Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

 Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) array’s efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection. VC 2014 Author(s)

Addendum: Abubakar, U.; Mekhilef, S.; Mokhlis, H.; Seyedmahmoudian, M.; Horan, B.; Stojcevski, A.; Bassi, H.; Rawa, M.J.H. Transient Faults in Wind Energy Conversion Systems: Analysis, Modelling Methodologies and Remedies. Energies 2018, 11, 2249

The authors would like to make the following addition to their paper [...

Investigating the Role of Virtual Reality in Planning for Sustainable Smart Cities

With rapid population growth, urban designers face tremendous challenges to accommodate the increasing size of the population in urban areas while simultaneously considering future environmental, social, and economic impacts. A “smart city” is an urban development vision that integrates multiple information and communication technologies to manage the assets of a city, including its information systems, transportation systems, power plants, water supply networks, waste management systems, and other community services provided by a local department. The goal of creating a smart city is to improve the quality of life of citizens by using technology and by addressing the environmental, social, cultural, and physical needs of a society. Data modeling and data visualization are integral parts of planning a smart city, and planning professionals currently seek new methods for real-time simulations. The impact analysis of “what-if scenarios” frequently takes a significant amount of time and resources, and virtual reality (VR) is a potential tool for addressing these challenges. VR is a computer technology that replicates an environment, whether real or imagined, and simulates the physical presence and environment of a user to allow for user interaction. This paper presents a review of the capacity of VR to address current challenges in creating, modeling, and visualizing smart cities through material modeling and light simulation in a VR environment. This study can assist urban planners, stakeholders, and communities to further understand the roles of planning policies in creating a smart city, particularly in the early design stages. The significant roles of technologies, such as VR, in targeting real-time simulations and visualization requirements for smart cities are emphasized

Near State Vector Selection-Based Model Predictive Control with Common Mode Voltage Mitigation for a Three-Phase Four-Leg Inverter

A high computational burden is required in conventional model predictive control, as all of the voltage vectors of a power inverter are used to predict the future behavior of the system. Apart from that, the common mode voltage (CMV) of a three-phase four-leg inverter utilizes up to half of the DC-link voltage due to the use of all of the available voltage vectors. Thus, this paper proposes a near state vector selection-based model predictive control (NSV-MPC) scheme to mitigate the CMV and reduce computational burden. In the proposed technique, only six active voltage vectors are used in the predictive model, and the vectors are selected based on the position of the future reference vector. In every sampling period, the position of the reference current is used to detect the voltage vectors surrounding the reference voltage vector. Besides the six active vectors, one of the zero vectors is also used. The proposed technique is compared with the conventional control scheme in terms of execution time, CMV variation, and load current ripple in both simulation and an experimental setup. The LabVIEW Field programmable gate array rapid prototyping controller is used to validate the proposed control scheme experimentally, and demonstrate that the CMV can be bounded within one-fourth of the DC-link voltage

Investigating the cooling effect of a green roof in Melbourne

‘Green Our Rooftop’ aims to transform the rooftop of Treasury Place, a state government building in the inner city of Melbourne, into an intensive green roof under the notion of ‘Garden of Victorian Landscapes’. The concept behind the innovative green roof design is to break down perceived barriers to green roof retrofitting, limit the global temperature rise and help cool the city by advocating modifications in urban infrastructure (e.g. greening projects). This study quantified the cooling effect of the complex green rooftop of Treasury Place (which is characterised by a diverse range of plant types and topographies) by using ENVI-met to assess the air temperature and outdoor thermal comfort at rooftop and pedestrian levels. After verification through field measurements, the study also investigated how the adjustments in the green roof's design settings (e.g. leaf area index [LAI], plant height, soil moisture and additional green coverage) can further improve the green roof's thermal performance. The findings from our study indicate that the implemented green roof configuration effectively lowered the air temperatures at the roof level by 1.5 °C, simultaneously enhancing thermal comfort by 2.38 °C during hot summer days. This optimum performance was achieved when soil moisture levels were set at 0.6, plant height at 0.6, and LAI at 2.5. Our statistical analysis indicates that all these scenarios exhibited equivalent cooling benefits. Thus, a holistic approach optimizing LAI, plant height, soil moisture, and tree coverage combined is essential to maximise cooling impact when integrating green roofs into future developments in inner city areas

Augmented reality visualization of modal analysis using the finite element method

Modal analysis provides the dynamic behavior of an object or structure, and is often undertaken using the Finite Element Method (FEM) due to its ability to deal with arbitrary geometries. This article investigates the use of Augmented Reality (AR) to provide the in situ visualization of a modal analysis for an aluminum impeller. Finite Element Analysis (FEA) software packages regularly use heat maps and shape deformation to visualize the outcomes of a given simulation. AR allows the superimposition of digital information on a view of the real-world environment, and provides the opportunity to overlay such simulation results onto real-world objects and environments. The presented modal analysis undertaken herein provides natural frequencies and the corresponding deformation of an aluminum impeller. The results indicate the ability for the design part and finite element analysis results to be viewed on the physical part. A mobile AR-FEA-based system was developed for Modal Analysis result visualization. This study offers designers and engineers a new way to visualize such simulation results

