. The large magnitude of EV charging load and unpredictable mobility of EVs make them a challenge for the distribution network. Leveraging fasttimescale measurements and low-latency broadband communications enabled by the smart grid, we propose a distributed control algorithm that adapts the charging rate of EVs to the available capacity of the network ensuring that network resources are used efficiently and each EV charger receives a fair share of these resources. We obtain sufficient conditions for stability of this control algorithm in a static network, and demonstrate through simulation in a test distribution network that our algorithm quickly converges to the optimal rate allocation

Event detection and localization in distribution grids with phasor measurement units

The recent introduction of synchrophasor technology into power distribution systems has given impetus to various monitoring, diagnostic, and control applications, such as system identification and event detection, which are crucial for restoring service, preventing outages, and managing equipment health. Drawing on the existing framework for inferring topology and admittances of a power network from voltage and current phasor measurements, this paper proposes an online algorithm for event detection and localization in unbalanced three-phase distribution systems. Using a convex relaxation and a matrix partitioning technique, the proposed algorithm is capable of identifying topology changes and attributing them to specific categories of events. The performance of this algorithm is evaluated on a standard test distribution feeder with synthesized loads, and it is shown that a tripped line can be detected and localized in an accurate and timely fashion, highlighting its potential for real-world applications

On Identification of Distribution Grids

Large-scale integration of distributed energy resources into distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created ample opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of admittance parameters and topology of a polyphase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low-rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real and synthetic household demands

On Identification of Distribution Grids

Large-scale integration of distributed energy resources into distribution feeders necessitates careful control of their operation through power flow analysis. While the knowledge of the distribution system model is crucial for this analysis, it is often unavailable or outdated. The recent introduction of synchrophasor technology in low-voltage distribution grids has created ample opportunity to learn this model from high-precision, time-synchronized measurements of voltage and current phasors at various locations. This paper focuses on joint estimation of admittance parameters and topology of a polyphase distribution network from the available telemetry data via the lasso, a method for regression shrinkage and selection. We propose tractable convex programs capable of tackling the low-rank structure of the distribution system and develop an online algorithm for early detection and localization of critical events that induce a change in the admittance matrix. The efficacy of these techniques is corroborated through power flow studies on four three-phase radial distribution systems serving real and synthetic household demands