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To reduce maintenance costs of wind turbines (WTs), WT health monitoring has attracted wide attention, and different methods have been proposed. However, most existing WT temperature monitoring methods ignore the fact that various wind conditions can directly affect internal temperature of WT, such as main bearing temperature. This paper analyzes the effects of wind conditions on WT temperature monitoring. To reduce these effects, this paper also proposes a novel WT temperature monitoring solution. Compared with existing solutions, the proposed solution has two advantages: (1) wind condition clustering (WCC) is applied and then a normal turbine behavior model is built for each wind condition; (2) extreme learning machine (ELM) is optimized by an improved genetic algorithm (IGA) to avoid local minimum due to the irregularity of wind condition change and the randomness of initial coefficients. Cases of real SCADA data validate the effectiveness and advantages of the proposed solution.

In this study, a current model predictive controller (MPC) is designed for a permanent magnet synchronous motor (PMSM) where the speed of the motor can be regulated precisely. First, the mathematical model, the specifications, and the drive topology of the PMSM are introduced, followed by an elaboration of the design of the MPC. The MPC is then used to predict the current in a discrete-time calculation. The phase current at the next sampling step can be estimated to compensate the current errors, thereby modifying the three-phase currents of the motor. Next, Simulink modeling of the MPC algorithm is given, with three-phase current waveforms compared when the motor is operated under the designed MPC and a traditional vector control for PMSM. Finally, the speed responses are measured when the motor is controlled by traditional control methods and the MPC approach under varied speed references and loads. In comparison with traditional controllers, both the simulation and the experimental results suggest that the MPC for the PMSM can improve the speed-tracking performance of the motor and that this motor has a fast speed response and small steady-state errors under the rated load.

Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.

Inter-turn short-circuit faults can lead to further faults in motors. This makes monitoring and identifying such faults particularly important. However, because of interference in their working environment, fault signals can be weak and difficult to detect in permanent magnet synchronous motors. This paper proposes a method for overcoming this by extracting the inverter harmonics as an excitation source and then extracting characteristic of fault measurements from the negative sequence voltage. First of all, a model of permanent magnet synchronous motor faults is established and a fault negative sequence voltage is introduced to calculate the fault indicators. Then the high frequency harmonic excitation in the voltage is extracted. This is injected into the original voltage signal and the high frequency negative sequence component is separated and detected by a second-order generalized integrator. Simulation results show that the proposed method can effectively identify inter-turn short-circuit faults in permanent magnet synchronous motors while remaining highly resistant to interference. The method is especially effective when the severity of the fault is relatively small and the torque is relatively large.

High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

Wind power is a key development project. In recent years, the wind power industry has developed rapidly. The market needs enterprises with complete systems and higher work efficiency. Internal control is an important measure for the transparency and standardization of enterprise operations. Therefore, after implementing internal control on wind power companies, how to objectively evaluate the internal control construction of wind power companies has become a new research focus. This paper proposes a method to quantitatively evaluate the internal control of wind power enterprises. First, the internal control objectives are decomposed into multiple factor indicators, and then the internal control evaluation system of wind power enterprises is constructed, which is realized by the use of analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The calculation example shows that the quantitative evaluation method helps to find the deficiencies of the internal control of wind power enterprises, and provides a reference for the establishment and implementation of the system and the efficiency of business operation.

This paper is concerned with the stability and disturbance attenuation properties of switched linear system with dwell time constraint. A novel time-scheduled Lyapunov function is introduced to deal with the problems studied in this paper. To numerically check the existence of such time-scheduled Lyapunov function, the discretized Lyapunov function technique usually used in time-delay system is developed in the context of switched system in continuous-time cases. Based on discretized Lyapunov function, sufficient conditions ensuring dwell-time constrained switched system global uniformly asymptotically stable are established, then the disturbance attenuation properties in the sense of L 2 gain are studied. The main advantage of discretized Lyapunov function approach is that the derived sufficient conditions are convex in subsystem matrices, which makes the analysis results easily used and generalized. Thus, the H ∞ control synthesis problem is considered. On the basis of analysis results in hand, the control synthesis procedures including both controller and switching law design are unified into one-step method which explicitly facilitates the control synthesis process. Several numerical examples are provided to illustrate the results within our paper.

Despite global warming, renewable energy has gained much interest worldwide due to its ability to generate large-scale energy without emitting greenhouse gases. The availability and low cost of wind energy and its high efficiency and technological advancements make it one of the most promising renewable energy sources. Hence, capturing large amounts of wind energy is essential today. The large-scale integration of wind power sources must be evaluated and mitigated to develop a sustainable future power system. Wind energy research and the government are working together to overcome the potential barriers associated with its penetration into the power grid. This paper reviews the social, environmental, and cost-economic impacts of installing large-scale wind energy plants. A valuable review of wind energy technology and its challenges is also presented in this paper, including the effects of wind farms on nearby communities, generation uncertainty, power quality issues, angular and voltage stability, reactive power support, and fault ride-through capability. Besides, socioeconomic, environmental, and electricity market challenges due to the grid integration of wind power are also investigated. Finally, potential technical challenges to integrating large-scale wind energy into the power grid are reviewed regarding current research and their available mitigation techniques.

This paper mainly focuses on the generalized mutual synchronization between two controlled interdependent networks. First, we propose the general model of controlled interdependent networks A and B with time-varying internetwork delays coupling. Then, by constructing Lyapunov functions and utilizing adaptive control technique, some sufficient conditions are established to ensure that the mutual synchronization errors between the state variables of networks A and B can asymptotically converge to zero. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results and to explore potential application in future smart grid. The simulation results also show how interdependent topologies and internetwork coupling delays influence the mutual synchronizability, which help to design interdependent networks with optimal mutual synchronizability.

This paper deals with a combined test rig for a traction system in the laboratory environment. An experimental system was designed and implemented to verify the performance of the traction system for a metro train. For a highly accurate control of the system, a hybrid control algorithm combining vector control and slip frequency control was applied to control the traction inverter. The design method of the flywheels, which represent the equivalent model of the train moment inertia, was elaborated. A train runtime diagnosis system was completed by adopting the multifunction vehicle bus (MVB) protocol. The dynamic performance of the metro power traction system was emulated under the control of the train runtime diagnosis system. Using the combined test rig, the performances of the traction system in traction, braking, temperature rise, etc., were verified through traction and breaking experiments.