Note that (55) is based on fitness function maximization. Weve updated our privacy policy so that we are compliant with changing global privacy regulations and to provide you with insight into the limited ways in which we use your data. National Library of Medicine The fitness of each squirrel is then estimated. As a result, the superiority of BFO in this application could not be validated. Introduced by Eusuff and Lansely, the shuffled frog leaping algorithm (SFLA) is inspired the hunting strategy of frogs. To assess the accuracy of the proposed algorithms, seven datasets were used. However, the stator current waveforms for the conventional sliding mode controller and conventional PI controller were not provided, hence the superior or inferior quality of the stator current waveforms from the proposed controller could not be validated. This is shown as [124]: Where xsa(t) is the current position of a squirrel in an acorn tree, xsa(t+1) is the updated position of a squirrel in an acorn tree, xsn(t) is the current position of a squirrel in a normal tree, xsn(t+1) is the current position of a squirrel in a normal tree, xsh(t) is the current position of the squirrel in the hickory nut tree. Thereafter, the fitness value of each firefly a is compared to the fitness value of a randomly chosen firefly b. If this fitness value is superior to the previous fitness value of that specific firefly, that firefly updates its fitness (and hence position) to the better value. Trial Course - CertMaster Learn and CertMaster Labs for Security+ (Exam SY0-6 Data Engineer's Lunch #81: Reverse ETL Tools for Modern Data Platforms, Using Different Types of Questions to Further Dialogue1 .docx, RSG Sri Lanka Presentation - Ravindra Perera- Public Version.pdf, Ravindra Perera CC MBA(PIM), 6 Sigma (Green Belt). It is a random number between 0 and 1. The application of the SFLA, SSA and SFO to the control of the DFIG. Ozcan E., Mohan C. Particle swarm optimization: surfing the waves. However, it was observed that as the number of subsets (dimensions) increased, the performance of the proposed algorithm declined by a large extent. In terms of proportional-integral (PI) controllers, the various fitness functions (performance indices) are time varying functions of the integral of either the square of absolute value of the error being input into the PI controller [46], [47], [48]. In wind farms, the generator most commonly utilized are the DFIG and permanent magnet synchronous generator [9]. Another method is the CohenCoon tuning method, which is similar to that of the ZieglerNichols method but makes use of different formulae to determine optimal controller performance [16], [17], [18]. Evidently, WECS comprises of various mechanical and electrical components. The second rule states that the best nest (which contains the best quality eggs) has the best chance of being carried over to the next generation. The third and final rule states that the number of nests is unchangeable. Only the RSC was considered, and three PI controllers were tuned. This can be expressed as [144], [145], [146]: Where L0 is the maximum light intensity, L is the light intensity of firefly b as seen by firefly a, y is the distance between the two fireflies and Is the light absorption which is dependent of the medium in which the firefly exists. A comparison between PSO and SFLA is provided in [168]. If q is greater than 0.5 and the magnitude of A is greater than or equal to 1, the whale positions are updated randomly. Pillay N. A particle swarm optimization approach for tuning of SISO PID control loops. Who were the siblings in Fred Claus' Siblings Anonymous group besides Roger Clinton Stephen Baldwin and Frank Stallone? This frequency is calculated using a specified maximum and minimum frequency and a randomized number in the range [0,1]. BA and BFO are fared against each other in [169]. This continues until all iterations have been completed. If the new fitness of any wolf is superior to that of the previous fitness function of that same wolf, that wolf updates its fitness value (and hence position). Yang B., Wang J., Yu L., Shu H., Yu T., Zhang X., Yao W., Sun L. A critical survey on proton exchange membrane fuel cell parameter estimation using meta-heuristic algorithms. Considering the turbine speed, line voltage, dc link voltage and grid side real power, the FLWRBFN achieved a lower overshoot and faster settling time for all aspects for both the dynamic and transient responses. stationary magnetic field inside the machine, that the armature CSA produced the worst average results among all the techniques, showing the strong need for improvement. rotor windings. The proposed algorithm offers a superior performance with regards to the sizing and optimal placement of distributed generators. This is seen in Fig. field and generate an alternating voltage. Sarangi S.K., Panda R., Priyadarshni S., Sarangi A. Three weighting factors are used in this fitness function, which