MLD-MPC Approach for Three-Tank Hybrid Benchmark Problem

The present paper aims at validating a Model Predictive Control(MPC),based on the Mixed Logical Dynamical(MLD)model,for Hybrid Dynamic Systems(HDSs)that explicitly involve continuous dynamics and discrete events.The proposed benchmark system is a three-tank process,which is a typical case study of HDSs.The MLD-MPC controller is applied to the level control of the considered tank system.The study is initially focused on the MLD approach that allows consideration of the interacting continuous dynamics with discrete events and includes the operating constraints.This feature of MLD modeling is very advantageous when an MPC controller synthesis for the HDSs is designed.Once the MLD model of the system is well-posed,then the MPC law synthesis can be developed based on the Mixed Integer Programming(MIP)optimization problem.For solving this MIP problem,a Branch and Bound(B&B)algorithm is proposed to determine the optimal control inputs.Then,a comparative study is carried out to illustrate the effectiveness of the proposed hybrid controller for the HDSs compared to the standard MPC approach.Performances results show that the MLD-MPC approach outperforms the standardMPCone that doesn’t consider the hybrid aspect of the system.The paper also shows a behavioral test of the MLDMPC controller against disturbances deemed as liquid leaks from the system.The results are very satisfactory and show that the tracking error is minimal less than 0.1%in nominal conditions and less than 0.6%in the presence of disturbances.Such results confirm the success of the MLD-MPC approach for the control of the HDSs.

Energy Management Control StraEnergy Management Control Strategy for Renewable Energy System Based on Spotted Hyena Optimizertegy for Renewable Energy System Based on Spotted Hyena Optimizer

Hydrocarbons,carbon monoxide and other pollutants from the transportation sector harm human health in many ways.Fuel cell(FC)has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy.The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand.Therefore,adding energy storage systems is necessary.However,to manage and distribute the power-sharing among the hybrid proton exchange membrane(PEM)fuel cell(FC),battery storage(BS),and supercapacitor(SC),an energy management strategy(EMS)is essential.In this research work,an optimal EMS based on a spotted hyena optimizer(SHO)for hybrid PEM fuel cell/BS/SC is proposed.The main goal of an EMS is to improve the performance of hybrid FC/BS/SC and to reduce the amount of hydrogen consumption.To prove the superiority of the SHO method,the obtained results are compared with the chimp optimizer(CO),the artificial ecosystem-based optimizer(AEO),the seagull optimization algorithm(SOA),the sooty tern optimization algorithm(STOA),and the coyote optimization algorithm(COA).Two main metrics are used as a benchmark for the comparison:the minimum consumed hydrogen and the efficiency of the system.The main findings confirm that the minimum amount of hydrogen consumption and maximum efficiency are achieved by the proposed SHO based EMS.

Fractional-Order Control of a Wind Turbine Using Manta Ray Foraging Optimization

In this research paper,an improved strategy to enhance the performance of the DC-link voltage loop regulation in a Doubly Fed Induction Generator(DFIG)based wind energy system has been proposed.The proposed strategy used the robust Fractional-Order(FO)Proportional-Integral(PI)control technique.The FOPI control contains a non-integer order which is preferred over the integer-order control owing to its benefits.It offers extra flexibility in design and demonstrates superior outcomes such as high robustness and effectiveness.The optimal gains of the FOPI controller have been determined using a recent Manta Ray Foraging Optimization(MRFO)algorithm.During the optimization process,the FOPI controller’s parameters are assigned to be the decision variables whereas the objective function is the error racking that to be minimized.To prove the superiority of the MRFO algorithm,an empirical comparison study with the homologous particle swarm optimization and genetic algorithm is achieved.The obtained results proved the superiority of the introduced strategy in tracking and control performances against various conditions such as voltage dips and wind speed variation.

