Conventional PI Controller Research Articles

Particle Swarm Optimization tuned Hybrid Sliding Mode Controller based static synchronous compensator with LCL filter for Power Quality improvement

One of the supreme striking areas in electrical system design is power quality analysis and its associated problems. All the electronic devices are exceptionally susceptible to a slight variation in the power supply. Among the Flexible Alternating Current Transmission System (FACTS) devices, the Static Synchronous Compensator (STATCOM) is the most effective device, since it has much faster dynamic response for the load changes. A PSO Optimized Hybrid Sliding Mode Controller based STATCOM with LCL Filter is used in the proposed work for enhancing power factor and decreasing the Total Harmonic Detection (THD) value. The Sliding Mode Controller (SMC) is used to operate this STATCOM, power quality was tuned using the PSO technique. The effectiveness of the conventional PI controller with SMC in regulating reactive power at the Point of Common Coupling (PCC) is compared with the proposed SMC-based controller. Applying soft computing techniques for efficient grid integration and cleaner energy production is the aim of this research. The overall design and analysis are done in MATLAB Simulink with nonlinear and unbalanced loads. With the proposed controller, the power factor is improved to 0.98 with the THD of 2.35 % for nonlinear inductive load. The proposed controller outperforms well with the 2.66 % overshoot and 5.61 ms settling time.

Battery and supercapacitor assisted controller for stability enhancement of DC microgrid

AbstractDespite the widespread popularity of DC microgrid (DCMG), one of the key issues faced by it is the instability arising from supply‐demand imbalance, which is conventionally tackled via energy storage units such as batteries. But the life cycle of the battery, which may reduce when subjected to transients, can be prolonged by integrating fast acting supercapacitor (SC) with higher power density. Such a combination of energy storage units enables the DCMG to handle steady state and transient conditions effectively. However, the reduction in the operating voltage of SC may bring in ringing in the DC bus voltage, which will affect DCMG stability. Hence, this paper focuses on analysing the impact of SC voltage deviation on DCMG stability and to propose a controller design procedure with conventional PI controller and power management strategy to mitigate the aforementioned consequences. The simulation in MATLAB and real‐time validation in OP4510 real‐time simulator has been conducted. The results of both revealed that the proposed scheme has succeeded in eliminating ringing of DC bus voltage, irrespective of the SC operating voltage variations, thereby maintaining DCMG stability.

Optimization and model-based control of sustainable ethyl-methyl carbonate and diethyl carbonate synthesis through reactive distillation

As excellent solvents of lithum-ion electrolyte, ethyl-methyl carbonate (EMC) and diethyl carbonate (DEC) are synthesized through transesterification of dimethly carbonate (DMC) and ethanol. Process intensification, optimization and the corresponding control scheme of the synthesized process are essential when sustainability is becoming a necessity. In the present work, the intensified flowsheet for EMC and DEC production process was firstly proposed and optimized with conventional reactive distillation (RD) and reactive dividing wall column (RDWC), respectively. Compared with RD process, RDWC could achieve 25.3% and 8.2% total annualized cost reduction for EMC and DEC production process, respectively. And carbon dioxide emission could be decreased by 29.4% and 11.7% by further integrating RD and separation column into RDWC arrangement for DEC and EMC production. Then multi-loop PI control schemes were developed as benchmark and linear model predictive controller was designed to handle the persistent feed disturbance. The superiorities of model-based controllers for the strongly interactive and coupled system are demonstrated by the dynamic response comparisons in terms of settling time, overshoot and integral of squared error (ISE), especially for the controlled variables with poor control performance under conventional multi-loop PI control structure. To summarize, the comprehensive control performance index-ISE with MPC is reduced by 55.9%–99.1%, as compared to those under PI control strategy.

