# Mirsad Cosovic's research while affiliated with University of Sarajevo and other places

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## Publications (36)

We consider the problem of maximum likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive internet of things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for...

Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iterative Gauss-Newton method. The nonlinear SE presents some difficulties when considering inputs from both phasor measurement units and supervisory control and data acquisi...

p>We propose a linear state estimation (SE) model with complex coefficients and variables suitable for processing large-scale data in electric power systems observable by phasor measurement units. The presented model is based on factor graphs and solved using the belief propagation (BP) algorithm. The proposed algorithm is placed in the non-overlap...

p>We propose a linear state estimation (SE) model with complex coefficients and variables suitable for processing large-scale data in electric power systems observable by phasor measurement units. The presented model is based on factor graphs and solved using the belief propagation (BP) algorithm. The proposed algorithm is placed in the non-overlap...

Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore h...

The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission power systems, a fast SE solver capable of exploiting PMUs' high sample rates is required. To accomplish this, we present a method f...

Multimedia streaming over the Internet (live and on demand) is the cornerstone of modern Internet carrying more than 60% of all traffic. With such high demand, delivering outstanding user experience is a crucial and challenging task. To evaluate user QoE many researchers deploy subjective quality assessments where participants watch and rate videos...

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates...

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling ra...

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling ra...

The purpose of a state estimation (SE) algorithm is to estimate the values of the state variables considering the available set of measurements. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (\i...

The purpose of a state estimation (SE) algorithm is to estimate the values of the state variables considering the available set of measurements. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (\i...

The purpose of a state estimation (SE) algorithm is to estimate the values of the state variables considering the available set of measurements. The centralised SE becomes impractical for large-scale systems, particularly if the measurements are spatially distributed across wide geographical areas. Dividing the large-scale systems into clusters (\i...

The state estimation algorithm estimates the values of the state variables based on the measurement model described as the system of equations. Prior to applying the state estimation algorithm, the existence and uniqueness of the solution of the underlying system of equations is determined through the observability analysis. If a unique solution do...

We present a novel observability analysis approach based on the factor graphs and Gaussian belief propagation (BP) algorithm. The observable islands are identified by following the evolution of marginal variances of the state variables. The resulting algorithm, due to the sparsity of the underlying power network, has the linear computational comple...

Lighting systems based on light-emitting diodes (LEDs) possess many benefits over their incandescent counterparts including longer lifespans, lower energy costs, better quality of light and no toxic elements, all without sacrificing consumer satisfaction. Their lifespan is not affected by switching frequency allowing for better illumination control...

We present a detailed study on application of factor graphs and the belief propagation (BP) algorithm to the power system state estimation (SE) problem. We start from the BP solution for the linear DC model, for which we provide a detailed convergence analysis. Using BP-based DC model we propose a fast real-time state estimator for the power system...

Distributed energy trading among energy prosumers (i.e., energy producers that also consume energy) is expected to bring significant cost benefits for the participating actors. In terms of the system architecture, physical grouping into microgrids (MG) can be further enhanced by communication infrastructure that provides support for flexible organi...

We present a novel distributed Gauss-Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP). The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method. The re...

Machine-type communications and large-scale information processing architectures are among key (r)evolutionary enhancements of emerging fifth-generation (5G) mobile cellular networks. Massive data acquisition and processing will make 5G network an ideal platform for large-scale system monitoring and control with applications in future smart transpo...

We propose a fast real-time state estimator based on the belief propagation algorithm for the power system state estimation. The proposed estimator is easy to distribute and parallelize, thus alleviating computational limitations and allowing for processing measurements in real time. In fully distributed implementation, local modules compute state...

With transition towards 5G, mobile cellular networks are evolving into a powerful platform for ubiquitous large-scale information acquisition, communication, storage and processing. 5G will provide suitable services for mission-critical and real-time applications such as the ones envisioned in future Smart Grids. In this work, we show how emerging...

We present a detailed study of the applications of factor graphs and the belief propagation (BP) algorithm to the state estimation (SE) problem. Our methodology starts with the BP solution for the linearized DC model, and use insights obtained therein to derive the BP algorithm for the non-linear AC model. Then, we make a key further step, where we...

In this paper, we propose a solution to an AC state estimation problem in electric power systems using a fully distributed Gauss-Newton method. The proposed method is placed within the context of factor graphs and belief propagation algorithms and closed-form expressions for belief propagation messages exchanged along the factor graph are derived....

