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The virtual synchronous generator (VSG) was proposed to emulate a synchronous machine's dynamics when integrating power electronic converter-based distributed energy resources to the power grid. However, the VSG's synchronization stability during grid faults is not fully explored. The underlying mechanism of loss of synchronization still needs to be further revealed due to VSG's nonlinear characteristics. In this article, a step-by-step analytical method based on combining the linear and nonlinear models is proposed to analyze VSG's dynamic behaviors during a large disturbance. The relationship between the linear and nonlinear models is first brought to light, showing that the linear model is suitable for qualitative analysis to give an intuitive physical insight. Simultaneously, the latter is adopted for quantitative analysis to assess stability after a grid fault. Moreover, to avoid the conflict of the synchronization stability and the inertia response, a transient damping method is added in the active power control loop, which can simultaneously improve the synchronization stability and frequency stability. Design guidelines are also proposed to identify the parameter of the transient damping with different inertia requirements. Finally, the experimental results verify the analytical method and the theoretical analysis. [ABSTRACT FROM AUTHOR]

This study proposes an advanced current and position sensor fault diagnosis mechanism for permanent magnet synchronous motor (PMSM) applications. Machine nonlinear dynamics as well as parameter and load uncertainties are addressed. The features of the results of this study are summarized as follows: 1) a proportional-type full-state observer is combined with a disturbance observer to deal with the model uncertainties, 2) a partial optimal process is introduced for determining the observer gain, 3) the concept of normalized residuals is introduced for fault diagnosis criteria, and 4) a closed-loop analysis of the convergence, performance recovery, and offset-free guarantee is provided. The performance recovery property makes the proposed algorithm robust to parameter uncertainties and load variations, and sensitive only to sensor faults. In addition, the location and type of sensor faults can be identified by the fault diagnosis criteria using the normalized residuals. The performance of the sensor fault diagnosis algorithm was experimentally verified under several credible scenarios using a 7.5-kW PMSM. [ABSTRACT FROM AUTHOR]

This paper discusses a potential mechanism from bifurcation analysis to explain the occurrence of modulated oscillations (which are observed from real PMU measurements) in synchronous machine nonlinear dynamics. We show that in special system configurations synchronous machine dynamics can be very sensitive to sustained low frequency oscillations which are amplified to a great extent by nonlinear effects. A comprehensive bifurcation analysis illustrates that a small sustained oscillation evolves into a torus through the Neimark-Sacker bifurcation, creating a modulated oscillation with a higher amplitude and a much slower but new oscillatory frequency. The analysis further predicts a new bifurcation type, the Chenciner bifurcation, that has never been reported in power engineering. The Chenciner bifurcation introduces much more complex dynamical behaviors including co-existence of two stable attractors and chaos. It can be a potential reason for the sudden appearance of a benign modulated oscillation without any system configuration change. We use a detailed synchronous machine infinite bus system with an automatic voltage regulator and a steam turbine to demonstrate the phenomenon and to study the bifurcation. Then, we perform a model order reduction to establish the main states responsible for nonlinear effects and show a potential critical role of the exciter saturation for the investigated bifurcation. [ABSTRACT FROM AUTHOR]

Cyber-physical manufacturing systems (CPMS) can be defined by the integration of control, network communication, and computing with a physical manufacturing process. In this work, we present a hybrid model of CPMS combining sensor data, context information, and expert knowledge. We used the identification of global operational states and a multimodel framework to improve anomaly detection and diagnosis. The anomaly detection is based on context-sensitive adaptive threshold limits. Root cause diagnosis is based on classification models and expert knowledge. The proposed approach was implemented using the Internet of Things (IoT) to extract data from a computer numerical control machine. Results showed that using a context-sensitive modeling strategy allowed to combine physics-based and data-driven models for residual analysis to detect an anomaly in the part, machine, or process. The identification of root cause was improved by adding context information in classification models to identify worn or broken tools and wrong material. Note to Practitioners—Anomaly detection and diagnosis of manufacturing equipment is a complex problem. Some of the challenges are complex machine dynamics and nonstationary operating conditions. This paper describes a framework for modeling manufacturing equipment using a combination of sensor data, context information, and system knowledge. The proposed modeling framework is used to improve anomaly detection for diagnostics using a context-sensitive strategy. This work aims to support more effective maintenance actions by identifying problems in the machine, part, or process. The modeling and anomaly detection strategy was used to identify anomalies in computer numerical control machines and can be extended to other equipment on the plant floor. [ABSTRACT FROM AUTHOR]

Oscillations have been observed in wind farms with weak grid interconnections. While Texas observes 4 Hz low-frequency oscillations, the west region in China observes subsynchronous oscillations at 30 Hz. Furthermore, this oscillation mode caused torsional interactions with a remote synchronous generator and led to shutdown of the power plant. Inspired by those real-world events, this paper aims to present an analytical model of type-4 wind in weak grids that can demonstrate both low-frequency and subsynchronous frequency oscillations. Critical factors, e.g., the parameters of the phase-locked loop (PLL), are examined using small-signal analysis. The analysis results are validated using a testbed built in MATLAB/SimPowerSystems. Except power electronic switching sequences, the testbed has controls (e.g., wind turbine pitch control, maximum power point control, converter controls), machine dynamics and power system dynamics modeled. This testbed includes a 100 MW Type-4 wind farm, a 600 MW synchronous generator, a long transmission line and a grid. The testbed successfully demonstrates two types of dominant oscillations under different PLLs. In addition, the testbed demonstrates torsional interactions due to the proximity of the subsynchronous mode and one of the torsional modes. [ABSTRACT FROM AUTHOR]