Grey Wolf Optimization Based Tuning of Terminal Sliding Mode Controllers for a Quadrotor

The research on Unmanned Aerial Vehicles(UAV)has intensified considerably thanks to the recent growth in the fields of advanced automatic control,artificial intelligence,and miniaturization.In this paper,a Grey Wolf Optimization(GWO)algorithm is proposed and successfully applied to tune all effective parameters of Fast Terminal Sliding Mode(FTSM)controllers for a quadrotor UAV.A full control scheme is first established to deal with the coupled and underactuated dynamics of the drone.Controllers for altitude,attitude,and position dynamics become separately designed and tuned.To work around the repetitive and time-consuming trial-error-based procedures,all FTSM controllers’parameters for only altitude and attitude dynamics are systematically tuned thanks to the proposed GWO metaheuristic.Such a hard and complex tuning task is formulated as a nonlinear optimization problem under operational constraints.The performance and robustness of the GWO-based control strategy are compared to those based on homologous metaheuristics and standard terminal sliding mode approaches.Numerical simulations are carried out to show the effectiveness and superiority of the proposed GWO-tuned FTSM controllers for the altitude and attitude dynamics’stabilization and tracking.Nonparametric statistical analyses revealed that the GWO algorithm is more competitive with high performance in terms of fastness,non-premature convergence,and research exploration/exploitation capabilities.

Accurate Fault Location Modeling for Parallel Transmission Lines Considering Mutual Effect

:A new accurate algorithms based on mathematical modeling of two parallel transmissions lines system(TPTLS)as influenced by the mutual effect to determine the fault location is discussed in this work.The distance relay measures the impedance to the fault location which is the positive-sequence.The principle of summation the positive-,negative-,and zero-sequence voltages which equal zero is used to determine the fault location on the TPTLS.Also,the impedance of the transmission line to the fault location is determined.These algorithms are applied to single-line-to-ground(SLG)and double-line-to-ground(DLG)faults.To detect the fault location along the transmission line,its impedance as seen by the distance relay is determined to indicate if the fault is within the relay’s reach area.TPTLS under study are fed from one-and both-ends.A schematic diagrams are obtained for the impedance relays to determine the fault location with high accuracy.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm

The power transfer capability of the smart transmission gridconnected networks needs to be reduced by inter-area oscillations.Due to the fact that inter-area modes of oscillations detain and make instability of power transmission networks.This fact is more noticeable in smart grid-connected systems.The smart grid infrastructure has more renewable energy resources installed for its operation.To overcome this problem,a deep learning widearea controller is proposed for real-time parameter control and smart power grid resilience on oscillations inter-area modes.The proposed Deep Wide Area Controller(DWAC)uses the Deep Belief Network(DBN).The network weights are updated based on real-time data from Phasor measurement units.Resilience assessment based on failure probability,financial impact,and time-series data in grid failure management determine the norm H2.To demonstrate the effectiveness of the proposed framework,a time-domain simulation case study based on the IEEE-39 bus system was performed.For a one-channel attack on the test system,the resiliency index increased to 0.962,and inter-area dampingξwas reduced to 0.005.The obtained results validate the proposed deep learning algorithm’s efficiency on damping inter-area and local oscillation on the 2-channel attack as well.Results also offer robust management of power system resilience and timely control of the operating conditions.

Optimal Selection of Hybrid Renewable Energy System Using Multi-Criteria Decision-Making Algorithms

Several models of multi-criteria decision-making(MCDM)have identified the optimal alternative electrical energy sources to supply certain load in an isolated region in Al-Minya City,Egypt.The load demand consists of water pumping system with a water desalination unit.Various options containing three different power sources:only DG,PV-B system,and hybrid PV-DG-B,two different sizes of reverse osmosis(RO)units;RO-250 and RO-500,two strategies of energy management;load following(LF)and cycle charging(CC),and two sizes of DG;5 and 10 kW were taken into account.Eight attributes,including operating cost,renewable fraction,initial cost,the cost of energy,excess energy,unmet load,breakeven grid extension distance,and the amount of CO_(2),were used during the evaluation process.To estimate these parameters,HOMER®software was employed to perform both the simulation and optimization process.Four different weight estimation methods were considered;no priority of criteria,based on a pairwise comparisons matrix of the criteria,CRITIC-method,and entropy-based method.The main findings(output results)confirmed that the optimal option for the case study was hybrid PV-DG-B with the following specification:5 kW DG,RO-500,and load following control strategy.Under this condition,the annual operating cost and initial costs were$5546 and$161022,respectively,whereas the cost of energy was 0.077$/kWh.The excess energy and unmet loads were 40998 and 2371 kWh,respectively.The breakeven grid extension distance and the amount of CO_(2) were 3.31 km and 5171 kg per year,respectively.Compared with DG only,the amount of CO_(2) has been sharply reduced by 113939 kg per year.

