Showing papers on "PID controller published in 2017"

Potential-induced degradation in photovoltaic modules: a critical review

Abstract: Potential-induced degradation (PID) has received considerable attention in recent years due to its detrimental impact on photovoltaic (PV) module performance under field conditions. Both crystalline silicon (c-Si) and thin-film PV modules are susceptible to PID. While extensive studies have already been conducted in this area, the understanding of the PID phenomena is still incomplete and it remains a major problem in the PV industry. Herein, a critical review of the available literature is given to serve as a one-stop source for understanding the current status of PID research. This paper also aims to provide an overview of future research paths to address PID-related issues. This paper consists of three parts. In the first part, the modelling of leakage current paths in the module package is discussed. The PID mechanisms in both c-Si and thin-film PV modules are also comprehensively reviewed. The second part summarizes various test methods to evaluate PV modules for PID. The last part focuses on studies related to PID in the omnipresent p-type c-Si PV modules. The dependence of temperature, humidity and voltage on the progression of PID is examined. Preventive measures against PID at the cell, module and system levels are illustrated. Moreover, PID recovery in standard p-type c-Si PV modules is also studied. Most of the findings from p-type c-Si PV modules are also applicable to other PV module technologies.

Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimiser

Abstract: In this study, an efficient load frequency controller (LFC) for standalone two-area hybrid microgrid system (HμGS) is addressed. Social-spider optimiser (SSO) is applied to fine tune the proposed proportional–integral–derivative (PID) controllers by generating their optimal settings. The integral time multiplied summation of absolute deviations and the gains of PID controllers define the fitness function and control variables, respectively. The performance of the proposed SSO-based method is demonstrated on an isolated HμGS complete with variety of energy storage systems under number of scenarios. The scenarios include load fluctuations, variations in wind speed, and sun irradiance employing real site measurements. In this study, photovoltaic generating arrays and wind turbine generators are not participating in system frequency regulations due the proposal of their maximum power tracking operation strategy. The signatures of time-domain dynamic responses and cropped numerical results ascertain that the proposed SSO-based LFC scheme is promising in diminuting the signal deviations and in short time. Further validations for cropped results produced by SSO are made compared with powerful optimisation tool such as genetic algorithm which signify the tuned PID gains.

PID controller design for second order nonlinear uncertain systems

Abstract: Although the classical PID (proportional-integral-derivative) controller is most widely and successfully used in engineering systems which are typically nonlinear with various uncertainties, almost all the existing investigations on PID controller focus on linear systems. The aim of this paper is to present a theory on PID controller for nonlinear uncertain systems, by giving a simple and analytic design method for the PID parameters together with a mathematic proof for the global stability and asymptotic regulation of the closed-loop control systems. To be specific, we will construct a 3-dimensional manifold within which the three PID parameters can be chosen arbitrarily to globally stabilize a wide class of second order nonlinear uncertain dynamical systems, as long as some knowledge on the upper bound of the derivatives of the nonlinear uncertain function is available. We will also try to make the feedback gains as small as possible by investigating the necessity of the manifold from which the PID parameters are chosen, and to establish some necessary and sufficient conditions for global stabilization of several special classes of nonlinear uncertain systems.

BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems

Abstract: In the fast developing world of today, automatic generation control (AGC) plays an incredibly significant role in offering inevident demand of good quality power supply in power system. To deliver a quality power, AGC system requires an efficient and intelligent control algorithm. Hence, in this paper, a novel fractional order fuzzy proportional-integral-derivative (FOFPID) controller is proposed for AGC of electric power generating systems. The proposed controller is tested for the first time on three structures of multi-area multi-source AGC system. The gains and fractional order parameters such as order of integrator (λ) and differentiator (µ and γ) of FOFPID controller are optimized using bacterial foraging optimization algorithm (BFOA). Initially, the proposed controller is implemented on a traditional two-area multi-source hydrothermal power system and its effectiveness is established by comparing the results with FOPID, fuzzy PID (FPID) and PI/PID controller based on recently published optimization techniques like hybrid firefly algorithm-pattern search (hFA-PS) and grey wolf optimization (GWO) algorithm. The approach is further extended to restructured multi-source hydrothermal and thermal gas systems. It is observed that the dynamic performance of the proposed BFOA optimized FOFPID controller is superior to BFOA optimized FPID/FOPID/PID and differential evolution (DE)/genetic algorithm (GA) optimized PID controllers. It is also detected that the dynamic responses obtained under different power transactions with/without appropriate generation rate constraint, time delay and governor dead-zone effectively satisfy the AGC requirement in deregulated environment. Moreover, robustness of the suggested approach is verified against wide variations in the nominal initial loading, system parameters, distribution company participation matrix structure and size and position of uncontracted power demand.

