Standardization is essential for any technological advancement to have widespread adoption, ensure interoperability, enhance compatibility and create a consistent user experience across various platforms and sectors. In industries, Programmable Logic Controllers (PLCs) and Human-Machine Interfaces (HMIs) play a crucial role in automating processes, improving operational efficiency, ensuring safety and providing real-time monitoring and control. The evolution of the Internet of Things (IoT) has significantly improved the industrial automation process to a higher level by enhancing the connectivity and data-sharing capabilities of PLCs and HMIs, enabling predictive maintenance and remote management. To meet the increasing demand and seamless integration of diverse industrial devices, many modern PLCs have built-in support for IoT implementation (IoT-ready PLCs) using industrial communication protocols like Ethernet Industrial Protocol (Ethernet/IP), PROFINET, Modbus, etc., with a standard interface. On the other hand, many old PLCs (legacy PLCs) continue to remain influential in industrial automation, managing complex processes. However, they lack built-in support for IoT integration due to different communication interfaces, proprietary protocols, and obsolete technology. Interconnecting these legacy PLCs under a shared network for data exchange, diagnosis, and remote monitoring in the industrial setup is challenging. It demands a huge capital expenditure to replace the existing legacy PLC infrastructure with IoT-ready PLCs. This challenge needs to be addressed through standardization and middleware solutions. This paper presents the integration of different PLCs deployed at diverse locations for the Industrial Lighting Management System (LMS) with a centralized HMI through a shared network using IoT Technologies.

Adequate illumination is integral to performing any activity in manufacturing industries. In large-scale industries, various capacities and quantities of high bay luminaires (roof lights) are installed and distributed over the factory roofs to achieve the required illumination. Such enormous quantum is traditionally controlled by grouping them under multiple digital time switches distributed at diverse locations. The arduous activity of frequent rescheduling of time switches on a need basis is addressed by adopting a centralised controller called the Lighting Management System (LMS). However, the LMS lacks individual control of each distribution point (point-based) of roof lights due to limited control wires, leading to substantial energy wastage. This paper presents the design and implementation of point-based switching control using the Modbus Remote Terminal Unit (RTU) protocol on the existing control cable.

Operational requirements of photovoltaic (PV) modules result in their inherent exposure to harsh environmental conditions. The performance of solar cells decreases with increasing temperature, with both efficiency and power output getting affected. High ambient temperature coupled with irradiance absorption leads to an elevated photovoltaic cell operating temperature, adversely affecting the panels' lifespan. Superhydrophobic nanocoatings are the preferred solution to reduce the accumulation of dust (soiling) over the surface of the panels. This article aims to study the effects of nanocoatings on module operating temperature and temperature-dependent cell parameters, such as open-circuit voltage ( Voc ), short-circuit current ( Isc ) and power generation. The application of nanocoating over the surface of solar panels reduces the operating temperatures while improving power generation in a temperate location with high annual atmospheric temperatures.

Power generation of photovoltaic (PV) modules undergoes harsh environmental conditions. Accumulation of dust on the surface of PV modules, also referred to as soiling is one of the important factors. It limits the penetration of solar energy onto the solar cell and reduces the energy output. This article aims to study the detrimental effects of soiling losses on solar modules in a real-time industrial environment. The role of hydrophobic nanocoatings in mitigating these losses is also investigated. Reduction in accumulation of dust, both in terms of the particle composition and deposition densities, shows significant improvements in reducing the transmission loss. In this article, we tested and validated the performance of hydrophobic nanocoatings on PV modules, at Bharat Heavy Electricals Limited campus in Tiruchirappalli, Tamil Nadu, India [10°48′18′′N,78°41′8′′E]. Application of the nanocoating demonstrated a significant increase in power generation under various climatic conditions in the location.

This paper presents the design and development of a laboratory module to aid solar photovoltaic courses in the Electrical Engineering and Power systems programs. This hardware-based system is modeled for three-phase solar photovoltaic power system with multiple interfaces such as solar, battery and utility power. This system employs a photovoltaic power as the main input and an electrical storage system as secondary input. The system is supported by the utility grid in the case of deficiency or absence of photovoltaic power. The power flow control from the two sources is tailored for maximum utilization of solar photovoltaic power while maintaining required level of constant power for the industrial application. A microcontroller is programmed to operate the changeovers between sources for uninterrupted and constant level of power for the load. The complete design and experimentation of the proposed system are explained in this paper along with techno-economic analysis to make this more meaningful for power engineering graduates to apply them in real life situations.

In this paper, the design of servo inverter used for an industrial application is presented. In boiler component manufacturing processes, high performance machines are often equipped motion controls consisting of synchronous servo motors and servo inverters. The design of important aspects of servo inverter used in a five axis CNC tube machine are explained. The configuration of main stages of the system such as main dc supply unit, servo inverter drives, servo motors etc. are detailed with relevant illustrations. The servo inverters are supplied with a regenerative power supply unit producing main dc power and are controlled by a CNC controller. The calculation of mechanical power for drives, dc bus continuous and peak power, continuous and peak regenerative power, simultaneity factor are well explained in this paper with a real time comparison. A regenerative power supply unit with smart energy mode to reduce current and power peaks on the mains side is also proposed to limit the maximum device current to 1.1 fold value of the nominal current and to achieve energy savings. This paper will be a good guide for project planning for industrial applications using servo inverter systems.

Penetration of grid-connected photovoltaic systems can be increased substantially by devising area-specific power output forecasting methods. Meteorological conditions of the area are decisive for solar plant management and electricity generation. This paper estimates and forecasts the profile of power output of a grid-connected 20-kW(p) solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India, using artificial neural networks (ANNs). A multilayer perceptron-based ANN model is proposed for day-ahead forecasting of the power generation. An experimental database comprising of each day's solar power output and atmospheric temperature for a period of 70 days has been used for training the ANN. Various training algorithms, transfer functions, and learning rules in the hidden layers/output layers were employed on the database of 11,200 patterns in order to obtain the best mapping between the ANN's inputs and outputs. Statistical error analysis in terms of mean absolute percentage error calculated on the 24-h-ahead forecasting results is presented. Analysis of the variations in network forecasting performance caused by changing the neuron functional parameters has been carried out. The results are also utilized for load scheduling operations of the industrial grid for the next day. Reliable area-specific solar power production map can help in power system scheduling and investment productivity.

This paper discusses the details and results obtained from flash butt welding machine using bidirectional power flow controller. Flash butt welding is a type of resistance welding used in the manufacturing industries. The power flow control and the operational features of the flash butt machine are discussed in detail. The different modes of operation and the welding parameters such as welding current, welding voltage, displacement during flashing and pressure applied to the work pieces are well explained. The experimental results obtained during the welding process along with various welding factors are also documented in this work.

Solar Photovoltaic (PV) systems are gaining popularity as a form of alternative energy with increased environmental awareness, renewable energy usage and concern for energy security. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders tapping solar power on a large scale. The objective of this paper is to estimate the profile of produced power of a grid-connected 20 kWp solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816" N, 78° 47' 9.4524" E]. An Artificial Neural Network (ANN)-based model is proposed in this paper. An experimental database of solar power output (from 7th January 2014 to 10th February 2014) has been used for training the ANN. Simulations were carried out with the Neural Network Fitting Toolbox of MATLAB software. Day-Ahead Forecasting results indicate that the proposed model performs well with great accuracy and efficiency. Statistical error analysis in terms of Mean Absolute Percentage Error (MAPE) was conducted and the best result was found to be 0.2887%. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.

An increase in environmental awareness, renewable energy usage and concern for energy security have resulted in the advent of Solar Photovoltaic (PV) systems as a sustainable form of alternative energy. Lack of area-specific forecasts for the power output of grid-connected photovoltaic system hinders in tapping the full potential of abundant solar power. The objective of this paper is to estimate the profile of power output of a grid connected 20kW p solar power plant in a reputed manufacturing industry located in Tiruchirappalli, India [10° 44' 42.3816" N, 78° 47' 9.4524" E] using artificial intelligence techniques. An Artificial Neural Network (ANN) based model is proposed as a prediction model in this paper. An experimental database comprising of each day's solar power output and atmospheric temperature (from 31 st May 2014 to 31 st July 2014) has been used for training the ANN. The regression mapping of the neural network was carried out with the Neural Network Fitting Toolbox of MATLAB and simulated with Neuro Solutions development environment. Statistical error analysis in terms of Mean Squared Error (MSE) was calculated on the Day-Ahead Forecasting results and was found to be in the range of 0.019 to 0.025, signifying good accuracy and efficiency. Reliable area-specific solar power production map can provide better utilization of solar energy resource and help in power system management.