Real-Time Visualization Tool for Phasor Measurement Unit Data

Authors Details

Titos Avraamides, Markos Asprou, Lenos Hadjidemetriou, Angelos Christofi, Christos Panayiotou

Research Unit

KIOS Research and Innovation Center of Excellence, H. Wise Wire Energy Solutions Limited

Description

The Real-Time Visualization Tool for Phasor Measurement Unit (PMU) Data is designed to provide an intuitive and user-friendly interface for retrieving and visualizing crucial electrical parameters from power systems. The tool enables operators and engineers to monitor real-time information provided by PMUs, offering insights into the grid's stability and efficiency. The tool facilitates the visualization of time-series data for parameters such as voltage, current, active power, reactive power and frequency. This feature aids in identifying trends, patterns, and anomalies, while phasor diagrams offer critical insights into phase relationships between electrical quantities, helping diagnose synchronization and imbalance issues. Additionally, the tool includes robust data management, allowing users to archive retrieved PMU data for detailed analysis and research on system behavior during disturbances or equipment failures. This supports long-term studies aimed at improving grid resilience. An important feature of this tool is also the frequency anomaly detection, which uses a threshold-based approach to detect deviations from the nominal frequency, signaling potential grid issues. The tool incorporates algorithms for estimating transmission line parameters like resistance and reactance. This tool was applied to the Cyprus power system for collecting and analyzing data from 18 PMUs installed in selected substations of the Cyprus transmission system, while it is already used in the Transmission System Operator of Cyprus.

Development of green-tech functionalized, biodegradable fibrous plant nursery bags in ecological seedlings cultivation

Authors Details

Theodora Krasia, Petri Papaphilippou, Christos Christou, Ioanna Savva, Maria Karouzou, Vassilis Drakonakis, Andreas Grigoriou, Andreas Chimaris, Kritonas Onoufriou, Panos Protopapas, Vasileios Fotopoulos, Roghayyeh Mahmoudi

Research Unit

Department of Mechanical and Manufacturing Engineering

Description

Plant nursery bags that are typically used in seedlings production mainly consist of low-density polyethylene which is a non-biodegradable plastic material. As a consequence, upon seedling planting, a large amount of plastic waste ends up in the environment. The primary objective of PlantNGreen is the development of innovative biodegradable nano/microfibrous “green” plant nursery bags that will further functionalized with selected plant growth promoters for use in ecological seedlings cultivation, thus promoting both, environmental protection and seedlings growth promotion. The implementation of this project is based on a strong and effective collaboration established between 2 public academic institutions in Cyprus (UCY, CUT) and 2 local enterprises (Elysee, AmaDema).

A Comprehensive Investigation of Microplastics as Secondary Pollutants in Radionuclide Dispersion and Their Magnet-Assisted Removal Along with Radionuclides from Water.

Authors Details

Ioannis Ioannidis, Ioannis Pashalidis

Research Unit

Department of Chemistry

Description

This study investigates microplastics (MPs) as secondary pollutants for the dispersion of radionuclides in the environment. Specifically, the adsorption behavior of various MPs, such as PN6, PE, PET, PVC, PU, and PLA, for a range of (radio)toxic metals (Ra, Am, Eu, Th, Np, U), spanning oxidation states from +2 to +6, has been investigeted. Experiments were conducted at both trace and relatively high concentrations to investigate factors affecting the adsorption and desorption processes, including pH, ionic strength, temperature, the presence of complexing ligands, and the type and size of MPs. The adsorption onto modified MPs (e.g.,biofilm-coated, natural organic matter (NOM)-coated, oxidized MPs, magnetic MPs) and the desorption in simulated human digestive systems were also studied. Key findings show that MPs can adsorb radionuclides even at ultra-trace levels, with PN6 demonstrating the highest adsorption capacity, particularly for uranium. Adsorption values reached up to log(Kd) of 3.4 L/kg and qmax of ~0.03 mol/kg at higher concentrations. Desorption studies revealed that up to 100% of radionuclides can be released in complexing agents and simulated digestive systems, suggesting that MPs may serve as "Trojan horses" for radionuclides transport in the environment and living organisms. Finally, the results of this study indicate that magnetic MPs can be removed from environmental aqueous solutions using a magnet, achieving a removal capacity of around 97% for radionuclides.

ML-assisted Dynamic Security Assessment of Energy Systems

Authors Details

Georgios Paphitis, Marios Shimillas, and Mathaios Panteli

Research Unit

KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering

Description

Power system asset failures, caused by various reasons, for example extreme weather, often result in power interruptions and electricity customers disconnected. These interruptions occur when the power network is unable to meet the load demand. In some cases, failures can trigger a chain reaction, causing additional components in the network to fail and escalating into larger-scale power outages, often national-wide blackouts. System operators can intervene to minimize the spread of such cascading failures, but these incidents can unfold rapidly, making timely responses critical. To enhance decision-making during these events, machine learning (ML) techniques unlock significant benefits and applications. This work presents a multimodal ML model designed to aid in real-time disaster management and decision-making—a tool or strategy that helps anticipate, mitigate, and respond to power system crises. Specifically, graph neural networks (GNNs) are employed to predict the path and propagation of cascading failures. In parallel, physics-informed neural networks (PINNs) and traditional ML models estimate the resulting demand not served based on the evolving status of the network. These outputs are seamlessly integrated with a reinforcement learning (RL) framework, which is tasked with determining the optimal set of actions to minimize demand not served. This multimodal approach can be applied in real-time operations to guide system operators toward the best course of action during emergencies, while also supporting long-term operational planning to build more resilient power networks. A key solution already developed is the reconfiguration of the network and the formation of networked microgrids (NMGs), which are essential for enhancing the resilience and reliability of the power system.

Silicide Thermoelectrics for Energy Harvesting Developed by Recyclable Silicon

Authors Details

Panagiotis Mangelis, Panagiotis Ioannou, Savvas Hadjipanteli, Theodora Kyratsi

Research Unit

Powder Technology Lab, Department of Mechanical and Manufacturing Engineering

Description

Taking into account that ca. 66% of global energy consumption is emitted to the environment as waste heat, thermoelectric (TE) devices can offer a promising approach for waste heat recovery and energy savings when applied in combustion engines and industrial processes. Solid-state TE generators are able to provide great advantages since they directly convert heat into clean electrical power through the Seebeck effect, and operate without any CO2 or toxic emissions, vibration, or noise. Silicide thermoelectrics have attracted considerable attention because these compounds consist of earth-abundant and eco-friendly elements, combining good TE properties and suitable stability for mid-temperature TE applications. From the point of view of materials, the TE efficiency is determined by the dimensionless figure of merit, ZT, depending on the Seebeck coefficient, the electrical conductivity, the absolute temperature, and the thermal conductivity. The European program ICARUS attempts to utilize effectively recycled Si from PV manufacturing industry for the development of silicide thermoelectrics. Two types of Si kerf are used for the synthesis of n-type Mg(Si,Sn)-based compounds and p-type Higher Manganese Silicides (HMS) phases. A remarkable ZT of 1 is achieved for the kerf-based Mg(Si,Sn) materials. A prototype TE module (TEM) has been also fabricated and characterized in a TEM testing apparatus. High power density is achieved for the Si-kerf- based TEM device, and a good agreement between experimental and simulated performance is observed. ICARUS work is a successful paradigm of circular economy, providing sustainability and a strong silicon recycling solution in the field of green energy technologies.

Additive Manufacturing and Advanced Materials at the University of Cyprus

Authors Details

Theodora Kyratsi, Angelos Evangelou

Research Unit

Powder Technology Lab, Department of Mechanical and Manufacturing Engineering

Description

Since 2020, the Powder Tech Lab at the University of Cyprus has focused on advancing research in Additive Manufacturing (AM) of metals and the development of metal matrix composites (MMCs). Through a series of parametric studies, we successfully optimized the AM of 316L stainless steel and developed MMCs reinforced with oxides and ceramics. These efforts led to the synthesis of high-performance materials tailored to the needs of the maritime industry, addressing challenges such as corrosion and mechanical degradation. Utilizing techniques like ball milling and additive manufacturing, the lab has established itself as a hub for advanced materials research, contributing to the development of next-generation MMCs.

Building on this foundation, the AM2C3 - Additive Manufacturing and Advanced Materials Competence Centre has now been launched as a Horizon Europe Twinning project. AM2C3 shifts our focus towards the development of innovative aluminium alloys and MMCs, with a strategic emphasis on serving the aerospace industry and the concept of New Space. These sectors demand advanced material solutions that can operate in extreme conditions, including thermal and structural loads. With the creation of a cutting-edge AM lab, the project will provide research and development services to regional SMEs and support Cyprus’s efforts to build expertise in high technology readiness level (TRL) research. AM2C3 positions UCY as a key player in additive manufacturing and materials innovation, fostering competitiveness and collaboration both regionally and internationally.