Comparison of different regression models to estimate fault location on hybrid power systems

Ekici, Sami; Ünal, Fatih; Ozleyen, Umit

doi:10.1049/iet-gtd.2018.6213

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…Recently, several studies have utilized this technique for estimating the model uncertainty with a 95% CI. In addition, a number of studies have utilized this technique for identifying the faults or biased nature of any model/system (Bode et al, 2020;Ekici, Unal and Ozleyen, 2019;Fernandes et al, 2022;Saufi et al, 2019;Yang et al, 2019). A few studies have utilized this technique for estimating water quality model uncertainty (Jiang et al, 2021;Khoi et al, 2022;Xu et al, 2022).…”

Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

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Section: Prediction Of Wqi Model Uncertainty 251 Machine Learning Alg...mentioning

confidence: 99%

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

2023

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“…In other words, the AA( f ) and PO( f ) in Equation ( 3) can be characterized as expressions that are dependent on both the communication frequency and the topological structure parameters E in Equation (1).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…With more distributed energy resources (DERs) being connected to medium voltage (MV) distribution networks through inverter-based power sources (IBPS), the meshed networks are expected to become more prevalent. However, this shift from traditional radial systems to meshed networks presents novel challenges for fault location [1]. The single-phase-to-ground location in neutral isolated distribution networks has been a topic of research for a long time and there are numerous available methods [2,3], which can be categorized into four types as follows: steady-state signal-based (SSBM) [4,5], transient signal-based (TSBM) [6,7], signal or disturbance injectionbased (SIBM) [8], and artificial intelligence-based methods (AIBM) [9].…”

Section: Introductionmentioning

confidence: 99%

A PLC communication characteristics‐based fault location method in medium voltage meshed distribution networks

Bin,

Min‐gang,

Junliang

et al. 2024

IET Generation Trans & Dist

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A power line carrier (PLC) communication characteristics‐based method is proposed for single‐phase‐to‐ground fault location in neutral isolated medium voltage meshed distribution networks in this paper. The carrier signals with a time‐varying frequency and constant amplitude are processed by a set of PLC transmitters and receivers, whose placement is optimized by regarding the power network as an undirected graph. Two signal encoding and decoding algorithms for the PLC terminals are proposed to avoid using expensive timing systems between the terminals. The fault location technique is implemented by comparing the cosine similarity of amplitude attenuation and phase offset between the fault and a feature library. The node corresponding to the maximum cosine similarity of the characteristics between the present fault and the library is selected as the location of the current fault. Only one set of low‐cost PLC communication terminals and the widely available power lines are needed in the fault location system, making this approach highly practical. Numerical simulations using MATLAB/Simulink have been performed to verify the technique's feasibility. The results show that the method can accurately locate faults in neutral isolated medium voltage meshed distribution networks. Besides, the presented approach achieves a high level of accuracy in estimating transition resistance values.

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“…Furthermore, their study also found that the stepwise kernel has good potential for estimation with less error (R 2 = 0.88 and RMSE = 1.5 MJ•m −2 •day −1 ). Ekici et al [97] reported that the Matern 5/2 kernel in GPR produced better results in comparison to other supervised ML algorithms and kernels for a hybrid power system study. However, there are a limited number of studies on agricultural biomass and grain yield prediction using UAV-and ground-based optical sensor-derived VIs, using crop height as input crop parameters with comprehensive ML approaches, particularly on sodic soil.…”

Section: Yield Prediction On Rain-fed Sodic Soils Using MLmentioning

confidence: 99%

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

et al. 2021

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Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to assist crop phenotyping on sodic soil are limited. Hence, this study evaluated the ability of multispectral, hyperspectral, 3D point cloud, and machine learning techniques to improve estimation of biomass and grain yield of wheat genotypes grown on a moderately sodic (MS) and highly sodic (HS) soil sites in northeastern Australia. While a number of studies have reported using different remote sensing approaches and crop traits to quantify crop growth, stress, and yield variation, studies are limited using the combination of these techniques including machine learning to improve estimation of genotypic biomass and yield, especially in constrained sodic soil environments. At close to flowering, unmanned aerial vehicle (UAV) and ground-based proximal sensing was used to obtain remote and/or proximal sensing data, while biomass yield and crop heights were also manually measured in the field. Grain yield was machine-harvested at maturity. UAV remote and/or proximal sensing-derived spectral vegetation indices (VIs), such as normalized difference vegetation index, optimized soil adjusted vegetation index, and enhanced vegetation index and crop height were closely corresponded to wheat genotypic biomass and grain yields. UAV multispectral VIs more closely associated with biomass and grain yields compared to proximal sensing data. The red-green-blue (RGB) 3D point cloud technique was effective in determining crop height, which was slightly better correlated with genotypic biomass and grain yield than ground-measured crop height data. These remote sensing-derived crop traits (VIs and crop height) and wheat biomass and grain yields were further simulated using machine learning algorithms (multitarget linear regression, support vector machine regression, Gaussian process regression, and artificial neural network) with different kernels to improve estimation of biomass and grain yield. The artificial neural network predicted biomass yield (R2 = 0.89; RMSE = 34.8 g/m2 for the MS and R2 = 0.82; RMSE = 26.4 g/m2 for the HS site) and grain yield (R2 = 0.88; RMSE = 11.8 g/m2 for the MS and R2 = 0.74; RMSE = 16.1 g/m2 for the HS site) with slightly less error than the others. Wheat genotypes Mitch, Corack, Mace, Trojan, Lancer, and Bremer were identified as more tolerant to sodic soil constraints than Emu Rock, Janz, Flanker, and Gladius. The study improves our ability to select appropriate traits and techniques in accurate estimation of wheat genotypic biomass and grain yields on sodic soils. This will also assist farmers in identifying cultivars tolerant to sodic soil constraints.

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Comparison of different regression models to estimate fault location on hybrid power systems

Cited by 13 publications

References 26 publications

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

A PLC communication characteristics‐based fault location method in medium voltage meshed distribution networks

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

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