State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.

Comparative analysis of current and voltage-controlled photovoltaic Maximum Power Point tracking

Maximum Power Point tracking (MPPT) in photovoltaic (PV) systems may be achieved by controlling either the voltage or current of the PV device. There is no consensus in the technical literature about how is the best choice. This paper provides a comparative analysis performance among current and voltage control using two different MPPT strategies: the perturb and observe (P&O) and the incremental conductance techniques. © 2011 IEEE.

Confronto di algoritmi per l'inseguimento della massima potenza per convertitori fotovoltaici

Modellazione di un impianto fotovoltaico connesso alla rete. Simulazione con software Simulink del funzionamento dell'intero sistema fotovoltaico e confronto delle prestazioni legate all'utilizzo degli algoritmi MPPT più comuni, quali Hill Climbing e Incremental Conductance.

Single sensor boost converter-based maximum power point tracking algorithms

Two new maximum power point tracking algorithms are presented: the input voltage sensor, and duty ratio maximum power point tracking algorithm (ViSD algorithm); and the output voltage sensor, and duty ratio maximum power point tracking algorithm (VoSD algorithm). The ViSD and VoSD algorithms have the features, characteristics and advantages of the incremental conductance algorithm (INC); but, unlike the incremental conductance algorithm which requires two sensors (the voltage sensor and current sensor), the two algorithms are more desirable because they require only one sensor: the voltage sensor. Moreover, the VoSD technique is less complex; hence, it requires less computational processing. Both the ViSD and the VoSD techniques operate by maximising power at the converter output, instead of the input. The ViSD algorithm uses a voltage sensor placed at the input of a boost converter, while the VoSD algorithm uses a voltage sensor placed at the output of a boost converter. © 2011 IEEE.

Voltage sensing buck converter-based technique for maximum photovoltaic solar energy delivery

A voltage sensing buck converter-based technique for maximum solar power delivery to a load is presented. While retaining the features and advantages of the incremental conductance algorithm, this technique is more desirable because of single sensor use. The technique operates by maximising power at the buck converter output instead of the input.

Single-sensor maximum power point tracking algorithms

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC-DC converter output, instead of the input. © 2013 The Institution of Engineering and Technology.

Nonlinear backstepping controller design for sharing active and reactive power in three-phase grid-connected photovoltaic systems

In this paper, a nonlinear backstepping controller is designed for three-phase grid-connected solar photovoltaic (PV) systems to share active and reactive power. A cascaded control structure is considered for the purpose of sharing appropriate amount of power. In this cascaded control structure, the dc-link voltage controller is designed for balancing the power flow within the system and the current controller is designed to shape the grid current into a pure sinusoidal waveform. In order to balance the power flow, it is always essential to maintain a constant voltage across the dc-link capacitor for which an incremental conductance (IC) method is used in this paper. This approach also ensures the operation of solar PV arrays at the maximum power point (MPP) under rapidly changing atmospheric conditions. The proposed current controller is designed to guarantee the current injection into the grid in such a way that the system operates at a power factor other than unity which is essential for sharing active and reactive power. The performance of the proposed backstepping approach is verified on a three-phase grid-connected PV system under different atmospheric conditions. Simulation results show the effectiveness of the proposed control scheme in terms of achieving desired control objectives.

Cardiac output and leg and arm blood flow during incremental exercise to exhaustion on the cycle ergometer

