Support Vector Machine Based Voltage Relays for Voltage Disturbance Detection in Micro-Distribution Systems

2013 ◽  
Vol 291-294 ◽  
pp. 2084-2090
Author(s):  
Whei Min Lin ◽  
Chia Sheng Tu ◽  
Ting Chia Ou
Keyword(s):  
Support Vector Machine ◽  
Distribution Systems ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Characteristic Curves ◽  
Inference System ◽  
Trigger Time ◽  
Voltage Swell ◽  
Dynamic Errors ◽  
Disturbance Detection

This study proposes combining fuzzy inference system and support vector machine based voltage relays for voltage disturbance detection in micro-distribution systems (MDSs). Moreover, the coordination characteristic curves of the trigger time versus dynamic errors are proposed for under-voltage and over-voltage protection. Modified coordination characteristic curves use a critical trigger time to isolate the faults. An support vector machine (SVM) is a multi-layer decision-making model, which detects voltage disturbances, such as voltage swell, voltage sag, voltage unbalance, and faults. Computer simulations are conducted, using an IEEE 30-bus power system and micro-distribution systems, to show the effectiveness of the proposed voltage relays.

Thermal error modelling of motorised spindle in large-sized gear grinding machine

2017 ◽  
Vol 231 (5) ◽  
pp. 768-778 ◽  
Author(s):  
He Dai ◽  
Shilong Wang ◽  
Xin Xiong ◽  
Baocang Zhou ◽  
Shouli Sun ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Machine Tools ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Support Vector ◽  
Inference System ◽  
Thermal Errors ◽  
Gear Grinding ◽  
Neuro Fuzzy ◽  
Grinding Machine

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.

Hybrid wavelet-support vector machine approach for modelling rainfall–runoff process

2016 ◽  
Vol 73 (8) ◽  
pp. 1937-1953 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fuzzy Inference ◽  
The Other ◽  
Support Vector ◽  
Rainfall Runoff ◽  
Model Concept ◽  
Inference System ◽  
Svm Model ◽  
Artificial Intelligence Models

Because of the importance of water resources management, the need for accurate modeling of the rainfall–runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall–runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall–runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

2013 ◽  
Vol 27 (10) ◽  
pp. 3803-3823 ◽  
Author(s):  
Afiq Hipni ◽  
Ahmed El-shafie ◽  
Ali Najah ◽  
Othman Abdul Karim ◽  
Aini Hussain ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Levels ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Svm Model

scholarly journals Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Thanh-Hai Le ◽  
Cao-Thang Pham ◽  
Tien-Thinh Le ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Support Vector ◽  
Inference System ◽  
Stone Matrix Asphalt ◽  
Hybrid Artificial Intelligence ◽  
Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

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