Comparative study of control methods for steam condenser

This paper proposes optimal tuning of Proportional Integral (PI) controller using Grey Wolf Optimizer (GWO) to overcome the slow convergence into local optimum associated with the Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) tuning methods. The optimal gains of the PI controller obtained from tuning using GWO, PSO and GA were implemented in the pitch angle control of the fixed speed Wind Turbine integrated into the distribution system. The objectives are to enhance the efficiency and reduced the fluctuation in the Wind Turbine output. Similarly, the Ziegler Nichols (ZN) tuned PI controller, the PI-fuzzy logic and Fuzzy Logic controllers were implemented in the same distribution system. The implementation of the tuned PI controllers has enhanced the output power of the Wind Turbine compared to the value obtained using the Hybrid or Fuzzy controllers. Furthermore, the GWO tuned PI controller recorded the high average output power of the Wind Turbine with low mean power error compared to other controllers. Also, it recorded the second-position in smoothing the Wind Turbine output power. The significance of the research is it has enhanced the efficiency and smoothing the output power of the fixed speed Wind Turbine.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

In this paper Fuzzy adaptive proportional-integral (PI) controller is designed to control the level of interacting tank. In the interacting process the load on the first process affects the second process and vice versa. The conventional PI controller cannot reach satisfactory result. The gain parameters of PI control are obtained by Ziegler-Nichols (ZN) tuning method and can be adjusted automatically using fuzzy logic. This paper illustrates the comparative analysis of conventional PI and fuzzy adaptive PI controller.

This paper proposes a design of modified Guo Tao based optimized Proportional Integral (PI) controller for the level control system. The performance of a designed system is compared with Fuzzy PI and conventional PI controller. The parameters of PI controller are optimized using Genetic Algorithm (GA) with Elite Preservation Strategy (EPS). Guo Tao proposed a subspace searching GA (GTA) in his research. Limitations of Guo Tao's algorithm (GTA) motivate us to propose a modified GTA using elite preservation strategy. Multi-parent crossover is used with EPS to enhance the performance of controller. A transfer function of a level loop of fabricated experimental setup is obtained by using the step response analysis. The performance of proposed controller is checked and verified by transfer function analysis using Ziegler Nicholas (Z-N) method in MATLAB environment. The proposed controller algorithm possesses an excellent transient response as compare to Fuzzy PI and conventional PI controller.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

A Static Synchronous Compensator (STATCOM) is one of the Flexible AC Transmission System (FACTS) device which plays an vital role in power system network in alleviation of small and large transient disturbances and better voltage regulation. This paper proposed a self tuning Proportional-Integral (PI) controller for STATCOM which provided a voltage support at point of common coupling (PCC). Self tuning of PI controller was achieved by adapting PI controller gains with system parameters by using Bats Echolocation controller which mimics the Bats Echolocation behavior. In the proposed controller, the system voltage (parameter) at point of common coupling compared with reference voltage and then Bats Echolocation Controller updates the PI controller gains according to the variation obtained between them. This paper also proposed a Cascaded Multilevel Inverter (CMLI) instead of conventional Voltage Source Inverter (VSI) for STATCOM to obtain better voltage regulation at point of common coupling. The voltage imbalance between H-bridge cells in CMLI was rectified by prosing Multicarrier Pulse Width Modulation (PWM) techniques, The proposed controller was validated by comparing the simulation results with fixed gain PI controller and Self tuning PI controller using Neural controller and Particle Swarm Optimization.

This paper presents a load frequency control (LFC) of five areas connected to same tie-line and it consist of non-conventional main power grids (Thermal-Hydro) and micro-grids (Wind-Diesel). Performance of conventional PI controllers is compared with PSO (Particle Swarm Optimization) tuned PI controller in a regulated and deregulated environment. A five area power system model has been made using Matlab/simulink to analyse load frequency control measures under different conditions based on participation factors, load disturbances and variations in local load demands. Simulation results shows that PSO(Particle Swarm Optimization) tuned PI(Proportional & Integral) controller's gains give much better performance than the conventional PI controller parameters which are more difficult to determine and time-consuming. The effect of DFIG (Doubly fed Induction Generator) in the power system which helps to smooth the tie line power is also been described.

Modeling proportional–integral controllers in tracking and economic model predictive control

The coupled-Tank (CT) system remains an important tool for research by process control engineers. However, effective control of the system depends largely on the accuracy of the mathematical model that predicts its dynamic behavior. In this work, the nonlinear model for the CT system based on analytical technique has been investigated. Linear models for the CT system were obtained based on analytical and empirical techniques. Accuracy of the linear models were investigated based on integral absolute error (IAE), were empirical model was found to be more accurate. Proportional integral (PI) control systems based on Ziegler Nichols (ZN), Ciancone correlation (CCR), Cohen Coon (CC), IMC and pole placement (PP) tuning methods were designed for liquid level control in the CT system tank 2. IAE results showed that PI controller did not meet all the specified control objectives. To improve the response, a neural network feedforward controller was incorporated to the PI controller and the response was compared to that of a model predictive controller (MPC). Simulations results showed that both PI-plus-feedforward and MPC satisfied all control specifications. However, MPC response was more satisfactory in terms of disturbance handling and time response criteria.

