Line Rate Research Articles

The Spanish Experience: Squeezing Line Ampacities Through Dynamic Line Rating

Dynamic Line Rating (DLR), also known in some instances as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-time thermal rating</i> , is becoming a key component of modern energy management systems for transmission system operators (TSOs) or independent system operators (ISOs) to use transmission assets more efficiently while maintaining current reliability standards. However, the need for this tool will be even more overwhelming in a decarbonized future, given the foreseeable line congestion originating from the coincidence of several factors: 1) significant growth of the share of intermittent renewables; 2) the further electrification of the residential, transportation, and industrial energy demand; and 3) increasing difficulties and costs for the construction of new transmission assets.

A Trauma Center-Based Study for Victims of Domestic Violence: Protecting Women of Richmond and Surrounding Counties.

Domestic violence (DV) is a major health issue on both a national and global scale. In Richmond County, Georgia, there are 18.1 calls per 1000 women for DV, exceeding the national average of 5.9 and Georgia average of 15.3 calls per 1000 women. The goal of the study is to map epidemiologic and spatial trends in communities of DV survivors thereby assessing the feasibility of a prospective intervention initiative for high-risk areas. In partnership with "SafeHomes", a local women's shelter, a retrospective review of physical addresses was compiled from first-time residential clients 18 and older from January 1, 2019 to December 31, 2019. Hot zones were overlaid with Census tracts to assess sociodemographic correlates including race, ethnicity, age, poverty, education, and employment status. From all records (n = 85), 5 hot zones were identified. Analysis of census tract data revealed minority predominance in all hot zones, in addition to an unemployment rate above the state average (23%, 12%, 8%, 5%, and 19%, respectively, vs 3.4%). DV affects both the inner city and suburban areas of Richmond County. Hot zone frequency was disproportionately increased in the urban center where community demographics showed minority predominance, especially of African-Americans. These hot zones also have more individuals living below the federal poverty line and experience unemployment rates greater than the state and national average of 3.4 and 3.5, respectively. This study demonstrated that mapping domestic violence epidemiologic and spatial trends is possible, allowing for targeted support and intervention to reduce the rate of domestic violence.

Change in spin-down rate and detection of emission line in HMXB 4U 2206$$+$$54 with AstroSat observation

This work presents timing and spectral analysis of 4U 2206\(+\)54 using data obtained from LAXPC instrument onboard India’s AstroSat mission. This source was observed with AstroSat in September–October 2016. We report detection of 5648 (4) s pulsations at MJD 57669 in the latter observation of 4U 2206\(+\)54. The pulse profile is sinusoidal and the inherent shape is independent of energy up to 30 keV. The pulse fraction increases with energy from \(\sim \)0.5 to \(\sim \)0.8%. We report an updated spin-down rate of \(2.95 (14) \times 10^{-7}\) s s\(^{-1}\). This is about 0.40 times smaller than the previously reported long term value. The energy spectrum is best modeled with an absorbed power-law with high energy exponential cut-off. We have detected presence of broad emission line in 4U 2206\(+\)54 at an energy of 7 keV with equivalent width of \(\sim \)0.4 keV.

A dagging‐based deep learning framework for transmission line flexibility assessment

AbstractUncertainty in renewable energy generation, energy consumption, and electricity prices, as well as transmission congestion, pose a number of problems in modern power grids, necessitating stability on the supply, grid, and demand sides. Grid‐side stability can be achieved by dynamic line rating (DLR) forecasting, which reliably predicts the overall current carrying potential of overhead transmission lines. Long short‐term memory proved beneficiary in this field, owing to its ability to learn highly variable and uncertain data. To empower this network to tackle the non‐stationary nature of meteorological parameters, a novel machine learning (ML) architecture based on Dagging technique is proposed and tested on the data collected from a 400 kV overhead transmission line. Simulation results corroborate that the proposed Dagging‐based stacked LSTM can successfully handle the non‐stationary issue and outperform the decomposition‐based technique, as the state‐of‐the‐art algorithm, for various forecasting horizons. The results confirm the generalizability of models with an application in forecasting DLR over the line without utilizing additional sensors and communication networks. Moreover, the proposed model is compared to several ML architectures, including support vector machines (SVM), random forest (RF), and multi‐layer perceptron (MLP) in a comprehensive benchmark study. The introduced algorithm outperforms MLP by 3.4%, RF by 9.4%, and SVM by 6.7% in terms of average prediction accuracy.

