Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-levelHI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment

Graphical Convolution Network Based Semi-Supervised Methods for Detecting PMU Data Manipulation Attacks

With the integration of information and communications technologies (ICTs) into the power grid, electricity infrastructures are gradually transformed towards smart grid and power systems become more open to and accessible from outside networks. With ubiquitous sensors, computers and communication networks, modern power systems have become complicated cyber-physical systems. The cyber security issues and the impact of potential attacks on the smart grid have become an important issue. Among these attacks, false data injection attack (FDIA) becomes a growing concern because of its varied types and impacts. Several detection algorithms have been developed in the last few years, which were model-based, trajectory prediction-based or learning-based methods. Phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) system work together to monitor the power system operation. The unsecured devices could offer opportunities to adversaries to compromise the system. In the literature review part of this thesis, the main methods are compared considering computing accuracy and complexity. Most work about PMUs ignored the reality that the number of PMUs installed in a power system is limited to realize observability because of high installing cost. Therefore, based on observable truth of PMU and the topology structure of power system, the graph convolution network (GCN) is proposed in this thesis. The main idea is using selected features to define violated PMU, and GCN is used to classify susceptible violated nodes and normal nodes. The basic detection method is introduced at first. And then the calculation process of neural network and Fourier transform are described with more details about graph convolution network. Later, the proposed detection mechanism and algorithm are introduced. Finally, the simulation results are given and analyzed

Creating protective space for innovation in electricity distribution networks in Great Britain: the politics of institutional change

Innovation in electricity distribution networks will be an important element in the transition to a sustainable low-carbon energy system. The nature of networks as regulated monopolies means that the locus of the evolution of protective space for innovation is regulatory institutions, and that the politics of creating protective space is the politics of institutional change. In this paper, I examine the case of Britain, where protective space for research, development and demonstration projects was created over the course of the 2000s in the form of funding mechanisms within the regulatory regime. The case study is used to test structural and discursive theories of gradual institutional change. I conclude that these theoretical frameworks are consistent with the evidence, but that the characterisations of regime actors and of dominant paradigms are insufficiently flexible. I also conclude that the framework for innovation in the British regulator remains incomplete

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin

Modern solutions for precise fault localization in Low Voltage (LV) Distribution Networks (DNS) often rely on costly tools such as micro-Phasor Measurement Unit (μPMU), potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. Using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMS do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by industry partner Scottish Power Energy Networks (SPEN). Results show that the current estimation regressor significantly improves fault localization and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, enabling highly accurate fault detection using SM voltage-only data, with further refinement through estimation of CSC. The proposed DT offers automated fault detection, enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on the densely-noded distribution network

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin.

Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit ( PMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of PMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables' Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive PMU on a densely-noded distribution network

Smart Battery Technology for Lifetime Improvement

Applications of lithium-ion batteries are widespread, ranging from electric vehicles to energy storage systems. In spite of nearly meeting the target in terms of energy density and cost, enhanced safety, lifetime, and second-life applications, there remain challenges. As a result of the difference between the electric characteristics of the cells, the degradation process is accelerated for battery packs containing many cells. The development of new generation battery solutions for transportation and grid storage with improved performance is the goal of this paper, which introduces the novel concept of Smart Battery that brings together batteries with advanced power electronics and artificial intelligence (AI). The key feature is a bypass device attached to each cell that can insert relaxation time to individual cell operation with minimal effect on the load. An advanced AI-based performance optimizer is trained to recognize early signs of accelerated degradation modes and to decide upon the optimal insertion of relaxation time. The resulting pulsed current operation has been proven to extend lifetime by up to 80% in laboratory aging conditions. The Smart Battery unique architecture uses a digital twin to accelerate the training of performance optimizers and predict failures. The Smart Battery technology is a new technology currently at the proof-of-concept stage

