Computational Intelligence and Voltage Stability Analysis for Mitigation of Blackout

Shaikh, Fouzul Azim; Din, Zaheer ud; Asghar, M. S. Jamil

doi:10.5120/1987-2677

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…"Artificial Neural Network (ANN) is biologically motivated systems that change a set of inputs into a set of outputs by a network of neurons, where each neuron produces one output as a function of inputs". "Artificial neural networks (ANNs) could be described as being very simple arithmetic computing units (nodes) attached together in a highly parallel network of interrelated layers and as being able with this kind of structure to symbolize functions and to learn these functions from examples" [2,25,39] and [60].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Archives of Electrical Engineering

Olajuyin,

Olulope,

Fasina

2022

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A Computational Intelligence (CI) approach is one of the main trending and potent data dealing out and processing instruments to unravel and resolve difficult and hard reliability crisis and it takes an important position in intelligent reliability analysis and management of data. Nevertheless, just few little broad reviews have recapitulated the current attempts of Computational Intelligence (CI) in reliability assessment in power systems. There are many methods in reliability assessment with the aim to prolong the life cycles of a system, to maximize profit and predict the life cycle of assets or systems within an organization especially in electric power distribution systems. Sustaining an uninterrupted electrical energy supply is a pointer of affluence and nationwide growth. The general background of reliability assessment in power system distribution using computational intelligence, some computational intelligence techniques, reliability engineering, literature reviews, theoretical or conceptual frameworks, methods of reliability assessment and conclusions was discussed. The anticipated and proposed technique has the aptitude to significantly reduce the needed period for reliability investigation in distribution networks because the distribution network needs an algorithm that can evaluate, assess, measure and update the reliability indices and system performance within a short time. It can also manage outages data on assets and on the entire system for quick and rapid decisions making as well as can prevent catastrophic failures. Those listed above would be taken care of if the proposed method is utilized. This overview or review may be deemed as valuable assistance for anybody doing research.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Archives of Electrical Engineering

Olajuyin,

Olulope,

Fasina

2022

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“…There are millions of people affected due to blackouts on large scale across the world. This is the huge problem which the power world faces as shown in figure-1 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Figure-1 shows the major blackouts with the number of people affected and how long it stays in the system.…”

Section: Blackouts Problems In Conventional Gridsmentioning

confidence: 99%

“…Figure-1 shows the major blackouts with the number of people affected and how long it stays in the system. The analysis of the major blackouts is as shown in the table below in fig-2 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The table shows the time duration and brief causes of Blackout problem occurs in last fifteen years.…”

Section: Blackouts Problems In Conventional Gridsmentioning

confidence: 99%

International Grid: New Way to Prevent Blackouts

Shaikh

Gautam²,

Pandey³

et al. 2012

IJCSI

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Nowadays the conventional grids are facing lots of problems like poor efficiency, unaffordable maintenance, high power loss, overloads and blackout or power outages. The main objective of this paper is to present the new concept of International Grid and to root out the problems which are prominent in conventional grids. The evolution of International Grid is a smart way to ensure the prevention of blackouts which are drastically increasing even with the advent of new technologies. The International Grid concept is introduced to highlight alternative, automated technologies that may bring significant benefits to the transmission and distribution networks. The real time application of International Grid is not only easily feasible but also requires less number of components for installation. We are presenting the structure and efficient working along with synchronization of International Grid with various components.

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“…This database must contain the electrical variables that allow determining the actual condition of the power system in near real time. When the AI is trained, new information is fed to the AI; being the output a voltage stability indicator [12,13]. The following AI techniques using voltage stability assessment are reported in the scientific literature: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM).…”

Section: Introductionmentioning

confidence: 99%

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

2020

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This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.

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Computational Intelligence and Voltage Stability Analysis for Mitigation of Blackout

Cited by 4 publications

References 14 publications

Archives of Electrical Engineering

Archives of Electrical Engineering

International Grid: New Way to Prevent Blackouts

Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

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