Proceedings of MARESEC 2023

The 3rd European Workshop on Maritime Systems Resilience and Security (MARESEC) was dedicated to the research on Resilience, Security, Technology and related Ethical, Legal, and Social Aspects (ELSA) in the context of Maritime Systems, including but not restricted to Offshore/Onshore Infrastructures, Navigation and Shipping and Autonomous Systems. The event, organized by the Institute for the Protection of Maritime Infrastructures of the German Aerospace Center (DLR), took place virtually on June 27th , 2023, with over 60 participants. Out of all submitted extended abstracts, 13 were selected for oral presentations, and 2 keynotes were delivered on Maritime Surveillance and Networked Autonomous Underwater Robots. Additionally, 2 student presentations were held. The contributions to the conference came from institutions in 22 countries. The final schedule can be found in the appendix

Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective

Renewable energy sources generated locally are becoming increasingly popular in order to achieve carbon neutrality in the near future. Some of these sources are being used in neighbourhood (local, or energy communities) grids to achieve high levels of self-sufficiency. However, the objectives of the local grid and the distribution grid to which it is connected are different and can sometimes conflict with each other. Although the distribution grid allows access to all variable resources, in certain circumstances, such as when its infrastructure is overloaded, redispatch measures need to be implemented. The complexity and uncertainties associated with current and future energy systems make this a challenging bi-level multi-criteria optimisation problem, with the distribution grid representing the upper level and the neighbourhood grid representing the lower level. Solving these problems numerically is not an easy task. However, there are new opportunities to solve these problems with less computational costs if we decompose the flexibility in the lower lever. Therefore, this paper presents a mathematical approach to optimise grid management systems by aggregating flexibility from neighbourhood grids. This mathematical approach can be implemented with centralised or decentralised algorithms to solve congestion problems in distribution grids

Deep Learning Based Non-Intrusive Load Monitoring for a Three-Phase System

Non-Intrusive Load Monitoring (NILM) is a method to determine the power consumption of individual appliances from the overall power consumption measured by a single measurement device, which is usually the main meter. Increase in the adoption of smart meters has facilitated large scale implementation of NILM, which can provide information about individual loads to the utilities and consumers. This will lead to significant energy savings as well as better demand-side management. Researchers have proposed several methods and have successfully implemented NILM for residential sectors that have a single-phase supply. However, NILM has not been successfully implemented for industrial and commercial buildings that have a three-phase supply, due to several challenges. These buildings consume significant amount of power and implementing NILM to these buildings has the potential to yield substantial benefits. In this paper, we propose a novel deep learning-based approach to address some of the key challenges in implementing NILM for buildings that have a three-phase supply. Our approach introduces an ensemble learning technique that does not require training of multiple neural network models, which reduces the computational requirements and makes it economically feasible. The model was tested on a three-phase system that consists of both three- phase loads and single-phase loads. The results show significant improvement in load disaggregation compared to the existing methods and indicate its applicability

Robust Control of Autonomous Underwater Vehicles Using Delta-Sigma-Based 1-Bit Controllers

Autonomous underwater vehicles (AUVs) are robots capable of operating underwater without the need for human operators. Nonetheless, there is the essential requirement of remote operation capability in case of exceptional situations, such as the encounter of unforeseen obstacles or malfunctions. The corresponding robust controller design is a challenging task, especially due to limited communication bandwidth to the land based control system as well as the exposure to disturbances like water currents. The present study, therefore, proposes a Delta-Sigma-based 1-bit PID controller for such AUVs, which consumes less communication resources and is robust to various disturbances arising in the underwater environment. The proposed controller is designed using the Takagi-Sugeno (T-S) fuzzy model of the nonlinear AUV systems. A comparative performance investigation of this controller is carried out with an output feedback controller as reference design, which is based on the same T-S fuzzy model. The stability conditions of both controllers are established. Obtained simulation results indicate that in case of extreme disturbances and limited bandwidth, the reference controller could not stabilise the AUV system. In contrast, the proposed Delta-Sigma-based 1-bit PID controller performed well under all conditions, while using less hardware and communication resources compared to the reference design

