Conventional PI Controller Research Articles

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

Auto tuning of PI Gains using Cuttlefish Optimization for DC Link Voltage Control in a 5-level HB MMC D-STATCOM

The performance of a Half Bridge (HB) Modular Multilevel Converter (MMC) for D-STATCOM application is studied in this article. For the control of MMC switches, the Carrier-Based (CB) Pulse Width Modulation (PWM) scheme has been selected as the appropriate method. There are two types of CB PWM schemes such as phase shift and level shift. Phase Disposition (PD), Phase Opposition Disposition (POD), and Alternate Phase Opposite Disposition (APOD) are the three different types of PWM that are used in level shifts. The PD CB PWM is projected in this work due to its simplicity and accuracy. The conventional PI Controller (PIC) has its disadvantages, including that it is time-consuming and comes with complexity in the estimation of gain values. Hence, the Cuttlefish Optimization Scheme (COS) is proposed for the online tuning of the PI gain values. The verification of performance parameters, including active power, reactive power, DC voltage balance, and Total Harmonic Distortion (THD), is effectively conducted using the COS method and subsequently compared with the conventional PIC. The suggested research is executed using the Matlab/Simulink software package.

Fuzzy Logic Energy Management Based on Wind Turbine for Standalone Application

Wind turbines (WT) offer valuable environmentally friendly energy solutions, particularly for electricity generation, owing to their renewable nature. These renewable energy systems (RESs) present an indispensable solution for sites that are not associated with the electrical grid. However, their intermittent power generation, which can be more or less than that required by the load, due to variable wind patterns, poses a challenge. To overcome these weaknesses, the integration of robustly controlled storage systems is a good proposal as it enhances the stability of standalone applications in terms of voltages and powers. In this paper, an intelligent supervisory power control system is developed to manage the different components of the renewable energy generator (REG). A fuzzy logic proportional–integral PI controller is also introduced, in the permanent magnet synchronous generator (PMSG) and the batteries, to better regulate energy, compared to a conventional PI controller. The main objective of this study is to introduce a new control strategy to satisfy the energy requirement of the load. The configuration of the suggested control methodology consists of a fuzzy logic supervisor (FLS) elaborated to manage energy flows, ensuring equilibrium between production and consumption amidst weather and load changes. The presented control method was applied to an autonomous wind battery station, showcasing its ability to maintain energy supply, minimize fluctuations, and enhance stability. Simulation results, executed in MATLAB/Simulink, confirm the effectiveness of the proposed system under various scenarios of wind speed variability and storage system state of charge.

Optimization-based power management for battery/supercapacitor hybrid energy storage system with load estimation capability in a DC microgrid

This paper proposes a novel optimization-based power management strategy (PMS) for a battery/supercapacitor hybrid energy storage system (HESS) with a semi-active structure in a DC microgrid application. As the DC bus voltage regulation is the main purpose of the HESS, the supercapacitor control due to direct connection to the DC bus becomes more important. While, in the semi-active structure, there is no control over the supercapacitor. The proposed PMS solved this challenge by considering the supercapacitor current as a control target in determining the reference current of the battery. Extending the battery life span by drawing smooth current from the battery and responding the supercapacitor to load current changes, and charging the battery with a constant current as a new objective function, are the other optimization targets. This multi-objective problem is solved by the Karush–Kuhn–Tucker (KKT) condition which makes the proposed PMS suitable for real-time applications. In addition, in contrast to previous related studies, (i) the load current (grid excess/deficiency power) as one of the important inputs of PMS is estimated by the adaptive Kalman filter (AKF), and (ii) the converter control signal is determined by system average model instead of conventional PI controller. Finally, using simulation, the performance of the proposed PMS has been verified by comparing the results with fuzzy logic, droop control, and adaptive filter-based methods. As well, the presented control strategy has been implemented on an experimental setup, and its performance is investigated.

A novel ANFIS-controlled customized UPQC device for power quality enhancement

Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons.

Switched Reluctance Motor Speed and Torque Control using ACO and PSO Algorithms

A Switched Reluctance Machine (SRM) is an electric motor that operates based on the principle of magnetic reluctance. In addition, SRMs have several advantages over other types of electric motors. However, SRMs also have some disadvantages. They can produce high noise and vibration levels, especially at high speeds. This paper describes the torque and speed controller for SRM using two techniques, Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) combined with a PI corrector. The conventional PI controller is nowadays used in most engineering, being acknowledged for their ability to give up superior control in power electronic systems. Moreover, finding appropriate values for the PI controller is not easy. A solution based on ACO and PSO is used to overcome this problem and simplify tuning the PI controller parameters. Simulation in MATLAB-Simulink was used to demonstrate the effectiveness of the suggested controllers.

