Maximum Power Extraction Algorithm Research Articles

Parametric Observer Controlled Motor Invariant Electronic Commutation of Photovoltaic Powered BLDCM Without Position Sensor

The development of parameter independent position sensor free control of permanent magnet brushless DC (PMBLDC) motor is one of the most challenging tasks. A motor parameter independent parametric observer based electronic commutation control is developed here without using rotor position sensor. The effect of motor parameter variation along with the change of motor, on electronic commutation control without using the rotor position sensor is considered as the disruption parameter for the developed control algorithm to mitigate the external disruption. Mitigation of control loop disturbances is done using the parametric observer based feedback control. A hybrid maximum power extraction algorithm using traverse and incremental conductance is implemented here for effective solar maximum power extraction. The implemented parametric observer motor invariant (POMI) based position sensor free control algorithm is verified using experimental process. Performance of the implemented POMI control is observed better in performance in comparison with the conventional position sensor free control as parametric variation is not considered in conventional control. The solution proves to be the best fit for any type of BLDC motor based water pumping solution irrespective of the pump configuration.

Coordination of Enhanced Control Schemes for Optimal Operation and Ancillary Services of Grid-Tied VSWT System

Wind power’s increasing use and its integration into the utility grid have prompted scholars to focus more on the refinement of wind power harvesting systems, injecting stable power and providing ancillary services to the utility grid. This article introduces a novel hybrid approach termed as sine cosine algorithm and transient search optimization (HSCATSO) optimizer of better exploration and exploitation phases for optimal designing the power electronic converter control schemes (PECCS) of the grid-tied variable speed wind turbine (VSWT) system. The PECCS is simultaneously coordinated with a robust maximum power extraction algorithm (MPEA) to form enhanced control systems for achieving the best wind harnessing, improving the VSWT system performance, and supporting the utility grid stability. In this context, the HSCATSO optimally designs the PECCS parameters based on minimizing the summation of integral squared error (ISE) of multiple error signals in the developed control schemes in coordination with the MPEA. The superiority of the HSCATSO optimizer is validated using twenty benchmark functions and statistically analysed against four well-known optimization algorithms. Meanwhile, the effectiveness of the optimally designed PECCS using the HSCATSO is verified by the extensive simulation analysis in MATLAB/Simulink considering severe grid disturbance and real wind speed data taken from Kudat, Sabah, Malaysia to mimic realistic circumstances. The obtained results have been compared with that realized using the other algorithms-based design PECCS. The simulation outcomes affirmed that the PECCS designed by the HSCATSO and coordinated with MPEA resulted in higher power harvesting and enhanced the grid-tied VSWT system stability better than the competitive control schemes.

Integrated Duty Cycle Control for Multiple Renewable Energy Sources in a Grid Connected System

Due to their abundance, affordability, and clean energy production, Renewable Energy Sources (RESs) have emerged as crucial sources of electricity production. As a result, considerable effort has been made to integrate renewable energy sources into the grid to help it meet energy demands. One of the challenges is the Maximum Power Extraction (MPE), where the maximum power needs to be extracted from each RES. The cost of implementation rises linearly with the number of RESs if the MPE arrangements are being made for each RES individually. Additionally, the MPE through multiple RES systems gets more challenging, especially when several PhotoVoltaic (PV) strings are connected and each PV string receive non-uniform irradiance. This paper proposes an integrated MPE control system for a multi-RES grid-connected system. The proposed solution uses a single microcontroller to maximize power from all sources to overcome the linearly growing cost problem. Moreover, we propose an improved Multi-Dimensional Cuckoo (MDC) algorithm for MPE to tackle the non-uniform irradiation problem with multi-string PV, in contrast to prior works with grid-connected multiple sources. The proposed technique is first put up against the individual MPE control system, and then it is put up against the Jaya algorithm that is documented in the literature for a typical one-dimensional MPE.

PV Array Reconfiguration Based on Genetic Algorithm for Maximum Power Extraction and Energy Impact Analysis

Reconfiguration of PV arrays is one of the most suitable options to face issues affecting the power produced by panels, such as partial shading. This paper presents a reconfiguration procedure based on a genetic algorithm. The execution times obtained with the proposed approach validate its good performance compared with a traditional brute force algorithm. Finally, different shading patterns are considered in the simulations.

