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Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.

This article presents a carrier phase shifting technique for minimizing the aggregate harmonics in networks of asymmetric parallel-connected inverters for distributed power generation system applications. The proposed technique is: 1) implemented in a decentralized manner, relying only on local voltage and current measurements, and 2) optimal in the sense that it minimizes a cost function representing the carrier-frequency current harmonics. The analysis indicates that the proposed optimal carrier phase shifting technique can enable order-of-magnitude reductions in harmonic power, and also universal improvements compared to symmetric carrier interleaving for asymmetric inverter networks. Moreover, compared to existing methods that require either centralized communication or information exchange between inverters to coordinate carriers, the proposed technique is completely decentralized, which provides important practical benefits for implementation, including improved robustness and reduced cost. The technique is experimentally validated on a network of three single-phase 2-kW inverters and demonstrates a 36.5% reduction in the weighted total harmonic distortion factor of the aggregate inverter current, and the ability to converge to the optimal carrier phase spacing dynamically in less than one line frequency cycle (16.7 ms) in steady state and transient operating conditions.

This article introduces the notion of minimum distortion point tracking (MDPT) : a control strategy where switching waveforms are optimally phase shifted to minimize aggregate ripple power for networks of dc–dc converters that are connected in series or parallel at the input or output. In a sense, MDPT generalizes the ubiquitous concept of interleaving in balanced systems to a broad class of asymmetric series- or parallel-connected dc–dc converters. For networks of up to one hundred interconnected power converters, MDPT demonstrates a one to two order of magnitude reduction (−14 to −22 dB) in distortion power. We present and experimentally verify three algorithms that can dynamically solve the MDPT optimization problem on a network of three input-parallel connected dc–dc buck converters handling 1.8 kW. The experimental results illustrate an up to $\text{3.06}\times$ reduction in the peak-to-peak ripple of the parallel-side bus voltage and convergence close to an optimal steady-state solution in 5 ms.

Rooftop and building-integrated distributed photovoltaic (PV) systems are emerging as key technologies for smart building applications. This paper presents the design methodology, mathematical analysis, simulation study, and experimental validation of a digital twin approach for fault diagnosis. We develop a digital twin that estimates the measurable characteristic outputs of a PV energy conversion unit (PVECU) in real time. The PVECU constitutes a PV source and a source-level power converter. The fault diagnosis is performed by generating and evaluating an error residual vector, which is the difference between the estimated and measured outputs. A PV panel-level power converter prototype is built to demonstrate how the sensing, processing, and actuation capabilities of the converter can enable effective fault diagnosis in real time. The experimental results show detection and identification of ten different faults in the PVECU. The time to fault detection (FD) in the power converter and the electrical sensors is less than 290 $\mu$ s and the identification time is less than 4 ms. The time to FD and identification in the PV panel are less than 80 ms and 1.2 s, respectively. The proposed approach demonstrates higher fault sensitivity than that of existing approaches. It can diagnose a 20% drift in the electrical sensor gains and a 20% shading of a solar cell in the PV panel.

We present a decentralized control strategy that yields switch interleaving for parallel-connected dc–dc buck converters. Compared to state-of-the-art methods that are distributed at best, the proposed architecture requires no communication, and hence, presents a variety of advantages with regard to reliability, modularity, and cost. The method is based on the digital implementation of the dynamics of a Lienard-type oscillator circuit as the controller for the converters. Each controller only requires the locally measured output current to synthesize the pulsewidth modulation (PWM) carrier waveform. The intrinsic electrical coupling between converters drives the nonlinear-oscillator-based controllers to converge to an interleaved state with uniform phase spacing across PWM carriers, independent of the number of converters, the load, and initial conditions. We provide analytical guarantees for existence and stability of the interleaved state as well as extensive hardware results for a system of five 120 W 48 V-to-12 V dc–dc buck converters that demonstrate convergence to the interleaved state in the face of a variety of large-signal disturbances.

This paper presents a hybrid analog/digital computing circuit that solves a form of the selective harmonic minimization problem in a fast and scalable manner. Voltages in the circuit represent variables in the optimization problem, and, in steady state, converge to the optimal solution of the problem. A digital microcontroller sets voltages at particular nodes in the circuit as to program the cost function and other inputs to the optimization. We present a specific realization of the computing circuit that solves for eight independent switching angles for a quarter-wave symmetric PWM driven two-level single-phase inverter. This realization minimizes seven undesired harmonics while also maintaining control of the modulation index. The simulation results demonstrate that the proposed circuit can converge to the optimal solution in less than 5.0 ms. Our survey of prior methods indicates that this is substantially faster than existing published methods in the literature.

DC microgrids have become a prevalent topic in research in part due to the expected superior efficiency of DC/DC converters compared to their AC/DC counterparts. Although numerous side-by-side analyses have quantified the efficiency benefits of DC power distribution, these studies all modeled converter loss based on product data that varied in component quality and operating voltage. To establish a fair efficiency comparison, this work derives a formulaic loss model of a DC/DC and an AC/DC PFC boost converter. These converters are modeled with identical components and an equivalent input and output voltage. Simulated designs with real components show AC/DC boost converters between 100 W to 500 W having up to 2.5 times more loss than DC/DC boost converters. Although boost converters represent a fraction of electronics in buildings, these loss models can eventually work toward establishing a comprehensive model-based full-building analysis.

This paper presents the design, implementation, and experimental validation of a method for fault prognosis for power electronics systems using an adaptive parameter identification approach. The adaptive parameter identifier uses a generalized gradient descent algorithm to compute real-time estimates of system parameters (e.g., capacitance, inductance, parasitic resistance) in arbitrary switching power electronics systems. These estimates can be used to monitor the overall health of a power electronics system and to predict when faults are more likely to occur. Moreover, the estimates can be used to tune control loops that rely on the system parameter values. The parameter identification algorithm is general in that it can be applied to a broad class of systems based on switching power converters. We present a real-time experimental validation of the proposed fault prognosis method on a 3 kW solar photovoltaic interleaved boost dc–dc converter system for tracking changes in passive component values. The proposed fault prognosis method enables a flexible and scalable solution for condition monitoring and fault prediction in power electronics systems.

We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$ s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.

We present the design and experimental validation of a scalable dc microgrid for rural electrification in emerging regions. A salient property of the dc microgrid architecture is the distributed control of the grid voltage, which enables both instantaneous power sharing and a metric for determining the available grid power. A droop-voltage power-sharing scheme is implemented wherein the bus voltage droops in response to low supply/high demand. In addition, the architecture of the dc microgrid aims to minimize the losses associated with stored energy by distributing storage to individual households. In this way, the number of conversion steps and line losses are reduced. We calculate that the levelized cost of electricity of the proposed dc microgrid over a 15-year time horizon is $0.35/kWh. We also present the experimental results from a scaled-down experimental prototype that demonstrates the steady-state behavior, the perturbation response, and the overall efficiency of the system. Moreover, we present fault mitigation strategies for various faults that can be expected to occur in a microgrid distribution system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost-effective method for electrifying emerging regions.

This paper introduces the notion of minimum distortion point tracking (MDPT): a control paradigm for input- or output-parallel connected dc-dc converters where switching waveforms are optimally phase shifted to minimize the total dc-bus ripple power. In a sense, MDPT generalizes the ubiquitous concept of interleaving in balanced multiphase dc-dc converters to a broad class of asymmetric input- or output-parallel connected dc-dc converters. Realizing power-quality improvement with control design implies that a drastic reduction in passive input or output filters can be achieved. This paper presents the mathematical characterization of the minimum distortion point (MDP) and a technique for MDPT. An experimental case study for three 600 W dc-dc converters demonstrates a 3× reduction in the bus voltage ripple, convergence to a static MDP in 40 ms, and the ability to dynamically track a time-varying MDP.

The paper presents the theoretical analysis, practical design considerations, and experimental verification of an improved robust adaptive parameter identifier for dc-dc power electronic converters. The proposed method improves the performance of a conventional linear switched adaptive parameter identifier by designing the observer gain using performance criteria. The design approach ensures the optimality of the observer gain as well as provides a degree of robustness to measurement noise and circuit non-idealities. We develop the design methodology for the robust adaptive parameter identifier and present experimental results that validate the achievable improvement in real-time parameter identification. The results indicate wide parameter tracking ranges (10% to 66% parameter deviations) and sub-second convergence, exceeding the performance of classically designed adaptive parameter identifiers.

This paper presents the design, analysis, and experimental validation of a fault detection and identification (FDI) scheme for dc-dc power electronic converters. The FDI scheme includes two new classes of fault filters: (1) a linear-switched fault detection (FD) filter and (2) a bank of linear-switched fault identification (FI) filters. Both the FD filter and the bank of FI filters have a structure similar to that of Luenberger observer. The FD filter detects a fault event and the bank of FI filters identify a faulted converter component, for instance, failure in switching or passive components. We present simulation and experimental results for a prototype 2 kW solar photovoltaic (PV) boost dc-dc converter system to demonstrate the efficacy of the proposed FDI scheme. The experimental results demonstrate accurate fault detection and identification for a collection of catastrophic component faults in PV dc-dc power converters.

This paper presents the design and experimental demonstration of a method for photovoltaic condition monitoring and hot-spot detection using a real-time adaptive parameter identification approach. The identification algorithm tracks parameters in an equivalent ac circuit model of a PV panel, and uses these parameter estimates to predict when the PV panel is partially shaded or in a hot-spot condition. The proposed hot-spot detection method has low computational overhead and requires only passive electrical measurements at the terminal of the PV panel. We present simulation and experimental results that demonstrate the accuracy of the parameter identifier and its ability to detect partial shading in a 10 W monocrystalline silicon solar panel.