[EN] To determine central and peripheral hemodynamic responses to upright leg cycling exercise, nine physically active men underwent measurements of arterial blood pressure and gases, as well as femoral and subclavian vein blood flows and gases during incremental exercise to exhaustion (Wmax). Cardiac output (CO) and leg blood flow (BF) increased in parallel with exercise intensity. In contrast, arm BF remained at 0.8 l/min during submaximal exercise, increasing to 1.2 +/- 0.2 l/min at maximal exercise (P < 0.05) when arm O(2) extraction reached 73 +/- 3%. The leg received a greater percentage of the CO with exercise intensity, reaching a value close to 70% at 64% of Wmax, which was maintained until exhaustion. The percentage of CO perfusing the trunk decreased with exercise intensity to 21% at Wmax, i.e., to approximately 5.5 l/min. For a given local Vo(2), leg vascular conductance (VC) was five- to sixfold higher than arm VC, despite marked hemoglobin deoxygenation in the subclavian vein. At peak exercise, arm VC was not significantly different than at rest. Leg Vo(2) represented approximately 84% of the whole body Vo(2) at intensities ranging from 38 to 100% of Wmax. Arm Vo(2) contributed between 7 and 10% to the whole body Vo(2). From 20 to 100% of Wmax, the trunk Vo(2) (including the gluteus muscles) represented between 14 and 15% of the whole body Vo(2). In summary, vasoconstrictor signals efficiently oppose the vasodilatory metabolites in the arms, suggesting that during whole body exercise in the upright position blood flow is differentially regulated in the upper and lower extremities.

Enhancing an Incremental Clustering Algorithm for Web Page Collections

With the size and state of the Internet today, a good quality approach to organizing this mass of information is of great importance. Clustering web pages into groups of similar documents is one approach, but relies heavily on good feature extraction and document representation as well as a good clustering approach and algorithm. Due to the changing nature of the Internet, resulting in a dynamic dataset, an incremental approach is preferred. In this work we propose an enhanced incremental clustering approach to develop a better clustering algorithm that can help to better organize the information available on the Internet in an incremental fashion. Experiments show that the enhanced algorithm outperforms the original histogram based algorithm by up to 7.5%.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.

Incremental entrepreneurship : best practice professionalization across generations

This chapter will introduce Australia’s Dennis family – a case of ‘incremental entrepreneurship’ in the business transition from the first to the second generation. Following the second generation’s formal involvement and ownership in the business, Dennis Family Corporation (DFC) undertook a major professionalization process to formalize the family business and ensure its continued success. The members of the second generation have successfully sustained the entrepreneurial spirit of their family business (albeit in a different style), adding value to the firm in an ‘incremental’ manner. Throughout the chapter there will be a strong emphasis on the family element of DFC and the roles that each family member has played. Bert Dennis, as the founder and incumbent leader of the firm, has witnessed major changes to the business he built from the ground up. His children, in particular his son Grant Dennis as the primary next generation issue champion, have seen the changes from another perspective – ensuring the business remains within the family into the second generation and beyond. The professionalization process was sparked by a commitment from the second generation to continue to ‘make a real go’ of the family business rather than simply liquidating and distributing the assets. The dedication of all the family members to this objective has ensured the success of this process, and ultimately, the longevity of the firm. Although DFC has become more ‘professional’, it has not lost its entrepreneurial character; rather, it has improved the ways in which entrepreneurialism is fostered and pursued in the company. In essence, this case outlines how the implementation of appropriate governance and management practices has allowed the Dennis family to overcome the challenges and maximize the opportunities associated with owning and operating a multigenerational family fi rm. From a theoretical perspective, this case uses the concepts of entrepreneurial orientation (EO) (Lumpkin and Dess 1996) and the resource- based view (RBV) (Habbershon and Williams 1999; Barney 1991; Wernerfelt 1984) to demonstrate how the fi rm has leveraged its familiness to foster an enduring spirit of entrepreneurship and to maintain a sustained competitive advantage.

Managing open incremental process innovation : absorptive capacity and distributed learning

In this conceptual article, we extend earlier work on Open Innovation and Absorptive Capacity. We suggest that the literature on Absorptive Capacity does not place sufficient emphasis on distributed knowledge and learning or on the application of innovative knowledge. To accomplish physical transformations, organisations need specific Innovative Capacities that extend beyond knowledge management. Accessive Capacity is the ability to collect, sort and analyse knowledge from both internal and external sources. Adaptive Capacity is needed to ensure that new pieces of equipment are suitable for the organisation's own purposes even though they may have been originally developed for other uses. Integrative Capacity makes it possible for a new or modified piece of equipment to be fitted into an existing production process with a minimum of inessential and expensive adjustment elsewhere in the process. These Innovative Capacities are controlled and coordinated by Innovative Management Capacity, a higher-order dynamic capability.