Coordinated optimal control of active power of wind farms considering wake effect

In a large-scale wind farm, under the influence of the wake effect, the single-machine maximum power extraction control strategy would not be able to function at the ideal optimal value. It is important to study the coordinated operation strategy of the wind farm under the wake effect to improve the output power of wind farms and improve the economic benefit. In this paper, a practical wake model called the PARK model is used and a wake superposition model based on energy balance is derived. Based on these models, an optimization problem is formulated to maximize the output power of the wind farm considering the wake effect. Taking the Horns Rev offshore wind farm as an example, the stochastic points method, particle swarm optimization, and the pattern search algorithm are implemented and compared with the single-machine maximum power extraction algorithm. Test results show that the particle swarm optimization and the pattern search algorithm have better performance. The output power of the wind farm increases by about 10 percent. The particle swarm optimization requires less computation while the pattern search algorithm obtains better and more practical results. Finally, the pattern search algorithm is used to improve economic benefits under different wind conditions.

An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters

This paper aims at exploring different approaches utilized to track the Maximum operating Power Point (MPP) of PV (Photovoltaic) modules, fulfilling the largest amount of available power extraction, hence achieving cost and energy efficiencies of PV systems. This procedure takes place within two divisions: algorithm implementation and DC-DC Power Processing Units (PPUs) design. First and foremost, all MPP algorithms fall under two major categories: local MPP (happens during homogeneous solar radiations) and global MPP (occurs with partial shading conditions). Each of the two groups possesses various methodologies for algorithm generation. Secondly, PPUs were generally classified as either isolated or non-isolated DC-DC converters. Each collection reigns a buck, boost and buck-boost converters with diverse circuit layouts. A critical assessment and comparison were conducted for algorithms and converters in terms of efficiency, reliability and complexity. After analysis and comparison of different maximum power point tracking schemes, it is found that the best algorithm to be adapted is from the global maximum power point tracking set, under exploitation of characteristic curve topologies. For PPUs architecture, it is concluded that a non-isolated buck-boost converter is the best to be chosen while designing an MPP tracker.

Modified leaky LMS‐based control strategy for reliable operation of single‐stage three‐phase grid‐tied PV system

This study presents development of a modified leaky least mean square (MLLMS)-based control strategy for a single-stage three-phase grid-tied photovoltaic (PV) system. In the proposed MLLMS-based control scheme, the authors employ an incremental conductance maximum power point tracking algorithm for maximum power extraction, MLLMS algorithm for extraction of fundamental active and reactive components of the load current, delivering PV power to the grid, balancing the grid current and compensating harmonics of the connected loads at the point of common coupling. By virtue of a leakage factor and selection of sum of the exponential of the adaptation error in the cost function, the MLLMS algorithm overcomes the problems of drifting, low convergence and oscillations in weights encountered by some popular adaptive algorithms, e.g. least mean square (LMS) and least mean fourth (LMF) algorithms. The proposed control scheme is simulated in MATLAB/Simulink environment under different load and environmental conditions. Subsequently, the proposed control scheme is realised on a prototype grid-tied PV system developed in the laboratory. From both the simulation and experimental results, it is observed that the proposed MLLMS algorithm outperforms LMS and LMF algorithms in terms of mean square error, oscillation in weights and total harmonic distortions of the grid currents.

Performance and Implementation of Grid Connected Single Phase Photovoltaic System and FPGA-Design Based MPPT

Solar Photo Voltaic system are used for power generation by the process of photovoltaic effect. The solar power is varying continuously on a particular day due the variations in the temperature and irradiances. To overcome the power loss of PV system maximum power point tracking techniques are used to generate maximum solar power . Generally digital signal processors are used for implementing maximum power point tracking algorithms, but the performance is limited. The high degree of flexibility is achieved by the use of field-programmable gate arrays (FPGA) chips. The PV systems are used to provide stable and reliable power. The paper presents the single-phase implementation of the grid connected PV system and FPGS based P&O MPPT algorithm. The proposed system consists of P&O MPPT algorithm for maximum solar power extraction, bi-directional DC-DC converter for is used for controlling the battery. The solar power is used to serve the connected load and the excess power is used for serving grid utility. The single-phase inverter is used for DC-AC conversion. The Xilinx ISE is used for simulation of MPPT algorithm. The grid connected PV system is simulated using MATLAB/SIMULINK.

