Foundations on Expansive soils

https://www.econstor.eu/bitstream/10419/244055/1/1694119025.pdf

An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

TELKOMNIKA : Telecommunication, Computing, Electronics and Control

Enhancing the efficiency of rooftop solar photovoltaic panel with simple cleaning mechanism

The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

https://www.mdpi.com/2411-9660/5/3/55/pdf

Cost-Effective Design of IoT-Based Smart Household Distribution System

Solar power is mainly harnessed from photovoltaic (PV) panels which are arranged in multiple arrays in a solar farm or solar system. Though, power generation from PV solar system is characterised by uncertain efficiency, many countries with high insolation prefer solar as an alternative way of generating clean energy. However, the efficiency of energy generated from PV panels is affected by the accumulation of dust and debris, even on one panel in an array. This condition leads to the need for regular cleaning of the surface of PV panels. Current labour-based cleaning methods for photovoltaic arrays are costly in time, water and energy usage as well as lacking in automation capabilities. To overcome this problem, a fully automatic solar panel cleaning system with/without water is proposed. Hence, in this paper, the design of a robot for automated cleaning of the surface of PV panel is presented. The design utilizes an Arduino controller system to control the robot movement during the cleaning process. In addition, it is equipped with two rough sponge and a water pump system that can be used to clean dust or debris found on PV panel surfaces. The efficiency of the PV panels before and after the cleaning process is also observed. The result shows that the developed solar panel cleaning robot is able to clean the panel effectively and increase back the output current as well as the maximum power of the panel by 50%, after the dust on the PV panel is cleaned.

/pdf/development-of-solar-panel-cleaning-robot-using-arduino-2xdyt9axzh.pdf

Development of solar panel cleaning robot using Arduino

One of the major challenges forbattery energy stowage system is to design a supervisory controller whichcan yield high energy concentration, reducedself-discharge rate and prolong the battery lifetime. A regulatory PV-Battery Management System (BMS) based State of Charge (SOC) estimation is presented in this paper that optimally addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and utilizesthe Maximum Power PointTracking(MPPT)schemeof the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. A case study(SOC estimation) is demonstrated as well to depictthe efficiency(Error 0.082%) of the proposed modelusing real time data. The numerical simulation structured through real-time information concedes that the projected control mechanismis robust and accomplishes several objectives of integratedPV-BMS for instance avoiding overcharging and deep discharging manner under different solar radiations

/pdf/designing-a-control-system-based-on-soc-estimation-of-bms-10ejkd5o6v.pdf

Designing a control system based on SOC estimation of BMS for PV-Solar system

This paper proposes a neural network control scheme of a DC-DC Flyback converter that will step up a 12V DC and applied it on brushless DC motor with 12 and 24V dc. In this technique, a back propagation learning algorithm is derived. The controller is designed to track the output voltage of the DC-DC converter and to improve performance of the Flyback converter during transient operations. Furthermore, to investigate the effectiveness of the proposed controller, some operations such as starting-up and reference voltage variations are verified. The numerical simulation results show that the proposed controller has a better performance compare to the conventional PI-Controller method. DOI: http://dx.doi.org/10.11591/ijpeds.v2i1.154

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/4882/4281

Voltage Tracking of a DC-DC Flyback Converter Using Neural Network Control

Electric motor drive systems (EMDS) have been recognized as one of the most promising motor systems recently due to their low energy consumption and reduced emissions. With only some exceptions, EMDS are the main source for the provision of mechanical energy in industry and accounts for about 60% of global industrial electricity consumption. Large energy efficiency potentials have been identified in EMDS with very short payback time and high-cost effectiveness. Typical, during operation at rated mode, the motor drive able to hold its good efficiencies. However, a motor usually operates out from rated mode in many applications, especially while under light load, it reduced the motor’s efficiency severely. Hence, it is necessary that a conventional drive system to embed with loss minimization strategy to optimize the drive system efficiency over all operation range. Conventionally, the flux value is keeping constantly over the range of operation, where it should be highlighted that for any operating point, the losses could be minimize with the proper adjustment of the flux level to a suitable value at that point. Hence, with the intention to generate an adaptive flux level corresponding to any operating point, especially at light load condition, an online learning Artificial Neural Network (ANN) controller was proposed in this study, to minimize the system losses. The entire proposed strategic drive system would be verified under the MATLAB/Simulink software environment. It is expected that with the proposed online learning Artificial Neural Network controller efficiency optimization algorithm can achieve better energy saving compared with traditional blended strategies.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/20560/13226

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

A renewable energy source that works alone can’t achieve customers’ requirements for a stable power supply. Therefore, the paper proposes a multi-input converter for hybrid renewable energy system. This converter is designed for two input sources, PV and wind generator in order to design high efficiency and high performance converters for renewable energy applications. The proposed multi-input converter is composed by interleaved technique with two step-up converters and the two inputs are accommodated with some extra semiconductors, inductances and diodes. The modes of operation based on the status of the four switches, where S1 and S2 operate as main switches in order to deliver energy from both voltage sources. A constant output power to the load is provided by switching S3 switch, which guarantied the appropriate output voltage by reduce the ripple and improve the reliability. Simulations of multi-input converter has been performed using MATLAB/SIMULINK. The simulation results confirm the validity of the proposed method, which can be seen as a promising new topology that ensure multi-input converter suitable for renewable energy applications.

Sim Sy Yi

Papers

Development of solar panel cleaning robot using Arduino

Designing a control system based on SOC estimation of BMS for PV-Solar system

Voltage Tracking of a DC-DC Flyback Converter Using Neural Network Control

Loss minimization DTC electric motor drive system based on adaptive ANN strategy

Multi-Input Boost Converter for Hybrid PV and Wind Generator Systems