Fuzzy Logic Controllers and Applications

Front matter

Fuzzy Logic Controllers and Applications Edited by P. Balasubramaniam and N. Ramesh Babu Edited by P. Balasubramaniam and N. Ramesh Babu This book is divided into two sections: “Fuzzy Logic Controllers” and “Applications of Fuzzy Logic.” It provides a comprehensive collection, with two chapters dedicated to fuzzy logic control theory and three chapters focusing on its practical applications. By merging foundational theory with real-world examples, the book offers both novice and experienced engineers a thorough understanding of how fuzzy logic can enhance system performance, particularly in cases where traditional methods struggle to model complex behaviors. Implementing fuzzy logic in control systems is an exciting and rapidly evolving field. This book aims to highlight its potential, inspiring further research and innovation. It encourages the next generation of engineers to harness the power of fuzzy logic in addressing the complex challenges of the future’s technology-driven world. Published in London, UK © 2025 IntechOpen © vsijan / nightcafe.studio ISBN 978-0-85014-874-9

Fuzzy Logic Controllers and Applications Edited by P . Balasubramaniam and N. Ramesh Babu Published in London, United Kingdom http:/ /dx.doi.org/10.5772/intechopen.1004663 Edited by P. Balasubramaniam and N. Ramesh Babu Contributors Chiheb Reddouane, Fatima Ouzayd, Hamid Ech-cheikh, Israa Salih H. Alajab, Kirenia de la Caridad Viamontes Rabelo, Luis Benigno Corrales Barrios, Manal Tamir, Mehdi Dalir, Mohammed Osman Hassan, Namal Rathnayake, Nooshin Bigdeli, Tuan Linh Dang, Upaka Rathnayake, Yainier Otero Rosell, Yukinobu Hoshino © The Editor(s) and the Author(s) 2025 The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECHOPEN LIMITED’s written permission. Enquiries concerning the use of the book should be directed to INTECHOPEN LIMITED rights and permissions department (permissions@intechopen.com).

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Section 1 Fuzzy Logic Controllers Section 1 Fuzzy Logic Controllers

Chapter 1 A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management Israa Salih H. Alajab and Mohammed Osman Hassan Abstract Over the past two decades, prodigious prosperity has occurred in the utility of fuzzy logic controllers in power systems and power electronic applications. One of the major issues for the power system engineer is to monitor and rank the network contingencies related to an outage of one or more elements on the power network. Contingencies denote a single contingency (N-1) for the outage of one component or multiple contingencies for more than one element. When contingency occurs, the system may operate out of its stability margin and become insecure. Hence, it is necessary to manage that according to their impact on the network. The chapter discusses one of the cutting-edge applications of fuzzy logic control systems on electrical power systems by presenting an intelligent approach of fuzzy logic to rank the N-1 in the Power Grid. The intrinsic objective of this study is to develop a MATALB fuzzy logic model for ranking contingencies on the abridge network of Sudan’ s national grid and compare this ranking with that which was chiefly accomplished using NEPLAN software. The results on applied partial network confirmed that the Fuzzy Logic applied algorithm is an effective and proper ranking process for the grid single contingencies (N-1).

Keywords: fuzzification, defuzzification, membership function, severity indices, national Sudanese grid (NSG), violation, contingency ranking 1. Introduction Fuzzy logic controllers (FLCs) have produced a great deal of interest in several utilizations and have been imported into power electronics. They possess multiple advantages compared with the conventional PI controllers, which can operate with imprecise input and handle nonlinearity, in addition to their higher durableness than PI controllers, also they do not need an accurate mathematical model [1]. Fuzzy logic control has expressed a momentous sector of fuzzy reasoning, which is illustrated by control theory based on mathematical models of the open-loop progress to be controlled [2].

