Moving vehicle load identification from bridge responses based on method of moments (MOM)

A MOM-based algorithm (MOMA) is proposed for identifying of the time-varying moving vehicle loads on a bridge in this paper. A series of numerical simulations and experiments in laboratory have been studied and the proposed MOMA are compared with the existing time domain method (TDM). A few main parameters, such as basis function terms, executive CPU time, Nyquist fraction of digital filter, two different solutions to the ill-posed system equation, etc, have been investigated. Both the numerical simulation and experimental results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent, but its CPU execution time is just less than one tenth of the TDM

Material tracking with dynamic torque adaptation for tension control in wire rod mill

Material tracking is an important part of the automation control system which has a major impact on the product quality. This paper addresses a stand load identification in wire rod mill as a new algorithm added to existing control system. Tension control approaches are described and a modification of existing tracking system is proposed in order to eliminate tracking faults. Proposed method is based on dynamic torque calculation and its performance was experimentally verified on the industrial wire rod mill. Experimental results show significant reduction of the errors

Non-intrusive load identification for smart outlets

An increasing interest in energy-efficiency combined with the decreasing cost of embedded networked sensors is lowering the cost of outlet-level metering. If these trends continue, new buildings in the near future will be able to install \u27smart\u27 outlets, which monitor and transmit an outlets power usage in real time, for nearly the same cost as conventional outlets. One problem with the pervasive deployment of smart outlets is that users must currently identify the specific device plugged into each meter, and then manually update the outlets meta-data in software whenever a new device is plugged into the outlet. Correct meta-data is important in both interpreting historical outlet energy data and using the data for building management. To address this problem, we propose Non-Intrusive Load Identification (NILI), which automatically identifies the device attached to a smart outlet without any human intervention. In particular, in our approach to NILI, we identify an intuitive and simple-to-compute set of features from time-series energy data and then employ well-known classifiers. Our results achieve accuracy of over 90% across 15 device types on outlet-level energy traces collected from multiple real homes

Augmented Tikhonov Regularization Method for Dynamic Load Identification

We introduce the augmented Tikhonov regularization method motivated by Bayesian principle to improve the load identification accuracy in seriously ill-posed problems. Firstly, the Green kernel function of a structural dynamic response is established; then, the unknown external loads are identified. In order to reduce the identification error, the augmented Tikhonov regularization method is combined with the Green kernel function. It should be also noted that we propose a novel algorithm to determine the initial values of the regularization parameters. The initial value is selected by finding a local minimum value of the slope of the residual norm. To verify the effectiveness and the accuracy of the proposed method, three experiments are performed, and then the proposed algorithm is used to reproduce the experimental results numerically. Numerical comparisons with the standard Tikhonov regularization method show the advantages of the proposed method. Furthermore, the presented results show clear advantages when dealing with ill-posedness of the problem

Load Identification Using Harmonic Based on Probabilistic Neural Network

Due to increase power quality which are caused by harmonic distortion it could be affected malfunction electrical equipment. Therefore, identification of harmonic loads become important attention  in the power system. According to those problems, this paper proposes a Load Identification using harmonic based on probabilistic neural network (PNN). Harmonic is obtained by experiment using prototype, which it consists of microcontroller and current sensor. Fast Fourier Transform (FFT) method to analyze of current waveform on loads become harmonic load data. PNN is used to identify the type of load. To load identification, PNN is trained to get the new weight. Testing is conducted To evaluate of the accuracy of the PNN from combination of four loads. The results demonstrate that this method has high accuracy to determine type of loads based on harmonic loa

Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism

Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. The method is used to estimate appliance-level power consumption from aggregated power measurements. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM), featuring an integrated attention mechanism, all within the context of disaggregating low-frequency power data. While prior research has been mainly focused on high-frequency data disaggregation, our study takes a distinct direction by concentrating on low-frequency data. The proposed hybrid CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level. This accuracy is further enhanced by the attention mechanism, which aids the model in pinpointing crucial parts of the data for more precise event detection and load disaggregation. We conduct simulations using the existing low-frequency REDD dataset to assess our model performance. The results demonstrate that our proposed approach outperforms existing methods in terms of accuracy and computation time

Buildings Sustainability — The Non-Intrusive Load-Identification System Contribution

Buildings are responsible for an important share of the global energy consumed with the associated consequences at economic and environmental level. To overcome this actual concern several objectives were put in perspective, being one of them the energetic performance of systems and appliances. Efficiency depends on working on optimal conditions and user behavior. Monitoring of the energy consumption of each electric load is important but the use of decentralized energy is not feasible at present due to the huge number of loads connected to the electric grid. An alternative consists on the use of a centralized measurement device able to identify loads. This work presents a measurement infrastructure that have, among others, the possibility to make the identification of electrical loads data will be used to improve the energetic performance of households and buildings and increase the sustainability of the energy system.info:eu-repo/semantics/publishedVersio

Response Transmissibility for Load Identification Improved By Optimal Sensor Locations

A knowledge of loads acting on a structure is important for analysis and design. There are many applications in which it is difficult to measure directly the dynamic loads acting on a component. In such situations, it may be possible to estimate the imposed loads through a measurement of the system output response. Load identification through output response measurement is an inverse problem that is not only ill-conditioned, but in general leads to multiple solutions. Therefore, additional information, such as number and locations of the imposed loads must be provided ahead of time in order to allow for a unique solution. This dissertation focuses on cases where such information is not readily accessible and presents a method for identification of loads applied to a structure using the concept of response transmissibility. The solution approach is divided into two phases that involve finding the number and location of forces first followed by a reconstruction of the load vector. To achieve the first phase, a complete description of the structure in terms of degrees of freedom needs to be specified and a numerical model, usually a finite element model is built. In order to determine the number of forces and their locations, the proposed algorithm combines the dynamic responses measured experimentally along with the transmissibility matrices obtained from the numerical model. Once the number of loads and their locations are known, a regeneration of the load vector is achieved during the second phase by combining the measured dynamic responses with the transmissibility matrix from the numerical model. In this dissertation, identification of loads through measurement of structural response at a finite number of optimally selected locations is also investigated. Optimum sensor locations are identified using the D-optimal design algorithm. Two different types of measurements are considered, acceleration measurements using accelerometers and the strain measurements using strain gages. A series of simulated results on multi-degree of freedom (MDOF) discrete and continuous systems are presented to illustrate the load identification technique based on response transmissibility. One of the factors that affects the accuracy of load reconstruction is the number of vibration modes included in the analysis, which can be a large number. Improvements using model order reduction, not only help reconstruct the input forces accurately, but it also reduces the computational burden significantly. The developed algorithms are implemented using the finite element tool ANSYS in conjunction with MATLAB software. Numerical sensitivity analysis is also implemented to examine the effect of presence of uncertainties (noise) in experimental data. The results obtained confirm that the techniques presented are robust even in the presence of simulated noise; it is seen that the applied loads are recovered accurately