Improved droop control based on virtual impedance and virtual power source in low-voltage microgrid

Abstract: Droop control is a common method in the universal microgrid applications. Conventional droop control is unpractical for low-voltage microgrid, where the line impedance among distributed generation units (DGs) is mainly resistive to generate the active and reactive power of DG is coupled. Besides, accurate reactive power sharing is not achieved due to the voltage deviation of DGs caused by disparate line impedance in microgrid. An improved droop control based on the virtual power source (VPS) and composite virtual impedance, which is constituted by a negative resistance and a negative inductance, is proposed for low-voltage microgrid. The virtual negative resistance counteracts the line resistance to decouple power. The reactive power sharing accuracy based on the virtual negative inductance and VPSs control increases through analysing the line and virtual voltage drop. Finally, the fractional-order PID controller is adopted to control the inverter voltage. The optimal controller parameters are obtained by the differential evolution algorithm. To verify the viability and availability of improved control strategy, simulations are carried out on MATLAB/Simulink.

Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study

Abstract: In Photovoltaic (PV) systems, maximum power point tracking (MPPT) is an indispensable task. To date, various MPPT techniques have been proposed in the literature using classical and artificial intelligence methods. However, those techniques are tested on different PV systems and under different environmental conditions. In this work, we attempt to summarize and to give a comprehensive comparative study of the most adopted Artificial Intelligence (AI)-based MPPT techniques. The MPPT techniques which will be described are based on: Proportional-Integral-Derivative (PID), Fuzzy Logic (FL), Artificial Neural Network (ANN), Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The developed MPPT controllers are tested under the same weather profile in the same photovoltaic system which is composed of a PV module, a DC-DC Buck-Boost converter and a DC load. Initially, Modelling and simulation of the system is performed using the MATLAB/Simulink environment. Thereafter, the sliding mode control is applied to the converter in order to improve its performance. In a further stage, the different steps of development for each MPPT technique are presented. Simulation is performed to confirm the validity of the proposed controllers under the same variable temperature and solar irradiance conditions. Finally, a comparative study is carried out in order to evaluate the developed techniques regarding two principal criteria: the performance and the implementation cost. The performance is evaluated using comparative analysis of the tracking speed, the average tracking error, the variance and the efficiency. To estimate the implementation cost, a classification is carried out according to the type of the used sensors, the type of circuitry and the software level complexity. Recommendations that expected to be useful for researchers in the MPPT area about the validity of each MPPT technique are given in the last section.

Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)

Abstract: This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

Robust Adaptive Fault-Tolerant PID Control of MIMO Nonlinear Systems With Unknown Control Direction

Abstract: This paper shows that the structurally simple and computationally inexpensive PID control, popular with single-input single-output (SISO) linear time-invariant systems, can be generalized and extended to control nonlinear multi-input multi-output (MIMO) systems with nonparametric uncertainties and actuation failures. By utilizing the Nussbaum-type function and the matrix decomposition technique, nonsquare systems with unknown control direction are also considered. Furthermore, with the proposed analytic algorithms for adaptively tuning PID gains, the resultant PID control can be made robust, adaptive, and fault-tolerant, and applicable to nonlinear systems with nonvanishing uncertainties and unexpected actuation faults. Both theoretical analysis and numerical simulation verify the effectiveness and benefits of the design.

A Cooperative Operation of Novel PV Inverter Control Scheme and Storage Energy Management System Based on ANFIS for Voltage Regulation of Grid-Tied PV System

Abstract: In this paper, the voltage regulation problem in low-voltage power distribution networks integrated with increased amount of solar photovoltaics (PV) has been addressed. This paper proposes and evaluates the cooperative performance of a novel proportional-integral-derivative (PID) control scheme for PV interfacing inverter based on intelligent adaptive neuro-fuzzy inference system (ANFIS) and an ANFIS-based supervisory storage energy management system (EMS) for regulating the voltage of three-phase grid-connected solar PV system under any nonlinear and fluctuating operating conditions. The proposed ANFIS-based PID control scheme (ANFISPID) dynamically controls the PV inverter to inject/ absorb appropriate reactive power to regulate the voltage at point of common coupling (PCC) and provides robust response at any system worst case scenarios and grid faults. And the proposed ANFIS-based supervisory EMS controls the charge/discharge of the energy storage system when there is voltage deviation to cooperate with ANFISPID in PCC voltage regulation. The proposed ANFISPID-based PV inverter control scheme and ANFIS-based supervisory EMS are developed and simulated in MATLAB/ Simulink environment and their dynamic cooperative performances are compared with cooperative performances of conventional PID-based PV inverter control scheme and state-based EMS.

