Fractional Order Load-Frequency Control of Interconnected Power Systems Using Chaotic Multi-objective Optimization

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Fractional order proportional-integral-derivative (FOPID) controllers are designed for load frequency control (LFC) of two interconnected power systems. Conflicting time domain design objectives are considered in a multi objective optimization (MOO) based design framework to design the gains and the fractional differ-integral orders of the FOPID controllers in the two areas. Here, we explore the effect of augmenting two different chaotic maps along with the uniform random number generator (RNG) in the popular MOO algorithm - the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Different measures of quality for MOO e.g. hypervolume indicator, moment of inertia based diversity metric, total Pareto spread, spacing metric are adopted to select the best set of controller parameters from multiple runs of all the NSGA-II variants (i.e. nominal and chaotic versions). The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time. In addition, the Pareto optimal fronts showing the trade-off between the two conflicting time domain design objectives are compared to show the advantage of using the FOPID controller over that with simple PID controller. The nature of fast/slow and high/low noise amplification effects of the FOPID structure or the four quadrant operation in the two inter-connected areas of the power system is also explored. A fuzzy logic based method has been adopted next to select the best compromise solution from the best Pareto fronts corresponding to each MOO comparison criteria. The time domain system responses are shown for the fuzzy best compromise solutions under nominal operating conditions. Comparative analysis on the merits and de-merits of each controller structure is reported then. A robustness analysis is also done for the PID and the FOPID controllers

Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants

An optimized fractional order PID controller for suppressing vibration of AC motor

Fractional order Proportional-Integral-Derivative (PID) controller is composed of a number of integer order PID controllers. It is more accurate to control the complex system than the traditional integer order PID controller. The values of parameters of the fractional order PID controller play a decisive role for the control effect. Because the fractional order PID controller added two adjustable parameters than the traditional PID controller, it is very difficult to tune parameters. So the Back Propagation (BP) neural network is selected to optimize the parameters of the fractional order PID controller in order to obtain the high performance. Then the optimized fractional order PID controller and the traditional PID controller are used to control AC motor speed governing system. And the vibration spectrum and stator current spectrum under different rotating speeds are compared and analyzed in detail. The results show that the optimized fractional order PID controller has better vibration suppression performance than the traditional PID controller. The reason is that the optimized fractional order PID controller changed the stator current component, and further changed the frequency components and the amplitude of the vibration signal of the motor

PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid

Due to global warming problems and increasing environmental pollution, there is a strong tendency to install and apply renewable energy power plants (REPPs) around the world. On the other hand, with the increasing development of information and communication technology (ICT) infrastructures, power systems are using these infrastructures to act as smart grids. In fact, future modern power systems should be considered as smart grids with many small and large scale REPPs. One of the main problems and challenges of the REPPs is uncertainty and fluctuation of electrical power generation. Accordingly, a suitable solution can be combination of different types of REPPs. So, the penetration rate of large-scale wind-PV farms (LWPF) is expected to increase sharply in the coming years. Given that the LWPFs are added to the grid or will replace fossil fuel power plants, they should be able to play the important roles of synchronous generators such as power low-frequency oscillation (LFO) damping. In this paper, an LFO damping system is suggested for a LWPF, based on a phasor measurement unit (PMU)-based fractional-order proportional–integral–derivative (FOPID) controller with wide range of stability area and proper robustness to many power system uncertainties. Finally, the performance of the proposed method is evaluated under different operating conditions in a benchmark smart system

Fractional Order AGC for Distributed Energy Resources Using Robust Optimization

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell and aqua electrolyzer along with other energy storage devices like the battery and flywheel. The controller is placed in a remote location while receiving and sending signals over an unreliable communication network with stochastic delay. The controller parameters are tuned using robust optimization techniques employing different variants of Particle Swarm Optimization (PSO) and are compared with the corresponding optimal solutions. An archival based strategy is used for reducing the number of function evaluations for the robust optimization methods. The solutions obtained through the robust optimization are able to handle higher variation in the controller gains and orders without significant decrease in the system performance. This is desirable from the FO controller implementation point of view, as the design is able to accommodate variations in the system parameter which may result due to the approximation of FO operators, using different realization methods and order of accuracy. Also a comparison is made between the FO and the integer order (IO) controllers to highlight the merits and demerits of each scheme

Model predictive control for load frequency control of an interconnected power system.