The influence of the element outage on the power system, when the rest of the system is in service, is called a contingency study [3]. The security operation of any power grid is a highly significance issue; hence, the contingency ranking is an integral section in the power networks security assessment [4]. The main target of contingency capturing and ranking is to hastily and meticulously shortlist critical contingencies from a massive list of reliable contingencies and rank them depending on their severity for advanced conclusive analysis [5]. In recent years many researchers have going ahead on the contingency analysis, Alam proposed a method to determine the power system contingency ranking by using voltage deviation indices and power deviation indices [6], and Christie and Talukdar presented a consolidation technique for evaluation contingencies depends on pundit systems for contingency election and interpretation, but the execution depends on the knowledge base which becomes so slow due to the knowledge base castles [7]. The using of intelligent systems in the contingency ranking had a wide range For instance, Mohsena et al., provide an integrated algorithm for contingency ranking by taking into account the indices of voltage violation, line flow violation, locational marginal price and congestion cost using the neural network to deter - mine the main parameters of power system [8]. Fuzzy logic was also used in contingency studies by Ramesh et al. to remove the mask effect after ranking contingency by using apparent power overloading and voltage violations. In this chapter, a fuzzy logic system has been used to accomplish the process of ranking for single contingencies [9].

As shown previously, although there are different studies discuss the fuzzy logic in the contingency analysis, no one of them applies the fuzzy logic system in ranking the single contingencies in the Sudan national grid which is the novelty of this study which contributes to advances in fuzzy logic system by applying this technique in a real national grid to rank the single contingencies.

Sections of this chapter may contain the following: the methodology of research in section 2 and overall reviews on the implemented network characterization in Sections 3. Section 4 represented the simulation of a fuzzy logic control system with its two processes (fuzzification and defuzzification). Section 5 illustrated the fuzzy logic simulation results analysis and discussion compared to those contingency raking obtained from NEPLAN software contingency in which both subsections discussed the results of single contingency ranking from NEPLAN software and results analysis of fuzzy logic system contingency ranking. The later sections is the conclusion and recommendations which they summarized the chapter results. Finally, the references for the previously published literature have been included. 2. Methodology According to the objectives of the study, the base requirement is to accomplish the contingency ranking by using the fuzzy logic control system, and the process relies on two base electrical amounts: the first is voltage and the second is megawatt loading which is used as inputs for fuzzy logic system that both of voltage profile and line power flow had a created membership function of trapezoidal type “trapmf ” on the Mamdani model membership at fuzzy interface system (FIS) toolbox on MATLAB. The output of the (FIS) is the severity indices of voltage profile (SIVP), and the severity indices of line flow (SILF). The algebraic summation of (SIVP) and (SILF) typify a composite index (CI) which the ranking process depends on. The most severe contingencies have a higher (CI) and vice versa. This process is illustrated in Figure 1 which represents the generation of CI.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 For reliability purposes, the results of contingency ranking were compared with those that were investigated at normal load in NEPLAN software. NEPLAN (Network Planning) is a network simulation program that acts multi-tasks of the power system analysis such as (load flow , short circuit, capacitance location, contingency analysis, etc.). Ranking of contingency at NEPLAN depends upon the concept of violation level that an element outage causes voltage violation on remaining network elements, if the violation is above (100%), the system is insecure and the contingency according to the violated element has been ranked as most severe. 3. The implemented network characterization A partial grid of the national Sudanese grid is implemented in this study, as shown in Figure 2. The simplified eighty-two (82) bus-bars operate at four multifarious voltage levels (500, 220, 110, and 66 kV), which form the main part of the transmission system in the Sudan electrical grid. The total number of transmission lines on the network had been summarized as eighty-one (81) which connected the main supplied areas.

The data was taken at normal load conditions where the network simplified to total generation and total loading as: (1603.517 MW , and 227 .008 MV Ar) and (1553.3 MW and 1083.3 MV Ar) respectively.

The total generation in the grid is generated from different sources (Hydro generation, Solar, and most are thermal power plants in addition to the interconnection tie line feeders from both Ethiopia and Egypt). 4. Simulation of fuzzy logic control system As clarified previously on the methodology, the membership functions had already been created for voltage profile and load flow indices as can be shown in Figures 3 and 4.

The outputs of the two membership functions represent the severity indices of (SIVP) and (SILF) which can be shown in Figures 5 and 6. Figure 1.

Composite index identifying based on fuzzy logic system [10].

Figure 4.

The created membership function of line flow.

Figure 2.

Single-line diagram of the implemented partial Sudan grid [11]. 500 kV , 220 kV , 110 kV , 66 kV .

Figure 3.

The created membership function of the voltage profile [11].

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 4.1 V oltage profile and line power flow fuzzification The fuzzification which already had been processed through the Mamdani Functions model (MFs), the IF-then rules were set as in Table 1. Table 1 can be implemented on the rule editor at (FIS) on MATLAB as shown in Figures 7 and 8.

The If-then rules formulated in Figures 7 and 8 can be depicted clearly on the rule viewer at FIS as shown in Figures 9 and 10.

Figure 5.

The severity index of voltage profile membership function. Figure 6.

The severity index of the line flow membership function. Table 1.

Fuzzification rules of voltage profile and line flow.

4.2 Severity indices defuzzification Defuzzification of the output is the process in which the severity indices of voltage profile and line flow (SIVP and SILF) are illustrated as numbers two different membership functions with linguistic variables of voltage profile data and line flow data of the network [15], on the other hand, linguistic values of these two membership functions represent both of severity index of voltage (SIVP) and severity index of line flow (SILF). This defuzzification process can be interpreted clearly in Figures 11–14. As mentioned earlier in the methodology section, the ranking of the fuzzy approach is based on the composite index (CI) [17]. The CI expresses the total Figure 7.

The rules editor of voltage profile MF [11].

Figure 8.

The rules editor of line flow MF [12].

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 Figure 9.

The rules viewer of the voltage profile membership function [13]. Figure 10.

The rules viewer of the line flow membership function [14]. Figure 11.

Voltage profile fuzzy logic controller block [10].

severity index for the specific contingency case. The CI can be identified mathematically as the common summation of total severity indices of voltage profile (∑SIVP), and total severity indices of line flow (∑SILF), where both (∑SIVP), and (∑SILF) are the outputs of the two blocks in Figures 11 and 13, respectively. 5. Fuzzy logic simulation results analysis compared to NEPLAN contingency ranking In this section, the results of ranking through the fuzzy logic control system have been compared with the ranking that had previously obtained from the NAPLAN program. The first subsection represents the NAPLAN results as a reference. The second subsection represents the detailed results from fuzzy interface system (FIS) blocks. The results are obtained for all elements in the implemented network but for simplification purposes, only the most severe five contingencies had been reviewed. Figure 12.

Inserting of voltage profile MF on fuzzy logic control block [16]. Figure 13.

Line flow fuzzy logic controller block.

Figure 14.

Inserting of voltage profile MF on fuzzy logic control block.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 5.1 Results of single contingency ranking from NEPLAN software Table 2 represents the NEPLAN ranking for the most severe five contingencies that had been used as reference ranking. 5.2 Results analysis of fuzzy logic system contingency ranking This subsection represents the fuzzy logic control ranking results for the same most severe single contingencies which had been illustrated in Section 5.1. 5.2.1 Contingency1: outage of line 23 Table 3 represents the severity indices for each node’ s voltage profile when an outage on line 23 occurred.

Contingency No.

Type of contingency From To Violation (%) Violated element Ranking 1 Line 23 Bus Bus 142.63 Line 36 1 2 Line 37 Bus Bus 110.72 Node 66 2 3 Line 20 Bus Bus 88.52 Node 61 3 4 Line 26 Bus Bus 88.17 Node 73 4 5 Line 10 Bus 1 Bus 83.82 Node 12 5 Table 2.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 Line 23 takes into consideration the effect of the high violation on line 36, which reached (142.63%) and the nodes with the contingency of line 23 connected between them are node (61) and node (62), both two nodes have a severity index of (43.75) which classified as the highest value for the severity indices on Table 3 and defined as More Severe (MS) on the membership function editor at the fuzzy set. On the other hand, the violated element itself connected through the nodes (59) and (68); their severity indices are (33.4450 and 35.5912), respectively, and this is defined as below severe (BS) as set in Table 1 in Section 4.1.

As shown in Table 4, the light concentrated on the line flow index for line 36 which is the violated element for this contingency of line 23 outage. The severity index is (13.7797) which is defined on the fuzzy set as the less severe (LS) range. When these results are compared with NAPLAN results, line 36 has a violation of (89.50%), that is, less than 100% which is below the severity limit of the network. From the whole mentioned above, it can be judged that by the contingency of line 36, the system is secure.

By returning to the main contingency of line 23, the composite index (CI) must be accounted for to rank the contingency of line 23. From the defuzzification results in Tables 3 and 4: ( )=∑ ∑ +∑ = + =line23 line23SIVP SILF 2539.4040 429.2228 .CI 2968 6268 (1) Node name V oltage profile (p.u) SIVP 19 0.9971 26.25 27 0.9974 26.25 39 1.0026 26.25 44 0.9975 26.25 17 0.9978 26.25 37 1.0019 26.25 41 0.9986 26.25 46 0.9986 26.25 34 0.9987 26.25 33 0.9988 26.25 38 1.0006 26.25 1 1.00 26.25 18 1.00 26.25 28 1.00 26.25 31 1.00 26.25 49 1.00 26.25 71 1.00 26.25 ∑ VPSI 2539.404 *The yellow line represents the buses (61,62) which link line 23 where both busses the most higher SIVP in the table and also the results represent that line 23 has the highest line flow severity index [11]. *Buses (59,68) in T able 3 link the transmission line in parallel with line 26 in contingency no4 this means that the outage of this line will have a severity index near equal to line 26 [11]. Table 3.

Severity indices for line 23 contingency [11].

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 The previous illustration can be represented clearly by graphs in Figures 15 and 16. 5.2.2 Contingency2: outage of line 37 The results of a single contingency of line 37 can be represented and clarified as in Figures 17 and 18.

Figure 17 represents the severity indices voltage profile against the voltage profile when a single contingency of line 23 occurs on the system. From the network in Figure 1, transmission line 37 is connected through basbars 66 and 61. Hence, the severity index for these two nodes represents the highest value, for instance, node 66 violated with (110.7%), and the severity indices for node 61 and its around nodes is (43.75) which is located in more severe (MS) at the fuzzy set.

Severity indices for line flow index [11].

Figure 15.

Severity index of voltage profile for the contingency of line 23. Figure 16.

Severity index of line flow for the contingency of line 23. Figure 17.

Severity index of voltage profile for the contingency of line 23.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 From the results of line flow severity indices shown in Figure 18, it is clear that the contingency of line 23, has a severity index of 37 .623 which is defined in a range of above severe (AS) at a fuzzy set of membership function, although the other lines have a severity index classified as (LS) and (VLS) at fuzzy logic setting as in Table 2, Section 4.1.

With the typical defuzzification process, the results of various severity indices for line 37 contingency can be obtained in tables as in contingency 1. It found that the value of (∑SIVP) is 2494.3840, and the value of ∑SILF is 428.9414. Hence, the composite index (CIline 37) can be obtained as: ( )=∑ ∑ +∑ = + =line37 line37SIVP SILF 2494.3840 428.9414 .CI 2923 3254 (2) 5.2.3 Contingency3: outage of line 20 The defuzzification results of the contingency line 20 shown in Figures 19 and 20. Figure 18.

Severity index of line flow for the contingency of line 37. Figure 19.

Severity index of voltage profile for the contingency of line 20.

