Academic literature on the topic 'Sensorless maximum power point tracking'

Abstract This paper proposes a new photovoltaic panel maximum-power-point optimizer based on a buck converter. It can be connected to the DC-link distributed energy harvesting system that should perform the true maximum-power-point tracking algorithm based on maintaining a constant DC link voltage. The algorithm is based on the sensorless hysteresis control and ensures high efficiency. Three different realizations of proposed hysteresis optimizers have been analyzed in the paper, including operation principle and adjustment of hysteresis intervals. An experimental study has been performed for a portable low-power photovoltaic system in case of different loads and irradiance levels. The efficiency of maximum power point tracking has been calculated analytically for different hysteresis intervals and validated by experiment, which proved a 97-98 % efficiency of tracking for different PV panel temperatures. The proposed solution is recommended to be used in small- and medium-sized power systems where the price of the conventional maximum power point tracking converter is very high and is comparable to the cost of the individual panel

Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

<strong><em>Introduction.&nbsp;</em></strong><em>A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors.&nbsp;<strong>The novelty.&nbsp;</strong>The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose.</em>&nbsp;<em>Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement.</em>&nbsp;Methods.&nbsp;<em>In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.</em>

This manuscript presents current sensorless algorithms for maximum power point tracking (MPPT) in partially shaded photovoltaic (PV) systems. The necessity of a current sensor is eliminated with the use of mathematical modeling of the power electronics converter. This approach significantly reduces the implementation cost and the inherent disadvantages in the current sensor circuitry. MPPT techniques based on soft computing are employed, in addition to Perturb and Observe (P&amp;O), due to their ability to explore a larger search space. This feature is advantageous because it minimizes convergence risk to a local maximum, a limitation of traditional techniques. Simulation and experimental results are presented and each algorithm is evaluated through different metrics, such as search time for the global maximum power point (GMPP) and efficiency. The tests consider dynamic irradiance profiles, producing a tracking factor (TF) above 99% and a remarkable fast convergence time.

A novel energy-based modelling and control strategy is developed and implemented to solve the maximum power point tracking problem when a photovoltaic cell array is connected to consumption loads. A mathematical model that contains key characteristic parameters of an energy converter stage connected to a photovoltaic cell array is proposed and recast using the port-Hamiltonian framework. The system consists of input-output power port pairs and storage and dissipating elements. Then, a current-sensorless control loop for a maximum power point tracking is designed, acting over the energy converter stage and following an interconnection and damping assignment passivity-based strategy. The performance of the proposed strategy is compared to a (classical) sliding mode control law. Our energy-based strategy is implemented in a hardware platform with a sampling rate of 122 Hz, resulting in lower dynamic power consumption compared to other maximum power point tracking control strategies. Numerical simulations and experimental results validate the performance of the proposed energy-based modelling and the novel control law approach.

In recent years, wind energy has become one of the rapid growing renewableenergy sources. According to the new power report from the European Wind Energy Association (EWEA), it forecasts that by 2020 the European Union will achieve 20% of power generation from renewable-energy sources, e.g. wind, solar and bio-fuels. Wind energy is a clean and inexhaustible energy source. It is available in all locations, especially remote ones with rich wind resources and plentiful land, which are suitable for developing large-scale wind farms. Typically, there are two well-known strategies for operating wind turbine generator (WTG) systems, including a fixed-speed strategy and a variable-speed strategy. The former strategy is suitable for large-scale WTG systems, which are directly connected to a grid via capacitor banks for adjusting the generated reactive power. Most of the fixed-speed WTG systems employ pitch angle controllers for extracting maximum wind turbine power from wind. The main disadvantages of the fixed-speed strategy are: first, the mechanical torques are highly affected under rapid wind speeds, i.e. wind gusts, which cause power surges on a grid