were chosen by the ABC algorithm. For all the investigated scenarios, the proposed techniques exhibited superior performance in both accuracy and stability. This is in terms of structure, mathematical modelling, shortcomings, advancements, and application to the control of the DFIG. The controller is easy to implement, does not require an accurate model, and exhibits a robust control performance. This can be represented as follows [44], [110], [111]: Where xi(t+1) is the updated position of the ith crow, xi(t) is the current position of the ith crow, fli(t) is the length of flight of the ith crow and be taken as a randomized number between 1 and 2, mj(t) is the location of the food of the jth crow (taken as the current position of a randomly chosen crow), rj is a randomized number between 0 and 1 and APjt is the probability of awareness of the jth crow to the intention of the ith crow. Careers, Discipline of Electrical, Electronics & Computer Engineering, University of KwaZulu-Natal, 238 Mazisi Kunene Road, Durban, 4041, South Africa. The overshoot superiority was once again exhibited by BFO, this time the result being 54.54% and a huge 834.62% superior to GA and PSO respectively. These algorithms are applied to three benchmark functions, at three dimension magnitudes. Feature selection based on bacteria foraging intelligence. The important fact, though, is that there has been a sharp rise in the utilization of wind energy for the production of electricity. The local search update is modified using the BFGS algorithm, which is based on the position of the best wolf. The inferiority to the best result, in terms of average value and standard deviation, was 93.56% and 94.69% respectively. Figure 24, Figure 25, Figure 26 depict the convergence curves of each algorithm, for each test function and at each of the dimension magnitudes utilized. This is to ensure that all the whales stay close to the prey. Model Predictive Control of Wind Energy Conversion Systems. If the fitness value of the nest is superior to that of the cuckoo, the cuckoo takes on the fitness (and hence position) of the nest. The second parameter, Smin is calculated based on the current and maximum iteration number [124]. Yang B., Zhu T., Wang J., Shu H., Yu T., Zhang X., Yao W., Sun L. Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition. Required fields are marked *. The FOPID controller makes use of two additional parameters, which ensures that the performance does not degrade if the rotor resistance varies. Metaheuristic Optimization Techniques, as the name suggests, are problem independent control techniques which has gain rapid popularity in the application of complex engineering problems. The PD can be calculated by utilizing the number of sardines and sailfish present. Reference [83] combines both the tracking and seeking modes and applied this to the GWO algorithm. PSO is one of the few swarm-based MOT which displays fast convergence, hence modification and incorporation of intelligent behavior of fish and birds may result in PSO once again being a superior optimization technique. [84]. The results showed that the proposed algorithm outperformed the supervisory control method in all aspects i.e., settling time, rise time, peak time, and percentage overshoot. To allow for a fair comparison, the number of search agents and particles were kept uniform across all three algorithms. 17 The conventional FA suffers the demerits of being easily trapped in the local optima [146], [149] and a slow convergence rate [146], [150]. International Conference on Computer, Control, Electrical, and Electronics Engineering; ICCCEEE, Khartoum; 2021. Zhai Q., Xia X., Feng S., Huang M. Optimization design of LQR controller based on improved whale optimization algorithm. Upon completion of this article, the authors propose the following future scope of work to be completed: All authors listed have significantly contributed to the development and the writing of this article. An improved firefly algorithm with specific probability and its engineering application. The authors in [157] proposed a SFLA which introduces the application of a weighting factor based on chaos memory and an absolute balance group strategy. Consider the following [102], [103]: Where Xi(t) is the current position of the ith whale and D is the number of search space dimensions. Legesse A.N., Saha A.K.C.R.P. This is based on the current and maximum iteration numbers, a randomized number in the range [0,2], a randomized number which lies in the range [0,2] and the values of A and D obtainable from the WOA. The total global installed capacity of WECS has rapidly increased in modern times [4]. Note that k and j are chosen randomly, but k needs to be different from i. The damping time and dynamic response of all three algorithms appear to be the same, with any variance being negligible. Considering stability, WOA is superior for F1 and F2, but is inferior to both PSO and ABC for F3. The