Intelligent Electric Grid Maintenance via an Adaptive Predictive Maintenance Optimization Algorithm
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Abstract
This study presents a novel framework for the predictive maintenance of electric grid infrastructure, leveraging an Adaptive Predictive Maintenance Optimization (APMO) algorithm. Our comprehensive system architecture integrates IoT sensors and drone surveillance, harnessing grid computing for data processing and machine learning for analytics. The APMO algorithm, underpinned by reinforcement learning, dynamically refines maintenance schedules, enhancing operational efficiency. Results from simulated sensor data exhibit a negligible correlation, indicating a multi-faceted approach to predictive analytics. Specifically, the Health Index distribution showed a wide range of 0.7 to 1.0, while the electrical current maintained a stable mean at 10 A. Further, the APMO algorithm demonstrated a promising improvement in maintenance efficiency from 40% to 75% over a year. This research introduces a scalable, robust system for grid management, paving the way for smarter infrastructure maintenance.
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References
L. Karsenti and R. Khellou, “Enedis IoT smart grid solutions for more efficiency,” in CIRED 2021 - The 26th International Conference and Exhibition on Electricity Distribution, 2021.
M. Asaad, F. Ahmad, M. S. Alam, and Y. Rafat, “IoT enabled monitoring of an optimized electric vehicle’s battery system,” Mob. Netw. Appl., vol. 23, no. 4, pp. 994–1005, 2018.
V. S. R. Tappeta, B. Appasani, S. Patnaik, and T. S. Ustun, “A review on emerging communication and computational technologies for increased use of plug-in electric vehicles,” Energies, vol. 15, no. 18, p. 6580, 2022.
M. Asaad, F. Ahmad, M. Saad Alam, and Y. Rafat, “IoT enabled electric vehicle’s battery monitoring system,” in Proceedings of the The 1st EAI International Conference on Smart Grid Assisted Internet of Things, 2017.
P. Choudhary, M. Choudhary, and B. S. Ram, “A preview of big data analytics in power systems,” 2019.
M. Sharma, B. S. Rajpurohit, S. Agnihotri, and S. N. Singh, “Data analytics based power quality investigations in emerging electric power system using sparse decomposition,” IEEE Trans. Power Deliv., vol. 37, no. 6, pp. 4838–4847, 2022.
J. Cepeda, I. Gomez, F. Calero, and A. Vaca, “Big data platform for real-time oscillatory stability predictive assessment using recurrent neural networks and WAProtector’s records,” in 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), 2022.
P. D. Diamantoulakis and G. K. Karagiannidis, “Big data analytics for smart grids,” in Big Data Recommender Systems - Volume 2: Application Paradigms, Institution of Engineering and Technology, 2019, pp. 125–144.
A. Yousefi, O. Ameri Sianaki, and T. Jan, “Big data analytics for electricity price forecast,” in Advances in Intelligent Systems and Computing, Cham: Springer International Publishing, 2019, pp. 915–922.
A. Lazzaro, D. M. D’Addona, and M. Merenda, “Comparison of machine learning models for predictive maintenance applications,” in Lecture Notes in Networks and Systems, Cham: Springer International Publishing, 2023, pp. 657–666.
H. P. H. Luu, “Advanced machine learning techniques based on DCA and applications to predictive maintenance. (Techniques avancées d’apprentissage automatique basées sur DCA et applications à la maintenance prédictive),” 2022.
A. Potturu, “Machine learning applications for industry 4.0 predictive maintenance and high conformity quality control,” 2020.
P. F. Suawa, A. Halbinger, M. Jongmanns, and M. Reichenbach, “Noise-robust machine learning models for predictive maintenance applications,” IEEE Sens. J., vol. 23, no. 13, pp. 15081–15092, 2023.
J. Pedro Serrasqueiro Martins, F. Martins Rodrigues, and N. Paulo Ferreira Henriques, “Modeling system based on machine learning approaches for predictive maintenance applications,” KnE Eng., 2020.
H. Fang et al., “Fuzzy-based adaptive optimization of unknown discrete-time nonlinear Markov jump systems with off-policy reinforcement learning,” IEEE Trans. Fuzzy Syst., vol. 30, no. 12, pp. 5276–5290, 2022.
Y. J. Tan et al., “Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications,” Opt. Express, vol. 30, no. 15, p. 27763, 2022.
N. J. Wispinski, A. Butcher, K. W. Mathewson, C. S. Chapman, M. M. Botvinick, and P. M. Pilarski, “Adaptive patch foraging in deep reinforcement learning agents,” arXiv [cs.AI], 2022.