Sensorless SynRG Based Variable Speed Wind Generator and Single-Stage Solar PV Array Integrated Grid System With Maximum Power Extraction Capability

This article presents a grid-integrated hybrid renewable energy sources based system comprising a solar photovoltaic (PV) array and a wind energy conversion system (WECS). The WECS uses a position-sensorless synchronous reluctance generator (SynRG) for the electric power generation from the wind turbine (WT), wherein, a sensorless field-oriented control (FOC) is made use of for the maximum power extraction (MPE). A second-order flux estimation (SFE) method along with frequency-locked loop (FLL) is utilized for the accurate flux estimation from the SynRG stator voltages and currents. A set of back-to-back connected three-phase two leg voltage source converter (VSC) topology is selected for the grid integration of WECS. This system has a common dc link where the solar PV array and the machine-side VSC (MSC) of the wind generator, are directly connected. The power output from the solar PV array and WECS, is shared between the grid and the local loads. The maximum power generation from SynRG in the WECS, is achieved by operating the SynRG at the speed estimated by the MPE algorithm. The maximum power is drawn from the solar PV array by adjusting the dc-link voltage, which is decided by the algorithm. For the proper power control and power quality improvement, the grid-side converter (GSC) is adequately controlled by implementing an observer-based control technique. The real-time validation of the system, is carried out using a developed laboratory prototype.

Design of Enhanced Perturb and Observe Algorithm for Maximum Power Extraction From Renewable Resources

Design of Enhanced Perturb and Observe Algorithm for Maximum Power Extraction From Renewable Resources

Photovoltaic System Enhancing Power Quality of Electrical Network

This paper focuses on the power quality improvement with active power filter supplied by a photovoltaic system which consists of a conventional DC/DC boost converter for the impedance matching between the PV array and DC link, a conventional DC/AC inverter and a power grid model with a non-linear load. A three-phase grid connected photovoltaic generation system for improving the power factor taking into account the influence of variable solar radiation is presented. Using the instantaneous power theory (p-q), a reduced inverter model and different control strategies are proposed and compared such as conventional PID controller, Fuzzy logic controller and hysteresis current controller. It is shown that with an extended P&O maximum power extraction algorithm, the hardware required to achieve performance improvement can be reduced to few components such as a single inverter and a boost converter. In this paper, the solution to improve the power quality, in particular the harmonic reduction through active power filters, passive power filters and hybrid power filters, is carefully studied and implemented in a fully scaled three-phase simulation environment. Extensive simulations show how the harmonics from a solar power generation system can be eliminated and how a stable steady state is rapidly achieved after an asymmetrical grid fault (opened phase fault in the point of common coupling) is occurred.

A Time-Interleave-Based Power Management System with Maximum Power Extraction and Health Protection Algorithm for Multiple Microbial Fuel Cells for Internet of Things Smart Nodes

Microbial Fuel Cell (MFC) technology is a novel Energy Harvesting (EH) source that can transform organic substrates in wastewater into electricity through a bioelectrochemical process. However, its limited output power available per liter is in the range of a few milliwatts, which results very limited to be used by an Internet of Things (IoT) smart node that could require power in the order of hundreds of milliwatts when in full operation. One way to reach a usable power output is to connect several MFCs in series or parallel; nevertheless, the high output characteristic resistance of MFCs and differences in output voltage from multiple MFCs, dramatically worsens its power efficiency for both series and parallel arrangements. In this paper, a Power Management System (PMS) is proposed to allow maximum power harvesting from multiple MFCs while providing a regulated output voltage. To enable a more efficient and reliable power-harvesting process from multiple MFCs that considers the biochemical limitations of the bacteria to extend its lifetime, a power ranking and MFC health-protection algorithm using an interleaved EH operation was implemented in a PIC24F16KA102 microcontroller. A power extraction sub-block of the system includes an ultra-low-power BQ25505 step-up DC-DC converter, which integrates Maximum Power Point Tracking (MPPT) capabilities. The maximum efficiency measured of the PMS was ~50.7%. The energy harvesting technique presented in this work was tested to power an internet-enabled temperature-sensing smart node.