and second, additional expensive equipment, e.g. motors, actuators and drivers, are required to implement a pitch angle controller. In literatures, the first problem was tackled by keeping the reference pitch angle constant at rapid wind speed variations in order to decrease mechanical stresses on a wind turbine tower. Whilst, the variable-speed strategy has been widely employed for maximising the output power of WTG systems using maximum power point tracking (MPPT) controllers, which can be applied via power electronic converters. The power delivered by a WTG system is dependent on the swept area of a wind turbine, wind speeds, power coefficients of a wind turbine and the current v drawn from a generator. The only controllable factor is the power coefficient, which varies with operating tip speed ratios (TSR). For coming wind speeds, there is a unique optimal TSR that keeps power coefficients at its maximum value. In order to achieve the optimal TSR, it is required to control rotor speeds of a WTG system to follow reference rotor speeds, which can be produced by a TSR controller based on measurement or estimation of wind speeds. In Chapter 2, a comparison study between a classic direct field oriented controller (FOC) and an optimised direct FOC, has been presented. The proposed VTG system comprises a vertical-axis wind turbine (VAWT), a permanent magnet synchronous generator (PMSG), a three-phase controlled rectifier and a stand-alone DC load. The objectives of these controllers are for improving the efficiency and the dynamic performance of a WTG system as well as minimising rotor speed overshoots under rapid wind speed variations. The developed controllers are based on a well-known FOC method, through adjusting stator currents and consequently electromagnetic torque. FOC transforms three-phase stator currents into two currents in the rotational reference frame, i.e. d-axis and q-axis currents, using the Park transformation. These d-axis and q-axis currents act as DC currents. To apply FOC, reference rotor speeds or reference electromagnetic torques are required to generate reference q-axis currents, whilst reference d-axis currents are usually set as zero for minimising loss. It is important to note that the Park transformation needs the knowledge of rotor positions, which can be measured by an encoder. In practice, an encoder cannot measure an accurate initial position, which may lead to wrong calculations of d-axis and q-axis currents. It is worth noting that the parameters of a PI current controller are firstly tuned using a classic zero and pole placement method and secondly optimised using a particle swarm optimisation (PSO) algorithm. The PSO algorithm is adopted due to the following advantages: such as easy to implement with simulations in real-time, a high computational efficiency and stable convergence characteristics. An accurate model for a PMSG is important for the design of a high-performance PMSG control system, because the performance of such control systems is influenced by PMSG physical parameter variations under real operation conditions. In this research, electrical parameters of a PMSG are optimally identified, e.g. the stator resistance per phase, the stator inductance per phase and the rotor permanent magnet flux linkage, using also a PSO algorithm. It is important noting that the bounds of these parameters are obtained using standard tests, e.g. an open-circuit test, a short-circuit test and a load test. The aim is to increase the accuracy of parameter identification, reduce the search space of parameters and decrease the convergence time of a psa algorithm, i.e. the computation time required to reach an optimal solution. One of the difficulties for implementing the direct vector control strategy is the requirement to fix an anemometer close to wind turbine blades in order to obtain accurate wind speed measurements, otherwise inaccurate calculations of reference rotational speeds are obtained causing a WTG system not to rotate at optimal speeds. For cost and reliability consideration, a sensorless MPPT controller, which is based on a novel TSR observer is developed. The purpose of the proposed TSR observer is for estimating TSRs and consequently reference rotor speeds without the knowledge of wind speeds. The proposed TSR observer is based on the well-known perturbation and observation (P&O) method. It is also known as the hill-climbing searching method, which doesn't require any previous knowledge of wind turbine and generator characteristics. In spite of these advantages, it has some problems, which considerably decrease its dynamic performance. These problems include the steady-state oscillations around a maximum power point, a slow tracking speed, a perturbation process in a wrong direction and a high rotor speed overshoot under fast wind speed variations. In this research, these problems are tackled by using adaptive perturbation