Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods

Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller

[EN] Power converters are electronic devices widely applied in industry, and in recent years, for renewable energy electronic systems, they can regulate voltage levels and actuate as interfaces, however, to do so, is needed a controller. Proportional-Integral-Derivative (PID) are applied to power converters comparing output voltage versus a reference voltage to reduce and anticipate error. Using PID controllers may be complicated since must be previously tuned prior to their use. Many methods for PID controllers tunning have been proposed, from classical to metaheuristic approaches. Between the metaheuristic approaches, bio-inspired algorithms are a feasible solution; Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are often used; however, they need many initial parameters to be specified, this can lead to local solutions, and not necessarily the global optimum. In recent years, new generation metaheuristic algorithms with fewer initial parameters had been proposed. The Grey Wolf Optimizer (GWO) algorithm is based on wolves¿ herds chasing habits. In this work, a comparison between PID controllers tunning using GWO, PSO, and GA algorithms for a Boost Converter is made. The converter is modeled by state-space equations, and then the optimization of the related PID controller is made using MATLAB/Simulink software. The algorithm¿s performance is evaluated using the Root Mean Squared Error (RMSE). Results show that the proposed GWO algorithm is a feasible solution for the PID controller tunning problem for power converters since its overall performance is better than the obtained by the PSO and GA.The authors wish to thank the Institute of Energy Engineering of the Polytechnic University of Valencia, Spain, and the Department of Water and Energy Studies of the University of Guadalajara, Mexico, for all their support and collaboration.Águila-León, J.; Chiñas-Palacios, C.; Vargas-Salgado Carlos; Hurtado-Perez, E.; García, EXM. (2021). Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller. Advances in Science, Technology and Engineering Systems Journal. 6(1):619-625. https://doi.org/10.25046/aj060167S61962561J. Aguila-Leon, C.D. Chinas-Palacios, C. Vargas-Salgado, E. Hurtado-Perez, E.X.M. Garcia, "Optimal PID Parameters Tunning for a DC-DC Boost Converter: A Performance Comparative Using Grey Wolf Optimizer, Particle Swarm Optimization and Genetic Algorithms," in 2020 IEEE Conference on Technologies for Sustainability, SusTech 2020, 2020, doi:10.1109/SusTech47890.2020.9150507.H. Sira-Ramírez, R. Silva-Ortigoza, Control Design Techniques in Power Electronic Devices, 2013, doi:10.1017/CBO9781107415324.004.G.A. Raiker, S.R. B, P.C. Ramamurthy, L. Umanand, S.G. Abines, S.G. Vasisht, "Solar PV interface to Grid-Tie Inverter with Current Referenced Boost Converter," in 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), IEEE: 343-348, 2018, doi:10.1109/ICIINFS.2018.8721313.S.E. Babaa, G. El Murr, F. Mohamed, S. Pamuri, "Overview of Boost Converters for Photovoltaic Systems," Journal of Power and Energy Engineering, 06(04), 16-31, 2018, doi:10.4236/jpee.2018.64002.J. Berner, K. Soltesz, T. Hägglund, K.J. Åström, "An experimental comparison of PID autotuners," Control Engineering Practice, 73, 124-133, 2018, doi:10.1016/J.CONENGPRAC.2018.01.006.K. Ogata, Modern Control Engineering, 5th ed., Prentice Hall, 2010.K. Nisi, B. Nagaraj, A. Agalya, "Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry," International Journal of Machine Learning and Cybernetics, 10(8), 2015-2025, 2019, doi:10.1007/s13042-018-0831-8.M.T. Özdemir, D. Öztürk, "Comparative performance analysis of optimal PID parameters tuning based on the optics inspired optimization methods for automatic generation control," Energies, 10(12), 2017, doi:10.3390/en10122134.G.