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Publications

Publications by José Ricardo Andrade

2021

INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE ON SCADA DATA

Authors
Almeida, B; Santos, J; Louro, M; Santos, M; Ribeiro, F; Bessa, J; Gouveia, C; Andrade, R; Silva, E; Rocha, N; Viana, P;

Publication
IET Conference Proceedings

Abstract
As AI algorithms thrive on data, SCADA would be considered a natural ground for Artificial Intelligence (AI) applications to be developed, translating that avalanche of information into meaningful and fast insights to human operators. However, presently, the high complexity of the events, the data semantics, the large variety of equipment and technologies translate into very few AI applications developed in SCADA. Aware of the enormous potential yet to be explored, E-REDES partnered with INESC TEC to experiment on the development of two novel AI applications based on SCADA data. The first tool, called Alarm2Insights, identifies anomalous behaviours regarding the performance of the protection functions associated with HV and MV line panels. The second tool, called EventProfiler, uses unsupervised learning to identify similar events (i.e., with similar log messages) in HV line panels, and supervised learning to classify new events into previously defined clusters and detect unique or rare events. Aspects associated to data handling and pre-processing are also discussed. The project's results show a very promising potential of applying AI to SCADA data, enhancing the role of the operator and support him in doing better and more informed decisions. © 2021 The Institution of Engineering and Technology.

2022

Data-Driven Anomaly Detection and Event Log Profiling of SCADA Alarms

Authors
Andrade, JR; Rocha, C; Silva, R; Viana, JP; Bessa, RJ; Gouveia, C; Almeida, B; Santos, RJ; Louro, M; Santos, PM; Ribeiro, AF;

Publication
IEEE ACCESS

Abstract
Network human operators' decision-making during grid outages requires significant attention and the ability to perceive real-time feedback from multiple information sources to minimize the number of control actions required to restore service, while maintaining the system and people safety. Data-driven event and alarm management have the potential to reduce human operator cognitive burden. However, the high complexity of events, the data semantics, and the large variety of equipment and technologies are key barriers for the application of Artificial Intelligence (AI) to raw SCADA data. In this context, this paper proposes a methodology to convert a large volume of alarm events into data mining terminology, creating the conditions for the application of modern AI techniques to alarm data. Moreover, this work also proposes two novel data-driven applications based on SCADA data: (i) identification of anomalous behaviors regarding the performance of the protection relays of primary substations, during circuit breaker tripping alarms in High Voltage (HV) and Medium Voltage (MV) lines; (ii) unsupervised learning to cluster similar events in HV line panels, classify new event logs based on the obtained clusters and membership grade with a control parameter that helps to identify rare events. Important aspects associated with data handling and pre-processing are also covered. The results for real data from a Distribution System Operator (DSO) showed: (i) that the proposed method can detect unexpected relay pickup events, e.g., one substation with nearly 41% of the circuit breaker alarms had an 'atypical' event in their context (revealed an overlooked problem on the electrification of a protection relay); (ii) capability to automatically detect and group issues into specific clusters, e.g., SF6 low-pressure alarms and blocks with abnormal profiles caused by event time-delay problems.

2022

ML-Assistant for Human Operators to Solve Faults and Classify Events Complexity in Electrical Grids

Authors
Campos, V; Andrade, R; Bessa, J; Gouveia, C;

Publication
IET Conference Proceedings

Abstract
Nowadays, human operators at grid control centers analyze a large volume of alarm information during outage’s events, and must act fast to restore the service. Currently, after the occurrence of short-circuit faults and its isolation via feeder protection, fault location and isolation is achieved via remotely controlled switching actions defined by operator’s experience. Despite operator’s experience and knowledge, this makes the process sub-optimal and slower. This paper proposes two novel machine learning-based algorithms to assist human operator decisions, aiming to: i) classify the complexity of a fault occurrence (Occurrences Classifier) based on its alarm events; ii) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes the alarm information of an occurrence and classifies it as a “simple” or “complex” occurrence. The Data2Actions takes a sequence of alarm information from the occurrence and suggests to the operator the more adequate sequence of switching actions to isolate the fault section on the overhead medium voltage line. Both algorithms were tested in real data from a Distribution System Operator between 2017 and 2020, and showed i) an accuracy of 86% for the Data2Actions, and ii) the Occurrences Classifier reached 74% accuracy for “simple” occurrences and 58% for “complex” ones, leading to an overall 65% accuracy. © 2022 IET Conference Proceedings. All rights reserved.

2024

Uncertainty-Aware Procurement of Flexibilities for Electrical Grid Operational Planning

Authors
Bessa, RJ; Moaidi, F; Viana, J; Andrade, JR;

Publication
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract
In the power system decarbonization roadmap, novel grid management tools and market mechanisms are fundamental to solving technical problems concerning renewable energy forecast uncertainty. This work proposes a predictive algorithm for procurement of grid flexibility by the system operator (SO), which combines the SO flexible assets with active and reactive power short-term flexibility markets. The goal is to reduce the cognitive load of the human operator when analyzing multiple flexibility options and trajectories for the forecasted load/RES and create a human-in-the-loop approach for balancing risk, stakes, and cost. This work also formulates the decision problem into several steps where the operator must decide to book flexibility now or wait for the next forecast update (time-to-decide method), considering that flexibility (availability) price may increase with a lower notification time. Numerical results obtained for a public MV grid (Oberrhein) show that the time-to-decide method improves up to 22% a performance indicator related to a cost-loss matrix, compared to the option of booking the flexibility now at a lower price and without waiting for a forecast update.

2023

Easing Predictors Selection in Electricity Price Forecasting with Deep Learning Techniques

Authors
Silva, AR; Fidalgo, JN; Andrade, JR;

Publication
2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM

Abstract
This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

2023

CHALLENGES AND CONSIDERATIONS FOR THE DESIGN AND IMPLEMENTATION OF A CENTRALIZED PROTECTION AND CONTROL SOLUTION FOR MV NETWORKS

Authors
Aleixo, AC; Dias Jorge, R; Gomes, F; Antunes, L; Barraca, JP; Carvalho, R; Antunes, M; Gomes, D; Gouveia, C; Carrapatoso, A; Alves, E; Andrade, J; Gonçalves, L; Falcão, F; Pinho, B; Pires, L;

Publication
IET Conference Proceedings

Abstract
The present paper presents the implementation of next-generation centralized Protection, Automation, and Control (PAC) solution for Medium Voltage (MV) power grids, developed in the scope of the SCALE project [1]. The main goals of the project are the development, testing, and field pilot deployment of an innovative, fully digital PAC system for Substation Automation (SAS), centralizing in a single device the functionalities of several bay-level Intelligent Electronic Devices (IED). The envisioned system, comprised of a Centralized Protection and Control (CPC) device and Merging Units (MU)/Process Interface Units (PIU), constitutes a highly flexible, resilient, future-proof solution that relies both on modern IEC 61850 standards and on legacy industrial protocols to guarantee multi-vendor interoperability and continued integration with multi-generation devices inside and outside of the substation. Centralizing SAS functionalities in a single device provides access to a wide range of data and measurements that unlocks technologically advanced substation-centric network automation applications. © The Institution of Engineering and Technology 2023.

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