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A Computational Intelligence (CI) approach is one of the main trending and potent data dealing out and processing instruments to unravel and resolve difficult and hard reliability crisis and it takes an important position in intelligent reliability analysis and management of data. Nevertheless, just few little broad reviews have recapitulated the current attempts of Computational Intelligence (CI) in reliability assessment in power systems. There are many methods in reliability assessment with the aim to prolong the life cycles of a system, to maximize profit and predict the life cycle of assets or systems within an organization especially in electric power distribution systems. Sustaining an uninterrupted electrical energy supply is a pointer of affluence and nationwide growth. The general background of reliability assessment in power system distribution using computational intelligence, some computational intelligence techniques, reliability engineering, literature reviews, theoretical or conceptual frameworks, methods of reliability assessment and conclusions was discussed. The anticipated and proposed technique has the aptitude to significantly reduce the needed period for reliability investigation in distribution networks because the distribution network needs an algorithm that can evaluate, assess, measure and update the reliability indices and system performance within a short time. It can also manage outages data on assets and on the entire system for quick and rapid decisions making as well as can prevent catastrophic failures. Those listed above would be taken care of if the proposed method is utilized. This overview or review may be deemed as valuable assistance for anybody doing research.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
425--443
Opis fizyczny
Bibliogr. 97 poz., tab., wz.
Twórcy
autor
- Bamidele Olumilua University of Education, Science and Technology Ikere Ekiti, Nigeria
autor
- Ekiti State University Ado Ekiti, Nigeria
autor
- Ekiti State University Ado Ekiti, Nigeria
Bibliografia
- [1] Adegboye B.A., Dawal E., Outage Analysis and System Integrity of an 11 kV Distribution System, Advanced Material Research, vol. 367, pp. 151–158 (2012), DOI: 104028/www.scientific.net/AMR. 367.151.
- [2] Olajuyin E.A., Wara S.T., Olubakinde E., Adetumbi A.O., Long Term Load Forecasting Using Artificial Neural Network, American Journal of Engineering Research (AJER), vol. 7, no. 11, pp. 14–17 (2018).
- [3] Akhavein A., Fotuhi-Firuzabad M., Billinton R., Farokhzad D., Adequacy equivalent development of composite generation and transmission systems using network screening, IET Gener. Transm. Distrib., vol. 5, no. 11, pp. 1141–1148 (2011).
- [4] Allan R.N., Billinton R., Shaidehpour S.M., Singh C., Bibliography on the Application of Probability methods in power system Reliability Evaluation, IEEE Transactions on Power Systems, vol. 3, no. 4 (1988).
- [5] Amiri M., Ghassemi-Tari F., Mohtashami A., Sadaghiani J.S., A Methodology for Analyzing the Transient Availability and Survivability of a System with the Stand by Components in Two Cases: The Identical Components and Non-Identical Components, Journal of Applied Sciences, vol. 8, pp. 4105–4112 (2008).
- [6] Andrea M.R., Armando M.L.S., Jorge L.J., Joao C.O.M., Static and dynamic aspects in bulk power system reliability evaluations, IEEE Trans. Power Systems, vol. 15, no. 1, pp. 189–195 (2000).
- [7] Armando M.L., Leonidas C.R., Luiz Antônio da F.M., Vladimiro M., Composite reliability assessment based on monte carlo simulation and artificial neural networks, IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1202–1209 (2007), DOI: 10.1109/TPWRS.
- [8] Athraa A.K., Noor Izzri A.W., Ishak A., Jasronita J., Ahmed N. Abdalla, Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory, Energies, pp. 2–13 (2017), DOI: 110.3390/en10030343.
- [9] Athraa A.K., Noor Izzri A.W., Ishak A., Jasronita J., Ahmed N.A., Computational techniques for assessing the reliability and sustainability of electrical power systems: A review, Renewable and Sustainable Energy Reviews, vol. 80, pp. 1175–1186 (2017), DOI: 10.10016/j.rser.2017.
- [10] Bolun W., Yong W., Ying D., Ming L., Cong Z., Interval Reliability Assessment of Power System under Epistemic Uncertainty Based on Belief UGF Method, 2nd Asia Conference on Power and Electrical Engineering (ACPEE) (2017), DOI: 10.1088/1757-899X/199/1/012070.
- [11] Brunette E.S., Flemmer R.C., Flemmer C.L., A Review of Artificial Intelligence, Proceedings of the 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand, pp. 385–392 (2009).
