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Advanced heart failure (HF) is used to characterized patients in HF with severe symptoms, recurrent decompensation and severe cardiac dysfunction. The prevalencekof HFkis approximatelyg1-2% of thecadult population inhdeveloped countries and it will be rising more than 10%pamongapeoplec>70ayears of age, whereas estimated theoprevalence ofcpatients with advanced HF is about 1% until 10%cof thekoverallkHFopopulation. Most ofkthe HF hospitalizationssare due to signs and symptoms of fluidcoverload.Recurrent congestionccould worsen patientssoutcomes.Loopdiureticssare recommended for thectreatmentcofkcongestionqinprHF patient. cHowever,cdiureticvresistanceeispavcommon problem issueiinpacuteqdecompensationtofcadvancedochronicrheartwfailureq(ACHF) patients and established prognostic factor. Some early reports estimated the prevalence of diuretic resistance about 20%-30% in HF population. In this review, we will be discuss how to diagnose the advancedvheartufailurepand the underlying mechanism of diuretic resistancebin HF patients. We also describe pharmacologicalvand non-pharmacologicalstrategies to overcome this issue.

Impedance‐based fault location (IBFL) approaches are the most commonly used fault location methods in digital relays. However, each IBFL approach is designed specific to a line or network configuration and thus is not universal. For example, complex line configurations such as lines with mutual coupling between them and three‐terminal lines have to employ individual IBFL algorithms derived specifically for them. Furthermore, they suffer from several sources of errors such as non‐homogeneous system, and CT saturation. Hence, this paper presents a novel fault location approach that utilises a system model to overcome these limitations. The proposed model‐based fault location (MBFL) approach estimates the fault location by identifying the closest match among various anticipated fault scenarios obtained using the system model and the actual fault scenario. It uses a dynamic search technique to implement the MBFL efficiently. A key highlight of the proposed approach is identifying the location of a fault on a neighbouring line using limited measurements, as few as only the through fault current flowing in a neighbouring line. The advantages of the approach and its practical applicability have been demonstrated by implementing it in complex network configurations as well as field data.

Low-frequency high-voltage alternating-current (LF-HVac) transmission scheme has been recently proposed as an alternative solution to conventional 50/60-Hz HVac and high-voltage direct-current (HVdc) schemes for bulk power transfer. This paper proposes an optimal planning and operation for loss minimization in a multi-frequency HVac transmission system. In such a system, conventional HVac and LF-HVac grids are interconnected using back-to-back (BTB) converters. The dependence of system MW losses on converter dispatch as well as the operating voltage and frequency in the LF-HVac is discussed and compared with that of HVdc transmission. Based on the results of the loss analysis, multi-objective optimization formulations for both planning and operation stages are proposed. The planning phase decides a suitable voltage level for the LF-HVac grid, while the operation phase determines the optimal operating frequency and power dispatch of BTB converters, generators, and shunt capacitors. A solution approach that effectively handles the variations of transmission line parameters with the rated voltage and operating frequency in the LF-HVac grid is proposed. The proposed solutions of the planning and operation stages are evaluated using a multi-frequency HVac system. The results show a significant loss reduction and improved voltage regulation during a 24-hour simulation., Comment: arXiv admin note: text overlap with arXiv:1908.02832

This work proposes a generic method to utilize customer smart meter measurements to automatically and simultaneously estimate topology, line parameters, and customer and line phasing connections in low voltage (LV) distribution systems. This generic approach is applicable to single, two, and three-phases lines and customers. Hence, it is suitable not only for North American systems but also for European and South American systems. This generic estimation is conducted by using a multiple linear regression model applied to data supplied by customers meters. The acceptance of each estimated parameter is carried out through comparisons with mathematical (e.g., coefficient of determination and relative standard deviation) and physical constraints (e.g., resistances, line length, and conductor X/R ratios). Granularity and sensitivity analyses are also conducted taking into account smart meter data quality (e.g., update ratio, metering errors, resolution, clock desynchronization). The obtained results show the high performance of the method to correctly estimate the system topology, line parameters, and line and customers phasing based on a specification of 15-day sample size with 60-min resolution as a general compromise solution between data acquisition and accuracy.