A Modified Total Energy Control Scheme for Unmanned Aircraft

The paper focuses on safety and simplicity of unmanned aircraft longitudinal control and proposes a new combination of total energy control (TECS) and conventional control. The introduced new modified total energy control (TECSMOD) method applies IAS priority all the time. IAS is controlled through the elevator while the total energy of the system and so the altitude is maintained with throttle. Neither engine fault or stall detection nor switching logic is required while stall is prevented keeping the solution simple and safe. To prove the viability of the new concept it is compared to a conventional multiple zone PI controller and the TECS solution in simulation and real flight tests. First, the six degrees of freedom simulation model of the Sindy test UAV (developed and built in Institute for Computer Science and Control, HUN-REN, Hungary) is verified comparing its inputs and outputs to flight results. Then a simulation campaign is done for all three controllers with special test cases which can be critical according to the literature. Finally, real flight test comparison is done considering IAS and altitude tracking and engine fault handling. The new method was the best in IAS tracking with acceptable results in altitude tracking and successful stall prevention upon engine fault (without any fault detection or switching). Future improvements can be fine tuning for improved altitude tracking with the price of decreased IAS tracking performance and the introduction of a glideslope tracking mode for landing scenarios.

Research on control strategy of flywheel energy storage system based on active disturbance rejection control

The flywheel energy storage system (FESS) has been attracting the attention of national and international academicians gradually with its benefits such as high energy power density, high conversion productivity, and inexpensive pollution. For the mutual limitation problem of reaction speed and overshoot of the conventional PI controller, it is hard to satisfy the demand of high efficiency control. In this study, the Active Disturbance Rejection Controller (ADRC) is adopted to substitute the classical PI controller in the flywheel energy storage control system. The control system of an external loop of speed and an internal loop of current is adopted at the motor side. The standard ADRC is adopted by increasing the new nonlinear control function. The control system adopted the control strategy of the DC bus voltage external loop and the current internal loop on the grid side, as well as the second-order Linear Active Disturbance Rejection Controller (LADRC), to enhance the control ability of the DC bus voltage. In the charge/discharge control of the FESS, both the velocity external loop and current internal loop control strategies are utilized to simplify the system structure. The performance shows that the improved control system could effectively enhance the charging and discharging speed of the FESS and effectively suppress the DC bus voltage rise caused by flywheel state switching so that the system has better robustness.

A Study on H∞-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty

In this study, an H∞-fuzzy controller is proposed for application in wind turbines with uncertainties and nonlinearities. The performance of the proposed controller was validated via dynamic simulations using a commercial aero-elastic code and wind tunnel experiments employing a scaled wind turbine. The simulation and the experimental results were then compared with those of the conventional PI and LQR control algorithms presented in our previous study. In the simulation, the perturbation and the sensor noise were applied to reflect uncertainty and nonlinearity effects. In addition, in the wind tunnel experiment, a control system using a commercial Bachmann PLC was established with an accelerometer to estimate the fatigue load exerted by the rotor thrust. It was confirmed through experiments that the robustness and adaptation of the control system improved in the situation of pitch system failure. As a result of the experiment, the proposed H∞ controller was able to reduce the rotor speed fluctuation by 39.9%, the power fluctuation by 32.0%, and the fatigue load by 2.4% compared with the LQR fuzzy controller, which had better performance than the conventional PI controller. In addition, it was confirmed through experiments that the robustness and adaptation of the control system were well maintained. This was even true in the situation of one-blade pitch system failure.

A hybrid DMO-RERNN based UPFC controller for transient stability analysis in grid connected wind-diesel-PV hybrid system

AbstractIn this paper, a novel hybrid technique is proposed for transient stability analysis on grid connected Wind-Diesel-PV hybrid system. The proposed hybrid methodology is combination of the dwarf mongoose optimization algorithm (DMO) and the recalling enhanced recurrent neural network (RERNN) named DMO-RERNN. The main purpose of this work is to consider various elements on hybrid system for the analysis of transient stability according to different conditions. The voltage profile of hybrid system is enhanced using the proposed unified power flow controller (UPFC), which also has higher performance improving transient performance compared to the conventional ANN, PI and fuzzy-sliding mode controller. Considering the proposed technique, DMO is used to find the optimal global solution for the fault predicted by the RERNN approach. The proposed system is executed on MATLAB work platform; its performance with existing systems is analyzed. The result proves that the proposed hybrid technique based UPFC controller provides better results compared with other existing technique. The efficiency of the PI is 82.136, ANN is 77, Fuzzy Sliding Mode is 65.097% and proposed technique is 97.99038%.

