The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable...
详细信息
The proliferation of renewable energy sources within distribution systems has given rise to a new structure known as microgrids. These microgrids are small power grids comprised of both controllable and uncontrollable loads. In the distribution system, microgrids can use renewable energy sources to be operated in far-away regions at lower investment costs. The energy industry faces numerous problems, including problems with energy efficiency, ensuring system confidence, and reducing the destructive environmental effects. Load management programs can help address the challenges confronting the energy industry. This essay proposes a method for evaluating the load responsiveness in microgrids. A combined algorithm, including a gravitationalsearchalgorithm as well as particle swarm optimization, to address a multi-objective optimization function. The Latin hypercube sampling method is adopted to create diverse scenarios, which are reduced by the K-means method. The objective function includes grid losses, generation costs, confidence index, and voltage stability. The suggested method is implemented on a modified 69-bus system consisting of wind turbines, solar power stations, and energy storage systems using the combined optimizationalgorithm in Matlab. The results show that increasing renewable energy production reduces power losses, power consumption, and cost per kilo and improves voltage deviation. Adding renewables and energy storage also reduces power fluctuations and grid losses. Adding renewable sources brings benefits such as reduced production costs, losses, and improved voltage profiles. From the simulation outcomes, it can be seen that the average voltage profile has been improved by about 4%. Also, increasing the responsive load has improved the mains voltage profile and increased by about 10%.
This paper presents an effective traffic video surveillance system for detecting moving vehicles in traffic scenes. Moving vehicle identification process on streets is utilized for vehicle tracking, counts, normal spe...
详细信息
This paper presents an effective traffic video surveillance system for detecting moving vehicles in traffic scenes. Moving vehicle identification process on streets is utilized for vehicle tracking, counts, normal speed of every individual vehicle, movement examination, and vehicle classifying targets and might be executed under various situations. In this paper, we develop a novel hybridization of artificial neural network (ANN) and oppositional gravitational search optimization algorithm (ANN-OGSA)-based moving vehicle detection (MVD) system. The proposed system consists of two main phases such as background generation and vehicle detection. Here, at first, we develop an efficient method to generate the background. After the background generation, we detect the moving vehicle using the ANN-OGSA model. To increase the performance of the ANN classifier, we optimally select the weight value using the OGSA algorithm. To prove the effectiveness of the system, we have compared our proposed algorithm with different algorithms and utilized three types of videos for experimental analysis. The precision of the proposed ANN-OGSA method has been improved over 3% and 6% than the existing GSA-ANN and ANN, respectively. Similarly, the GSA-ANN-based MVD system attained the maximum recall of 89%, 91%, and 91% for video 1, video 2, and video 3, respectively.
暂无评论