conventional SFO has the merits of a fast convergence rate and being not easily trapped in the local optima [134]. coils are spun in. It was observed that the proposed algorithm produced a superior global search capability in all the tested functions. Bats use echolocation to perform various functions, such as locating prey, avoided obstacles, and finding other bats. PSO consists of a population of particles which move at a given velocity. [92]. The scholars in [120] attempted to enhance the local search capability of the algorithm, as well as prevent premature convergence. No data was used for the research described in the article. [124]. The proposed algorithm was tested on a range of unimodal, multimodal, and fixed dimensional multimodal benchmark functions. International Federation of Automatic Control. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Karaboga D., Basturk B. An induction generator requires less maintenance because of its robust construction. However, even if the acceleration constants and maximum velocity are selected correctly, there is a possibility that the particles would continue to diverge. The authors in [141] incorporated the method of differential evolution in (37). The Internet of Things for Food - An integrated socio-economic and technologi InsurTech Evaluation The different perspectives, Trajectory 2022 - Shifting Cloud Native Observability to the Left, Asslam - CPA Week - Perth 13 June 2022Online Upload.pptx. In binary particle swarm optimization (BPSO), the same equation as shown in (2) is used. An improved Bernoulli shift map was introduced in [109] to initialize the population of whales so to enhance the algorithm global search ability. Two variables are defined. Small-signal stability analysis of a DFIG-based wind power system under different modes of operation. The number of sailfish is based on the initial sardine population. Total global capacity of wind energy systems (in GW) from 20132019 [5]. DPC control directly the stator reactive and active powers. Jebaraj L., Rajan C.A.A., Sakthivel S. Incorporation of SSSC and SVC devices for real power and voltage stability limit enhancement through shuffled frog leaping algorithm under stressed conditions. A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal. The proposed algorithm was compared to various MOT, including the conventional CuSA. Tu J.V. At first, the relevant parameters (number of particles, iteration number, initial acceleration constant and initial inertial weight) are defined. This is primarily due to the many advantages doubly-fed induction generators offer over other types of generators in applications where the mechanical power provided by the prime mover driving the generator varies greatly . This is to enhance the exploration capability of the algorithm. The positions of each sailfish and sardine can be represented as shown in [134], with the fitness values of the sailfish and sardines represented in a matrix also shown in [134]. circuit is referred to as an "applied voltage". With regards to DFIG application, The BA and ALO show promise, but require much more rigorous testing to be validated. The convergence rate of the proposed algorithm was only displayed for three of the unimodal and multimodal functions and two fixed dimension functions. When the controllers were optimized for 8 m/s, there exists stability for the synchronous and super synchronous modes, but not sub synchronous. Despite the advantages of the PMSG, the DFIG proves to be the more efficient generator. These factors are the air density (usually 1.204kg.m3), the gliding velocity (usually 5.25m.s1), the surface area of the body of the squirrel (usually 154cm2), the friction coefficient (usually 0.6) and a randomized number which lies in the range [0.675,1.5]. Looks like youve clipped this slide to already. This sardine is then eliminated from the system. Considering the multi machine bus system under small disturbance, UCDP-PSO and C-PSO produce the same percentage overshoot and damping time, which was superior to that of R-PSO. Considering the standard deviation, there existed a couple of scenarios whereby the WOA proved to be dominant. 2014. All five convergence factors are based on the current and maximum iteration numbers. since it can be magnetised from the rotor circuit too, 4- the size of the converter is not related to the total Consider that i(j,k,l) is a representation of the position of the ith bacterium, at the jth chemotactic, kth reproductive and lth elimination-dispersal step. 9 [140]. There was no comparison between optimization using BFO and optimization using another method, like Ziegler Nichols. 