Intelligent series inducing switching scheme (ISISS) to enhance power delivery in shaded photovoltaic array

It is crucial to improve the photovoltaic (PV) system efficiency and to develop the reliability of PV generation control systems. One of the approaches to increase the efficiency of PV power generation system is to operate the PV systems optimally at the maximum power point. However, the PV system can be optimally operated only at a specific output voltage; otherwise the output power fluctuates under intermittent weather conditions. In addition, it is very difficult to test the performance of PV systems controller under the same weather condition during the development process where the field testing is costly and time consuming. For these reasons, the presentation is about the state of the art techniques to track the maximum available output power of photovoltaic systems called maximum power point tracking (MPPT) control systems. This topic could be also one of the most challenges in photovoltaic systems application that has been receiving much more attention worldwide. The talks will cover the application of intelligent techniques by means the artificial neural network (ANN) and fuzzy logic controller scheme using polar information to develop a novel real-time simulation technique for MPPT control by using dSPACE real-time interface system. In this case, the three-layer feed-forward ANN is trained once for different scenarios to determine the global MPP voltage and power and the fuzzy logic with polar information controller takes the global maximum power point (MPP) voltage as a reference voltage to generate the required control signal for the power converter. This type of fuzzy logic rules is implemented for the first time in MPPT control application. The proposed method has been tested using different solar cell technologies such as 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. In other cases, one of the main causes of reducing energy yield of photovoltaic systems is the partially shaded condition. Although the conventional MPPT control algorithms operate well in a uniform solar irradiance, they do not operate well in non-uniform solar irradiance conditions. 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 power point may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognize the global operating point under partially shaded conditions. From these reasons, the presentation will address the effectiveness of the proposed MPPT method to solve the partially shaded conditions under the experimental real-time simulation technique based dSPACE real-time interface system for different size of PV arrays, such as 3x3(0.5kW) and 20x3(3.3kW) and different interconnected PV arrays, for instance series-parallel (SP), bridge link (BL) and total cross tied (TCT) configurations.

Solar energy continues to be a viable renewable energy source owing to its eco-friendly nature and long-term economic prospect. The photovoltaic (PV) systems enjoy the trend of being commercialized in many countries due to their potential long-term benefits. Solar irradiance and temperature appear to be responsible for the nonlinear nature of I–V and P–V characteristics of the PV array. The nonlinear characteristics create an optimal point in the sense when the PV system is operated at the maximum power point; it plays a vital role in optimizing the PV power. One of the simplest methods that allow improving the efficiency of the PV systems relates to maximum power point tracking (MPPT). The MPPT aims to track the maximum power point when the weather conditions change, the variation of the solar irradiation, and the temperature leads to a change of the maximum power point. The extraction of the maximum power follows the matching of the power–voltage operating point of the PV modules with that of the corresponding power converter. However, owing to the nonlinear variation of the power output of the solar panel, the MPPT control method becomes an important part of any solar system. The maximum power point tracker (MPPT) ensures the optimal utilization of a large PV array when employed in conjunction with the power converter. The control becomes more complicated when the entire PV array does not receive uniform irradiance – a condition known as partial shading. Partial shading invites considerable interest due to its significance in influencing the energy yield of a PV system. It can be ascertained by the statistical measure of power loss due to partial shading that varies from 10 to 70% of the system yield. Though many conventional MPPT schemes remain in vogue, most of them remain suited when the irradiance varies very slowly and becomes ineffective when subjected to a sudden change in environmental conditions. The maximum power point tracking (MPPT) algorithm becomes crucial in attaining the maximal PV power, facilitating optimal PV cell performance. The MPPT algorithms demonstrate excellent tracking efficiency in uniform insolation conditions. However, under partially shaded conditions, when the entire array does not receive uniform insolation, the PV characteristics become more complex, displaying multiple peaks, of which one of them constitutes to be the global peak (GP) and the rest being local peaks. The occurrence of partially shaded conditions being quite common (e.g., due to clouds, trees, etc.) echoes a need to develop special MPPT schemes that can track the GP under these conditions. It becomes significant to propose advanced MPPT techniques for a large PV system with an array of PV panels. However, in the scenario of distributed PV system planning, decentralized MPP tracking schemes gather significance, and MPP tracking in a single PV panel under uniform and partial shading conditions needs attention. The main emphasis involves a two-stage approach to track the global peak, wherein it orients to explore the use of the maximum power from the solar PV system under the partially shaded environment. It augurs the role of the closed-loop controller to vary the duty cycle of the converter interface and arrive at the delivery of the maximum power to the load. The exercise relates to modeling the solar PV system under a partially shaded environment and analyzing the performance for different shading patterns in the solar panel. The focus incites improving the global peak tracking in all conditions through an algorithm that can operate in the vicinity of the global peak. It includes the identification of different possible combinations of partially shaded patterns and requires being tested for the effectiveness of the scheme. It further necessitates the implementation of the technique through a prototype model and therefrom demonstrates the effectiveness of the use of a simple controller in place of sophisticated MPPT methods.

