A Review on Expert System Applications in Power Plants

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.DOI:http://dx.doi.org/10.11591/ijece.v4i1.502

A Comparative Analysis of Self-Rectifying Turbines for the Mutriku Oscillating Water Column Energy Plant

Oscillating Water Column (OWC) based devices are arising as one of the most promising technologies for wave energy harnessing. However, the most widely used turbine comprising its power take-off (PTO) module, the Wells turbine, presents some drawbacks that require special attention. Notwithstanding different control strategies are being followed to overcome these issues; the use of other self-rectifying turbines could directly achieve this goal at the expense of some extra construction, maintenance, and operation costs. However, these newly developed turbines in turn show diverse behaviours that should be compared for each case. This paper aims to analyse this comparison for the Mutriku wave energy power plant.This work was supported by the MINECO through the Research Project DPI2015-70075-R (MINECO/FEDER, UE) and in part by the University of the Basque Country (UPV/EHU) through PPG17/33. The authors would like to thank the collaboration of the Basque Energy Agency (EVE) through Agreement UPV/EHUEVE23/6/2011, the Spanish National Fusion Laboratory (EURATOM-CIEMAT) through Agreement UPV/EHUCIEMAT08/190, and EUSKAMPUSCampus of International Excellence

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Applications of aerospace technology in the electric power industry

An overview of the electric power industry, selected NASA contributions to progress in the industry, linkages affecting the transfer and diffusion of technology, and, finally, a perspective on technology transfer issues are presented

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

A Dynamic Performance Model for Hybrid Wind/Gas Power Plants

The scope of this chapter is to assess the performance of hybrid power plants and more specifically demonstrate the challenges of partnering the wind turbines with gas turbines. A dynamic engine model of a gas turbine along with a wind turbine model is developed to simulate plethora of scenarios for optimizing their operation in terms of efficiency, fuel consumption and NOx emissions. Moreover, a comparison between the hybrid power plant and a twin gas turbine power plant is carried out to assess the improvement in both NOx emissions and fuel consumption. The results demonstrate and illustrate the significant impact that dynamic performance modeling has in the optimization and controller design of hybrid power plant

REAL TIME PROGNOSTIC STRATEGIES APPLICATION TO GAS TURBINES

Gas turbines are increasingly deployed throughout the world to provide electrical and mechanical power in consumer and industrial sectors. The efficiency of these complex multi-domain systems is dependant on the turbine\u27s design, established operating envelope, environmental conditions, and maintenance schedule. A real-time health management strategy can enhance overall plant reliability through the continual monitoring of transient and steady-state system operations. The availability of sensory information for control system needs often allow diagnostic/prognostic algorithms to be executed in a parallel fashion which warn of impending system degradations. Specifically, prognostic strategies estimate the future plant behavior which leads to minimized maintenance costs through timely repairs, and hence, improved reliability. A health management system can incorporate prognostic algorithms to effectively interpret and determine the healthy working span of a gas turbine. The research project\u27s objective is to develop real-time monitoring and prediction algorithms for simple cycle natural gas turbines to forecast short and long term system behavior. Two real-time statistical and wavelet prognostic methods have been investigated to predict system operation. For the statistical approach, a multi-dimensional empirical description reveals dominant data trends and estimates future behavior. The wavelet approach uses second and fourth-order Daubechies wavelet coefficients to generate signal approximations that forecast future plant operation. To complement the empirical models, a real-time analytical, lumped parameter mathematical model has been developed that describes normal transient and steady-state gas turbine system operation. The model serves as the basis to understand a simple cycle gas turbine\u27s operation, and may be utilized in model-based diagnostic algorithms. To validate the model and the prognostic strategies, extensive data has been gathered for a 4.5 MW Solar Mercury 50 and a 85 MW General Electric 7EA simple cycle gas turbine. For the dynamic gas turbine model, the comparison between the field data and simulation results for five Mercury 50 gas turbine signals (e.g., shaft speed, power, fuel flow, turbine rotor inlet temperature, and compressor delivery pressure) demonstrate a high degree of correspondence. Although there are some deviations between the analytical and experimental results during the transient phase, the estimated steady state results are within 2.0% of the actual data. The direct comparison of the two forecasting methods revealed that the wavelet method is superior since the forecasting error is 2.4% versus 4.0% for the statistical method on the Mercury 50 simple cycle gas turbine steady-state signals (e.g., compressor delivery pressure and turbine rotor inlet temperature). Similarly, the General Electric 7EA steady-state signal (e.g., turbine inlet temperature) offered a forecasting error of 9.23% for the wavelet and 11.47% for the statistical methods, respectively. The developed approaches successfully estimate and predict the system operation and may be used with a diagnostic algorithm to monitor gas turbine system health. An excellent opportunity exists to apply the algorithms to gas turbines for improved operation and reliability

INNOVATIVE OPEN AIR BRAYTON COMBINED CYCLE SYSTEMS FOR THE NEXT GENERATION NUCLEAR POWER PLANTS

The purpose of this research was to model and analyze a nuclear heated multi-turbine power conversion system operating with atmospheric air as the working fluid. The air is heated by a molten salt, or liquid metal, to gas heat exchanger reaching a peak temperature of 660 0C. The effects of adding a recuperator or a bottoming steam cycle have been addressed. The calculated results are intended to identify paths for future work on the next generation nuclear power plant (GEN-IV). This document describes the proposed system in sufficient detail to communicate a good understanding of the overall system, its components, and intended uses. The architecture is described at the conceptual level, and does not replace a detailed design document. The main part of the study focused on a Brayton -- Rankine Combined Cycle system and a Recuperated Brayton Cycle since they offer the highest overall efficiencies. Open Air Brayton power cycles also require low cooling water flows relative to other power cycles. Although the Recuperated Brayton Cycle achieves an overall efficiency slightly less that the Brayton -- Rankine Combined Cycle, it is completely free of a circulating water system and can be used in a desert climate. Detailed results of modeling a combined cycle Brayton-Rankine power conversion system are presented. The Rankine bottoming cycle appears to offer a slight efficiency advantage over the recuperated Brayton cycle. Both offer very significant advantages over current generation Light Water Reactor steam cycles. The combined cycle was optimized as a unit and lower pressure Rankine systems seem to be more efficient. The combined cycle requires a lot less circulating water than current power plants. The open-air Brayton systems appear to be worth investigating, if the higher temperatures predicted for the Next Generation Nuclear Plant do materialize

Impacts of renewable energy resources on effectiveness of grid‐integrated systems: succinct review of current challenges and potential solution strategies

This study is aimed at a succinct review of practical impacts of grid integration of renewable energy systems on effectiveness of power networks, as well as often employed state‐of-the‐art solution strategies. The renewable energy resources focused on include solar energy, wind energy, biomass energy and geothermal energy, as well as renewable hydrogen/fuel cells, which, although not classified purely as renewable resources, are a famous energy carrier vital for future energy sustainability. Although several world energy outlooks have suggested that the renewable resources available worldwide are sufficient to satisfy global energy needs in multiples of thousands, the different challenges often associated with practical exploitation have made this assertion an illusion to date. Thus, more research efforts are required to synthesize the nature of these challenges as well as viable solution strategies, hence, the need for this review study. First, brief overviews are provided for each of the studied renewable energy sources. Next, challenges and solution strategies associated with each of them at generation phase are discussed, with reference to power grid integration. Thereafter, challenges and common solution strategies at the grid/electrical interface are discussed for each of the renewable resources. Finally, expert opinions are provided, comprising a number of aphorisms deducible from the review study, which reveal knowledge gaps in the field and potential roadmap for future research. In particular, these opinions include the essential roles that renewable hydrogen will play in future energy systems; the need for multi‐sectoral coupling, specifically by promoting electric vehicle usage and integration with renewable‐based power grids; the need for cheaper energy storage devices, attainable possibly by using abandoned electric vehicle batteries for electrical storage, and by further development of advanced thermal energy storage systems (overviews of state‐of‐the‐art thermal and electrochemical energy storage are also provided); amongst others

Alternative Energy Sources

The search for alternative sources of energy is an attempt to solve two of the main problems facing the modern world. Today's resources are mainly based on fossil flammable substances such as coal, oil, and natural gas. The first problem is related to the expected and observed depletion of deposits, not only those available but also less accessible. Another is related to global warming from emissions of greenhouse gases (mainly carbon dioxide) as well as emissions of other pollutants in the atmosphere. Mitigating the harmful effects of fossil fuel use is an obvious challenge for mankind. This Special Issue includes articles on the search for new raw materials and new technologies for obtaining energy, such as those existing in nature, methane hydrates, biomass, etc., new more efficient technologies for generating electricity, as well as analyses of the possibilities and conditions of use of these resources for practical applications