The increasing deployment of triggerless power quality monitors provides valuable data about power systems and their components. However, efficient and accurate data mining techniques are necessary for parsing through large datasets and obtaining useful insights about the power system operation. While multiple studies can be found in the literature for detection of power quality events, they either detect only severe disturbances or are applicable to a specific subset of disturbances. This study proposes a universal approach for detecting power quality disturbances within large datasets regardless of the event root causes. It is based on the comparison between the shapes of multiple cycles of waveform data through similarity scores. Pre-processing techniques to enhance the detector accuracy are presented. They include correction of time misalignment between successive cycles caused by variations in the power system operating frequency, and reduction of load variation effects. The threshold values for detecting abnormal waveforms is time-adaptive, so that detection is based on local outliers. The proposed framework performance is assessed through multiple types of field datasets, and its superiority over other waveform abnormality detectors is empirically demonstrated. Moreover, current waveform is shown to be more suitable than that of voltage for novelty detection in power quality data.

Low-frequency high voltage ac transmission scheme has recently been proposed as an alternative approach for bulk power transmission. This paper proposes a multi-period optimal power flow (OPF) for a multi-frequency HVac transmission system that interconnects both conventional 50/60-Hz and low-frequency grids using back-to-back converters with a centralized control scheme. The OPF objective is to minimize system losses by determining the optimal dispatch for generators, shunt capacitors, and converters. The OPF constraints include the operational constraints of all HVac grid and converter stations. The resulting mixed-integer nonlinear programing problem is solved using a proposed framework based on the predictor-corrector primal-dual interior-point method. The proposed OPF formulation and solution approach are verified using a multi-frequency HVac transmission system that is modified from the IEEE 57-bus system. The results with the optimal dispatch from the proposed method during a simulated day show a significant loss reduction and an improved voltage regulation compared to those when an arbitrary dispatch is chosen.

When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system’s state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.

Background Acute aortic dissection is a life threatening condition that remains a challenge to diagnose and treat. 67% patients was presented with Acute type A aortic dissection (ATAAD) and often accompanied with some complications. Case Summary A 40-year-old male with risk factor hypertension was presented with sudden sharp tearing-like chest pain and radiating to the back 3 hours before admission. He had transient decrease of consciousness and waken up spontaneously. He was compos mentis, with normal JVP, normal heart sound, no pulsus deficit, hypotension, cold acral and oliguria. He got fluid rehydration and the BP was stabilized. Haemoglobin was normal with increase D-Dimer. The ECG was normal and chest radiography revealed cardiomegaly and widening mediastinum. From eyeballing echocardiogram showed moderate pericardial effusion without right atrial and right ventricle collapse. A CT scan angiography was performed and revealed an Aortic Dissection Stanford A Debakey I. Discussion The development of an evidence-based strategy to rule out aortic dissection in patients presenting chest pain would be useful to diagnose patients with chest pain. Rapid diagnosis of dissection is most likely when CT is a part of the diagnostic testing. Hypotension and shock occurred in > 25% of patients with type ATAAD and in this patient was associated transient loss of consciousness. This patient was in impending rupture condition. The definitive treatment for this patient was emergency aortic repair surgery. We was succeded to stabilize the patient then referred to a cardiothoracic surgeon in 24 hours later.

Present-day radial electric power distribution circuit faces multiple challenges due to the increased photovoltaic (PV) generation. This paper aims to examine and design modern distribution circuit topologies for accommodating high PV penetration, while addressing power quality concerns. The central hypothesis of the proposed design approach is that by decreasing the Thevenin impedance at the buses where PVs are connected, the impacts on feeder voltages due to PV generation become less pronounced thereby additional PV capacity can be integrated at the corresponding buses. A novel two-stage optimization framework is proposed. The first stage is a mixed-integer linear programing based formulation to design new optimal configuration for any given distribution circuit. The formulation allows the possibility that the circuit can be operated in either a radial or loop configuration. The second stage, a nonlinear programing based formulation, then finds PV hosting capacity for the identified optimal configuration. The proposed two-stage framework is evaluated for IEEE 123-bus test feeder. It is demonstrated that the PV hosting capacity of the feeder can be increased by 53% by optimally adding two new distribution lines with tie-switches.

Aging of power system assets challenges utilities to maintain reliable service while minimizing their maintenance costs. Although malfunction of devices in the transmission or distribution networks can have catastrophic consequences, their failure is seldom unforeseen. Typically, the device aging occurs gradually over time until its health condition reaches a point of complete breakdown. The overall objective of this paper is to devise a non-invasive, online condition monitoring technique of circuit switchers for shunt capacitor banks. It aims to detect abnormal operation of these devices based on features derived from raw voltage and current waveforms, such that a predictive maintenance strategy may be employed before a complete failure occurs. The data analyzed in this paper are collected at a transmission capacitor bus, where the banks are equipped with pre-insertion impedance. Through a baseline range of values for selected parameters, the proposed technique is able to detect missing pre-insertion impedance during the capacitor energizing and failure of pre-insertion impedance switch out a few cycles after the energizing operation.

