Economic dispatch (ED) is an optimisation strategy to ensure power systems operate in an economic manner. This paper proposes a multi-objective optimisation method to minimise the total generation cost and total system loss simultaneously and find the best adjustment for this economic dispatch problem. This study focused on solving the multi-objective economic dispatch problem using a Heuristic Optimisation (HO) method, namely Multi-Objective Evolutionary Programming (MOEP). The Weighted Sum Method (WSM) is integrated with EP to find a trade-off solution between two objectives: total generation cost minimisation and total system loss minimisation. The practicable proposed method was tested on the IEEE 30-Bus Reliability Test System (RTS) for three different scenarios. MATLAB programming language was used to run the designated algorithm of MOEP. The performance of MOEP to solve the multi-objective ED problem was then compared with another method; the Multi-Objective Artificial Immune System (MOAIS). The experimental results show that MOEP dominates in all cases that have been tested, proving that MOEP is superior than MOAIS in providing high-quality solution to economic dispatch problem with multiple objectives in terms of cheap total generation cost and low total system loss.
This paper presents the application of a hybrid optimization technique termed as Immune-Evolutionary Programming (IEP) to solve Economic Emission Load Dispatch (EELD) of power system. IEP has been tested on the IEEE 30-Bus Reliability Test System (RTS) with two case studies: base case and loaded case. The objective of the optimization process is to minimize the total emission. Total system loss and total generation cost are calculated and observed while minimizing the emission. It is found that IEP is suitable to be used to solve EELD problemin giving better total emission, total system loss and total generation cost compared to the pre-optimization results for the both base case and loaded case.
Most countries over the past few decades have modernized their economies and become more reliant on electricity to run, so the electrical power system has also expanded greatly. Optimal Reactive Power Dispatch (ORPD) has a big influence on the reliability, security, and economic operation of the power system. Another thing to note is that ORPD has a few major targets and objectives which are to reduce the active or real power losses, to improve the voltage profile, to reduce transmission costs, and to increase system stability. Non-convex, non-linear, and multimodal problems make the development of intelligent algorithms to solve the reactive power dispatch problem highly relevant. Some researchers chose to compare and contrast optimization techniques from the past with each other in order to answer some remaining uncertainties such as the effectiveness and complexity of the technique toward the chosen objective function(s). Thus, this paper proposed applying the Multistage Artificial Immune System (MAIS) optimization method for solving the ORPD problem with the objective of reducing the power system losses. This algorithm was made by modifying and upgrading the classical AIS optimization method. Instead of only going through the process one time in the classical AIS algorithm, this MAIS method going through the processes more than one time in multiple stages of the same processes. This process includes cloning and mutation as well as selection. These modifications also aid in the development of new and unique solutions, as opposed to the classical AIS optimization process. Therefore, these enhancements could lead to a rise in the accuracy of the results' because there have been increased comparisons. This study confirms that MAIS optimization can deliver superior results in less time than AIS. Keywords—Optimal reactive power dispatch, computational intelligence, multistage artificial immune system, loss minimization.
This paper presents the design and development of a mini portable cooler that utilizes the Peltier effect to cool the human expressed breast milk (EBM) within 24 hours at the temperature range between 4°C-15°C to maintain the high quality of the EBM for use at a later date and the health of the baby who consume it. It is developed to overcome the tediousness of using cooler bag/box, which requires ice packs that need repetitive freezing. A few prototypes with different sizes and body materials, namely polystyrene, polypropylene and aluminium, were successfully developed and tested to get the minimum temperature using 5V power supply. In all graphs, the temperature is decreasing with time. The smallest size of the cooled space (18cm of width, 12cm of length and 14cm of height) has the lowest temperature compared to the other sizes for each body material. However, the lowest temperature of all body materials is obtained from aluminium with 11.2°C at 100 minutes. It also reaches below 15°C the fastest which is at 80 minutes compared to the other prototypes that need longer time. All in all, aluminium is the best body material for the mini portable cooler developed in this project.
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