- [12] Carl J.W., Lina B., Le Anh T., Risk and reliability assessment for electrical distribution systems and impacts of regulations with examples from Sweden, International Journal of Systems Assurance Engineering and Management, Springer, vol. 1, no. 2, pp. 87–95 (2010), DOI: 10.1007/s13198-010-0017-6.
- [13] Carl J.W., Patrik H., Sabina S., Asset management framework applied to power distribution for costeffective resource allocation, International Transactions on Electrical Energy Systems Int. Trans. Electr. Energ. Syst. (2013).
- [14] Chanan S., Lingfeng W., Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems, Proceedings of ELCO (2007).
- [15] Chanan S., Lingfeng W., Role of Artificial Intelligence in the Reliability Evaluation of Electric Power Systems, Turk. J. Elec. Engin., vol. 3, no. 16, pp 189–200 (2017).
- [16] Carneiro P., Paula F., The Economic, Environmental and Strategic Value of Biomass, Renewable Energy, vol. 44, pp. 17–22 (2012).
- [17] Danyel P.R., Lavelle A.A., Richard E.B., Overcoming Data Problems in Predictive Reliability Distribution Modeling, IEEE Computer Applications in Power (2001).
- [18] Dogan U., Chanan S., Power System Reliability Evaluation using Monte Carlo Simulation and Multi Label Classifier, Proceedings of the National Power Systems Conference (NPSC), NIT Tiruchirappalli, India, pp. 1–6 (2018).
- [19] Dogan U., Chanan S., Power System Reliability Evaluation using Monte Carlo Simulation and Multi Label Classifier, Proceedings of the National Power Systems Conference (NPSC), NIT Tiruchirappalli, India, pp. 14–16 (2018).
- [20] Donald L.T., Asset Management System Processes: Implementation of Sensor and Artificial Intelligence, Industrial Engineering and Management, ISSN: 2169-0316, vol. 6, no. 4 (2017), DOI: 10.4172/2169-0316.1000231.
- [21] Fabíola F.C.V., Carmen L.T.B., Andrea M.R., A comparison of load models for composite reliability evaluation by non-sequential Monte Carlo simulation, IEEE Trans. Power Systems, vol. 25, no. 2, pp. 649–656 (2010).
- [22] Faiz R.B., Eran A.E., Decision Making for Predictive Maintenance in Asset Information Management, Interdisciplinary Journal of Information, Knowledge, and Management, vol. 4 (2009).
- [23] Fangxing L., Nura S., Monte Carlo Simulation to Evaluate the Reliability Improvement with DG connected to Distribution Systems, Proceedings of the 8th WSEAS International Conference on Electric Power Systems, High Voltages and Electric Machines, ISBN: 978-960-474-026-0 (2008).
- [24] Franklin Onime, Adegboyega G.A., Reliability Analysis of Power Distribution System in Nigeria: A Case Study of Ekpoma Network, Edo State, International Journal of Electronics and Electrical Engineering, vol. 2, no. 3, pp. 175–82 (2014), DOI: 10.12720/ijeee.2.3.175-182.
- [25] Fouzul Azim Shaikh, Zaheeruddin Z., Jamil Asghar M.S., Computational Intelligence and Voltage Stability Analysis for Mitigation of Blackout, International Journal of Computer Applications, vol. 16, no. 2, pp. 6–11 (2011), DOI: 10.5120/1987-2677.
- [26] Gupta B.R., Power System Analysis and Design, Chand and Company Ltd., India (2011).
- [27] Gupta J.B., Transmission and Distribution of Electrical power, S.K. Kataria and Sons, India (2012).
- [28] Goel L., Billinton R., Overall adequacy assessment of an electric power system, IEE Proc. Gener. Transm. Distrib., vol. 139, no. 1, pp. 57–63 (1992).
- [29] Goel L., Billinton R., Indexing pertinent factors in the adequacy evaluation of an overall electric power system, IEE Proc. Gener. Transm. Distrib., vol. 142, no. 4, pp. 337–342 (1995).
- [30] Gregory L., Computational Intelligence in Reliability Engineering, Springer, vol. 39, pp. 1–41 (2006).
- [31] Hao Y., Tiantian X., Paszczynski S., Wilamowski B.M., Advantages of Radial Basis Function Networks for Dynamic System Design, IEEE Transactions on Industrial Electronics, vol. 12, no. 58, pp. 5438–5450 (2011), DOI: 10.1109/TIE.2011.2164773.
- [32] He J., Sun Y., Kirschen D.S., Singh C., Cheng L., State-space partitioning method for composite power system reliability assessment, IET Gener. Transm. Distrib., vol. 4, no. 7, pp. 780–792 (2010).
