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Because of their high power density and compact size, permanent-magnet (PM) motors have been commonly used to drive rotary blood pumps (RBPs), which are focused on the treatment of end-stage heart failure or as the bridge to a heart transplant. In this paper, a bearingless PM motor has been proposed for axial blood pump applications. The finite-element method (FEM) is used to predict the electromagnetic characteristics of the designed motor with improved performance. Two topologies are investigated, namely the integral-slot and distributed-windings method and the fractional-slot and double-layer concentrated windings method. Both motors are analyzed and optimized. FEM reveals that, compared with the integral-slot motor, the fractional-slot motor offers significantly enhanced performance, including reduced cogging torque, improved back electromotive force (back EMF), and decreased magnetic flux leakage. Finally, hydraulic experiments have been conducted in a mock-circulation loop to validate the feasibility of the designed motor for an axial blood pump. The results show that the fractional-slot bearingless PM motor can drive the RBP to produce physiological blood flow with reasonable efficiency.

Presently, Coronary Acute Syndrome (CAS) is a widespread and extreme heart disease which is considered as one of the major concerns for death in the world with long-term disability. For early intervention and care, the prediction of CAS clinical risk is more critical during analysis. A minimum number of manually selected dimensions of risk are used for current CAS risk assessment models and statistical variables are often dichotomized to optimize storage in the Internet of medical things platform(IoMT) on the Encoder layer during data analysis. This research develops a Convolution Denoising Regularized Auto Encoder Stacked Method (CDRAESM) to normalize CAS patient's medical risks from high volumes of patient records and the characteristics have been analyzed during prediction. In this research, a true medical dataset of 3,464 CAS samples is used for experimental analysis and numerical reliability has been analyzed in Area Characteristics Curve (ACC) with an Accuracy range of 96.77%. The results show that the current health risk prediction using CDRAESM achieves competitive concert than conventional models which prevails in practice. Further, the reconstructive learning strategy approach can extract informational risk from the CAS and the risk factors has been identified with existing knowledge of the clinical domain and include theories that might be confirmed by further medical research.

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs. [ABSTRACT FROM AUTHOR]

Intensive use of photovoltaic (PV) inverter in the volt/var control (VVC) methods in active distribution networks can impair inverter reliability. This article proposes a PV inverter reliability-constrained (PiReCon-) VVC method with a power smoothing scheme under uncertainties. First, considering impacts on inverter reliability, new reliability constraints are developed with a power smoothing factor to constrict inverter apparent power variation. Second, a new VVC optimization model is proposed with the reliability constraints, which minimizes power losses by optimizing both inverter var output and PV curtailment. Uncertainties of PV generation and loads are fully considered and addressed via a stochastic optimization method. Third, this article develops a penalty convex-concave programming method to effectively tackle nonconvexity of the proposed optimization model. The proposed PiReCon-VVC method is tested on a 33-bus distribution network, and simulation results verify its high efficiency in both minimizing the power losses and enhancing the PV inverter reliability. [ABSTRACT FROM AUTHOR]

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery. [ABSTRACT FROM AUTHOR]

Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

While high volume of power electronic converters with renewable energy are being integrated into the grid, low system strength of grid causes reduction in system inertia and loss of synchronization with power converters. This paper proposes a novel interactive control scheme of voltage source converter (VSC) connected to weak grid. Firstly, a grid synchronization approach is performed using VSC side voltage transformed on rotating reference frame with respect to grid voltage, which is carried out based on lowest short circuit ratio (SCR) as approved by utilities such as Australian Energy Market Operator’s system strength report. The transformed voltage is then fed into the active power and reactive power control through PLL. Secondly, the grid synchronization approach through PLL is made interactive by adding virtual synchronous generator (VSG) control into the outer loop, which generates virtual inertia to mimic the synchronous generators of a power system. The design of the VSG control parameters is realized based on small signal stability analysis. Furthermore, the control method is evaluated as per the National Electricity Rules clauses S5.2.5.1-S5.2.14 under AEMO and National Service Providers of Australian strict Industry guidelines. The time-domain simulation results are conducted on PSCAD platform. [ABSTRACT FROM AUTHOR]

False data injection attack (FDIA) is a major threat in wide-area monitoring systems. Being able to differentiate FDIA from normal grid contingencies is a paramount necessity for a grid operator to decide the correct response on a critical prompt basis as well as reduce the overall FDIA's false alarms. Two FDIA's characterization algorithms are developed in this paper. The first is based on the principal component analysis (PCA) while the second is based on the canonical correlation analysis (CCA). Both algorithms are developed in an online platform to reduce the computational complexity. The various designed test cases demonstrate a promising FDIA characterization performance utilizing both algorithms. The testing results of three machine learning-based classifiers indicate that the proposed FDIA's characterization algorithms provide better classification models than conventional PCA-based characterization algorithm with CCA illustrating advanced characterization and detection results. [ABSTRACT FROM AUTHOR]