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A novel model-based unbalance monitoring and prognostics for rotor-bearing systems is introduced in the paper. An analytical method is first applied for rotor modeling and the calculated first natural frequency is validated by an FEM model. The rotor-bearing model with the identified bearing parameters is next validated with an operational 3-stage turbine-bearing’s machine on the first critical speed. The novelty of the approach is that the unbalance proceeding with optimization schemes is evaluated in two phases. In phase I, the bearing parameters and the initial unbalances are simultaneously evaluated based on the operational data soon after an overhaul. In phase II, the unbalance deterioration with time is identified through every day’s measured vibration at two bearings. A set of operational data over 16 months, provided by a local company, are used to test the approach. The evaluated unbalance deterioration trend is verified by the collaborated company from two consecutive overhauls. Five optimization algorithms are also tested and the results prove the robustness of the derived approach. Finally, the unbalance forecasting capability extrapolating from historical unbalance curve is demonstrated and that can work as prognostics in a condition-based maintenance strategy.

Given that the existing passive arc suppression technology of the distribution network cannot eliminate three-phase unbalanced overvoltage and the difficulties in dynamic measurement and hardware implementation of active current arc suppression equipment based on power electronics, a new technology of unbalanced overvoltage suppression and voltage arc suppression based on flexible control of distribution network zero sequence voltage is proposed. During normal operation, we inject current to eliminate three-phase unbalanced overvoltage; in case of a grounding fault, the system neutral point voltage shall be actively regulated, the fault phase voltage shall be reduced, the arcing conditions shall be destroyed, and the fault arcing shall be fundamentally realized. The 10 kV distribution network model is built in PSCAD/EMTDC simulation environment to verify the new technology of unbalanced overvoltage suppression and voltage arc suppression of zero sequence voltage flexible control. The simulation results show that the new technology can effectively suppress three-phase unbalanced over-voltage, reduce fault phase voltage, and realize reliable arc suppression of grounding faults.

Phase unbalance is widespread in the distribution networks in the UK, continental Europe, US, China, and other countries and regions. This paper first reviews the mass scale of phase unbalance and its causes and consequences. Three challenges arise from phase rebalancing: the scalability, data scarcity, and adaptability (towards changing unbalance over time). Solutions to address the challenges are: 1) using retrofit table, maintenance-free, automatic solutions to overcome the scalability challenge; 2) using data analytics to overcome the data-scarcity challenge; and 3) using phase balancers or other online phase rebalancing solutions to overcome the adaptability challenge. This paper categorizes existing phase rebalancing solutions into three classes: 1) load/lateral re-phasing; 2) using phase balancers; 3) controlling energy storage, electric vehicles, distributed generation, and micro-grids for phase rebalancing. Their advantages and limitations are analyzed and ways to overcome their limitations are recommended. Finally, this paper suggests future research topics: 1) long-term forecast of phase unbalance; 2) the whole-system analysis of the unbalance-induced costs; 3) the phase unbalance diagnosis for data-scarce LV networks; 4) techno-commercial solutions to exploit the flexibility from large three-phase customers for phase balancing; 5) the optimal placement of phase balancers; 6) the transition from single-phase customers to three-phase customers.

An AC filter tripping accident occurred in a converter station of the North China Power Grid in 2019, which seriously affected the safe and stable operation of the power grid. In this paper, combined with the configuration of the AC filter in the converter station and the principle of capacitor unbalance protection, the tripping mechanism of the AC filter is explained. It is considered that the direct cause of the tripping of the AC filter is the short-circuit discharge phenomenon between the layers caused by the dead trees (grass) branches on the capacitor tower, which causes the instantaneous fault between the layers of the low-voltage capacitor, and finally leads to the action of the capacitor unbalance protection. From the perspective of operation and maintenance, some practical preventive measures are put forward, which will help to improve the safety of AC filter operation in the future, and then improve the reliability of DC transmission system, especially during the peak summer, peak winter and special power protection period, to ensure that there will be no fluctuation in DC load transmission and protect the safe and stable operation of power grid.

Root-associated microbiomes contribute to plant growth and health, and are dynamically affected by plant development and changes in the soil environment. However, how different fertilizer regimes affect quantitative changes in microbial assembly to effect plant growth remains obscure. Here, we explore the temporal dynamics of the root-associated bacteria of soybean using quantitative microbiome profiling (QMP) to examine its response to unbalanced fertilizer treatments (i.e., lacking either N, P or K) and its role in sustaining plant growth after four decades of unbalanced fertilization. We show that the root-associated bacteria exhibit strong succession during plant development, and bacterial loads largely increase at later stages, particularly for Bacteroidetes. Unbalanced fertilization has a significant effect on the assembly of the soybean rhizosphere bacteria, and in the absence of N fertilizer the bacterial community diverges from that of fertilized plants, while lacking P fertilizer impedes the total load and turnover of rhizosphere bacteria. Importantly, a SynCom derived from the low-nitrogen-enriched cluster is capable of stimulating plant growth, corresponding with the stabilized soybean productivity in the absence of N fertilizer. These findings provide new insights in the quantitative dynamics of the root-associated microbiome and highlight a key ecological cluster with prospects for sustainable agricultural management. Root-associated microbiomes contribute to plant growth and health. Here, the authors unveil the quantitative development of the root microbiome under unbalanced fertilization and highlight a key microbial cluster for soybean productivity.

