Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
نویسندگان
چکیده
منابع مشابه
Multiple Complex Extreme Learning Machine using Holomorphic Mapping for Prediction of Wind Power Generation System
In this paper, a wind prediction system for the wind power generation using ensemble of multiple complex extreme learning machines (C-ELM) is proposed. The extreme learning machines is a single layer feed forward neural network having a fast learning and better generalization ability than the gradient-based learning methods. C-ELM is chosen as base classifier because it is very suitable for pro...
متن کاملBankruptcy prediction using Extreme Learning Machine and financial expertise
Bankruptcy prediction has been widely studied as a binary classification problem using financial ratios methodologies. In this paper, Leave-One-Out-Incremental Extreme Learning Machine (LOO-IELM) is explored for this task. LOO-IELM operates in an incremental way to avoid inefficient and unnecessary calculations and stops automatically with the neurons of which the number is unknown. Moreover, C...
متن کاملTransient Stability Improvement of Multi-machine Power System using Fuzzy Controlled TCSC
Power system is subjected to sudden changes in load levels. Stability is an important concept which determines the stable operation of power system. In general rotor angle stability is taken as index, but the concept of transient stability, which is the function of operating condition and disturbances deals with the ability of the system to remain intact after being subjected to abnormal deviat...
متن کاملLearning of a single-hidden layer feedforward neural network using an optimized extreme learning machine
This paper proposes a learning framework for single-hidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (O-ELM). In O-ELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov’s regularization in order to improve the ...
متن کاملEnsembles of extreme learning machine networks for value prediction
Value prediction is an important subproblem of several reinforcement learning (RL) algorithms. In a previous work, it has been shown that the combination of least-squares temporal-difference learning with ELM (extreme learning machine) networks is a powerful method for value prediction in continuous-state problems. This work proposes the use of ensembles to improve the approximation capabilitie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2015
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2015/529724