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

MCHO – A new indicator for insulation conditions in transmission lines

AbstractConventionally monitoring operating conditions of a power transmission line is accomplished by periodic inspections along this line. This monitoring allows corrective maintenance by finding faults during the inspection. But in more efficient maintenance, predictive techniques that are characterized by real-time monitoring should be employed. Such predictive techniques allow for verifying the working status of the line by using normal working models to detect faults and fault models for diagnosis. This paper presents a study that used a mathematical model appropriate for application to predictive maintenance of transmission line segments at low cost, without the need for sensors distributed along the line, and presenting a new indicator of transmission line operation conditions. By tracking the leakage current of transmission lines, this model allows for estimating the current line insulation status. Once the current line insulation status is known, it is possible to compare it against other future status and verify the progress of the insulation conditions of that line. The model uses a new indicator, called MCHO, which can detect and diagnose both normal and abnormal operating conditions of a power transmission line. This new indicator is the capacitance of the harmonic frequencies of the transmission line leakage current. The model was validated through measurements obtained on a stretch of transmission line

Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis

This paper proposes an approach for maintenancemanagement of wind turbines based on their life. The proposedapproach uses performance analysis and anomaly detection(PAAD) which can detect anomalies and point out the originof the detected anomalies. This PAAD algorithm utilizes neuralnetwork (NN) technique in order to detect anomalies in theperformance of the wind turbine (system layer), and then appliesprincipal component analysis (PCA) technique to uncover theroot of the detected anomalies (component layer). To validatethe accuracy of the proposed algorithm, SCADA data obtainedfrom online condition monitoring of a wind turbine are utilized.The results demonstrate that the proposed PAAD algorithm hasthe capability of exposing the cause of the anomalies. Reducingtime and cost of maintenance and increasing availability and inreturn profits in form of savings are some of the benefits of theproposed PAAD algorithm.QC 20170824</p

Optimising a Microgrid System by Deep Reinforcement Learning Techniques

The deployment of microgrids could be fostered by control systems that do not require very complex modelling, calibration, prediction and/or optimisation processes. This paper explores the application of Reinforcement Learning (RL) techniques for the operation of a microgrid. The implemented Deep Q-Network (DQN) can learn an optimal policy for the operation of the elements of an isolated microgrid, based on the interaction agent-environment when particular operation actions are taken in the microgrid components. In order to facilitate the scaling-up of this solution, the algorithm relies exclusively on historical data from past events, and therefore it does not require forecasts of the demand or the renewable generation. The objective is to minimise the cost of operating the microgrid, including the penalty of non-served power. This paper analyses the effect of considering different definitions for the state of the system by expanding the set of variables that define it. The obtained results are very satisfactory as it can be concluded by their comparison with the perfect-information optimal operation computed with a traditional optimisation model, and with a Naive model