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Cyberattacks (CAs) on modern interconnected power systems are currently a primary concern. The development of information and communication technology (ICT) has increased the possibility of unauthorized access to power system networks for data manipulation. Unauthorized data manipulation may lead to the partial or complete shutdown of a power network. In this paper, we propose a novel security unit that mitigates intrusion for an interconnected power system and compensates for data manipulation to augment cybersecurity. The studied two-area interconnected power system is first stabilized to alleviate frequency deviation and tie-line power between the areas by designing a fractional-order proportional integral derivative (FPID) controller. Since the parameters of the FPID controller can also be influenced by a CA, the proposed security unit, named the automatic intrusion mitigation unit (AIMU), guarantees control over such changes. The effectiveness of the AIMU is inspected against a CA, load variations, and unknown noises, and the results show that the proposed unit guarantees reliable performance in all circumstances.
Faisal Badal; Zannatun Nayem; Subrata Sarker; Dristi Datta; Shahriar Rahman Fahim; S. Muyeen; Islam Sheikh; Sajal Das. A Novel Intrusion Mitigation Unit for Interconnected Power Systems in Frequency Regulation to Enhance Cybersecurity. Energies 2021, 14, 1401 .
AMA StyleFaisal Badal, Zannatun Nayem, Subrata Sarker, Dristi Datta, Shahriar Rahman Fahim, S. Muyeen, Islam Sheikh, Sajal Das. A Novel Intrusion Mitigation Unit for Interconnected Power Systems in Frequency Regulation to Enhance Cybersecurity. Energies. 2021; 14 (5):1401.
Chicago/Turabian StyleFaisal Badal; Zannatun Nayem; Subrata Sarker; Dristi Datta; Shahriar Rahman Fahim; S. Muyeen; Islam Sheikh; Sajal Das. 2021. "A Novel Intrusion Mitigation Unit for Interconnected Power Systems in Frequency Regulation to Enhance Cybersecurity." Energies 14, no. 5: 1401.
Accurate fault classification and detection for the microgrid (MG) becomes a concern among the researchers from the state-of-art of fault diagnosis as it increases the chance to increase the transient response. The MG frequently experiences a number of shunt faults during the distribution of power from the generation end to user premises, which affects the system reliability, damages the load, and increases the fault line restoration cost. Therefore, a noise-immune and precise fault diagnosis model is required to perform the fast recovery of the unhealthy phases. This paper presents a review on the MG fault diagnosis techniques with their limitations and proposes a novel discrete-wavelet transform (DWT) based probabilistic generative model to explore the precise solution for fault diagnosis of MG. The proposed model is made of multiple layers with a restricted Boltzmann machine (RBM), which enables the model to make the probability reconstruction over its inputs. The individual RBM layer is trained with an unsupervised learning approach where an artificial neural network (ANN) algorithm tunes the model for minimizing the error between the true and predicted class. The effectiveness of the proposed model is studied by varying the input signal and sampling frequencies. A level of considered noise is added with the sample data to test the robustness of the studied model. Results prove that the proposed fault detection and classification model has the ability to perform the precise diagnosis of MG faults. A comparative study among the proposed, kernel extreme learning machine (KELM), multi KELM, and support vector machine (SVM) approaches is studied to confirm the robust superior performance of the proposed model.
Shahriar Rahman Fahim; Subrata K. Sarker; S. M. Muyeen; Rafiqul Islam Sheikh; Sajal K. Das. Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews. Energies 2020, 13, 3460 .
AMA StyleShahriar Rahman Fahim, Subrata K. Sarker, S. M. Muyeen, Rafiqul Islam Sheikh, Sajal K. Das. Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews. Energies. 2020; 13 (13):3460.
Chicago/Turabian StyleShahriar Rahman Fahim; Subrata K. Sarker; S. M. Muyeen; Rafiqul Islam Sheikh; Sajal K. Das. 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews." Energies 13, no. 13: 3460.