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

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System

Keeping computer reliability to confirm reliable, secure, and truthful correspondence of data between different enterprises is a major security issue. Ensuring information correspondence over the web or computer grids is always under threat of hackers or intruders. Many techniques have been utilized in intrusion detections, but all have flaws. In this paper, a new hybrid technique is proposed, which combines the Ensemble of Feature Selection (EFS) algorithm and Teaching Learning-Based Optimization (TLBO) techniques. In the proposed, EFS-TLBO method, the EFS strategy is applied to rank the features for choosing the ideal best subset of applicable information, and the TLBO is utilized to identify the most important features from the produced datasets. The TLBO algorithm uses the Extreme Learning Machine (ELM) to choose the most effective attributes and to enhance classification accuracy. The performance of the recommended technique is evaluated in a benchmark dataset. The experimental outcomes depict that the proposed model has high predictive accuracy, detection rate, false-positive rate, and requires less significant attributes than other techniques known from the literature

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system

Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability. Power transformers, Phase Shift Transformers or Phase Angle Regulators, and transmission lines are critical components in power systems, and ensuring their safety is a primary issue. Differential relays are commonly employed to protect transformers, whereas distance relays are utilized to protect transmission lines. Magnetizing inrush, overexcitation, and current transformer saturation make transformer protection a challenge. Furthermore, non-standard phase shift, series core saturation, low turn-to-turn, and turn-to-ground fault currents are non-traditional problems associated with Phase Angle Regulators. Faults during symmetrical power swings and unstable power swings may cause mal-operation of distance relays, and unintentional and uncontrolled islanding. The distance relays also mal-operate for transmission lines connected to type-3 wind farms. The conventional protection techniques would no longer be adequate to address the above-mentioned challenges due to their limitations in handling and analyzing the massive amount of data, limited generalizability of conventional models, incapability to model non-linear systems, etc. These limitations of conventional differential and distance protection methods bring forward the motivation of using machine learning techniques in addressing various protection challenges. The power transformers and Phase Angle Regulators are modeled to simulate and analyze the transients accurately. Appropriate time and frequency domain features are selected using different selection algorithms to train the machine learning algorithms. The boosting algorithms outperformed the other classifiers for detection of faults with balanced accuracies of above 99% and computational time of about one and a half cycles. The case studies on transmission lines show that the developed methods distinguish power swings and faults, and determine the correct fault zone. The proposed data-driven protection algorithms can work together with conventional differential and distance relays and offer supervisory control over their operation and thus improve the dependability and security of protection systems

Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.Comment: 13 pages, 9 figures. Submitted to IEEE access. A supplementary video for the work presented in this paper can be accessed at: https://www.youtube.com/watch?v=dz3WTFU7W7c. This version includes minor style edits for appendix and reference

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems