With the rapid development of sensing, communication, computing technologies, and analytics techniques, today’s manufacturing is marching towards a new generation of sustainability, digitalization, and intelligence. Even though the significance of both sustainability and intelligence is well recognized by academia, industry, as well as governments, and substantial efforts are devoted to both areas, the intersection of the two has not been fully exploited. Conventionally, studies in sustainable manufacturing and smart manufacturing have different objectives and employ different tools. Nevertheless, in the design and implementation of smart factories, sustainability, and energy efficiency are supposed to be important goals. Moreover, big data based decision-making techniques that are developed and applied for smart manufacturing have great potential in promoting the sustainability of manufacturing. In this paper, the state-of-the-art of sustainable and smart manufacturing is first reviewed based on the PRISMA framework, with a focus on how they interact and benefit each other. Key problems in both fields are then identified and discussed. Specially, different technologies emerging in the 4th industrial revolution and their dedications on sustainability are discussed. In addition, the impacts of smart manufacturing technologies on sustainable energy industry are analyzed. Finally, opportunities and challenges in the intersection of the two are identified for future investigation. The scope examined in this paper will be interesting to researchers, engineers, business owners, and policymakers in the manufacturing community, and could serve as a fundamental guideline for future studies in these areas.

Enhancing sustainability and energy efficiency in smart factories: A review

The tremendous advances in information and communications technology (ICT), as well as the embedded systems, have been led to the emergence of the novel concept of the internet of things (IoT). Enjoying IoT-based technologies, many objects and components can be connected to each other through the internet or other modern communicational platforms. Embedded systems which are computing machines for special purposes like those utilized in high-tech devices, smart buildings, aircraft, and vehicles including advanced controllers, sensors, and meters with the ability of information exchange using IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer networks. In this study, the application of IoT in smart grids is addressed. Hence, at first, an introduction to the necessity of deployment of IoT in smart grids is presented. Afterwards, the applications of IoT in three levels of generation, transmission, and distribution is proposed. The generation level is composed of applications of IoT in renewable energy resources, wind and solar in particular, thermal generation, and energy storage facilities. The deployment of IoT in transmission level deals with congestion management in power system and guarantees the security of the system. In the distribution level, the implications of IoT in active distribution networks, smart cities, microgrids, smart buildings, and industrial sector are evaluated.

IoT Architecture for Smart Grids

The environmentally clean nature of solar photovoltaic (PV) technology causes PV power generation to be embraced by all countries across the globe. Consequently, installation and utilization of PV power systems have seen much growth in recent years. Although PV arrays of such systems are robust, they are not immune to faults. To guarantee reliable power supply, economic returns, and safety of both humans and equipment, highly accurate fault detection, diagnosis, and interruption devices are required. In this paper, an overview of four major PV array faults and their causes are presented. Specifically, ground fault, line-line fault, arc fault, and hot spot fault have been covered. Next, conventional and advanced fault detection and diagnosis (FDD) techniques for managing these faults are reviewed. Moreover, a single evaluation metric has been proposed and utilized to evaluate the performances of the advanced FDD techniques. Finally, based on the papers reviewed, PV array fault management future trends and possible recommendations have been outlined.

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Review and Performance Evaluation of Photovoltaic Array Fault Detection and Diagnosis Techniques

An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia

Currently, a huge number of photovoltaic plants have been installed worldwide and these plants should be carefully protected and supervised continually in order to be safe and reliable during their working lifetime. Photovoltaic plants are subject to different types of faults and failures, while available fault detection equipment are mainly used to protect and isolate the photovoltaic plants from some faults (such as arc fault, line-to-line, line-to-ground and ground faults). Although a good number of international standards (IEC, NEC, and UL) exists, undetectable faults continue to create serious problems in photovoltaic plants. Thus, designing smart equipment, including artificial intelligence and internet of things for remote sensing and fault detection and diagnosis of photovoltaic plants, will considerably solve the shortcomings of existing methods and commercialized equipment. This paper presents an overview of artificial intelligence and internet of things applications in photovoltaic plants. This research presents also the most advanced algorithms such as machine and deep learning, in terms of cost implementation, complexity, accuracy, software suitability, and feasibility of real-time applications. The embedding of artificial intelligence and internet of things techniques for fault detection and diagnosis into simple hardware, such as low-cost chips, may be economical and technically feasible for photovoltaic plants located in remote areas, with costly and challenging accessibility for maintenance. Challenging issues, recommendations, and trends of these techniques will also be presented in this paper.

Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions

Using the Internet Of Things Technology for supervising solar photovoltaic power generation can greatly enhance the performance, monitoring and maintenance of the plant. With advancement of technologies the cost of renewable energy equipments is going down globally encouraging large scale solar photovoltaic installations. This massive scale of solar photovoltaic deployment requires sophisticated systems for automation of the plant monitoring remotely using web based interfaces as majority of them are installed in inaccessible locations and thus unable to be monitored from a dedicated location. The discussion in this paper is based on implementation of new cost effective methodology based on IoT to remotely monitor a solar photovoltaic plant for performance evaluation. This will facilitate preventive maintenance, fault detection, historical analysis of the plant in addition to real time monitoring.

Soham Adhya

Papers

An IoT based smart solar photovoltaic remote monitoring and control unit