Dynamic Security Assessment For Power System Using Attribute Selection Technique

The evaluation of the dynamic security of the electrical power system after the occurrence of disturbances in the network is one of the most important tools that the control center uses to maintain the system in a safe operating mode, as well as prevent cases of system out of control and cases of complete shutdown. With the annual increase in the size of the electrical system and its distribution over a very wide geographical area, this led to a new challenge to assess dynamic security assessment (DSA), which is dealing with a huge and varied amount of data that requires processing in a very short time. To address these challenges, this study presented a new technique of artificial intelligence, which is the attribute selection technique, to reduce the size of this data and thus improve the accuracy and speed of results. This method relied on the combination of decision tree algorithms and a technique (Attribute selection) in the data obtained from the test system (IEEE-30Bus). The results of this method showed a significant reduction in the number of data used, which amounted to (45.55%) of the total data, Which led to an improvement in the classification accuracy, as the classification accuracy reached (97.27%). This reduction is very important when dealing in the online operating environment, as it saves the time necessary to reach the most accurate evaluation decision and thus issue gives a greater opportunity to take the appropriate decision in the event of disturbances and keep the electrical system in a secure state

Recurrent Neural Networks RNNs and Decision Tree DT Machine Learning-Based Approaches For Transmission System Faults Diagnosis

Accurate and prompt detection of system faults are crucial to maintain sufficient protection of system equipment, avoid false tripping, and cascaded failures.This paper presents a comprehensive study on the effectiveness of machine learning techniques for electrical fault detection and classification. Specifically, a comparative analysis is conducted between two prominent algorithms: Recurrent Neural Networks (RNNs) and Decision Tree (DT). The study employs a dataset comprising real-world electrical fault scenarios to evaluate the performance of RNNs and DT in identifying and categorizing faults. While DT algorithm showed slightly better accuracy in some cases, the RNN exhibited better generalization capabilities and a lower risk of overfitting. The analysis involves various performance metrics such as accuracy, precision, recall, and confusion matrices to comprehensively assess the algorithms\u27 capabilities. The findings provide valuable insights into the strengths and limitations of each approach in the context of electrical fault management. This paper contributes to the selection of suitable techniques based on specific application requirements, advancing the field of predictive maintenance and fault mitigation in electrical systems. Keywords- Decision Tree, Electrical Faults, Fault Classification, Fault Detection, Machine Learning, Recurrent Neural Networks

Influence of Renewable Energy Sources on Day Ahead Optimal Power Flow Based on Meteorological Data Forecast Using Machine Learning: A case study of Johor Province

This article investigates a day ahead optimal power flow considering the intermittent nature of renewable energy sources that involved with weather conditions. The article integrates the machine learning into power system operation to predict precisely day ahead meteorological data (wind speed, temperature and solar irradiance) that influence directly on the calculations of generated power of wind turbines and solar photovoltaic generators. Consequently, the power generation schedulers can make appropriate decisions for the next 24 hours. The proposed research uses conventional IEEE -30-bus as a test system running in Johor province that selected as a test location. algorithm designed in Matlab is utilized to accomplish the day ahead optimal power flow. The obtained results show that the true and predicted values of meteorological data are similar significantly and thus, these predicted values demonstrate the feasibility of the presented prediction in performing the day ahead optimal power flow. Economically, the obtained results reveal that the predicted fuel cost considering wind turbines and solar photovoltaic generators is reduced to 645.34 USD/h as compared to 802.28 USD/h of the fuel cost without considering renewable energy sources. Environmentally, CO2 emission is reduced to 340.9 kg/h as compared to 419.37 kg/h of the conventional system. To validate the competency of the whale optimization, the OPF for the conventional system is investigated by other 2 metaheuristic optimization techniques to attain statistical metrics for comparative analysis

Recurrent Neural Networks RNNs and Decision Tree DT Machine Learning-Based Approaches For Transmission System Faults Diagnosis

Accurate and prompt detection of system faults are crucial to maintain sufficient protection of system equipment, avoid false tripping, and cascaded failures.This paper presents a comprehensive study on the effectiveness of machine learning techniques for electrical fault detection and classification. Specifically, a comparative analysis is conducted between two prominent algorithms: Recurrent Neural Networks (RNNs) and Decision Tree (DT). The study employs a dataset comprising real-world electrical fault scenarios to evaluate the performance of RNNs and DT in identifying and categorizing faults. While DT algorithm showed slightly better accuracy in some cases, the RNN exhibited better generalization capabilities and a lower risk of overfitting. The analysis involves various performance metrics such as accuracy, precision, recall, and confusion matrices to comprehensively assess the algorithms\u27 capabilities. The findings provide valuable insights into the strengths and limitations of each approach in the context of electrical fault management. This paper contributes to the selection of suitable techniques based on specific application requirements, advancing the field of predictive maintenance and fault mitigation in electrical systems. Keywords- Decision Tree, Electrical Faults, Fault Classification, Fault Detection, Machine Learning, Recurrent Neural Networks

Application of Machine Learning Methods for Asset Management on Power Distribution Networks

This study aims to study the different kinds of Machine Learning (ML) models and their working principles for asset management in power networks. Also, it investigates the challenges behind asset management and its maintenance activities. In this review article, Machine Learning (ML) models are analyzed to improve the lifespan of the electrical components based on the maintenance management and assessment planning policies. The articles are categorized according to their purpose: 1) classification, 2) machine learning, and 3) artificial intelligence mechanisms. Moreover, the importance of using ML models for proper decision making based on the asset management plan is illustrated in a detailed manner. In addition to this, a comparative analysis between the ML models is performed, identifying the advantages and disadvantages of these techniques. Then, the challenges and managing operations of the asset management strategies are discussed based on the technical and economic factors. The proper functioning, maintenance and controlling operations of the electric components are key challenging and demanding tasks in the power distribution systems. Typically, asset management plays an essential role in determining the quality and profitability of the elements in the power network. Based on this investigation, the most suitable and optimal machine learning technique can be identified and used for future work. Doi: 10.28991/ESJ-2022-06-04-017 Full Text: PD

A Review on Application of Artificial Intelligence Techniques in Microgrids

A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Voltage Stability Margin Estimation Using Machine Learning Tools

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results

Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment

Static security assessment (SSA) is fundamental in electrical network analysis. However, the growing complexity and variability of grid’s operating conditions can make it tedious, slow, computationally intensive, and limited or impractical for on-line applications when traditional approaches are considered. Since this may hinder the emerging analytical duties of system operators, data-driven alternatives are required for faster and sophisticated decision-making. Although different machine learning algorithms (MLAs) could be applied, Convolutional Neural Networks (CNNs) are one of the most powerful models used in many advanced technological developments due to their remarkable capability to identify meaningful patterns in challenging and complex data sets. According to this, a CNN based approach for fast SSA of power systems with N-1 contingency is presented in this paper. To contribute to the automation of model building and tuning, a settings-free strategy to optimize a set of hyperparameters is adopted. Besides, permutation feature importance is considered to identify only a subset of key features and reduce the initial input space. To illustrate the application of the proposed approach, the simulation model of a practical grid in Mexico is used. The superior performance of the CNN alternative is demonstrated by comparing it with two popular MLAs

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed