1,414 research outputs found
CFD AND NEURAL NETWORK-BASED EXPERT SYSTEM FOR THE SUPERVISION OF BOILERS AND FURNACES
CFD (Computational Fluid Dynamics) tools were used to build a virtual furnace, validated with experimental data. This model was used to simulate both normal and “faulty” behaviours, regarding parameters such as energy conversion efficiency, steam leakage and fouling. A database was developed comprising normal situations and simulated fault sets, characterized by virtual sensor outputs used in the evaluation of diagnostic parameters patterns to be processed and recognized by the diagnostic system. The database was processed using Neural Networks, with satisfactory results even in their most simple form (backpropagation networks) trained using standard algorithms. Pattern recognition was thus performed, identifying root causes of simulated anomalies. Interactions with related research areas and future proposed developments are also discussed
Applications of Computational Intelligence to Power Systems
In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty
Artificial Neural Network and its Applications in the Energy Sector – An Overview
In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists
have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an
overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few
ANALYSIS OF BOILER TUBE LEAKAGE BY USING ARTIFICIAL NEURAL NETWORK
Artificial neural network (ANN) models, developed by training the network with
data from an existing plant, are very useful especially for large systems such as Thermal
Power Plant. The project is focusing on the ANN modeling development and to examine
the relative importance of modeling and processing variables in investigating the unit
trip due to steam boiler tube leakage.
The modeling and results obtained will be used to overcome the effect of the boiler tube
leakage which influenced the boiler to shutdown if the tube leakage continuously
producing the mixture of steam and water to escape from the risers into the furnace. The
Artificial Intelligent-ANN has been chosen as the system to evaluate the behavior of the
boiler because it has the ability to forecast the trips.
Hence, the objective of this study has been developed to design an ANN to detect and
diagnosis the boiler tube leakage and to simulate the ANN using real data obtained from
Thermal Power Plant. The feed-forward with back-propagation, (BP) ANN model will
be trained with the real data obtained from the plant.
Training and validation of ANN models, using real data from an existing plant, are very
useful to minimize or avoid the trip occurrence in the plants. The study will focus on
investigating the unit trip due to tube leakage of risers in the boiler furnace and
developing the ANN model to forecast the trip
Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines
153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale
Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast
The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win
Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control
Post-combustion carbon capture (PCC) with chemical absorption has strong interactions with coal-fired power plant (CFPP). It is necessary to investigate dynamic characteristics of the integrated CFPP-PCC system to gain knowledge for flexible operation. It has been demonstrated that the integrated system exhibits large time inertial and this will incur additional challenge for controller design. Conventional PID controller cannot effectively control CFPP-PCC process. To overcome these barriers, this paper presents an improved neural network inverse control (NNIC) which can quickly operate the integrated system and handle with large time constant. Neural network (NN) is used to approximate inverse dynamic relationships of integrated CFPP-PCC system. The NN inverse model uses setpoints as model inputs and gets predictions of manipulated variables. The predicted manipulated variables are then introduced as feed-forward signals. In order to eliminate steady-state bias and to operate the integrated CFPP-PCC under different working conditions, improvements have been achieved with the addition of PID compensator. The improved NNIC is evaluated in a large-scale supercritical CFPP-PCC plant which is implemented in gCCS toolkit. Case studies are carried out considering variations in power setpoint and capture level setpoint. Simulation results reveal that proposed NNIC can track setpoints quickly and exhibit satisfactory control performances
On the analysis and design of genetic fuzzy controllers : An application to automatic generation control of large interconnected power systems using genetic fuzzy rule based systems.
Frequency Control of large interconnected power systems is governed by means
of Automatic Generation Control (AGC), which regulates the system frequency
and tie line power interchange at its nominal parameter set points. Conventional
approaches to AGC controller design is centered around the Proportional, Integral
and Derivative (PID) controller structures, which have found widespread
application within industry.
However, the dynamic changes experienced throughout the life cycle of power
systems have many contributing factors, in part attributed to unknown knowledge
of system behavior, neglected process dynamics and a limited knowledge of
system interactions, which makes modeling for AGC systems particularly trying
for conventional AGC controller design approaches.
Therefore, in this study, Genetic - Fuzzy controllers (GA - Fuzzy) are applied as
plausible candidates for Automatic Generation Controller design and application.
In GA - Fuzzy controllers, genetic algorithms which are based on the foundation
of evolutionary heuristics are used as a global search method for FLC design.