step sizes instead of fixed ones. For implementing the proposed MPPT controller, a costeffective power-electronics converter, which consists of a three-phase diode rectifier and a DC-DC boost converter, is constructed for experiments. Furthermore, a complete transfer function of the proposed system has been derived, which is employed to design a speed observer for estimating rotor speeds and consequently, rotor positions and for testing the stability of the developed rotor speed observers and controllers. In this thesis, another robust sensor less MPPT controller has been proposed for maximising the output power of a WTG system. A switch-mode rectifier (SMR), which includes a three-phase diode rectifier and a DC-DC boost converter without a boost inductance with an input capacitor filter for harmonic mitigation, is employed for implementing the proposed sensorless MPPT controller. The proposed sensorless MPPT controller is based on two novel observers, i.e. an adaptive sliding-mode observer (SMO) and an adaptive P&O algorithm. The former is used for estimating back-EMFs and consequently rotor speeds without the knowledge of rotor positions using an adaptive PMSG model in the stationary ex-/3 reference frame, an adaptive sliding gain and an adaptive cutoff-frequency LPF. The purpose is to eliminate the chattering effect (which occurs in conventional S1\,1Os ) and decrease estimation errors. The adaptive P&O algorithm is developed to estimate reference rotor speeds and optimal duty cycles based upon turbine coefficient errors and rotor speed errors, respectively. It uses adaptive variables compared with some widely used P&O algorithms, which use an adaptive perturbation step size but a fixed observation period. The adaptive variables are: (i) a perturbation step size, which decreases steady-state oscillations around optimal operating power points and (ii) an observation period, which is another contribution of this work. It increases the tracking speed and ensures that MPPT is always executed in the right direction with small rotor speed overshoots under fast wind speed variations. It should be noted that the developed sensorless MPPT controllers are experimentally validated using a WTG simulator. The data acquisition and control stage of the power electronic converters are implemented using a digital signal processing and control engineering (dSPACE) controller. In this thesis, the analysis of experimental results has been undertaken to verify the proposed observers and controllers. Finally, future research work is suggested.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (p. 79-80).<br>This thesis describes the design, and validation of a maximum power point tracking DC-DC converter capable of following the true global maximum power point in the presence of other local maximum. It does this without the use of costly components such as analog-to-digital converters and microprocessors. It substantially increases the efficiency of solar power conversion by allowing solar cells to operate at their ideal operating point regardless of changes in load, and illumination. The converter switches between a dithering algorithm which tracks the local maximum and a global search algorithm for ensuring that the converter is operating at the true global maximum.<br>by Joseph Duncan.<br>M.Eng.

This thesis begins with providing a basic introduction of electricity requirements for small developing country communities serviced by small scale generating units (focussing mainly on small wind turbine, small Photo Voltaic system and Micro-Hydro Power Plants). Scenarios of these small scale units around the world are presented. Companies manufacturing different size wind turbines are surveyed in order to propose a design that suits the most abundantly available and affordable turbines. Different Maximum Power Point Tracking (MPPT) algorithms normally employed for these small scale generating units are listed along with their working principles. Most of these algorithms for MPPT do not require any mechanical sensors in order to sense the control parameters like wind speed and rotor speed (for small wind turbines), temperature and irradiation (for PV systems), and water flow and water head (for Micro-Hydro). Models for all three of these systems were developed in order to generate Maximum Power Point (MPP) curves. Similarly, a model for Permanent Magnet Synchronous Generators (PMSGs) has been developed in the d-q reference frame. A boost rectifier which enables active Power Factor Correction (PFC) and has a DC regulated output voltage is proposed before implementing a MPPT algorithm. The proposed boost rectifier works on the principle of Direct Power Control Space Vector Modulation (DPC-SVM) which is based on instantaneous active and reactive power control loops. In this technique, the switching states are determined according to the errors between commanded and estimated