Optimal Proportional-integral Controller Research Articles

Hardware-in-the-loop simulation for the testing of smart control in grid-connected solar power generation systems

Generally, the power of a solar photovoltaic panel is strongly related to climate conditions. Hence, a grid-connected system should have a good response over variations in the solar radiation and temperature. Consequently, this work describes the application of the control based general type-II fuzzy system for the grid-connected solar power generation system. In this study, general type-2 fuzzy logic sets (GT2FLS) and the Modified Backtracking Search Algorithm (MBSA) techniques for the adaptive tuning of the most popular existing proportional-integral (PI) controller is integrated in order to tackle these uncertainties. The achieved results are compared with conventional PI controller and Optimal Fuzzy PI (OFPI) controller results, which are the most recent researches into in the present issue to evaluate the proficiency of the proposed controller. Finally, the extensive studies and hardware-in-the-loop simulations are presented to show the effectiveness of the proposed controller.

Hardware-in-the-loop simulation for the testing of smart control in grid-connected solar power generation systems

Generally, the power of a solar photovoltaic panel is strongly related to climate conditions. Hence, a grid-connected system should have a good response over variations in the solar radiation and temperature. Consequently, this work describes the application of the control based general type-II fuzzy system for the grid-connected solar power generation system. In this study, general type-2 fuzzy logic sets (GT2FLS) and the Modified Backtracking Search Algorithm (MBSA) techniques for the adaptive tuning of the most popular existing proportional-integral (PI) controller is integrated in order to tackle these uncertainties. The achieved results are compared with conventional PI controller and Optimal Fuzzy PI (OFPI) controller results, which are the most recent researches into in the present issue to evaluate the proficiency of the proposed controller. Finally, the extensive studies and hardware-in-the-loop simulations are presented to show the effectiveness of the proposed controller.

Frequency Stabilization of Renewable Power Systems Based on MPC With Application to The Egyptian Grid

Abstract This paper presents a Model Predictive Control (MPC) technique for frequency stabilization of renewable power systems with inherent nonlinearities. To prove the effectiveness of the proposed technique, the Egyptian Power System (EPS), which includes both conventional generation units (i.e., non-reheat, reheat and hydraulic power plants) and wind turbines is tested using Matlab/SIMULINK® software. Furthermore, the performance of the proposed MPC technique is compared with an optimal Proportional-Integral (PI) controller-based Particle Swarm Optimization (PSO) algorithm under different load profiles and system uncertainties. The simulation results emphasize the superior robustness of the proposed MPC-based LFC in comparison to the optimal PI controller.

Dynamic modeling, adaptive control and energy performance simulation of a hybrid hydronic space heating system

A dynamic model of a hybrid hydronic heating system has been developed. The overall model consists of a boiler, a heat exchanger, a ground loop heat pump, a ground loop heat exchanger, baseboard heaters (convector–radiators), and radiant floor hydraulic piping system. Two control strategies for improving the overall system performance were explored: (1) a conventional proportional-integral (PI) control and (2) an adaptive gain control. The simulation results showed that the performance of the adaptive controller is better than the fixed gain PI controller in disturbance rejection and stability. Energy simulations under three different operating strategies were conducted: (1) a fixed set-point PI control, (2) an outdoor air temperature reset control, and (3) an optimal set-point adaptive PI control. It was shown that the outdoor temperature reset strategy can save 4.5 and 19.9% energy under cold day and mild day conditions compared to the fixed set-point PI control strategy. The optimal adaptive PI control strategy resulted in higher energy savings of 6.6 and 22% as compared to the PI control under cold and mild day conditions, respectively. Practical application: Energy efficiency and sustainability are a major issue of importance in the design and operation of space heating systems. The proposed hybrid system combines a ground-source heat pump and a hot water boiler for space heating of both residential and commercial building applications. The heating system consists of radiant floor piping for the residential zones and hot water baseboard heaters (convector–radiators) for the commercial zones. An energy optimal adaptive control strategy is designed to improve energy efficiency and temperature control performance of the hybrid system. The control strategy is simple and can be implemented on the existing building control systems.

Analytical design of an industrial two-term controller for optimal regulatory control of open-loop unstable processes under operational constraints

This paper proposes a novel optimization-based approach for the design of an industrial two-term proportional-integral (PI) controller for the optimal regulatory control of unstable processes subjected to three common operational constraints related to the process variable, manipulated variable and its rate of change. To derive analytical design relations, the constrained optimal control problem in the time domain was transformed into an unconstrained optimization problem in a new parameter space via an effective parameterization. The resulting optimal PI controller has been verified to yield optimal performance and stability of an open-loop unstable first-order process under operational constraints. The proposed analytical design method explicitly takes into account the operational constraints in the controller design stage and also provides useful insights into the optimal controller design. Practical procedures for designing optimal PI parameters and a feasible constraint set exclusive of complex optimization steps are also proposed. The proposed controller was compared with several other PI controllers to illustrate its performance. The robustness of the proposed controller against plant-model mismatch has also been investigated.

