A novel maximum power point tracking in partially shaded PV systems using a hybrid method

To exploit photovoltaic systems, a major point that merits attention is to find maximum output power for the efficiency increase. The output power of solar cells depends on the ambient temperature and intensity of solar radiation. The cloud phenomenon creates a partially shaded on solar arrays; in this condition, the power–voltage curve of a solar array has several local maximum points. When there is uniform radiation, conventional methods of maximum power point tracking (MPPT) can be used. However, these methods are not efficient in partially shaded, due to the existence of several Maximum Power Points (MPPs) in the power-voltage characteristic. In this paper, a novel method for MPP tracking under the partially shaded is proposed, which is a combination of observational tracking and constant–voltage methods. When there is uniform radiation, the tracking operation is observed and tracked by a fuzzy logic–based approach. The proposed method is based on the existence of a relationship between radiation intensity and MPP voltage. In the existence of this relationship, the MPP voltage can be calculated by measuring the intensity of radiation at any moment. In addition, partially shaded works using the constant-voltage method. To verify the simulation results, laboratory implementation was performed. The results show that with considering the MPPs, the output power increases about 10% while the partially shaded is applied.     

Feasibility of solar tracking systems for PV panels in hot and cold regions

Solar tracking systems would probably increase the efficiency of a PV module, but when and where. There are many factors that affect the performance of PV panels, especially crystalline silicon panels, e.g. overheating due to excessive exposure to solar irradiance in a hot climate as in Sunbelt countries. So, it could be the case that a tracking system is not necessary for a Sunbelt country. The objective of this research is to determine mathematically the performance of a PV panel as a function of tracking the sun and the operating conditions. The used mathematical model is validated experimentally and then applied for several environments, i.e. hot as well as cold regions. It has been found that the gain in electrical energy from tracking the sun is about 39% in case of a cold city as Berlin, Germany. While the gain in energy does not exceed 8% in case of a hot city as Aswan, Egypt, due to overheating of the PV panels. However, if the energy needed for running the tracking system, which ranges from 5% to 10% of the energy generated, is included in this analysis then tracking the sun will not be feasible in hot countries.     

Modeling and control of photovoltaic and fuel cell based alternative power systems

Photovoltaic (PV) systems and fuel cells (FCs) represent interesting solutions as being alternative power sources with high performance and low emission. This work presents a modeling and control study of two power generators; photovoltaic array and fuel cell based systems. An MPPT approach to optimize the PV system performances is proposed. The PV system consists of a PV array connected to a DC-DC buck converter and a resistive load. A maximum power point tracker controller is required to extract the maximum generated power. Based on Incremental Conductance (INC) principle, the idea of the proposed control is to use a Fuzzy Logic Controller (FLC) that allows the choice of the duty cycle step size which is used to be fixed in conventional MPPT algorithms. The variable step is computed according to the value of the PV power-voltage characteristic slope. The second working system comprises a controlled DC-DC converter fed by a proton exchange membrane fuel cell (PEMFC) and supplies a DC bus. The mathematical model of the PEMFC system is given. The converter duty cycle is adjusted in order to regulate the DC bus voltage. Obtained simulation results validate the control algorithms for both of studied power systems.     

Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system

This paper proposes a method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems. The system is composed of a boost converter and a single-phase inverter connected to a utility grid. The maximum power point tracking control is based on adaptive fuzzy logic to control a switch of a boost converter. Adaptive fuzzy logic controllers provide attractive features such as fast response, good performance. In addition, adaptive fuzzy logic controllers can also change the fuzzy parameter for improving the control system. The single phase inverter uses predictive current control which provides current with sinusoidal waveform. Therefore, the system is able to deliver energy with low harmonics and high power factor. Both conventional fuzzy logic controller and adaptive fuzzy logic controller are simulated and implemented to evaluate performance. Simulation and experimental results are provided for both controllers under the same atmospheric condition. From the simulation and experimental results, the adaptive fuzzy logic controller can deliver more power than the conventional fuzzy logic controller.     