values of active and reactive powers. The PMSG and Wind Turbine behaviour are simulated at various wind speeds. Similarly, simulation of the proposed PFC boost rectifier is performed in matlab/simulink. The output of these models are observed for the variable wind speeds which identifies PFC and boosted constant DC output voltage is obtained. A buck converter that employs the MPPT algorithm is proposed and modeled. The model of a complete system that consists of a variable speed small wind turbine, PMSG, DPC-SVM boost rectifier, and buck converter implementing MPPT algorithm is developed. The proposed MPPT algorithm is based upon the principle of adjusting the duty ratio of the buck converter in order reach the MPP for different wind speeds (for small wind turbines) and different water flow rates (Micro-Hydro). Finally, a prototype DPC-SVM boost rectifier and buck converter was designed and built for a turbine with an output power ranging from 50 W-1 kW. Inductors for the boost rectifier and buck DC-DC converter were designed and built for these output power ranges. A microcontroller was programmed in order to generate three switching signals for the PFC boost rectifier and one switching signal for the MPPT buck converter. Three phase voltages and currents were sensed to determine active and reactive power. The voltage vectors were divided into 12 sectors and a switching algorithm based on the DPC-SVM boost rectifier model was implemented in order to minimize the errors between commanded and estimated values of active and reactive power. The system was designed for charging 48 V battery bank. The generator three phase voltage is boosted to a constant 80 V DC. Simulation results of the DPC-SVM based rectifier shows that the output power could be varied by varying the DC load maintaining UPF and constant boosted DC voltage. A buck DC-DC converter is proposed after the boost rectifier stage in order to charge the 48 V battery bank. Duty ratio of the buck converter is varied for varying the output power in order to reach the MPP. The controller prototype was designed and developed. A laboratory setup connecting 4 kW induction motor (behaving as a wind turbine) with 1kW PMSG was built. Speed-torque characteristic of the induction motor is initially determined. The torque out of the motor varies with the motor speed at various motor supply voltages. At a particular supply voltage, the motor torque reaches peak power at a certain turbine speed. Hence, the control algorithm is tested to reach this power point. Although the prototype of the entire system was built, complete results were not obtained due to various time constraints. Results from the boost rectifier showed that the appropriate switching were performed according to the digitized signals of the active and reactive power errors for different voltage sectors. Simulation results showed that for various wind speed, a constant DC voltage of 80 V DC is achieved along with UPF. MPPT control algorithm was tested for induction motor and PMSG combination. Results showed that the MPPT could be achieved by varying the buck converter duty ratio with UPF achieved at various wind speeds.

An investigation into the design of a stand-alone photovoltaic water pumping system for supplying rural areas is presented. It includes a study of system components and their modelling. The PV water pumping system comprises a solar-cell-array, DC-DC buck chopper and permanent-magnet DC motor driving a centrifugal pump. The thesis focuses on increasing energy extraction by improving maximum power point tracking (MPPT). From different MPPT techniques previously proposed, the perturb and observe (P&O) technique is developed because of its ease of implementation and low implementation cost. A modified variable step-size P&O MPPT algorithm is investigated which uses fuzzy logic to automatically adjust step-size to better track maximum power point. Two other MPPT methods are investigated: a new artificial neural network (ANN) method and fuzzy logic (FL) based method. These use PV source output power and the speed of the DC pump motor as input variables. Both generate pulse width modulation (PWM) control signals to continually adjust the buck converter to maximize power from the PV array, and thus motor speed and the water discharge rate of a centrifugal pump. System elements are individually modelled in MATLAB/SIMULINK and then connected to assess performance under different PV irradiation levels. First, the MP&O MPPT technique is compared with the conventional P&O MPPT algorithm. The results show that the MP&O MPPT has faster dynamic response and eliminates oscillations around the MPP under steady-state conditions. The three proposed MPPT methods are implemented in the simulated PV water pumping system and compared. The results confirm that the new methods have improved energy extraction and dynamic tracking compared with simpler methods.

<p> The SIUe solar car team lacks a competitive communication system. To enable the competitive edge a major upgrade to the electronics and wiring was required. A new maximum power point tracker and driver support system was developed to give them the competitive edge.</p>

This thesis proposes a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems using Kalman filter. The Perturbation & Observation (P&O) method is widely used due to its easy implementation and simplicity. The P&O usually requires a dithering scheme to reduce noise effects, but the dithering scheme slows the tracking response time. Tracking speed is the most important factor for improving efficiency under frequent environmental change. The proposed method is based on the Kalman filter. An adaptive MPPT algorithm which uses an instantaneous power slope has introduced, but process and sensor noises disturb its estimations. Thus, applying the Kalman filter to the adaptive algorithm is able to reduce tracking failures by the noises. It also keeps fast tracking performance of the adaptive algorithm, so that enables using the Kalman filter to generate more powers under rapid weather changes than using the P&O. For simulations, a PV system is introduced with a 30kW array and MPPT controller designs using the Kalman filter and P&O. Simulation results are provided the comparison of the proposed method and the P&O on transient response for sudden system restart and irradiation changes in different noise levels. The simulations are also performed using real irradiance data for two entire days, one day is smooth irradiance changes and the other day is severe irradiance changes. The proposed method has showed the better performance when the irradiance is severely fluctuating than the P&O while the two methods have showed the similar performances on the smooth irradiance changes.<br>Master of Science

La creixent demanda energètica, l’esgotament dels combustibles fòssils i l’augment de l’escalfament global a causa de l’emissió de carboni ha donat lloc a la necessitat d’un sistema energètic alternatiu, global i respectuós amb el medi ambient. L’energia solar es considera una de les formes d’energia més inesgotables d’aquest univers, però té el problema de la baixa eficiència a causa de les diferents condicions ambientals. El panell solar presenta un comportament no lineal en condicions climàtiques reals i la potència de sortida fluctua amb la variació de la irradiació solar i la temperatura. El canvi de les condicions meteorològiques i el comportament no lineal dels sistemes fotovoltaics suposen un repte en el seguiment de diferents PowerPoint màxims. Per tant, per extreure i lliurar contínuament la màxima potència possible del sistema fotovoltaic, en determinades condicions ambientals, s’ha de formular l’estratègia de control de seguiment del punt de potència màxima (MPPT) que funcioni contínuament el sistema fotovoltaic al seu MPP. Es necessita un controlador no lineal robust per garantir el MPPT mitjançant la manipulació de les línies no lineals d’un sistema i el fa robust contra les condicions ambientals canviants. El control de mode lliscant (SMC) s’utilitza àmpliament en sistemes de control no lineals i s’ha implementat en sistemes fotovoltaics (PVC) per rastrejar MPP. SMC és robust contra les pertorbacions, les incerteses del model i les variacions paramètriques. Representa fenòmens indesitjables com el xerramec, inherent al fet que provoca pèrdues d’energia i calor. En aquesta tesi, en primer lloc, es formula un controlador SMC d’ordre sencer per extreure la màxima potència d’un sistema solar fotovoltaic en condicions climàtiques variables que utilitzen l’esquema MPPT de pertorbació i observació (P \ & O) del sistema fotovoltaic autònom proposat. El sistema proposat consta de dos esquemes de bucles, a saber, el bucle de cerca i el bucle de seguiment. P&O MPPT s’utilitza al bucle de cerca per generar el senyal de referència i un controlador SMC de seguiment s’utilitza a l’altre bucle per extreure la màxima potència fotovoltaica. El sistema fotovoltaic es connecta amb la càrrega mitjançant el convertidor d’alimentació electrònic DC-DC de potència. Primer es deriva un model matemàtic del convertidor d’augment i, basat en el model derivat, es formula un SMC per controlar els impulsos de la porta del commutador del convertidor d’augment. L’estabilitat del sistema de bucle tancat es verifica mitjançant el teorema d’estabilitat de Lyapunov. L’esquema de control proposat es prova amb diferents nivells d’irradiació i els resultats de la simulació es comparen amb el controlador de derivades integrals proporcionals clàssiques (PID). El SMC clàssic representa fenòmens indesitjables com el xerramec, inherent al fet que provoca pèrdues d’energia i calor. A la següent part d’aquesta tesi, es discuteix el disseny del controlador de mode lliscant adaptatiu (ASMC) per al sistema fotovoltaic proposat. El control adoptat s’executa mitjançant un ASMC i la millora s’actualitza mitjançant un algorisme d’optimització MPPT de mètode de cerca de patrons millorats (IPSM). S’utilitza un MPPT IPSM per generar la tensió de referència per tal de comandar el controlador ASMC subjacent. S’ha dut a terme una comparació amb altres dos algoritmes d’optimització, a saber, Perturb \ & Observe (P&O) i Particle Swarm Optimization (PSO) amb IPSM per MPPT. Com a estratègia no lineal, l’estabilitat del controlador adaptatiu es garanteix mitjançant la realització d’una anàlisi de Lyapunov. El rendiment de les arquitectures de control proposades es valida comparant les propostes amb la del conegut i àmpliament utilitzat controlador PID.<br>La creciente demanda de energía, el agotamiento de los combustibles fósiles y el aumento del calentamiento global debido a la emisión de carbono han hecho surgir la necesidad de un sistema energético alternativo, de eficiencia general y respetuoso con el medio ambiente. La energía solar se considera una de las formas de energía más inagotables de este universo, pero tiene el problema de la baja eficiencia debido a las diferentes condiciones ambientales. El panel solar exhibe un comportamiento no lineal en condiciones climáticas reales y la potencia de salida fluctúa con la variación de la irradiancia solar y la temperatura. Las condiciones climáticas cambiantes y el comportamiento no lineal de los sistemas fotovoltaicos plantean un desafío en el seguimiento de la variación máxima de PowerPoint. Por lo tanto, para extraer y entregar continuamente la máxima potencia posible del sistema fotovoltaico, en determinadas condiciones ambientales, se debe formular la estrategia de control de seguimiento del punto de máxima potencia (MPPT) que opere continuamente el sistema fotovoltaico en su MPP. Se requiere un controlador no lineal robusto para asegurar MPPT manejando las no linealidades de un sistema y haciéndolo robusto frente a condiciones ambientales cambiantes. El control de modo deslizante (SMC) se usa ampliamente en sistemas de control no lineales y se ha implementado en sistemas fotovoltaicos (PVC) para rastrear MPP. SMC es robusto contra perturbaciones, incertidumbres del modelo y variaciones paramétricas. Representa fenómenos indeseables como el parloteo, inherentes a él, que provocan pérdidas de energía y calor. En esta tesis, en primer lugar, se formula un controlador SMC de orden entero para extraer la máxima potencia de un sistema fotovoltaico solar en condiciones climáticas variables empleando el esquema MPPT de perturbar y observar (P&O) para el sistema fotovoltaico autónomo propuesto. El sistema propuesto consta de dos esquemas de bucles, a saber, el bucle de búsqueda y el bucle de seguimiento. P&O MPPT se utiliza en el bucle de búsqueda para generar la señal de referencia y se utiliza un controlador SMC de seguimiento en el otro bucle para extraer la máxima potencia fotovoltaica. El sistema fotovoltaico está conectado con la carga a través del convertidor elevador DC-DC electrónico de potencia. Primero se deriva un modelo matemático del convertidor elevador y, en base al modelo derivado, se formula un SMC para controlar los pulsos de puerta del interruptor del convertidor elevador. La estabilidad del sistema de circuito cerrado se verifica mediante el teorema de estabilidad de Lyapunov. El esquema de control propuesto se prueba bajo diferentes niveles de irradiancia y los resultados de la simulación se comparan con el controlador clásico proporcional integral derivado (PID). El SMC clásico describe fenómenos indeseables como el parloteo, inherente a él, que causa pérdidas de energía y calor. En la siguiente parte de esta tesis, se analiza el diseño del controlador de modo deslizante adaptativo (ASMC) para el sistema fotovoltaico propuesto. El control adoptado se ejecuta utilizando un ASMC y la mejora se actualiza utilizando un algoritmo de optimización MPPT del Método de búsqueda de patrón mejorado (IPSM). Se utiliza un IPSM MPPT para generar el voltaje de referencia para controlar el controlador ASMC subyacente. Se ha realizado una comparación con otros dos algoritmos de optimización, a saber, Perturb \ Observe (P&O) y Particle Swarm Optimization (PSO) con IPSM para MPPT. Como estrategia no lineal, la estabilidad del controlador adaptativo está garantizada mediante la realización de un análisis de Lyapunov.