RETRACTED: Cuckoo Search Optimization based PI Controller Tuning for Hopper Tank System

The paper work focuses on soft computing and Conventional controller tuning approach to design of PI controller, for a nonlinear hopper tank liquid level control system process Industries. The automation industries provide conventional techniques where it is impossible to maintain its settling time which motivates to do the research in this field. The system processes the combination of a conical and cylindrical tank for providing Multi-region based mathematical modelling to obtain the first order with delay time (FOPDT) process transfer function model. The Ziegler Nichols, Cohen-coon, Tyreus Luben, CHR (Chien, Hrones, and Reswick), IMC (Internal Model Control), Direct Synthesis, FOPI(Fractional Order PI) Conventional tuning formulae and Cuckoo Search Optimization (CSO) algorithm are used to optimize the servo regulatory responses of PI controller. The integral and proportional gain of the PI controller is said to produce the fastest settling time and reduces the error using performance indices and achieves Liquid Level control in hopper tank. Comparison is made for the various conventional controller tuning methods with Cuckoo Search Optimization tuning responses and identified to CSO-PI method offers enhanced Optimized Performance of settling time which was about 105.58 s which is relatively less while comparing to Conventional PI controller tuning methods for a different region based system.

Development of Neuroxellar Algorithms for Adaptive Pi Adjustable Speed Control Parameters in Mining

This article discusses the adaptive Pi regulator based on the basic function of a neural network (RBF) and it is used to control the velocity of a vector directional asynchronous motor. The structure of the control circuit consists of the RBF identifier of the standard Pi controller. The RBF Identifier is used to identify the Jacobian meaning of the asynchronous motor online. The "online" change of neural network parameters is performed by the gradient access method without prior training. Pi controller training is performed by using the "online" identification RBF model. It is advisable to test this controller under different conditions to finally make sure the reliability of the management technology we offer. Research has shown that the proposed controller provides good steadiness and stability of the steering system compared to what a conventional Pi controller provides.

Three‐vector model predictive power control for three‐level T‐type rectifier based on dead‐beat control

This paper presents a three-vector model predictive power control with neutral-point voltage balance (TVMPPC-NPVB) and a dead-beat control for three-phase three-level T-type rectifier. Three vectors are adopted to reduce the current ripple, balance the neutral-point voltage and achieve unity power factor on the AC side simultaneously, while the conventional model predictive power control suffers from large current ripple, difficulty in designing the weighting factor and computational burden. In the proposed strategy, two cost functions are derived to select the optimal space vector sequence and reduce the computational burden. The action time of each vector is introduced in detail. The traditional PWM rectifier utilizes PI controller to regulate the output voltage. But PI controller tends to have a long transition time and big variation on output voltage when the load changes suddenly. The proposed dead-beat control is capable of solving these problems caused by conventional PI controller. Both simulation and experimental results are shown to confirm the effectiveness of the proposed method.

Optimal Energy Management Scheme of Battery Supercapacitor-Based Bidirectional Converter for DC Microgrid Applications

Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot.

Interval type-2 fuzzy logic controller based grid-tied solar PV inverter with active and reactive power control

Solar photovoltaic industry is continuously thriving to improve the performance of power management systems with technology advancement. In the power system, balancing active and reactive power injection and absorption plays a pivotal role in maintaining Grid regulation and power factor and thus improving the efficiency and power handling capability. The Grid-tied solar PV inverters can be the cost-effective solution for reactive power compensation. Solar photovoltaic (SPV) system with the Grid-tied inverter is to produce active and reactive power in various ecological conditions. The Grid-tied PV inverter injects active and reactive power into the grid according to the desired value by the Grid requirements. The Proposed system uses decoupled P-Q theory with Interval type-2 Fuzzy Logic Controller (IT2-FLC). Also, the proposed controller minimizes the Total Harmonic Distortion (THD) and produces regulated output power compared to the other control techniques. The proposed IT2-FLC is compared with a Type-1 Fuzzy Logic Controller (T1-FLC) and conventional mathematical PI controller for performance analysis using simulation. A 75 kW Grid-tied inverter is designed in simulation to prove the effectiveness of the system through Matlab/Simulink. A smaller rating prototype model is developed to verify the simulation results.