The Material Test Facility (MTF) houses a medium-caliber electromagnetic (EM) railgun that is designed for ease of access. As such, it is ideal for developing diagnostics for measuring the performance of EM launchers. Multiple in situ diagnostics have been fielded on the MTF railgun including field sensors for position location and thermal sensors. Kinematics of the launch package are of particular interest. B-dot loops are the usual method, but many such B-dots are required to obtain a detailed evolution of position and velocity. This requires many channels and consumes data acquisition resources. Naval Research Lab (NRL) has developed several versions of a flux ladder diagnostic, which include many pick-up loops on one data channel. Some versions have multiple flux ladders interleaved to maintain pulse separation in a single channel, but which increase the overall spatial resolution. A typical MTF configuration has 58 positions monitored in just three channels. Careful processing has yielded velocity and even acceleration. NRL is also developing a flux loop diagnostic that has the potential for continuous kinematics with the time resolution of the digital sampling rate. However, flux loops are more sensitive to spurious signal sources. Preliminary flux loop results are presented. [ABSTRACT FROM PUBLISHER]

The design of the drive system for an automatic machine and its correct sizing is a very important competence for an electrical or mechatronic engineer. This requires knowledge that crosses the fields of electrical engineering, electronics and mechanics, as well as the skill to choose commercial components based upon their technical documentation. A good way to help students to achieve these competencies is to set them a project-oriented task, consisting of a real case study they have to solve. The aim is to give to students enrolled in the courses Functional Mechanical Design and Dynamics of Machines at the Polytechnic of Milan, Milan, Italy, the opportunity to put their knowledge and skills into practice on a real industrial case regarding the design of an automatic machine, with particular attention being paid to the problem of the analysis and synthesis of the drive system. [ABSTRACT FROM AUTHOR]

In recent research, a field construction technique (FCT) was derived to enable more efficient evaluation of the magnetic fields and forces within induction machines. Using the FCT, the results of two finite-element (FE) solutions are used to establish basis functions for the flux densities in the airgap of the machine. The basis functions are then used to predict the magnetic fields and forces under arbitrary stator excitation. In this paper, a multirate FCT(MRFCT)is proposed to enable efficientFCTmodeling ofmachines that are connected to power electronic converters. Within theMRFCT, the low- and high-frequency components of the stator current are partitioned. The partitioned currents are then used to calculate the flux density and forces at time steps commensurate with the respective low- and high-frequency dynamics. It is shown that applying the MRFCT, the forces and fields of a machine connected to a power electronic circuit can be obtained at a small fraction of the time required for a coupled FE/circuit model. [ABSTRACT FROM AUTHOR]

In this paper, an interior permanent-magnet traction drive machine with single layer non-overlapping concentrated stator winding is analyzed. In the single layer winding design, the number of stator slots and a winding factor are specifically considered. Moreover, the effect of varying the coil pitch on the winding factor and torque pulsations is investigated. A new calculation method is proposed, whereby the constant torque and field-weakening speed characteristics of the machine drive can be predicted using torque and flux linkage functions. The measured and predicted field-weakening performance of a 150-kW direct traction drive machine is presented. [ABSTRACT FROM AUTHOR]

The author discusses the development of a snake-like robot, with emphasis on its mechanical aspect. It explains various movements of the snake which were categorized into serpentine movement, rectilinear movement, concertina movement and side winding movement. Furthermore, a snake-like robot is composed of concentrated joints which were classified into different types which include active bending joint type, active bending joint and active wheel type, and passive bending joint and active wheel type. Required power for serpentine motion is also solved.

We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by taking into account both the deterministic prior knowledge and the stochastic data in an intelligent manner. Like a conventional RBF, the proposed BVC-RBF has a linear-in-the-parameter structure, such that it is advantageous that many of the existing algorithms for linear-in-the-parameters models are directly applicable. The BVC satisfaction properties of the proposed BVC-RBF are discussed. Finally, numerical examples based on the combined D-optimality-based orthogonal least squares algorithm are utilized to illustrate the performance of the proposed BVC-RBF for completeness. [ABSTRACT FROM AUTHOR]

Examines the control of an industrial injection molding machine (IMM) for both mold filling and mold packing. The machine is a Boy 50 Ton system operated by hydraulics with electronically controlled valves. A nonlinear model of the system is determined for the filling and packing stages, then verified against experimental data. The model includes several key aspects of the actual machine dynamics including stick-slip friction, time delays, nonlinear valve flow characteristics, and deadzones. A feedforward control input is determined via an iterative learning control scheme. This input is combined with either a feedback strategy or an open-loop strategy for the filling and packing control phases, respectively. The time delay, which exists in both stages, is accounted for by time shifting the learning feedforward signal relative to the feedback control signal. Simulation and experimental investigations indicate the benefits of the proposed strategy for control of electrohydraulic IMMs. Subsequent to demonstrating the benefits for fill-stage and pack-stage learning control, a complete cycle, including fill-to-pack transfer, is demonstrated [ABSTRACT FROM PUBLISHER]