Abstract

In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.

Introduction

Due to advancements in powerful computing tools, hardware, widespread use of cloud data, and the evolution of the Internet of Things, the accessibility of large datasets has significantly improved. This has promoted the emergence of many efficient machine learning (ML) algorithms. These algorithms, aided by continuous advancements in data analysis technology, have demonstrated effectiveness in training models and solving specific problems. If ML algorithms are complex and deep enough in terms of layers, then ML can become so-called deep learning (DL) [1]. Regardless of the name, ML seems to have a wide range of applications in modern society, because the achievements of artificial intelligence (AI) often exceed those achieved by manual control design.

In 2021, Nature Photonics further advanced its exploration of the hot topic of optics/photonics through a focused issue called “Machine learning of Light”. As an important type of microphysics, optics seems to have similarities in complexity with some of the “black box” processes in ML. In the Q&A session, David Pille and Aydogan Ozcan discussed the two primary drivers behind the current application of ML in photonics [2]. One direction is to use AI methods on hardware to design optical structures and devices with specific performance. Another direction is to use optical systems as fully optical/hybrid statistical inference models to achieve AI calculations.

Optics and photonics applied to ML computing offer unique advantages, allowing for ultrafast calculations at extreme frame rates and low energy consumption [3], [4], [5], [6]. For instance, Feldmann et al. [7] proposed a fully optical neural synaptic system. They use wavelength division multiplexing technology to implement a scalable circuit architecture for photonic neural networks (NNs) and use optical pulse signals to regulate the phase change materials that encapsulate neurons. This phase change material can regulate transmittance and reflectivity through a controllable laser. This system can perform both supervised and unsupervised learning. The work successfully demonstrates direct pattern recognition in the full optical system. This photonic NN has the potential to leverage the inherent high-speed and high bandwidth of optical systems, thereby enabling the direct processing of full optical communication and visual data. However, logic gates, computation, and NNs are all linear (passive) systems that cannot address nonlinear (active) issues. The lack of effective nonlinear optical processes as activation functions for many nodes poses challenges for all-optical implementation [8].

Conclusion and future perspectives

This paper reviews the application of the ML algorithm in optical fiber sensors. In recent years, the advent of ML has greatly impacted optical development. Compared with static demodulation algorithms and complex spatial optical paths, the ML algorithm offers better signal processing mechanisms for optics. ML algorithms enable automated processing, analysis, and recognition of spectrums, thereby enhancing the efficiency and accuracy of spectrum analysis. Using ML algorithms to classify and identify the spectrum of different substances, enabling qualitative and quantitative analysis of substances. By learning from known corresponding spectrums and building models, ML algorithms can predict and analyze unknown spectrums. ML algorithms can extract and recognize features from the spectrum, analyze the content and proportion of various components, and reveal the intrinsic nature of substances. With the continuous development of spectroscopy and ML algorithms, the application prospects of ML in spectroscopy will become increasingly widespread. This advancement will contribute to more efficient and accurate analytical methods in chemical analysis, environmental monitoring, food safety, and other fields, promoting progress in scientific research and expanding application fields.

Abstract

In recent years, floating photovoltaic (FPV) systems have emerged as a promising technology for generating renewable energy using the surface of water bodies such as reservoirs, lakes, and oceans. FPV systems offer several advantages over traditional land-based solar arrays, including increased land-use efficiency, reduced water evaporation, and improved cooling and maintenance. However, like all solar power systems, FPVs are subject to variability and intermittency due to changes in weather, seasons, and time of day. The environmental impact is discussed along with the deployment consideration and the feasibility for a better understanding of the system. Challenges associated with this are addressed by progressed research suggesting the integration of FPVs with various energy storage and hybrid systems. The most promising areas researched in this paper look at hybrid FPV hydropower plants (HPP), in parts of the world experiencing droughts HPP is not working to its optimum capacity. A review of available literature has been conducted on the topic of offshore and onshore floating solar electricity generation using floating solar photovoltaics to identify the challenges and opportunities presented. This work looks at a variety of other hybrid FPV energy sources with varying technology readiness levels. This paper concludes with the possibility of integrating different renewable technologies with existing FPVs and highlights the boons of doing so with some examples. Ultimately, current as well as future perspectives have been provided which consolidate the current research being done and give recommendations for future research work.

Introduction

One of the biggest challenges that is faced by the world is global warming due to which both humans and the planet are suffering as a whole, this is directly linked to the burning of the fossil fuels that we rely on to facilitate our daily life. The Intergovernmental Panel on Climate Change (IPCC) set out the impacts of global warming 1.5 °C above preindustrial levels [1]. We are not currently on course to limit 21st-century global warming to under 1.5 °C or even 2 °C [2]. Another key issue is the lack of clean electrification for a vast population which is hampering the development of the planet in tackling the climate change issue as many rely on the burning of fossil fuel for various daily requirements from generating electricity to cooking. It has been estimated that about 675 million people are still forced to live in the dark most of them belong to sub-Saharan Africa according to 2021 data. Though there has been an increase in the rate of access to electricity from 87% in 2015 to 91% in 2019, this has provided electricity to nearly 800 million people [3]. This is where solar PV can play a substantial role, solar PV has the benefit of being a renewable energy source, producing electricity from solar irradiance without any greenhouse emission [4].

