A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods

A Randomized Greedy Algorithm for Near-Optimal Sensor Scheduling in Large-Scale Sensor Networks

We study the problem of scheduling sensors in a resource-constrained linear dynamical system, where the objective is to select a small subset of sensors from a large network to perform the state estimation task. We formulate this problem as the maximization of a monotone set function under a matroid constraint. We propose a randomized greedy algorithm that is significantly faster than state-of-the-art methods. By introducing the notion of curvature which quantifies how close a function is to being submodular, we analyze the performance of the proposed algorithm and find a bound on the expected mean square error (MSE) of the estimator that uses the selected sensors in terms of the optimal MSE. Moreover, we derive a probabilistic bound on the curvature for the scenario where{\color{black}{ the measurements are i.i.d. random vectors with bounded $\ell_2$ norm.}} Simulation results demonstrate efficacy of the randomized greedy algorithm in a comparison with greedy and semidefinite programming relaxation methods

LU Factorization Algorithm with Minimum Degree Ordering in Power Distribution Network Problems

Power systems computations for nowadays common large distributed systems typically involve the usage of very large sparse matrices, whose analysis and verification is very time and memory consuming. When blocked, sparse matrices can be processed much more efficiently, and this made blocked sparse matrices widely used in acquiring solutions for power system problems. The established sparse matrix storage and reordering techniques however do not fully utilize the existing computer architecture, thus search for efficient sparse system solution is ongoing. This paper presents adjustments of well-known LU factorization algorithm suitable for use in power distribution network applications. LU factorization algorithm processes data in blocks and uses minimum degree ordering to accelerate the computations

Modern Distribution Management System and Voltage VAR Control

This paper describes modern Distribution Management System (DMS) and Voltage/VAR Control (VVC) as one of its important components. Importance of DMS with respect to latest changes such as renewable energy sources, distribution generation, demand-respond is significant for the complete power system stability and control. In this paper VVC, as one of the most important applications in DMS, is explained and analyzed. VVC uses power system control equipment and calculates new optimal operational state. Typical VVC objective function is minimization of system power losses, violations of bus voltage limits, feeder capacity limits or combinations of these. Changes of controllable devices are presented through their injected current used in current iteration method for power flow. Test of Voltage/VAR control is performed on modified IEEE13 test network and results show that proper adjustments of OLTC transformers, capacitors and DG significantly reducepower losses while satisfying all operation constraints

Power Distribution Management System revisited: Single-thread vs. Multithread Performance

Power Distribution Management System (PDMS) uses very sophisticated algorithms to deliver reliable and efficient functioning of power distribution networks (PDN). PDNs are represented using very large sparse matrices, whose processing is computationally very demanding. Dividing large PDNs into smaller sub-networks results in smaller sparse matrices, and further processing each sub-network in parallel significantly improves the performance of PDMS. Using multithreading to further process each sub-network however degrades PDMS performance. Single-thread processing of sub-network sparse matrices gives much better performance results, mainly due to the structure of these matrices (indefinite and very sparse) and synchronization overhead involved in multi-thread operations. In this paper an overview of PDMS system is presented, and its performance given single-thread and multiple threads is compared. The results have shown that for some applications, single-threaded implementation in multi-process parallel environment gives better performance than multithreaded implementation

Design and accuracy analysis of multi-level state estimation based on smart metering infrastructure

While the first aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multi-level state estimator that exploits smart meter measurements for monitoring both low and medium voltage grids. The goal of the paper is to present an architecture able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-time smart meter measurements for state estimation purposes were enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at the different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at the different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multi-level estimator in coordination with the cloud-based platform

An Intelligent Method Based on WNN for Estimating Voltage Harmonic Waveforms of Non-monitored Sensitive Loads in Distribution Network

An intelligent method based on wavelet neural network (WNN) is presented in this study to estimate voltage harmonic distortion waveforms at a non-monitored sensitive load. Voltage harmonics are considered as the main type of waveform distortion in the power quality approach. To detect and analyze voltage harmonics, it is not economical to install power quality monitors (PQMs) at all buses. The cost associated with the monitoring procedure can be reduced by optimizing the number of PQMs to be installed. The main aim of this paper is to further reduce the number of PQMs through recently proposed optimum allocation approaches. An estimator based on WNN is presented in this study to estimate voltage-harmonic waveforms at a non-monitored sensitive load using current and voltage at a monitored location. Since capacitors and distributed generations (DGs) have a special role in distribution networks, they are considered in this paper and their effects on the harmonic voltage waveform estimator are evaluated. The proposed technique is examined on the IEEE 37-bus network. Results indicate the acceptable high accuracy of the WNN estimator