Enhanced Atmospheric Turbulence Resiliency With Successive Interference Cancellation DSP in Mode Division Multiplexing Free-Space Optical Links

We experimentally demonstrate the enhanced atmospheric turbulence resiliency in a 137.8 Gbit/s/mode mode-division multiplexing free-space optical communication link through the application of a successive interference cancellation digital signal processing algorithm. The turbulence resiliency is further enhanced through redundant receive channels in the mode-division multiplexing link. The proof of concept demonstration is performed using commercially available mode-selective photonic lanterns, a commercial transponder, and a spatial light modulator based turbulence emulator. In this link, 5 spatial modes with each mode carrying 34.46 GBaud dual-polarization quadrature phase shift keying signals are successfully transmitted with an average bit error rate lower than the hard-decision forward error correction limit. As a result, we achieved a record-high mode- and polarization-division multiplexing channel number of 10, a record-high line rate of 689.23 Gbit/s, and a record-high net spectral efficiency of 13.9 b/s/Hz in emulated turbulent links in a mode-division multiplexing free-space optical system.

Railway car routing optimization based on the coordinated utilization of station‐line capacities

AbstractTo optimize railway car routing based on balanced station‐line capacities, thereby improving railway freight traffic organization efficiency, and reducing transportation costs, a railway car routing optimization model based on the coordinated utilization of station‐line capacities was developed. Based on the theory of the membership function in fuzzy mathematics, the level of railway station‐line coordination based on capacity utilization was presented. The occupancy of a railway line by each car was taken as a binary decision variable. Using a set of deviation constraints, the deviation between the station‐line flow of the railway network and the utilization of a certain level was expressed in the form of decision variables that were then penalized to different degrees in the objective function. The total transportation cost and the penalty cost were taken as the objective function for minimization. Based on the characteristics of the fuzzy membership degree, the relationship between the penalty for capacity deviation and the membership function in the optimization model was analyzed, and the calculation method of the penalty for the capacity deviation considering the membership function was presented. An adaptive large‐scale neighbourhood search (ALNS) algorithm was designed to solve the model, and its effectiveness was verified. An actual 36‐station railway network was used to validate the proposed railway car routing optimization model through comparison with existing models. The optimization results showed that, at the expense of increasing the transportation cost by 1.05%, the coordination penalty cost was reduced by 11.24%, the overall cost was reduced by 3.21%, and most of the situation with 90% utilization rate of stations and lines in the railway network was eliminated. The model proposed in this paper can help reduce the pressure on lines and stations with tight capacity in the network, improve the utilization of virtual lines and stations and coordination of station‐line capacities.

Cyanobacteria Application Ameliorates Floral Traits and Outcrossing Rate in Diverse Rice Cytoplasmic Male Sterile Lines.

In rice, cytoplasmic male sterility (CMS) represents an irreplaceable strategy for producing high-yielding hybrid rice based on the commercial exploitation of heterosis. Thereupon, enhancing floral traits and outcrossing rates in CMS lines increase hybrid seed production and ensure global food security. The exogenous application of cyanobacteria could enhance outcrossing rates in CMS lines and, accordingly, hybrid rice seed production. In the present study, we aimed at exploring the impact of cyanobacteria implementation such as Anabaena oryzae, Nostoc muscorum, and their mixture to promote the floral traits, outcrossing rates, and seed production in hybrid rice. The impact of cyanobacteria (Anabaena Oryza (T2), Nostoc muscorum (T3), and their combination (T4) versus the untreated control (T1) was investigated for two years on the growth, floral, and yield traits of five diverse CMS lines, namely IR69625A (L1), IR58025A (L2), IR70368A (L3), G46A (L4), and K17A(L5). The evaluated CMS lines exhibited significant differences in all measured floral traits (days to heading (DTH), total stigma length (TSL), stigma width (SW), duration of spikelet opening (DSO), spikelet opening angle (SOA)). Additionally, L4 displayed the uppermost total stigma length and stigma width, whereas L1 and L5 recorded the best duration of spikelet opening and spikelet opening angle. Notably, these mentioned CMS lines exhibited the highest plant growth and yield traits, particularly under T4 treatment. Strong positive relationships were distinguished between the duration of the spikelet opening, panicle exertion, panicle weight, seed set, grain yield, total stigma length, spikelet opening angle, stigma width, and number of fertile panicles per hill. Cyanobacteria is a potential promising tool to increase floral traits and seed production in hybrid rice.