Polar coordinated fuzzy controller based real-time maximum-power point control of photovoltaic system

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. There are two ways to increase the efficiency of PV power generation system. The first is to develop materials offering high conversion efficiency at low cost. The second is to operate PV systems optimally. However, the PV system can be optimally operated only at a specific output voltage and its output power fluctuates under intermittent weather conditions. Moreover, it is very difficult to test the performance of a maximum-power point tracking (MPPT) controller under the same weather condition during the development process and also the field testing is costly and time consuming. This paper presents a novel real-time simulation technique of PV generation system by using dSPACE real-time interface system. The proposed system includes Artificial Neural Network (ANN) and fuzzy logic controller scheme using polar information. This type of fuzzy logic rules is implemented for the first time to operate the PV module at optimum operating point. ANN is utilized to determine the optimum operating voltage for monocrystalline silicon, thin-film cadmium telluride and triple junction amorphous silicon solar cells. The verification of availability and stability of the proposed system through the real-time simulator shows that the proposed system can respond accurately for different scenarios and different solar cell technologies.     

PV-battery energy storage system operating of asynchronous motor driven by using fuzzy sliding mode control

This article tends on the designing of renewable-storage electrical system for asynchronous machine application. The machine requires a photovoltaic energy as a primary source of renewable energy. Such as the major drawback of solar sources are neither continuous nor regular in the time. To overcome this problem, a battery energy storage system will be added to make sure the continuity of the operation machine. Generally, electrical machine is driven by using sliding mode control where the major problem in this control technique design is chattering phenomenon which can be defeated using a sliding surface based on fuzzy logic. The proposed technique is performed with the proposed control system. A simulation test of the system for varied load motor conditions proved that the system is preferment. When, the generated power from PV is superior that the demands, the rest is stored in the battery. In the opposite condition, PV and battery required load. Moreover, the system which consists of machine and its control is enabling to produce electrical energy when it is functioning in the other sense of rotation. Four quadrant machine operations guarantee many possibilities of charging and discharging of the battery that assure the continuity of operating system.     

Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control

Currently, the grid-connected large PV farms are extensively installed in power systems. Nevertheless, in addition to the load change, the intermittent power output of PV farms may lead to the serious problem of the system frequency fluctuation. To handle this problem, this paper proposes a new design of Sugeno fuzzy logic controller based on particle swarm optimization (PSO-SFLC) of intelligent PV farms for the frequency stabilization in a multi-area interconnected power system. To handle various scenarios, the frequency deviations and solar insolations are used as input signals of the PSO-SFLC. The output signal of the PSO-SFLC is a command signal for adjusting PV output power. The output power of PV is controlled by the PSO-SFLC to meet the load demand so that the system frequency fluctuation can be suppressed. Without the difficulty of trial and error, the optimal input and output membership functions, and control rules of PSO-SFLC are automatically achieved by PSO. Simulation study in a three-area loop interconnected power system with large PV farms elucidates that the frequency stabilizing performance and robustness of the PV equipped with the PSO-SFLC is much superior to that of the PV with the SFLC and the PV with the maximum power point tracking control in scenarios with various solar insolations and loading conditions.     

GA-based fuzzy logic control of a solar power plant using distributed collector fields

A novel genetic algorithm (GA) based fuzzy logic control (FLC) system has been developed for the solar power plant, Plataforma Solar de Almería (PSA) at Tabernas, in Almería, Spain. The rule base encompasses an empirical set of 49 “if-then” rules. Chromosomes consisting of 49 genes of 5-bit data are created to link to the rule base. The 5-bit data of each gene represents the stength of the corresponding ‘If-Then’ rule. The GA performs the basic operations of reproduction, crossover and mutation on a pool of chromosomes to search for the best rule base which optimises the response time of the plant to input temperature or power demand by controlling the distributed collector field of the plant. The collect field is essentially an array of parabolic mirrors and oil pipes in which the transversal of solar energy takes place. Simulation results on the plant with an optimised rule-base using the 100th generation of the chromosome show that the proposed GA-FLC scheme gives a better and more robust performance of the plant than other schemes previously implemented.     

