The operational challenges of the integration of electric vehicles (EV), air conditioning and photovoltaic panels (PV) are prompting the upgrade of distribution grids, seen here as cyber-physical infrastructures. An important upgrading feature of the cyber-side is the electrical grid monitoring, which needs to expand both in data coverage and granularity. The challenge is to decide the data strategy, or in other words, which level of granularity is actually needed in active distribution networks. This work proposes a framework to assist grid planners in selecting the level of data expansion needed, by quantifying the impact of extended data granularity on control capabilities, and corresponding grid performance. The framework combines machine learning with AC optimal power flow and state estimation to select incremental upgrades of the cyber-physical infrastructure. Grid planning and operation are simulated and tested for the IEEE 33-bus test system over a 5-year span to assess the role of granularity in grid performance for different cyber-infrastructures. The results show that extending data granularity is critical for mitigating voltage violations under high penetration of EVs, air conditioning and PVs. By modeling the relationships between data, grid planning and operation, and grid performance, the framework supports efficient cyber system upgrades to mitigate operational violations while accounting for budget limitations.

High Voltage Circuit Breakers (HVCBs) are critical components in power systems to maintain reliable operation. Accurate condition monitoring of HVCBs is vital to reduce maintenance costs and consequently to enhance grid reliability. However, achieving this with low-cost measurement devices, which often provide noisy signals, poses a significant challenge. In this paper, a novel defect classification framework for HVCBs is proposed that uses the Savitzky-Golay filter to preprocess the most common condition monitoring signal, which is the trip/close coil current. This filter is well-known for denoising while preserving critical signal features. Following signal preprocessing, a robust defect detection and classification methodology is introduced, combining time series similarity assessment techniques, such as Euclidean distance and dynamic time warping, with machine learning algorithms. Moreover, an experimental setup is designed to emulate the behavior of an HVCB's coil mechanism. To further enhance model transparency, Shapley additive explanations analysis is applied, providing interpretability into feature contributions toward model decisions. The obtained results validate the effectiveness of the proposed hybrid approach, demonstrating its potential to provide a cost-effective, accurate, and reliable solution for HVCB condition monitoring.

In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.

This study investigates outage statistics in the Swedish power system. More specifically, this paper delves into the critical analysis and enhancement of data quality, focusing on inconsistencies and missing values, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work not only illuminate the current challenges within outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation. 

This study investigates outage statistics in the Swedish power system. More specifically, this paper highlights the critical importance of addressing data quality issues such as inconsistencies and missing values, including unknown outage causes and unidentified faulty equipment. Existing research often overlooks the depth of these data quality challenges, leaving significant gaps in the reliability and utility of outage statistics. This paper reveals noticeable deficiencies in the current data and proposes a structured format for improving outage reporting through a database with three relations: outage summary, outage breakdown, and customer breakdown. To tackle these issues, a detailed qualitative analysis of the data is conducted, complemented by the exploration and testing of various machine learning algorithms. These algorithms are employed to predict unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and enabling more precise analytical capabilities. Specifically, methods such as decision trees and random forests are utilized to address the data gaps. The findings and proposals within this work not only illuminate the current challenges in outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation.

The reliable operation of high voltage circuit breakers (HVCBs) is crucial for ensuring the security of the power system. Therefore, the accurate calculation of the health index (HI) for proper maintenance of HVCBs is essential. However, existing methods often neglect two key challenges: proper signal preprocessing for feature extraction and incorporating measurement accuracy into the HI model. This paper addresses these shortcomings by proposing a methodology for improving HI representation for HVCBs. We account for measurement accuracy by considering the varying precision of different monitoring schemes. Furthermore, the impact of signal preprocessing on the condition monitoring data is analyzed. Finally, a case study exhibits how the HI based failure rate can affect the reliability centered maintenance scheduling in the power system.

Dynamic thermal rating allows transformers to operate beyond the nameplate rating according to the actual weather and loading conditions. This paper proposes a methodology to improve the application of this technology in the design of new transformers or in the operation of existing transformers connected to wind farms by accurately predicting their load profiles, accounting for the influence of wake effect and turbine availability. Specifically, the variation of turbine availability due to the intermittent wind is considered in the load profile estimation. Additionally, a correction method, which can be incorporated into any wake model, is proposed to improve the accuracy of wake loss computation. A case study shows that the wake effect and the changing turbine availability shorten the time that the transformers maintain at full load, thereby reducing the aging rate of the wind farm export transformers. The findings suggest that considering these two factors in the DTR application can benefit the longevity and efficiency of wind farm exported transformers.

Reliability indices like the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) serve as the Key Performance Indicators (KPIs) for Distribution System Operators (DSOs). They effectively measure the frequency and impact of outages on end users. Given the criticality of the electrical grid to many functions of the modern world, minimizing these values has been and continues to be a priority for DSOs. SAIDI and SAIFI can, however, be influenced by many factors including but not necessarily limited to the network topology, the type of installed components and the quantity of customers connected to the grid. In this work we thus attempt to predict the reliability indices of a DSO based on financial, customer and grid composition statistics reported to regulatory bodies by DSOs in Sweden between 2010 and 2021. By decomposing which features are the strongest predictors for SAIDI and SAIFI, DSOs can see how changes in their customer base and grid composition impact their reliability KPIs. In addition these indices can potentially be used to indicate which parts of the grid are most vulnerable to outages and thus prioritize mitigations at those locations.

The energy system, including the electrical power system, is currently undergoing major changes to meet increased demands and climate target plans, and to stand against potential malicious activities and all sorts of disruptions. Specifically, the electrical power system is drastically changing with regards to consumption, production, transmission, control, monitoring, markets, and digitalization. Such a change, however, makes the power system an attractive and vulnerable target to all kinds of disruptive events and social-cyber-physical attacks since the system is crucial for the functioning of the society and economy. In this work, to act against such events and to study the future power system's susceptibility and resilience towards social-cyber-physical attacks, the Resilient Digital Sustainable Energy Transition (REDISET) project has shown the need for a new model that is able to describe the future electrical power system in a way that reflects the future reality. In this paper, existing power system models, the changing landscape of power systems, the drivers for a new model, the suggested model that comprises 7 building blocks instead of today's 3, and finally a direction of future related work are presented.