Touria El-Mezyani

Power Flow Control and Network Stability in an All-Electric Ship

The concept of an all-electric ship, while offering unprecedented advantages from the point of view of efficiency and flexibility of operation, has introduced new challenges in terms of stability and power flow control. The advent of a full power electronics power system has raised new questions from the point of view of system dynamics, particularly when dealing with the new medium-voltage direct current distribution. The overall goal of guaranteeing a secure operation of the power system has brought researchers to consider two main approaches: reducing the dynamics of the large load to operate in a range of dynamics compatible with traditional generation systems, or making the generator set smarter through its power electronics interface. This paper compares these approaches to stable operation, focusing on the latter considered more in line with the progress of technology and in general more appealing.

Robust adaptive droop control for DC microgrids

Abstract There are tradeoffs between current sharing among distributed resources and DC bus voltage stability when conventional droop control is used in DC microgrids. As current sharing approaches the setpoint, bus voltage deviation increases. Previous studies have suggested using secondary control utilizing linear controllers to overcome drawbacks of droop control. However, linear control design depends on an accurate model of the system. The derivation of such a model is challenging because the noise and disturbances caused by the coupling between sources, loads, and switches in microgrids are under-represented. This under-representation makes linear modeling and control insufficient. Hence, in this paper, we propose a robust adaptive control to adjust droop characteristics to satisfy both current sharing and bus voltage stability. First, the time-varying models of DC microgrids are derived. Second, the improvements for the adaptive control method are presented. Third, the application of the enhanced adaptive method to DC microgrids is presented to satisfy the system objective. Fourth, simulation and experimental results on a microgrid show that the adaptive method precisely shares current between two distributed resources and maintains the nominal bus voltage. Last, the comparative study validates the effectiveness of the proposed method over the conventional method.

An Alternative Distributed Control Architecture for Improvement in the Transient Response of DC Microgrids

Distributed secondary control plays an important role in dc microgrids, since it ensures system control objectives, which are power sharing and dc-bus voltage stability. Previous studies have suggested using a control architecture that utilizes a parallel secondary bus voltage and current sharing compensation. However, the parallel controllers have a mutual impact on each other, which degrades the transient performance of the system. This paper reports on an alternative distributed secondary control architecture and controller design process, based on small-signal analysis to alleviate the mutual effect of the current sharing and bus voltage compensation, and to improve the transient response of the system. Experimental results confirm the improved transient performance in the current sharing control and dc-bus voltage stability utilizing the proposed control architecture.

Role of Power Hardware in the Loop in Modeling and Simulation for Experimentation in Power and Energy Systems

The area of modeling and simulation is a critical aspect in the basic research to commercialization and instantiation cycle. This paper reports on modeling and simulation in the context of verification, validation, and experimentation of power and energy systems and associated electrical apparatus via the utilization of power hardware in the loop (PHIL)-based strategies. PHIL is a powerful technique for testing and demonstration of systems in a rigorous and dynamic manner that is not achievable with other methodologies; however, it must only be conducted with foreknowledge of the technique and its challenges in order to realize its significant benefits. This paper reports on the state of the art in PHIL and its challenges and presents sample case studies illustrating its impact.

Predictive Control for Energy Management in Ship Power Systems Under High-Power Ramp Rate Loads

Electrical weapons and combat systems integrated into ships create challenges for their power systems. The main challenge is operation under high-power ramp rate loads, such as rail-guns and radar systems. When operated, these load devices may exceed the ships generators in terms of power ramp rate, which may drive the system to instability. Thus, electric ships require integration of energy storage devices in coordination with the power generators to maintain the power balance between distributed resources and load devices. In order to support the generators by using energy storage systems, an energy management scheme must be deployed to ensure load demand is met. This paper proposes and implements an energy management scheme based on model predictive control to optimize the coordination between the energy storage and the power generators under high-power ramp rate conditions. The simulation and experimental results validate the proposed technique in a reduced scale, notional electric ship power system.

Parity space approach for enhanced fault detection and intelligent sensor network design in power systems

In this study, the model-based fault detection and isolation (FDI) approach of parity-space is adapted to the diagnosis of sensor faults in power systems. Hardware redundancy is conventionally utilized to overcome this problem. However, this is an expensive solution. Instead, we propose to detect and locate faults by the systematic use of the systems analytical redundancy, with a global view of the system. This redundancy can be used to detect and isolate sensor failure as well. We also give necessary and sufficient conditions for a sensor network to be able detect faults, sensor failures included. Hence, the sensor configuration problem boils down to an optimization problem that can be intelligently guided by our method. The principle of parity-space approach is described in detail and illustrated on a simple power system model. The method is then validated through simulation.

Real-time stability assessment utilizing non-linear time series analysis

In this study, the complexity of the nonlinear behavior of a Solid State Transformer is quantified to assess the stability of the system in real-time under different operating conditions. This system is more prone to instability compared to the traditional 60 Hz transformers due to the nonlinear behavior of power electronic components. Commonly used linear stability techniques such as Nyquist and Bode are not effective for assessing the dynamic stability in nonlinear systems. The nonlinear stability techniques are computationally complex for the implementation and real-time stability assessment. A complexity-based approach, which involves only the output terminal measurement, is proposed in this research. This approach is straightforward for real-time application and cost effective compared to existing approaches that require complex models or adding perturbation to the system. The system is modeled in MATLAB/SIMULINK, and the simulation results are discussed in different operating conditions in order to validate the proposed technique.

Dynamic Behavioral Observation in Power Systems Utilizing Real-Time Complexity Computation

In this paper, a novel real-time complexity measurement (RCM) through the approximate entropy calculation is put forward to detect the dynamical changes in the power system. These dynamical changes (can be seen as irregularities) are able to drive the system into instability. The detection of these irregularities based on the RCM technique brings a clear understanding of the system interactions when the system undergoes changes in terms of parameters or the topology. Unlike the conventional linear-based tools, which are highly reliant on the model, this technique is independent of the model. In this paper, three solid state transformers (SSTs) in a micro-grid are considered as a test system. The test system is evaluated under different scenarios implying different types of dynamics for each of three SSTs. The performance of the proposed RCM method is verified using a real-time processor coordinated with the test system.

Sensor optimization and placement for enhanced power system monitoring using entropy

In this paper we propose a new methodology for sensor optimization and placement in a power system. The objectives of this study are to identify basic concepts on sensor optimization and placements to enhance the reliability and for efficient sensor data collection, processing, and transmission. Two approaches based on automatic control and information theory have been proposed. Condition of observability and fault detectability and isolability is developed to determine the optimal number of sensors and to determine the set of candidate sensors necessary for state estimation and fault detection and isolation. An entropy-based heuristic is proposed for the selection of the best sensors candidate that increases the information gain, thereby decreasing the drawback of system complexity and information uncertainty.