Solar photovoltaic (PV) is a crucial renewable energy source that converts sunlight into electricity using silicon-based semiconductor materials. However, due to the non-linear characteristic behavior of the PV module, the module’s output power varies according to the solar radiation and the ambient temperature. To address this challenge, maximum power point tracking (MPPT) techniques are employed to extract the maximum amount of power from the PV modules. This paper offers a comprehensive review of widely used traditional and advanced MPPT approaches in PV systems, along with current developments and future directions in the field. Under uniform insolation, these methods are compared based on their strengths and weaknesses, including sensed parameters, circuitry, tracking speed, implementation complexity, true MPPT, accuracy, and cost. Additionally, MPPT algorithms are evaluated in terms of their performance in reaching maximum power point (MPP) under partial shading condition (PSC). Existing research clearly demonstrates that the advanced techniques exhibit superior efficiency in comparison to traditional methods, although at the cost of increased design complexity and higher expenses. By presenting a detailed review and providing comparison tables of widely used MPPT techniques, this study aims to provide valuable insights for researchers and practitioners in selecting appropriate MPPT approaches for PV applications.

A Proportional-Integral (PI) based Maximum Power Point Tracking (MPPT) control algorithm is proposed in this study where it is applied to a Buck-Boost converter. It is aimed to combine regular PI control and MPPT technique to enhance the generated power from photovoltaic PV) panels. The perturb and observe (P&O) technique is used as the MPPT control algorithm. The study proposes to reduce converter output oscillation owing to implemented MPPT control technique with additional PI observer. Furthermore aims to optimize output power using PI voltage mode closed-loop structure.

It is one of the topics that have been studied extensively on maximum power point tracking (MPPT) recently. Traditional or soft computing methods are used for MPPT. Since soft computing approaches are more effective than traditional approaches, studies on MPPT have shifted in this direction. This study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for MPPT. The meta-heuristic training algorithms used are particle swarm optimization (PSO), harmony search (HS), cuckoo search (CS), artificial bee colony (ABC) algorithm, bee algorithm (BA), differential evolution (DE) and flower pollination algorithm (FPA). The antecedent and conclusion parameters of neuro-fuzzy are determined by these algorithms. The data of a 250 W photovoltaic (PV) is used in the applications. For effective MPPT, different neuro-fuzzy structures, different membership functions and different control parameter values are evaluated in detail. Related training algorithms are compared in terms of solution quality and convergence speed. The strengths and weaknesses of these algorithms are revealed. It is seen that the type and number of membership function, colony size, number of generations affect the solution quality and convergence speed of the training algorithms. As a result, it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.

The proposed study deals with modeling and implementation of a multilevel string inverter with non-isolated dc-dc converter design. The modeled system is intended to be used for solar applications that the photovoltaic panels are connected in string structure. However, the dc-dc converter and multilevel inverter are capable to be used in distributed energy generation applications owing to the proposed control infrastructure. The entire design includes dual buck converter at the dc coupling input where both of the converters simultaneously operate to regulate photovoltaic string voltages. Each dc converter is capable to manage a string comprised of two photovoltaic arrays at the input. The maximum power of each solar array is allowed to be 1.2 kWp while the maximum value of total input power is limited to 4.8 kWp regarding to the related international standards such as AS4777-2013, IEC62109, and IEC61727. The output voltages of dc converters are set to 120V by the proportional-integral controller and are coupled over dc busbar to supply 9-level string inverter. The multilevel string inverter is comprised of two H-bridges in symmetrical topology where it is arranged according to regular 5-level symmetrical inverter. Furthermore, the regular sinusoidal pulse width modulator is enhanced to increase voltage level of inverter to nine instead of regular 5-level output. The inverter provides unity power factor with 0.63% total harmonic distortion ratio.

Design and Implementation of a Wind Turbine Emulator Using an Induction Motor and Direct Current Machine

This paper proposes monitoring of the dual dc-dc converters that independently operate of each other in the same topology (Buck-Boost). The dual converter structure is proposed to implement asymmetrical string inverter that is intended to be used variously arranged PV strings. The converters are operated by independently running MPPT algorithms. The converters are supplied by programmed dc sources emulating freely arranged solar arrays, and different loads are connected to each converter output. The converters allow using independent power sources like hybrid power techniques owing to these features. Current and voltage sensors measure the output powers of each converter, where the obtained values are evaluated by P&O algorithm on MCU and the PWM signals are generated. At the same time, obtained values are transmitted to computer by a microcontroller on optically isolated UART to USB unit. The received values are saved in a database to enable historical and instant analyse capability of the converters.