This paper has two contributions. First, we introduce a clustering basic benchmark. Second, we study the performance of k-means using this benchmark. Specifically, we measure how the performance depends on four factors: (1) overlap of clusters, (2) number of clusters, (3) dimensionality, and (4) unbalance of cluster sizes. The results show that overlap is critical, and that k-means starts to work effectively when the overlap reaches 4% level.

To analyze unbalanced electrical systems, the mathematical technique “symmetrical components method” developed by Charles LeGeyt Fortescue in the early 20th century has been very successful in this field. By decomposing three-phase systems into three symmetrical components: positive sequence, negative sequence, and zero sequence, the Fortescue theory provides an important analyzing method. It allows for the calculation of these symmetrical components, which helps in understanding and addressing issues related to unbalance in amplitude within electrical systems. This theory deals only with amplitude unbalances in electrical systems to analyze and solve those problems. Since this technique is limited only to amplitude unbalance, our objective is to propose a modified Fortescue theory, which will resolve frequency unbalance problems in electrical systems. The new balanced components, at the conclusion of this new theory, will be used as references to be assigned in the adopted control strategies in a subsequent research paper.

As unmanned aerial vehicles (UAVs) are witnessing a rapid increase in usage in regard to many different applications, it has become paramount to classify blade faults and unbalances in preflight processes. This paper aims to introduce and show the effectiveness of new unbalance classification models using two different machine learning techniques; support vector machine (SVM) and k nearest neighbor (kNN). Screw loosening, motor base shifting, and other faults were simulated as weight-adding unbalances on the blades of a quadcopter UAV. The vibration-based signal processing features in the time domain were extracted with the aid of a 3-axis accelerometer sensor and a data acquisition system while in hover mode. ReliefF supreme feature scoring and ranking method were used to improve prediction accuracy to acquire effective features and suppress insufficient ones. The performance of the models was validated using several methods by comparing them with the basic models. Accuracy increased from 92.52 to 98.85% and from 95.1 to 96.41% in SVM and kNN machine learning classifiers, respectively. The enhanced models proved reliance on locating unbalanced blades as the processing time decreased noticeably. The results stipulate that the proposed system transcended current developments in predicting blade faults of UAVs and showed good promise for future development of embedded systems-based quadcopter fault diagnosis.

This paper presents a compensation method for unbalanced voltage through active and reactive power control by utilizing a smart inverter that improves the voltage unbalance index and detects an unbalanced state of voltage magnitude and phase, and thus enhances power quality by minimizing the voltage imbalance. First of all, this paper presents an analysis of a mathematical approach, which demonstrates that the conventional voltage unbalanced factor (VUF) using the symmetrical component cannot correctly detect the imbalanced state from index equations; and by only minimizing the VUF value, it cannot establish a balanced condition for an unbalanced state of the voltage profile. This paper further discusses that intermittent photovoltaic (PV) output power and diversified load demand lead to an unexpected voltage imbalance. Therefore, considering the complexity of unbalanced voltage conditions, a specific load and an PV profile were extracted from big data and applied to the distribution system model. The effectiveness of the proposed scheme was verified by comparing VUF indices and controlling the active and reactive power of a smart inverter through a numerical simulation.

Operation of distribution networks involves a series of criteria that should be met, aiming for the correct and optimal behavior of such systems. Some of the major drawbacks found when studying these networks is the real losses related to them. To overcome this problem, distribution network reconfiguration (DNR) is an efficient tool due to the low costs involved in its implementation. The majority of studies regarding this subject treat the problem by considering networks only as three-phase balanced, modeled as single-phase grids with fixed power demand, which is far from representing the characteristics of real networks (e.g., unbalanced loads, variable power and unbalance indexes). Due to the combinatorial nature of the problem, metaheuristic techniques are powerful tools for the inclusion of such characteristics. In this sense, this paper proposes a study of DNR considering balanced and unbalanced systems with variable power demand. An analysis of the direct influence of voltage unbalance index (VUI) and current unbalance index (CUI) is carried out for unbalanced cases. To solve the DNR problem, a selective bio-inspired metaheuristic based on micro bats’ behavior named the selective bat algorithm (SBAT) is used together with the EPRI-OpenDSS software (California, US, EPRI). Tests are initially conducted on balanced systems, aiming to validate the technique proposed for both demands and state their differences, and then they are conducted on unbalanced systems to study the influence of VUI and CUI in the DNR solution.