1)To get power output from induction generator, it should always be rotated above synchronous speed. The advantages and disadvantages of various traditional SSR analysis methods have been summarized in Table 2 according to the prevailing literature. C is based only on a random number in the range [0,1]. The rest are considered to be in the normal trees. Participation of wind energy to global capacity of various nations [6]. Bouderbala M., Bossoufi B., Aroussi H.A., Lagrioui M., Taoussi M., Ihedrane Y., Ghamrasni M.E. To encircle the prey, each of the whales update their positions based on the best position found thus far. In the sailfish algorithm, both the sailfish and sardines are critical aspects to consider [134]. Considering the rise time, the ABC exhibited dominancy to the other techniques by a magnitude greater than 100% When the RSC and GSC gains were considered, it was seen that ABC yielded the best overshoot value, being superior to GWO and the advisory method by 97.7% and 169.73% respectively. This type of representation should be investigated thoroughly so as to ensure strong simulation of the effect of these holes on ants. [5]. The proposed algorithm, however, produced the best convergence rate (after a maximum of 75 iterations). Workshop on Particle Swarm Optimization; Indianapolis; 2001. The authors in [114] proposed five unique factors of convergence to enhance the global search capability of the algorithm. It can operate at variable speed, sub- or super synchronously. The authors in [97] proposed a new method to update the position of a bee. It was observed that after 100 iterations, the proposed algorithm produced a 1.28% improvement in the result. If not, then the ith crow is successful in stealing the food of the jth crow. Provide a review on the merits and demerits of each algorithm, as well as a review on recent advancements for mitigation of relevant demerits. The corresponds to a superiority of 17.5% and 253.21% respectively for the settling time. More specifically, these motors are often referred to as Butterworth-Heinemann, Elsevier; 2015. To produce a new solution from the old solution, the following is used [90]: Where vij is the new solution, xij is the previous solution of the bee (either employed bee or onlooker bee), k[1,2S], j[1,2D] and ij is a random number which lies in the range [1,1]. The individual best and global best are obtained based on a fitness function which is defined by the user [52]. IHorizon-Enabled Energy Management for Electrified Vehicles. The SlideShare family just got bigger. away from the field, and the armature can usually develop only a Eberhart R., Shi Y., Kennedy J. Morgan Kauffman; San Mateo: 2001. The proposed algorithm is employed on the optimal distribution of flexible fault current limiters and applied to the revised IEEE 33-BUS distribution systems with distributed generation and IEEE 30-BUS benchmark system. The third term describes the particles propensity to gravitate towards to global best i.e., the best of all the particles. Then, each bacterium is randomly positioned. Zhang S., Liu S. A discrete improved artificial bee colony algorithm for 01 knapsack problem. Gao H., Shi Y., Pun C., Kwong S. An improved artificial bee colony algorithm with its application. The squirrel in the hickory nut tree, as well as the squirrels in the acorn trees, are determined. This means that the rotor voltages will be higher and currents respectively lower. This continues until all iterations have been completed. What are the disadvantages of doubly-fed induction generator? While the techniques provide strong performances in general, many of the conventional algorithm suffer the demerits of a poor rate of convergence and being easily entrapped in the local minima. Your email address will not be published. It was observed that the proposed algorithm produced the most accurate result in 7 of the 9 cases. To the authors best knowledge, there has been no established demerits of the SFO algorithm. The advantages of variable-speed turbines are that their annual energy capture is about 5% greater than the fixed-speed technology and that the active and reactive powers generated can be easily controlled. The proposed algorithm was applied to the 14-BUS system for reactive power optimization and compared to the conventional PSO. BFO produced the best overshoot, which was 25.33% and 132.8% superior to GA and PSO respectively. Also, when compared to the squirrel cage induction machine, these machines produce a lower level of stress on the machine components. Also, the slip rings on the wound-rotor induction machine used to implement the doubly fed induction generator require periodic maintenance while no such rings are required on the rotor of the . The final assumption is that the loudness changes between a specified maximum and minimum. The authors acknowledge the supports from the School of Engineering, University of KwaZulu-Natal. Wang P., Kong Y., He X., Zhang M., Tan X. The doubly fed induction generator (DFIG) wind energy conversion system (WECS) has many merits and, as a result, large numbers have been installed to date. This corresponded to 0.61% and 1.36% respectively for a second order system with a first order filter.
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