Figure 19 represents that the highest (SIVP) value is 43.75, which appeared at nodes 53 and 54, respectively, because they are the holder nodes for line 20 itself. The severity spread around its neighbor directly connected nodes that had possessed the same severity index. The remaining nodes on the network have severity indices in the range of (MS) set.

Regarding the transmission lines flow severity indices shown in Figure 20, most SILFs are in the range of very less severe (VLS) and limited lines in less severe (LS) according to the fuzzy set in Table 1, Section 4.1. An outage of line 20 has a high influence on system voltage stability that most of the nodes had an (MS) severity index; this can be noticed clearly from Eq. (3) that the weight of the composite index consists of high (∑ SIVP). The composite index of line 20 (CIline 20) contingency is: ( )=∑ ∑ +∑ = + =line20 line20SIVP SILF 2461.7530 451.8530 .CI 2913 6060 (3) Figures 19 and 20 show the severity indices of line 26 outage. 5.2.4 Contingency4: outage of line 26 The single contingency of line 26 will be illustrated in this section with the aid of Figures 21 and 22.

Figure 21 represents SIVP on line 26 contingency against the voltage profile on the line; the most severe element is node 73, and its connected nodes are affected by it with high severity index voltage profile (SIVP) and the remaining nodes at the network had been possessed (SIVP) in the (BS) limits at the fuzzy set. Figure 22 represents the values of SILF according to the line flow index in the network at the outage of line 26. Most of the severity indices of line flow (SILF) for this contingency are within limits of (VLS), and fewer of them are within (LS) limit as in the fuzzy set in Table 1. As a result, it can be noticed that the contingency of line (26) outage had a less (∑SILF) impact on the composite index, this can be shown in Eq. (4): ( )=∑ ∑ +∑ = + =line26 line26SIVP SILF 1708.767 344.5316 .CI 2053 2986 (4) Figure 20.

Severity index of line flow for the contingency of line 20.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 5.2.5 Contingency5: outage of line 10 In this subsection, results for line 10 single contingency, will be clarified as shown in Figures 23 and 24.

Figure 23 represents the magnitudes of SIVP depending on the voltage profile at the outage of line 10. In this contingency, the most affected node is node 12 that it violated with (83.82%), the other bus-bars on the network had possessed severity indices of voltage profile (SIVP) in the limits of below severe (BS) on the fuzzy logic classified previously in Section 4.1. Hence, it is worth mentioning that the single contingency of line 10 had a less severe influence on the voltage security. Figure 24 shows the SILF according to the implemented elements load flow index, when returning to the network diagram shown in Figure 1 in Section 3, transmission line 10 is connected through busbar 1 and busbar 2, so, it is normally to find that the most affected lines with line 10 contingency, those which closely connected to Figure 21.

Severity index of voltage profile for the contingency of line 26. Figure 22.

Severity index of line flow for the contingency of line 26.

busbar 1 and busbar 2 that they had been in the top of severity indices list in the limits of below severe as a fuzzy setting in Table 1. The remaining transmission lines of the system possessed values SILF in limits between (LS) and (VLS) of severity index for the other lines are varied within the range of (LS) and (VLS). The range of total algebraic summation for the severity indices voltage profile in this contingency is less value compared with that for severity indices line flow which had been arranged in limits of (BS, LS, and VLS).

According to which mentioned above, it can be handed down that line 10 single contingency has a high impact on system power loading (MW) which can give the operator engineer an indicator to concentrate on the megawatt administration techniques to monitor this contingency. Eq. (5) below represents the composite index for line 10 contingency (CIline 10) is: ( )=∑ ∑ +∑ = + =line10 line10SIVP SILF 812.3472 987.7966 1800.1438CI (5) Figure 24.

Severity index of line flow for the contingency of line 10. Figure 23.

