Dynamic Modeling of Chatter Vibration in Cylindrical Plunge Grinding Process

Cylindrical plunge grinding process is a machining process normally employed as a final stage in precision machining of shafts and sleeves. The occurrence of chatter vibrations in cylindrical plunge grinding limits the ability of the grinding process to achieve the desired accuracy and surface finish. Moreover, chatter vibration leads to high costs of production due to tool breakages. In this paper, a theoretical model for the prediction of chatter vibration in cylindrical grinding is developed. The model is based on the geometric and dynamic interaction of the work piece and the grinding wheel. The model is validated with a series of experiments. Results show that variation in the grinding wheel and work piece speeds, and in-feed lead to changes in the vibration modes and amplitudes of vibration

Design of an Adaptive Controller for Cylindrical Plunge Grinding Process

In modern competitive manufacturing industry, machining processes are expected to deliver products with high accuracy and good surface integrity. Cylindrical plunge grinding process, which is a final operation in precision machining, suffers from occurrence of chatter vibrations which limits the ability of the grinding process to achieve the desired surface finish. Further, such vibrations lead to rapid tool wear, noise and frequent machine tool breakages, which increase the production costs. There is therefore a need to increase the control of the machining processes to achieve shorter production cycle times, reduced operator intervention and increased flexibility. In this paper, an Adaptive Neural Fuzzy Inference System (ANFIS) based controller for optimization of the cylindrical grinding process is developed. The proposed controller was tested through experiments and it was seen to be effective in reducing the machining vibration amplitudes from a 10-1 µm to a 10-2 µm range

Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods

Optimal sizing of grid connected multi-microgrid system using grey wolf optimization

Renewable distributed energy resources (DERs) offer a promising and environmentally sustainable solution for providing energy. Nowadays, there has been significant attention in wind, solar Photovoltaic (PV), and hydrogen-based fuel cell (FC) systems due to their ability to provide cost-effective energy to replace conventional generations. However, the intermittent nature of renewable energy sources presents challenges and operational issues for fully renewable energy systems. To address this, integrating energy storage systems and effectively managing uncertainties related to both load and generation resources are crucial for mitigating such challenges. This paper proposes a hybrid grid-connected PV-wind-FC generation-based Multi-microgrid (MMG) system integrated with a Battery Energy Storage System (BESS) to meet the entire load demand of the adopted MMG-based IEEE 14-bus system. The aim is to ensure cost-effectiveness and enable energy trading with the main grid by optimizing system configurations. The study incorporates stochastic analysis to handle uncertainties related to load, meteorological data, and energy prices to optimize the configuration of DERs and BESS in the MMGs. A Grey Wolf Optimization (GWO) algorithm is employed to determine the optimal sizing of the proposed grid-connected MMG. The proposed algorithm has reduced the NPC from $431.796 million to$428.832 million and LCOE to 0.267$/kWh when load and generation data uncertainty and dynamic energy price has considered. The robustness of the proposed approach is evaluated by comparing results with those obtained using Particle Swarm Optimization (PSO) and JAYA algorithms. The GWO method demonstrates superior performance, resulting in lower total Net Present Cost (NPC), lower system capacity, and a lower Levelized Cost of Energy (LCOE) compared to its counterparts. Moreover, the GWO algorithm exhibits the fastest convergence, indicating its accuracy and robustness compared to PSO and JAYA algorithms

A state of the art review on energy management techniques and optimal sizing of DERs in grid-connected multi-microgrids

In recent times, there has been a growing focus on multi-micro-grids (MMGs) system, owing to its well-suited structures for efficiently accommodating large-scale integration of distributed energy resources (DER). This attention is driven by the system’s cost-effectiveness, enhanced efficiency, stability, and reliability performance, achieved through the collaborative exchange of power flow among individual micro-grids (MGs) and the main grid. Fundamental strategies for attaining optimal energy flow and sharing involve the optimal sizing of MGs and the implementation of an Energy Management System (EMS). These strategies play a crucial role in addressing uncertainties associated with intermittent generation, load fluctuations and energy market dynamics. This paper offers a review of grid-connected MMG topologies, EMS structures, coordination methods and current optimization approaches designed to meet EMS objectives. To address the inherent volatilities, the paper introduces various uncertainty quantification techniques along with current challenges. Additionally, it suggests future directions, emphasizing intelligent and predictive modeling to handle uncertainties, as well as recommending the incorporation of energy storage systems (ESSs) to align with emerging trends.</p