Power Optimization Analysis Research Articles

Analysis of power optimization and enhanced routing protocols for wireless sensor networks

Computers are connected through a network. Wireless sensor nodes are used in sensor networks to keep tabs on their surroundings. The building components of a network are sensor nodes. Processors, transceiver and memory are all onboard as well as a power supply. A WSN is a network of devices. Lifespan, data collection, and security are WSN parameters. Wireless sensors' low cost and quick deployment make them ideal for real-time applications. WSN's key problem is long-term energy supply. Energy resource, energy harvesting, topology, and effective routing may extend network lifespan. WSN energy is improved using an efficient manner. The recommended technique relies heavily on grouping and selecting the best possible path. The energy levels of the sensor nodes decide the cluster leader. Chikoo search algorithm selects the best path. This algorithm saves more energy than others. Optimum search ideas identify optimal sink location. Based on the network's energy dissipation and distribution, this technique finds the ideal location for all sinks to improve the network's lifespan and move with intelligent sink placement.

RETRACTED ARTICLE: Implications for sustainability in supply chain management and the circular economy using machine learning model

The definition of “Industry 4.0” would contribute to that distributed manufacturing systems, real-time information and inevitably a lot more product information. In this case, the optimization of capabilities goes beyond the conventional goal often adds to the competitiveness and efficiency of the company. Lean management and continuous development strategies, in turn, suggest an enhancement rather than maximization of energy. Furthermore, the analysis of Power Optimization and Costing Models is an important area of research that needs functional theoretical feedback. Hence in this paper, a statistical approach for power control based upon multiple costing frameworks using a machine learning model (SCM–MLM) has been designed and developed for power optimization and cost factor. This paper introduces and addresses a generalized model that has been built to evaluate idleness and create techniques to optimize the profitability of the enterprise. The maximization of trade-off capacity against organizational performance is demonstrated and it is seen to be organizational inefficiency by power optimization has been validated in this paper.

Reactive Power Optimization Analysis of 35KV Regional Power Grid Based on the Genetic Algorithm

As we know, the load of power grid keeps increasing in recent years, and the difference of power pink and trough is large. This situation causes the low efficiency of power consuming. In this paper, the integer coded genetic algorithm is used to carry on the reactive optimization of 35KV power gird, which includes operations of genetic algorithm, such as coding, selecting, crossing, and mutating. The genetic algorithm is illustrated in IEEE30 points system and the optimization of maximum operational mode, the results show that this method decreases the loss and ensure the qualified rate of voltages, realizing the optimization of the power gird.

"Floater" Material Stability Calculation and Mould Power Optimization Analysis of 409l Stainless Steel

For Containing Titanium Stainless Steel, in Liquid Steel the Existence of the Element such as Ti, N, O Are Easily Generating High Melting Point Compounds Tin, Tio2, Etc. the Compounds Easily Deteriorate Slag. the Relationship of Tin between Solubility Product and Temperature in 409 Stainless Steel Is lg([%Ti][%N])=4.6-17036.9/T . the Relationship of Tio2 between Solubility Product and Temperature in 409 Stainless Steel Is lg([%Ti][(%O)2])=11.6-35728/T .The Relationship of Al2O3 between Solubility Product and Temperature in 409 Stainless Steel Is lg（[%Al]2[%O]3）=20.2-62787.9/T .Protective Slag Contains Compounds of B2O3 Mno and Fe2O3 Can Solve the Problem of “floater”.