Randomness from the power load demand and renewable generations causes frequency oscillations among interconnected power systems. Due to the requirement of synchronism of the whole grid, load ...frequency control (LFC) has become one of the essential challenges for power system stability and security. In this paper, by modeling the disturbances and parameter uncertainties into the LFC model, we propose an adaptive supplementary control scheme for the power system frequency regulation. An improved sliding mode control (SMC) is employed as the basic controller, where a new sliding mode variable is specifically proposed for the LFC problem. The adaptive dynamic programming strategy is used to provide the supplementary control signal, which is beneficial to the frequency regulation by adapting to the real-time disturbances and uncertainties. The stability analysis is also provided to guarantee the reliability of the proposed control strategy. For comparison, a particle swarm optimization-based SMC scheme is developed as the optimal parameter controller for the frequency regulation problem. Simulation studies are performed on single-area and multiarea benchmark systems, and comparative results illustrate the favorable performance of the proposed adaptive approach for the frequency regulation under load disturbances and parameter uncertainties.
Currently, vibration analysis has been widely considered as an effective way to fulfill the fault diagnosis task of gearboxes in wind turbines (WTs). However, vibration signals are usually with ...abundant noise and characterized as nonlinearity and nonstationarity. Therefore, it is quite challenging to extract robust and useful fault features from complex vibration signals to achieve an accurate and reliable diagnosis. This paper proposes a novel feature representation learning approach, named stacked multilevel-denoising autoencoders (SMLDAEs), with the aim to learn robust and discriminative fault feature representations through a deep network architecture for diagnosis accuracy improvement. In our proposed approach, we design an MLD training scheme, which uses multiple noise levels to train AEs. It enables to learn more general and detailed fault feature patterns simultaneously at different scales from the complex frequency spectra of the raw vibration data, and therefore helps enhance the feature learning and fault diagnosis capability. Furthermore, SMLDAE-based fault diagnosis is performed with an unsupervised representation learning procedure followed by a supervised fine-tuning process with label information for classification. Our approach is evaluated by using the field vibration data collected from a self-designed WT gearbox test rig. The results show that our proposed approach learned more robust and discriminative fault feature representations and achieved the best diagnosis accuracy compared with the traditional approaches.
When the modern electrical infrastructure is undergoing a migration to the Smart Grid, vulnerability and security concerns have also been raised regarding the cascading failure threats in this ...interconnected transmission system with complex communication and control challenge. The DC power flow-based model has been a popular model to study the cascading failure problem due to its efficiency, simplicity and scalability in simulations of such failures. However, due to the complex nature of the power system and cascading failures, the underlying assumptions in DC power flow-based cascading failure simulators (CFS) may fail to hold during the development of cascading failures. This paper compares the validity of a typical DC power flow-based CFS in cascading failure analysis with a new numerical metric defined as the critical moment (CM). The adopted CFS is first implemented to simulate system behavior after initial contingencies and to evaluate the utility of DC-CFS in cascading failure analysis. Then the DC-CFS is compared against another classic, more precise power system stability methodology, i.e., the transient stability analysis (TSA). The CM is introduced with a case study to assess the utilization of these two models for cascading failure analysis. Comparative simulations on the IEEE 39-bus and 68-bus benchmark reveal important consistency and discrepancy between these two approaches. Some suggestions are provided for using these two models in the power grid cascading failure analysis.
The modern power system is evolving towards a new generation of smart grid, with significant benefits from the latest computer-based communication network technologies. Furthermore, as the ...incremental deployment of phase measurements units (PMUs) and the use of Smart Meters, there will be a substantial increase of the real-time system measurements. Under this trend, event-triggered control (ETC) will play an important role in reducing the communication and computation cost. In this paper, a novel ETC architecture design for load frequency control (LFC) with supplementary adaptive dynamic programming (ADP) is presented. The primary proportional-integral (PI) controller uses different proportional and integral thresholds for updating the actions, while the supplementary ADP controller is updated in an aperiodic manner. A strategy for the parameters calculation is introduced in a systematic way, and theoretical analysis of the ultimate boundedness for the closed-loop event-triggered system is also included. Simulation studies are carried out on one-area and three-area IEEE LFC benchmarks, and the results demonstrate the efficiency and effectiveness of the proposed design.
Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be ...expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.
Low-frequency oscillation is one of the main barriers limiting power transmission between two connected power systems. Although power system stabilizers (PSSs) have been proved to be effective in ...damping inner-area oscillation, inter-area oscillation still remains a critical challenge in today's power systems. Since the low-frequency oscillation between two connected power systems is active power oscillation, power modulation through energy storage devices (ESDs) can be an efficient and effective way to maintain such power system stability. In this paper, we investigate the integration of a new goal representation heuristic dynamic programming (GrHDP) algorithm to adaptively control ESD to damp inter-area oscillation. A particle swarm optimization (PSO)-based power oscillation damper (POD) has also been proposed for comparison. Various simulation studies with residue-based POD controller design, the proposed PSO optimized controller design, and the GrHDP-based controller design over a four-machine-two-area benchmark power system with energy storage device have been conducted. Simulation results have demonstrated the efficiency and effectiveness of the GrHDP-based approach for inter-area oscillation damping in a wide range of system operating conditions.
The piezoelectric effect of biological piezoelectric materials promotes bone growth. However, the material should be subjected to stress before it can produce an electric charge that promotes bone ...repair and reconstruction conducive to fracture healing. A novel method for in vitro experimentation of biological piezoelectric materials with physiological load is presented. A dynamic loading device that can simulate the force of human motion and provide periodic load to piezoelectric materials when co-cultured with cells was designed to obtain a realistic expression of piezoelectric effect on bone repair. Hydroxyapatite (HA)/barium titanate (BaTiO
) composite materials were fabricated by slip casting, and their piezoelectric properties were obtained by polarization. The d
of HA/BaTiO
piezoelectric ceramics after polarization was 1.3 pC/N to 6.8 pC/N with BaTiO
content ranging from 80% to 100%. The in vitro biological properties of piezoelectric bioceramics with and without cycle loading were investigated. When HA/BaTiO
piezoelectric bioceramics were affected by cycle loading, the piezoelectric effect of BaTiO
promoted the growth of osteoblasts and interaction with HA, which was better than the effect of HA alone. The best biocompatibility and bone-inducing activity were demonstrated by the 10%HA/90%BaTiO
piezoelectric ceramics.
Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and ...efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (CI) community have demonstrated that CI research could provide key technical innovations into this challenging problem. In this paper, we propose a sensitivity analysis approach based on both trajectory and frequency domain information integrated with evolutionary algorithm to achieve the optimal control of doubly-fed induction generators (DFIG) based wind generation. Instead of optimizing all the control parameters, our key idea is to use the sensitivity analysis to first identify the critical parameters, the unified dominate control parameters (UDCP), to reduce the optimization complexity. Based on such selected parameters, we then use particle swarm optimization (PSO) to find the optimal values to achieve the control objective. Simulation analysis and comparative studies demonstrate the effectiveness of our approach.
Guided tissue regeneration (GTR) membranes are key materials used to create barriers between soft and bone tissues during bone defect repair; however, single-function barrier GTR membranes cannot ...meet clinical requirements. In this paper, we propose a GTR membrane with drug release, bone guidance, and barrier functions; this membrane is a poly(lactic-co-glycolic acid) (PLGA)/hydroxyapatite (HA) (core)-collagen/amoxicillin (shell) nanofiber membrane fabricated by coaxial electrospinning. The shell of each nanofiber is composed of collagen/amoxicillin to promote wound healing through drug release, and its core is composed of PLGA/HA to block fibroblast growth into bone defects and promote bone growth. The effects of collagen content in the shell spinning solution on drug release time are studied, and results show that the total release time of amoxicillin can reach 40 h when a shell spinning solution containing 4 wt% collagen is used. Mineralization occurs on the nanofiber membrane surface after 8 weeks of degradation, indicating the ability of the membrane to induce apatite deposition. Fibroblasts were also co-cultured on one side of the core/shell nanofiber membranes, and results showed that fibroblasts on the cultured side grew well; moreover, no fibroblasts were observed on the opposite side of the membrane after 48 h of culture. The barrier membrane developed in this work may be applied in the GTR of dental implants and orthopedic transplants.
In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency ...deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.
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