IMPLEMENTATION OF FUZZY LOGIC CONTROLLER BASED DSTATCOM FOR POWER QUALITY ENHANCEMENT IN DISTRIBUTION SYSTEMS

This research work, presents Fuzzy Logic Controller (FLC) based D-STATCOM for power quality (PQ) enhancement in power distribution network. In the power distribution system, PQ is the major issue that is occurring due to non-linearity and dynamic changes in the connected loads. The proposed work utilizes FLC for generating switching Pulses for IGBT switches in the D-STATCOM to enhance quality of power in distribution systems. This research work also shows superior performance over conventional PI controllers in mitigation of harmonics by using proposed FLC topology. The proposed system is simulated with Matlab/Simulink software to ensure effective realization.

Performance assessment and validation of inverter control current controllers in reduced sensor maximum power point tracking based photovoltaic‐grid tied system

A relative assessment on conventional and adaptive current controllers used in reduced sensor-maximum power point tracking (MPPT) based photovoltaic (PV)-grid tied inverter systems for the improvement of system power quality is suggested. The steady-state and transients errors produced in the conventional PI and proportional resonant controllers, which are used to generate the references, can be fixed by using an intelligent ADALINE-LMS adaptive controller; moreover, it helps in reducing the %THD (total harmonic distortion) level measured at different power zones. Also, to track the maximum PV power, which is further integrated to DC-bus, a reduced sensor-based technology is added into the circuit that sidesteps the problem of tracking local MPP instead of global MPP and the drawbacks of using current sensors. The use of a reduced sensor-based MPPT controller confirms extraction of maximum PV power and it guarantees a constant DC-link voltage under all the possible test conditions. The overall control architectures and system performances, which are tested under different system dynamics, are validated through MATLAB/Simulink as well as experimental findings obtained using the dSPACE RTI 1202 interfacing kit. These experimental results confirm that the adaptive control technique used in reduced sensor-MPPT based PV-grid tied inverter systems performs unbeatably with balanced load and grid voltages, less harmonics, quick response time etc. under the operation of linear, non-linear and transient loads, whereas, conventional controllers are best only for the linear loads.

PIL Implementation of Feedback Linearisation-SVM Control of 3-Phase Multifunctional Grid-Tied Solar PV Integrated UPQC

In this paper, an implementation of a feedback linearisation-based on SVM control of a multifunctional unified power quality conditioner (UPQC) combined with a photovoltaic generator (PVG) using STM32F429I-DISC DSP board has been presented. Further, The PVG-UPQC is acting as a universal conditioner for power quality enhancement and renewable energy integration simultaneously with harmonics reduction in both grid voltage and current. Furthermore, the system can also compensate reactive power in presence of nonlinear loads. The presented system is made up of a shunt active power filter which acts as a current harmonics compensator in addition to a series filter that compensates voltage harmonics and fluctuations such as voltage sag/swell. In order to enhancing the PVG-UPQC performances, a nonlinear feedback linearisation control technique combined with SVM controller is applied. The performance of the suggested control scheme is validated by extensive PIL co-simulation technique. The obtained results prove the high performances and reliability of the presented system in the case of nonlinear load variations as well as eliminating voltage and current harmonics. These results accomplish the superiority and effectiveness of those obtained by a compared to a conventional PI controller.