However, there are challenges that must be addressed in order to fully realize the potential of solar energy and traditional photovoltaics [5]. These challenges include land usage, intermittency, storage, and integration into existing energy grids. One promising and upcoming alternative to traditional land-based photovoltaics is Floating Photovoltaics (FPV) or flotavoltaics [6]. The majority of renewable energy sources, such as biomass, solar, and others, take considerable footprint areas to generate electricity on a larger scale, which restricts the use of land for agriculture [[7], [8], [9]]. This sparked the discussion over whether land should be used for food production or energy production [10,11], encouraging research into offshore renewable technologies [12], and led to the creation of the floating photovoltaic (PV) array concept for the production of commercial electricity [13]. FPV technology is a concept in which solar panels are placed on platforms that float on water bodies such as natural lakes, man-made reservoirs, and the seas and oceans [14]. Fig. 1 shows a typical standalone floating photovoltaic system with all the components including an inverter, pontoons, solar panels, and cables connected to the grid.

Conclusion

This review article has examined the current state of research on the integration of floating photovoltaics with different storage and hybrid systems, including batteries, pumped hydro storage, compressed air energy storage, hydrogen storage and mixed energy storage options as well as the hybrid systems of FPV wind, FPV aquaculture, and FPV hydrogen production. The findings suggest that such systems have the potential to significantly increase the efficiency and reliability of renewable energy generation, as well as provide additional flexibility in managing electricity supply and demand.

FPV has many benefits over ground-mounted such as reduced land costs, increased efficiency, reduced soiling, reduced shading, reduced evaporation, and lower visual impact. Hybrid FPV has all of these benefits while being able to reduce its lifetime costs when paired with another technology a reduction in O&M costs, grid infrastructure and cable pooling. Overall, hybrid FPV has all the benefits of FPV plus reduced costs, while enhancing another technology. While FPV is set to rise this will initially be in land first with offshore following, the priority should be covering HPP first due to its complementary nature and benefits for both technologies. Countries, which have HPP reservoirs, should be looking into all the benefits of combining FPV with it. As FPV becomes more common it will be seen moving offshore, as the offshore wind increases it creates the opportunity for FPV to become a hybrid system.

Abstract

     Small- and medium-sized enterprises (SMEs) play a crucial role within a country’s economy considering that they provide a source of skills and innovation for entrepreneurship and their capacity for introducing, integrating and disseminating new technologies is incomparable. However, the world may be looking at an impending devastating recession delivered by the coronavirus pandemic. As governments intensify restrictions on business to halt the spread of the pandemic, the fear of the virus also reconfigures the very concept of business and the workplace. Therefore, there is a need for a greater focus on digital transformation considering that physical distancing requirements to curb the spread of this virus may become a cultural orientation for businesses and the workplace. A real digital transition is anticipated for small businesses with the 5G network technology. 5G network will not just be a technological connection but will affect different socio-economic sectors and will stimulate innovation in small companies. The goal of this paper was to carry out a literature review on the potential ways small businesses can leverage on 5G network for digital innovation. The paper proposed that small and medium businesses can leverage the 5G Mobile Technology through enhanced interpersonal communication, expanded remote work opportunities, innovative digital tools and supply chain efficiency. Finally, while many individuals and small business owners will profit from 5G technology some will work to undermine it. As a result, companies must keep this in mind when implementing 5G, or other new technology, and adjust their security protocols accordingly to remain secure.

Introduction

     Small businesses can expect a true digital transition with 5G (fifth generation of broadband data-networking digital communications technology) network technology (Tullberg et al., 2016). With an estimated annual growth rate of 97% and approximately 40% of the global population using IoT apps on a 5G network, the new mobile technology has the potential to generate a $251 trillion economic impact by 2025. This growth can be attributed to the fact that the 5G network will not only provide a technological connection but will also have an impact on various socioeconomic sectors and encourage small business innovation (Webb, 2016). The new technological link will make it possible to implement well-known initiatives, such as Smart City, Smart Company, and artificial intelligence in business (Zhu et al., 2016). Because of increased data transfer speed and reduced time between signal transmission and reception, the 5G network will redefine the role of connectivity and connections in global society, providing a new foundation for business innovation, particularly in small businesses. The goal of this paper is to conduct a review of the literature on the various ways small businesses can use the 5G network for digital innovation.

Conclusion

     The goal of this article was to establish a link between 5G technology and digital innovation, particularly in SMEs. The article began by establishing the context of the paper by summarising current knowledge and background information on the subject. This was accomplished by stating the purpose of the paper, the research question/problem, briefly explaining the methodological approach, and emphasising the study’s potential outcomes. Conceptual framework and synthesis were created to demonstrate the key relationships between the focal constructs (5G technology and digital innovation). This was done to aid comprehension of the conceptual framework. This paper contends that the implementation of 5G technology creates a platform for digital innovation in SMEs’ communication, remote working, supply chains, and marketing activities. This can simplify and improve the efficiency of work processes and operations within SMEs, resulting in increased productivity. 5G technology will create enormous opportunities for new revenue streams and will serve as the foundation for business innovation and development on a scale that companies in almost every industry have yet to fully explore.

Abstract

     The latest 5G technology is being introduced the Internet of Things (IoT) Era. The study aims to focus the 5G technology and the current healthcare challenges as well as to highlight 5G based solutions that can handle the COVID-19 issues in different arenas. This paper provides a comprehensive review of 5G technology with the integration of other digital technologies (like AI and machine learning, IoT objects, big data analytics, cloud computing, robotic technology, and other digital platforms) in emerging healthcare applications. From the literature, it is clear that the promising aspects of 5G (such as super-high speed, high throughput, low latency) have a prospect in healthcare advancement. Now healthcare is being adopted 5G-based technologies to aid improved health services, more effective medical research, enhanced quality of life, better experiences of medical professionals and patients in anywhere–anytime. This paper emphasizes the evolving roles of 5G technology for handling the epidemiological challenges. The study also discusses various technological challenges and prospective for developing 5G powered healthcare solutions. Further works will incorporate more studies on how to expand 5G-based digital society as well as to resolve the issues of safety–security–privacy and availability–accessibility–integrity in future health crises.

Introduction

     The latest wireless mobile phone technology, Fifth Generation (5G), was first widely deployed in 2019. While 5G network is at developing stage, some countries including China, South Korea, the UK and the US commercially deployed 5G networks, and some other developed countries are being expecting to put forward commercial 5G networks by 2025 [1]. Comparing to the existing wireless networks, 5G provides high data rate, lower latency, high volume of devices connectivity with energy efficiency, high reliability and mobility support [2]. Thus, the services offered by 5G mobile networks are generally categorized into three group, namely (i) “enhanced mobile broadband (eMBB)”, (ii) “ultra-reliable low latency communications (URLLC)”, and (iii) “massive machine-type communications (mMTC)” [3]. In 2019, 67% of the world people have subscribed to cellar devices, of which 65% have used smart phones [4], and 204 billion apps have been downloaded [5]. About 3.8 billion public have eagerly used social media reported in January 2020 [6]. As the number of digital devices connectivity with 5G is dramatically increasing, the extent of variation in exposure to radiofrequency arenas is still under investigation.

Conclusion

     The use of 5G network in integrating other digital technologies (like AI and machine learning, IoMTs, big data analytics, cloud computing, etc.) is now a reality in healthcare. From the literature, it is clear that 5G enabled technologies as well as intelligent systems have a prospect to improve healthcare services, such as medical diagnosis, health monitoring, treatment, management, etc. As the 5G poses non-ionizing radiation, there is no significant health risk of 5G RF wave below the recommended safety level. There is no evidence that 5G signal causes the spread of the coronavirus. Moreover, 5G enabled digital technologies have been used for limiting the COVID-19 outbreak, and enhancing the public-health strategies in 2020. Some advanced technology giants are conducting their researches on 5G related applications that will combat against the health risks of any undesirable epidemics. The successful utilization of 5G technology to tackle healthcare challenges will enhance the acceptance of such technologies for other sectors in the future.

     As it is a new field of research, some issues are to be considered in deployment of 5G network in healthcare including infrastructure development, technical standards, effective regulations and policy, privacy protection and security, as well as availability of research data. Further works will incorporate more study on how to expand 5G based digital society as well as to resolve some issues, such as safety–security–privacy, availability–accessibility–integrity, and to augment resilience to future health crises. It is also required to compile laws and prepare proper guidelines to deal with ethical and legal counteractions in managing the online services during the emergency situations. With the new technological innovations, 5G network will provide a complete pathway for a smart society with various promising healthcare applications.

Abstract

1. Introduction

2. Microgrid Control

3. Overview of AI framework for microgrid control

4. AI in Hierarchical control – review and possible improvement

5. Networked microgrid

6. Discussion and future scopes

7. Conclusion

Acknowledgement

References

Abstract

     Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and forming essential consumer/prosumer centric integrated energy systems. Integration, coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task. Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks. Therefore, this paper briefly reviews the control architectures, existing conventional controlling techniques, their drawbacks, the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers. This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments. It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

Introduction

     Microgrids can be distinguished from any distribution network containing DERs by two distinct features. First, their capabilities to operate in an islanded mode confirms the resiliency and reliability of the network. Second, to appear as controlled and coordinated units viewing from the upstream network [1]. Microgrids provide noteworthy benefits to consumers as well as utilities, the majority of which include; higher reliability by incorporating flexibility at the community layer distribution network, improved power quality by managing flexible loads, reduced carbon emission by the diverse DERs, lowering transmission & distribution losses, cheap energy supply utilising more renewable resources, and the possibility of active participation in the energy markets [2], and must fully satisfy the network and load requirements at islanded mode [3].