- [33] H’ector C., Lesile M., Gerardo R., Conditional Monte Carlo With Intermediate Estimations for Simulation of Markovian Systems, Electronics Notes in Theoretical Computer Science, vol. 321, pp. 3–16 (2016), DOI: 10.1016,www.elsevier.com.
- [34] Hongzhou W., Hoang P., Survey of Reliability and Availability Evaluation of Complex Networks Using Monte Carlo Technique, Microelectron, Reliab., Elsevier Science Ltd., Great Britain, vol. 2, no. 31, pp. 187–209 (1997).
- [35] Hongan L., Tomas E., Barbro B., Hannu V., Visual Data Mining: Using Self Organizing Maps for Electricity Distribution Regulation, Springer-Verlag Berlin Heidelberg, pp. 631–645 (2011), DOI: 10.1007/978-3-642-22603-8_55.
- [36] Hossein A.H., Hamed M.R., An assessment of Paradigm shift barriers and prospects: Power Systems big data analytics, Energy Reports 4, pp. 91–100 (2018).
- [37] https://www.weibull.com.
- [38] https://www.tdworld.com/grid-innovations/article/20972827/the-potential-of-ai-for-utilities.
- [39] Pasupathi Nath R., Nishanth Balaji V., Artificial Intelligence in Power Systems, http://www.iosrjournals.org.
- [40] Hossain F.M., Hasanuzzaman M., Rahim N.A., Ping H.W., Impact of Renewable Energy on Rural Electrification in Malaysia: A Review, Clean Technologies and Environmental Policy, vol. 17, no. 4 (2015).
- [41] Istvan M., Laios J., An Expert-system-assisted Reliability Analysis of Electric Power Networks, Engineering Applic. Artif. Intell., Elsevier Science Ltd., Great Britain, vol. 4, no. 8, pp. 449–460 (1994).
- [42] Jaime C., Om P., Information and Communication Technologies in Condition Monitoring and Maintenance, IFAC (International Federation of Automatic Control) (2006).
- [43] Janne S.N., John D.S., Bayesian Estimation of Remaining Useful Life for Wind Turbine Blades, Energies, pp. 1–13 (2017).
- [44] Jose L., López-Prado Jorge, Vélez I., Guisselle A., Garcia-Llinás G.A., Reliability Evaluation in Distribution Networks with Microgrids: Review and Classification of the Literature, Energies, vol. 13, no. 23 (2020), DOI: 10.3390/en13236189.
- [45] Jonathan H., Nal K., Bayesian Inference for Large Scale Image Classification, Brain, Amsterdam, (2019).
- [46] Kabi R.P., Ash Bahadur S., Kezang P., Roshan Chhetri, Reliability Assessment of Distribution System through Cost Analysis, International Journal of Scientific Research and Engineering Development, vol. 3, no. 4 (2020).
- [47] Kovalev G.F., Lebedeva L.M., Reliability of power systems, Springer, pp. 237 (2019).
- [48] Krupenev D., Boyarkin D., Iakubovskii D., Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method, Reliability Engineering and System Safety, vol. 204 (2020).
- [49] Leite da Silva A.M., de Resende L.C., Fonseca Manso L.A., Miranda V., Well-being analysis for composite generation and transmission systems based on pattern recognition techniques, IET Gener. Transm. Distrib., vol. 2, no. 2, pp. 202–208 (2008).
- [50] Lina B., Ron A., Roland E., A reliability-centered asset maintenance method for assessing the impact of maintenance in power distribution systems, IEEE Transactions (2011).
- [51] Lingfeng W., Integration of Renewable Energy Sources: Reliability-Constrained Power System Planning and Operations Using Computational Intelligence, a dissertation submitted to the Office of Graduate Studies of Texas A&M University (2008).
- [52] Lin J., Lian R., Intelligent Control of Active Suspension Systems, IEEE Transactions on Industrial Electronics, vol. 2, no. 58, pp. 618–628 (2011).
- [53] Martorell S., Carlos S., Sanchez A. et al., Constrained optimization of test intervals using a steady-state genetic algorithm, Rel. Eng. Syst. Saf., vol. 67, pp. 215–232 (2000).
- [54] Mahind R., Patil A., A Review Paper on General Concepts of Artificial Intelligence and Machine Learning, International Advanced Research Journal in Science, Engineering and Technology National Conference on Innovative Applications and Research in Computer Science and Engineering (NCIARCSE-2017), vol. 2, no. 4, pp. 79–82 (2017), DOI: 10.17148/IARJSET/NCIARCSE.2017.22.