For the optimal operation of photovoltaic system, The MPPT (Maximum Power Point Tracking) control unit is an essential part for the photovoltaic system. In addition to the protection function, this command ensures the continuation of the maximum power point (MPPT) and allows the PV generator to deliver its maximum power regardless of the variation in climatic conditions (sunshine and) temperature). This work intends to provide an artificial neural network (ANN) maximum power point tracking (MPPT) method which is fast and precise in finding and tracking the maximum power point (MPP) in photovoltaic (PV) applications, under rapidly changing of solar irradiation, and the P&O algorithm. ANN and P&O MPPT algorithms, and other components of the MPPT control system which are PV module and DC-DC boost converter, are simulated in MATLAB/ Simulink, we used in The proposed ANN two inputs which are irradiation and ambient temperature, and one output is the optimum voltage of the PV system. The proposed ANN was analyzed under different irradiation conditions. The response of the proposed ANN for MPPT controllers found to be lesser oscillation at MPP and faster tracking response compared with the P&O algorithm. Comparisons of MPPT with P&O algorithm and without MPPT tracker are also shown in this paper. It is demonstrated that the neural network based MPPT tracking require less time and provide more accurate results than the P&O algorithm based MPPT.

Analog maximum power point tracking (MPPT) controller circuit for photovoltaic solar panels/cells is an attractive solution due to its low cost and simple implementation. The paper presents an analog Maximum Power Point Tracking (MPPT) controller for a Photovoltaic (PV) solar system that utilizes the load current to achieve maximum output power from the solar panel. Compared to the existing MPPT controller circuitry which requires multiplication of the sensed PV panel voltage and current to yield panel power, the cost and size of the proposed circuit are reduced. The tracking performance of the proposed MPPT controller is validated by simulation results. The proposed MPPT controller circuit can be further implemented by CMOS technology and fabricated on-chip to perform distributed maximum power point tracking (DMPPT) in PV system.

Transient PV System Models for Power Quality Studies

Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.

A photovoltaic (PV) system uses the maximum power point tracking (MPPT) controller used in a photovoltaic (PV) system to get the maximum power operating point at different temperatures and irradiance conditions. Several optimization methods from conventional to soft computing methods have been applied to software and hardware platforms to generate duty cycles and optimize fuzzy membership functions. The PV system with partial shading condition is also considered for better tracking of power peaks. Merits and demerits of different MPPT optimization methods have been discussed to conclude better. The results obtained by recently developed algorithms in the MPPT controller have been compared to show better performance and effectiveness of the algorithm. This chapter references undertaking research work to optimize MPPT controllers in PV systems under partial shading conditions.

Shamsodin Taheri Shamsodin Taheri, from the University of Quebec Outaouais, Canada, talks to us about his group's submission “Analysis of electrical behaviour of PV arrays covered with nonuniform snow” page 192. I am an Associate Professor of Electrical Engineering in the Department of Computer Science and Engineering at the Université du Québec en Outaouais (UQO). The expertise of my research group in the field of power energy has advanced our understanding of the performance of photovoltaic (PV) sources in cold-climate conditions. This research was inspired by the issues that PV plants in Canada experience during cold months. In recent years, we have collaborated with several industry partners, who own utility-scale PV plants, to evaluate the performance of their facilities. This ongoing research helps mitigate the technical challenges facing the integration of renewable energy sources into power grids in cold regions. PV systems have found different applications in cold areas for a long time, such as remote research facilities in the South and North Poles and space travelling. The majority of PV energy plants are presently installed in geographical cold locations with a considerable amount of snowfall every year. Furthermore, the solar energy industry is expanding competitively in cold climate regions. For example, in Canada, the installed capacity of solar PV grew from 95 MW to more than 2500 MW during 2009–2016. PV panel efficiency is not only influenced by PV technology, but environmental conditions can also influence their energy production. Accumulation of snow/ice decreases solar radiation reaching the PV panels, which results in a significant loss of power generation. Non-uniform snow accretion on PV panels often occurs due to ambient conditions such as wind, temperature variation, partial snow shedding and ground interference. This leads to power loss that is dependent on the configuration of the PV system. Owing to the increasing deployment of PV systems, there is a significant interest in optimal utilisation of PV potential in cold climate regions. A fundamental need for knowledge of the impact of nonuniform snow accretion on PV systems is therefore essential. This research has shown that the snow-covered PV panels can still produce a significant amount of energy depending on the pattern and depth of the snow accumulation. The portion of this energy that could be collected is decided by configuration of the PV array as well as the maximum power point tracking (MPPT) technique. Investigations concerning the effect of bypass diodes have shown that the PV string without bypass diodes generally experiences more power loss. In addition, the vertical and horizontal PV array layouts were tested to determine power loss due to nonuniform snow accretion. The horizontal PV array layout would be more effective in snowy climates. This work is helpful for PV system performance assessment, MPPT development and testing, power generation prediction, and proper arrangement of panels in cold regions. The accumulation of snow on PV modules weakens the intensity of incoming solar radiation and hence limits their ability to generate electricity during cold months. However, the true extent of such impact on the energy production of PV modules cannot be specified in a straightforward way, since it is not proportional to the snow-covered area. In some instances, snow covering only one cell can reduce the efficiency of a whole module by a noticeable amount. Snow is a complex phenomenon, and a quantitative characterisation of radiation transmittance through snow coverage requires knowledge of the physical properties of snow. In fact, the interaction of sunlight with snow to determine the influence of snow coverage on the PV modules is a challenge. This leads to changing the electrical characteristics of the PV array and producing multiple, local peaks on PV characteristics. Accurate modelling of the electric behaviour of PV modules and systems is a key element in improving system operation and efficiency. Hence, proper modelling approaches of the operation of PV generators under varying environmental conditions that improve their performance control and assist the design of PV converters and their MPPT controller have been achieved. Moreover, an appropriate tool has been proposed to determine the efficiency of PV farms under cold conditions. Furthermore, a high performance MPPT technique has been proposed to harvest the maximum energy of a PV array with multipeak characteristic under partial shading condition. Major progress must be made on the proper design and arrangement of PV arrays and power electronic interfaces, minimising the effects of snow on large-scale PV farms and the increasing the use of bifacial PV modules as a candidate for cold regions over the next ten years.

Renewable energy growing fast, causing solar PV to be widely used in everyday life. The power generated by solar PV is strongly influenced by environmental and weather conditions. The problem becomes important to be resolved, so that solar panels require controls that can keep the power is in maximum condition such as Maximum Power Point Tracking (MPPT) control. This control able to maximize the output power from solar PV in normal condition. Unfortunately, there are problems that arise for MPPT control when solar PV are shaded by objects. Under normal conditions without shadows, solar PV have only one peak power that is Global Maximum Power Point (GMPP). However, with the shadow on the surface of the solar PV it will cause the emergence of several peak power on the solar PV that is called GMPP and Local Maximum Power Point (LMPP). This causes the conventional MPPT controler can be fail to determine the GMPP and will be trapped at LMPP. Therefore, this research will proposed (FPA) Flower Pollination Algorithm method as MPPT control under partial shading condition. The FPA method is chosen to solve partial shading problems in solar PV, so it can reach GMPP without being trapped in LMPP. The FPA method will be implemented through hardware using Coupled Inductor SEPIC converter. Based on the experimental results, the performance of MPPT using FPA method is superior when compared with P&O method. The FPA can improve 40% power with 2,2 second tracking under partial shading condition.

