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Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method

  • Research Article-Electrical Engineering
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Abstract

The increasing demand on electrical power consumption all over the world makes the need of stable and reliable electrical power grids is indispensable. However, one of hostile obstacles which delays reaching out to that desired goal is occurrence of faults. Despite to fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides such as validation and automation. In this paper, an electrical fault detection system based on the concept of anomaly detection is presented. The main salient advantages of the proposed system are overcoming the limitations of existed counterpart systems and its compatibility with real-world power grids. To enhance the performance of the proposed system, two vital stages are involved in its design prior to training, namely, data preprocessing and pre-training. Whereas the former is to prepare raw signals to be modeled, the latter is dedicated for model’s hyperparameter selection using the particle swarm optimization metaheuristic. Moreover, two well-known anomaly detection models, namely, One-Class Support Vector Machines and principal component analysis are utilized to validate the proposed system as well as real-time data (VSB dataset) are used to train and test models. Finally, the experimental results and discussion emphasize that there is a performance improvement in detecting of electrical faults when using the proposed system.

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  1. https://www.python.org

  2. https://www.numpy.org

References

  1. Wadi, M.; Elmasry, W.: Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: a case study. Electr. Eng. 103, 2573–2594 (2021). https://doi.org/10.1007/s00202-021-01254-0

    Article  Google Scholar 

  2. Wadi, M.; Elmasry, W.: Modeling of wind energy potential in marmara region using different statistical distributions and genetic algorithms. In: 2021 International Conference on Electric Power Engineering–Palestine (ICEPE-P) IEEE. pp. 1–7 (2021)

  3. Raza, A.; Benrabah, A.; Alquthami, T.; Akmal, M.: A Review of Fault Diagnosing Methods in Power Transmission Systems. Appl. Sci. 10(4), 1312 (2020)

    Article  Google Scholar 

  4. Elmasry, W.; Wadi, M.: Enhanced anomaly-based fault detection system in electrical power grids. Int. Trans. Electr. Energy Syst. (2022). https://doi.org/10.1155/2022/1870136

    Article  Google Scholar 

  5. Elmasry, W.; Wadi, M.: EDLA-EFDS: a novel ensemble deep learning approach for electrical fault detection systems. Electr. Power Sys. Res. 207, 107834 (2022). https://doi.org/10.1016/j.epsr.2022.107834

    Article  Google Scholar 

  6. Prasad, A.; Edward, J.B.; Ravi, K.: A review on fault classification methodologies in power transmission systems: Part-I. J. Electrical Syst. inf. Technol. 5(1), 48–60 (2018)

    Article  Google Scholar 

  7. Prasad, A.; Edward, J.B.; Ravi, K.: A review on fault classification methodologies in power transmission systems: Part-II. J. Electr. Syst. Inf. Technol. 5(1), 61–67 (2018)

    Article  Google Scholar 

  8. Chen, K.; Huang, C.; He, J.: Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High Volt. 1(1), 25–33 (2016)

    Article  Google Scholar 

  9. Elmasry, W.; Akbulut, A.; Zaim, A.H.: Empirical study on multiclass classification-based network intrusion detection. Comput. Intell. 35(4), 919–954 (2019). https://doi.org/10.1111/coin.12220

    Article  MathSciNet  Google Scholar 

  10. Chandola, V.; Banerjee, A.; Kumar, V.: Outlier detection: a survey. ACM Comput. Surv. 14, 15 (2007)

    Google Scholar 

  11. Hodge, V.; Austin, J.: A survey of outlier detection methodologies. Artif. intell. Rev. 22(2), 85–126 (2004)

    Article  MATH  Google Scholar 

  12. Elmasry, W.; Akbulut, A.; Zaim, A.H.: Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comp. Netw. 168, 107042 (2020)

    Article  Google Scholar 

  13. Elmasry, W.; Akbulut, A.; Zaim, A.H.: Deep learning approaches for predictive masquerade detection. Security Commun. Netw. (2018). https://doi.org/10.1155/2018/9327215

    Article  Google Scholar 

  14. Elmasry, W.; Akbulut, A.; Zaim, A.H.: A design of an integrated cloud-based intrusion detection system with third party cloud service. Open Comput. Sci. 11(1), 365–379 (2021). https://doi.org/10.1515/comp-2020-0214

    Article  Google Scholar 

  15. One-Class Support Vector Machine. Microsoft Docs. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/one-class-support-vector-machine (2019)

  16. Schölkopf, B.; Platt, J.C.; Shawe-Taylor, J.; Smola, A.J.; Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)

    Article  MATH  Google Scholar 

  17. PCA-Based Anomaly Detection. Microsoft Docs. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/pca-based-anomaly-detection (2019)

  18. ENET Centre. VSB. https://cenet.vsb.cz/en/ (2021)

  19. VSB Power Line Fault Detection. Kaggle. https://www.kaggle.com/c/vsb-power-line-fault-detection/data (2018)

  20. Singh, R.: Fault detection of electric power transmission line by using neural network. Int. J. Emerg. Technol. Adv. Eng. 2(12), 530–538 (2012)