Framework of Maximum Power Extraction From Solar PV Panel Using Self Predictive Perturb and Observe Algorithm

This paper deals with a new version of perturb and observe tracking algorithm for maximum power extraction from the solar photovoltaic panel, which has self-predictive and decision taking ability. The working principle of self-predictive perturb and observe (SPP&O) algorithm is based on three consecutive operating points on the power-voltage characteristic. Out of three points, first two points very smartly detects the dynamic condition, as well as in normal condition, quickly searches the maximum power point (MPP) region. Moreover, by using a circular analogy, all points decide the optimal operating position for next iteration, which is responsible for quick MPP tracking as well as improved dynamic performance. Here, in every new iteration, the step-size is reduced by 90% from the previous step-size, which provides an oscillation-free steady-state performance. The effectiveness of the proposed technique is validated by MATLAB simulation as well as tested on hardware prototype. Moreover, comparison between SPP&O algorithm and state of art methods is made. Its satisfactory dynamic and steady-state behaviors with low algorithm complexity as well as the low computational burden of the SPP&O algorithm show the superiority over state of the art methods.

Modeling and control of variable speed wind turbine using laboratory simulator

An important challenge associated with wind power is the unpredictable nature of the wind. As wind power forecasting is usually based on the relationship between wind speeds and wind power, wind farm data is a critical prerequisite for wind speed prediction, meanwhile it plays an important role on the reliability and safety of the grids. For development of new controls, converter topologies, modulation methods, and maximum power extraction algorithms for wind energy conversion systems, the provision of a controlled test environment showing dynamic and steady state characteristics of wind turbines is desired. In this paper, a small scale wind turbine experimental laboratory model based on separately excited dc-shunt motor and permanent magnet synchronous generator (PMSG) is proposed, which reproduces the static and dynamic characteristics of the wind turbine without any wind environment. A programmable wind speed model is established that can satisfactorily simulate the spatial effect of the wind behavior, including gust change, ramp variations, and noise effect. The dynamics of the simulator is studied using the drive train model of the armature controlled dc shunt motor. A microcontroller along with high frequency buck converter has been used for acceleration and deceleration of the dc motor. It is shown through the frequency response characteristics that the simulator model approximates the wind turbine model dynamics. Simulation and experimental results illustrate that the speed of the motor changes with variation in the applied terminal voltage, like in the real wind turbine where the turbine speed changes according to the wind speed. Simulation results are obtained through PSCAD/EMTDC simulation software and experimental results are obtained through the programmable microcontroller used with the DC-motor-PMSG set.

Power Management System With Integrated Maximum Power Extraction Algorithm for Microbial Fuel Cells

Microbial fuel cells (MFC) are alternative renewable power sources that can directly produce electricity from biodegradable substances. However, due to their low power and voltage production, power management systems (PMS) are required to process the MFC power to a more readily usable level. For this application, a monolithic PMS with an integrated maximum power extraction algorithm (MPEA) is proposed. The MPEA allows for quick and accurate pin-pointing of the matching conditions for maximum power transfer from the MFC to the PMS. The PMS delivers a regulated fixed voltage from low fluctuating voltages produced by MFC at maximum power point to a supercapacitor from which electronic devices, such as wireless sensors, for extended operation time can be directly powered. Along with the MPEA system, the PMS is composed of a dc-dc boost converter operating in discontinuous conduction mode to maximize efficiency. In addition, a zero current switching tracking loop is proposed to improve overall system efficiency and minimize losses in the PMS through accurate P-type Metal-oxide-semiconductor field-effect transistor on/off timing control. The PMS circuit was fabricated in 0.5-μm CMOS technology. The maximum dynamic efficiency measured was ~58% for a load of ~250 μW.