The Photovoltaic (PV) system has been recently attained a considerable interest around the world due to their sustainability, limitlessness and cleanliness. Despite that, the PV system must be equipped with an accurate and robust tracker to enhance its operational efficiency using maximum power point tracking (MPPT) control system. In virtue of inefficient performance of traditional MPPT controller, high fluctuation occurs around the MPP during irradiation and temperature variations. To reach an exact and reliable solution, Sliding Mode Controller (SMC)-based MPPT as a robust and non-linear tracker has been structured. But, the chattering phenomenon in conventional SMC can considerably impact on the controller tracking performance and system stability. Hence, this paper proposes a novel high-order super-twisting SMC (HOSTSMC)-based incremental conductance strategy as MPPT controller of PV system to accurately and smoothly track MPP with low chattering ripple and steady-state error under varying irradiation and temperature conditions. The imprecise adjustment of controller parameters according to the several objectives cannot lead to correct result. So, the MPPT control scheme is improved by the multi-objective equilibrium optimizer (MOEO) thanks to its exploration-exploitation dominance technique. To evaluate the performance of proposed MPPT controller, it has been compared with conventional incremental conductance-based MPPT, SMC-based MPPT and fuzzy SMC-based MPPT. The chattering ripple percentage (|PPV,max-PPV,min|/Pref*100) has been respectively extracted 0.0467%, 0.207%, 0.473% and 0.234% for proposed HOSTSMC controller, Fuzzy-SMC controller, conventional SMC controller and conventional incremental conductance, and also steady-state deviation percentage (|PPV-Pref|/Pref*100) has been respectively attained 0.3681%, 0.403%, 0.624% and 0.971% for these mentioned controllers. At last, both the prominent factors have clearly validated the high tracking performance of HOSTSMC-based incremental conductance with low steady-state deviation and chattering ripple as compared to other controllers.

The output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells [1] and the load impedance. In order to maximize the conversion efficiency of the PV system, it is necessary to track the maximum power point (MPP) of the PV array. In this paper, a prototype Self-tuning Factor Fuzzy Logic based maximum power point tracking (MPPT) controller for photovoltaic (PV) systems is proposed under variable temperature and irradiance conditions. A simulation study in MATLAB is presented under variable environment conditions.

A maximum power point tracking (MPPT) controller was used to make the photovoltaic (PV) module operate at its maximum power point (MPP) under changing temperature and sunlight irradiance. Under partially shaded conditions, the characteristic power–voltage (P–V) curve of the PV modules will have more than one maximum power point, at least one local maximum power point and a global maximum power point. Conventional MPPT controllers may control the PV module array at the local maximum power point rather than the global maximum power point. MPPT control can be also implemented by using soft computing methods (SCM), which can handle the partial shade problem. However, to improve the robustness and speed of the MPPT controller, a hybrid MPPT controller has been proposed that combines two SCMs, the Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Matlab was used in a simulation of a GA-ACO MPPT controller where four SunPower SPR-305NE-WHT-D PV modules with a maximum power of 305.226 W connected in series were used under conditions of partial shade to investigate the performance of the proposed MPPT controller. The results obtained were analyzed and compared with others obtained under perturb and observe (P&O) MPPT and conventional ACO MPPT controllers were observed.

In this paper we propose the control of a parallel multi‐stage photovoltaic (PV) system using a reliable and precise Maximum Power Point Tracking (MPPT) control strategy. This technique is based on the acquisition of the electrical output quantities (voltage, current, and power), common to DC/DC converters, and the control of the power switches, in alternation. In the case of two stages, during the time ti, converter 1 is regulated in real time by the MPPT control, and the second converter keeps the previous MPPT optimization parameters (ti − 1). During the following time ti + 1, converter 1 retains the previous MPPT optimization parameters (ti) and converter 2 is regulated in real time by the MPPT controller. The proposed technique is experimented on a photovoltaic system that feeds a solar cooker. This system is formed by a thermal resistance, two types of PV panels (200 and 230 W) and two DC/DC converters. Compared to the classical technique (instantaneous control), the results obtained show a significant performance and improvements: Convergence speed towards the maximum power point PPM of 48%, variations the electrical quantities gaps (current, voltage, power) at the input and output of the DC/DC converters by a factor of 2.46, electrical energy production of 4%, plate heating temperature of 27.3%, and an efficiency of 5%. The temperatures of the thermal resistances are validated by the thermal models established during this work. The improvements in DC/DC converter performance and solar cooker heating temperature are attributed, on the one hand, to the nature of the proposed MPPT control, which reduces the dispersion of electrical quantities at the input and output of the DC/DC converters, and, on the other hand, to the rapid heating of the solar cooker by the electrical energy produced by the photovoltaic panels.

Unique performance of a standalone solar photovoltaic energy system with SEPIC converter

A Maximum Power Point Tracking (MPPT) Controller using a buck converter has been designed and developed for a standalone Photovoltaic(PV) array. Perturb and Observe (P&O) algorithm has been implemented on a microcontroller to achieve the maximum power output from PV array. The whole developed hardware system has been tested with and without MPPT in a conventional PV array. The results show that the introduction of MPPT controller increases the efficiency of the conventional PV array.