Application of Extreme Learning Machine in Transient Stability Assessment of Power Systems

2013 ◽  
Vol 392 ◽  
pp. 544-547 ◽  
Author(s):  
Yang Li ◽  
Xue Ping Gu
Keyword(s):  
Power Systems ◽  
Extreme Learning Machine ◽  
Rough Sets ◽  
Transient Stability ◽  
Test System ◽  
System Level ◽  
Stability Assessment ◽  
Fuzzy Rough Sets ◽  
Operation Condition ◽  
Learning Machine

This paper presents a new method for transient stability assessment (TSA) of power systems using kernel fuzzy rough sets and extreme learning machine (ELM). Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features were firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets were used to reduce the dimension of input space, and ELM was employed to build a TSA model. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus test system.

Novel Real-Time Stability Assessment Algorithm Based on Synchro-Phasors Measurement and Parallel Algorithms for Multi-Machine Networks.

2012 ◽  
Vol 13 (3) ◽  
Author(s):  
Mohamed Abdelwahhab Ali ◽  
Wagdy Mohamed Mansour ◽  
Wael Refaat Anis ◽  
Fahmy Metwally Bendary
Keyword(s):  
Power Systems ◽  
Parallel Algorithms ◽  
Real Time ◽  
Transient Stability ◽  
Test System ◽  
System Stability ◽  
Stability Assessment ◽  
Varying Coefficients ◽  
Rotor Angle ◽  
Time Varying Coefficients

Abstract The introduction of wide area measurements has brought a need for real time assessment methods of power systems, which are accurate and fast. The time varying coefficients in synchronous machine equations make it difficult to find solutions to obtain machine voltages, currents and flux linkages when expressed in phase quantities under transient conditions. The paper presents an approach to design power system transient stability assessment using direct methods for a multi-machine network based on multiple synchronized phasors, measured from Phasor Measurement Units (PMUs) and generator parameters. The generator rotor angle was derived from phasor measurements of voltage and current, and generator parameters using direct algorithm . The method assumes that a temporary fault is applied to the system therefore the pre-fault and post-fault conditions are similar. The multi-machine system was reduced to groups denoted Single Machine to Equivalent Bus (SMEB) models and another groups denoted Load Equivalent Bus (LEB) using Parallel Algorithms (PAs) [1]. The use of these PAs eliminates the SPMUs at each bus in the system, and it is required number of SPMUs only equals the number of generator buses. So that, the Equal Area Criterion in both rotor angle domain and time domain can be applicable for the SMEBs groups to assess the system stability in real-time through the Synchro-Phasors Measurements Units (SPMUs). A temporary three phase fault was simulated at test system comprises 2-machine, 8-bus network for validating the novel algorithm.

scholarly journals Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yanjun Zhang ◽  
Tie Li ◽  
Guangyu Na ◽  
Guoqing Li ◽  
Yang Li
Keyword(s):  
Power Systems ◽  
Power System ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Transient Stability ◽  
Stability Prediction ◽  
Learning Machine ◽  
Elm Classifier ◽  
Ipso Algorithm ◽  
Synchronized Measurements

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

Real-Time Transient Stability Assessment Based on Genetic Algorithm-Extreme Learning Machine

2013 ◽  
Vol 427-429 ◽  
pp. 1390-1393
Author(s):  
Bo Wang ◽  
Ke Wang ◽  
Da Hai You ◽  
Wei Hua Chen ◽  
Gang Wang
Keyword(s):  
Genetic Algorithm ◽  
New England ◽  
Real Time ◽  
Extreme Learning Machine ◽  
Transient Stability ◽  
Assessment Method ◽  
Stability Assessment ◽  
Electromagnetic Power ◽  
Hidden Biases ◽  
Learning Machine

In this paper an genetic algorithm-extreme learning machine (ELM) based real-time transient stability assessment method is proposed. This method uses genetic algorithm (GA) to search optimal input weights and hidden biases in the principle of cross validation to establish GA-ELM classifier. In order to do real-time transient stability assessment, generator trajectories of rotor angle, rotor speed, voltage magnitude, electromagnetic power and imbalance power in-and post-disturbance are chosen as original features for the quick access based synchronously sampled values. Simulation results of New-England 39-bus system show that this method has good performance in power system transient stability assessment.

scholarly journals Random Vector Functional Link Network Optimized by Jaya Algorithm for Transient Stability Assessment of Power Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jianhong Pan ◽  
Jiashu Fan ◽  
Aidi Dong ◽  
Yang Li
Keyword(s):  
Power Systems ◽  
Random Vector ◽  
Transient Stability ◽  
Probabilistic Neural Network ◽  
System Level ◽  
Measurement Unit ◽  
Support Vector ◽  
Jaya Algorithm ◽  
Stability Assessment ◽  
Functional Link

A novel transient stability assessment (TSA) approach using random vector functional link (RVFL) network optimized by Jaya algorithm, called Jaya-RVFL, is proposed for power systems in this paper. First, by extracting system-level features from phasor measurement unit (PMU) measurements as predictors, an RVFL-based TSA model is proposed. In order to improve the performance of RVFL classifiers, a quantile scaling approach is utilized to optimize the randomization range of input weights via the Jaya algorithm. The simulation results on IEEE 39-bus system and a real-world power system show that the presented method outperforms other popular methods comprising multilayer perception, probabilistic neural network, and support vector machine.

scholarly journals Power flow variation based on extreme learning machine algorithm in power system

2019 ◽  
Vol 10 (3) ◽  
pp. 1244
Author(s):  
Labed Imen ◽  
Labed Djamel
Keyword(s):  
Power System ◽  
Extreme Learning Machine ◽  
Power Flow ◽  
Transient Stability ◽  
Flow Analysis ◽  
Test System ◽  
Electrical Network ◽  
Neural Network Approach ◽  
Algorithm Efficiency ◽  
Learning Machine

<p>The main focus of this paper is a study that empowers us to understand how the temperature variation affects the transmission line resistance and as a result the power flow analysis with a specific end goal to assess losses in the electrical network. The paper is composed of two sections; the first part is a power flow study under normal conditions utilizing the neural network approach while the second investigated extreme learning machine algorithm efficiency and exactitude. Extreme learning machine algorithm has been used to settle several complications in power system: load forecasting, fault diagnosis, economic dispatch, security, transient stability; Thus, we proposed to study this technique to figure out this sort of complex issue.</p>The study was conducted for IEEE 30 bus test system. The simulation results are exposed and analyzed in detail at the end of this paper.

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