     Taking into consideration the large share of stochastic natured DERs comes with lots of uncertainty and a range of instances where effective controlling of the microgrid network becomes very critical. Interconnecting multiple microgrids as a network of microgrids can be an effective solution to accommodate and improve the operation quality of the large number of DERs. It has also been recognised that when multiple microgrids, geographically close to each other, are tied together through the distribution line to form a networked microgrid, the reliability and resilience of the interconnected microgrid can be significantly increased [4, 5].

Conclusion

     This paper provides an overall review of AI-based control in microgrid environments. An overview of existing traditional control methods, their drawbacks, the need for AI techniques and their implementation at the different levels have been reviewed and future scopes have been presented. Despite system model complexities and challenges, it is found that AI can certainly be an important tool to enable the seamless integration and control of DERs at the local and networked levels. NN-based models are getting more focus on all levels. Most of the implemented AI techniques are physical model-based, whereas data-driven techniques should also gain more interest. Since a data-driven model doesn’t require extensive physical system information, the design becomes less complex. However, in some specific applications like inertia estimation in the primary control, there is a lack of data available at the low voltage distribution network level. As ESS is becoming a core part of decarbonising the smart and microgrid networks, the datasets for ESS SoC levels which are accessible and open to the public require extensive preprocessing to improve the data quality and better predictability.

An ever-increasing interest on integrating solar power to utility grid exists due to wide use of renewable energy sources and distributed generation. The grid-connected solar inverters that are the key devices interfacing solar power plant with utility play crucial role in this situation. Although three-phase inverters were industry standard in large photovoltaic (PV) power plant applications, the microgrid regulations increased the use of single-phase inverters in residential power plants and grid interconnection. This paper presents a detailed review on singlephase grid-connected solar inverters in terms of their improvements in circuit topologies and control methods. Even though there are many reviews have been proposed in the current literature, this study provides a differentiating approach by focusing on novel circuit topologies and control methods of string and micro inverters. The single and multi-stage solar inverters are reviewed in terms of emerging DC-DC converter and unfolding inverter topologies while the novel control methods of both stages have been surveyed in a comprehensive manner. The isolated and transformerless circuit topologies have been investigated by reviewing experimental and commercial devices. The soft computing, evolutionary and swarm intelligence based algorithms have been summarized in MPPT methods section while current injection and grid-connection control methods of unfolding inverters stage have been presented with and without PLL architecture. There are many papers have been compared and listed in each section to provide further outcomes which is followed by a summarizing discussion section and conclusion.

Introduction

The use of renewable energy sources (RESs) is increasing day by day in electricity generation due to their variety and support to utility grids. Besides the opportunities, the wide integrations of RESs to utility grid have brought several challenges in terms of planning, operating, maintenance and management issues. The distributed generation (DG) policies set by governments and demand side management (DSM) requirements of distribution system operators (DSOs) have leveraged the interoperability plans for generation plants including solar photovoltaics (PVs), wind turbines, fuel cells, and micro sources. A DG system comprised by various type of energy sources requires appropriate power electronic devices for power conversion for coupling at a single bus bar. The grid-connected inverters which are required for RES and DG integration to utility play crucial role in resource management. Moreover, the DC-DC converters are also required for regulating the DC power generated by PV or fuel cell sources while inverters are interfacing the entire power plant with grid (Kabalci, 2015; Kabalci and Kabalci, 2018, 2017). The microgrid applications have promoted efficient power conversion requirements especially in low-voltage range (Schweizer and Kolar, 2013) where the wide variety of power converter studies on DC-DC converters and cascaded multilevel inverters have been proposed in the literature. The PV power plants hold the biggest share by increasing their installed capacity up to 400 GWp by the end of 2017 which is estimated to tackle 1000 GWp capacity by 2022 (JaegerWaldau, 2017; Masson et al., 2018; Schmela, 2018). The total installed capacity of global PV power plants has been increased more than 100 GWp in 2017 as seen in Fig. 1. The main contribution is supported by subsidies provided by US and European governments.

1- Introduction

2- MPPT selection criteria

3- MPPT techniques

4- Conclusion

References

Abstract

This paper presents a comprehensive overview on various maximum power point tracking (MPPT) techniques, which have been recently designed, simulated and/or experimentally validated in the PV literature. The primary goal of each MPPT technique is to optimize the output of shaded/unshaded photovoltaic (PV) array under static and dynamic weather conditions. Though each MPPT technique has its own pros and cons, an optimized MPPT technique is characterized in many aspects like hardware and software simplicity, implementation, cost effectiveness, sensors required, popularity, accuracy and convergence speed. In this paper the rating of various MPPT methods has been done based on the benchmark P&O method. The rating criteria is separately calculated for the techniques that are capable to work in full-sun and partial shading conditions. A rule based table is set to evaluate the MPPT against the algorithm’s complexity, hardware implementation, tracking speed, and steady state accuracy or detection of global maximum. Moreover, special consideration has been given to bio-inspired MPPT algorithms. The bio-inspired algorithms are compared side by side with their specific application in PV system. A tree diagram is also designed to see the emergence of partial shading algorithms over a period of time. The traits presented in this paper are novel and provide bottom-line for the researchers to select and implement an appropriate MPPT technique.