Quality aware path finding algorithm for mixed line rates WDM/DWDM networks

Abstract The optical network plays a vital role in the current technology for providing high-speed data communication, which the networks operate at Tb/s. In this case, different modulation techniques can be used for different line rates to achieve high-speed data transmission. The light paths and various data rates such as 10, 40, and 100 Gb/s are the important parameters for mixed line rate networks. The wavelengths and line rates are powerful tools for mixed line rates networks. It can exist on different optical fibers. In this paper, advanced modulation techniques achieve a relative performance with the required Q-factor. This paper analysis for different matrix computations to achieve a superior Q-factor. It can affect the data rates and quality of transmission. This paper also proposed an algorithm that can improve the Q-factor. Q-factor is analyzed and proposes routing and wavelength assignment (RWA) techniques based on the Q-factor obtained at different line rates. This paper is a brief overview of a quality-aware path-finding algorithm for mixed line rates WDM/DWDM networks are present and discussed.

MAP4: A Pragmatic Framework for In-Network Machine Learning Traffic Classification

Self-driving networks guided by machine-learning (ML) algorithms are the driving force for building networks of the future. ML is effective at making inferences about data that is too complex or too unpredictable for humans. The network softwarization enabled by a deep programmability approach opens up new opportunities to deploy ML at the programmable data plane. In this paper, we introduce the MAP4 as a framework that explores the feasibility of mapping ML models in programmable network devices. To achieve this, we rely on the P4 language to deploy a pre-trained model into a programmable switch, utilizing the ML model to accurately classify flows at line rate. Our approach demonstrates that ML models working as classifiers can better fit the data by using the new levels of network programmability from the P4 language. The results showed that with few packets, most of the flows are properly classified. In some use cases, with two packets in the flow, 97% of traffic can be correctly classified, and all classes are properly labeled with a maximum of four packets.

Virtual Filter for Non-Duplicate Sampling With Network Applications

Sampling is key to handling mismatch between the line rate and the throughput of a network traffic measurement module. Flow-spread measurement requires non-duplicate sampling, which only samples the elements (carried in packet header or payload) in each flow when they appear for the first time and blocks them for subsequent appearances. The only prior work for non-duplicate sampling incurs considerable overhead, and has two practical limitations: It lacks a mechanism to set an appropriate sampling probability under dynamic traffic conditions, and it cannot efficiently handle multiple concurrent sampling tasks. This paper proposes a virtual filter design for non-duplicate sampling, which reduces the processing overhead by about half and reduces the memory overhead by an order of magnitude or more under some practical settings. It has a mechanism to automatically adapt its sampling probability to the traffic dynamics. It can be modified to handle sampling for multiple independent tasks with different probabilities. We also enhance the virtual filter for flow spread measurement and super spreader detection with a large measurement period.

Review of measures to improve the lightning performance of transmission lines

• Measures for improving the lightning performance of transmission lines. • Reducing the backflashover rate of transmission lines. • Techniques for improving TL lightning performance. • Threshold tower-footing grounding resistance for improved lightning performance. • Impulse impedance of tower-footing electrodes of transmission lines. This paper discusses fundamental aspects related to the measures for improvement of the lightning performance of transmission lines, considering the advantages and constraints on their application. The content reflects the experiences developed by the authors facing exciting challenges related to the improvement of the performance of real transmission lines installed under very unfavorable conditions. The work considers the approaches and the calculating procedure the authors adopted for providing the support required for decisions about the measure to be adopted. The results seem to be useful for academics and professionals involved with theoretical and practical aspects related to the lightning performance of transmission lines.

A comparison of functional and radiological outcome of combine compression antegrade intertrochanteric nail (InterTan) and proximal femoral nail anti-rotation II (PFNA-II) in elderly patients with intertrochanteric fractures.