Severity index of voltage profile for the contingency of line 10.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 5.3 Results compression of contingencies ranking Table 5 shows that any element of the selected contingencies has the same ranking within both the NAPLAN program and the severity indices obtained from the fuzzy logic system (FIS) [18]. 6. Conclusion This chapter represented an intelligent technique based on the fuzzy logic controller for ranking a single contingency. The fuzzy logic ranking depends on the severity index (SI) denoted with the composite index (CI) which is accumulated of two severity indices, the first one, is the severity index of voltage profile (SIVP) and the other is the severity index of line flow (SILF).

In this chapter, the technique of fuzzy logic Interface system has been applied to a partial Sudanese transmission network. The results were compared to those obtained from NEPLAN software and the fuzzy logic system approach had been approved for the same ranking with the additional advantage that it is more reliable and robust in ranking the single contingencies. Also, contingency ranking based on CI imparted the fuzzy logic technique more precession because it indicates the source of insecurity on the network that the security problem refers to voltage problem if (∑SIVP) is higher than (∑SILF) on the (CI), and the security problem refers to power flow on the network if the (∑SILF) is the higher component of CI, this significant issue because it guides the network dispatcher to take the proper control action according to system deficiency. 7. Recommendations • Applying the fuzzy logic ranking technique on the extended actual grids for its high reliability. • Upgrading the technique by linking it through the adaptive neuro-fuzzy interface system (ANFIS) function in MATLAB and simulating it through the Sugenomodel system instead of the Mamdani model which is used in this chapter. Cont. No. Type of contingency Violation (%) from NEPLAN Ranking of contingency through NEPLAN CI ∑(∑SIVP,∑SILF) from fuzzy interface system Ranking of contingency through fuzzy logic approach 1 Line 23 142.63 1 2968.6268 1 2 Line 37 110.72 2 2923.3254 2 3 Line 20 88.52 3 2913.6060 3 4 Line 26 88.17 4 2053.2986 4 5 Line 10 83.82 5 1800.1438 5 Table 5.

Compression of fuzzy logic system contingencies ranking with the NEPLAN contingency ranking for the same five single contingencies.

Author details Israa Salih H. Alajab1* and Mohammed Osman Hassan2 1 Sudanese Electricity Transmission Co. Ltd, Khartoum, Sudan 2 Sudan University of Science and Technology, Khartoum, Sudan * Address all correspondence to: israamm9119@gmail.com © 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A Fuzzy Logic Controller for Enhancement of the Power Grid Single Contingency Management DOI: http://dx.doi.org/10.5772/intechopen.1006581 References [1] Mikklii S, Panda AK. Real-time implementation of PI and fuzzy logic controllers based shunt active filter control strategies for power quality improvement. International Journal of Electrical Power & Energy Systems. 2012;143(1):1114-1126. DOI: 10.1016/j. ijepes.2012.06.045 [2] Iancu I. A Mamdani Type Fuzzy Logic Controller. Intech Open, Open Access books. ISBN: 978-953-51-0396-7; 2012 [3] Lo KL, Abdelaal AKI, et al. Fuzzy logic based contingency analysis. In: IEEE Conference; 04-07 April 2000.

London UK: IEEE; 2000. DOI: 10.1109/ DRPT .2000.855715 [4] Krishnakumar B, Subaashini M, Ganesh Kumar E, Arthi R. Power flow based contingency analysis using fuzzy logic. Procedia Engineering. 2012;38:3603-3613 [5] Manjula BG, Suma AP , Kulkarni AD, Kumar N, Ananthapadmanabha T .

Contingency ranking in power systems employing fuzzy based analysis.

International Journal of Electrical and Electronics Engineering Studies (IJEEES). 2012;4(1):1-7 [6] Alam M. Determination of Power System Contingency Ranking Using Novel Indices. Singapore: Springer Nature; 2022 [7] Nagendra Prasad HK, Kantharaj B, Kavitha KM, Ramesh E. Contingency analysis of a power system by using fuzzy approach. International Journal of Engineering Research & Technology (IJERT). 2014;3(6):1659-1661. ISSN: 2278-0181 [8] Mohsena S, Seyavashc C, Sepided Y , Alie S. A new method for power system contingency ranking using a combination of neural network and data envelopment analysis. Journal of Intelligent & Fuzzy Systems. 2017;32(6):3859-3866.