Modeling and Comparison of Fuzzy-PI and Genetic Control Algorithms for Active and Reactive Power Flow between the Stator (DFIG) and the Grid

This paper performs a comparison between Fuzzy-PI regulators and Genetic Algorithm (GA) for controlling an active and reactive Doubly-Fed Induction Generator (DFIG) for providing power to the electrical grid. Theoretical analysis, modeling, and simulation studies are provided. Control strategies were developed for both active and reactive forces in order to optimize energy production. The performance of the two control strategies was examined and compared using benchmarks for durability and reference traceability. This paper studied a system consisting of a wind turbine operating at variable wind speed and a two-feed asynchronous machine (DFIG) connected to the grid by the stator and fed by a transducer at the side of the rotor. The conductors were separately controlled for active and reactive power flow between the stator (DFIG) and the grid, which was achieved in this article using conventional PI and fuzzy logic controllers. The considered controllers generated reference voltages for the rotor to ensure that the active and reactive power reached the required reference values. This was done in order to ensure effective tracking of the optimum operating point and the maximum output of electrical power. System modeling and simulation were examined in Matlab/Simulink. Dynamic analysis of the system was performed under variable wind speed.

Direct Torque Control With Variable Flux for an SRM Based on Hybrid Optimization Algorithm

This article presents an optimal direct torque control (DTC) strategy with variable flux for a switched reluctance motor using the improved linear active disturbance rejection control (LADRC) plus the hybrid optimization algorithm (HOA). First, the constant flux amplitude is substituted by the variable flux DTC (VF-DTC) to reduce the torque ripple. Then, the LADRC with the improved extended state observer applied in speed controller is utilized instead of the conventional PI control to improve the speed of the observer, antidisturbance ability, and robustness. Moreover, the HOA is employed to search for the optimal control parameters and acquire satisfactory dynamic performances. Finally, the optimal VF-DTC system is implemented on a 12/8 SRM. Simulation and experimental results are carried out to compare the performances of the conventional DTC, the VF-DTC with LADRC, the VF-DTC with PI using HOA, and the proposed optimal VF-DTC using HOA. The results show that the proposed control method has a faster speed response, superior antidisturbance ability, and lower torque ripples.

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

Control of Photovoltaic Plants Interconnected via VSC to Improve Power Oscillations in a Power System

This paper presents an integrated methodology applied to photovoltaic (PV) plants for improving the dynamic performance of electric power systems. The proposed methodology is based on primary frequency control, which adds an ancillary signal to the voltage reference of the DC-link for the voltage source converter (VSC) in order to reduce power oscillations. This ancillary signal is computed by relating the energy stored in the VSC of the DC-link and the energy stored in the synchronous machine’s shaft. In addition, the methodology considers the operating limits of the VSC, which prioritizes active power over reactive power. Furthermore, the VSC control is assessed with interconnection and damping assignment passivity-based control (IDA-PBC), as well as compared to conventional PI control. IDA-PBC is employed to design a Lyapunov asymptotically stable controller using the Hamiltonian structural properties of the open-loop model of the VSC. A 12-bus test system that considers PV plants is employed to compare the proposed IDA-PBC control with a classical proportional-integral control approach. The impact of the proposed methodology is analyzed in four scenarios with different PV penetration levels (10%, 30%, 50%, and 80%) and four large disturbances in the test power system. In addition, a decrease in the inertia of the synchronous machines from 100 to 25% is analyzed. The time-domain simulation results show that the frequency oscillations are reduced by 16.8%, 38.43%, 37.53%, and 76.94% in comparison with the case where the proposed methodology was not implemented. The simulations were conducted using the SimPowerSystems toolbox of the MATLAB/Simulink software.

Robust soft computing control algorithm for sustainable enhancement of renewable energy sources based microgrid: A hybrid Garra rufa fish optimization – Isolation forest approach