To compare the functional and radiological outcome of combine compression interlocking intramedullary nail (InterTan) and proximal femoral nail anti-rotation II (PFNA-II) in the treatment of elderly patients with intertrochanteric fractures. As a retrospective cohort study, records of 88 patients with intertrochanteric fractures treated in our hospital from January 1st, 2019 to July 31st, 2021 were retrospectively reviewed. According to treatment records, it included 45 patients treated with InterTan (Group-A) and 43 patients treated with PFNA-II (Group-B). The operation safety and functional rehabilitation of the two groups were compared and analyzed. This study included 88 patients with intertrochanteric fractures (mean [SD] age, 68.72 [0.10] years at baseline), of whom 52 (59.09%) were males and 36 (40.91%) were females. Operation time and intraoperative blood loss in Group-B were less than Group-A, while fracture healing time was shorter in Group-A. The fracture separation distance was measured four weeks after the operation. The widening rate of the fracture line in Group-A was lower than Group-B (4.4% vs.18.6%; P<0.05). The incidence of complications in Group-A was lower than Group-B (4.4% vs.18.6%; P<0.05). At three, six and twelve months after the operation, the Harris hip score of the two groups was higher than at discharge (P<0.05), with no significant difference between groups (P>0.05). We found no significant difference in the functional outcome in elderly patients with intertrochanteric fractures treated with InterTan and PFNA-II. Early fracture healing and reduced complication rate however has been noted with InterTan.

Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating

It is well known that dynamic thermal line rating has the potential to use power transmission infrastructure more effectively by allowing higher currents when lines are cooler; however, it is not commonly implemented. Some of the barriers to implementation can be mitigated using modern battery energy storage systems. This paper proposes a combination of dynamic thermal line rating and battery use through the application of deep reinforcement learning. In particular, several algorithms based on deep deterministic policy gradient and soft actor critic are examined, in both single- and multi-agent settings. The selected algorithms are used to control battery energy storage systems in a 6-bus test grid. The effects of load and transmissible power forecasting on the convergence of those algorithms are also examined. The soft actor critic algorithm performs best, followed by deep deterministic policy gradient, and their multi-agent versions in the same order. One-step forecasting of the load and ampacity does not provide any significant benefit for predicting battery action.

Targeted manipulation of grain shape genes effectively improves outcrossing rate and hybrid seed production in rice.

Stigma exsertion rate (SER) of the male sterile line is a key limiting factor for hybrid seed production in rice. Although a large number of quantitative trait loci associated with SER have been reported, few genes have been molecularly cloned and functionally characterized, severely hindering the genetic improvement of SER of the male sterile line and the breeding efficiency of hybrid rice. In this study, we identified three grain shape regulatory genes, GS3, GW8 and GS9, as potential candidate genes for targeted manipulation of grain shape and SER. We show that simultaneously knocking out these three genes could effectively increase SER by increasing the ratio of spikelet length/spikelet width and length of stigma and style, without negative impacts on other agronomic traits. Cellular examination and transcriptomic analyses revealed a role of these genes in coordinated regulation of transverse and longitudinal cell division in the pistils. Moreover, we demonstrate that targeted manipulation of these grain shape genes could significantly improve the outcrossing rate in both the ZH11 (a japonica variety) and Zhu6S (an indica male sterile line) backgrounds. Our results provide new insights into the mechanisms of rice SER regulation and develop an effective strategy to improve SER and out-crossing rate in rice, thus facilitating hybrid rice production.

Overexpression of Maize Glutathione S-Transferase ZmGST26 Decreases Drought Resistance of Arabidopsis

Drought stress critically endangers the growth and development of crops. Glutathione S-transferase plays a vital role in response to abiotic stress. However, there are few studies on the role of glutathione S-transferase in maize drought stress. In this study, the significantly downregulated expression of ZmGST26 in roots under drought stress was analyzed by qRT-PCR. Promoter analyses showed that there were several cis-acting elements related to drought stress and that were involved in oxidative response in the promoter region of ZmGST26. Subcellular localization results showed that ZmGST26 was localized in the nucleus. The transgenic lines of the Arabidopsis over-expressing ZmGST26 were more sensitive to drought stress and ABA in seed germination and inhibited ABA-mediated stomatal closure. Under drought stress, phenotypic analyses showed that the germination rate, root length and survival rate of ZmGST26 overexpressing lines were significantly lower than those of wild-type lines. The determination of physiological and biochemical indexes showed that the water loss rate, malondialdehyde, O2− and H2O2 of the overexpression lines significantly increased compared with wild-type Arabidopsis, but the antioxidant enzyme activities (CAT, SOD and POD), and proline and chlorophyll contents were significantly reduced. Subsequently, the qRT-PCR analysis of drought stress-related gene expression showed that, under drought stress conditions, the expression levels of DREB2A, RD29A, RD29B and PP2CA genes in ZmGST26 overexpression lines were significantly lower than those in wild-type Arabidopsis. In summary, ZmGST26 reduced the drought resistance of plants by aggravating the accumulation of reactive oxygen species in Arabidopsis.