An Efficient Fuzzy Logic Fault Detection and Identification Method of Photovoltaic Inverters

Fuzzy logic control(FLC)systems have found wide utilization in several industrial applications.This paper proposes a fuzzy logic-based fault detection and identification method for open-circuit switch fault in grid-tied photovoltaic(PV)inverters.Large installations and ambitious plans have been recently achieved for PV systems as clean and renewable power generation sources due to their improved environmental impacts and availability everywhere.Power converters represent the main parts for the grid integration of PV systems.However,PV power converters contain several power switches that construct their circuits.The power switches in PV systems are highly subjected to high stresses due to the continuously varying operating conditions.Moreover,the grid-tied systems represent nonlinear systems and the system model parameters are changing continuously.Consequently,the grid-tied PV systems have a nonlinear factor and the fault detection and identification(FDI)methods based on using mathematical models become more complex.The proposed fuzzy logic-based FDI(FL-FDI)method is based on employing the fuzzy logic concept for detecting and identifying the location of various switch faults.The proposed FL-FDI method is designed and extracted from the analysis and comparison of the various measured voltage/current components for the control purposes.Therefore,the proposed FL-FDI method does not require additional components or measurement circuits.Additionally,the proposed method can detect the faulty condition and also identify the location of the faulty switch for replacement and maintenance purposes.The proposed method can detect the faulty condition within only a single fundamental line period without the need for additional sensors and/or performing complex calculations or precise models.The proposed FL-FDI method is tested on the widely used T-type PV inverter system,wherein there are twelve different switches and the FDI process represents a challenging task.The results shows the superior and accurate performance of the proposed FL-FDI method.

Load Frequency Control of Multi-interconnected Renewable Energy Plants Using Multi-Verse Optimizer

A reliable approach based on a multi-verse optimization algorithm(MVO)for designing load frequency control incorporated in multi-interconnected power system comprising wind power and photovoltaic(PV)plants is presented in this paper.It has been applied for optimizing the control parameters of the load frequency controller(LFC)of the multi-source power system(MSPS).The MSPS includes thermal,gas,and hydro power plants for energy generation.Moreover,the MSPS is integrated with renewable energy sources(RES).The MVO algorithm is applied to acquire the ideal parameters of the controller for controlling a single area and a multi-area MSPS integrated with RES.HVDC link is utilized in shunt with AC multi-areas interconnection tie line.The proposed scheme has achieved robust performance against the disturbance in loading conditions,variation of system parameters,and size of step load perturbation(SLP).Meanwhile,the simulation outcomes showed a good dynamic performance of the proposed controller.

Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control design.In this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Recently,implementation of Battery Energy Storage(BES)with photovoltaic(PV)array in distribution networks is becoming very popular in overall the world.Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source.PV can be able to generate constant output power during 24-hours by installing BES with it.Therefore,this paper presents a new application of a recent metaheuristic algorithm,called Slime Mould Algorithm(SMA),to determine the best size,and location of photovoltaic alone or with battery energy storage in the radial distribution system(RDS).This algorithm is modeled from the behavior of SMA in nature.During the optimization process,the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints.In addition,the presented function is based on the probabilistic for PV output and different types of system load.The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor(PLSF).IEEE 69-bus RDS with different types of loads is used as a test system.The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The main goal of this research is to develop and apply a robust Artificial Neural Networks(ANNs)model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer.Concentrated salt and sugar solutions were used as the osmotic solutions at 27◦C.Series of experiments were performed at various temperatures of 35◦C,40◦C,and 55◦C for conduction heat input under vacuum(−760 mm Hg)condition.Some experiments were also performed in a pure vacuum without heat addition.Dimensionless moisture content(DMC),effective moisture diffusivity,and mass flux were considered as the performance parameters in this study.Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87%compared to 5.3%in the salt solution.Furthermore,a significant enhancement of drying performance of about 27%in DMC was observed for both samples at vacuum and 40◦C compared to pure vacuum drying conditions.Using the experimental data,a robust artificial neural network(ANN)was proposed to describe the osmotic dehydration’s behavior on the drying process.The ANN model outputs are the dimensionless moisture contents(DMC),the diffusivity,and the mass flux.Whereas the ANN inputs were the drying time,the percent of sugar solution,and the percent of salt solution.For the ANN apple’s model,the minimum root mean square error(RMSE)values were 0.0261,0.0349 and 0.0406,for DMC,diffusivity,and mass flux,respectively.Whereas the best correlation coefficients of the above three parameters’determination values were 0.9909,0.9867 and 0.9744,respectively.For the ANN potato’s model,the minimum RMSE values were 0.0124,0.0140 and 0.0333,for DMC,diffusivity,and mass flux,respectively.And the best correlation coefficients of the parameters’values were found 0.9969,0.9968 and 0.9736,respectively.Accordingly,the ANN model’s prediction has a perfect agreement with the experimental dataset,which confirmed the ANN model’s accuracy.

