CONSTRAINED OPTIMAL CONTROL OF LIQUID LEVEL LOOP USING A CONVENTIONAL PROPORTIONAL-INTEGRAL CONTROLLER

Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller

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.

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

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.

This paper presents a fuzzy proportional-integral (PI) controller, which is a discrete-time version of a conventional PI controller. The data base as well as the constant PI control gains are optimized by using a genetic algorithm according to the following design specifications: minimizing the overshoot, the settling time and smoothing of the output curve. Thus, the optimization problem close to a multiobjective optimization one, resulting in an optimal fuzzy PI controller. Simulation results are shown to demonstrate the improvement over a conventional one.

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.

In voltage‐controlled voltage source inverters (VSIs)‐based microgrids (MGs), the inner control is of prime interest task for guaranteeing safe and stable operation. In this paper, an in‐depth investigation of the modelling, control design, and analysis of the voltage and current inner control loops intended for single‐phase voltage‐controlled VSIs is established. The main objective of this work is to provide a comprehensive study of the mathematical modelling, control design, and performance evaluation of the inner control's loops considering different proportional‐integral (PI) controller types with and without compensation, and to determine the optimal scheme that can offer better performance in terms of implementation simplicity, robustness, and transient and steady‐state responses. Thus, the mathematical closed‐loop models of designed outer voltage and inner current control schemes based on PI, P, and feedforward controllers with and without compensation are, first, derived. Following this, a systematic and effective control design for tuning the different PI controllers’ parameters is proposed. Furthermore, an analysis revealing the performance of the designed voltage and current control schemes is provided. This analysis enables us to choose a P controller and PI feedforward controller for the current control loop and the voltage control loop, respectively. The chosen P and PI controllers should be simple; meanwhile, they should offer a wide bandwidth. A simulation study is carried out in MATLAB/Simulink software to assess the performance of the adopted inner control scheme for both linear and non‐linear loads. In addition, an experimental setup, based on a TMS320F2837xD μ C, of a single‐phase VSI supplying linear and non‐linear loads is built to verify the effectiveness and the robustness of the adopted inner controller. The results demonstrated: (1) the necessity of introducing the compensation term, which is responsible for offering control improvement against voltage perturbation, (2) the high tracking performance of the chosen controller in terms of dynamic and steady‐state responses as well as its simplicity of implementation.

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.

Proportional Integral (PI) controller has attracted researchers in industrial control processes because of its simplicity and robust performance in a wide range of operating conditions. It has been used to provide proportional delay differentiation on web servers in previous work. However, PI controller cannot achieve satisfactory results due to (1) the web server's non-linearity properties, and (2) the difficulty of building an accurate model for the web server. To address these issues, a nonlinear fuzzy PI controller is proposed in this paper, which has the advantage of fuzzy controller while maintaining the simplicity and robustness of PI controller. The proposed controller are self-tuned according to the periodical online performance measurement. The experimental results demonstrate that our fuzzy PI controller outperforms the PI controller in several aspects.

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.

Many DC/DC converters with voltage boosting functions have zeros on the right half of the complex plane; they are non-minimum phase systems. Conventional linear controllers have trouble obtaining the ideal control quality of simultaneously satisfying the performance requirements with respect to stability, speed, accuracy, robustness, and simplicity. This paper combines a proportional integral (PI) control rule with an adaptive parameter-adjusting circuit composed of memristors and adaptively adjusts the controller parameters through a memristor circuit to adapt to changes in system operation. The parameter update rules of voltage- and current-controlled memristors are discussed. The analysis indicates that under the control of the memristor-based adaptive PI controller, the closed-loop system’s output deviation continuously decreases along the gradient and approaches a minimum. Simulations and experimental verification are performed using a boost converter as the controlled object. The results show that, compared with a conventional PI controller, the proposed controller is more robust, and it is suitable for controlling a power electronic system with nonlinearly time-varying uncertainty and disturbance.

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

This paper presents simulation and analysis of intelligent control-based boost converter interfaced with three-level-diode clamped inverter (TL-DCI) for renewable energy and AC motor drive applications. The main objective of a DC–DC boost converter is to maintain a constant output voltage with an optimum duty cycle despite variations in input voltage or load current using an intelligent control, i.e., fuzzy logic control (FLC) approach. Further, better conversion efficiency with reduced ripple can be obtained by keeping optimum range of output voltage. To achieve the desired results from a DC–DC converter, an intelligent controller is required. The open loop and conventional proportional integral (PI) controllers have certain limitations to improve dynamic performance due to variation of parasitic into components. The FLC uses voltage output as feedback for significantly improving the dynamic performance of boost DC–DC converter. In addition, an intelligent control-based boost converter interfaced with TL-DCI using sine pulse width modulation (PWM) to apply for AC motor drive applications. This paper shows an improved dynamic performance of boost converter using FLC compared to open loop and conventional PI controllers using MATLAB or Simulink. The performance of TL-DCI using sine PWM integrated with FLC-based boost converter has been analyzed in terms power quality with respect to voltage and current waveforms.KeywordsBoost converterPI controlIntelligent controlTL-DCISine PWM

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