This paper presents the analysis and design of an adaptive parameter estimator for power electronics circuits. Adaptive parameter estimation has been demonstrated to be a useful technique for enabling novel controls, monitoring, and fault diagnosis techniques in power electronics systems. We present an analysis of factors that affect the performance, stability, and feasibility of a gradient-type parameter estimation, along with simulation and experimental results that validate the analysis. Moreover, we derive theoretical limits on the convergence speed of the parameter estimation and demonstrate design techniques to maximize this limit while maintaining the robustness of the estimation. The paper provides a comprehensive analysis and design process for adaptive estimation schemes as applied to systems of switching power converters.

We present a decentralized control strategy that yields switch interleaving among parallel-connected dc-dc buck converters. The proposed method is based on the digital implementation of the dynamics of a nonlinear oscillator circuit as the controller. Each controller is fully decentralized, i.e., it only requires the locally measured output current to synthesize the pulse width modulation (PWM) carrier waveform and no communication between different controllers is needed. By virtue of the intrinsic electrical coupling between converters, the nonlinear oscillator-based controllers converge to an interleaved state with uniform phase-spacing across PWM carriers. To the knowledge of the authors, this work presents the first fully decentralized strategy for switch interleaving in paralleled dc-dc buck converters.

This paper develops and experimentally demonstrates a new class of high-fidelity model-based fault detection and isolation filters for three-phase AC-DC power electronics systems. The structure of these filters is similar to that of a piecewise linear observer and in the absence of faults the filter residual converges to zero. On the other hand, whenever a fault occurs, by appropriately choosing the filter gain, the filter residual will exhibit certain geometric characteristics that allow the fault to be detected and, in certain cases, also isolated. Key advantages of these filters include fast detection of all possible component faults and the ability to capture slow degradation in individual components. In order to experimentally demonstrate their feasibility, the filters are implemented on an ultra-fast application-specific real-time processor. While the theoretical framework developed is general, the analysis, simulations, and experiments are focused on widely used power electronics systems implementing three-phase AC-DC converters that are used in, e.g., motor drive applications and distributed static compensators.

The ability to detect hot temperatures is critical to maintaining body temperature and avoiding injury in diverse animals from insects to mammals. Zebrafish embryos, when given a choice, actively avoid hot temperatures and display an increase in locomotion similar to that seen when they are exposed to noxious compounds such as mustard oil. Phylogenetic analysis suggests that the single zebrafish ortholog of TRPV1/2 may have arisen from an evolutionary precursor of the mammalian TRPV1 and TRPV2. As opposed to TRPV2, mammalian TRPV1 is essential for environmentally relevant heat sensation. In the present study, we provide evidence that the zebrafish TRPV1 ion channel is also required for the sensation of heat. Contrary to development in mammals, zebrafish TRPV1+neurons arise during the first wave of somatosensory neuron development, suggesting a vital importance of thermal sensation in early larval survival.In vitroanalysis showed that zebrafish TRPV1 acts as a molecular sensor of environmental heat (≥25°C) that is distinctly lower than the sensitivity of the mammalian form (≥42°C) but consistent with thresholds measured in behavioral assays. Usingin vivocalcium imaging with the genetically encoded calcium sensor GCaMP3, we show that TRPV1-expressing trigeminal neurons are activated by heat at behaviorally relevant temperatures. Using knock-down studies, we also show that TRPV1 is required for normal heat-induced locomotion. Our results demonstrate for the first time an ancient role for TRPV1 in the direct sensation of environmental heat and show that heat sensation is adapted to reflect species-dependent requirements in response to environmental stimuli.

This paper presents the design, analysis, and implementation of a fault diagnosis method for photovoltaic (PV) energy conversion systems. We present a model-based state estimation approach for detecting and identifying three types of faults — (1) converter input faults (e.g. faults in a PV panel), (2) converter component faults (e.g. switch faults or passive component degradation), and (3) sensor faults (e.g. voltage and current sensors) for PV panel-integrated power electronics systems. The state estimator model includes a dynamic model of the PV source and a linear-switched model of the switching power converter. The estimated state values are compared with measured values from the physical power stage, which generates an error residual vector. This residual is used to detect and identify faults in either the PV source or the switching power converter. The estimator, fault detection and identification logic, and the PV converter control system (including PWM generation and maximum power point tracking) are implemented entirely on a single all-programmable system-on-chip (SoC) device, which includes an FPGA and ARM core. We present simulation and experimental results for a prototype 2 kW PV energy conversion system to demonstrate the efficacy of the proposed fault diagnosis and control platform. The experimental results demonstrate successful fault detection within 2 ms and precise fault identification within 31 ms for a collection of input, component, and sensor faults.

