A novel multilevel DC - AC converter from green energy power generators using step-square waving and PWM technique

Green energy sources or renewable energy system generally utilize modular approach in their design. This sort of power sources are generally in DC form or in single cases AC. Due to high fluctuation in the natural origin of this energy (wind & solar) source they are stored as DC. DC power however are difficult to transfer over long distances hence DC to AC converters and storage system are very important in green energy system design. In this work we have designed a novel multilevel DC to AC converter that takes into account the modular design of green energy systems. A power conversion efficiency of 99% with reduced total harmonic distortion (THD) was recorded from our simulated system design

Neural network prediction for efficient waste management in Malaysia

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on population growth factor. This study uses Malaysian population as sample size and the data for weight is acquired via authorized Malaysia statistics’ websites. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with ten and five nodes were used respectively. The result portrayed that there will be an increase of 29.03 percent of SWG in year 2031 compared to 2012. The limitation to this study is that the data was not based on real time as it was restricted by the government

Design of smart waste bin and prediction algorithm for waste management in household area

Maintaining current municipal solid waste management (MSWM) for the next ten years would not be efficient anymore as it has brought many environmental issues such as air pollution. This project has proposed Artificial Neural Network (ANN) based prediction algorithm that can forecast Solid Waste Generation (SWG) based on household size factor. Kulliyyah of Engineering (KOE) in International Islamic University Malaysia (IIUM) has been chosen as the sample size for household size factor. A smart waste bin has been developed that can measure the weight, detect the emptiness level of the waste bin, stores information and have direct communication between waste bin and collector crews. This study uses the information obtained from the smart waste bin for the waste weight while the sample size of KOE has been obtained through KOE’s department. All data will be normalized in the pre-processing stage before proceeding to the prediction using Visual Gene Developer. This project evaluated the performance using R2 value. Two hidden layers with five and ten nodes were used respectively. The result portrayed that the average rate of increment of waste weight is 2.05 percent from week one until week twenty. The limitation to this study is that the amount of smart waste bin should be replicated more so that all data for waste weight is directly collected from the smart waste bin

Automatic metal waste separator system in Malaysia

Metal recycling is an issue that needs attention and should be practiced by people as it has many advantages to human and Earth. In order to get a metal from Earth, the process called mining are needed where it can harm our natural resources due to depletion of the area to be mined. If the process is not controlled, most of the areas on Earth will have huge excavation holes. So, people should be responsible to prevent this from happening to preserve the environment in a good quality by recycling the metal material. By metal recycling, it can save an energy and resources as it can reduce the demand for raw materials, hence maintain the natural resources for the future. The proposed automatic metal waste separation system is intended to ease the people to separate the waste material. Besides, it will make the metal recycling industry work easier because the metal waste is already isolated at garbage collection side. The purpose of the project is to design a system to separate the metal recyclable household waste automatically and record the data waste collected. There are total of four detectors used to separate the non-metal, steel, copper and aluminum metal waste. The average time used to complete metal separation process by using the proposed prototype is 14.5 seconds. This project includes a mechanical part, programming part and an electronic design. The system will be programmed using Arduino Mega as a microcontroller to control all the electronic component in the system

Smart street light using intensity controller

Smart street light is an intelligent control of street lights to optimize the problem of power consumption of the street, late in night. Currently, usual street lights are automatically turn on when it becomes dark and turn off when it becomes bright. This is huge waste of energy in the entire world as it is an essential community service, but current implementation is not efficient. Conventional street lights are being replaced by Light Emitting Diode (LED) street lighting system, which reduces the power consumption. The focus of this project is to design a system of street lights controller to provide a reduction in power consumption. The prototype is design by using Light Dependent Resistor (LDR), Infrared sensor (IR), battery and LED. All this component was controlled by Arduino UNO as the microcontroller. The brightness of the lamps is being controlled in this project to reduce the power consumption. The dimming of the lamps depends on the speed of object motion detected such as pedestrians, cyclists and cars. The higher speed of moving object, the greater the level of intensity. For this idea, the innovation of street lights is not quite the same as conventional street lights that are controlled by timer switch or light sensor which automatically turns the street lights on during sunset and off during sunrise. According to the study, motion detection devices may help to save up to 40% of energy per month