In this paper, we model an extended DC state estimation (SE) in an electric power system as a factor graph (FG) and solve it using belief propagation (BP) algorithm. The DC model comprises bus voltage angles as state variables, while the extended DC model includes bus voltage angles and bus voltage magnitudes as state variables. By applying BP to s...

In this paper, we propose a solution to an AC state estimation problem in electric power systems using a fully distributed Gauss-Newton method. The proposed method is placed within the context of factor graphs and belief propagation algorithms and closed-form expressions for belief propagation messages exchanged along the factor graph are derived....

In this paper, we model an extended DC state estimation (SE) in an electric power system as a factor graph (FG) and solve it using belief propagation (BP) algorithm. The DC model comprises bus voltage angles as state variables, while the extended DC model includes bus voltage angles and bus voltage magnitudes as state variables. By applying BP to s...

The European Marie Curie Project ADVANTAGE (Advanced Communications and Information processing in smart grid systems) was launched in 2014. It represents a major inter-disciplinary research project into the topic of Smart Grid technology. A key aspect of the project is to bring together and train 13 early stage researchers from the traditionally se...

## Citations

... Contributions: In this work, we propose a data-driven nonlinear state estimator based on graph attention networks [15] operating on the factor-graph-like structure [16] obtained by transforming the bus/branch power system model. The proposed approach is an extension of our previous work on linear SE with PMUs [17] and linear SE with PMUs considering covariances of measurement phasors represented in rectangular coordinates [18]. The proposed method takes into account all of the legacy measurements, as well as bus voltage and branch current phasor measurements, and provides a trivial way to remove or add additional measurements by altering the corresponding factor nodes in the graph. ...

... Inspired by the APC algorithm, our previous work designed a distributed channel estimation algorithm for mmWave massive MIMO communication systems [5]. Moreover, there are a number of references on the topics related to the APC algorithm, e.g., distributed algorithms for systems of linear equations [2], for state estimation [3], for gradient-descent method [7], [9], for linear transforms [10], [11], for coded matrix multiplication [12], and for phase retrieval [13]. ...

... Due to the recent advances in smart grid technology, local trading schemes have gained significant interest in the energy sector, e.g., in the community microgrid management and operation [5,6]. The concept is motivated by the microgeneration and local consumption of energy at the edge of the distribution network [16]. ...

... BP was applied to real-time SE for the DC model in [4], while in [5], an extended DC model that contributes to the establishment of BP in decentralized electrical networks was analyzed. The potential of the BP framework in energy networks was further expanded in [6]. It guarantees the same accuracy as the centralized algorithm while retaining the advantages of the BP algorithm. ...

... The study showed state estimation accuracy can be significantly affected by a varying cell coverage range and number of contending devices in the system. Cosovic et al. in [13], [14] propose to leverage 5G cellular technologies to enable a distributed state estimation for smart grid. Latency and reliability of distributed state estimation on 5G communication networks are analyzed. ...

... 4]. While the centralised matrix decomposition techniques provide reliable and fast solution, they become impractical for large-scale systems, thus calling for various iterative methods [12]- [14]. To speed up computing by taking the advantage of the system architecture, iterative methods can be applied in parallel [15]- [17] or distributed [18]- [22] fashion. ...

... In essence, we provide algorithms that solve systems of linear and non-linear equations with real coefficients and variables. Consequently, the implications of our results go far beyond SE in electric power systems and can be applied in different areas, such as for demand response [1] or water distribution systems [2]. ...

... This overcomes the main drawback of traditional solutions based on the WLS or RLS algorithms. More precisely, the GBP is capable of integrating a wide range of variances, from small values v i → 0 to large values v i → ∞ [27]. This property allows inference over the factor graph that reflects the entire network of sensors, regardless of whether the data is available at a given time. ...

... It moves the estimation subproblems from the control center into multiple edge servers (ESs) that are distributed near data sources, to deliver low communication delays and timely data processing [11]. Based on this, we should decompose a large-scale network into several subareas, where an ES is placed in each subarea as the local controller to acquire information and perform DSE [12]. In this scenario, the network partition and the ES placement are two critical factors to facilitate the real-time performance of DSE, as the network partition can balance the sizes of subareas to shorten the runtime of DSE [13], and the ES placement aims to shorten the average distance between ES and data sources to reduce communication delays [11]. ...

... Compared to our recent work on BP-based SE [4], [5] that addresses classical (static) SE problem, this paper is an extension to the real-time model that operates continuously and accepts asynchronous measurements from different measurement subsystems. More precisely, we assume presence of both SCADA and WAMS infrastructure, and without loss of generality, we observe active power flow and injection measurements (from SCADA), and voltage phase angle measurements (from WAMS). ...