DOI: 10.3233/IFS-162169 [9] Ramesh E, Prakash R, Lekshmi M, Y ogeesh S. Case study on fuzzy logic based network contingency ranking.

International Journal of Engineering Research & Technology (IJERT). 2013;2(7):830-834. DOI: 10.17577/ IJERTV2IS70268 [10] Hassan M, O, Alajab ISH. Sudan national grid contingency ranking through fuzzy logic approach.

International Journal of Advanced Engineering and Nano Technology (IJAENT). 2022;9(4):3-6. DOI: 10.35940/ ijaent.C0468.049422. ISSN: 2347-6389 (Online) [11] Alajab ISH, Hassan MO. Thesis of: “National Sudanese Grid Contingency Ranking through Fuzzy Logic Approach” .

Sudan: Sudan University; 2017 . Available from: http://repository.sustech.edu/ handle/123456789/18488 [12] Poshtan M, Rastgoufard P , Singh B.

Contingency ranking for voltage stability analysis of large scale power systems.

In: Proceeding of IEEE/PES Power System Conference and Exposition. 2004. pp. 1595-1602 [13] Mohammed MM. Development of Intelligent Systems for Evaluating V oltage Profile and Collapse under Contingency Operation. Egypt: M. S., Zagazig University; 2004 [14] Shankar S, Ananthapadmanabha T .

Fuzzy Approach to Critical Bus Ranking Under Normal and Line Outage Contingencies. Dubai, UAE:

International Journal of Soft Computing (IJSC); 2011;2(1) [15] Magaji UA. Contingency analysis using fuzzy logic. In: 2nd International Conference on Science, Technology and Management, University of Delhi (DU), Conference Centre, India; 27 September, [16] Fuzzy Logic Toolbox User’ s Guide “Fuzzy Logic Toolbox for Use with MATLAB” , COPYRIGHT by The MathWorks, U.S; 1995-1999. pp. 6-63 [17] Christy, Priscilla D, Christy A.

Contingency ranking method using fuzzy controller. International Journal of Innovative Technology and Exploring Engineering (IJITEE). (Online). 2019;8(12):3104-3108. ISSN: 2278-3075 [18] Zanzoni A, Montecchi-Palazzi L, Quondam MX. Mint: A molecular interaction database. FEBS Letters. 2002;513:135-140. DOI: 10.1016/ s0014-5793(01)03293-8

Chapter 2 The Design of Fractional-Order Robust Adaptive Intelligent Controller for Fractional-Order Nonlinear Systems with Time Delay, Uncertainty and Disturbance Mehdi Dalir and Nooshin Bigdeli Abstract Recently, many practical systems have been described via nonlinear fractional-order dynamic models. The design of controller for such systems is not a straightforward task, where their complex behaviors may result in chaos and bifurcation. This challenge becomes more serious when the system experiences time delays, uncertainties and disturbances. This chapter presents the controller design strategies for fractional-order nonlinear systems with time delays, uncertainties and disturbances via robust adaptive intelligent structure. Time-varying heterogeneous time delays are considered in inputs and states; and two robust adaptive intelligent strategies are designed and compared based on sliding mode and backstepping control methods. Besides, the stability analysis of the controllers is carried out based on Lyapunov-Krasovskii method and Barbalat’s Lemma. Simulation examples show the effectiveness of the proposed method for such systems. To demonstrate the effectiveness of Adaptive Network-based Fuzzy Inference System (ANFIS) on designed controller, their performance is compared with that of fractional-order robust adaptive controllers, as well. Keywords: adaptive intelligent control, neuro-fuzzy estimator, time delay, robust control, fractional-order nonlinear system, uncertain fractional-order system 1.