Erratum to “Steady-state analysis of PV supplied separately excited DC motor fed from IDB converter” [Sol. Energy Mater. Sol. Cells 71 (4) (2002) 493–510]

Interleaved dual boost converter is capable of extracting more amount of power from the photovoltaic source than the conventional boost converter. Power extraction capabilities of the conventional boost and interleaved dual boost converters from the photovoltaic array are verified through experimental studies. As an application the effectiveness of this interleaved dual boost converter for PV supplied separately excited DC motor is studied through simulation. Extensive studies are made by formulating the mathematical models for photovoltaic source, interleaved dual boost converter, DC motor and load. Steady-state performance of the motor coupled to centrifugal pump load is analyzed for maximum power, gross mechanical energy operations. From the simulation studies, it is found that the motor performance is improved with gross mechanical energy output operation as compared to maximum power operation of solar cell array. Furthermore, the use of interleaved dual boost converter reduces the ripple content both in the source and load waveforms, thus resulting in reduced filtering requirements and improved photovoltaic array performance.     

Optimal design parameters for a PV array coupled to a DC motor viaa DC-DC transformer

A study is presented of the optimal operating parameters of a system comprising a photovoltaic solar array and a DC electromechanical energy converter (motor) driving a mechanical load. The analysis and design procedure includes the addition of a variable DC-DC matching transformer placed between the array and the motor. It is responsible for adjusting the load curve seen by the array to coincide with its maximum power point. The model takes into consideration the effect of different temperature as well as isolation profiles along the year. The analysis procedure guides the design of DC motors as well as variable DC transformers especially suited to be operated in conjunction with PV arrays. The procedure determines optimal motor constants which lead to an improved overall design in terms of maximizing the total annual gross mechanical energy delivered to a load of a given torque-speed characteristic     

Steady-state and dynamic performance analysis of PV supplied DC motors fed from intermediate power converter

The steady-state and transient performance of PM and series motors coupled to centrifugal pump supplied from Photovoltaic source through intermediate buck-boost converter is analyzed. The effect of duty ratio selection based on maximum power operation of PV source and maximum daily gross mechanical power is investigated on the solar cell array operating point, motor armature voltage, armature current and motor efficiency variation. Studies are carried out by formulating the mathematical models for photovoltaic source, DC motors, power converter and load. Starting torque variation, Torque magnification factors expressions are derived and their variations plotted for the above two cases. Simulation software is developed for the transient and steady-state analysis of PV supplied DC motors for different duty ratios of power converter and solar insolations. Steady-state and transient performance characteristics are presented. The performance of PM DC motor is compared with the series motor operating under identical conditions.     

Simulation model of ANN based maximum power point tracking controller for solar PV system

In this paper the simulation model of an artificial neural network (ANN) based maximum power point tracking controller has been developed. The controller consists of an ANN tracker and the optimal control unit. The ANN tracker estimates the voltages and currents corresponding to a maximum power delivered by solar PV (photovoltaic) array for variable cell temperature and solar radiation. The cell temperature is considered as a function of ambient air temperature, wind speed and solar radiation. The tracker is trained employing a set of 124 patterns using the back propagation algorithm. The mean square error of tracker output and target values is set to be of the order of 10⁻⁵ and the successful convergent of learning process takes 1281 epochs. The accuracy of the ANN tracker has been validated by employing different test data sets. The control unit uses the estimates of the ANN tracker to adjust the duty cycle of the chopper to optimum value needed for maximum power transfer to the specified load.     

Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances

In recent years, many different techniques are applied in order to draw maximum power from photovoltaic (PV) modules for changing solar irradiance and temperature conditions. Generally, the output power generation of the PV system depends on the intermittent solar insolation, cell temperature, efficiency of the PV panel and its output voltage level. Consequently, it is essential to track the generated power of the PV system and utilize the collected solar energy optimally. The aim of this paper is to simulate and control a grid-connected PV source by using an adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) controller. The data are optimized by GA and then, these optimum values are used in network training. The simulation results indicate that the ANFIS-GA controller can meet the need of load easily with less fluctuation around the maximum power point (MPP) and can increase the convergence speed to achieve the MPP rather than the conventional method. Moreover, to control both line voltage and current, a grid side P/Q controller has been applied. A dynamic modeling, control and simulation study of the PV system is performed with the Matlab/Simulink program.     

Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms

This work presents an experimental comparison of two algorithms developed in order to maximize the output power from a photovoltaic (PV) system for the same given set of conditions. The maximum power point tracking (MPPT) methods proposed in this study are two extended algorithms: Perturb and Observe and Incremental Conductance. The numerical modelling of the PV system shows the MPPT interest and then the extended MPPT algorithms are highlighted. In this paper, a PV system based on a boost converter as MPPT device is considered. A programmable DC electronic load is fed by two identical PV systems in which the MPPT control converter algorithms are different. This experimental platform operates under the same conditions such as changing solar radiation and cell temperature. The experimental results obtained with a dSPACE controller board show the MPPT energy efficiency of the proposed algorithms.     

A photovoltaic powered electrolysis converter system with maximum power point tracking control

One of the main problems for renewable and other innovative energy sources is the storage of energy for sustainability. This study focuses on two different scenarios to benefit from solar energy more efficiently. Photovoltaic (PV) energy is converted to the desired voltage level using a buck converter for generating hydrogen with electrolysis process. A maximum power point tracking (MPPT) algorithm is used to benefit from the photovoltaic sources more efficiently. The basic electrolysis load for hydrogen production needs low voltage and high current and controlled sensitively to supply these conditions. The photovoltaic powered buck converter for electrolysis load was simulated in MATLAB/Simulink software using a perturb and observe (P and O) MPPT algorithm and PI controller. The simulation results show that in normal, short circuit and open circuit working conditions the PV and load voltages are stabilized. The efficiency of the proposed system is reached more than 90% for high irradiance levels.     

Fractional order extremum seeking approach for maximum power point tracking of photovoltaic panels

Due to the high interest in renewable energy and diversity of research regarding photovoltaic (PV) array, a great research effort is focusing nowadays on solar power generation and its performance improvement under various weather conditions. In this paper, an integrated framework was proposed, which achieved both maximum power point tracking (MPPT) and minimum ripple signals. The proposed control scheme was based on extremum-seeking (ES) combined with fractional order systems (FOS). This auto-tuning strategy was developed to maximize the PV panel output power through the regulation of the voltage input to the DC/DC converter in order to lead the PV system steady-state to a stable oscillation behavior around the maximum power point (MPP). It is shown that fractional order operators can improve the plant dynamics with respect to time response and disturbance rejection. The effectiveness of the proposed controller scheme is illustrated with simulations using measured solar radiation data.     

New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks

The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm.     

Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power?voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.     

A photovoltaic utilization system with bang–bang self-adjusting maximum energy tracking controller

The paper presents an error driven controller for maximum energy utilization of photovoltaic PV renewable energy interface schemes. The error driven comprises of regulation loops ensuring on-line dynamic tracking of the maximum power point under different variations in solar insulation levels or sudden mechanical load excursions. The bang–bang regulator limiter block is self-adjusting as it is driven by the error excursion vector magnitude. This ensures that the control signal is modulated in magnitude by the distance of error deviation in the error hyper plane. The paper details a digital simulation MATLAB/SIMULINK model of a PV−PMDC motor utilization system driving a ventilation, pumping or air-conditioning mechanical load. © 1998 John Wiley & Sons, Ltd.     

Maximum power point traking controller for PV systems using neural networks

This paper presents a development and implementation of a PC-based maximum power point tracker (MPPT) for PV system using neural networks (NN). The system consists of a PV module via a MPPT supplying a dc motor that drives an air fan. The control algorithm is developed to use the artificial NN for detecting the optimal operating point under different operating conditions, then the control action gives the driving signals to the MPPT. A PC is used for data acquisition, running the control algorithm, data storage, as well as data display and analysis. The system has been implemented and tested under various operating conditions.The experimental results showed that the PV system with MPPT always tracks the peak power point of the PV module under various operating conditions. The MPPT transmits about 97% of the actual maximum power generated by the PV module. The MPPT not only increases the power from the PV module to the load, but also maintains longer operating periods for the PV system. The air velocity and the air mass flow rate of the mechanical load are increased considerably, due to the increase of the PV system power. It is also found that, the increase in the output energy due to using the MPPT is about 45.2% for a clear sunny day.     

Evaluation of a photovoltaic energy mechatronics system with a built-in quadratic maximum power point tracking algorithm

The historically high cost of crude oil price is stimulating research into solar (green) energy as an alternative energy source. In general, applications with large solar energy output require a maximum power point tracking (MPPT) algorithm to optimize the power generated by the photovoltaic effect. This work aims to provide a stand-alone solution for solar energy applications by integrating a DC/DC buck converter to a newly developed quadratic MPPT algorithm along with its appropriate software and hardware. The quadratic MPPT method utilizes three previously used duty cycles with their corresponding power outputs. It approaches the maximum value by using a second order polynomial formula, which converges faster than the existing MPPT algorithm. The hardware implementation takes advantage of the real-time controller system from National Instruments, USA. Experimental results have shown that the proposed solar mechatronics system can correctly and effectively track the maximum power point without any difficulties.     