In this paper, a two-stage optimization framework is proposed for optimal placement and centralized real-time control of low-voltage static var compensators (LV-SVCs) in unbalanced distribution circuits to achieve feeder level benefits namely voltage regulation and energy loss minimization. The number, locations, and the optimal real-time reactive power injections from the LV-SVCs are determined from a proposed three-phase unbalanced ac optimal power flow (ACOPF). The ACOPF is formulated as a multi-objective optimization with operating constraints written in rectangular coordinates. The resulting nonlinear nonconvex problem is solved using the predictor-corrector primal-dual interior point method. The proposed approach is scalable for application to large distribution circuits, treats the constraints of physical space limitations effectively, and can take advantage of communication capabilities of LV-SVCs for centralized real-time control. The benefits of the proposed real-time control of LV-SVCs as compared to their operation with local autonomous voltage-based distributed control is demonstrated. The results show that, the proposed approach addresses the LV-SVC placement and control problem effectively to minimize energy losses while maintaining the voltage regulation.

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This paper presents three advances in BESS SoC forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational data is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. The proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.

Power quality disturbances commonly observed in power systems have been studied for decades, resulting in numerous algorithms for detecting the events that affect the voltage and/or current waveforms. However, a considerable amount of disturbances is not visually observable in the raw waveforms, especially switching operations. These events must be detected through an alternative feature, such as abrupt variations in the root-mean-square (rms) voltage profile. This study examines the methods commonly used for detecting power quality disturbances in the waveform or rms voltage profile domains and identifies their limitations. Afterwards, a novel step change detector is proposed based on a modified median filter and rms voltage gradient values to overcome the deficiencies of the existing methods. The effectiveness of the proposed method is assessed by applying it to both simulated and field data. This assessment shows that the method detects all switching events with no false positives for the datasets under analysis.

Data recorded by intelligent electronic devices such as protective relays contain a wealth of valuable information that can be used beyond post-mortem analysis of fault events. The objective of this paper is to propose algorithms to estimate zero-sequence line impedance and fault resistance using protective relay data collected during short-circuit fault events. Protective relaying data may be available from one or both ends of the line. For the latter, they may be collected at different sampling rates with dissimilar fault time instants. Furthermore, they may be unsynchronized. This paper presents approaches which uses measurement data from one and both ends of the line. The efficacy of the proposed algorithms presented in this paper are demonstrated using a test case as well as field data. The error percentage of the zero-sequence line impedance magnitude estimated using the data from both ends of a two-terminal line was under 3% for all the test cases. The error percentage in estimating the fault resistance was less than 1% in the test cases presented.

The location of protective devices, such as circuit breakers, reclosers, sectionalizers, and fuses, along with isolating switches in a distribution network is a key factor impacting the reliability performance. Furthermore, automatic restoration from intentional islanding with renewable-based distributed generators (DGs) or from alternate feeders can reduce outage times. In this paper, a mixed-integer linear program (MILP) formulation is proposed for protective device and switch allocation considering intentional islanding with distributed generation in distribution systems. The specific impact of each protective device type and isolating switch is modeled, e.g., momentary interruptions caused by reclosers. Efficient graph search algorithms combined with a directed graph representation of the distribution system allows for preprocessing of the network data and facilitates the formulation of an MILP. The formulation is able to efficiently compute optimal device allocations for multiple scenarios, revealing key insights, e.g., the location and capacity of DGs providing the greatest reliability benefit for a fixed protection budget. Numerical tests on realistic feeders and comparison with prior solutions show improved device allocations and lower objective function values.

