Proportional-integral Controller Parameters Research Articles

Computation of Robust PI-Based Pitch Controller Parameters for Large Wind Turbines

This paper deals with the computation of all proportional-integral (PI)-based pitch controllers which achieve the desired frequency-domain specifications, namely, gain and phase margins (GPMs) of a large wind turbine (LWT) with communication delays. An efficient graphical method based on extracting the boundaries of stability regions in PI controller parameter space having user-defined GPMs has been employed to determine GPM-based stability regions for a wide range of time delays. The theoretical region boundaries are validated by using a powerful numerical method known as the quasi-polynomial mapping-based root finder (QPmR) and time-domain simulations. Results indicate that the proposed scheme gives an improved dynamic response compared to the recently developed scheme based on stability only for the pitch control of LWTs with communication delays.

An assessment of the proportional-integral control tuning rules applied to Photovoltaic Irrigation Systems based on Standard Frequency Converters

In this work, a systematic study of different tuning rules of proportional-integral (PI) control parameters applied to Photovoltaic Irrigation Systems (PVIS) based on Standard Frequency Converters (SFCs) has been carried out. A laboratory system has been implemented to obtain the PI control parameters by means of the most widely accepted tuning rules in control systems. Based on the step-input response modeling, we have been applied the tuning rules of Ziegler-Nichols (ZNs), Chien-Hrones-Reswick (CHR), Cohen-Coon (C-C), Lambda and AMIGOs. Based on frequency response modeling we have been applied the tuning rules of Ziegler-Nichols (ZNf) and AMIGOf. The PI controller parameters obtained from these tuning rules have been evaluated by performing two types of tests: start-up and sudden drop of the available PV power. The experimental results show that the parameter sets obtained by the ZNs, CHR and AMIGOf rules are the only ones that pass all the start-up tests and that the tests of sudden drop of the available PV power are only passed by the configuration obtained by the AMIGOf rules. Therefore, it has experimentally verified that the AMIGO tuning rules based on the frequency response modeling are the only ones that provide adequate values for the PI control parameters for PV irrigation systems.

Network-Based Modeling and Proportional–Integral Control for Direct-Drive-Wheel Systems in Wireless Network Environments

This paper focuses on the network-based modeling and proportional-integral (PI) control for a continuous-time direct-drive-wheel system in a wireless network environment. The developed system can simplify configuration, reduce bus cables, and realize vehicle height adjustment. A novel network-based model is first established by constructing a PI control system and taking network-induced delays and stochastic packet dropouts into account. By using two different artificial delays to characterize the update of proportional and integral control signals, the network-based PI control system is modeled as a stochastic impulsive system with two input delays and reset equations at updating instants. Then, through involving the reset states and the relationship among two delayed states and the current state in the discontinuous Lyapunov-Krasovskii functional and actively introducing the upper bounds of nonzero network-induced delays, some exponential mean-square stability and H∞ performance conditions with less conservatism are derived in terms of tractable linear matrix inequalities. An algorithm is presented to determine the minimum H∞ performance and the corresponding PI control parameters by combining a particle swarm optimization technique with the performance condition. These results can be extended to a network-based PI control of general continuous-time linear systems. A ZigBee-based network simulation platform is finally built and some simulation results are provided to validate the proposed methods.

Applying a Relay Feedback Test for Autotuning the Electrohydraulic Controller of a Strength Testbed

This paper describes the problem of automation of parameter selection of an electrohy-draulic drive for a single-channel testbed for the strength load of aviation elements. It is proposed to tune the proportional-integral controller parameters of the testbed on the basis of the results of the tests based on relay feedback and time-scale separation method. The results obtained allow for autotuning the strength testbed controller parameters and increasing the accuracy of the strength load cyclograms, which eventually accelerates the strength tests. The results of applying this method on the experimental testbed are given.

A PI controller optimized with modified differential evolution algorithm for speed control of BLDC motor

ABSTRACTIn this paper, a proportional integral (PI) controller that optimized with the modified different evolutional (DE) algorithm is proposed for speed control of brushless direct-current (BLDC) motor. The parameters of PI controller are tuned by the modified DE algorithm which based on adaptive mutation factor, multivariable fitness function and the starting rule for the modified algorithm. The performances of proposed controller, the conventional PI controller and the PI controller optimized with standard DE controller (PI-SDE controller) are investigated and compared in simulation. Also, the proposed controller is compared with other optimization controller in this study. The simulation results and the experimental verification show that the proposed controller leads to the smaller overshoot, less setting time and rising time compared to other controllers in this study. The results also show that the proposed controller can accelerate the response speed of BLDC motor, strengthen the robustness and guarantee motor runs smoothly as well as precisely. This work indicates the distinguished performance of proposed controller for the speed control of BLDC motor.