Introduction

The worldwide energy demand is increasing on a faster pace than the energy generation because of enhanced industrialization, growing population and improved living standards. According to international energy agency (IEA) reports, the energy consumption of world will increase by 44% from 2006 to 2030 [1]. In the present scenario, the fossil fuels such as oil, gas and coal are depleting with every passing day, which are the primary energy drivers and pollution contributors [2–4]. Therefore, the world is moving to new energy resources, which are promising, socially compatible, renewable, and sustainable. These sources are solar, wind, biomass, small hydro, geothermal, and ocean tidal/thermal. Photovoltaic (PV) is a prominent renewable energy source due to its sustainability, local availability, environmental friendly nature, simple technology, increasing cost effectiveness and less balance of systems. In photovoltaic, the sunlight is converted into direct current using semiconductor materials without any moving parts and carbon emissions. For the past decades, the power generation from PV systems have showed extensive penetration in commercial and domestic facilities. PV has now turned into the third most essential renewable energy source after hydro and wind. At present, more than 100 nations are utilizing ground mounted or building integrated PV systems for domestic, commercial and grid applications. According to IEA-PVPS (international energy agency for photovoltaic power systems), the worldwide installed PV capacity has expanded to 227 GW in March 2015 and is expected to reach up to 500 GW by the end of 2020 [5,6]. The current-voltage (I-V) curve of PV array relies on irradiance and temperature conditions. When the irradiance increases with constant temperature, the photovoltaic current also increases in direct proportion with negligible effect on PV voltage. Similarly, if the temperature increases with constant irradiance, the PV voltage decreases substantially while the PV current increases slightly. Under uniform condition, PV array exhibits unique maximum power point (MPP) on its I-V curve. The tracking mechanism in PV system, known as maximum power point tracking (MPPT) technique, is inevitable to search the MPP as innate characteristic of PV varies non-linearly with irradiance, temperature, and load.

1- Introduction

2- Electrical micro-force sensors

3- Optical micro-force sensors

4- Discussion

5- Conclusion

References

Abstract

Micro/nano manipulation for both mechanical and biological structures is currently a popular research field. To protect small-scale structures and acquire their mechanical properties, a micro-scale force sensor is needed. This paper focuses on reviewing the research on non-contact micro-force sensor parts that can be integrated in this manipulation system. The content involves the structure, working principle, resolution and sensitivity of different force sensor parts, including electrical and optical force sensors. The electrical force sensors include piezoresistive, piezoelectric, capacitive, electrothermal and strain gauge-based types; while the optical force sensors focus on but are not restricted to the optical fiber-based force sensors and the vision-based sensing systems. All of these sensors are analysed and compared. Electrical force sensors are currently widely used but are restricted by the sensing properties and size; optical force sensors have high sensitivity, small structure and anti-electromagnetic-interference properties, but they are hardly applied in micro/nano manipulation systems for force measurement. As a result, optical force sensors may become the new generation of sensors that can be integrated with micro/nano manipulation systems.

Introduction

Micro/nano manipulation is a popular interdisciplinary research field of this century. This approach is common for the fabrication of three-dimensional (3D) microstructures, the movement of nanostructures, the transportation and motion control of particles and, especially applications in biology [1,2]. Based on this technology, manipulation and characterization of biological cells, such as cell transfer, isolation, immobilization and injection have been widely used in many biological applications over the past decade [3]. As the handled biological cells can be easily damaged, the capability of precisely measuring small forces at the micro/nanoscale is necessary forproviding force feedbackduringmanipulation[4]. The measurement of force can also be used for the mechanical characterization of cells since genetic mutations and pathogens can result in changes to cell mechanical properties such as elasticity and viscosity [5]. In [6], changes in the mechanical properties of mouse zona pellucida post fertilization were tested for investigating the hardening process. Other than biological cells, force measurement is essential for sample protection and mechanical properties detection of micro/nano samples. Additionally, the force sensing system is vital for the experimental verification of the micro-force theoretical model. Different kinds of micro-force sensors were designed during the past decades, including piezoresistive, piezoelectric, capacitive, and electrothermal sensors and those based on visual detection. Reference [7,8] demonstrated two kinds of triaxial piezoresistive micro-force sensors with millinewton resolution, while reference [9,10] presented two piezoresistive force sensors with the piezoresistance attached to the membranes. In [11,12], force sensors were constructed using the polyvinylidene fluoride(PVDF) sensing material by measuring the electric charge of the PVDF membrane. Beyeler et al. [13] devised a six-axial force sensor using seven capacitors with a micronewton resolution. Bulut Coskun et al. [14] designed a micro-electrothermal force that used feedback control to nullify displacements within the device with a high resolution of 7.8 nN. Rajagopalan et al. [15] put forward a displacement-based force sensor with the resolution of 50 pN. The force was obtained by optically measuring the displacement of the probe with respect to a fixed reference beam.

1- Introduction

2- Resilience of power systems

3- Microgrids as a resilience resource: microgrid formation

4- Strategies used by microgrids for enhancing resilience

5- Communication, event-specific, and component resilience

6- Research gaps and future directions

7- Conclusion

References

Abstract

Microgrids are considered as a potential solution to deal with major power disruption events due to their ability of islanding and potential to sustain the penetration of renewables. In order to elaborate the role of microgrids in enhancing the resilience of power systems, a three-step analysis is carried out in this paper. In the first step, the general backgrounds of resilience in power systems are presented, which comprise of disaster modeling, resilience analysis methods, and resilience enhancement efforts by different regions. In the second step, the use of microgrids as a resilience resource is analyzed, where formation of microgrids, networked microgrids, and dynamic microgrids along with resiliency of multi-energy networks are explored. In the third step, the strategies utilized by microgrids for enhancing their resilience during major outage events are analyzed. These strategies include proactive scheduling, outage management, feasible islanding, and advanced operation strategies for reducing the impact of major disruptions. The classification of these operation strategies is based on the event occurrence and clearance times. In addition, the resilience strategies used by different types of microgrids, types of energy management systems, communication resilience, and resilience of individual components in microgrids are also analyzed. Finally, research gaps in the existing literature and future directions for improving the available resilience-oriented operation methods for enhancing the resilience of microgrids are presented.

Introduction

Resilience enhancement of power grids against natural disasters has become a major consideration for power and energy sector researchers and engineers in recent years. Due to lesser incidences of natural disasters, these events were initially known as low-probability high-impact events. However, the frequency of these events has increased in the last few decades due to climate change [1]. Among various other major events, the intensity and severity of weather-related events have significantly increased in the last decades [2]. Seven of the ten major storms that occurred during the last four decades have occurred in the last 10 years and each event caused damages of over 1-billion dollars [3]. In 2017 only, eight major weather-related events have struck the world, especially the US [4–11]. Five of these eight events have disrupted power to over a million consumers and the least number of consumers affected during any single event were around 0.3million. The existing electrical power systems can assure service reliability during normal conditions and abnormal but foreseeable and low impact contingencies. However, the continuity of service during unexpected and high impact events is still a challenge [12]. Therefore, existing power systems are known to be reliable but not resilient. The resiliency of a system is defined as its ability to return to the equilibrium (stable operation point) after a major disruption event [13]. Due to the absence of a universally accepted definition of resilience in power systems, the authors in [14] have analyzed various aspects of system resilience and defined the power system resilience.

1- Introduction

2- Requirements for sensors to determine the state of an anaerobic digester

3- Algorithm-based monitoring

4- Utilisation of colourimetric sensors in anaerobic digestion

5- Concluding remarks

References

Abstract

Although biogas is not a new approach to producing renewable fuel, it could further be developed to improve its potential as an alternative energy source. To achieve this, vast improvements in the efficiency and cost of biogas production are essential. These enhancements require detailed systematic monitoring to attain a near-optimal biogas production process. To date, there is a striking imbalance between the inherent biological complexity of anaerobic digestion, and the minimal information currently measured on-line. The objective of this review is to discuss how improvements in availability and cost of sensor technology used for determining the key compounds and their dynamics within the biogas processing plant will facilitate the further understanding of the biogas production process, preventing the biological process failure. In particular, colourimetric assays (sensor assays based on coloured dyes) for variable detection in anaerobic digestion provide a stable, multivariate system for the detection of Volatile Fatty Acids (VFAs), but also provide a much deeper insight into the process by assessing other parameters, which, to date have never been measured on-line. These sensor improvements will allow the biogas production, even on a small scale, to be guided in the optimum direction, avoiding the biological process from collapsing. This will result in improved efficiency, at a reduced operational cost. The potential of colourimetric assay methods for use in anaerobic digestion as a sensor technology with associated data analysis methodologies has not previously been observed. Here, a 23-dye colourimetric sensor array was experimentally assessed to exhibit the differentiation of 10 mM acetic acid, 5 mM propionic acid and 0.3 mM butyric acid. The feasibility of on-line, cost-effective, rapid, and efficient detection of VFAs together with other key parameters by these colourimetric sensor arrays is intended to be assessed to advocate their usage in AD.

Introduction

One route to production of renewable, clean energy is through biogas production, where biogas is derived from biological waste materials. Biogas is produced through anaerobic digestion (AD) of organic waste materials by a variety of microorganisms [1]. The inherent complexity of this ecosystem resulting from the dynamical interaction of several hundreds of bacterial and archaeal species [2], developing on a mixture of substrates, results in significant difficulty in determining the state of the digestion process [1]. The most straightforward information is provided by monitoring of gases (H2, CH4 and CO2) measured in the gas-phase of the anaerobic digester. Their fluctuations can provide information about the digesters’ productivity [3]. Measurement of pH is also important. A pH outside the range of 6–8 often indicates process deterioration, limiting methane production and eventually collapse of the entire biological process [4]. Concentrations of volatile fatty acids (VFAs; mainly acetic, butyric and propionic acid) have been suggested as useful control parameters, as these acids are indirectly indicative of the activity of the methanogenic consortia [3,4]. VFA accumulation can be interpreted as organic overload or inhibition of the methanogenic microbial communities [5]. Acidogenic microorganisms transform hydrolysis products into VFAs, while acetogenic microorganisms then convert VFAs into acetate, H2 and CO2. Methane is then produced by the methanogens [6], with the majority being produced by acetotrophic methanogens, transforming acetate into methane and CO2 [7]. VFA concentrations are a required control parameter for biogas production monitoring [8,9], and it is essential that VFAs are monitored to understand the biological process. Additionally, the ratio of VFAs over total alkalinity is often suggested as an indicator of the processes stability, with the objective of this ratio remaining below 0.3 [10].

An overview of capacitive technologies based on carbon materials (energy storage in electrical double-layer capacitors (EDLCs), capacitive deionization (CDI), energy harvesting, capacitive actuation, and potential controlled chromatography) is presented. The review reveals the role of carbon for these scientific and industrial purposes with disclosing the benefits and limitations of various nanostructured carbons for a certain application. A special attention is placed on the electrical double-layer (EDL) formation mechanisms affected by the porous texture of carbon and the electrode architecture. The importance of a careful selection of the electrolytic solution for the EDL formation inside the intraparticle pores of carbon electrodes is also enlightened.

Introduction

During the last fifty years, the worldwide industrial development accompanied by improving wealth in emerging markets and growth of the human population have driven a steadily increasing demand for new energy and fresh water supplies. The emission of noxious gases during fossil fuel consumption and the depletion of their reserves have led to introduce renewables (sun and wind) in the energy mix. Unfortunately, due to their intermittent character, the renewable technologies are not able to ensure a real-time balance of electricity supply.[1] Therefore, in order to adapt the energy delivery to the demand and improve the energy efficiency, current researches are focused on introducing energy-storage technologies which would meet sustainability, environmental requirements and be cost-effective. What is more, the automotive industry faces the challenge to reduce the exhaust gases emission and amount of energy dissipated in urban areas by internal combustion engine powered vehicles. Encouraging efficient energy management should accelerate commercialization of the powertrain technologies to be applied in low carbon vehicles (LCV), fast-charging infrastructures in the stations, wireless monitoring systems or electricity load leveling in stationary and transportation systems. Apart from energy related issues, another critical problem worldwide concerns the shrinking access to potable water sources. It is commonly known that sea or brackish water is not suitable to drink without being desalinated. Therefore, alternative options to i.e. expensive vacuum distillation processes, reverse osmosis and cogeneration desalination plants, using either fossil fuels or nuclear power, are still highly sought in order to meet the global needs for clean water with reduced carbon footprint. Electrochemical technologies are able to bring some response to the issues related with efficient energy management, reduction of greenhouse gases emissions and water desalination by utilizing the concept of electrical double-layer (EDL) created at the surface of nanoporous electrodes.[2-4] When an electrode is polarized, the ions of opposite charge present in the electrolyte migrate to the electrode surface, where they create an EDL.[5] The double-layer capacitance depends on the electrode surface area able to accumulate electrical charge by pure electrostatic forces and on the charge separation distance imposed by dimensions of the charge carriers. Therefore, electrodes made from nanostructured carbon materials (also called carbons further in the text), characterized by a highly extended surface area and pores which match with the size of electrolyte ions, are generally considered to be capacitive.[4, 6-13] The capacitive technologies include: i) capacitive energy storage in electrical double-layer capacitors (EDLCs);[14, 15] ii) capacitive energy harvesting where small amount of ambient energy is captured, accumulated, and converted into electrical energy;[16, 17] and iii) capacitive deionization (CDI) of sea or brackish water where the salt ions are removed from the feed water, electrostatically held in the EDL and released in a brine stream during discharge.[18-20] Besides, the chargedependent ions separation can be also employed in a new type of liquid chromatography (called potentialcontrolled chromatography) to enhance the anolyte concentration or to introduce withholding of certain species.[21] What is more, the distribution of electric charges within the porosity of carbon material entails changes of geometric surface area of the electrodes triggered by charging and discharging, so-called capacitive actuation. The flexible capacitive devices where the electrodes deformation is controlled by a voltage stimulation can be used in soft robotics to make shape-changing robots.[22] In this context, the paper reveals the benefits and limitations of various nanostructured carbons, with a special attention to the charge storage mechanism in EDLCs affected by the porous texture of carbon electrodes. It then reviews the state-of-the-art of capacitive technologies based on carbon materials with discussing in some detail how the double-layer concept is being utilized for their development.

1- Introduction

2- Layouts

3- Sampling techniques

4- Communication technology

5- Prediction models

6- Decision support systems

7- Conclusion and future work

References

Abstract

Integration of Wireless Sensor Networks (WSN) in the field of agriculture has provided a new direction for the production of crops. The same can be applied particularly to greenhouses. Greenhouses provide a protected environment for plants and crops. This paper reviews the role of WSN in monitoring of greenhouses. The paper presents an end-to-end survey; starting from the layout of crops in greenhouses, wireless technology used for communication of sensors, techniques to choose transmission interval or rate. Layouts and sampling techniques are also classified and compared. Other than these, the paper also studies various prediction models and decision supporting techniques adopted in greenhouses for efficient integration and management of WSN. These prediction models are also comprehensively analyzed in this work.

Introduction

Modern day scenario of agriculture has changed. Over the years, population has increased. According to UN’s world population prospect 2017 report, population is estimated to reach 9.8 billion by 2050 (United Nations, 2017). Along with population, demand for food is also multiplying. According to Food and Agriculture Organization of the United Nations (FAO), although there are 30,000 types of edible plants available, humans cultivate only 4% of it (FAO, 1999). The agricultural land has considerably reduced due to various factors like urbanization and industrialization. To cope up with these situations, modern-day technological solutions are required. Other than this, water scarcity, increase in fertilization and dynamic climate changes have also strained to integrate technology to achieve required production in agriculture with minimum wastage of resources. WSN is one prominent technology that has developed over past few years and has found its path in many applications like military, defense, healthcare, agriculture, etc. In agriculture, WSN is particularly used to achieve a state called Precision Agriculture (PA). PA works on the phenomenon of observing (using sensors) and responding (using actuators) and tries to achieve parametric values and conditions required for optimum health and yield of crop. PA also focuses on optimizing resources used for production. Fig. 1 shows the complete process of monitoring and control in a field. Sensors are placed at required stations in suitable and optimal deployment. Sensed information is sent to central repository through available wireless channel.

1- Introduction

2- Methodology

3- Results and discussion

4- Conclusions

References

Abstract

Despite the considerable contribution of hydropower in driving the American economy for over a century, the rationale for hydropower in the U.S. energy mix needs to be reassessed in the context of advanced science and technology. Other alternative-yet-cheaper energy resources have been identified and hazards associated with aging hydro-dams have escalated in recent years. Furthermore, research has shown more negative environmental consequences associated with hydro-dams—and dams in general. To compare the contribution of hydro-electricity to the total energy production in the U.S., and to identify its regional distribution and contemporary patterns, we conducted a systematic analysis of large-scale multi-year data from U.S. federal agencies and tallied the nameplate capacities of major hydro-dams against their existing energy production values. We found that despite continuous efforts at upgrading hydro-facilities, since 2000 the mean contribution of hydroelectricity has remained less than 10% of the total generated energy in the U.S. and has been declining since then. Based on our results, we conclude that reservoir- and dam-based hydroelectricity may not be an efficient energy resource—at least from the American perspective, and perhaps it is timely to consider promoting other non-conventional renewable resources for energy production.

Introduction

Hydropower has been an integral component of American electricity production for nearly 130 years and has played a substantial role in the nation’s industrial revolution [1]. Differences in regional precipitation and runoff, river morphologies, local settlement patterns and concordant energy needs and alternatives, and the nature of hydropower facilities has varied widely across the conterminous U.S. However, over time as energy demand has outstripped the feasibility to bring additional hydropower capacity online, the contribution of hydropower has declined in the nation’s energy mix while, at the same time, annual total and per capita electricity usage in the U.S. has been increasing [2]. The feasibility of satisfying expanding energy needs from alternative renewable energy sources (e.g., wind, solar and biomass fuel) has been increasing with the rapid decline of their costs and improvements in their efficiency. Indeed, the leading U.S. federal labs have reported a consistent annual decline of the levelized cost of energy (LCOE) for land-based- and offshore-windfarms, and utility-scale solar installations in recent years. For instance, the LCOE value of a typical land-based wind project in the U.S. declined from $71/Megawatt hour (MWh) in 2010 to $49/MWh in 2016, and it declined from $225/MWh in 2010 to $173–207/MWh in 2016 for a typical offshore windfarm [3,4]. Hydroelectricity generation from impounded water is still viewed as providing a high level of energy supply services, probably because even in the recent context of other non-traditional sources becoming cheaper, it contributed a substantial 16.4% of the global energy mix in 2016 with an estimated total installed capacity of 1096 GW [5]. From the energy production perspective, three main types of hydro-projects, namely reservoir-based, run-of-river and pumped-storage have been identified; each with its own inherent merits and demerits. Perhaps the most important appeal of a hydroelectric plant is its capacity to load balance instantly as the electricity demand varies on a diurnal basis [6]. Initially, hydropower was promoted as a “carbon-emission free” energy resource and a panacea to mitigate atmospheric pollution; however, recent studies, emerging from tropical to temperate areas—including the U.S., have debunked those assertions by reporting significant emission of greenhouse gases (GHGs) from reservoirs into the atmosphere [7,8].