This is particularly motivated by the fact that Fuzzy controllers, especially where
there are large data sets, unknown process knowledge and insu cient expert data
available, FLC controller design proves to be a daunting task.
Therefore, this thesis explores the automatic design of FLC controllers through
evolutionary heuristics and applies the designed controller to the AGC problem
of large interconnected power systems. The design methodology followed is to
understand power system interactions through power plant modeling and the
simulation power plant models for the basis for AGC controller design.
It is shown in this study that the performance of the GA - Fuzzy controller
have favourable characteristics in terms of robust performance, robustness properties
and compares favorably with conventional AGC controller techniques. The
analysis of the GA - Fuzzy controller shows that problem formulation and chromosome
encoding of the problem search space forms an important prerequisite
for controller design by evolutionary methods.
Therefore the study concludes by stating that GA - Fuzzy controllers are plausible
for application within the power industry because of its desirable attributes
and that future work would include extending this research into areas of renewable
energy for study and application
DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS
Steam boilers represent the main equipment in the power plant. Some boiler trips may
lead to an entire shutdown of the plant, which is economically burdensome. An early
detection and diagnosis of the boiler trips is crucial to maintain normal and safe
operational conditions of the plant. Numbers of methodologies have been proposed in
the literature for fault diagnosis of power plants. However, rapid deployment of these
methodologies is difficult to be achieved due to certain inherent limitations such as
system inability to learn or a dynamically improve the system performance and the
brittleness of the system beyond its domain of expertise. As a potential solution to
these problems, two artificial intelligent monitoring systems specialized in boiler trips
have been proposed and coded within the MA TLAB environment in the present work.
The training and validation of the two systems have been performed using real
operational data which was captured from the plant integrated acquisition system of
JANAMANJUNG coal-fired power plant. An integrated plant data preparation
framework for seven boiler trips with related operational variables, has been proposed
for the training and validation of the proposed artificial intelligent systems. The feedforward
neural network methodology has been adopted as a major computational
intelligent tool in both systems. The root mean square error has been widely used as a
performance indicator of the proposed systems. The first intelligent monitoring
system represents the use of the pure artificial neural network system for boiler trip
detection. The final architecture for this system has been explored after investigation
of various main neural network topology combinations which include one and two
hidden layers, one to ten neurons for each hidden layer, three types of activation
function, and four types of multidimensional minimization training algorithms. It has
been found that there was no general neural network topology combination that can
be applied for all boiler trips. All seven boiler trips under consideration had been
detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic
algorithms and artificial neural networks as a hybrid intelligent system. For this
hybrid intelligent system, the selection of appropriate variables from hundreds of
boiler operation variables with optimal neural network topology combinations to
monitor boiler trips was a major concern. The encoding and optimization process
using genetic algorithms has been applied successfully. A slightly lower root mean
square error was observed in the second system which reveals that the hybrid
intelligent system performed better than the pure neural network system. Also, the
optimal selection of the most influencing variables was performed successfully by the
hybrid intelligent system. The proposed artificial intelligent systems could be adopted
on-line as a reliable controller of the thermal power plant boiler
A review of the different boiler efficiency calculation and modeling methodologies
A review of the different mathematical methodologies for calculating energy efficiency in boilers was carried out in this work, considering both the methods included in standards and the proposals and applications published in research works. The classification was delimited in analytical methods, mechanistic modeling, and empirical modeling; moreover, the main equations for each of the methodologies are presented, which allows building a compilation that is expected to be useful for a first approach to the subject. It is displayed that those mechanistic models are used to evaluate subsystems or specific cases that require a high level of detail, while analytical models are used to make a first approximation to the systems described, and empirical models stand out in terms of their use at the industrial level if there is access to a starting database to adjust them.En el presente trabajo se realizó una revisión de las diferentes metodologías matemáticas de cálculo de eficiencia energética en calderas, considerando tanto los métodos incluidos en normas como las diferentes propuestas y aplicaciones publicadas en trabajos investigativos. Se delimitó la clasificación en métodos analíticos, modelados mecanicistas y modelados empíricos. Se exponen las principales ecuaciones para cada una de las metodologías, lo que permite construir una compilación, que se espera que sea de utilidad para una primera aproximación a la temática. Se evidencia que los modelos mecanicistas se emplean para evaluar subsistemas o casos puntuales que requieren alto nivel de detalle, mientras que los modelos analíticos se emplean para realizar una primera aproximación a los sistemas descritos, y los modelos empíricos destacan en cuanto al uso a nivel industrial, siempre y cuando se tenga acceso a una base de datos de partida para ajustarlos
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