Energy efficient smart street light for smart city using sensors and controller

Smart street light is an intelligent control of street lights to optimize the problem of power consumption of the street, late in night. Conventional street lights are being replaced by Light Emitting Diode (LED) street lighting system, which reduces the power consumption. The focus of this project is to design a system of street lights controller to provide a reduction in power consumption. The prototype was designed by using Light Dependent Resistor (LDR), Infrared sensor (IR), battery and LED. The brightness of the lamps is being controlled in this project to reduce the power consumption. The dimming of the lamps depends on the speed of object motion detected such as pedestrians, cyclists and cars. The higher speed of moving object, the greater the level of intensity. For this idea, the innovation of street lights is not quite the same as conventional street lights that are controlled by timer switch or light sensor which automatically turns light on during sunset and off during sunrise. According to the study, motion detection devices may help to save up to 40% of energy per month

Classification of different types of metal from recyclable household waste for automatic waste separation system

Nowadays, solid waste has become a major problem in Malaysia. However, most people in Malaysia are not aware and take this problem for granted. The rising number of population and massive development in recent years indirectly generated an enormous value of household waste, making the household waste the main generator for solid waste in Malaysia. It stated that only 5 percent of an average 30,000 tons of waste have been recycled in Malaysia. The purpose of the paper is to design a system to separate the metal recyclable household waste automatically and record the data waste collected. There are total of four detectors used to separate the non-metal, steel, copper and aluminum metal waste. The average time used to complete metal separation process by using the proposed prototype is 14.5 seconds. This paper includes a mechanical part, programming part, an electronic design and also the data collected from this proposed system. The system will be programmed using Arduino Mega as a microcontroller to control all the electronic component in the system

Discrimination of residual and recyclable household waste for automatic waste separation system

Most of the countries in the world are confronting a gigantic issue of waste management system. Due to the rapid urbanization and increasing population, huge volume of waste is produced year by year. Improper waste management system has created a deep concern among people which lead to disturbance to the environment and human health. In this paper, a fully automated system that discriminate residual and recyclable household waste is proposed. It is shown that the prototype system are able to automatically separate waste into residual and recyclable waste by employing a moisture sensor to sort them into wet (residual) and dry (recyclable) waste. The state of the waste is determined by its resistance to current value and the percentage of water content

Material classification of recyclable waste using the weight and size of waste

Nowadays, insufficient landfills problem had increased the needs to decrease the waste and recycling them. However, despite the efforts done by the government and local authorities on promoting recycling culture by introducing new laws and regulations, the awareness and willingness among the community is still low. One of the possible reasons to this is lack of effort to categorize the waste into the designated category which are paper, glass, plastic and metal. In order to address this problem, it is important to design a system that will ease the process of categorizing the waste. This can be achieve by the automation of the said process. In this work, a system consist of an algorithm and hardware to automatically categorize recyclable waste is proposed. The proposed system are utilizing weight sensor and ultrasonic sensors in order to capture the characteristics of the waste item, which are weight and size so that it can be categorized into paper, glass, plastic and metal. Here, an algorithm to compensate minimum usage of hardware, namely the type and number of sensors is presented

Resonant configuration topology exploration for inductive link power transfer

This paper investigates the performance of circuit topology used in wireless power applications to optimize the level of maximum efficiency. We analyse the series and the parallel resonant topologies for use in an inductive coupling link to derive power transfer efficiency expressions verified using MATLAB. We look into the two topologies into the link under resonant conditions for selectively supplying the device with power. The results are obtained analytically which are verified subsequently by MATLAB simulation. We then analyse the links to see how maximum power transfer efficiency for a given pair of coils can be achieved. The topology at a given tuning frequency is used for powering a selected resistive load. The method is presented using a given pair of coils simulated and the results agree well with the theoretical explanation and derivations