Maximum Power Point Tracking for Photovoltaic Systems Under Partial Shading Conditions Using Bat Algorithm

The vibrant, noiseless, and low-maintenance characteristics of photovoltaic (PV) systems make them one of the fast-growing technologies in the modern era. This on-demand source of energy suffers from low-output efficiency compared with other alternatives. Given that PV systems must be installed in outdoor spaces, their efficiency is significantly affected by the inevitable complication called partial shading (PS). Partial shading occurs when different sections of the solar array are subjected to different levels of solar irradiance, which then leads to a multiple-peak function in the output characteristics of the system. Conventional tracking techniques, along with some nascent/novel approaches used for the tracking maximum power point (MPP), are unsatisfactory when subjected to PS, eventually leading to the reduced efficiency of the PV system. This study aims at investigating the use of the bat algorithm (BA), a nature-inspired metaheuristic algorithm for MPP tracking (MPPT) subjected to PS conditions. A brief explanation of the behavior of the PV system under the PS condition and the advantages of using BA for estimating the MPPT of the PV system under PS condition is discussed. The deployment of the BA for the MPPT in PV systems is then explained in detail highlighting the simulation results which verifies whether the proposed method is faster, more efficient, sustainable and more reliable than conventional and other soft computing-based methods. Three testing conditions are considered in the simulation, and the results indicate that the proposed technique has high efficiency and reliability even when subjected to an acute shading condition

Short-Term Forecasting of the Output Power of a Building-Integrated Photovoltaic System Using a Metaheuristic Approach

The rapidly increasing use of renewable energy resources in power generation systems in recent years has accentuated the need to find an optimum and efficient scheme for forecasting meteorological parameters, such as solar radiation, temperature, wind speed, and sun exposure. Integrating wind power prediction systems into electrical grids has witnessed a powerful economic impact, along with the supply and demand balance of the power generation scheme. Academic interest in formulating accurate forecasting models of the energy yields of solar energy systems has significantly increased around the world. This significant rise has contributed to the increase in the share of solar power, which is evident from the power grids set up in Germany (5 GW) and Bavaria. The Spanish government has also taken initiative measures to develop the use of renewable energy, by providing incentives for the accurate day-ahead forecasting. Forecasting solar power outputs aids the critical components of the energy market, such as the management, scheduling, and decision making related to the distribution of the generated power. In the current study, a mathematical forecasting model, optimized using differential evolution and the particle swarm optimization (DEPSO) technique utilized for the short-term photovoltaic (PV) power output forecasting of the PV system located at Deakin University (Victoria, Australia), is proposed. A hybrid self-energized datalogging system is utilized in this setup to monitor the PV data along with the local environmental parameters used in the proposed forecasting model. A comparison study is carried out evaluating the standard particle swarm optimization (PSO) and differential evolution (DE), with the proposed DEPSO under three different time horizons (1-h, 2-h, and 4-h). Results of the 1-h time horizon shows that the root mean square error (RMSE), mean relative error (MRE), mean absolute error (MAE), mean bias error (MBE), weekly mean error (WME), and variance of the prediction errors (VAR) of the DEPSO based forecasting is 4.4%, 3.1%, 0.03, −1.63, 0.16, and 0.01, respectively. Results demonstrate that the proposed DEPSO approach is more efficient and accurate compared with the PSO and DE

The prospective non-conventional alternate and renewable energy sources in Pakistan - A focus on biomass energy for power generation, transportation, and industrial fuel

Pakistan is experiencing an undersupply of electricity, causing load shedding several hours per day due to the adherence to conventional energy resources having quantitative and environmental limitations. Fossil fuels generate more than half of the country's total electricity, but they will ultimately run out due to their limited supply. Their combustion emits greenhouse gases, posing environmental threats. Since the world is tending toward efficient and sustainable alternative methods for harvesting energy from nature, Pakistan has also been investigating an elevated deployment of renewable energy projects. This paper presents a critical analysis of the present energy sector of Pakistan along with global scenarios. Pakistan relies on mainly thermal, hydro, and nuclear energy for power generation. National solar, wind, geothermal, and biomass resources have not been extensively explored and implemented. This paper provides an insight into the potential of these resources in Pakistan to generate electricity for the national grid on a large scale. It focuses on biomass energy, which can be harnessed from bagasse, poultry waste, and municipal waste for power production, and biomass-based fuel for industries and transportation. It concludes that biomass is the most sustainable, available, implementable, and environment-friendly resource that can be utilized to lessen the energy demand and supply gap in Pakistan.University of Malay