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Real-time power line outage detection (POD) and localization is an important monitoring task for the modern smart grid. Reliable monitoring of power lines status plays a critical role in the system-wide blackout prevention. In this paper, the main aim is to address the multiple POD problems by exploiting the compressive system identification—a time-efficient approach in a complex network analysis. A typical power network is considered as a single graph, and the mathematical formulation of the POD problem is initialized using the dc power-flow model and graph theory concepts. Next, a sparse representation-based formulation for this problem (POD-SRP) is reported and further improved and generalized in case of multiple large-scale outages. Practical and technical challenges associated with this sparse recovery problem are partially addressed by developing new SRP solvers. Furthermore, a new sparse-based mathematical formulation for POD is introduced and termed as “Binary-POD-SRP,” which specifically deals with two particular issues, namely, the high coherence and the signal dynamic outrange. Finally, the identification performance of the proposed framework is evaluated by a variety of case studies, which are modeled using IEEE standard test-beds. We specifically discuss how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations. [ABSTRACT FROM AUTHOR]

Penetration rate of grid-connected photovoltaic (PV) generation to the existing utility grid is rapidly increasing over the years. Since the power generated from PV systems fluctuates according to the weather condition, e.g., cloud passing, this can significantly disturb the stability of a weak utility grid. The integration of energy storage devices and its ramp-rate control technique are required to reduce the impact of PV systems output fluctuations and augment the stability of the utility grid. In this paper, ramp-rate control is applied to the direct connection of energy storage devices in PV generation system configuration. The direct connection of supercapacitors string and battery combination scheme is proposed to reduce the number of power converters so that the efficiency of the system is increased. In this work, the PV system output is controlled by directly controlling the energy storage system (ESS) to limit the changing rate of the PV output to a desired ramp-rate value, hence reducing the battery charge/discharge cycles and extending the expected lifetime of the ESS. The performance of the proposed direct connection scheme of the ESS and its ramp-rate control strategy is verified using a 1-kW PV system prototype. [ABSTRACT FROM AUTHOR]

This paper proposes a simplified small-signal model for output voltage control of a single-phase asymmetrical cascaded H-bridge multilevel inverter (ACHMI). The ACHMI is an n-series connected H-bridge converter, each one with a unique value at the dc link and usually scaled at 1:2:6:… or 1:3:9:…. By assuming that the small-signal variation component is equal in all n converter terminal ports, a simplified small-signal model is obtained. This assumption is carefully described and justified. To verify the veracity of the proposed model, two distinct control strategies are applied. One is a single-loop control scheme based on a modified proportional-integral (PI) controller. The other one is a double-loop control scheme based on a PI controller with feedforward action of the load current. Both controllers are tuned based on the dynamic behavior of the proposed model. Since the designed controllers based on the simplified model make the ACHMI output voltage to follow the reference without steady-state error, the proposed simplified model truly represents the inverter. Experimental results show the efficacy of the simplified model of the ACHMI through the two mentioned control strategies as well as the ACHMI installed in a microgrid. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

The use of power converters is very important in maximizing the power transfer from renewable energy sources such as wind, solar, or even a hydrogen-based fuel cell to the utility grid. An LCL filter is often used to interconnect an inverter to the utility grid in order to filter the harmonics produced by the inverter. Although there is an extensive amount of literature available describing LCL filters, there has been a gap in providing a systematic design methodology. Furthermore, there has been a lack of a state-space mathematical modeling approach that considers practical cases of delta- and wye-connected capacitors showing their effects on possible grounding alternatives. This paper describes a design methodology of an LCL filter for grid-interconnected inverters along with a comprehensive study of how to mitigate harmonics. The procedures and techniques described in this paper may be used in small-scale renewable energy conversion systems and may be also retrofitted for medium- and large-scale grid-connected systems. [ABSTRACT FROM PUBLISHER]

This paper presents the modeling approach for a quasi-2-D tubular anode-supported solid-oxide fuel cell (SOFC) for real-time control implementation. The proposed system considers a multidomain electrochemical, fluidic, and thermal dynamic modeling and it is experimentally validated against a SOFC real world implementation. Implicit iterative algebraic equations have been introduced and implemented in C language in order to have fast real-time execution. The methodology for implementing such an iterative solver is discussed in details and the results demonstrate practical feasibilities in advancing real-time control of SOFC [ABSTRACT FROM AUTHOR]