Constraint handling optimal PI control of open-loop unstable process: Analytical approach

This paper proposes the closed-form analytical design of proportional-integral (PI) controller parameters for the optimal control of an open-loop unstable first order process subject to operational constraints. The main idea of the design process is not only to minimize the control performance index, but also to cope with the constraints in the process variable, controller output, and its rate of change. To derive an analytical design formula, the constrained optimal control problem in the time domain was transformed to an unconstrained optimization in a parameter space associated with closed-loop dynamics. By taking advantage of the proposed analytical approach, a convenient shortcut algorithm was also provided for finding the optimal PI parameters quickly, based on the graphical analysis for the optimal solution of the corresponding optimization problem in the parameter space. The resulting optimal PI controller guarantees the globally optimal closed-loop response and handles the operational constraints precisely.

Optimal fractional order PI control applicability for enhanced dynamic behavior of on-grid solar PV systems

This paper aims at finding the optimal gain scheduling of the conventional Proportional Integral (PI) and the Fractional Order (FOPI) through the use of the advanced meta-heuristic optimization techniques in PV-grid connected systems. Therefore, the hybridization of the optimization techniques with both PI and FOPI controllers is proposed for attaining an optimal dynamic performance through a specific criterion. This latter is a function of different response characteristics such as the settling time, maximum overshoot, steady state error and execution time. Based on the proposed criterion, the meta-heuristic technique that provides the best enhanced dynamic performance can be simply identified. For this purpose, a comprehensive state of art of the on-grid PV system performance dilemma is initially discussed. The problem of designing current and voltage regulators for the PV-grid connected system is addressed, seeking for an adequate dynamic behavior. Then, different metaheuristic optimization techniques such as Artificial Bee Colony (ABC), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), Mine Blast Algorithm (MBA), Whale Optimization Algorithm (WOA) and Moth Swarm Algorithm (MSA) are employed for determining the optimal PI/FOPI controller gains, forming a hybrid optimal FOPI or PI control. From the numerical simulation analysis of Matlab™/Simulink results, the significant dynamic behavior enhancement of the overall on-grid PV system is illustrated using PI/FOPI controllers optimally tuned with the proposed meta-heuristic algorithms considering solar irradiation variation conditions. The impact of the optimized PI and FOPI controllers on the overall system dynamic responses is explicitly analyzed while using different meta-heuristic algorithms through qualitative and quantitative comparisons.

Small-signal modeling and multivariable PI control design of VSC-HVDC transmission link

This paper employs a multivariable PI (proportional-integral) controller for inner-loop control and SISO (single-input single-output) PI controllers for outer-loop control for a VSC-HVDC (voltage source converter-based high voltage direct current) transmission system. To design the control system, first, a state-space model of VSC-HVDC transmission link is obtained in dq-coordinate frame. Then, using linearization technique the small-signal model is obtained. Next, a multivariable optimal PI controller is designed for the (inner-loop) linearized model using LQR (linear quadratic regulator) approach. Finally, the outer-loop SISO PI controller parameters are obtained using sequential design method (i.e., assuming that the inner-loop is must faster than the outer-loop). The advantage of using the MIMO (multi-input multi-output) PI controller over the conventional decouple-based PI controller for inner-loop control is studied through different performance and robustness measures, and also verified through simulations in presence of several disturbances and parameter variations.

Adaptive PI controller to voltage regulation in power systems: STATCOM as a case study

Static synchronous compensator (STATCOM) provides the means to improve quality and reliability of a power system as it has the functional capability to handle dynamic disturbances, such as transient stability and power oscillation damping as well as to providing voltage regulation. In this paper, a robust adaptive PI-based optimal fuzzy control strategy is proposed to control a STATCOM used in distribution systems. The proposed intelligent strategy is based on a combination of a new General Type-II Fuzzy Logic (GT2FL) with a simple heuristic algorithm named Teaching Learning Based Optimization (TLBO) Algorithm. The proposed framework optimally tunes parameters of a Proportional-Integral (PI) controller which, similar to most of other researchers regarding control of STATCOM, are in charge of controlling the device. The proposed controller guaranties robustness and stability against uncertainties caused by external disturbances or ever-changing nature of the power systems. The TLBO optimizes the parameters of the controller as well as the input and output membership functions. To validate the efficiency of the proposed controller, the obtained simulation results are compared with those of the two most recent researches applied in this field, namely, conventional Proportional Integral (PI) controller and Optimal Fuzzy PI (OFPI) controller. Results demonstrate the successfulness and effectiveness of the proposed online-TLBO General Type-2 Fuzzy PI (OGT2FPI) controller and its superiority over conventional approaches.