Enhanced Fault Classification and Localization in Microgrids Using Machine Learning

The identification and positioning of faults are crucial in microgrids to enhance their performance and control. However, conventional protection methods are not effective due to significant variations in fault currents caused by diverse operational scenarios in microgrids. Additionally, they cannot locate the fault. Thus, the authors propose a deep learning-based system that uses discrete wavelet transform, wavelet energy entropy, and artificial neural networks to classify and locate the faults in the distribution network of the microgrid. The system is designed to quickly isolate the fault and restore power supply. MATLAB/Simulink is used to simulate the microgrid and train the neural networks. The study shows that the proposed system achieves high accuracy in fault classification and localization within a short period

Maximizing Efficiency in Commercial Power Systems with an Optimized Load Classification and Identification Method Using Deep Learning and Ensemble Techniques

Due to the continuous rise of energy demand and electricity costs, the need for a detailed metering option has become crucial. Non-intrusive load monitoring is such an approach that requires less hardware compared to the other load monitoring options, significantly improving consumer comfort. Due to this reason, researchers are encouraged to implement more advanced machine learning techniques capable of accurate load classification and identification; among them, most focus on residential applications due to fewer complications. However, commercial power systems present considerable challenges compared to residential power systems due to the greater diversity of loads and significant imbalances. In order to overcome these challenges, we introduce a novel neural network design that incorporates sequence-to-sequence, WaveNet, and Ensembling techniques to identify and classify single-phase and three-phase loads in commercial power systems. We tested our approach by identifying and classifying nine appliances - five single-phase and four three-phase - for three months, revealing a significant improvement in accuracy

Optimal Scheduling of a Solar-Powered Microgrid Using ML-Based Solar and Load Forecasting

Microgrids, powered by distributed energy resources, are gaining traction as decentralized power systems. However, optimizing microgrid operation poses challenges due to intermittent renewable energy sources and dynamic load patterns. To tackle this, we propose an AI-driven day-ahead optimal scheduling approach for a grid-connected AC microgrid equipped with a solar panel and a battery energy storage system. Our approach leverages Genetic Algorithm, a popular optimization algorithm, to generate demand response strategies and optimal battery dispatch schedule. Additionally, we utilize LightGBM, a decision tree-based machine learning method, for solar and load forecasting prior to scheduling. Our objective is to minimize operational costs while ensuring the sustainability of the microgrid. Our simulation results showcase the effectiveness of our approach in reducing costs, with a 13.86% decrease in electricity costs observed in the University of Moratuwa microgrid under the tariff structure in Sri Lanka. Our proposed demand response optimizing strategies further contribute to cost reduction. Our approach showcases the power of AI in addressing the challenges of microgrid operation and optimization, with promising results in reducing costs and ensuring sustainability

A Data-Driven Approach Based on Artificial Neural Networks for the Detection and Classification of Bearing Anomalies in Power Generation Plants

Power generation plants play a crucial role in modern societies, but they are vulnerable to different types of anomalies and faults that can have serious economic and environmental consequences. Bearing anomaly detection is an effective approach to recognize potential failures beforehand and avoid their occurrence. Recently, artificial neural networks (ANNs) have emerged as a promising approach for detecting anomalies in power generation plants, owing to their capability of acquiring intricate patterns and adapting to diverse operating circumstances. The presented study proposes a novel method to detect bearing anomalies in power generation plants using artificial neural networks. The approach aims to enhance the precision and dependability of anomaly detection by incorporating diverse features extracted from bearing data signals. Experimental validation was carried out on vibration data obtained from a real-world power generation plant to demonstrate the effectiveness of the proposed approach for detecting bearing anomalies. The results indicate that the proposed approach surpasses conventional methods, emphasizing the potential of ANNs for detecting vibration anomalies in power generation plants with higher accuracy and reliability