154 research outputs found

    Frequency Control of Microgrid Network using Intelligent Techniques – ANN, PSO and ANFIS

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    The electric grid is a complex system that transmits electricity from the point of generation to the point of consumption. According to the IEA, worldwide energy-related carbon emissions in 2021 will be 36.3Gt, 60% greater than at the start of the industrial revolution. Researchers have used intelligent solutions for power system frequency regulation to ensure that the system\u27s frequency is maintained. A proper frequency control of the microgrid necessitates the modeling and study of the systems. To emulate the operation of the human brain, frequency control employs a variety of artificial intelligence-based computer algorithms. This thesis generates a complete state space model of a microgrid composed of solar power plants, wind turbines, battery storage systems, and backup generators. The system frequency control was created for this system and analyzed against a benchmark PID controller utilizing several intelligent controllers such as PSO optimized PID, ANN, and ANFIS. The suggested intelligent frequency controllers were be simulated and validated using MATLAB/ Simulink

    The 1st International Conference on Computational Engineering and Intelligent Systems

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    Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

    Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

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    Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

    Frequency deviations stabilizations in restructured power systems using coordinative controllers

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    Modern restructured power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. An efficient and robust control strategy is obligatory to minimise deviations in the system frequency and tie-line to avoid any possible blackout. Hence, in this research, to achieve this target, automatic generation control (AGC) is utilized as a secondary controller to alleviate the changes in interconnected restructured systems at uncertainties. The objective of AGC is to quickly stabilize the deviations in frequency and tie-line power following load fluctuations. This thesis addresses the performance of AGC in two-area restructured power systems with many sophisticated control strategies in the presence of renewable and traditional power plants. As per literature of research work, there are quite a few research studies on AGC of a restructured system using optimized coordinative controllers. Besides, investigations on advanced optimized-based coordinative controller approaches are also rare to find in the literature. So, various combinations of two degrees of freedom (2DOF) controllers are utilized as supplementary controllers to diminish the frequency deviations. Nevertheless, the interconnected tie-lines are typically congested in areas with huge penetration of renewable sources, which may reduce the tie -line capability. Therefore, distinct FACTS controllers and ultra-capacitor (UC) are integrated into two-area restructured systems for strengthening the tie-line power and frequency. Further, new optimization techniques such as cuckoo search (CS), bat algorithm (BA), moth-flame optimization (MFO) are utilized in this work for investigating the suggested 2DOF controllers and compared their performance in all contracts of restructured systems. As per the simulation outcomes, the amalgamation of DPFC and UC with MFObased 2DOF PID-FOPDN shows low fluctuation rate in frequency and tie-line power. Besides, the settling times (ST) of two areas are 9.5 S for ΔF1, 8.2 S for ΔF2, and 10.15 S for ΔPtie. The robustness of the suggested controller has been verified by ±25% variations in system parameters and loading conditions

    Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

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    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

    Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

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    Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure

    Advanced Mathematics and Computational Applications in Control Systems Engineering

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    Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

    Load frequency controllers considering renewable energy integration in power system

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    Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

    Bayesian reinforcement learning with MCMC to maximize energy output of vertical axis wind turbine

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    Optimization of energy output of small scale wind turbines requires a controller which keeps the wind speed to rotor tip speed ratio at the optimum value. An analytic solution can be obtained if the dynamic model of the complete system is known and wind speed can be anticipated. However, not only aging but also errors in modeling and wind speed prediction prevent a straightforward solution. This thesis proposes to apply a reinforcement learning approach designed to optimize dynamic systems with continuous state and action spaces, to the energy output optimization of Vertical Axis Wind Turbines (VAWT). The dynamic modeling and load control of the wind turbine are accomplished in the same process. The proposed algorithm is a model-free Bayesian Reinforcement Learning using Markov Chain Monte Carlo method (MCMC) to obtain the parameters of an optimal policy. The proposed method learns wind speed pro les and system model, therefore, can utilize all system states and observed wind speed pro les to calculate an optimal control signal by using a Radial Basis Function Neural Network (RBFNN). The proposed method is validated by performing simulation studies on a permanent magnet synchronous generator-based VAWT Simulink model to compare with the classical Maximum Power Point Tracking (MPPT). The results show signi cant improvement over the classical method, especially during the wind speed transients, promising a superior energy output in turbulent settings; which coincide with the expected application areas of VAWT
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