- [55] Mammath K.B., Durga P.M., Srinivas S., Software Reliability Assessment using Neural Networks of Computational Intelligence Based on Software Failure Data, Baltic Journal, Modern Computing, vol. 4, no. 4, pp. 1016–1037 (2016), DOI: 10.22364/bjmc.2016.4.4.26.
- [56] Martinez E.A., Nicholas G., Angeliki L.AS., Pierluigi M., Techno-Economic and Business Case Assessment of Multi-Energy Microgrids with Co-Optimization of Energy, Reserve and Reliability Services, Applied Energy, vol. 2, no. 10, pp. 896–913 (2018).
- [57] Mauricio S., Jorge C., Constructing Markov Models for Reliability Assessment with Self-Organizing Maps, 9th International Conference on Probabilistic Methods Applied to Power System KTH, Stockholm, Sweden, pp. 1–5 (2016).
- [58] Michalis K.T., Petros D., https://deepai.org/publication/gradient-based-adaptive-markov-chain-montecarlo (2019).
- [59] Mohammad E.H., Mohmmad S.G., Vahid H., Pierluigi S., Reliability modeling of process-oriented smart monitoring in the distribution systems, Electrical Power and Energy Systems, vol. 109, pp. 20–28 (2019).
- [60] Moody J., Darken J., Fast Learning in Networks of Locally-Tuned Processing Units, Neural Computation, Massachusetts Institute of Technology, pp. 281–294 (1989).
- [61] Mohamad F., Teh J., Impacts of Energy Storage System on Power System Reliability: A Systematic Review, Energies, vol. 11, p. 1749 (2018).
- [62] Okorie P.U., Aliyu U.O., Jimoh B., Sani S.M., Reliability Indices of Electric Distribution Network System Assessment, Quest Journal (2015).
- [63] Olulope P.K., Transient Assessment of Hybrid Distributed Generation Using Computation Intelligence Approaches, University of Cape Town, PhD Thesis (2014).
- [64] Olajuyin E.A., Olubakinde E., Adetunmbi A.O., Factors affecting Power Supply in Nigeria and the way forward, International Journal of Electrical Engineering and Ethics, vol. 2, no. 1 (2019).
- [65] Onime F., Adegboyega G.A., Reliability Analysis of Power Distribution System in Nigeria; A Case Study of Ekpoma Network, Edo State, International Journal of Electronics and Electrical Engineering, vol. 2, no. 3, pp. 175–182 (2014).
- [66] Preet L., Shelly V., State of Art on Reliability Evaluation of Systems Involving Non-Renewable and Renewable Energy Resources, International Journal of Control and Automation, ISSN: 2005-4297 vol. 12, no. 6, pp. 532–553 (2019).
- [67] Pathomthat C., Sunti Y., Atthapol N., Optimal Allocation of Multi-DG on Distribution System Reliability and power Losses Using Differential Evolution Algorithm, 4 𝑡 ℎ International Conference on power and Energy Systems Engineering, CPESE 2017 Berlin, Germany, pp. 25–29 (2017).
- [68] Rajesh C., Artificial Intelligence: An Advanced Approach in Power Systems, https://www.electrical india.in/artificial-intelligence-an-advanced-approach-in-power-systems (2017).
- [69] Sunny O., Computational Intelligence in Electrical Power Systems: A Survey of Emerging Approaches, British Journal of Science, vol. 12, no. 2, pp. 23–45 (2015).
- [70] Theo Wai Lip et al., Review of Distributed Generation (DG) System Planning Optimisation Technique: Comparison of Numerical and Mathematical Modelling Methods (2017).
- [71] Tripathi A., Shilipi S., Comparative Study of Reliability Assessment Technique for Composite Power System Planning and Application (2014).
- [72] Uhunmwangho R., Omorogiuwa E., Reliability Prediction of Port Harcourt Electricity Distribution Network Using NEPLAN, The International Journal of Engineering and Science (IJES), vol. 3, no. 12, pp. 68–79 (2014).
- [73] Ulas E., Ridan U., Reliability Analyses of Electrical Distribution Systems, International Journal of Engineering and Science (IRJES), ISSN 2319-1821, vol. 12, no. 5, pp. 94–105 (2016).
- [74] Vrana T.K., Johansson E., Overview of Analytical Power System Reliability Assessment Technique, CIGRE, Norwegian University of Science and Technology Norway, pp. 1–11 (2011).
- [75] Xufeng X., Joydeep M., Distribution system Reliability evaluation using credibility theory, International Journal of Engineering, Science and Technology, Multicraft Limited, vol. 3, no. 2, pp. 107–118 (2010).
