A comparative study on procedure and state of the art of conventional maximum power point tracking techniques for photovoltaic system

Due to the increasing world energy demand, renewable energy systems have been significantly applied in the power generation sector. Among the renewable energy options, photovoltaic system is one of the most popular resources which has been experiencing a huge attention during recent decades. The remarkable advantages, such as static and movement free characteristics, low maintenance costs, and longevity are the primary factors for the popularity of solar generation in the late years. Nevertheless, the low PV conversion efficiency in one side and high PV material cost in the other side have made PV generation comparably expensive system. Consequently, a capable maximum power point tracking (MPPT) is all important to elicit the maximum energy from the production of PV systems. Different researches have been conducted to design a fast, simple and robust MPPT technique under uniform conditions. However, due to the series and parallel connection of PV modules and according to the use of bypass diodes, in the structure of PV modules, a conventional techniques are unable to track a true MPP. Recently, several studies have been undertaken to modify these conventional methods and enable them to track the global MPP under rapidly changing environments and partial shading (PS) conditions. This report concentrates on the state of the art of these methods and their evolution to apply under PS conditions. The recent developments and modifications are analyzed through a comparison based on design complexity, cost, speed and the ability to track the MPP under rapid environmental variations and PS conditions.

A new fuzzy-based combined prediction interval for wind power forecasting

This paper makes use of the idea of prediction intervals (PIs) to capture the uncertainty associated with wind power generation in power systems. Since the forecasting errors cannot be appropriately modeled using distribution probability functions, here we employ a powerful nonparametric approach called lower upper bound estimation (LUBE) method to construct the PIs. The proposed LUBE method uses a new framework based on a combination of PIs to overcome the performance instability of neural networks (NNs) used in the LUBE method. Also, a new fuzzy-based cost function is proposed with the purpose of having more freedom and flexibility in adjusting NN parameters used for construction of PIs. In comparison with the other cost functions in the literature, this new formulation allows the decision-makers to apply their preferences for satisfying the PI coverage probability and PI normalized average width individually. As the optimization tool, bat algorithm with a new modification is introduced to solve the problem. The feasibility and satisfying performance of the proposed method are examined using datasets taken from different wind farms in Australia.

Improved electrical efficiency by active cooling of building integrated photovoltaic panels

The electrical efficiency of photovoltaic devices can be directly related to the temperature of the photovoltaic cells.Tn this study a BIPVT solar collector was analysed and key parameters affecting its electrical efficiency were identified.

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A mutation-based evolving neural network model and its application to condition monitoring

Data analysis using intelligent systems is a key solution to many industrial problems. In this paper, a mutation-based evolving artificial neural network, which is based on an integration of the Fuzzy ARTMAP (FAM) neural network and evolutionary programming (EP), is proposed. The proposed FAMEP model is applied to detect and classify possible faults from a number of sensory signals of a circulating water system in a power generation plant. The efficiency of FAM-EP is assessed and compared with that of the original FAM network in terms of classification accuracy as well as network complexity. In addition, the bootstrap method is used to quantify the performance statistically. The results positively demonstrate the usefulness of FAM-EP in tackling data classification problems.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

An adaptive fuzzy min-max conflict-resolving classifier

This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz . an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.

A cascade genetic algorithm and particle swarm approach for solving unit commitment and economic dispatch problems

Positive Unit commitment and economic dispatch are two important decisions in thermal power generation scheduling. The tasks involve determination and allocation of power generation to thermal units that minimize the total power generation cost and satisfy the production constraints.This paper presents a cascade Genetic Algorithm and Particle Swarm Optimization (GA-PSO) approach for solving thermal power generation scheduling based on a layered matrix encoding structure.The proposed hybrid method is compared to layered matrix encoding GA using the thermal power generation problem given in Williams [1] to demonstrate its effectiveness in generating an optimal, cost-effective power generation schedule.The results showed that cascade GA-PSO outperformed the layered matrix encoding GA in minimizing the total power production cost for unit commitment and power dispatch problems.

Rule pruning in a fuzzy rule-based classification system

In this paper, we purpose a rule pruning strategy to reduce the number of rules in a fuzzy rule-based classification system.A confidence factor, which is formulated based on the compatibility of the rules with the input patterns is under deployed for rule pruning.The pruning strategy aims at reducing the complexity of the fuzzy classification system and, at the same time, maintaining the accuracy rate at a good level.To evaluate the effectiveness of the pruning strategy, two benchmark data sets are first tested. Then, a fault classification problem with real senor measurements collected from a power generation plant is evaluated.The results obtained are analyzed and explained, and implications of the proposed rule pruning strategy to the fuzzy classification system are discussed.

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

Running in a minimalist and lightweight shoe is not the same as running barefoot : a biomechanical study

Aim The purpose of this study was to determine the changes in running mechanics that occur when highly trained runners run barefoot and in a minimalist shoe, and specifically if running in a minimalist shoe replicates barefoot running.

Methods Ground reaction force data and kinematics were collected from 22 highly trained runners during overground running while barefoot and in three shod conditions (minimalist shoe, racing flat and the athlete's regular shoe). Three-dimensional net joint moments and subsequent net powers and work were computed using Newton-Euler inverse dynamics. Joint kinematic and kinetic variables were statistically compared between barefoot and shod conditions using a multivariate analysis of variance for repeated measures and standardised mean differences calculated.

Results There were significant differences between barefoot and shod conditions for kinematic and kinetic variables at the knee and ankle, with no differences between shod conditions. Barefoot running demonstrated less knee flexion during midstance, an 11% decrease in the peak internal knee extension and abduction moments and a 24% decrease in negative work done at the knee compared with shod conditions. The ankle demonstrated less dorsiflexion at initial contact, a 14% increase in peak power generation and a 19% increase in the positive work done during barefoot running compared with shod conditions.

Conclusions Barefoot running was different to all shod conditions. Barefoot running changes the amount of work done at the knee and ankle joints and this may have therapeutic and performance implications for runners.

Impacts of DG penetration in the Bamaga and Gununa isolated distribution networks

Isolated distribution systems are dispersed throughout regional Queensland to supply small isolated communities that are distant from the main supply grid. The costs of maintaining the electricity supply to these areas is costly; mainly due to the cost of diesel fuel. Furthermore, there is a community focus on climate change, and Ergon Energy aims to reduce the reliance on fossil fuels whilst optimising cost efficiencies and greenhouse gas emissions. The objective of this study is to examine the impacts of renewable energy sources in isolated power systems. For the locations studied, viable renewable energy sources have been integrated into these networks. Anticipated challenges and issues with the integration of the intermittent renewable energy sources were addressed, using mitigation techniques, including energy storage solutions. The investigation and findings demonstrated that network improvements can be achieved by an ideal level of renewable penetration, which has been the main focus of the project. The project involved the development and simulation of MATLAB Simulink and SINCAL models of the two isolated networks at Gununa and Bamaga. The subsequent analysis of these systems has shown a modest penetration level of renewables can be combined with energy storage solutions, which reduces fuel consumption and greenhouse gas emissions at these locations.

Distribution grid codes : opportunities and challenges

Traditional distribution networks were not originally designed to accommodate power generation facilities. The installation of distributed generation (DG) units with significatn capacity in these passive networks can cause reverse power flows which will result in some conflicts with the operation of the existing protection system. In this context, utilities around the world have started establishing requirements to ensure safe and reliable interconnection of generators in low- and medium-voltage networks. Grid interconnection is presently one of the most important issues involving DG. This paper presents a critical review of the requirements adopted by distribution companies in selected countries such as the USA, the UK, germany and Australia to facilitate the connection of DG. Critical issues such as voltage regulation, islanding operation, dynamic interactions among DG and loads are discussed to identify a few points where attention is still needed to improve the reliability of distribution systems

Improving fault ride through capability of DFIG during RSC flashover fault

Wind power generation is growing rapidly. However, maintaining the wind turbine connection to grid is a real challenge. Recent grid codes require wind turbines to maintain connected to the grid even during fault conditions which increases concerns about its sensitivity to external faults. So, researchers have given attention to investigating the impact of various external faults, and grid disturbances such as voltage sag and short circuit faults, on the fault ride through (FRT) capability of the doubly fed induction generator (DFIG). However, no attention has been given to the impact of internal faults on the dynamic performance of the machine when the fault occurs within the voltage source converters (VSCs) that interface the DFIG with the grid. This paper investigates the impact of the rotor side converter (RSC) IGBT flashover fault on the common coupling (PCC) reactive power and the FRT is proposed. The DFIG compliance with numerous and recently released FRT grid codes under the studied fault, with and without the STATCOM are examined and compared. Furthermore, the capability of a proposed controller to bring the voltage profile at the point of PCC to the nominal steady-state level; maintain the unity power factor operation; and, maintain the connection of the wind turbine to the grid are examined