Simulation of SCADA System for Advanced Metering Infrastructure in Smart Grid

R, Prajwal Gupta C; Ramesh, A.; Satvik, Dandu; Nagasundari, S; Honnavalli, Prasad B

doi:10.1109/icosec49089.2020.9215432

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The system's performance is directly impacted by effective and secure communication between system components, making it of utmost importance. A severe problem in industrial control systems could result from defective device characteristics [9]. In addition, the sensing and data actuation of the system components are affected by significant security concerns [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems.

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Section: Introductionmentioning

confidence: 99%

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Rabie

Balachandran²,

Shitharth

2022

Electronics

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“…It is certainly advisable to develop and implement Smart Grid on a massive scale in the domestic power grid to ensure the desired flexibility and responsiveness to changes in the demand for power or to breakdowns. The massive implementation of AMI will contribute to better monitoring of the LV grid and to its integration with the system SCADA [11].…”

Section: Introductionmentioning

confidence: 99%

Advanced Metering Infrastructure—Towards a Reliable Network

Kornatka¹,

Popławski²

2021

Energies

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In order to ensure continuous energy supply, Distribution System Operators (DSOs) have to monitor and analyze the condition of the power grid, especially checking for random events, such as breakdowns or other disturbances. Still, relatively little information is available on the operation of the Low Voltage (LV) grid. This can be improved thanks to digital tools, offering online processing of data, which ultimately increases effectiveness of the power grid. Among those tools, the use of the Advanced Metering Infrastructure (AMI) is especially conducive for improving reliability. AMI is one of the elements of the system Supervisory Control and Data Acquisition (SCADA) for the LV grid. Exact knowledge of the reliability conditions of a power grid is also indispensable for optimizing investment. AMI is also key in providing operational capacity for carrying out energy balance in virtual power plants (VPPs). This paper deals with methodology of identification and location of faults in the AMI-supervised LV grid and with calculating the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) on the basis of the recorded events. The results presented in the paper are based on data obtained from seven MV/LV transformer stations that supply over 2000 customers.

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A historical distribution network state estimator using smart distribution transformer meters

Lin

Liao

et al. 2021

2021 International Conference on Power System Technology (POWERCON)

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Simulation of SCADA System for Advanced Metering Infrastructure in Smart Grid

Cited by 3 publications

References 19 publications

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

A Proficient ZESO-DRKFC Model for Smart Grid SCADA Security

Advanced Metering Infrastructure—Towards a Reliable Network

A historical distribution network state estimator using smart distribution transformer meters

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