Enhanced IMC based PID controller design for non-minimum phase (NMP) integrating processes with time delays.

Abstract: Internal model control (IMC) with optimal H2 minimization framework is proposed in this paper for design of proportional-integral-derivative (PID) controllers. The controller design is addressed for integrating and double integrating time delay processes with right half plane (RHP) zeros. Blaschke product is used to derive the optimal controller. There is a single adjustable closed loop tuning parameter for controller design. Systematic guidelines are provided for selection of this tuning parameter based on maximum sensitivity. Simulation studies have been carried out on various integrating time delay processes to show the advantages of the proposed method. The proposed controller provides enhanced closed loop performances when compared to recently reported methods in the literature. Quantitative comparative analysis has been carried out using the performance indices, Integral Absolute Error (IAE) and Total Variation (TV).

Fractional order PID control design for semi-active control of smart base-isolated structures: A multi-objective cuckoo search approach.

Abstract: Fractional order PID (FOPID) controllers are introduced as a general form of classical PID controllers using fractional calculus. As this controller provides good disturbance rejection and is robust against plant uncertainties it is appropriate for the vibration mitigation in structures. In this paper, an FOPID controller is designed to adjust the contact force of piezoelectric friction dampers for semi-active control of base-isolated structures during far-field and near-field earthquake excitations. A multi-objective cuckoo search algorithm is employed to tune the controller parameters. Considering the resulting Pareto optimal front, the best input for the FOPID controller is selected. For seven pairs of earthquakes and nine performance indices, the performance of the proposed controller is compared with those provided by several well-known control techniques. According to the simulation results, the proposed controller performs better than other controllers in terms of simultaneous reduction of the maximum base displacement and story acceleration for various types of earthquakes. Also, it provides acceptable responses in terms of inter-story drifts, root mean square of base displacements and floor acceleration. In addition, the evaluation of robustness for a stiffness uncertainty of ±10% indicates that the proposed controller gives a robust performance against such modeling errors.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

Abstract: The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control Firstly, the vehicle dynamics model (ie, transfer function) is established according to the vehicle parameters Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control

Application of flower pollination algorithm in load frequency control of multi-area interconnected power system with nonlinearity

Abstract: This work presents an application of bio-inspired flower pollination algorithm (FPA) for tuning proportional–integral–derivative (PID) controller in load frequency control (LFC) of multi-area interconnected power system. The investigated power system comprises of three equal thermal power systems with appropriate PID controller. The controller gain [proportional gain (K p), integral gain (K i) and derivative gain (K d)] values are tuned by using the FPA algorithm with one percent step load perturbation in area 1 (1 % SLP). The integral square error (ISE) is considered the objective function for the FPA. The supremacy performance of proposed algorithm for optimized PID controller is proved by comparing the results with genetic algorithm (GA) and particle swarm optimization (PSO)-based PID controller under the same investigated power system. In addition, the controller robustness is studied by considering appropriate generate rate constraint with nonlinearity in all areas. The result cumulative performance comparisons established that FPA-PID controller exhibit better performance compared to performances of GA-PID and PSO-PID controller-based power system with and without nonlinearity effect.

Evolving an interval type-2 fuzzy PID controller for the redundant robotic manipulator

Abstract: Robotic manipulators are a multi-input multi-output, dynamically coupled, highly time-varying, complex and highly nonlinear systems wherein the external disturbances, parameter variations, and random noise adversely affects the performance of the robotic system. Therefore, in order to deal with such complexities, however, an intriguing task for control researchers, these systems require an efficient and robust controller. In this paper, a novel application of genetic algorithms (GA) optimization approach to optimize the scaling factors of interval type-2 fuzzy proportional derivative plus integral (IT2FPD+I) controllers is proposed for 5-DOF redundant robot manipulator for trajectory tracking task. All five controllers' parameters are optimized simultaneously. Further, a procedure for selecting appropriate initial search space is also demonstrated. In order to make a fair comparison between different controllers, the tuning of each of the controllers' parameters is done with GA. This optimization technique uses the time domain optimal tuning while minimizing the fitness function as the sum of integral of multiplication of time with square error (ITSE) for each joint. To ascertain the effectiveness of IT2FPID controller, it is compared against type-1 fuzzy PID (T1FPID) and conventional PID controllers. Furthermore, robustness testing of developed IT2FPID controller for external disturbances, parameter variations, and random noise rejection is also investigated. Finally, the experimental study leads us to claim that our proposed controller can not only assure best trajectory tracking in joint and Cartesian space, but also improves the robustness of the systems for external disturbances, parameter variations, and random noise.