David A. Cartes

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.

Power system islanding is an efficient 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 a number of efficient islanding solutions for large-scale power systems due to its computational efficiency. First, desired generators groups is obtained using 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 resulted islanding solutions provide good static and dynamic stability. Simulations for power systems of different scales demonstrate the effectiveness of the proposed algorithm.

High performance application of 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 operations 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) approach. As an intelligent computational method based on stochastic search, PSO 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 discussed, the method is generally applicable to other types of electrical motors, and as well as other dynamic systems with nonlinear model structure.

Power system defensive islanding is an efficient way to avoid catastrophic wide area blackouts, such as the 2003 North American blackout. Finding defensive islands of large scale power systems is a combinatorial explosion problem. Thus, it is very difficult to find an optimal solution, if it exists, within reasonable time using analytical methods. This paper proposes utilizing the computational efficiency property of an angle modulated particle swarm optimization to find some efficient islanding solutions for large scale power systems. The solutions are referred to as optimal according to a fitness function considering the real power balance between generations and loads in islands, the relative importance of customers, and the desired number of islands. The algorithm can also provide necessary load shedding information. Furthermore, the algorithm can provide a number of candidate solutions for the check of transmission system capacity constraint. Simulations with power systems of different scales demonstrate the accuracy and effectiveness of the proposed algorithm.

Advanced agent technology provides significant potential contribution to distributed intelligent controls. Several agent based applications have been developed in the area of power systems. Although these applications have shown some very good results, agent technology has yet to make a significant impact in the power system area. Many agent based applications are not practical due to limitations such as - computation speed, communication bandwidth, unstable realtime operation, and problem complexity. In this paper strength, weaknesses, threats and opportunities of the existing agent framework in power systems are discussed. Attempts are been made to envisage the future framework for the agent based applications in this area. Need for mutual collaboration among experts and a paradigm for integrated approach for agent application are stressed. Examples of incremental success stories at the Center for Advanced Power Systems of Florida State University are used to highlight the concepts of this paper.

A framework for establishing neighborhood based integrated energy systems, which are highly but not solely dependant on renewable energy is presented. Integrated energy systems is a whole system concept. It includes knowledge, design, analysis, construction, and long term utilization of a community's electrical, mechanical, thermal, educational, and governing systems at all levels, individual homes, communities, local and regional infrastructures, etc. The authors depend heavily on their experiences in the USA State of Florida. The guidance should be generally applicable to world wide communities with additional considerations. Integrated energy systems will provide significant benefit to both the community and the national grid infrastructure. These systems can provide economic, ideological, and aesthetic satisfaction to certain homeowners who believe a greater dependence on renewable energies is demanded by world events. These systems can also provide significant performance improvements for a national grid that faces rapidly increasing demand at a pace that surpasses the pace of additional transmission or even generation in some places of the world. In this paper we touch on considerations for the national infrastructure, the actual neighborhood microgrid design, the energy sources, the economic analysis and education. This is intended to be a start of discussion and not an exhaustive analysis or case for integrated energy systems.

In this article, an expert system based load shedding scheme for ship power systems is revisited. In this load shedding scheme, a popular decision making tool termed the analytical hierarchy process, is utilized for prioritizing the loads to be shed onboard a notional all-electric ship power system. The analytical hierarchy process based load shedding scheme overcomes some of the major drawbacks of traditional load shedding schemes implemented on ship power systems by minimizing the number of loads disconnected, differentiating the priority of loads based on system conditions, and considering a wider ranger of factors that may potentially influence the shedding of electrical loads. An illustration of the developed load shedding scheme implementation to a destroyer class shipboard power system simulated in a real time digital simulator is presented.

This paper presents a combined hardware/software simulation framework for testing and evaluating decentralized control methodologies for integrated mechanical and electrical systems on board US Navy ships. Integrated ship system control is a challenging domain as it involves a set of highly complex and inter-dependent systems, including electrical power, HVAC, and an underlying communications network. This control problem is especially difficult when damage events or current operational objectives require collaboration between the individual systems to make intelligent tradeoffs that maximize overall effectiveness.

It is envisioned that in the future a naval electric ship should be operated with minimum number of human operators, while having the intelligence to automatically reconfigure its power plant under various scenarios. This should be accomplished with less response time and adequate configurations when compared to the systems operated by humans. Multi agent systems have been shown as a promising technology to achieve this goal. In this paper, the authors present a study of various agent systems being developed for Navy shipboard power systems. A particular focus is given on agent based solutions for fault detection and reconfiguration of a shipboard power system. Test scenarios are presented for each agent based solution.