A Novel Auto-Scaling MPPT Algorithm based on Perturb and Observe Method for Photovoltaic Modules under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) controller is required in a solar photovoltaic (PV) system to deliver the maximum power to load from PV module. This paper proposes a novel stepped MPPT method to realize a simple MPPT controller, which can track the real maximum power point (RMPP) even under partial shading conditions. The proposed algorithm is started by scanning the characteristic curve of the PV modules to detect the global maximum power point and then the algorithm will be switched to the conventional P&O algorithm to track the true maximum power point. The obtained simulation results, using Power electronic simulation software (PSIM), are compared with those found using the P&O method to confirm the performance of our proposed MPPT method even under non-uniform solar irradiation.     

Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter

In this study, a maximum power point tracking DC–DC quadratic boost converter for high conversion ratio required applications is proposed. The proposed system consists of a quadratic boost converter with high step-up ratio and fuzzy logic based maximum power point tracking controller. The fuzzy logic based maximum power point tracking algorithm is used to generate the converter reference signal, and the change in PV power and the change in PV voltage are selected as fuzzy variables. Determined membership functions and fuzzy rules which are design to track the maximum power point of the PV system generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/Simulink simulation and experimental results that the quadratic boost converter provides high step-up function with robustness and stability. In addition, this process is achieved with low duty cycle ratio when compared to the traditional boost converter. Furthermore, simulation and experimental results have validated that the proposed system has fast response, and it is suitable for rapidly changing atmospheric conditions. The steady state maximum power point tracking efficiency of the proposed system is obtained as 99.10%. Besides, the output power oscillation of the converter, which is a major problem of the maximum power point trackers, is also reduced.     

Matching of photovolatic motor-pump systems for maximum efficiency operation

A PV array is a nonlinear d.c. source and its operation has to be carefully matched to that of its equivalent electrical load in order to extract the maximum available energy. Two PV pumping schemes are investigated to get the maximum gross mechanical power. The system based on the separately-excited d.c. motor is matched through the control of the motor excitation, while for the system based on the induction motor, the voltage source inverter frequency is controlled by maximum mechanical power operation.     

Vectorial command of an asynchronous motor fed by a photovoltaic generator

In order to promote renewable energy, Tunisia has developed a large program to exploitate photovoltaïc systems (PV) to provide electric power in rural electrification. These needs increase continually following standard of living improvements, from lighting and media communication (radio, TV) to motors, refrigeration and pumping. The fluctuation of solar energy on one hand, and the necessity to optimise available solar energy on the other, it is useful to develop new efficient and flexible modes to control motors. A vectorial control of an asynchronous motor fed by a photovoltaïc system is proposed. In this case, the control of the circulating current becomes an important objective in the algorithm design. This paper presents an efficient current controller scheme that can achieve high accuracy and a fast dynamic response of induction machine. This scheme uses voltage decoupling and proportional integral controller loops (PI). Furthermore, to operate the PV array at its maximum power point for every instant, the PV system must contain a maximum power point tracking controller (MPPT). Good static and dynamic performances were obtained in simulation of the proposed structure.     

Matching of a DC motor to a photovoltaic generator using a step-upconverter with a current-locked loop

A photovoltaic (PV) generator is a nonlinear device having insolation-dependent volt-ampere characteristics. Because of its relatively high cost, the system designer is interested in optimum matching of the motor and its mechanical load to the PV generator so that maximum power is obtained during the entire operating period. However, since the maximum-power point varies with solar insolation, it is difficult to achieve an optimum matching that is valid for all insolation levels. In this paper it is shown that for maximum power, the generator current must be directly proportional to insolation. This remarkable property is utilized to achieve insolation-independent optimum matching. A shunt DC motor driving a centrifugal water pump is supplied from a PV generator via a step-up converter whose duty ratio is controlled using a current-locked feedback loop     

A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive

Artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm makes use of the advantages of ANNs such as noise rejection capability and not requiring any prior knowledge of the physical parameters relating to PV system. This paper proposes a genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive. The major objective of this design is to eliminate dc–dc converter and its accompanying losses. Implementing off-line ANN in DSP needs optimization of ANN structure to obtain an ideal size. GA optimization was used in this study to determine neuron numbers in multi-layer perceptron neural network. Another objective of this work is to prevent the necessity of the trade-off between the tracking speed and the oscillations around the maximum power point. Hence, varying step size is used in MPPT algorithm and PI-controller is adopted for simple implementation. Simulation and experimental results have been used to demonstrate effectiveness of the proposed method.     

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.