GNSS Related Threats to Power Grid Applications

As power grid environments are moving towards the smart grid vision of the future, the traditional schemes for power grid protection and control are making way for new applications. The advancements in this field have made the requirements for power grid’s time synchronization accuracy and precision considerably more demanding. So far, the signals provided by Global Navigation Satellite Systems have generally addressed the need for highly accurate and stable reference time in power grid applications. These signals however are highly susceptible to tampering as they are being transmitted. Since electrical power transmission and distribution are critical functions for any modern society, the risks and impacts affiliated with satellite-based time synchronization in power grids ought to be examined. This thesis aims to address the matter. The objective is to examine how Global Navigation Satellite Systems are utilized in the power grids, how different attacks would potentially be carried out by employing interference and disturbance to GNSS signals and receivers and how the potential threats can be mitigated. A major part of the research is done through literature review, and the core concepts and different implementations of Global Navigation Satellite Systems are firstly introduced. The literature review also involves the introduction of different power grid components and subsystems, that utilize Global Positioning System for time synchronization. Threat modeling techniques traditionally practiced in software development are applied to power grid components and subsystems to gain insight about the possible threats and their impacts. The threats recognized through this process are evaluated and potential techniques for mitigating the most notable threats are presented.Sähköverkot ovat siirtymässä kohti tulevaisuuden älykkäitä sähköverkkoja ja perinteiset sähköverkon suojaus- ja ohjausmenetelmät tekevät tilaa uusille sovelluksille. Alan kehitys on tehnyt aikasynkronoinnin tarkkuusvaatimuksista huomattavasti aikaisempaa vaativampia. Tarkka aikareferenssi sähköverkoissa on tähän saakka saavutettu satelliittinavigointijärjestelmien tarjoamien signaalien avulla. Nämä signaalit ovat kuitenkin erittäin alttiita erilaisille hyökkäyksille. Sähkönjakelujärjestelmät ovat kriittinen osa nykyaikaista yhteiskuntaa ja riskejä sekä seuraamuksia, jotka liittyvät satelliittipohjaisten aikasynkronointimenetelmien hyödyntämiseen sähköverkoissa, tulisi tarkastella. Tämä tutkielma pyrkii vastaamaan tähän tarpeeseen. Päämääränä on selvittää, miten satelliittinavigointijärjestelmiä hyödynnetään sähköverkoissa, kuinka erilaisia hyökkäyksiä voidaan toteuttaa satelliittisignaaleja häiritsemällä ja satelliittisignaalivastaanottimia harhauttamalla ja kuinka näiden muodostamia uhkia voidaan lieventää. Valtaosa tästä tutkimuksesta on toteutettu kirjallisuuskatselmoinnin pohjalta. Työ kattaa satelliittinavigointijärjestelmien perusteet ja esittelee erilaisia tapoja, kuinka satelliittisignaaleja hyödynnetään sähköverkoissa erityisesti aikasynkronoinnin näkökulmasta. Työssä hyödynnettiin perinteisesti ohjelmistokehityksessä käytettyjä uhkamallinnusmenetelmiä mahdollisten uhkien ja seurausten analysointiin. Lopputuloksena esitellään riskiarviot uhkamallinnuksen pohjalta tunnistetuista uhkista, sekä esitellään erilaisia menettelytapoja uhkien lieventämiseksi

Dynamic Restoration of Active Distribution Networks by Coordinated Repair Crew Dispatch and Cold Load Pickup

This article presents a dynamic restoration strategy for active distribution networks (ADNs) by coordinating repair crew dispatch and frequency-constrained cold load pickup. To incorporate the stochastic repair time, the repair crew dispatch is formulated as “event-driven” with the implementation of model predictive control (MPC). The stochastic repair time is estimated, convexified, and updated dynamically with each MPC execution. The finish of a repair task triggers the subsequent cold load pickup model, where the frequency dynamics are computed and linearly constrained with the help of a uniform frequency response model for low-inertia systems. Next, a co-optimization framework of the two models is developed to coordinate the repair crew dispatch and cold load pickup under a unified time scale. Numerical results on a modified IEEE 33-node test feeder and a real-world 136-node distribution system have verified the effectiveness of the proposed model.©2024 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