A Maximum Power Loading Factor (MPLF) Control Strategy for Distributed Secondary Frequency Regulation of Islanded Microgrid

Microgrids rely on both primary and secondary frequency control techniques to maintain system stability. Secondary frequency control effectively minimizes frequency fluctuations by adjusting the active power reference in each power inverter, but requires complex and costly interequipment communication. In this paper, we propose a distributed secondary frequency control strategy for microgrids containing multiple virtual synchronous generator (VSG) units based on a new maximum power loading factor (MPLF) concept. The MPLF algorithm facilitates power sharing by dynamically identifying the maximum VSG loading factor at each time instance, and then using this value as a unified reference signal for all the VSGs in the microgrid. The active power reference for each VSG will be adjusted based on the unified reference signal, subsequently the secondary frequency control can be realized. The proposed strategy does not require high-bandwidth communication since the MPLF data are transmitted among the VSGs using low-bandwidth communication. We also develop small-signal models for the control architecture to analyze the influence of major proportional–integral control parameters and communication latency. The MPLF control strategy is implemented using custom digital signal processor controllers, and experimentally validated using hardware in loop simulations. Finally, the new control paradigm demonstrates significant tolerance for communication delay or failure, which we purposely introduced in our investigation.

PI Controller of Speed Regulation of Brushless DC Motor Based on Particle Swarm Optimization Algorithm with Improved Inertia Weights

The brushless director current (DC) motor is a new type of mechatronic motor that has been developed rapidly with the development of power electronics technology and the emergence of new permanent magnet materials. Based on the speed regulation characteristics, speed regulation strategy, and mathematical model of brushless DC motor, a parameter optimization method of proportional-integral (PI) controller on speed regulation for the brushless DC motor based on particle swarm optimization (PSO) algorithm with variable inertia weights is proposed. The parameters of PI controller are optimized by PSO algorithm with five inertia weight adjustment strategies (linear descending inertia weight, linear differential descending inertia weight, incremental-decremented inertia weight, nonlinear descending inertia weight with threshold, and nonlinear descending inertia weight with control factor). The effectiveness of the proposed method is verified by the simulation experiments and the related simulation results.

Small Signal Modeling of Inverter-based Grid-Connected Microgrid to Determine the Zero-Pole Drift Control with Dynamic Power Sharing Controller

This paper presents a small signal state space modeling of three-phase inverter-based microgrid (MG) system with consideration of improved droop control. The complete system matrices for one distribution source-grid connects to the local load have been elaborated by applying high, medium and low-frequency clusters to the system without considering the switching action on the inverter during power-sharing. Moreover, the final matrices will be used to determine the location of the eigenvalues for the control parameters gains due to dynamic effect of the MG, by observing the root locus graph on cluster identification. Sensitivity analysis of all types of frequency cluster showed that power-sharing control parameters such as load current, source current, and inverter voltage are influencing system stability and must be considered when designing the proportional-integral (PI) control when different load scenarios have been applied from the zero-pole drifting. Those eigenvalues of the system model are indicating the frequency and damping oscillatory components when there is sudden changed at the inverter-grid connection. The matrices’ eigenvalues are being plotted using MATLAB/Simulink to identify system stability region and find the PI controller parameters.

Analytical design of constraint handling optimal two parameter internal model control for dead-time processes

This work presents an advanced and systematic approach to analytically design the optimal parameters of a two parameter second-order internal model control (IMC) filter that satisfies operational constraints on the output process, the manipulated variable as well as rate of change of the manipulated variable, for a first-order plus dead time (FOPDT) process. The IMC parameters are designed to minimize a control objective function composed of the weighted sum of the error between the process variable and the set point, and the rate of change of the manipulated variable, and to satisfy the desired constraints. The feasible region of the constrained IMC control parameters was graphically analyzed, as the process parameters and the constraints varied. The resulting constrained IMC control parameters were also used to find the corresponding industrial proportional-integral controller parameters of a Smith predictor structure.

Closed-loop active power control of wind farm based on frequency domain analysis

To further improve the wind power quality and stability, it is necessary to break the existing open-loop wind farm scheduling method and bring new strategies in both wind farm and wind turbine active power control. In this paper, a closed-loop active power control method is proposed based on the active power tracking dynamic characteristics of wind turbines, where the power fluctuations caused by the modeling errors and wind turbulence are isolated and suppressed by a proportional-integral (PI) controller. The PI controller parameters are obtained through the frequency domain analysis with bode diagrams. Then a frequency-domain aggregation and approximation method is proposed to establish the wind farm active power tracking model, and the wind farm-level controller is obtained similarly with the unit-level one but with an event-driven integral structure. Simulation results show the significant improvement and good robustness of the proposed controller.

