Mohammad Babakmehr

Compressive Sensing-Based Topology Identification for Smart Grids

Smart grid (SG) technology transforms the traditional power grid from a single-layer physical system to a cyber-physical network that includes a second layer of information. Collecting, transferring, and analyzing the huge amount of data that can be captured from different parameters in the network, together with the uncertainty that is caused by the distributed power generators, challenge the standard methods for security and monitoring in future SGs. Other important issues are the cost and power efficiency of data collection and analysis, which are highlighted in emergency situations such as blackouts. This paper presents an efficient dynamic solution for online SG topology identification (TI) and monitoring by combining concepts from compressive sensing (CS) and graph theory. In particular, the SG is modeled as a huge interconnected graph, and then using a dc power-flow model under the probabilistic optimal power flow (P-OPF), TI is mathematically reformulated as a sparse-recovery problem (SRP). This problem and challenges therein are efficiently solved using modified sparse-recovery algorithms. Network models are generated using the MATPOWER toolbox. Simulation results show that the proposed method represents a promising alternative for real-time monitoring in SGs.

Designing smart inverter with unified controller and smooth transition between grid-connected and islanding modes for microgrid application

A three-phase smart grid-connected inverter (SGCI) for grid integration of PV system is designed where an appropriate controller for grid-connected (GC) operation is developed to avoid transients and distortion usually caused by switching internal current/voltage controllers. The presented control technique uses d-q decoupled electrical axis and after the controller detects any impeding fault in the grid voltage, a unified control allows a very smooth transition between the grid-connected and islanded modes of operation. In addition, a floating interleaved boost converter (FIBC) is implemented in this system for photovoltaic array energy conversion and energy storage has an optimized lifetime algorithm, used to increase the power handling capability of the PV-modules. Analysis and results verify the effectiveness of the proposed control framework under different operating conditions.

Application of compressive sensing for distributed and structured power line outage detection in smart grids

Fast and accurate identification and localization of power line outages is one of the critical issues for efficient monitoring and control tasks in future smart grids. In this work, considering the whole power network (PN) as a single graph, the recently introduced sparse formulation of the Power line Outage Identification (POI-SRP) is represented. Our main contribution is to improve and generalize the POI-SRP results for single and multiple line outage identification using matrix analysis and the structured pattern in multiple POI. We address the high coherence and high correlation issues in corresponding POI-SRP sensing matrices. In order to solve these problems, we perform a comprehensive study on the corresponding matrices of the IEEE standard networks in POI-SRP. Our main approach will be based on the necessary and sufficient conditions that these matrices should satisfy in order to be applicable to the Sparse Recovery Problem (SRP). First, we discuss the effect that node-line connection structures in the PN model have on the coherence. We describe a modification of the PN model in order to decrease the coherence. Next, using the IEEE standard 118-BUS as a case study, we discuss how the high correlation problem can be solved by applying mathematical matrix analysis such as matrix decomposition and improving the final POI results. The third main contribution is the identification of the structured outages in POI-SRP. In this work, the boundary conditions of the Clustered OMP recovery algorithm are modified (MCOMP) and finally it will be shown how the existence of structured sparsity in multiple POI problems (Structured-POI-SRP) helps MCOMP to improve the POI results.

Designing an intelligent low power residential PV-based Microgrid

In this work a bi-level (Supervisory-Local) PV-based Microgrid configuration is proposed for low power residential applications. In the supervisory level a long-term control scheme is assigned to define the set points for local controllers. The local level is mainly formed from a set of controllers which are basically responsible to control the power electronic interfaces and converters. Within the supervisory level a dynamic price scheduling framework with load and solar energy forecasting is implemented using time series-based regression technique. In the local level, adaptive double mode controllers are developed to realize intelligent inverters with smart grid-tied (GT) capabilities and smooth transition between GT and stand-alone modes. The effectiveness of the proposed architecture is examined using simulation in PSIM software. Next, hardware in the loop is implemented using the real time simulator OPAL-RT with DSP module as a controller for a board range of conditions and within different practical scenarios.

Smart-Grid Topology Identification Using Sparse Recovery

Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve the analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification and monitoring activities in SG. The basic idea combines optimization-based sparse-recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the dc power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse-recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this study, we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and MATLAB-based simulation results have indicated the promising performance of the proposed method for real-time topology identification (TI) in SGs.

Smart grid topology identification using sparse recovery

Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve the analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification and monitoring activities in SG. The basic idea combines optimization-based sparse-recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the dc power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse-recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this study, we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and MATLAB-based simulation results have indicated the promising performance of the proposed method for real-time topology identification (TI) in SGs.

Multi-functional double mode inverter for power quality enhancement in smart-grid applications

This paper introduces a new multi-functional double mode inverter (MFDMI) scheme, which is able to operate under a variety of operational conditions for aggregation of PV-based renewable energy resources. Detecting islanded situation within a new and fast approach, regulating the voltage in the islanded mode, smooth transition between islanded and grid-tied (GT) modes, injecting both active and reactive powers to the grid in addition to compensating the harmonics from nonlinear loads are beyond the most notable functionalities of the proposed framework. Technically, we exploit a combinational control scheme formed by instantaneous power theory, vector-control and a proportional integral resonant (PIR) controller to address the required functionalities. To deal with the low output power issue, photovoltaic (PV) cells are aggregated through a high gain DC-DC floating interleaved boost converter (FIBC). Moreover, a battery back-up module with bidirectional DC-DC floating interleaved buck-boost converter (FIBBC) is used to improve the system reliability and dispatch ability. The effectiveness of the proposed framework has been first verified within a comprehensive PSIM simulation results and then has been examined under realistic situations using the real-time simulator OPAL-RT with DSP modules (hardware in the loop) for a broad range of conditions and within different practical scenarios.

Compressive Sensing for Power System Data Analysis

Chapter Overview Within this chapter, we will introduce the applications of a state-of-the-art theorem in signal processing and system identification, named as compressive sensing-sparse recovery (CS-SR), in smart power networks monitoring, data analysis, security, and reliability. The sparse nature of the electrical power grids as well as electrical signals is exploited to introduce alternative mathematical formulations to address some of the most famous system modeling problems in power engineering through a compressive signal processing or a sparse system identification framework. First, a short background on CS-SR theorems and techniques is presented. Next, the state of the art in CS-SR applications in smart grid technology is discussed, and finally, the following three data analyses and power network control problems are specifically addressed in detail. The CS-SR techniques are exploited to propose novel methods for distribution system state estimation (DSSE), single and simultaneous fault location in smart distribution and transmission networks, and partial discharge pattern recognition.

Modeling and tracking Transmission Line Dynamic Behavior in Smart Grids using structured sparsity

In this work a new and fast network-wide framework is addressed for modeling and tracking the dynamic behavior of transmission lines in Power Networks (PN). A sparse-based mathematical formulation for Transmission Line Dynamic Behavior Tracking (TLDBT) is formed by incorporating a PN Port-Hamiltonian model. Among the TLDBT a new set of intermediate parameters called the line dynamic index coefficients (LDIC) are defined based on the wave propagation analysis of current waves in transmission lines. It is shown how these coefficients can reflect the dynamic behavior of the transmission lines. The online monitoring of variations in these index coefficients is interpreted as an alternative approach for TLDBT in power grids. Finally, exploiting the inherent sparsity in the PN structure this TLDBT problem is reformulated as a Structured Sparse Recovery Problem (SSRP) and the TLDBT-SSRP is solved for LDICs. The simulation results indicate that the proposed framework can be considered as an alternative approach to address the new challenges in the future generation of smart power grids modeling, monitoring and congestion-management strategies.