Enhanced control strategies for a hybrid battery/photovoltaic system using FGS-PID in grid-connected mode

This paper suggests improved control strategies using Fuzzy Gain Scheduling of Proportional-Integral-Derivative (FGS-PID) controller for a hybrid Photovoltaic (PV) and Battery Energy Storage (BES) system under different weather conditions. The proposed scheme is implemented using a two-level control system structure, combining the benefits of the PID as well as the fuzzy logic controller (FLC) for maximum power point tracking (MPPT). Ziegler-Nichols tuning method is also employed to select the initial values of PID gains. Within the period of steady-states and transients, FGS-PID adopts the gains to ensure the stability of the control scheme. It also damps out transient fluctuations and reduces settling time. Also, BES could be employed to provide a stable and reliable power from the output of PV sources to loads. It can enhance the performance of the entire power system during the grid-connected mode. The simulation results under Matlab/Simulink show that the suggested control strategies are robustness, fast transient response and proper steady-state performance in the grid-connected mode in comparison other presented methods.     

Fuzzy logic based DSP controlled servo position control for ultrasonic motor

In this paper, position control of an ultrasonic motor was implemented on the basis of fuzzy reasoning. A digitally controllable two phase serial resonant inverter was developed to drive the ultrasonic motor by using a TMS320F243 digital signal processor. The driving frequency was used as a control input in the position control loop. The position characteristics obtained from the proposed drive and control system were demonstrated and evaluated by experiments. The experimental results verify that the developed position control scheme is highly effective, reliable and applicable for the ultrasonic motor.     

Neuro-fuzzy control for autonomous wind–diesel systems using biomass

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers.     

Power management control for off-grid solar hydrogen production and utilisation system

Climate change concerns, increasing global energy demand, coupled with pending peak supply of fossil fuels, calls for development of new power source. The rapid price drops for solar technologies and combined with international and national policy changes makes solar energy more affordable and accessible for widespread adoption. Solar energy also contributes towards the reduction of greenhouse gas emissions. The combination of electrolysis of water and fuel cells, which use hydrogen as an energy carrier extends the utility of the solar energy. For an integrated solar powered hydrogen production, storage and utilisation system, one of the elements that needs to be designed carefully is the power management system. Power management strategy has a complex function in this type of solar hydrogen system. This paper presents a power management strategy based on fuzzy logic technology to address the problems.     

A novel fuzzy logic based safe operation oriented control technique for driving HHO dry cell systems based on PWM duty cycle

The presented study provides details on Fuzzy Logic based control of HHO generators aims at protecting the HHO generator from extreme temperature effects while maximizing the hydrogen production. The Fuzzy Logic Controller mainly focuses on protecting the generator from the harsh effects of process related over heating problems while maximizing the hydrogen production. The designed controller takes two process specific parameters, such as temperature and the HHO flow rate, and optimizes the operation of the reactor for maximization of production. In order to prove the effectiveness of the developed Fuzzy Logic based control approach, a comparative study on performance of conventional and presented novel Fuzzy Logic based systems are presented. The advantage of the Fuzzy Logic Controller is that it protests the reactor and allows continuous production of HHO gas while the conventional approach leads to damage to the reactor or interrupted operation due to overheating.     

Fuzzy self-adaptive PID control technique for driving HHO dry cell systems

When the standard operating temperatures are exceeded in hydroxy generators, production performance decreases and power consumption increases. Self-adaptive fuzzy proportional integral derivative systems have been used to prevent this situation and to maintain optimum production conditions. The Fuzzy Logic is provided to tune the optimum PID parameters by using a gain scheduling method. The tuning scheme is demonstrated by a fuzzy decision process, which consists of fuzzification, knowledge and rule base, inference and defuzzification. Results of developed fuzzy proportional integral derivative (FPID), on-off, conventional PID and fuzzy control approach are discussed and compared. The presented result shows that self-adaptive fuzzy PID controllers achieve better control performance than conventional control methods mentioned. The last but not the least, developed approach minimizes the expertise needed to apply pulse width modulation technique.     

A robust control method for a PV-supplied DC motor using universal learning networks

In this paper, a new robust control method and its application to a photovoltaic (PV) supplied, separately excited DC motor loaded with a constant torque is discussed. The robust controller is designed against the load torque changes by using the first and second ordered derivatives of the universal learning networks (ULNs). These derivatives are calculated using the forward propagation algorithm, which is considered as an extended version of real time recurrent learning (RTRL). In this application, two ULNs are used: The first is the ULN identifier trained offline to emulate the dynamic performance of the DC motor system. The second is the ULN controller, which is trained online not only to make the motor speed follow a selected reference signal, but also to make the overall system operate at the maximum power point of the PV source. To investigate the effectiveness of the proposed robust control method, the simulation is carried out at four different values of the robustness coefficient γ in two different stages: The training stage, in which the simulation is done for a constant load torque. And the control stage, in which the controller performance is obtained when the load torque is changed. The simulation results showed that the robustness of the control system is improved although the motor load torque at the control stage is different from that at the training stage.     