The primary function of microgrid (MG) is to share the power accurately among different distributed generation (DG) units according to the load demands. The droop control methods are normally used for appropriate power sharing. However, these control techniques suffer badly due to the feeder impedances and uncontrollable power coupling issues. Many techniques have been proposed to share power accurately, most of them rely on communication link. In this work a localized/communication free signal generating mechanism is proposed. The mechanism injects the transient reactive power in frequency droop characteristics by observing the changes in microgrid. This injection disturbs the actual active power sharing which is helpful for the computation of reactive power sharing errors. These errors are minimized by utilizing the proportional integral (PI) controller. The PI controller modifies the y-intercept and the slop of Q-E (Reactive Power –Voltage) droop characteristic for the correction of reactive power sharing error. Moreover, the parameters of droop and PI controller of inner control loop are optimized by Particle Swarm Optimization (PSO) to obtain high precision and reliability. The proposed technique has the following salient features:1. Accurately shares the reactive power without the information of feeder line impedances2. This accurate reactive power sharing scheme has been started locally without the need of communication signal from the central controller. Thus, there is no delay in monitoring the notable changes in microgrid caused by DGs. 3. The careful selection of PI & droop controller parameters through PSO makes the system more stable as compared to the conventional methods in terms of transients and steady state responses.

Power flow control in double circuit transmission lines is crucial for maintaining system stability and ensuring optimal utilization of resources. This paper proposes a novel approach using a Five Level Unified Power Flow Controller (UPFC) with a Grey Wolf Optimizer (GWO) tuned Proportional-Integral (PI) controller. The Five Level UPFC acts as a flexible and efficient solution, facilitating the concurrent regulation of both active and reactive power flow in transmission lines. By applying the GWO algorithm, the aim is to improve the performance of the UPFC control system through the optimization of the PI controller's parameters. By imitating the natural hunting behaviour of grey wolves, the GWO algorithm achieves exceptional convergence and solution accuracy. The suggested control scheme is implemented and tested on a double circuit transmission line system. The system's effectiveness is evaluated under various operating conditions and compared against alternative control approaches. The findings establish the efficacy of the Five Level UPFC with a GWO-optimized PI controller in effectively regulating power flow and enhancing overall system stability. Furthermore, the proposed approach exhibits excellent robustness against parameter variations, load fluctuations, and system disturbances. The GWO optimization technique ensures optimal tuning of the PI controller, this leads to decreased power losses, enhanced voltage profile, and improved system reliability. Overall, the application of the Five Level UPFC with GWO-tuned PI controller provides an efficient and reliable solution for power flow control in double circuit transmission lines. The suggested method can make a substantial contribution to enhancing the operational efficiency of power systems, this enables the seamless integration of renewable energy sources and the progression of smart grid infrastructures. The circuit is simulated using the MATLAB/SIMULINK software. And the following results from the simulation are presented below

Feedback control of an experimental electrically-heated steam methane reformer

Advanced process control of a B-9 Permasep ® permeator desalination pilot plant

This paper presents a modern approach for speed control of an induction motor (IM) using the particle swarm optimization (PSO) method for determining the optimal parameters, Kp and Ki, of the integral proportional (IP) controller. The use of PSO as an optimization algorithm makes the drive robust, with faster dynamic response, higher accuracy and insensitive to load variation. Comparison between different controllers is achieved, using an IP controller which is tuned by two methods, firstly manually and secondly using the PSO technique. Integral time absolute error, integral absolute error and integral square error performance indices are considered to satisfy the required criteria in output speed of an IM. From the simulation results it is observed that the IP controller designed with PSO yields better results when compared to the traditional method in terms of performance index.

To solve the neutral-point (NP) voltage unbalancing problem, a conventional proportional integral (PI) controller is used for NP clamped (NPC) three-level grid-connected inverters under balanced grid conditions. However, when the three-phase grid voltage is unbalanced, the NP voltage unbalancing problem becomes more serious and complex, which cannot be solved by a conventional PI controller with the variation of the unbalanced grid. Based on the transfer function of the injected zero-sequence component to the NP voltage, the NP voltage module has different characteristic performances in frequency-domain, which is a leading or lagging system with the variation of the unbalanced grid and output power factor. Besides, being appropriate for this NP voltage module in two cases, a PI controller with hybrid parameters (HP-PI controller) is proposed to balance the NP voltage in this paper. According to varied unbalanced grid and output power factor, hybrid parameters are designed for this controller in two cases by the Lagrange multiplier method appropriately. Furthermore, frequency-domain characteristics and the balancing time of the proposed HP-PI control method are analyzed. Experimental and simulation results show that, compared with conventional PI and quasi-proportional resonant (PR) control methods, the proposed HP-PI controller has a strong ability to balance NP voltage with a good dynamic and steady-state performance under varied unbalanced grid conditions, which also improves the quality of grid currents.