- [76] Wan Z., Min-Ping L., Xiao-An., Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis, Chin. J. Mech. Eng., vol. 30, pp. 782–795 (2017), DOI: 10.1007/s10033-017-0150-0.
- [77] Wenyuan Li, Jiaqi Zhou, Probabilistic Reliability Assessment of Power System Operations, Electric Power Components and Systems (2008).
- [78] Yong Liu, Chanan Singh, Reliability evaluation of composite power systems using Markov Cut-Set method, IEEE Trans. Power Systems, vol. 25, no. 2, pp. 777–785 (2010).
- [79] Zhong W., Wang L., Liu Z., Hou S., Reliability Evaluation and Improvement of Islanded Microgrid Considering Operation Failures of Power Electronic, Equipment J. Mod. Power Syst. Clean Energy, vol. 8, pp. 111–123 (2020).
- [80] Onwualu A.P., Oluka S.I., Offfiong A., Principle of Engineering Project Management, SNAAP Press Ltd., Enugu (2002).
- [81] Allan R.N., Billinton R., Lee S.H., Bibliography on the Application of probability methods in power system reliability Evaluation, IEEE Transactions on power Apparatus and Systems, vol. 103, no. 2 (1984).
- [82] Langevine R., AbouRizk S., Allouche M., Development of a decision support system for building maintenance management, Proceedings of Annual Conference of the Canadian Society for Civil Engineering, pp. 1–10 (2002).
- [83] Juan L., Carlos D., Horst H., Karin F., Condition Assessment and Asset Management in Electric Power T and D Networks, CIRED Workshop-Lyon, pp. 7–8 (2010).
- [84] Yang J.E., Sung T.Y., Yin Y., Optimization of the surveillance test interval of the safety systems at the plant level, Nucl. Tech., vol. 132, pp. 352–365 (2000).
- [85] Mojgan H., Hashim H., Norman M., Senan M.A., Comparative Analysis of ATC Probabilistic Methods, Journal of American Science, vol. 6, no. 9, Dublin (2010).
- [86] Kezunovic M., Dokic T., Chen P., Big Data Used for Risk Assessment in Predictive Outage and Asset Management, Dublin (2017).
- [87] Akhikpemelo A., Eyibo N., Adeyi A., Reliability Analysis of Power Distribution Network, Continental Journal Engineering Sciences, vol. 11, no. 2, pp. 53–63 (2016),
- [88] Hsiang-Hua Y., Kuo-Hao C., Hsin-Wei H., Robert C., A Monte Carlo simulation-based decision support system for reliability analysis of Taiwan’s power system: Framework and empirical study, vol. 178, pp. 252–262 (2019).
- [89] Kazemi S., Reliability evaluation of smart distribution grids, Alto University Publication series, doctoral dissertations (2011).
- [90] Leonardo V., David M.H., Mauro C., Ales P., An artificial intelligence system for predicting Customer default in e-commerce, Expert Systems with Applications Journal, vol. 1, no. 4, pp. 1–21 (2018).
- [91] Srimawar S., Muhamma B.N., Andarin A., Bayu T.U., Prediction of lightning density value tower based on Adaptive Neuro-fuzzy Inference System, Archives of Electrical Engineering, vol. 70, no. 3, pp. 499–511 (2021), DOI: 10.24425/aee.2021.137570.
- [92] https://www.geeksforgeeks.org/introduction-to-recurrent-neural network.
- [93] Alex S., Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network, Elsevier journal Physica D: Nonlinear Phenomena, Special Issue on Machine Learning and Dynamical Systems, vol. 404, pp. 1–43 (2020).
- [94] Olajuyin E.A., Akinyede J.A., Akinyede T., Evaluation of reliability of protective devices in power distribution network, Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), vol. 12, no. 1, pp. 13–18 (2021).
- [95] Olajuyin E.A., Olubakinde E., Electrical Load forecasting in Power System, International Journal of Innovative Science and Research Technology, vol. 4, no. 8, pp. 845–847 (2019).
- [96] Mohammed Y.S., Mahmood T.A., High impedance fault detection in radial distribution network using discrete wavelet transform technique, Archives of Electrical Engineering, vol. 70, no. 4, pp. 873–886 (2021), DOI: 10.24425/aee.2021.138267.
- [97] Adoghe A.U., Awosope C.O.A., Ekeh J.C., Asset maintenance planning in electric power distribution network using statistical analysis of outage data, Electrical Power and Energy Systems, vol. 47, pp. 424–435 (2013).
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-73ebd596-7a36-453f-a12e-963707670c89