This article introduces the key innovations of the 5Growth service platform to empower verticals industries with an AI-driven automated 5G End-to-End (E2E) slicing solution which allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, Transportation and Energy), identify the key 5G requirements to enable them and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control, (ii) an AI-driven closed-loop for automated service management with Service Level Agreement (SLA) assurance, and, (iii) Multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.
As the integration of High Voltage Direct Current (HVDC) systems on modern power networks continues to expand, challenges have appeared in different fields of the network architecture. In the Supervisory, Control and Data Acquisition (SCADA) field, software and toolboxes are expected to be modified to meet the new network characteristics. Therefore, this paper presents a unified Weighted Least Squares (WLS) state estimation algorithm suitable for hybrid HVDC/AC transmission systems, based on Voltage Source Converter (VSC). The mathematical formulas of the unified approach are derived for modelling the AC, DC and converter coupling components. The method couples the AC and DC sides of the converter through power and voltage constraints and measurement functions. Two hybrid power system test cases have been studied to validate this work, a 4-AC/4-DC/4-AC network and Cigre B4 DC test case network. Furthermore, comparison between the fully decentralized state estimation and the unified method is provided, which indicated an accuracy improvement and error reduction.
The growing integration of rooftop photovoltaics (PVs) and energy storage units (ESUs) in customer households has resulted in changes in the customer load profiles. This is likely to influence the accuracy of state estimation (SE) carried out based on previously assumed load profiles. In this paper, a statistical model for modern low voltage (LV) customers was developed using Gaussian mixture model (GMM). The resulting model was subsequently applied to SE using weighted least squares (WLS) algorithm. LV network with high penetration of customer-owned PV and ESUs have been simulated. Different scenarios which include load profiles: with PVs integrated but without ESUs, ESUs alone, and with hybrid systems (combination of PVs and ESUs) have been considered. The results are presented and discussed.
The High Voltage Direct Current (HVDC) is an emerging technology for transmitting power over long distances with a higher capacity than the traditional AC systems. The integration of the HVDC systems has demanded changes on the Supervisory, Control and Data Acquisition (SCADA) systems. Several power system applications and toolboxes in the SCADA have to be modified to meet the modern power network characteristics. One of the essential toolboxes is the state estimator, which estimates the network AC and DC systems states. On several occasions, the state estimator fails to deal with severely corrupted data, known as bad data. Therefore, an additional data treatment is required. This paper presents a unified bad data detection block for Weighted Least Squares (WLS) state estimation algorithm suitable for hybrid Voltage Source Converter (VSC)-HVDC/AC transmission systems. The bad data detection block improves the traditional Largest Normalized Residual (LNR) method by integrating the Gaussian Mixture Model (GMM) algorithm. The modifications aim to reduce the time performance of the bad data detection, increase the algorithm robustness, and enhance the state estimation accuracy. The Cigre B4 network is used as a test case to validate this work on a hybrid VSC-HVDC/AC network. UK national grid load profile data is used to construct the simulation measurements set and the GMM model. The work has concluded that the modified GMM-LNR has considerably reduced the bad data detection time and improved the WLS state estimation accuracy.
⎯ Questions related to power quality definitely challenges engineers all over the world. Amongst these, the matter of supply reliability emerges as an important factor towards the improvement of continuity indexes such as the duration and frequency of interruptions. In such a way, information associated to the operational conditions of equipment such as transformers, cables, etc. have a relevant role in the overall electrical complex behavior. Focusing the insulated cables operational status, the occurrence of physical degradation phenomenon such as the so called water tree appears as a common cause for cable life expectance reduction. Having in mind this target, this paper considers the basis related to this effect and describes a procedure to estimate the actual conditions of a given cable. A hardware and software structure is then presented and first experimental results are described to highlight the proposal potentiality at finding operational indexes about the component life expectance.
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