Early-Term Results of the Cadence Total Ankle Prosthesis: An European Noninventor Study.

Although considerable literature can be found on the outcome of total ankle replacement (TAR), only a few studies have reported the results of the fixed-bearing Cadence prosthesis. This noninventor study reports a consecutive series of 60 Cadence TAR systems with a mean of 2.9 years' follow-up, focusing on clinical and radiographic outcomes and early complications. This study is the first to assess true postoperative radiographic ankle prosthesis range of motion (ROM) and to report an unanticipated serious adverse device effect. Sixty patients who underwent primary TAR with the Cadence prosthesis between July 2016 and July 2019 were clinically and radiographically evaluated preoperatively and at last follow-up after the procedure. Revisions, additional procedures, implant failure, and complications were reported according to the classifications of Vander Griend and Glazebrook. Radiographic outcomes included radiographic TAR ROM, bone-implant interface, and alignment parameters. The survival rate of the prosthesis was 98.3%. The mean radiographic ankle ROM at the last follow-up was 24 degrees (9 degrees of dorsiflexion and 15 degrees of plantarflexion). The coronal and sagittal alignment of TAR was 90.8 degrees and 3.9 degrees, respectively. Bone-implant interface analysis revealed osteolysis in 9 ankles (15%) and radiolucent lines in 33 ankles (55%) occurring at both component interfaces. Intraoperative complications were 3 periprosthetic malleolar fractures (5%). Five talar implant fractures (implant failure of 8.3%) were observed, and 1 unexplained persistent pain that required a conversion from TAR to a tibiotalocalcaneal arthrodesis. Clinical, radiograph ROM, implant position outcomes, and survival rate at an early-term follow-up of 2.9 years were similar to those reported in recent Cadence studies. However, this study reports 5 unanticipated talar implant fractures and a high rate of posterior radiolucent lines. Level IV, retrospective case series.

Evaluation of Advanced Mutant Restorer Lines for Enhancing Outcrossing Rate and Hybrid Seed Production of Diverse Rice Cytoplasmic Male Sterile Lines

Hybrid rice seed production predominantly relies on the pollen ability of restorer lines and the stigma properties of the cytoplasmic male sterile (CMS) lines. Improving the pollen ability and agronomic performance of restorer lines could be achieved using mutation to reinforce the outcrossing rate and seed set percentage of CMS lines. Two commercial restorer cultivars (Giza-178 and Giza-179) were treated with three different doses of gamma-ray; 200, 300, and 400 Gy. The derived mutant restorer lines were selfed and constantly evaluated with their corresponding parental cultivars until the M6 generation. Six promising mutant lines were chosen based on their performance to be evaluated with their parents for their pollen and agronomic characteristics. The novel-induced mutant restorer lines exhibited significant differences in all studied pollen and agronomic characteristics. The mutant restorer lines R4 and R7 exhibited superior desirable anther length (2.36 and 2.38 mm, in the same order), anther width (0.47 and 0.45 mm), pollen fertility (97.50 and 97.31%), basal pore length (218.8 and 299.5 mm), apical length (103.6 and 108.1), number of pollen grains per anther (1810 and 1832), plant height (121.8 and 115.9 cm), fertile tillers (23.50 and 18.89), number of spikelets per panicle (203.2 and 202.5), panicle length (25.34 and 24.26 cm), number of filled grains per panicle (195.5 and 191.8), seed set percentage (95.56 and 96.63%), and grain yield (11.01 and 11.36 t/ha). Based on the results of the first two seasons, the two novel promising restorers and their parental cultivars were crossed with four diverse CMS lines. The seed set percentage and hybrid seed production of the crossed CMS lines were evaluated for further two seasons. The results exposed that the combinations derived from restorer lines R4 and R7 with most of the evaluated CMS lines produced the uppermost seed set percentage outcrossing rate. Particularly in combination with CMS2, the restorer lines R4 and R7 displayed the highest seed set percentage (39.57 and 34.80%), number of tillers fertile per hill (25.31 and 23.32), seed yield (2.02 and 1.81 t/ha), and harvest index (23.88 and 22.66%). Conclusively, the derived gamma-ray-induced mutant restorer lines R4 and R7 could be exploited as a new source for ensuring desirable pollen and anther characteristics in order to improve the outcrossing rate of CMS lines and hybrid rice seed production.

