Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

This paper investigates the use of fractional order Proportional, Integral and Derivative (FOPID) controllers for the frequency and power regulation in a microgrid power system. The proposed microgrid system composes of renewable energy resources such as solar and wind generators, diesel engine generators as a secondary source to support the principle generators, and along with different energy storage devices like fuel cell, battery and flywheel. Due to the intermittent nature of integrated renewable energy like wind turbine and photovoltaic generators, which depend on the weather conditions and climate change this affects the microgrid stability by considered fluctuation in frequency and power deviations which can be improved using the selected controller. The fractional-order controller has five parameters in comparison with the classical PID controller, and that makes it more flexible and robust against the microgrid perturbation. The Fractional Order PID controller parameters are optimized using a new optimization technique called Krill Herd which selected as a suitable optimization method in comparison with other techniques like Particle Swarm Optimization. The results show better performance of this system using the fractional order PID controller-based Krill Herd algorithm by eliminates the fluctuations in frequency and power deviation in comparison with the classical PID controller. The obtained results are compared with the fractional order PID controller optimized using Particle Swarm Optimization. The proposed system is simulated under nominal conditions and using the disconnecting of storage devices like battery and Flywheel system in order to test the robustness of the proposed methods and the obtained results are compared.У статті досліджено використання регуляторів пропорційного, інтегрального та похідного дробового порядку (FOPID) для регулювання частоти та потужності в електромережі. Запропонована мікромережева система складається з поновлюваних джерел енергії, таких як сонячні та вітрогенератори, дизельних генераторів як вторинного джерела для підтримки основних генераторів, а також з різних пристроїв для накопичування енергії, таких як паливна батарея, акумулятор і маховик. Через переривчасту природу інтегрованої відновлювальної енергії, наприклад, вітрогенераторів та фотоелектричних генераторів, які залежать від погодних умов та зміни клімату, це впливає на стабільність мікромережі, враховуючи коливання частоти та відхилення потужності, які можна поліпшити за допомогою вибраного контролера. Контролер дробового порядку має п’ять параметрів порівняно з класичним PID-контролером, що робить його більш гнучким та надійним щодо збурень мікромережі. Параметри PID-контролера дробового порядку оптимізовані за допомогою нової методики оптимізації під назвою «зграя криля», яка обрана як підходящий метод оптимізації порівняно з іншими методами, такими як оптимізація методом рою частинок. Результати показують кращі показники роботи цієї системи за допомогою алгоритму «зграя криля», заснованого на PID-контролері дробового порядку, виключаючи коливання частоти та відхилення потужності порівняно з класичним PID-контролером. Отримані результати порівнюються з PID-контролером дробового порядку, оптимізованим за допомогою оптимізації методом рою частинок. Запропонована система моделюється в номінальному режимі роботи та використовує відключення накопичувальних пристроїв, таких як акумулятор та маховик, щоб перевірити надійність запропонованих методів та порівняти отримані результати

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

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

PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

Optimal fuzzy-PID controller with derivative filter for load frequency control including UPFC and SMES

A newly adopted optimization technique known as sine-cosine algorithm (SCA) is suggested in this research article to tune the gains of Fuzzy-PID controller along with a derivative filter (Fuzzy-PIDF) of a hybrid interconnected system for the Load Frequency Control (LFC). The scrutinized multi-generation system considers hydro, gas and thermal sources in all areas of the dual area power system integrated with UPFC (unified power flow controller) and SMES (Super-conducting magnetic energy storage) units. The preeminence of the offered Fuzzy-PIDF controller is recognized over Fuzzy-PID controller by comparing their dynamic performance indices concerning minimum undershoot, settling time and also peak overshoot. Finally, the sensitiveness and sturdiness of the recommended control method are proved by altering the parameters of the system from their nominal values and by the implementation of random loading in the system

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybrid- particle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line

MULTI-DOMAIN, MULTI-OBJECTIVE-OPTIMIZATION-BASED APPROACH TO THE DESIGN OF CONTROLLERS FOR POWER ELECTRONICS

Power converter has played a very important role in modern electric power systems. The control of power converters is necessary to achieve high performance. In this study, a dc-dc buck converter is studied. The parameters of a notional proportional-integral controller are to be selected. Genetic algorithms (GAs), which have been widely used to solve multi-objective optimization problems, is used in order to locate appropriate controller design. The control metrics are specified as phase margin in frequency domain and voltage error in time-domain. GAs presented the optimal tradeoffs between these two objectives. Three candidate control designs are studied in simulation and experimentally. There is some agreement between the experimental results and the simulation results, but there are also some discrepancies due to model error. Overall, the use of multi-domain, multi-objective-optimization-based approach has proven feasible

Synthesis of Minimal Error Control Software

Software implementations of controllers for physical systems are at the core of many embedded systems. The design of controllers uses the theory of dynamical systems to construct a mathematical control law that ensures that the controlled system has certain properties, such as asymptotic convergence to an equilibrium point, while optimizing some performance criteria. However, owing to quantization errors arising from the use of fixed-point arithmetic, the implementation of this control law can only guarantee practical stability: under the actions of the implementation, the trajectories of the controlled system converge to a bounded set around the equilibrium point, and the size of the bounded set is proportional to the error in the implementation. The problem of verifying whether a controller implementation achieves practical stability for a given bounded set has been studied before. In this paper, we change the emphasis from verification to automatic synthesis. Using synthesis, the need for formal verification can be considerably reduced thereby reducing the design time as well as design cost of embedded control software. We give a methodology and a tool to synthesize embedded control software that is Pareto optimal w.r.t. both performance criteria and practical stability regions. Our technique is a combination of static analysis to estimate quantization errors for specific controller implementations and stochastic local search over the space of possible controllers using particle swarm optimization. The effectiveness of our technique is illustrated using examples of various standard control systems: in most examples, we achieve controllers with close LQR-LQG performance but with implementation errors, hence regions of practical stability, several times as small.Comment: 18 pages, 2 figure

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

A Hankel Matrix Based Reduced Order Model for Stability Analysis of Hybrid Power System Using PSO-GSA Optimized Cascade PI-PD Controller for Automatic Load Frequency Control

This paper presents the automatic load frequency control (ALFC) of two-area multisource hybrid power system (HPS). The interconnected HPS model consists of conventional and renewable energy sources operating in disparate combinations to balance the generation and load demand of the system. In the proffered work, the stability analysis of nonlinear dynamic HPS model was analyzed using the Hankel method of model order reduction. Also, an attempt was made to apply cascade proportional integral - proportional derivative (PI-PD) control for HPS. The gains of the controller were optimized by minimizing the integral absolute error (IAE) of area control error using particle swarm optimization-gravitational search algorithm (PSO-GSA) optimization technique. The performance of cascade control was compared with other classical controllers and the efficiency of this approach was studied for various cases of HPS model. The result shows that the cascade control produced better transient and steady state performances than those of the other classical controllers. The robustness analysis also reveals that the system overshoots/undershoots in frequency response pertaining to random change in wind power generation and load perturbations were significantly reduced by the proposed cascade control. In addition, the sensitivity analysis of the system was performed, with the variation in step load perturbation (SLP) of 1% to 5%, system loading and inertia of the system by ±25% of nominal values to prove the efficiency of the controller. Furthermore, to prove the efficiency of PSO-GSA tuned cascade control, the results were compared with other artificial intelligence (AI) methods presented in the literature. Further, the stability of the system was analyzed in frequency domain for different operating cases