David A. Cartes

Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System

Microgrids are a new concept for future energy distribution systems that enable renewable energy integration and improved energy management capability. Microgrids consist of multiple distributed generators (DGs) that are usually integrated via power electronic inverters. In order to enhance power quality and power distribution reliability, microgrids need to operate in both grid-connected and island modes. Consequently, microgrids can suffer performance degradation as the operating conditions vary due to abrupt mode changes and variations in bus voltages and system frequency. This paper presents controller design and optimization methods to stably coordinate multiple inverter-interfaced DGs and to robustly control individual interface inverters against voltage and frequency disturbances. Droop-control concepts are used as system-level multiple DG coordination controllers, and control theory is applied to device-level inverter controllers. Optimal control parameters are obtained by particle-swarm-optimization algorithms, and the control performance is verified via simulation studies.

Particle swarm optimization-based parameter identification applied to permanent magnet synchronous motors

The high-performance application of high-power permanent magnet synchronous motors (PMSM) is increasing. PMSM models with accurate parameters are significant for precise control system designs. Acquisition of these parameters during motor operation is a challenging task due to the inherent nonlinearity of motor dynamics. This paper proposes an intelligent model parameter identification method using particle swarm optimization (PSO). PSO, an intelligent computational method based on stochastic search, is shown to be a versatile and efficient tool for this complicated engineering problem. Through both simulation and experiment, this paper verifies the effectiveness of the proposed method in identification of PMSM model parameters. Specifically, stator resistance and load torque disturbance are identified in this PMSM application. Though PMSM is presented, the method is generally applicable to other types of electrical motors, as well as other dynamic systems with nonlinear model structure.

A multiagent based algorithm for ring-structured shipboard power system reconfiguration

A shipboard power system (SPS) supplies energy to electrical loads on a ship. It is critical for the system to be reconfigurable for the purpose of survivability and reliability. In our earlier work an agent based de-centralized approach for a radial SPS reconfiguration is successfully developed. Each agent in this system only communicates with its immediate neighbors, which reduces the dependency on the system topology. However, when expanding the technique to a meshed structure in the SPS, this lead to the problem of redundant information accumulation among the agents, making the information flow in the system unstable. In this paper, the authors propose a spanning tree algorithm for an agent system, which breaks the mesh-structured system into a single tree-structured system. Then an algorithm for calculating the information flow among agents without redundant information accumulation is presented. Finally, the proposed methodology is illustrated by a test case on a simplified SPS

Fault Detection, Diagnostics, and Prognostics: Software Agent Solutions

Fault diagnosis and prognosis are important tools for the reliability, availability and survivability of navy all-electric ships. Extending the fault detection and diagnosis into predictive maintenance increases the value of this technology. The traditional diagnosis can be viewed as a single diagnostic agent having a model of the whole system to be diagnosed. This becomes inadequate when the system becomes large and distributed as on the electric ships. For such systems, the software multi-agents may offer a solution. This paper first presents a brief review on the traditional fault diagnosis method with an emphasis on its application to electric motors as important components on the all-electric ship. The software agent technology is then introduced. The discussion is made about how this technology supports the drastic manning reduction requirements for the future navy ships. Examples are given on the existing naval applications of diagnostic and prognostic software agents.

An Improved Adaptive Detection Method for Power Quality Improvement

A new adaptive harmonic selective detection method is proposed in this paper. This adaptive method has better convergence properties compared to previous adaptive or neuron detection methods. The adaptive gains of this method can be relatively large to obtain faster convergence. The stability of the proposed method is guaranteed and its properties are analyzed when compared to its predecessors. This proposed adaptive method is simple and effective in extracting fundamental and harmonic current information from nonlinear load currents. The extracted fundamental or harmonic currents therefore can be used as the reference signals for power quality improvement, harmonic selective cancellation or reactive power compensation. The method is also phase-independent and can thus be easily applied to single- or three-phase unbalanced systems. Simulation and experimental results verify the good performance of this improved adaptive detection method. Moreover, two application cases are built and tested to demonstrate its effectiveness in harmonic selective cancellation and reactive power compensation.

