LSA-SVM Fusion Algorithm for Enhancing Power Network Security

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.May 1 2024

In a paper published in the journal Scientific Reports, researchers addressed the security threats by introducing a fusion algorithm merging the flash search algorithm with support vector machine (SVM) technology. This algorithm formed the backbone of an advanced power network security risk evaluation model.

Results demonstrated its superiority, boasting high accuracy, low error rates, and rapid convergence of the loss function curve. The model achieved remarkable computation speed. Empirical analysis confirmed its effectiveness, empowering maintenance staff to preemptively identify and mitigate potential security threats, ensuring the uninterrupted and safe operation of the power system.

Background

Previous research has applied ML algorithms across various domains, including power systems and privacy protection. Techniques such as modified complex methods with lightning search algorithm (LSA) and SVM have minimized power loss and brain magnetic resonance imaging (MRI) classification, respectively.

Additionally, efforts have focused on fault prediction models for power systems and scalable algorithms for security assessment. These studies highlight the ongoing need for innovation in security and reliability within power networks.

Power Security Optimization

The study proposes the integration of the LSA with the SVM to enhance the performance of the SVM in power network security risk evaluation. This enhancement addresses the dependency of SVM on parameters and optimizes its operational efficiency and risk-detection capabilities.

By combining LSA and SVM, the study develops the LSA-SVM algorithm, which forms the foundation of the power network security risk evaluation model (PNSREM). This model utilizes the improved LSA-SVM algorithm to assess and predict security risks within power systems.

The study establishes a comprehensive power network security risk indicator system (SRAIS) to define power communication networks' characteristics and security requirements (PCNs) to construct the PNSREM. This system is the basis for developing security risk assessment indices, ensuring a scientific and systematic approach to assessing PCN security. The construction of SRAIS involves selecting and validating evaluation indicators using weighted judgment methods and expert ratings, ensuring the accuracy and reliability of the assessment model.

Furthermore, the study implements the LSA-SVM algorithm to optimize SVM parameters for more efficient and accurate risk assessment. The LSA-SVM algorithm simulates lightning behavior to search for optimal parameters efficiently, improving SVM's performance in handling high-dimensional feature spaces and small-sample data characteristic of power networks.

By integrating LSA-SVM into the PNSREM, the model can effectively assess security risks, provide real-time monitoring, and offer early warnings to mitigate potential threats to power systems.

In practical application, the PNSREM facilitates data collection, preprocessing, feature extraction, label generation, model training, evaluation, and performance optimization. Through these steps, the model evaluates power system security risks, identifies potential threats, and assists decision-makers in implementing timely and effective security measures. Overall, integrating LSA-SVM into the PNSREM enhances power network security risks' assessment and prediction capabilities, contributing to power systems' safe and reliable operation.

Empirical Validation Summary

The empirical analysis of the PNSREM based on the LSA-SVM algorithm focuses on evaluating its effectiveness and applicability. Firstly, the study utilizes datasets and the LSA-SVM algorithm to construct the PNSREM, ensuring parameter optimization and operational efficiency.

The study conducts performance comparison experiments using various datasets, including the Canadian Institute for Cybersecurity (CIC)-Intrusion Detection Systems (IDS) 2017 dataset. It assesses the accuracy and reliability of the proposed model, highlighting its superior performance compared to other algorithms such as artificial bee colony (ABC)-SVM and genetic algorithm (GA)-SVM.

Secondly, the study conducts empirical analysis and expert evaluations to validate the efficacy of the PNSREM. The study utilizes the Institute of Electrical and Electronics Engineers (IEEE) 33-bus node standard test system to compare different risk assessment models, including GA-based and Bayesian network-based models, thereby showcasing the superior accuracy and efficiency of the LSA-SVM approach. Expert scoring and satisfaction evaluations further confirm the model's practicality and reliability, with the LSA-SVM model outperforming other comparative models in accuracy, error rates, and computation time.

Thirdly, the robustness and generalization ability of the LSA-SVM model is evaluated through network reconstruction and intelligent soft-switching tests. These tests demonstrate the model's effectiveness in assessing power grid safety risks under different scenarios, such as distributed photovoltaic access nodes in AC/DC hybrid distribution networks. The results highlight the model's capability to accurately identify and defend against security risks while prolonging the life cycle of power grid engineering equipment and reducing investment losses.

The empirical analysis confirms the effectiveness and practical applicability of the LSA-SVM-based PNSREM in evaluating power network security risks. By combining empirical testing, expert evaluation, and scenario-based assessments, the study provides comprehensive evidence of the model's superiority over existing risk assessment approaches, underscoring its potential for real-world application in ensuring power systems' safe and reliable operation.

Conclusion

In summary, integrating LSA and SVM in the PNSREM enhanced accuracy and processing speed, making it a valuable tool for power network security. The LSA-SVM model efficiently combined lightning search technology with an SVM, ensuring robustness in power grid systems. However, as power grid systems evolved, there was a need for further exploration of complex environmental factors and risk assessment methods. Future research should address these challenges to ensure the safety and stability of power systems amid advancing technologies.