An Adaptive Control Strategy for Low Voltage Ride Through Capability Enhancement of Grid-Connected Photovoltaic Power Plants

This paper presents a novel application of continuous mixed $p$ -norm (CMPN) algorithm-based adaptive control strategy with the purpose of enhancing the low voltage ride through (LVRT) capability of grid-connected photovoltaic (PV) power plants. The PV arrays are connected to the point of common coupling (PCC) through a DC-DC boost converter, a DC-link capacitor, a grid-side inverter, and a three-phase step up transformer. The DC-DC converter is used for a maximum power point tracking operation based on the fractional open circuit voltage method. The grid-side inverter is utilized to control the DC-link voltage and terminal voltage at the PCC through a vector control scheme. The CMPN algorithm-based adaptive proportional-integral (PI) controller is used to control the power electronic circuits due to its very fast convergence. The proposed algorithm updates the PI controller gains online without the need to fine tune or optimize. For realistic responses, the PV power plant is connected to the IEEE 39-bus New England test system. The effectiveness of the proposed control strategy is compared with that obtained using Taguchi approach-based an optimal PI controller taking into account subjecting the system to symmetrical, unsymmetrical faults, and unsuccessful reclosing of circuit breakers due to the existence of permanent fault. The validity of adaptive control strategy is extensively verified by the simulation results, which are carried out using PSCAD/EMTDC software. With the proposed adaptive-controlled PV power plants, the LVRT capability of such system can be improved.

Multichannel optimal control algorithm for adaptive optics system

In this paper we propose a multichannel optimal control algorithm (MOCA) which can be applied to practical real-time closed-loop adaptive optics system (AOS). The MOCA, doesn't like the traditional ones, considers both the effect of atmospheric turbulence and of the time delay. The control parameters are optimized timely through iterative calculations in order to let the system working at the best controlling state. Here we study, by the means of numerical simulations, the multichannel optimal control method with Recursive Least-Square (RLS) algorithm, considering a 61-element AOS as an example. Analyses were made on convergence, control performance and bandwidth of this algorithm. Compared with the classical optimal proportional-integral control algorithm, simulation results show that the residual error caused by the atmospheric turbulence and servo lag in the system are reduced more effectively by using MOCA which has good adaptability and could improve the close-loop bandwidth of the AOS.

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.

ANALYSIS OF MULTI-TERMINAL HVDC TRANSMISSION SYSTEM FEEDING VERY WEAK AC NETWORKS

This paper presents a line commutated converter (LCC) based multi-terminal HVDC transmission (MTDC) system feeding very weak AC networks with hybrid reactive power compensators (RPC’s) at the inverter AC side. The hybrid compensator is accomplished by the equal mixing of any two of the following compensators: synchronous compensator (SC); static var compensator (SVC); static synchronous compensator (STATCOM). The four-terminal HVDC transmission system model is implemented in the Matlab with the firefly algorithm based optimal proportional integral (PI) controller for rectifiers and inverters control. The transient performances of hybrid RPC’s (SC+SVC, SVC+STATCOM and SC+STATCOM) are studied under various fault conditions and the results are compared with the performance of the SC, SVC and STATCOM to focus the high quality of the hybrid compensators. The simulation results authorize that the equivalent mixture of SC and STATCOM has a steady and fastest response. The results also reveal the supremacy of the firefly algorithm based optimal PI controller over the conventional PI controller. The harmonic present in the inverter side AC quantities is also calculated under steady state operation to assure the quality of power supply.

Dual Proportional Integral Controller of Two-Area Load Frequency Control Based Gravitational Search Algorithm

Gravitational Search Algorithm (GSA) has recently been explored to develop a novel algorithm for distributed optimization and control. This paper proposes a dual Proportional Integral (PI) controller of Load Frequency Control (LFC) based GSA to enhance the damping of oscillations in a two-area power system. A two-area non-reheat thermal system is considered to be equipped with dual PI controller. GSA is utilized to search for optimal controller parameters by minimizing a time-domain based objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller, and PI controller tuned by GSA in order to demonstrate the superior efficiency of the proposed dual PI controller tuned by GSA. Simulation results emphasis on the better performance of the optimized dual PI controller based on GSA in compare to optimized PI controller based on GSA and conventional one over wide range of operating conditions, and system parameters variations. DOI: http://dx.doi.org/10.11591/telkomnika.v15i3.8454

Artificial Intelligence in Performance Analysis of Load Frequency Control in Thermal-Wind-Hydro Power Systems