A novel optimally tuned super twisting sliding mode controller for active and reactive power control in grid‐interfaced photovoltaic system

AbstractIn photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510.

Power Quality Improvement of a Hybrid Renewable Energy Sources Based Standalone System Using Neuro-Fuzzy Controllers

Wind and Photovoltaic (PV) based hybrid standalone systems for supplying electricity to remote consumers are becoming increasingly popular around the world. In general, power generation from both PV and wind is always variable due to irradiance and speed fluctuations. Furthermore, the load is unpredictably variable and changes at random. As a result, one of the main challenges in such standalone micro grids is maintaining power quality. To maintain power quality in such standalone systems, a novel controller should be developed. Furthermore, due to the unpredictable random changes in wind speed, solar irradiance, and load, a battery bank must be integrated into the standalone system via a bidirectional converter. Furthermore, the interconnection of multiple wind turbines and PV panels can create a powerful hybrid system. This paper examines a 1 MW stand-alone power generation system using numerous PV and wind systems. Maximum power points tracking devices with perturbed and observe algorithms are used to optimize the utilization of individual PV modules and wind turbines. Because of its superior priority over conventional PI controllers, a Neuro-Fuzzy integrated controller-based novel control scheme for both inverter and bidirectional converter is proposed to achieve precise response while maintaining power quality. This paper discusses extensive results obtained by establishing hardware-in-the-loop on the OPAL-RT platform to evaluate the responses of proposed controllers. The experimental results also included to provide strength to the paper.

Comparison of Intelligent Control Methods Performance in the UPFC Controllers Design for Power Flow Reference Tracking

The Unified Power Flow Controller (UPFC) is one of such emerging FACTS devices that can manage the power flow in Transportation System. Based on the control system associated with the management of UPFC, both the controllers of shunt and series converters of UPFC are controlled conventionally using PIs controllers. However, the PI system alone cannot control the UPFC in the presence of uncertainty of system parameters and disturbances. For this reason, the use of novel robust techniques based on artificial intelligence theory in the design control of UPFC is very efficient for improving its performances. In this regard, four approaches have been used in this study to replace the conventional PI controller and enhance the performance of the UPFC for power flow reference tracking. In the first approach, the fuzzy logic technique (Mamdani and Sugeno types) has been employed to provide dynamic control of series and shunt converters of UPFC, whereas in the second method, the controllers of UPFC are performed through the optimization techniques such as: PSO, PSO_SQP, and GA techniques. The proposed techniques based UPFC are tested in MATLAB/Simulink software by using UPFC with a 48-pulse voltage source converter (VSC) equipping a High Voltage (HV) electrical network. In terms of dynamic performance, the simulations’ results and the comparative analysis clearly show the superiority of the intelligent controllers regardless of the variation of powers that affecting the model plant. Compared to PI controller, they ensure good performance in terms of tracking and decoupling.

Artificial Neural Network controller for Solar PV based Electric Vehicle Charging Station with Supercapacitor

In this article, a hybrid energy system for a solar-powered electric vehicle charging station is proposed. A hybrid energy storage system is made up of a battery and a supercapacitor. If the solar power varies, a fast shift in supply from the storage system is required. Furthermore, power fluctuation is observed when power is drawn from the storage system. A standalone battery cannot adjust for this quick shift in supply. To address these issues, a hybrid energy storage system consisting of a supercapacitor and a battery is proposed for solar-powered electric vehicle charging stations. Power fluctuation is also compensated for using the hybrid energy system. The findings are obtained using a traditional PI controller and an ANN controller. The result taken with ANN controller are found better in comparison to results taken with the conventional PI controller. The results are obtained using MATLAB SIMULINKS.