    Google Scholar 

  21. Tayeb, E.B.M.: Faults detection in power systems using artificial neural network. Am. J. Eng. Res. 2(6), 69–75 (2013)

    Google Scholar 

  22. Jamil, M.; Sharma, S.K.; Singh, R.: Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus 4(1), 1–13 (2015)

    Article  Google Scholar 

  23. Koley, E.; Verma, K.; Ghosh, S.: An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only. Springerplus 4(1), 551 (2015)

    Article  Google Scholar 

  24. Uzubi, U.; Ekwue, A.; Ejiogu, E.; Artificial neural network technique for transmission line protection on Nigerian power system. In: IEEE PES PowerAfrica. IEEE. pp. 52–58 (2017)

  25. Eboule, P.S.P.; Pretorius, J.H.C.; Mbuli, N.; Leke, C.; Fault detection and Location in power transmission line using concurrent neuro fuzzy technique. In: IEEE Electrical Power and Energy Conference (EPEC). IEEE, pp. 1–6 (2018)

  26. Singh, M.; Panigrahi, B.; Maheshwari, R.: Transmission line fault detection and classification. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology. IEEE (2011) pp. 15–22

  27. Gururajapathy, S.S.; Mokhlis, H.; Illias, H.A.B.: Classification and regression analysis using support vector machine for classifying and locating faults in a distribution system. Turkish J. Electr. Eng. Comput. Sci. 26(6), 3044–3056 (2018)

    Google Scholar 

  28. Dong, M.; Sun, Z.; Wang, C.A.; pattern recognition method for partial discharge detection on insulated overhead conductors. In: IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). IEEE. pp. 1–4 (2019)

  29. Qu, N.; Li, Z.; Zuo, J.; Chen, J.: Fault detection on insulated overhead conductors based on DWT-LSTM and partial discharge. IEEE Access 8, 87060–87070 (2020)

    Article  Google Scholar 

  30. Wadi, M.; Elmasry, W.: An anomaly-based technique for fault detection in power system networks. In: 2021 International Conference on Electric Power Engineering - Palestine (ICEPE- P). IEEE. pp. 1-6 (2021)

  31. Wadi, M.: Fault detection in power grids based on improved supervised machine learning binary classification. J. Electr. Eng. 72(5), 315–322 (2021)

    Google Scholar 

  32. Himeur, Y.; Ghanem, K.; Alsalemi, A.; Bensaali, F.; Amira, A.: Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives. Appl. Energy 287, 116601 (2021)

    Article  Google Scholar 

  33. Biswas, S.; Nayak, P.K.: A fault detection and classification scheme for unified power flow controller compensated transmission lines connecting wind farms. IEEE Syst. J. 15(1), 297–306 (2020)

    Article  Google Scholar 

  34. Rose, T.; Kifayat, K.; Abbas, S.; Asim, M.: A hybrid anomaly-based intrusion detection system to improve time complexity in the Internet of Energy environment. J. Parallel Distrib. Comput. 145, 124–139 (2020)

    Article  Google Scholar 

  35. Alamiedy, T.A.; Anbar, M.; Alqattan, Z.N.; Alzubi, Q.M.: Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J. Ambient Intell. Humaniz. Comput. 11(9), 3735–3756 (2020)

    Article  Google Scholar 

  36. Himeur, Y.; Alsalemi, A.; Bensaali, F.; Amira, A.: Smart power consumption abnormality detection in buildings using micromoments and improved K-nearest neighbors. Int. J. Intell. Syst. 36(6), 2865–2894 (2021)

    Article  Google Scholar 

  37. Aker, E.; Othman, M.L.; Veerasamy, V.; Aris, I.B.; Wahab, N.I.A.; Hizam, H.: Fault detection and classification of shunt compensated transmission line using discrete wavelet transform and naive bayes classifier. Energies 13(1), 243 (2020)

    Article  Google Scholar 

  38. Chen, H.; Jiang, B.; Zhang, T.; Lu, N.: Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems. Neurocomputing 396, 429–437 (2020)

    Article  Google Scholar 

  39. Himeur, Y.; Alsalemi, A.; Bensaali, F.; Amira, A.: A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks. Cognit. Comput. 12(6), 1381–1401 (2020)

    Article  Google Scholar 

  40. Naveed, K.; Akhtar, M.T.; Siddiqui, M.F.; Ur Rehman, N.: A statistical approach to signal denoising based on data-driven multiscale representation. Digital Signal Process. 108, 102896 (2021)

    Article  Google Scholar 

  41. García-Gil, D.; Luengo, J.; García, S.; Herrera, F.: Enabling smart data: noise filtering in big data classification. Inf. Sci. 479, 135–152 (2019)

    Article  Google Scholar 

  42. Yue, X.: Data decomposition for analytics of engineering systems: Literature review, methodology formulation, and future trends. In: 58745 of International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers (2019) V001T02A011

