Online estimation of voltage stability margin via deep neural network with consideration of the local structures in power grid

2020 ◽  
Vol 30 (11) ◽  
Author(s):  
Xiwei Bai ◽  
Jie Tan ◽  
Shiying Ma ◽  
Daowei Liu
Keyword(s):  
Neural Network ◽  
Voltage Stability ◽  
Deep Neural Network ◽  
Power Grid ◽  
Stability Margin ◽  
Online Estimation ◽  
Local Structures ◽  
Voltage Stability Margin

scholarly journals Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Akash Saxena ◽  
Ankit Kumar Sharma
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Voltage Stability ◽  
Control Strategies ◽  
Additive White Gaussian Noise ◽  
Stability Margin ◽  
Operating Conditions ◽  
Voltage Stability Margin ◽  
Radial Basis

Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.

Voltage Stability Margin Assessment Using Multilayer Feed Forward Neural Network

2014 ◽  
Vol 573 ◽  
pp. 661-667 ◽  
Author(s):  
G.S. Naganathan ◽  
C.K. Babulal
Keyword(s):  
Neural Network ◽  
Power Flow ◽  
Voltage Stability ◽  
Reactive Power ◽  
Stability Margin ◽  
Feed Forward Neural Network ◽  
Continuation Power Flow ◽  
Feature Sets ◽  
Input Feature ◽  
Voltage Stability Margin

With the deregulation of electricity markets, the system operation strategies have changed in recent years. The systems are operated with smaller margins. How to maintain the voltage stability of the power systems have become an important issue.This paper presents an Artificial Feed Forward Neural Network (FFNN) approach for the assessment of power system voltage stability. This paper uses some input feature sets using real power, reactive power, voltage magnitude and phase angle to train the neural network (NN). The target output for each input pattern is obtained by computing the distance to voltage collapse from the current system operating point using a continuation power flow type algorithm. This paper compared different input feature sets and showed that reactive power and the phase angle are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. The proposed method has been applied to the IEEE 14 and IEEE 30 bus test system. The continuation power flow technique run with PSAT and the proposed method is implemented in MATLAB.

scholarly journals Multiple-deme parallel genetic algorithm based on modular neural network for effective load shedding

2021 ◽  
Author(s):  
Ali Gholami-Rahimabadi ◽  
Hadi Razmi ◽  
Hasan Doagou-Mojarrad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Voltage Stability ◽  
Stability Margin ◽  
Test System ◽  
Load Shedding ◽  
Parallel Genetic Algorithm ◽  
Detailed Model ◽  
Modular Neural Network ◽  
Voltage Stability Margin

Abstract One of the most effective corrective control strategies to prevent voltage collapse and instability is load shedding. In this paper, a multiple-deme parallel genetic algorithm (MDPGA) is used for a suitable design of load shedding. The load shedding algorithm is implemented when the voltage stability margin index of the power system is lower than a predefined value. In order to increase the computational speed, the voltage stability margin index is estimated by a modular neural network method in a fraction of a second. In addition, in order to use the exact values of the voltage stability margin index for neural network training, a simultaneous equilibrium tracing technique has been employed considering the detailed model of the components of the generating units such as the governor and the excitation system. In the proposed algorithm, the entire population is partitioned into several isolated subpopulations (demes) in which demes distributed in different processors and individuals may migrate occasionally from one subpopulation to another. The proposed technique has been tested on New England-39 bus test system and the obtained results indicate the efficiency of the proposed method.

scholarly journals Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Garima Singh ◽  
Laxmi Srivastava
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Voltage Stability ◽  
Back Propagation ◽  
Stability Margin ◽  
Electric Power Industry ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Accurate Estimation ◽  
Voltage Stability Margin

With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN) has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.

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