A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Lévy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy

Electrical Characteristics Estimation of Photovoltaic Modules via Cuckoo Search—Relevant Vector Machine Probabilistic Model

This work presents an optimized probabilistic modeling methodology that facilitates the modeling of photovoltaic (PV) modules with measured data over a range of environmental conditions. The method applies cuckoo search to optimize kernel parameters, followed by electrical characteristics estimation via relevance vector machine. Unlike analytical modeling techniques, the proposed cuckoo search-relevance vector machine (CS-RVM) takes advantages of no required knowledge of internal PV parameters, more accurate estimation capability and less computational effort. A comparative study has been done among the electrical characteristics predicted by back-propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), Villalva's model, relevance vector machine (RVM), and the CS-RVM. Experimental results show that the proposed CS-RVM provides the best prediction in most scenarios

A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction

A New Real-Time Pinch Detection Algorithm Based on Model Reference Kalman Prediction and SRMS for Electric Adjustable Desk

This paper presents a new algorithm based on model reference Kalman torque prediction algorithm combined with the sliding root mean square (SRMS). It is necessary to improve the accuracy and reliability of the pinch detection for avoiding collision with the height adjustable desk and accidents on users. Motors need to regulate their position and speed during the operation using different voltage by PWM (Pulse Width Modulation) to meet the requirement of position synchronization. It causes much noise and coupling information in the current sampling signal. Firstly, to analyze the working principle of an electric height adjustable desk control system, a system model is established with consideration of the DC (Direct Current) motor characteristics and the coupling of the system. Secondly, to precisely identify the load situation, a new model reference Kalman perdition method is proposed. The load torque signal is selected as a pinch state variable of the filter by comparing the current signal. Thirdly, to meet the need of the different loads of the electric table, the sliding root means square (SRMS) of the torque is proposed to be the criterion for threshold detection. Finally, to verify the effectiveness of the algorithm, the experiments are carried out in the actual system. Experimental results show that the algorithm proposed in this paper can detect the pinched state accurately under different load conditions

Fabrication Of An Ir Hollow-Core Bragg Fiber Based On Chalcogenide Glass Extrusion

The theoretical analysis and experimental preparation of a hollow-core Bragg fiber based on chalcogenide glasses are demonstrated. The fiber has potential applications in bio-sensing and IR energy transmission. Two chalcogenide glasses with, respectively, high and low refractive indexes are investigated in detail for the fabrication of hollow-core Bragg fibers. The most appropriate structure is selected; this structure is composed of four concentric rings and a center air hole. Its band gap for the Bragg fiber is analyzed by the plane wave method. The chalcogenide glasses Ge15Sb20S58.5I13 and Ge15Sb10Se75 are chosen to extrude the robust multi-material glass preform with a specialized punch and glass container. The glass preform is simultaneously protected with a polyetherimide polymer. The hollow-core Bragg fibers are finally obtained after glass preform extrusion, fiber preform fabrication, and fiber drawing. Results showed that the fiber has a transparency window from 2.5 to 14 μm, including a low-loss transmission window from 10.5 to 12 μm. The location of this low-loss transmission window matches the predicted photonic band gap in the simulation