Masters Degree. University of KwaZulu-Natal, Durban.Reliable load frequency control (LFC) is of importance in modern power system generation, transmission and distribution, it has been the basis of research on advanced control theory and application in recent years. In LFC scheme, local load disturbance, inter-area ties power fluctuation, frequency deviation, generation rate constraints (GRC), and governor dead band (GDB) are the major nonlinear factors on the control scheme that affect the dynamic response of the system to a large extent. Over the years, many methods have been designed for LFC problem of which model predictive controller (MPC) stands out due to its advantages. MPC is a control approach that simulates the feature behaviour of a system it controls and based on the result of the simulation attempt to find a control output such that the simulated system behaves optimally. When applied to LFC it copes with the perturbation. In this dissertation, robust distributed model predictive control (RDMPC) is developed as a controller scheme for LFC and is compared with a proportional integral derivative (PID) controller using MATLAB/Simulink for two-area and three-area hydro-thermal interconnected power system. From the simulation result, RDMPC significantly shows robustness over PID when compared in frequency deviation and area control error. It is observed that RDMPC still lags, from system varying dynamics and uncertainty despite its robustness over PID, hence an adaptive model predictive control (AMPC) is developed to improve on the performance of RDMPC. In order to evaluate the efficacy of this proposed controller, robustness and comparative analysis is performed using MATLAB/Simulink between the conventional PID, RDMPC, and AMPC with respect to performance indices such as settling time, undershoot and peak overshoot when subjected to frequency deviation, tie-line active power deviation, and area control error. Also, the dynamic response of the hydrothermal systems is analysed and compared in the presence of nonlinear constraints such as generator rate constraint (GRC) and governor dead band (GDB). The result from the simulation tests shows that AMPC has a better dynamic response when compared with PID, and RDMPC with a significant improvement

Tuning and comparison of design concepts applying Pareto optimality. A case study of Cholette bioreactor

[EN] The linear control PI (D) and its variants are control structures (design concepts) that are still used in industrial processes. The control engineer will prefer one over another according to a desired tradeoff among complexity and performance indices. Given that this exchange might be in conflict, an analisis using multiobjective optimisation tools could be interesting. With this perspective, different Pareto fronts from different design concetps are compared, enabling a global, and not punctual, performance comparison. In this work a global methodology for comparing design concepts in dfferent stages was developed. The first step was to establish a region of stability. In the second stage, the stability region was considered as a search space for the multiobjective optimization process, approximating a Pareto set and front. In the third stage, a multicriteria analysis of the Pareto fronts was carried out, together with the simulation in the time domain for the output and control signals. As case study to validate this proposal the Cholette’s biorreactor was selected. The proposed methodology allows a better understanding of a conceptual solution, justifies and determines the use of a design concept thus meeting the needs of the designer.[ES] El control lineal PI(D) y sus variantes, son estructuras de control (conceptos de diseño) que actualmente se siguen utilizando en procesos industriales. La elección de una estructura de control sobre otra reside en el intercambio de prestaciones entre complejidad y rendimiento. Dado que este intercambio de prestaciones normalmente estará en conflicto, un análisis desde el punto de vista multiobjetivo puede ser de interés. Desde tal perspectiva, se analizan frentes de Pareto de diferentes conceptos de diseño, con lo que se realiza una comparación global y no puntual de tales conceptos. En este trabajo se plantea una propuesta metodológica para dicha comparación en diferentes etapas. La primera, fue establecer una región de estabilidad. En la segunda etapa se consideró la región de estabilidad como espacio de búsqueda para el proceso de optimización multiobjetivo calculando un conjunto y frente de Pareto. En la tercera etapa se realizó un análisis multicriterio de los frentes de Pareto, junto con la simulación en el dominio del tiempo para las señales de salida y de control. Como caso de estudio para validar la propuesta se ha elegido el biorreactor de Cholette que presenta diferentes condiciones de operación. 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