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.

An Optimal Load Shedding Methodology for Radial Power Distribution Systems to Improve Static Voltage Stability Margin using Gravity Search Algorithm

2014 ◽  
Vol 68 (3) ◽  
Author(s):  
Aziah Khamis ◽  
H. Shareef ◽  
A. Mohamed ◽  
Erdal Bizkevelci
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Stability Margin ◽  
Maximum Amount ◽  
Load Shedding ◽  
Voltage Stability Margin

Voltage stability is one of the major concerns in operational and planning of modern power system. Many strategies have been implemented to avoid voltage collapse, which the load shedding considered as the last option. However, optimization is needed to estimate the minimum amount to shed so as to prevent voltage instability. In this paper, an effective method is presented for estimating the optimal amount of load to be shed in a distribution system based on the gravitational search algorithm (GSA). The voltage stability margin (VSM) of the system has been considered in the objective function. The optimization problem is formulated to maximize the VSM of the system and at the same time satisfying the operation and security constraints. The optimum solution depends on the predefined constraints such as the number of load buses available to shed and the maximum amount of load permitted to shed. Simulation result conducted on the IEEE 33 bus radial distribution system shows that the system voltage stability can be improved by optimally shedding the loads at critical system buses. The results also indicate that the numbers of load buses available for load shedding does not have a significant impact on voltage stability margin, but it is highly dependent on the maximum amount of load permitted to shed. 

scholarly journals Decentralized Load Shedding Method Based on Voltage Stability Margin Index Using Synchrophasor Measurement Technology

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 277 ◽  
Author(s):  
Yunhwan Lee ◽  
Hwachang Song
Keyword(s):  
Power Systems ◽  
Voltage Stability ◽  
Stability Margin ◽  
Load Shedding ◽  
Measurement Technology ◽  
Dynamic Simulations ◽  
Stability Margins ◽  
Voltage Instability ◽  
Voltage Stability Margin ◽  
Simulation Results

This study develops an analytical method for assessing the voltage stability margins of a decentralized load shedding scheme; it then examines the challenges related to the existing load shedding scheme. It also presents a practical application for implementing the proposed method, based on the synchrophasor measurement technology in modern power grid operations. By applying the concept of a continuously-computed voltage stability margin index to the configuration of the Thévenin equivalent system, the maximum transfer power could be used as an index to monitor the voltage instability phenomenon and thus determine the required load shedding amount. Thus, the calculated voltage stability margin might be a useful index for system operators in the critical decision-making process of load shedding. Dynamic simulations are performed on real Korean power systems as case studies. Simulation results, when comparing the existing and proposed methods, showed that there was a considerable reduction in the amount of load shedding in the voltage instability scenario. This indicates that the synchrophasor measurement technology has a considerable effect on the proposed load shedding method. The simulation results have validated the performance of the proposed method.

Static Voltage Stability Margin Enhancement Using Shunt Capacitor, SVC and STATCOM

2015 ◽  
Vol 781 ◽  
pp. 288-291 ◽  
Author(s):  
Natakorn Thasnas ◽  
Apirat Siritaratiwat
Keyword(s):  
Power Flow ◽  
Voltage Stability ◽  
Stability Margin ◽  
Test System ◽  
Stability Study ◽  
Flow Process ◽  
Static Voltage Stability ◽  
Voltage Stability Margin ◽  
Shunt Compensation ◽  
Shunt Capacitor

This paper presents the study of static voltage stability margin enhancement using shunt capacitor, SVC and STATCOM. AC and DC representations of shunt compensation devices are used in the continuation power flow process in static voltage stability study. Various performance measures including PV curves, voltage profiles, and power losses are compared. Placement and sizing techniques of shunt compensation devices are proposed for loading margin enhancement. The study has been carried out on the IEEE 14 bus test system.

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.

scholarly journals ACTIVE POWER LOSS REDUCTION & STATIC VOLTAGE STABILITY MARGIN ENHANCEMENT BY AERIFORM NEBULA ALGORITHM

2017 ◽  
Vol 5 (10) ◽  
pp. 375-389
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Voltage Stability ◽  
Reactive Power ◽  
Stability Margin ◽  
Test System ◽  
Superior Performance ◽  
Static Voltage Stability ◽  
Optimal Reactive Power Dispatch ◽  
Voltage Stability Margin ◽  
Power Loss Reduction

In this paper, Aeriform Nebula Algorithm (ANA) has been used for solving the optimal reactive power dispatch problem. Aeriform Nebula Algorithm (ANA) is stirred from the deeds of cloud. ANA imitate the creation behavior, modify behavior and expand deeds of cloud. The projected Aeriform Nebula Algorithm (ANA) has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Aeriform Nebula Algorithm (ANA) in reducing the real power loss and voltage stability has been enhanced.

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