Estimating sewage flow rate in Jefferson County, Kentucky, using machine learning for wastewater-based epidemiology applications

Abstract Direct measurement of the flow rate in sanitary sewer lines is not always feasible and is an important parameter for the normalization of data used in wastewater-based epidemiology applications. Machine learning to estimate past wastewater influent flow rates supporting public health applications has not been studied. The aim of this study was to assess wastewater treatment plant influent flow rates when compared with weather data and to retrospectively estimate flow rates in Louisville, Kentucky (USA), based on other data-types using machine learning. A random forest model was trained using a range of variables, such as feces-related indicators, weather data that could be associated with dilution in sewage systems, and area demographics. The developed algorithm successfully estimated the flow rate with an accuracy of 91.7%, although it did not perform as well with short-term (one-day) high flow rates. This study suggests that using variables such as precipitation (mm/day) and population size are more important for wastewater flow estimation. The fecal indicator concentration (cross-assembly phage and pepper mild mottle virus) was less important. Our study challenges currently accepted opinions by showing the important public health potential application of artificial intelligence in wastewater treatment plant flow rate estimation for wastewater-based epidemiological applications.

Developing a novel zonal congestion management based on demand response programs considering dynamic transmission ratings

Congestion is one of the main obstacles to fair competition in today’s electricity markets. In this regard, a set of rules are legislated to create a fair competitive environment and ensure adequate control over consumers to maintain an acceptable level of reliability with maximum economic efficiency. In this paper, using power tracing techniques and sensitivity analysis, a new dynamic zonal congestion management (ZCM) model based on the demand response program (DRP) is developed. The congestion control zones are determined by an introduced congestion index (CI) including some candidates with both high effectiveness in congestion alleviation (the sensitivity analysis) and high responsibility in the congestion creation (the power tracing technique). Then, through the intelligent implementation of DRP in the zone, a conceptual framework with a comprehensive solution method for congestion alleviation is proposed. Throughout the proposed partitioning based DRP, the optimal amount of load decrease and incentives are determined considering economic issues. In power system operation, contingency analysis such as generator and line outages and uncertainty of lines capacities are inevitable, so they will be taken into account in our proposed methodology. The capacity fluctuates a lot due to the environmental conditions and it will be shown through a comparative study that dynamic line rating (DLR) has a significant impact on results. Using the proposed method, the system is simplified and power system operators can put their focus on the candidate zone than the whole network and alleviate the congestion effectively in the least possible time. The proposed methodology is applied on IEEE 118-bus test system and the results show the effectiveness of the proposed model.

DLR related model development and performance analysis in the framework of FLEXITRANSTORE

This paper demonstrates a practical implementation of a dynamic line rating (DLR) system and an effective ice prediction and prevention method using line monitoring sensors within the FLEXITRANSTORE international project framework. The presented anti-icing system operated continuously for two years. As a part of the project, 4 line monitoring sensors and 2 weather stations were installed on that section of the power line where icing is most likely to occur. These sensors worked continuously and reliably during the demonstration period. The sensor measurements were supplemented with the load value from the SCADA system and weather forecasts, with the help of which, in addition to the real-time line rating and sag-clearance analysis, a predetermined line rating could also be determined. The investigated system also included an ice prediction subsystem as well as a sag-calculation-based ice detection subsystem. Based on the comparison of the measured and calculated parameters, it can be said that both the operation of the sensors and the quality of the data provided by them were satisfactory. In the case of conductor temperature calculation, the developed mathematical models, which can be applied independently, provided information to the system operator with almost similar accuracy than sensors’ measurements. During the observed period, the ice prediction and detection subsystem were also tested and functioned correctly. As an overview of the project, it can be concluded that an efficient DLR-sensor-based anti-icing system is possible to be realized on the high voltage distribution power grid.