4,892 research outputs found
Control Theory in Engineering
The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation
Power System Stability Analysis using Neural Network
This work focuses on the design of modern power system controllers for
automatic voltage regulators (AVR) and the applications of machine learning
(ML) algorithms to correctly classify the stability of the IEEE 14 bus system.
The LQG controller performs the best time domain characteristics compared to
PID and LQG, while the sensor and amplifier gain is changed in a dynamic
passion. After that, the IEEE 14 bus system is modeled, and contingency
scenarios are simulated in the System Modelica Dymola environment. Application
of the Monte Carlo principle with modified Poissons probability distribution
principle is reviewed from the literature that reduces the total contingency
from 1000k to 20k. The damping ratio of the contingency is then extracted,
pre-processed, and fed to ML algorithms, such as logistic regression, support
vector machine, decision trees, random forests, Naive Bayes, and k-nearest
neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden
layers with 25%, 50%, 75%, and 100% data size is considered to observe and
compare the prediction time, accuracy, precision, and recall value. At lower
data size, 25%, in the neural network with two-hidden layers and a single
hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing
the hidden layer of NN beyond a second does not increase the overall score and
takes a much longer prediction time; thus could be discarded for similar
analysis. Moreover, when five, seven, and ten hidden layers are used, the F1
score reduces. However, in practical scenarios, where the data set contains
more features and a variety of classes, higher data size is required for NN for
proper training. This research will provide more insight into the damping
ratio-based system stability prediction with traditional ML algorithms and
neural networks.Comment: Masters Thesis Dissertatio
Enhancing the performance of flexible AC transmission systems (FACTS) by computational intelligence
The thesis studies and analyzes UPFC technology concerns the management of active and reactive power in the power networks to improve the performance aiming to reach the best operation criteria. The contributions of the thesis start with formatting, deriving, coding and programming the network equations required to link UPFC steady-state and dynamic models to the power systems. The thesis derives GA applications on UPFC to achieve real criteria on a real world sub-transmission network.
An enhanced GA technique is proposed by enhancing and updating the working phases of the GA including the objective function formulation and computing the fitness using the diversity in the population and selection probability. The simulations and results show the advantages of using the proposed technique. Integrating the results by linking the case studies of the steady-state and the dynamic analysis is achieved. In the dynamic analysis section, a new idea for integrating the GA with ANFIS to be applied on the control action procedure is presented.
The main subject of the thesis deals with enhancing the steady-state and dynamics performance of the power grids by Flexible AC Transmission System (FACTS) based on computational intelligence. Control of the electric power system can be achieved by designing the FACTS controller, where the new trends as Artificial Intelligence can be applied to this subject to enhance the characteristics of controller performance. The proposed technique will be applied to solve real problems in a Finnish power grid. The thesis seeks to deal, solve, and enhance performances until the year 2020, where the data used is until the conditions of year 2020. The FACTS device, which will be used in the thesis, is the most promising one, which known as the Unified Power Flow Controller (UPFC).
The thesis achieves the optimization of the type, the location and the size of the power and control elements for UPFC to optimize the system performance. The thesis derives the criteria to install the UPFC in an optimal location with optimal parameters and then designs an AI based damping controller for enhancing power system dynamic performance. In this thesis, for every operating point GA is used to search for controllers' parameters, parameters found at certain operating point are different from those found at others. ANFISs are required in this case to recognize the appropriate parameters for each operating point
Adaptive Critic Designs for Optimal Control of Power Systems
The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances
Risk-Based Machine Learning Approaches for Probabilistic Transient Stability
Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive expansion plans or conservative operating limits. With the increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate probabilistic transient stability (PTS) analysis. Moreover, the time-domain simulation approach, for transient stability evaluation, involving differential-algebraic equations, can be very computationally intensive, especially for a large-scale system, and for online dynamic security assessment (DSA).
The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this research proposes risk-based, machine learning (ML) approaches, for PTS enhancement by replacing circuit breakers, including the impact of wind generation. Artificial Neural Network (ANN) was used for predicting the benefit-cost ratio (BCR) to reduce the computation effort. Moreover, both ANN and support vector machine (SVM) were used and consequently, were compared, for PTS classification, for online DSA. The training of the ANN and SVM was accomplished using suitable system features as inputs, and PTS status indicator as the output. DIgSILENT PowerFactory and MATLAB was utilized for transient stability simulations (for obtaining training data for ML algorithms), and applying ML algorithms, respectively. Results obtained for the IEEE 14-bus test system demonstrated that the proposed ML methods offer a fast approach for PTS prediction with a fairly high accuracy, and thereby, signifying a strong possibility for ML application in probabilistic DSA.
Advisor: Sohrab Asgarpoo
Advanced fault diagnosis techniques and their role in preventing cascading blackouts
This dissertation studied new transmission line fault diagnosis approaches using
new technologies and proposed a scheme to apply those techniques in preventing and
mitigating cascading blackouts. The new fault diagnosis approaches are based on two
time-domain techniques: neural network based, and synchronized sampling based.
For a neural network based fault diagnosis approach, a specially designed fuzzy
Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap-
plication issues were solved by coordinating multiple neural networks and improving
the feature extraction method. A new boundary protection scheme was designed by
using a wavelet transform and fuzzy ART neural network. By extracting the fault gen-
erated high frequency signal, the new scheme can solve the difficulty of the traditional
method to differentiate the internal faults from the external using one end transmis-
sion line data only. The fault diagnosis based on synchronized sampling utilizes the
Global Positioning System of satellites to synchronize data samples from the two ends
of the transmission line. The effort has been made to extend the fault location scheme
to a complete fault detection, classification and location scheme. Without an extra
data requirement, the new approach enhances the functions of fault diagnosis and
improves the performance.
Two fault diagnosis techniques using neural network and synchronized sampling
are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree
analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring
and control scheme for preventing and mitigating cascading blackouts is proposed.
The local relay monitoring tool was coordinated with the system-wide monitoring
and control tool to enable a better understanding of the system disturbances. Case
studies were presented to demonstrate the proposed scheme.
An improved simulation software using MATLAB and EMTP/ATP was devel-
oped to study the proposed fault diagnosis techniques. Comprehensive performance
studies were implemented and the test results validated the enhanced performance
of the proposed approaches over the traditional fault diagnosis performed by the
transmission line distance relay
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