<br>The increasing energy demands, depleting fossil fuels, and increasing global warming due to carbon emission has arisen the need for an alternate, overall efficiency, and environment-friendly energy system. Solar energy is considered to be one of the most inexhaustible forms of energy in this universe, but it has the problem of low efficiency due to varying environmental conditions. Solar panel exhibits nonlinear behavior under real climatic conditions and output power fluctuates with the variation in solar irradiance and temperature. Changing weather conditions and nonlinear behavior of PV systems pose a challenge in the tracking of varying maximum PowerPoint. Hence, to continuously extract and deliver the maximum possible power from the PV system, under given environmental conditions, the maximum power point tracking (MPPT) control strategy needs to be formulated that continuously operates the PV system at its MPP. A robust nonlinear controller is required to ensure MPPT by handling nonlinearities of a system and making it robust against changing environmental conditions. Sliding mode control (SMC) is extensively used in non-linear control systems and has been implemented in photovoltaic systems (PV) to track MPP. SMC is robust against disturbances, model uncertainties, and parametric variations. It depicts undesirable phenomena like chattering, inherent in it causing power and heat losses. In this thesis, first, an integer order SMC controller is formulated for extracting maximum power from a solar PV system under variable climatic conditions employing the perturb and observe (P&O) MPPT scheme for the proposed stand-alone PV system. The proposed system consists of two loops schemes, namely the searching loop and the tracking loop. P&O MPPT is utilized in the searching loop to generate the reference signal and a tracking SMC controller is utilized in the other loop to extract the maximum PV power. PV system is connected with load through the power electronic DC-DC boost converter. A mathematical model of the boost converter is derived first, and based on the derived model, an SMC is formulated to control the gate pulses of the boost converter switch. The closed-loop system stability is verified through the Lyapunov stability theorem. The proposed control scheme is tested under varying irradiance levels and the simulation results are compared with the classical proportional integral derivative (PID) controller. Classical SMC depicts undesirable phenomena like chattering, inherent in it causing power and heat losses. In the next part of this thesis, the design of the adaptive sliding mode controller (ASMC) is discussed for the proposed PV system. The adopted control is executed utilizing an ASMC and the enhancement is actualized utilizing an Improved Pattern Search Method (IPSM) MPPT optimization algorithm. An IPSM MPPT is used to generate the reference voltage in order to command the underlying ASMC controller. Comparison with two other optimization algorithms, namely, a Perturb & Observe (P&O) and Particle Swarm Optimization (PSO) with IPSM for MPPT has been conducted. As a non-linear strategy, the stability of the adaptive controller is guaranteed by conducting a Lyapunov analysis. The performance of the proposed control architectures is validated by comparing the proposals with that of the well-known and widely used PID controller. The simulation results validate that the proposed controller effectively improves the voltage tracking, system power with reduced chattering effect, and steady-state error. A tabular comparison is provided at the end of each optimization algorithm category as a resume quantitative comparison. It is anticipated that this work will serve as a reference and provides important insight into MPPT control of the PV systems.

In grid connected photovoltaic (PV) generation systems, inverters are used to convert the generated DC voltage to an AC voltage. An additional dc-dc converter is usually connected between the PV source and the inverter for Maximum Power Point Tracking (MPPT). An iterative MPPT algorithm searches for the optimum operating point of PV cells to maximise the output power under various atmospheric conditions. It is desirable to be able to represent the dynamics of the changing PV power yield within stability studies of the AC network. Unfortunately MPPT algorithms tend to be nonlinear and/or time-varying and cannot be easily combined with linear models of other system elements. In this work a new MPPT technique is developed in order to enable linear analysis of the PV system over reasonable time scales. The new MPPT method is based on interpolation and an emulated-load control technique. Numerical analysis and simulations are employed to develop and refine the MPPT. The small-signal modelling of the MPPT technique exploits the fact that the emulated-load control technique can be linearised and that short periods of interpolation can be neglected. A small-signal PV system model for variable irradiation conditions was developed. The PV system includes a PV module, a dc-dc boost converter, the proposed controller and a variety of possible loads. The new model was verified by component-level time-domain simulations. Be cause measured signals in PV systems contain noise, it is important to assess the impact of that noise on the MPPT and design an algorithm that operates effectively in pr esence of noise. For performance assessment of the new MPPT techniques, the efficiencies of various MPPT techniques in presence of noise were compared. This comparison showed superiority of the interpolation MPPT and led to conclusions about effective use of existing MPPT methods. The new MPPT method was also experimentally tested.