Optimal Voltage and Frequency Control of an Islanded Microgrid Using Grasshopper Optimization Algorithm

Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

Optimal Tuning of SVC-PI Controller using Whale Optimization Algorithm for Angle Stability Improvement

This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.

Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance

<span lang="EN-US">This paper presents a stand-alone hybrid renewable energy system (SHRES) consisting of solar photovoltaic (PV), wind turbine (WT) and battery energy storage (BES) in an effort reduce the dependence on fossil fuels. The renewable energy sources have individual inverters and the PV inverter of the SHRES is operated using active and reactive power control. The PV inverter have two main control structures which are active power control and reactive power control and each contain a proportional integral (PI) controller. Accurate control of the PV inverter’s active power is essential for PV curtailment applications. Thus, this paper aims to enhance the performance of the SHRES in this work by optimizing the performance of the PV inverter’s active power PI controller parameters through the design of adaptive controllers. Therefore, an adaptive controller and an optimized adaptive controller are proposed in this paper. The performances of the proposed controllers are evaluated by minimizing the objective function which is the integral of the time weighted absolute error (ITAE) criterion and this performance is then compared with a controller that is tuned by the traditional trial and error method. Simulation results showed that the optimized adaptive controller is better as it recorded an error improvement of 42.59%. The dynamic optimized adaptive controller is more adept at handling the fast changes of the SHRES operation.</span>

A Hankel matrix reduced order SEPIC model for simplified voltage control optimization and MPPT

This study focuses on the realization of efficient solar power fed DC-DC Single Ended Primary Inductor Converter (SEPIC) using second order ACO (Ant Colony Optimisation) tuned PI (Proportional Integral) controller. Second order model of the DC-DC SEPIC converter is derived using Hankel Matrix method from the original fourth model to reduce the number of sensors used and to ease the controller design. PI controller parameters are derived from the second order model by using GA (Genetic Algorithm) based optimisation and ACO techniques. Statistical analysis shows that ACO yields better results when compared to GA. To harvest the maximum power from the Photo Voltaic (PV) panel, Incremental Conductance (IC) based Maximum Power Point Tracking (MPPT) method is chosen due to its high tracking accuracy. Extensive simulation studies have been carried out by using MATLAB/SIMULINK software tool. Simulation results for varying irradiation, temperature and load conditions have been presented in this paper. To substantiate the simulation results, an experimental prototype model of 120 Watts controlled by C2000 Piccolo TMS320F28035MCU digital controller is set up. Results reveal that the proposed cascade control technique can provide regulated output voltage and 80–99% of MPPT simultaneously using a single stage non-isolated DC-DC SEPIC Converter.

Computation of PI Controllers Ensuring Desired Gain and Phase Margins for Two-Area Load Frequency Control System with Communication Time Delays

This article deals with computation of all proportional-integral (PI) controllers which achieve the desired frequency-domain specifications namely, gain and phase margins (GPMs) of a traditional two-area load frequency control (LFC) system with constant communication delays. An efficient graphical method based on extracting the boundaries of stability regions in PI controller parameter space having user defined GPMs is proposed to obtain GPMs based stability regions. Regions are determined for a wide range of GPMs and time delay. The theoretical region boundaries are validated using a powerful numerical method known as the quasi-polynomial mapping-based root finder (QPmR) and time-domain simulations. Results indicate that the proposed scheme gives faster disturbance rejection and desired frequency response as compared to the recently developed scheme for LFC systems with communication delays.

Speed Control of PMSM using nature-inspired algorithms

A Proportional-Integral (PI) controller is a system designed to diminish the error between the desired reference value and the measured variable of the process to be regulated. The PI controller works according to two input parameters that must be calculated by a tuning process. In this work, the process is performed using intelligent swarm algorithms to seek for optimal parameters of PI controller. Three reactive, nature-inspired algorithms (bat, whale optimization and cuckoo search), were used to tune the PI controller. The control scheme based on linear regulators is used to improve the performance of the permanent magnet synchronous motor (PMSM) and to regulate the rotor speed to a desired reference value. In order to validate the effectiveness of the controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the PMSM.

