Lefteri H. Tsoukalas

From smart grids to an energy internet: Assumptions, architectures and requirements

Secure and reliable delivery of energy is essential to modern society. Achieving this goal is becoming more challenging with increasing demand and declining resources. The ongoing restructuring of the rather old delivery infrastructure is an attempt to improve its performance so that energy can be utilized with higher efficiency. Smart grids are an advanced concept with a number of unique features compared to their precedents, including early detection and self healing capabilities. An implementation of smart grids is an energy Internet where energy flows from suppliers to customers like data packets do in the Internet. Apparent benefits from an energy Internet are its openness, robustness and reliability. This paper uses electricity as an example to present some key assumptions and requirements for building the energy Internet. An example is presented.

Vertical two-phase flow identification using advanced instrumentation and neural networks

Abstract Most two-phase flow measurements, including void fraction measurements, depend on correct flow regime identification. There are two steps taken towards the successful identification of flow regimes: first, develop a non-intrusive instrument to demonstrate area-averaged void fluctuations and second, develop a non-linear mapping approach to perform objective identification of flow regimes. In this paper, an advanced non-intrusive impedance void-meter provides input signals to neural networks which are used to identify flow regimes. After training, both supervised and self-organizing neural network learning paradigms performed flow regime identification successfully. The methodology presented holds considerable promise for multiphase flow diagnostic and measurement applications.

Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation

Accurate information on battery state-of-charge, expected battery lifetime, and expected battery cycle life is essential for many practical applications. In this paper, we develop a nonchemically based partially linearized (in battery power) input-output battery model, initially developed for lead-acid batteries in a hybrid electric vehicle. We show that with properly tuned parameter values, the model can be extended to different battery types, such as lithium-ion, nickel-metal hydride, and alkaline. The validation results of the model against measured data in terms of power and efficiency at different temperatures are then presented. The model is incorporated with the recovery effect for accurate lifetime estimation. The obtained lifetime estimation results using the proposed model are similar to the ones predicted by the Rakhmatov and Virudhula battery model on a given set of typical loads at room temperature. A possible incorporation of the cycling effect, which determines the battery cycle life, in terms of the maximum available energy approximated at charge/discharge nominal power level is also suggested. The usage of the proposed model is computationally inexpensive, hence implementable in many applications, such as low-power system design, real-time energy management in distributed sensor network, etc.

Flow regime identification methodology with neural networks and two-phase flow models

Vertical two-phase flows often need to be categorized into flow regimes. In each flow regime, flow conditions share similar geometric and hydrodynamic characteristics. Previously, flow regime identification was carried out by flow visualization or instrumental indicators. In this research, to avoid any instrumentation errors and any subjective judgments involved, vertical flow regime identification was performed based on theoretical two-phase flow simulation with supervised and self-organizing neural network systems. Statistics of the two-phase flow impedance were used as input to these systems. They were trained with results from an idealized simulation that was mainly based on Mishima and Ishiis flow regime map, the drift flux model, and the newly developed model of slug flow. These trained systems were verified with impedance signals measured by an impedance void-meter. The results conclusively demonstrate that the neural network systems are appropriate classifiers of vertical flow regimes. The theoretical models and experimental databases used in the simulation are shown to be reliable.

Generalized fuzzy indices for similarity matching

The purpose of this paper is to introduce a new family of fuzzy similarity indices, referred to as the generalised Tversky index (GTI). The development of GTI is based on the theoretical findings by Amos Tversky regarding the human perception of similarity between different objects, as formulated by the Features Contrast model (FC). Although GTI was developed starting from Tverskys FC, it is shown that it provides a fuzzy extension and generalization of several widely used similarity indices like the Jaccard and Dice coefficients. The mathematical properties of two members of the GTI family, namely TIM and TIP, are studied and their interpretation of similarity is explained by comparison to other conventional indices.

