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.

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.

Parallel Algorithm for Transient Stability Online Simulation

2012 ◽  
Vol 433-440 ◽  
pp. 4446-4451
Author(s):  
Tian Liang Xue ◽  
Fang Zong Wang ◽  
Jing Ye
Keyword(s):  
Power Systems ◽  
Power System ◽  
Parallel Algorithm ◽  
Large Scale ◽  
Transient Stability ◽  
Online Assessment ◽  
Type Method ◽  
Online Simulation ◽  
Time Simulation ◽  
Implicit Trapezoidal Rule

Parallel computation is an effective approach to real-time simulation and transient stability online assessment of large-scale power systems. In this paper, the s-stage 2s-order symplectic Runge-Kutta-Nyström method is adopted for transient stability simulation of power system using classic model. Using Butcher transformation, a new parallel algorithm has been derived. The proposed algorithm has the convergence characteristic of a Newton type method and is of fully parallel-in-time. Through numerical simulation where the IEEE 145-bus power system is used, the proposed algorithm has been tested and compared with the conventional parallel-in-time Newton approach using implicit trapezoidal rule.

scholarly journals An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method

2021 ◽  
Author(s):  
Likai Liu ◽  
Zechun Hu ◽  
Nikhil Pathak ◽  
Haocheng Luo
Keyword(s):  
Power System ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Frequency Instability ◽  
Linearization Method ◽  
Large Power ◽  
Highly Nonlinear ◽  
Learning Machine ◽  
Scale Integration

Large-scale integration of converter-based renewable energy sources (RESs) into the power system will lead to a higher risk of frequency nadir limit violation and even frequency instability after the large power disturbance. Therefore, it is essential to consider the frequency nadir constraint (FNC) in power system scheduling. Nevertheless, the FNC is highly nonlinear and nonconvex. The <a>state-of-the-art</a> method to simplify the constraint is to construct a low-order frequency response model at first, and then linearize the frequency nadir equation. In this letter, an extreme learning machine (ELM)-based network is built to <a>derive </a>the linear formulation of FNC, where the two-step fitting process is integrated into one training process and more details about the physical model of the generator are considered to reduce the fitting error. Simulation results show the superiority of the proposed method on the fitting accuracy.

scholarly journals An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

2020 ◽  
Vol 10 (7) ◽  
pp. 2255
Author(s):  
Jun Liu ◽  
Huiwen Sun ◽  
Yitong Li ◽  
Wanliang Fang ◽  
Shuanbao Niu
Keyword(s):  
Feature Selection ◽  
Power Systems ◽  
Power System ◽  
Prediction Model ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Transient Stability ◽  
Stability Assessment ◽  
Stability Prediction ◽  
Electromagnetic Power

Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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.

scholarly journals An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method

2021 ◽  
Author(s):  
Likai Liu ◽  
Zechun Hu ◽  
Nikhil Pathak ◽  
Haocheng Luo
Keyword(s):  
Power System ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Frequency Instability ◽  
Linearization Method ◽  
Large Power ◽  
Highly Nonlinear ◽  
Learning Machine ◽  
Scale Integration

Large-scale integration of converter-based renewable energy sources (RESs) into the power system will lead to a higher risk of frequency nadir limit violation and even frequency instability after the large power disturbance. Therefore, it is essential to consider the frequency nadir constraint (FNC) in power system scheduling. Nevertheless, the FNC is highly nonlinear and nonconvex. The <a>state-of-the-art</a> method to simplify the constraint is to construct a low-order frequency response model at first, and then linearize the frequency nadir equation. In this letter, an extreme learning machine (ELM)-based network is built to <a>derive </a>the linear formulation of FNC, where the two-step fitting process is integrated into one training process and more details about the physical model of the generator are considered to reduce the fitting error. Simulation results show the superiority of the proposed method on the fitting accuracy.

scholarly journals An Extreme Learning Machine Based Adaptive VISMA for Stability Enhancement of Renewable Rich Power Systems

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 247
Author(s):  
Herlambang Setiadi ◽  
Rakibuzzaman Shah ◽  
Md Rabiul Islam ◽  
Dimas Anton Asfani ◽  
Tigor Hamonangan Nasution ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
Extreme Learning Machine ◽  
Power Plants ◽  
Test System ◽  
Operating Conditions ◽  
System Stability ◽  
Synchronous Machines ◽  
Machine Method ◽  
Learning Machine

Maintaining power system stability in renewable-rich power systems can be a challenging task. Generally, the renewable-rich power systems suffer from low and no inertia due to the integration of power electronics devices in renewable-based power plants. Power system oscillatory stability can also be affected due to the low and no inertia. To overcome this problem, additional devices that can emulate inertia without adding synchronous machines can be used. These devices are referred to as virtual synchronous machines (VISMA). In this paper, the enhancement of oscillatory stability of a realistic representative power system using VISMA is proposed. A battery energy storage system (BESS) is used as the VISMA by adding an additional controller to emulate the inertia. The VISMA is designed by using Fruit Fly Optimization. Moreover, to handle the uncertainty of renewable-based power plants, the VISMA parameters are designed to be adaptive using the extreme learning machine method. Java Indonesian Power Grid has been used as the test system to investigate the efficacy of the proposed method against the conventional POD method and VISMA tuning using other methods. The simulation results show that the proposed method can enhance the oscillatory stability of the power system under various operating conditions.

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