X. Jin, H. Ma, J. Tang, and Y. Kang, “A self-adaptive vibration reduction method based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm,” Appl. Sci. (Basel), vol. 12, no. 19, p. 9703, 2022.
S. Shen, G. Shen, Y. Shen, D. Liu, X. Yang, and X. Kong, “PGA: An efficient adaptive traffic signal timing optimization scheme using actor-critic reinforcement learning algorithm,” KSII Trans. Internet Inf. Syst., vol. 14, no. 11, 2020.
J. Santhappan and P. Chokkalingam, “A comparative analysis of predictive modeling techniques: A case study of device failure,” in Machine Learning and Information Processing, Singapore: Springer Singapore, 2020, pp. 223–233.
J. M. Wakiru, L. Pintelon, P. Muchiri, and P. Chemweno, “A comparative analysis of maintenance strategies and data application in asset performance management for both developed and developing countries,” Int. J. Qual. Reliab. Manag., vol. 39, no. 4, pp. 961–983, 2022.
S. S. Abdurakipov and E. B. Butakov, “Comparative analysis of algorithms of machine learning for predicting pre-failure and failure states of aircraft engines,” Optoelectron. Instrum. Data Process., vol. 56, no. 6, pp. 586–597, 2020.
P. Singh, S. Agrawal, and A. Chakraborty, “Multi-classifier predictive maintenance strategy for a manufacturing plant,” in 2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), 2021.
J. Y. Arpilleda, “Exploring the potential of AI and machine learning in predictive maintenance of electrical systems,” International Journal of Advanced Research in Science, Communication and Technology, pp. 751–756, 2023.
S. S. Thorat, M. Ashwini, A. Kelshikar, S. Londhe, and M. Choudhary, “IoT based smart parking system using RFID,” Int. J. Comput. Eng. Res. Trends, vol. 4, no. 1, pp. 9–12, 2017.
L. G. Titare and R. Qureshi, “‘cloud centric loT based farmer’s virtual market place,’” Int. J. Comput. Eng. Res. Trends, vol. 3, no. 12, pp. 654–658, 2016.
V. Jain, K. Poojari, and A. Chaudhari, “Prototype for design and development of video surveillance robot,” Int. J. Comput. Eng. Res. Trends, vol. 7, no. 4, pp. 28–34, 2020.
A. Gopi, P. R. Divya, L. Rajan, S. Rajan, and S. Renjith, “Accident tracking and visual sharing using RFID and SDN,” Int. J. Comput. Eng. Res. Trends, vol. 3, no. 10, pp. 544–549, 2016.
A. K. Pundir, P. Maheshwari, and P. Prajapati, “Machine learning based predictive maintenance model,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, 2022.
A. Erguido, A. Crespo, E. Castellano, and J. L. Flores, “After-sales services optimisation through dynamic opportunistic maintenance: a wind energy case study,” Proc. Inst. Mech. Eng. O. J. Risk Reliab., vol. 232, no. 4, pp. 352–367, 2018.
A. Turnbull and J. Carroll, “Cost benefit of implementing advanced monitoring and predictive maintenance strategies for offshore wind farms,” Energies, vol. 14, no. 16, p. 4922, 2021.
S. Ueyama, Y. Hashiba, K. Yokohata, H. Ueda, and H. Kameda, “Study on advanced maintenance strategies and asset management for substation equipment in japan: Investigation of recent technologies for advance maintenance strategies,” in 2022 9th International Conference on Condition Monitoring and Diagnosis (CMD), 2022.
S. Abou Malham, N. Touati, L. Maillet, and M. Breton, “The challenges of implementing advanced access for residents in family medicine in Quebec. Do promising strategies exist?,” Med. Educ. Online, vol. 23, no. 1, p. 1438719, 2018.
Kethireddy, J., Aravind, E., Kamal, M.V. (2023). Software Defects Prediction Using Machine Learning Algorithms. In: Reddy, V.S., Prasad, V.K., Wang, J., Rao Dasari, N.M. (eds) Intelligent Systems and Sustainable Computing. ICISSC 2022. Smart Innovation, Systems and Technologies, vol 363. Springer, Singapore. https://doi.org/10.1007/978-981-99-4717-1_10
D. Vasumathi, M. V. (2020). Unsupervised Learning Methods for Anomaly Detection and Log Quality Improvement Using Process Event Log. International Journal of Advanced Science and Technology, 29(1), 1109 - 1125.