Slow coherency and Angle Modulated Particle Swarm Optimization based islanding of large-scale power systems

Power system islanding is an effective way to avoid catastrophic wide area blackouts, such as the 2003 North American Blackout. Islanding of large-scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution within reasonable time using analytical methods. This paper presents a new method to solve this problem. In the proposed method, Angle Modulated Particle Swarm Optimization (AMPSO) is utilized to find islanding solutions for large-scale power systems due to its high computational efficiency. First, desired generator groups are obtained using the slow coherency algorithm. AMPSO is then used to optimize a fitness function defined according to both generation/load balance and similarity to the desired generator grouping. In doing so, the resulting islanding solutions provide good static and dynamic stability. Simulations of power systems of different scales demonstrate the effectiveness of the proposed algorithm.

Control parameter optimization for a microgrid system using particle swarm optimization

Microgrids are new concept of electric power networks consisting of distributed generators, renewable energy sources and sensitive loads. The goal of microgrid operation is to provide reliable and high-quality electric power regardless of faults or abnormal operating conditions. This paper presents control schemes for coordination of multiple microgrid generators, especially with voltage source inverter type interface, for both grid-connected and autonomous modes. To maintain required control performance and power quality during severe conditions, excessive trial-and-error-based repeated tuning process is required. This paper proposes an effective optimal control parameter-tuning method using the particle swarm optimization (PSO) algorithm. Requirements of power quality and load following performance during the island operation are considered in the PSO tuning.

Prony analysis for power system transient harmonics

Proliferation of nonlinear loads in power systems has increased harmonic pollution and deteriorated power quality. Not required to have prior knowledge of existing harmonics, Prony analysis detects frequencies, magnitudes, phases, and especially damping factors of exponential decaying or growing transient harmonics. In this paper, Prony analysis is implemented to supervise power system transient harmonics, or time-varying harmonics. Further, to improve power quality when transient harmonics appear, the dominant harmonics identified from Prony analysis are used as the harmonic reference for harmonic selective active filters. Simulation results of two test systems during transformer energizing and induction motor starting confirm the effectiveness of the Prony analysis in supervising and canceling power system transient harmonics.

Study On Grid Connected Inverter Used in High Power Wind Generation System

In high power wind power generation systems, grid connected voltage source inverters (VSI) are essential devices for power transporting and energy converting. Output currents of inverters increase harmonic distortion due to low switching frequencies. A series inductance usually is used to reduce switching harmonics entering the power distribution network. Another kind of filter, LCL filter, can achieve reduced levels of harmonic distortion at lower switching frequencies. However grid connected inverters with an LCL-filter require more complex control methods to make the system stable. This paper proposes a robust strategy to control grid currents entering a distribution network from a three-phase VSI connected via an LCL filter. This grid connected inverter system is used in high power wind generation systems for reduced current harmonics with low switching frequency. Different from previous control strategies integrating an outer loop grid current regulator with inner capacitor current regulation, this control strategy integrates one bridge current regulation loop with grid current and voltage feed forward (dual feed forwards) to stabilize the system. Therefore, the grid currents are controlled indirectly, and the current sensors number can be reduced. Linear analysis, comparisons with previous methods, simulation and experimental results verify the effectiveness of this control strategy across a range of operating conditions

Integration of a bi-directional dc-dc converter model into a large-scale system simulation of a shipboard MVDC power system

To improve energy flexibility and deal with peak energy demand in shipboard power system, a bi-directional dc/dc converter is investigated for a notional U.S. Navy Medium Voltage DC (MVDC) shipboard power system. Surplus energy due to light electric load or ship-speed variation can be captured by energy storages distributed in 800 V load zones and during heavy load or black starting condition, supplied to the rest of the 5 kV MVDC system through the bi-directional dc/dc converters. This paper presents the controller optimization process using the particle swarm optimization for an isolated-type bi-directional dc-dc converter. The control performance of the proposed controller is evaluated using small-signal average models and a large-scale simulation of the notional U.S. Navy MVDC system using the real-time digital simulator.