Smart inverters provide additional control capability to help optimize the operation of distribution systems. This paper proposes a framework for exact optimal active and reactive power dispatch of distributed photovoltaic (PV) generation, switched capacitors, and voltage regulators in large multi-phase unbalanced distribution systems. The objectives of the optimal dispatch are minimization of the energy loss, PV active power curtailment, and operations of capacitors and voltage regulators, in addition to elimination of voltage violations and reverse power flow. The optimization problem is formulated in rectangular coordinates as a nonlinear, nonconvex problem. Effective computational strategies are proposed to allow the application of predictor-corrector primal-dual interior point method to solve optimization problems in real-time with a large number of constraints and variables, including discrete variables corresponding to switched capacitors and voltage regulators. The accuracy of the numerical solution and the ability to implement the proposed framework are validated using the unbalanced multi-phase IEEE 34-bus and EPRI 2,998-bus distribution systems with 15-minute load and PV data. The results show a significant loss reduction and elimination of both voltage violations and reverse power flow.

The inverse-time operating characteristic of overcurrent relays is the primary protective element in distribution system protection schemes and has been utilized for several decades. As the distribution system becomes increasingly complex due to the growth of distributed generation, the protection task based on existing methodologies will become more difficult. Faster relay operating times while maintaining selectivity is critical. In this paper, a stochastic mixed-integer linear program is formulated to minimize a relay’s tripping time at discrete fault current intervals and considers the cost of tripping a relay as the objective function. The formulation takes into account the probabilistic nature of the fault current observed at each relay, which can be impacted by fault location, fault resistance, device failure, and DG output. Monte Carlo simulation is used to determine the empirical probabilities of each relay observing a particular fault current. Probabilistic fault scenarios are simulated on the IEEE 34-node test feeder. The proposed approach shows a decrease in expected energy loss due to faults up to 11.5% compared to conventional TCC curves for 10 000 Monte Carlo fault scenarios.

Phasor measurement units are widely used for enhancing awareness of the operation of transmission and distribution networks. Their synchronized measurements enable the development of multiple near-real-time applications that are unattainable with SCADA data only. However, the accuracy of PMU measurements is highly dependent on the chosen algorithms. This paper illustrates that a certain PMU model is unable to accurately estimate system frequency as it drifts away from its nominal value (the estimated frequency profile is contaminated with pseudo-oscillations). Moreover, three devices connected at the same location produce divergent estimates during off-nominal frequency operation. Then, a technique is proposed for directly estimating frequency from phase angle measurements through numerical derivatives. It is shown to accurately estimate the system frequency regardless of nominal or off-nominal frequency operation, and it eliminates the two issues presented previously.

Comunicació de congrés presentada a: Resilience Week 2020 (19 - 23 octubre 2020: Salt Lake City, Utah). https://resilience.inl.gov/resilience-week/ Windstorms represent a particular class of highimpact, low-probability events that is highly likely to damage distribution poles and pull down overhead lines in vulnerable areas. As a result, when a windstorm occurs, the costs associated with damaged overhead corridors and energy not supplied may be too high. Conversely, the costs associated with the installation of underground distribution lines are expensive in comparison to overhead distribution lines and may not compensate for the penalties avoided for the loads lost. In this scenario, this article assesses the costs and risks associated with underground and overhead power lines for a resilient, cost-effective planning and operation of power distribution networks under windstorms. Thus, it calculates the accumulated costs associated with installation, operation, and repair of power distribution lines, as well as the penalties for the energy not supplied, subject to the probability of failure of individual components over time, to determine which power line setting is the most appropriate in terms of resilience and costs This research was supported by the European Union’s Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project “Renewable penetration levered by Efficient Low Voltage Distribution grids”, grant agreement number 773715, and University of Girona scholarship. We acknowledge that this work benefits from discussion and collaboration with the project “Defending the Electricity Infrastructure against Extreme Weather Events, Now and in the Future”, funded by The University of Texas at Austin Energy Institute

Comunicació de congrés presentada a: 2020 IEEE PES Transmission & Distribution Conference & Exposition Latin America (28 setembre - 2 octubre 2020: Montevideo, Uruguay). https://www.ieee-tdla2020.org/ This article provides an evaluation of power system resilience enhancements in low-income neighborhoods. Lowincome households and communities may be subject to risks of different nature, such as natural hazards and human-made attacks, hereby considered as particular cases of high-impact, low-probability events that are highly likely to damage power grid infrastructures. Such events may result in long interruption times and lead to permanent disconnection from the grid in extreme cases, and consequently, the value of load lost may be much higher than the investment cost associated with prevention and mitigation alternatives. In this scenario, this article analyzes the value of load lost and the costs associated with installation, operation, and repair of grid components affected by extreme events to determine the benefits of different strategies for power system resilience improvements targeted at low-income neighborhoods This research was supported by the European Union’s Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project “Renewable penetration levered by Efficient Low Voltage Distribution grids”, grant agreement number 773715, and University of Girona scholarship