Simulation Comparisons and Implementation of Induction Generator Wind Power Systems

This paper describes the performance comparison of a wind power systems based on two different induction generators as well as the experimental demonstration of a wind turbine simulator for the maximum power extraction. The two induction machines studied for the comparison are the squirrel-cage induction generator (SCIG) and the doubly fed induction generator (DFIG). The techniques of direct grid integration, independent power control, and the droop phenomenon of distribution line are studied and compared between the SCIG and DFIG systems. Both systems are modeled in Matlab/Simulink environment, and the operation is tested for the wind turbine maximum power extraction algorithm results. Based on the simulated wind turbine parameters, a commercial induction motor drive was programmed to emulate the wind turbine and is coupled to the experimental generator systems. The turbine experimental results matched well with the theoretical turbine operation.

ROBUST FUZZY LOGIC CONTROL OF WIND ENERGY CONVERSION SYSTEMS WITH UNKNOWN INPUTS

The maximum power extraction algorithms for variable speed wind turbines subjected to a wide range of wind variation, unknown inputs disturbance and parameter uncertainties are discussed in this paper. The Takagi-Sugeno (TS) fuzzy model system with parametric uncertainties is adopted for modelling the nonlinear Wind Energy Conversion System (WECS) and establishing unknown input fuzzy observers. Sufficient conditions are derived for robust stabilization in the sense of Taylor series stability. The problem is formulated and solved using a Fuzzy Linear Matrix Inequalities (FLMIs). The main contribution of this paper is summarized in two points: (i) the proposed fuzzy control and fuzzy observer are completely robust to the unknown inputs disturbance, a wide range of wind speed variations and parameter uncertainties; (ii) maximizes the power that is produced from the wind. The control scheme is tested for random variation of wind speed pattern and provides satisfactory results.

An intelligent maximum power extraction algorithm for hybrid wind–diesel-storage system

This paper focuses on the development of maximum wind power extraction algorithms for variable speed wind turbines in hybrid wind–diesel storage system (HWDSS). The propose algorithm utilizes Takagi–Sugeno (T–S) fuzzy controller. This algorithm combines the merits of: (i) the capability for dealing with nonlinear systems; (ii) the powerful LMI approach to obtain control gains; (iii) the high performance of integral controller. The algorithm maximizes the power coefficient for a fixed pitch and suddenly load changes. Moreover, it reduces the voltage ripple and stabilizes the system over a wide range of wind speed variations. The control scheme is tested for different real profiles of wind speed pattern and provides satisfactory results.

Output Power Smoothing for Wind Turbine Permanent Magnet Synchronous Generators Using Rotor Inertia

Due to wind speed variations, the output power of wind turbines fluctuates. This power fluctuation can cause frequency deviations and power outage, particularly when wind power penetration is significant. Energy storage devices, such as batteries, ultracapacitors, super inductors, and flywheels can be utilized in a hybrid system to solve this problem. These methods are effective, but they impose a significant additional cost to the system. This article presents a novel control method to mitigate the power fluctuations using the rotor inertia as an energy storage component. Therefore, the additional energy storage system is not required. The proposed method is also modified to obtain better energy capturing efficiency. The efficiency of the algorithm is evaluated based on developed equations. The energy extracting capability using this method is comparable to other methods such as the maximum power extraction algorithm. Simulation studies for various cases are performed on a permanent magnet synchronous generator, which verify the theoretical analysis.

Novel maximum-power-extraction algorithm for PMSG wind generation system

The paper focuses on the development of a novel maximum-power-extraction algorithm (MPEA) including a maximum-power error-driven (MPED) mechanism and a maximum-power differential-speed (MPDS) control, for a wind generation system with a permanent-magnet synchronous generator (PMSG). In the proposed MPEA scheme, the MPED mechanism, operating like a traditional hill-climbing method, drives the output power gradually increasing to its maximum value by regulating the direction of current command according to the power variation trend. The MPDS control produces an additional step of current command based on the instantaneous difference of generator speeds, so that it can prevent the wind turbine from stalling at the suddenly dropping wind speed and achieve the object of maximum power extraction instantly as a stiff wind flowing through the wind turbine. In addition, the output is connected to a utility grid for providing energy flexibility via a unipolar full-bridge inverter controlled by a digital signal processor. The grid-connected experimentations of the proposed MPEA scheme, without any mechanical sensors for a wind-power emulation system via a PMSG driven by an induction motor, are given to examine its feasibility in practical applications.