A Grey Wolf Optimizer for Optimum Parameters of Multiple PI Controllers of a Grid-Connected PMSG Driven by Variable Speed Wind Turbine

This paper presents a novel application of a grey wolf optimizer (GWO) to improve the low voltage ride through (LVRT) capability and the maximum power point tracking (MPPT) of a grid-connected permanent-magnet synchronous generator driven directly by a variable-speed wind turbine (DD-PMSG-VSWT). The LVRT capability and MPPT enhancements are achieved by the optimal tuning of eight proportional-integral (PI) controllers in the cascaded control of the machine-side converter and the grid-side inverter, simultaneously. An online optimization is used and achieved by minimizing the integral-squared error of the error inputs of the PI controllers that are controlling dc link voltage, generated real power, and terminal voltages of the PMSG and the grid. The symmetrical and asymmetrical faults for testing the optimum gain parameters are simulated and examined using PSCAD/EMTDC. The obtained results of the optimum values of the GWO algorithm are compared with those attained using the optimum values of the genetic algorithm and the simplex method.

Optimal algorithms for the charge equalisation controller of series connected lithium‐ion battery cells in electric vehicle applications

A charge equalisation controller (CEC) was developed for continuously monitoring individual battery cells and equalising the charge or voltage levels of all cells in a series pack. A charge equalisation control algorithm was developed to equalise undercharged, overcharged, and unprotected cells through the use of a bidirectional fly-back converter. The equalisation involves charging and discharging by employing constant current-constant voltage and discontinuous current mode proportional-integral (PI) control techniques. Particle swarm optimisation is applied to optimising the PI controller parameters that generate the regulated pulse width modulation switching signal for the converter. A CEC model was applied to 90 lithium-ion battery cells (nominally 15.5 Ah and 3.7 V each) connected in series. The results showed that the developed CEC model performed well at equalising both undercharged and overcharged cells with ~92% efficiency and equalised every cell within the safe operation range of 3.73-3.87 V. The developed system realises excellent equalisation speed, a simple design and efficiency with low power loss. Thus, the CEC model has great potential for implementation in real-world electric vehicle energy storage systems.

Integrating adaptive control of renewable distributed Switched Reluctance Generation and feeder protection coordination

This paper proposes an adaptive Proportional Integral (PI) control scheme of interconnected wind turbine-based distributed Switched Reluctance Generation (SRG). The introduced control facilitates the integration of protective relaying coordination of the distribution feeder using Artificial Neural Network (ANN). It adapts the turn-off angle of the SRG by the ANN-PI controller to regulate the injected power (and accordingly limits the injected current) from the generation unit to the network to avoid incorrect overcurrent protection coordination during grid faults. The parameters of the PI control are tuned off-line using Genetic Algorithm (GA) in order to minimize the associated Integral of Square of Error (ISE) between the reference updated power (function of the SRG voltage) and the SRG power over a wide range of abnormal grid conditions. The values of the reference updated power, the voltage level at SRG, and the corresponding optimized PI control parameters by GA are used to off-line training ANN. This proposed ANN-PI controller turns the controller into on-line one. Therefore, the proposed adaptive ANN-PI techniques could, on-line, tune the PI controller parameters for realizing an optimal response and integrating the SRG with the protection coordination by adapting the turn-off angle of SRG. By evaluating the proposed control integration with the protection coordination of an 11kV Egyptian distribution feeder, the results provide evidences of efficient and robust performance of the proposed control for renewable distributed Switched Reluctance Generation either in steady state operation or during faults in distribution feeders.

A two-stage robust-intelligent controller design for efficient LFC based on Kharitonov theorem and fuzzy logic

This paper proposes an efficient load frequency control (LFC) approach based on robust and intelligent methods. Practically speaking, proportional-integral (PI) controller is widely deployed in LFC structure. Basically, the parameters of PI controller are adjusted based on trial-and-error or classic control methods. In such manners, robust performance of PI controller cannot be guaranteed in disturbances including load changes or parameter variations. In this research, at the first stage, the gain values of PI controller are tuned in an offline manner based on Kharitonov theorem which strengthens the validity of the controller against the variations in time constants of turbine and governor. As another aspect of uncertainty, power system loading demand is changed ceaselessly. To accommodate such conditions, at the second stage, the initial gain values based on Kharitonov theorem are adapted in an online manner based on fuzzy logic approach. The fuzzy controller, as an aspect of intelligence, adapts the proportional and integral gains through appropriate membership functions in an online fashion. Frequency deviation and its derivative are selected as efficient input signals for the fuzzy controller. Detailed numerical studies are conducted to assess performance of the proposed approach. Results demonstrate a reliable frequency performance against different uncertainties.