-Q. Zeng, X.-Q. Xie, M.-R. Chen, "An Adaptive Model Predictive Load Frequency Control Method for Multi-Area Interconnected Power Systems with Photovoltaic Generations," Energies, 10(11), 1840, 2017, doi:10.3390/en10111840.Y. Sawle, S.C. Gupta, A.K. Bohre, "Optimal sizing of standalone PV/Wind/Biomass hybrid energy system using GA and PSO optimization technique," Energy Procedia, 117, 690-698, 2017, doi:10.1016/j.egypro.2017.05.183.S. Surender Reddy, C. Srinivasa Rathnam, "Optimal Power Flow using Glowworm Swarm Optimization," International Journal of Electrical Power & Energy Systems, 80, 128-139, 2016, doi:10.1016/J.IJEPES.2016.01.036.C.Y. Acevedo-arenas, A. Correcher, C. Sánchez-díaz, E. Ariza, D. Alfonso-solar, C. Vargas-salgado, J.F. Petit-suárez, "MPC for optimal dispatch of an AC-linked hybrid PV / wind / biomass / H2 system incorporating demand response," Energy Conversion and Management, 186(February), 241-257, 2019, doi:10.1016/j.enconman.2019.02.044.M. Çelebi, "Efficiency optimization of a conventional boost DC/DC converter," Electrical Engineering, 100(2), 803-809, 2018, doi:10.1007/s00202-017-0552-0.Q.Y. Lu, W. Hu, L. Zheng, Y. Min, M. Li, X.P. Li, W.C. Ge, Z.M. Wang, "Integrated coordinated optimization control of automatic generation control and automatic voltage control in regional power grids," Energies, 5(10), 3817-3834, 2012, doi:10.3390/en5103817.J. Aguila‐Leon, C. Chiñas‐Palacios, E.X.M. Garcia, C. Vargas‐Salgado, "A multimicrogrid energy management model implementing an evolutionary game‐theoretic approach," International Transactions on Electrical Energy Systems, 30(11), 2020, doi:10.1002/2050-7038.12617.Ovat Friday Aje, Anyandi Adie Josephat, "The particle swarm optimization (PSO) algorithm application - A review," Global Journal of Engineering and Technology Advances, 3(3), 001-006, 2020, doi:10.30574/gjeta.2020.3.3.0033.N.K. Jain, U. Nangia, J. Jain, A Review of Particle Swarm Optimization, Journal of The Institution of Engineers (India): Series B, 99(4), 407-411, 2018, doi:10.1007/s40031-018-0323-y.B. Hekimoǧlu, S. Ekinci, S. Kaya, "Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm," in 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018, Institute of Electrical and Electronics Engineers Inc., 2019, doi:10.1109/IDAP.2018.8620833.S. Mirjalili, S.M. Mirjalili, A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, 69, 46-61, 2014, doi:10.1016/j.advengsoft.2013.12.007.S.-X. Li, J.-S. Wang, "Dynamic Modeling of Steam Condenser and Design of PI Controller Based on Grey Wolf Optimizer," Mathematical Problems in Engineering, 2015, 1-9, 2015, doi:10.1155/2015/120975.S. Yadav, S.K. Verma, S.K. Nagar, "Optimized PID Controller for Magnetic Levitation System," IFAC-PapersOnLine, 49(1), 778-782, 2016, doi:10.1016/J.IFACOL.2016.03.151.R.H.G. Tan, L.Y.H. Hoo, "DC-DC converter modeling and simulation using state space approach," 2015 IEEE Conference on Energy Conversion (CENCON), (2), 42-47, 2015, doi:10.1109/CENCON.2015.7409511