  43. Yasir, M.N.; Koh, B.H.: Data decomposition techniques with multi-scale permutation entropy calculations for bearing fault diagnosis. Sensors 18(4), 1278 (2018)

    Article  Google Scholar 

  44. Hyndman, R.J.; Fan, Y.: Sample quantiles in statistical packages. Am. Stat. 50(4), 361–365 (1996)

    Google Scholar 

  45. Azure machine learning studio. Microsoft. https://studio.azureml.net/ (2022)

  46. Barga, R.; Fontama, V.; Tok, W.H.: Introducing microsoft azure machine learning. In: Predictive Analytics with Microsoft Azure Machine Learning (pp. 21–43) Springer (2015)

  47. Elmasry, W.; Akbulut, A.; Zaim, A.H.: Comparative evaluation of different classification techniques for masquerade attack detection. Int. J. Inf. Comput. Security 13(2), 187–209 (2020)

    Google Scholar 

  48. Boughorbel, S.; Jarray, F.; El-Anbari, M.: Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PloS One 12(6), e0177678 (2017)

    Article  Google Scholar 

  49. Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica Biophysica Acta BBA-Protein Struct. 405(2), 442–451 (1975)

  50. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  51. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  52. Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  53. Sokolova, M.; Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  54. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32(200), 675–701 (1937)

  55. Sahmoud, S.; Elmasry, W.; Abudalfa, S.: Enhancement the Security of AES Against Modern Attacks by Using Variable Key Block Cipher. Int. Arab. J. Technol. 3(1), 17–26 (2013)

    Google Scholar 

  56. Cem kasapbaşi, M.; Elmasry, W.: New LSB-based colour image steganography method to enhance the efficiency in payload capacity, security and integrity check. Sādhanā (2018) 43(5): 1–14. https://doi.org/10.1007/s12046-018-0848-4

  57. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  58. Fister Jr, I.; Yang, X.S.; Fister, I.; Brest, J.: Memetic firefly algorithm for combinatorial optimization. arXiv preprint arXiv:1204.5165 (2012)

  59. Cervante, L.; Xue, B.; Zhang, M.; Shang, L.; Binary particle swarm optimisation for feature selection: A filter based approach. In: IEEE Congress on Evolutionary Computation. IEEE, 1–8 (2012)

  60. Zhou, Z.; Liu, X.; Li, P.; Shang, L.: Feature selection method with proportionate fitness based binary particle swarm optimization. In: Asia-Pacific Conference on Simulated Evolution and Learning. Springer (2014) 582–592

  61. Şenel, F.A.; Gökçe, F.; Yüksel, A.S.; Yiğit, T.: A novel hybrid PSO-GWO algorithm for optimization problems. Eng. Comput. 35(4), 1359–1373 (2019)

    Article  Google Scholar 

  62. Moosavi, S.H.S.; Bardsiri, V.K.: Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng. Appl. Artif. Intell. 60, 1–15 (2017)

    Article  Google Scholar 

  63. El-Kenawy, E.S.M.; Ibrahim, A.; Mirjalili, S.; Eid, M.M.; Hussein, S.E.: Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images. IEEE Access 8, 179317–179335 (2020)

    Article  Google Scholar 

  64. Karakonstantis, I.; Vlachos, A.: Bat algorithm applied to continuous constrained optimization problems. J. Inf. Optim. Sci. 42(1), 57–75 (2021)

    Google Scholar 

  65. Holland, J.H.; et al.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

  66. Saremi, S.; Mirjalili, S.; Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)

    Article  Google Scholar 

  67. Mirjalili, S.; Mirjalili, S.M.; Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  68. Mirjalili, S.; Mirjalili, S.M.; Hatamlou, A.: Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput. Appl. 27(2), 495–513 (2016)

    Article  Google Scholar 

  69. Mirjalili, S.; Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  70. Two-Class Boosted Decision Tree. Microsoft Docs. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-boosted-decision-tree (2019)

  71. Two-Class Decision Forest. Microsoft Docs. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-decision-forest (2019)

  72. Two-Class Decision Jungle. Microsoft Docs. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-decision-jungle (2019)

  73. Zeineddine, H.; Braendle, U.; Farah, A.: Enhancing prediction of student success: automated machine learning approach. Comput. Electr. Eng. 89, 106903 (2021)

    Article  Google Scholar 

  74. Havlíček, V.; Córcoles, A.D.; Temme, K.; et al.: Supervised learning with quantum-enhanced feature spaces. Nature 567(7747), 209–212 (2019)

    Article  Google Scholar 

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  1. Electrical and Electronics Engineering Department, Istanbul Sabahattin Zaim University, 34303, Istanbul, Turkey

    Wisam Elmasry & Mohammed Wadi

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  1. Wisam Elmasry
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Elmasry, W., Wadi, M. Detection of Faults in Electrical Power Grids Using an Enhanced Anomaly-Based Method. Arab J Sci Eng 47, 14899–14914 (2022). https://doi.org/10.1007/s13369-022-07030-x

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