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

With higher penetration of distributed photovoltaic (PV) resources and wider adoption of electric vehicles (EVs), it is reasonable to expect events at the distribution level to more actively influence the state of the network. However, traditional power flow formulations mostly consider either the transmission system or the distribution system. Recent work has been done in the development of a joint transmission and distribution (T&D) formulation to properly account for interactions between the two systems. This paper presents the analysis of effects of distribution system resources on transmission system voltages. The analysis relies on a recently developed power flow tool that combines distribution and transmission circuits. Results of multi-period power flow simulations are shown demonstrating how the location of PV and EV fast charging facilities in distribution circuits affects transmission bus voltages. Furthermore, the results are analyzed, and justifications are offered to explain the observed phenomena.

Low-voltage dc (LVDC) distribution systems are increasingly used nowadays. Faults affect the safe and secure operation of the distribution network. Among various types of faults, series arc faults are challenging to detect because they produce only small changes in the circuit current. Though a variety of series arc fault detection approaches have been developed in the past, each approach has its requirements and limitations. This paper identifies characteristics in the current and voltage waveforms to aid the detection of series arc faults in LVDC systems. Fault signatures are extracted from current and voltage measurements that do not require a high sampling frequency and installation of additional devices such as capacitors. The load current and voltage experience a step change when a series arc fault occurs. For this purpose, a step-change detector is presented in this paper. The paper also presents a novel approach to distinguish between steady stable arc conditions and rapidly fluctuating arc conditions. This provides information on arc conditions useful for fault analysis. The identified fault signatures are demonstrated and validated using experimental data.

This paper presents a time-domain, equivalent current source model (CSM) for inverter-based resources in EMTPRV for evaluating their harmonic impacts, behavior, and interactions with the grid. Whereas detailed inverter models can be accurate if the inverter design and parameters are precisely known, their simulations are time-consuming. Moreover, the lack of information about topology and control schemes from inverter manufacturers makes these models difficult to hand-tune and leads to loss of accuracy. The CSM proposed in this paper, simple yet reasonably accurate, represents an inverter-based resource along with its step-up transformer as a parallel combination of harmonic current sources. The development and validation of a CSM is presented using a grid-connected detailed inverter model as a basis. CSMs can be used to represent multiple inverter-based resources like solar farms in a network – to study harmonic interactions between them, evaluate their impact on weak grids, and develop mitigation solutions.

This paper demonstrates the application of current source models (CSMs) in EMTP-RV to study the interaction of harmonic currents from inverter-based solar farms with the grid. The CSM represents the harmonic current injections of a solar farm at the high voltage side of its transformer. This work also investigates the diversity of current harmonic profiles of multiple solar farms, which arises due to the varying generation levels and distinct locations of solar farms. The CSMs emulate the behavior of solar farms using data from known current harmonic spectra and the phase angle of the inverter bus voltage obtained from a steady-state load flow simulation. The process is explained using two different network simulation models in which the CSMs operate along with conventional generator plants. The results of the harmonic current distortion are shown for two cases at 230-kV with PV-controlled generator plants.

As the share of wind power generation increases, the power system stability may be adversely affected. Wind interconnection studies are conducted to evaluate and anticipate the interactions between the wind project and the power system. Various metrics have been developed to evaluate the system strength at the point of interconnection: short-circuit ratio, voltage stability limit, and system inertia. These metrics, unfortunately, rely on network data which may not be readily available. In this paper, a voltage phase angle based index is proposed for evaluating a transmission bus strength. The proposed metric describes the sensitivity of the change of voltage phase angle to the change of active power flow. The proposed metric requires no network information but solely rely on PMU measurements. The validity of the proposed metric is demonstrated by applying it to analyzing the system strength of the Panhandle region of the ERCOT transmission network. Through field data, it is shown that the proposed metric correctly identifies buses as strong or weak. Moreover, the metric can determine the system strength caused by varying wind power production. It is shown that the system strength decreases during high wind power generation, and vice versa.