Soft computing technologies in nuclear engineering applications

The application of soft computing technologies, particularly neural networks, fuzzy logic, and genetic algorithms, to the surveillance, diagnostics and operation of nuclear power plants and their components is an area that has great potential for exploitation. Areas of potential application are to the surveillance and diagnostics of complete nuclear power plants and to specific systems such as check valves, instrumentation systems, and rotating machinery. Applications include sensor surveillance and calibration verification, diagnostics of both plant transients and specific faults, efficiency optimization, vibration analysis, loose parts monitoring, and adaptive and/or optimal control. The synergistic benefits of combining the use of neutral networks, fuzzy systems and genetic algorithms are illustrated in several application. Although some of the work cited (e.g. vibration systems) are not necessarily associated with nuclear power plants, the results are directly applicable. Indeed, the methodologies of soft computing technologies have many applications outside the nuclear power field, e.g., fossil-fired power plants, chemical process facilities, high performance aerospace systems, financial market issues, sociological systems, and others.

Financial prediction and trading strategies using neurofuzzy approaches

Neurofuzzy approaches for predicting financial time series are investigated and shown to perform well in the context of various trading strategies involving stocks and options. The horizon of prediction is typically a few days and trading strategies are examined using historical data. Two methodologies are presented wherein neural predictors are used to anticipate the general behavior of financial indexes (moving up, down, or staying constant) in the context of stocks and options trading. The methodologies are tested with actual financial data and show considerable promise as a decision making and planning tool.

Neural-wavelet Methodology for Load Forecasting

Intelligent demand-side management represents a future trend of power system regulation. A key issue in intelligent demand-side management is accurate prediction of load within a local area grid (LAG), which is defined as a set of customers with an appropriate residential, commercial and industrial mix. Power consumption is deemed to be unpredictable in some sense due to the idiosyncratic behaviors of individual customers. However, the overall pattern of a group of consumers is possible to predict. The developed neural-wavelet approach is shown capable of handling the nonlinearities involved and provides a unique tool for intelligent demand-side management. The paper presents the neural-wavelet approach and its implementation to load identification and forecasting.

Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting

A useful tool for the efficient management of the electric power grid is the accurate, ahead-of-time prediction-of-load demand. A novel methodology for very-short-term load forecasting is introduced in this paper, and its performance is tested on a set of historical, demand-side, 5-min data. The approach employs an ensemble of kernel-based Gaussian processes (GPs) whose predictions constitute the terms of a linear model. Adoption of a set of cost functions assessing model accuracy allows the formulation of a multiobjective optimization problem with respect to model coefficients. A genetic algorithm (GA) is used to search for a solution based on the previous step data while Pareto optimality theory provides the necessary conditions to identify an optimal one. Thus, it is the optimized linear model that yields the final prediction over the designated time interval. The proposed methodology is examined on 5-min-interval predictions for 30-min-ahead horizon. It is compared with support vector regression (SVR) and autoregressive moving average (ARMA) models as well as the independent GP forecasters on a set of six cost functions. Results clearly promote the proposed forecasting method not only over individual GPs but also over SVR and ARMA.

Investigation of vertical slug flow with advanced two-phase flow instrumentation

Abstract Extensive experiments of vertical slug flow were carried out with an electromagnetic flowmeter and an impedance void-meter in an air–water two-phase experimental loop. The basic principles of these instruments in vertical slug flow measurements are discussed. Time series of the liquid velocity and the impedance were separated into two parts corresponding to the Taylor bubble and the liquid slug. Characteristics of slug flow, such as the void fractions, probabilities and lengths of the Taylor bubble and liquid slug, slug unit velocity, area-averaged liquid velocity, and liquid film velocity of the Taylor bubble tail, etc., were obtained. For the first time, the area-averaged liquid velocity of slug flow was revealed by the electromagnetic flowmeter. It is realized that the void fraction of the liquid slug is determined by the turbulent intensity due to the relative liquid motion between the Taylor bubble tail region and its wake region. A correlation of the void fraction of the liquid slug is developed based on experimental results obtained from a test section with 50.8 mm i.d. The results of this study suggest a promising improvement in understanding of vertical slug flow.