Modeling and Optimization of Surface Roughness of Epoxy/Nanoparticles Composite Coating

In power plants,flue gases can cause severe corrosion damage in metallic parts such as flue ducts,heat exchangers,and boilers.Coating is an effective technique to prevent this damage.A robust fuzzy model of the surface roughness(Ra and Rz)of flue gas ducts coated by protective composite coating from epoxy and nanoparticles was constructed based on the experimental dataset.The proposed model consists of four nanoparticles(ZnO,ZrO2,SiO2,and NiO)with 2%,4%,6%,and 8%,respectively.Response surface methodology(RSM)was used to optimize the process parameters and identify the optimal conditions for minimum surface roughness of this coated duct.To prove the superiority of the proposed fuzzy model,the model results were compared with those obtained by ANOVA,with the coefficient of determination and the root-mean-square error(RMSE)used as metrics.For Ra,for the first output response,using ANOVA,the coefficient-of-determination values were 0.9137 and 0.4037,respectively,for training and prediction.Similarly,for Rz,the second output response,the coefficient-of-determination results were 0.9695 and 0.4037,respectively,for training and prediction.In the fuzzy modeling of Ra,for the first output response,the RMSE values were 0.0 and 0.1455,respectively,for training and testing.The values for the coefficient of determination were 1.00 and 0.9807,respectively,for training and testing.The results prove the superiority of fuzzy modeling.For modeling the second output response Rz,the RMSE values were 0.0 and 0.0421,respectively,for training and testing,and the coefficient-of-determination values were 1.00 and 0.9959,respectively,for training and testing.

Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

This paper addresses improvements in fractional order(FO)system performance.Although the classical proportional-integral-derivative(PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems,the FOPID fuzzy controller has been proven to provide better results.This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques.This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer,social spider optimization,in order to improve the response of fractional dynamical systems.This group of systems had usually produced multimodal error surfaces/functions that occasionally had many variant local minima.The integral time of absolute error(ITAE)used in this study was the error function.The results showed that the strategy adopted produced superior performance regarding the lowest ITAE value.It reached a value of 88.22 while the best value obtained in previous work was 98.87.A further comparison between the current work and previous studies concerning transient-analysis factors of the model’s response showed that the strategy proposed was the only one that was able to produce fast rise time,low-percentage overshoot,and very small steady-state error.However,the other strategies were good for one factor,but not for the others.

A Robust Single-Sensor MPPT Strategy for Shaded Photovoltaic-Battery System

A robust single-sensor global maximum power point tracking(MPPT)strategy based on modern optimization for photovoltaic systems considering shading conditions is proposed in this work.The proposed strategy is designed for battery charging applications and direct current(DC)microgrids.Under normal operation,the curve of photovoltaic(PV)output power versus PV voltage contains only a single peak point.This point can be simply captured using any traditional tracking method like perturb and observe.However,this situation is completely different during the shadowing effect where several peaks appear on the power voltage curve.Most of these peaks are local with only a single global.This condition leads to the incapability of traditional tracking approaches to extract the global peak power due to their inability to distinguish between the local and global peak points.They are trapped in the first peak point even when the point is local.Therefore,global tracking approaches based on modern optimization are highly required.A recent marine predators algorithm(MPA)has been used to solve the problem of tracking the global MPP under shadowing influence.Different shadowing scenarios are used to test and evaluate the performance of MPA based tracker.The obtained results are compared with particle swarm optimization(PSO)and ant lion optimizer(ALO).The results of the comparison con-firmed the effectiveness and robustness of the proposed global MPPT-MPA based tracker over PSO and ALO.