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

Battery energy storage systems are often controlled through an energy management system (EMS), which may not have access to detailed models developed by battery manu-facturers. The EMS contains a model of the battery system’s performance capabilities that enables it to optimize charge and discharge decisions. In this paper, we develop a process for the EMS to calculate and improve the accuracy of its control model using the operational data produced by the battery system. This process checks for data salience and quality, identifies candidate parameters, and then calculates their accuracy. The process then updates its model of the battery based on the candidate parameters and their accuracy. We use a charge reservoir model with a first order equivalent circuit to represent the battery and a flexible open-circuit-voltage function. The process is applied to one year of operational data from two lithium-ion batteries in a battery system located in Sterling, MA USA. Results show that the process quickly learns the optimal model parameters and significantly reduces modeling uncertainty. Applying this process to an EMS can improve control performance and enable risk-averse control by accounting for variations in capacity and efficiency.

This article presents a methodology aimed at improving mid-term power system resilience at transmission substations in areas potentially affected by floods, combining hardening strategies and quantitative metrics. It takes into account flood forecasts from a hydrological model and the location of electrical equipment to perform impact assessment “as is” and with resilience planning strategies. Thus, the impact of floods on the grid is evaluated over a range of realistic flood scenarios, based on the accumulated cost and load energy unserved as metrics together with future transmission system expansion capacity projections. The mixed-integer linear programming formulation is aimed at minimizing accumulated cost and load energy unserved with optimal hardening of substations, assuming that any non-hardened substation disabled by flooding must be repaired. Furthermore, the methodology is demonstrated in the coastal area of Texas with simulations of floods based on the rainfall of Hurricane Harvey in 2017. Ultimately, the choice of the most appropriate mitigation strategies shall optimize resilience metrics and/or cost indicators with robustness over a range of scenarios.

The voluminous amount of raw waveform data recorded by triggerless power quality monitors contain conspicuous and inconspicuous disturbance events. Data reduction and detection techniques are needed to efficiently extract useful information hidden in the raw data and identify power quality disturbances. The overall objective of this study is to use step changes in the rms voltage profile as an alternative triggering feature for automatically detecting switching events. The full characterization of the event is based on processing a small portion of the voltage waveform selected around the detected rms voltage step change. A filtering method is proposed to smooth out rapid fluctuations in the rms voltage profile during steady-state operation, while preserving the sharp edges caused by rms voltage step changes. Once the rms voltage profile has been filtered, adaptive limits based on the median absolute deviation are computed for detecting rms voltage step changes. The effectiveness of the proposed technique is evaluated using triggerless voltage waveforms to detect capacitor switching events. The use of the filtered rms voltage profile allows accurate detection of capacitor energizing and de-energizing events, while more than 50% of the detections in the unfiltered profile correspond to false-positives.

Fault location algorithms require the input of voltage and current phasors when estimating the distance to a fault. In the case of momentary faults, phasor calculations are often complicated by the presence of an exponentially decaying dc offset. Fourier filters and cosine filters, popularly used by most phasor estimation algorithms in relays, are successful in filtering out most, but not all, of the exponentially decaying dc offset. This dc offset affects the accuracy of voltage and current phasors and may result in a significant error in location estimates. Therefore, this paper presents a dc offset removal algorithm to improve the fault location estimates of momentary faults. The algorithm uses the rms-wavelet method for fault detection and estimates voltage and current phasors using non-linear least squares methods. The proposed method uses variable window size in calculating phasors and estimates a single, but more accurate fault location than multiple locations estimated by the Fourier and cosine filters. The method is validated using simulated and actual field data. The location estimates are shown to be accurate within 5.6% in all cases.

Z -source inverters (ZSIs) are single-stage power converters with both voltage buck and boost capabilities provided by the unique impedance network and the ability to operate during shoot-through states. This study proposes a novel non-linear adaptive backstepping method for dc-side controllers in a multi-loop control scheme of the ZSI in grid-tied photovoltaic (PV) systems. Despite the variability of the capacitor and inductor values in the ZSI impedance network, the proposed controller guarantees robust and stable operation under varying levels of PV irradiance and temperature. The shoot-through duty ratio of the ZSI is obtained directly from the output of an MPPT algorithm and the measured PV and inductor currents. This strategy overcomes the disadvantages of the conventional approach such as the non-minimum phase at the dc side of the ZSI. It also eliminates the need to linearise the voltage/current characteristics of the PV arrays and ZSI model. The ac-side controllers consist of an outer proportional-integral voltage controller and an inner deadbeat current controller to achieve unity power factor and stable capacitor voltage in spite of grid voltage fluctuations. The efficacy of the proposed adaptive backstepping controller and the multi-loop control scheme is validated by offline and hardware-in-the-loop real-time simulations.