Robust Coordinated Designing of PSS and UPFC Damping Controller

This paper presents the simultaneous coordinated designing of the UPFC robust power oscillation damping controller and the conventional power system stabilizer. On the basis of the linearized Phillips-Herffron model, the coordinated design problem of PSS and UPFC damping controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with the eigenvalue-based multiobjective function which is solved by a particle swarm optimization algorithm (PSO) that has a strong ability to find the most optimistic results. The stabilizers are tuned to simultaneously shift the undamped electromechanical modes to a prescribed zone in the s-plane. To ensure the robustness of the proposed simultaneous coordinated controllers tuning, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed method is demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices studies under various disturbance conditions of over a wide range of loading conditions. The results of these studies show that the PSO based simultaneous coordinated controller has an excellent capability in damping power system oscillations and enhance greatly the dynamic stability of the power system

Robust Coordinated Designing of PSS and UPFC Damping Controller

This paper presents the simultaneous coordinated designing of the UPFC robust power oscillation damping controller and the conventional power system stabilizer. On the basis of the linearized Phillips-Herffron model, the coordinated design problem of PSS and UPFC damping controllers over a wide range of loading conditions and system configurations is formulated as an optimization problem with the eigenvalue-based multiobjective function which is solved by a particle swarm optimization algorithm (PSO) that has a strong ability to find the most optimistic results. The stabilizers are tuned to simultaneously shift the undamped electromechanical modes to a prescribed zone in the s-plane. To ensure the robustness of the proposed simultaneous coordinated controllers tuning, the design process takes into account a wide range of operating conditions and system configurations. The effectiveness of the proposed method is demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices studies under various disturbance conditions of over a wide range of loading conditions. The results of these studies show that the PSO based simultaneous coordinated controller has an excellent capability in damping power system oscillations and enhance greatly the dynamic stability of the power system

Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone

HOME ENERGY MANAGEMENT SYSTEM FOR DEMAND RESPONSE PURPOSES

The growing demand for electricity has led to increasing efforts to generate and satisfy the rising demand. This led to suppliers attempting to reduce consumption with the help of the users. Requests to shift unnecessary loads off the peak hours, using other sources of generators to supply the grid while offering incentives to the users have made a significant effect. Furthermore, automated solutions were implemented with the help of Home Energy Management Systems (HEMS) where the user can remotely manage household loads to reduce consumption or cost. Demand Response (DR) is the process of reducing power consumption in a response to demand signals generated by the utility based on many factors such as the Time of Use (ToU) prices. Automated HEMS use load scheduling techniques to control house appliances in response to DR signals. Scheduling can be purely user-dependent or fully automated with minimum effort from the user. This thesis presents a HEMS which automatically schedules appliances around the house to reduce the cost to the minimum. The main contributions in this thesis are the house controller model which models a variety of thermal loads in addition to two shiftable loads, and the optimizer which schedules the loads to reduce the cost depending on the DR signals. The controllers focus on the thermal loads since they have the biggest effect on the electricity bill, they also consider many factors ignored in similar models such as the physical properties of the room/medium, the outer temperatures, the comfort levels of the users, and the occupancy of the house during scheduling. The DR signal was the hourly electricity price; normally higher during the peak hours. Another main part of the thesis was studying multiple optimization algorithms and utilizing them to get the optimum scheduling. Results showed a maximum of 44% cost reduction using different metaheuristic optimization algorithms and different price and occupancy schemes

Performance Comparison of No-preference and Weighted Sum Objective Methods in Multi-Objective Optimization of AVR-PSS Tuning in Multi-machine Power System

Simultaneous optimization of controllers in power systems is a challenging research due to the inherent nonlinearity of such a system. Multi-objective optimization is a useful tool for tuning excitation controllers and minimizing oscillations that are described through definition of transient and small-signal stability in power systems. In this paper, a Two-Area-Four-Machine (TAFM) power system model is tested on multiple short circuit and load disturbances. A multi-objective performance analysis is investigated by observing the system\u27s behaviour in different cases involving the no-preference method and a priori method called weighted sum objective. The analysis is done through observation of two different objective functions. First objective function includes the sum of the integral of time-weighted absolute errors of rotor speed differences, generator voltage, and tie-line power transfer. Second objective function observes time domain elements: overshoot, undershoot, and settling time of machines\u27 rotor speeds. Results are compared for two methods combined with four different algorithms to provide better insight into the computational performance of each algorithm and objective search method. Algorithms used for controllers\u27 parametrization include two novel algorithms: multi-objective ant lion optimizer (MOALO) and salp swarm algorithm (MOSSA), and two classic algorithms: multi-objective particle swarm optimization with velocity relaxation (MOVRPSO) and simulated annealing (MOSA)

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

Voltage source converter based multi – terminal DC grids (VSC – MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional – integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi – terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method. In this paper, a four terminal VSC – MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.publishedVersio