A novel hybrid Garra rufa Fish optimization (GRFO) – isolation Forest (iForest) soft computing approach is proposed in this paper for optimizing the controller parameters in an isolated test microgrid. The test microgrid comprises of two PV units and one wind generator synchronized via Voltage Source Converters (VSC) in the ac side. Each VSC is regulated based on power generation in individual micro sources. The operation of VSC is the key factor maintaining the stability of the micro grid. Each VSC is controlled by outer power loop and inner current regulation loop employing PI controllers. The performance of the VSC is enhanced using the proposed hybrid GRFO – iForest algorithm for tuning the PI controller gains. The performance of the proposed approach is then compared with conventional tuning PI controller and optimization techniques like Ant Lion Optimization (ALO) and Modified Ant Lion optimization based Artificial Neural Network (MALANN). The gain parameter of the proposed controller is optimally tuned and the controller provides reliable sustainable microgrid system operation. The proposed approach is simulated in MATLAB/Simulink and validated using OP-4500 Real Time Simulator environment. Based on the results, the performance of the GRFO-iForest approach is superior during steady-state and transient operation. It enhances the sustainability of the microgrid by restoring normal operating conditions after a small physical disturbance and the microgrid remains stable with the optimized regulator. The stability of the overall system is proved with the mathematical model.

Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System

The wind speed information measured by the wind speed sensor in the wind turbine generator may differ from sufficient wind speed. Therefore, this paper proposes a new effective wind speed calculation method and applies it to the optimal maximum power point tracking (MPPT) of wind energy. After estimating the turbine torque and its rotor speed, the process reverses the turbine’s aerodynamic model. The extended state observer (ESO) based on sliding mode control is used to estimate the aerodynamic torque, which solves complex and challenging tuning of the traditional ESO parameters, and replaces the conventional PI controller, thereby improving the anti-interference and robustness of the system. The sliding mode observer (SMO) is used to estimate the rotor speed. While satisfying the conditions of Lyapunov’s inequality, the design of the SMO is discussed in detail. The wind speed estimation method proposed for evaluating permanent magnet semidirect drive wind turbine and its application performance in maximum power tracking. The simulation was carried out in MATLAB/Simulink; the simulation confirmed that the estimated wind speed under different wind conditions is accurate. Compared with the traditional PI control, the utilization rate of wind energy is increased by 2%, which can be used for the MPPT control of the wind energy conversion system.

Voltage Sag Mitigation with DVR optimized in DFIG based Wind Energy System

One of the most serious power quality problems in electrical networks is voltage drops. The DVR is known as the most common special power devices used in solving these power quality problems. In this study, optimization is made with WCA algorithm in DVR’S control circuit under wind energy system for fast and reliable detection of mains voltage disruptions, voltage drops and voltage spikes, Its performances were evaluated according to conventional PI controller. These control methods can instantly extract the amplitude and phase information of the mains voltage to detect voltage changes quickly and accurately and with the help of this information, The voltage that needs to be injected into the connection point can be obtained. In the simulation study, the tensions produced by DVR in DFIG based wind energy system and injected into the system were also presented using these two different control methods. The superiority of WCA-based PI controller is clearly seen in the results.

A Novel Adaptive Model Predictive Control for Proton Exchange Membrane Fuel Cell in DC Microgrids

In this paper, a novel adaptive model predictive control (AMPC) algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to improve its output power tracking performance in DC microgrids. The proposed AMPC algorithm is capable of producing superior PEMFC control performance over the conventional MPC and PI controllers. Compared with PI control, AMPC is able to handle physical constraints, which ensures the safe operation of PEMFC under different conditions. Furthermore, it overcomes the common problem of system model dependence that is shared by nearly all the model-based control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm under various operating conditions and system parameter variations are verified by simulation results.

Robust multi-variable control of continuous yeast drying

This work considers robust moisture content and activity control in a continuous convective drying for particulate bio-materials. The major process uncertainty is in the kinetics of the second (falling-rate) drying period. Robust feedback controllers in the multi-variable and de-central case are designed to guarantee desired product specifications. The advantages of the robust controllers are discussed in comparison with a conventional PI controller. The case study presented is the drying of baker’s yeast in a fluidized bed dryer. The results show constant performances concerning the uncertainty of the drying kinetics and disturbance rejection at different operating points.