Energy management hypothesis for hybrid power system of H2/WT/PV/GMT via AI techniques

This paper aims to attain an efficient and optimized energy management operation of Hybrid Power System (HPS) by using Artificial Intelligent (AI) controllers. The HPS comprises Wind Turbines (WTs) and Photovoltaic (PV) panels such as primary Renewable Energy Sources (RESs) in addition to both Fuel Cells (FCs) and Gas Micro–Turbines (GMTs) which are used as Backup Sources (BKUSs).To avoid the undesired negative impacts on the HPS functionality because of the RESs intermittency, the Hydrogen Storage System (HSS) is integrated into the system. Two different energy management strategies based on Neural Networks (NN) and Fuzzy Logic Control (FLC) respectively are applied to the HPS for minimizing the energy production cost and keeping the buffer role of HSS. Using MATLAB^?, the proposed two AI introduced solutions are used for reaching adequate energy management operation performance for the overall HPS during 24 h load variation. From the numerical simulations, the superiority of the FLC over the NN control approach is discussed. The proposed HSS can positively act as a buffer solution to avoid drawbacks of RESs during unexpected load peaks and/or discontinuous energy production.     

Tuning of asymmetrical fuzzy logic control algorithm for SPV system connected to grid

Solar Photovoltaic (SPV) cells and hydrogen fuel cells are green and efficient Distributed Energy Resources (DERs) with minimal environmental impact. Integration of DERs with conventional grid through inverters has proven to be an accepted technique for a continuous and good quality power to end users. For an efficient use of an inverter, its control algorithm plays an important role. In this paper, asymmetrical Fuzzy Logic Control (FLC) algorithm is designed, developed and simulated for a SPV system connected to the grid. In proposed asymmetrical FLC algorithm, the fuzzy functions of error input close to zero are used for fine-tuning, and the error input away from zero are used for coarse tuning. The effectiveness of the proposed control algorithm is demonstrated for controlling the PV inverter in unity power factor mode while maintaining the power quality standards and load balancing. Further, the system steady-state and transient response with the asymmetrical FLC algorithm is analyzed for linear as well as non-linear loads using MATLAB along with Simulink toolbox. Finally, a comparative analysis of asymmetrical FLC algorithm with other algorithms is presented to establish the superiority of the proposed algorithm.     

A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.     

Design and simulation of the power control system of a plant for the generation of hydrogen via electrolysis, using photovoltaic solar energy

The gradual exhaustion of natural resources, particularly energy sources, and the various problems involved in their life-cycle, makes it necessary to promote a renewably derived hydrogen economy, in which hydrogen is produced from clean sources.

In this paper, the control system for an installation for producing hydrogen via electrolysis using only a 250 kWp photovoltaic generator is presented. Computer simulation was used to design and confirm its correct performance.

The results obtained ensure the installation's high energy yield.     

Speed control of oil-hydraulic power take-off system for oscillating body type wave energy converters

The variable displacement oil-hydraulic pumps for the Power Take-Off (PTO) of wave energy converters must work above 80% of maximum displacement in order to have an overall efficiency of approximately 94.5%. This is achieved by controlling their rotational speed when the oil-hydraulic power fluctuates in time. Three speed control strategies have been presented, the first fixing the maximum possible speed in each sea state, the second by slowly varying the pump speed between speed peak values and average ones, and the third by working with highly variable speed reference values. The worst pump efficiency is achieved with the first strategy while the best one with the third strategy. However, the first has less impact than the third one in the pump lifecycle. On the other hand, the second strategy is used to make a trade-off between pump efficiency and lifecycle. However, this paper presents a fourth speed control strategy, which is a hybrid of the second and third strategies. So, the objectives of this paper were to know if these strategies are implementable in a test rig and also on a new PTO concept and determining what modifications should be introduced in these PTO strategies and hardware. This paper also contributes with the application of new methodologies in this field of research for the modelling of pump efficiency and pressure control, such as Neuro-Fuzzy modelling and Fuzzy Logic control systems.