Artificial Intelligence Approach for the Control of SPMSG Based Grid Connected WECS

In India, power plant requirement is rapidly increasing as non-conventional sources are in current need. Government financial aid plays important role for generator plant execution because of its tedious procedure. If the utilization of wind power production has to be doubled, then machines, turbines and converters are essential in two numbers. The research work includes parallel connection of two machines using star-delta-star transformer fashion resulting in six-phase permanent synchronous machine. This arrangement is more economical as the number of converters are reduced thereby increasing the efficiency by minimizing the switching losses. Initially rectifier setup is used wherein high frequency AC is converted to low frequency and maximum power point tracking is done with the aid of a converter. It is DC-DC conversion by decoupled DQ strategy. Later, it is again converted back to three phase AC so as to synchronize it to the grid. The system response is analysed by conventional PI controller by reducing the noise in the real & reactive power generation. Further, ANFIS-PI controller is implemented to improve the response of PI controller which is much essential for wind power generation. The MATLAB simulated results of PI controller system response and ANFIS-PI controller is presented in this paper.

Improvement DTC for Induction Motor Drives Using Modern Speed Controllers Tuning by PSO Algorithm

The work carried out in this paper proposes an improvement of the direct torque control (DTC) of induction machine by the design of modern, robust and more efficient controllers than the conventional PI controllers commonly used for speed control. A comparative study has been carried out using five controllers, i.e. PI anti-windup, first-order sliding mode control (SMC), second order sliding mode control (SOSMC), fuzzy logic controller (Fuzzy-PI) and hybrid Fuzzy second-order sliding mode controller (FSOSMC). An advanced optimization technique based on the particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers, the use of PSO for the determination of the different gains used in all controllers gives on the one hand a high accuracy performance and ensures on the other hand a reliable comparison between the different controllers in their optimal states. The simulation and analysis of each method with respect to robustness to disturbances was performed externally under various operating conditions and variations of the machine parameters.

Model Predictive PI Circulating Current Control for Modular Multilevel Converter

Significant circulating currents in the modular multilevel converter (MMC) increase system losses and complicate heat-sink design. Conventional PI and PR controllers can achieve steady-state error adjustment, but are sensitive to parameter changes and model uncertainty, heavily relying on coordinate transformations and careful design of model parameters. Model predictive control (MPC) has the characteristics of simple design, good robustness, and excellent dynamic response; however, it encountered the complexity of adjusting weighting factors. This paper proposed circulating the current model predictive proportional integral control (MPPIC) method in abc reference frame. This hybrid control solution utilized the predictive model and traditional PI algorithm to combine the advantages of nonlinear and linear control. Compared with existing suppression methods, this method avoided complex mathematical operations and a selection of weight coefficients, was easy to implement, and can effectively suppress circulating currents under different modulation ratios. Simulations were conducted on MATLAB/Simulink to verify the effectiveness of the proposed control strategy. MPPIC can not only distinctly suppress the circulating currents, but also reduce the overall voltage fluctuation of sub-modules capacitors under different modulation ratios, and had almost no any adverse effect on the performance of MMC.

PR controller-based droop control strategy for AC microgrid using Ant Lion Optimization technique

Microgrids are now an integral part of the modern power system to maintain the stability of the power distribution process. Ensuring the frequency and voltage stability in the autonomous mode is quite a challenging task indeed. These have now reached the extent of utilizing evolutionary intelligence techniques leading to the concept of intelligent microgrids. In this work, a novel droop control strategy is implemented with a proportional resonant controller, whose proportional gain is optimized by the Ant Lion Optimization algorithm (A.L.O). The efficacy of the A.L.O algorithm for its application in the primary level droop control for voltage stability is investigated in this work. The superiority of the A.L.O optimized PR controller over the conventional PI controller is compared in terms of error indices, timed domain specifications, speed of response, voltage regulation and T.H.D levels. It could ensure a higher speed of response, better voltage stability and higher power quality as against its PI counterpart. The performance of the proposed A.L.O optimized PR controller is validated by comparing it with the performance achieved by controllers tuned with two other algorithms namely, particle swarm optimization (P.S.O) and Satin bower bird optimization (S.B.O). The proposed controller could ensure better steady-state stability compared to the other two.

A Fuzzy Logic Controller Based Vector Control of IPMSM Drives

This paper explores a fuzzy-logic based speed controller of an interior permanent magnet synchronous motor (IPMSM) drive based on vector control. PI controllers were mostly used in a speed control loop based field oriented control of an IPMSM. The fundamentals of fuzzy logic algorithms as related to drive control applications are illustrated. A complete comparison between two tuning algorithms of the classical PI controller and the fuzzy PI controller is explained. A simplified fuzzy logic controller (FLC) for the IPMSM drive has been found to maintain high performance standards with a much simpler and less computation implementation. The Matlab simulink results have been given for different mechanical operating conditions. The simulated results confirmed that the FLC-PI has a lower ripple than the conventional PI controller