In this article, Load Frequency Control (LFC) of three area unequal interconnected thermal, wind and Hydro power generating units has been developed with Proportional-Integral (PI) controller under MATLAB/SIMULINK environment. Further, the PI controller gains values that optimized using trial and error method with two different objective functions, namely the Integral Time Square Error (ITSE) and the Integral Time Absolute Error (ITAE). The performance of ITAE objective function based PI controller is compared with the ITSE objective function optimized PI controller. Analysis reveals that the ITSE optimized controller gives more superior performance than ITAE based controller during one percent Step Load Perturbation (1% SLP) in area 1 (thermal area). In addition, Proportional–Integral –Derivative (PID) controller is employed to improve the same power system performance. The controller gain values are optimized using Artificial Intelligence technique based Ant Colony Optimization (ACO) algorithm. The simulation performance compares the ACO-PID controller to the conventional PI. The results proved that the proposed optimization technique based the ACO-PID controller provides a superior control performance compared to the PI controller. As the system using the ACO-PID controller yield minimum overshoot, undershoot and settling time compared to the conventional PI controlled equipped system performance.

ACO based speed control of SRM fed by photovoltaic system

Abstract This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.

Modelling and Simulation of Monopolar HVDC Transmission System Feeding a Strong AC Network with Firefly Algorithm based Optimal PI Controller

Transmission of electrical power through high voltage direct current (HVDC) has attracted the attention of a number of researchers in the recent years. For economic design and optimal operation, HVDC system requires a detailed simulation model. Therefore, in this paper a detailed Matlab simulation model of line commutated converter (LCC) based monpolar HVDC system, feeding a strong AC network, with a fixed capacitor (FC) as a reactive power compensator is presented. Firefly algorithm based optimal proportional integral (PI) controller has been proposed for the rectifier and the inverter control. The transient performances of the HVDC system under various AC and DC fault conditions were studied. The results show the supremacy of the firefly algorithm based optimal PI controller over the conventional PI controller. The harmonic analysis is also carried out under steady state operation to assure the quality of power supply on the inverter AC side.

Frequency control of a new topology in proton exchange membrane fuel cell/wind turbine/photovoltaic/ultra-capacitor/battery energy storage system based isolated networks by a novel intelligent controller

This paper proposes a new topology for hybrid isolated networks employing a wind turbine (WT), proton exchange membrane fuel cell (PEMFC), photovoltaic, ultra-capacitor (UC), and battery energy storage system (BESS). The PEMFC is used in parallel with the UC and BESS; a configuration that improves the performance of the PEMFC in terms of dynamic response and lifetime. The proposed topology of the PEMFC along with a new model of WT reduces the initial costs of the hybrid isolated network. Due to power demand fluctuations in isolated networks, to maintain the balance between power generation and consumption, intelligent and flexible control methods must be applied. The variations of the solar irradiance, of the wind speed, and of the power consumption in hybrid isolated networks render the classic controllers and even fuzzy controllers inefficient. To overcome this problem, we use a novel intelligent fuzzy controller which is optimized by the teaching-learning-based optimization method. In the structure of the new proposed controller, the range and place of membership functions are optimized to reduce network frequency oscillations and improve network frequency stability. The designed control methodology is evaluated in the proposed isolated network and its performance is compared with the fuzzy controller, proportional integral (PI) controller, and optimal PI controller. To demonstrate the effectiveness of the proposed control strategies, the above-mentioned detailed mathematical and dynamic models of the isolated network were integrated using MATLAB/Simulink.

Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity

This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional–Integral (PI) and Proportional–Integral–Derivative (PID) controllers for Automatic Generation Control (AGC) of an interconnected power system. Initially, a two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. In the proposed approach, the design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions are used for the design purpose. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO) technique for the same interconnected power system. It is noticed that, the dynamic performance of DE optimized PI controller is better than CPSO optimized PI controllers. Additionally, controller parameters are tuned at different loading conditions so that an adaptive gain scheduling control strategy can be employed. The study is further extended to a more realistic network of two-area six unit system with different power generating units such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC) and Governor Dead Band (GDB) non-linearity.

Particle Swarm Optimisation Based Feedback Controller Design for Induction Motor Soft-Startingd

The objective of this work is to design and implement a closed loop system for induction motor starting at rated current. Thyristorised AC voltage regulator is used as the starting equipment and motor current regulation is carried out using an optimally tuned proportionalintegral (PI) controller. Since AC voltage controller fed starting of induction motor is a non-linear process, identification of optimal values of PI controller constants is performed using particle swarm optimisation. The complete drive system including AC voltage controller fed induction motor in conjunction with optimal PI controller is first simulated in MATLAB and subsequently verified experimentally. The successful implementation with a low cost microcontroller illustrates the feasibility of the new approach.