Av den frigjorda energin för en lastbils bränsle är omkring 30% i form avspillvärme i avgassystemet. Med implementation av ett spillvärmeåtervinningsystem går det att återvinna en del av den frigjorda energin i form av elektricitet till lastbilens elsystem. Två termoelektriska generatorer använder avgaserna som värmekälla och ett kylmedel som kall källa för att åstakomma en temperaturdifferans i generatorerna. Med hjälp av Seebeck-effekten går det att omvandla temperaturdifferansen till elektricitet och på så sätt avlastas motorns generator vilket medför en lägre bränsleförbrukning. Detta examensarbete innefattar utvecklandet av en funktion som maximerar nettoeffekten utvunnen från systemet. Funktionen som utvecklats är döpt till Maximum Net-power Point Tracking (MNPT) och har som uppgift att beräkna referensvärden som styrningen av systemet skall uppnå för att få ut maximal nettoeffekt. En simuleringmiljö i Matlab/Simulink är uppbyggd för att kunna implementera en kontrollstrategi för styrningen av kylmedlet samt avgasledning via bypass-ventiler. Systemet har blivit implementerat i en motorstyrenhet på en testrack somkommunicerar via CAN där givare så som temperatur och tryck avläses. Systemet har ej blivit implementerat på lastbilen då samtliga fysiska komponenter ej blev färdigställda under examensarbetets gång. En fallstudie genomfördes i simuleringsmiljön och resultaten visade att användningen av en MNPT-funktion tillät upp till 300% ökning av den återinförda nettoeffekten till lastbilens elsystem jämfört med utan användning av kontrollalgoritmer, och upp till 50% ökning jämfört med statiska referensvärden.<br>About 30% of the released energy of a truck’s fuel is waste heat in the exhaustsystem. It is possible to recover some of the energy with a waste heat recovery system that generates electricity from a temperature difference by utilising the Seebeck-effect. Two thermoelectric generators are implemented on a truck and utilises the exhaust gas as a heat source and the coolant fluid as a cold source to accomplish a temperature difference in the generators. The electricity is reintroduced to the truck’s electrical system and thus reducing the load on the electrical generator in the engine which results in lower fuel consumption. This thesis includes the construction of a function that maximises the netpowerderived from the system. The function developed is named Maximum Net Power Point Tracking (MNPT) and has the task of calculating reference values that the controllers of the system must achieve in order to obtain maximumnet-power. A simulation environment has been developed in Matlab/Simulink in order to design a control strategy to three valves and one pump. The system has been implemented on a engine control unit that has been mounted on a test rack. The engine control unit communicates through CAN to connected devices. The system has not been implemented on the truck due that all the physical components were not completed during the time of the thesis. A case study has been conducted and the results proves that the use of an MNPT-function allows up to 300% increase in regenerated net power into the trucks electrical system compared with no control algorithms, and up to 50% compared with static reference values.

Operating faraway from maximum power point decreases the generated power from photovoltaic (PV) system. For optimum operation, it is necessary to continually track the maximum power point of the PV solar array. However with huge changes in external influences and the nonlinear relationship of electrical characteristics of PV panels it is a difficult problem to identify the maximum power point as a function of these influences. Many tracking control strategies have been proposed to track maximum power point such as perturb and observe, incremental conductance, parasitic capacitance, and neural networks. These proposed methods have some disadvantages such as high cost, difficulty, complexity and nonstability. This thesis presents a novel approach based on Adaptive NeuroFuzzy Inference System (ANFIS) to predict the maximum power point utilising the actual field data, which is performed in different environmental conditions. The short circuit current and open circuit voltage are used as inputs to PV panels instead of solar irradiation and cell junction temperature. The predicted $V_{max}$from ANFIS model is used as a reference voltage for fuzzy logic controller (FLC). The FLC is used to adjust the duty cycle of the electronic switch of two types of DC-DC converter. These DC-DC converters are used to interface between the load voltage and PV panels. The duty cycle of the electronic switch of the DC-DC converter is adjusted until the input voltage of the converter tracks the predicted $V_{max}$of the PV system. FLC rules and membership functions are designed to achieve the most promising performance at different environmental conditions, different load types and different rate of changes in the duty cycle of Buck-Boost and Buck converters. The membership functions and fuzzy rules of FLC are designed to balance between different required features such as quick tracking under different environmental conditions, high accuracy, stability and high efficiency.