We present the design, implementation, and experimental validation of FailSafe — a generalized methodology for fault detection, identification, and remediation (FDIR) for switching power converters in nanogrids. FailSafe is a dynamical systems approach to FDIR for switching power converters, and can be applied to a broad class of converters and fault types. FailSafe operates as part of the control loop of a switching power converter, and uses the measurements and inputs of the converter to achieve both fault detection and identification (FDI) and fault remediation. In this paper, we present two Modules for FDI — a model-based residual approach and a data-driven multiclass Support Vector Machine (one-vs-one) approach. Moreover, we describe the design of a fault remediation Module by designing optimal control actions in a pre-computed reach-avoid set. We present simulation and experimental results using a prototype nanogrid testbed. Simulation results for the multiclass Support Vector Machine (one-vs-one) FDI Module on a 6-phase interleaved boost converter demonstrate fault detection and identification with a classification accuracy of 98.9% for a current sensor fault and 90.8% for an output capacitor fault. Experimental results for the model-based residual FDI Module on a boost converter demonstrate fault detection and identification in 600 μs for a capacitor fault and 250 μs for a voltage sensor fault.

This paper presents an approach for nominally lossless regulation of the output voltage, and for design of tight closed-loop voltage control of a resonant switched-capacitor (ResSC) dc-dc converter. A switching pattern for the ResSC dc-dc converter that enables wide range lossless voltage regulation and zero voltage switching (ZVS) is developed. An appropriate small signal model is derived based on the generalized averaging method. In view of the dynamics of the developed small signal transfer functions, a compensation method based on a minor phase loop is introduced to stabilize the system. The steady state and transient responses of the system are evaluated based on the developed model. The performance of the proposed controller is verified by a switch-based simulation in a design example for on-chip power delivery application.

We present the analysis, design, and experimental implementation of a fault diagnosis method for switching power converters using a model-based estimator approach. The fault diagnosis method enables efficient detection and identification of component and sensor faults, and is implemented on the same computation platform as the control system. The model-based estimator operates in parallel with the switching power converter, and generates an error residual vector that can be used to detect and identify particular component or sensor faults. This paper presents an experimental demonstration for a 1.2 kW rack-level uninterruptable power supply (UPS) dc-dc converter for data center applications. Simulation and experimental results demonstrate fault detection and identification for various component and sensor faults in the converter. Moreover, we show that the proposed fault diagnosis design and analysis methods are applicable to a broad class of converter topologies and fault types.

We present the design and experimental validation of a scalable dc microgrid architecture for rural electrification. The microgrid design has been driven by field data collected from Kenya and India. The salient features of the microgrid are distributed voltage control and distributed storage, which enable developed world grid cost parity. In this paper, we calculate that the levelized cost of electricity (LCOE) for the proposed dc microgrid system will be less than $0.40 per kW-hr. We also present experimental results from a locally installed dc microgrid prototype that demonstrate the steady state behavior, the perturbation response, and the overall efficiency of the system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost effective method for electrifying emerging regions.

We describe the design, implementation, and experimental validation of an ultra-fast real-time hardware-in-the-loop emulation of a permanent magnet synchronous machine (PMSM) inverter drive. The power electronics converter and machine are modeled using a flexible piecewise linear state space approach and are simulated in hard real-time with 1 μs time step, which enables high-fidelity modeling of converter switching dynamics, including dead time and fully rectifying mode. We validate the fidelity of the real-time PMSM drive emulation by making real-time comparisons with a reference hardware model of a PMSM drive (scaled down system rated at 2.7 A and 200 V). Additionally, we experimentally demonstrate the capability of the hardware-in-the-loop PMSM drive emulation under various operating scenarios, including steady state, transient, and fault conditions.

In this paper, we describe the design and implementation of a state observer for power electronics converters, the dynamics of which can be described by a linear-switched state-space model. The observer is also described by a linear-switched state-space model, and for each particular subsystem, the observer essentially behaves as a Luenberger observer. As part of the design procedure, we provide sufficient conditions that guarantee the stability of the observer. In order to experimentally demonstrate its feasibility, the observer is implemented in a dedicated real-time processor architecture. We present experimental results that illustrate the observer behavior when used for state estimation in single-phase and two-phase boost converters.

This paper describes the design, implementation, and validation of a hardware-in-the-loop (HIL) test platform for electric vehicle drive applications. We implement a HIL platform by interfacing a variable speed drive controller with a real-time simulation of an electric vehicle drive. A real-time test bench simulation enables drive cycle testing and fault injection capability for the HIL platform. We demonstrate the prototyping capability of the HIL platform with the EPA Urban Dynamometer Driving Schedule (UDDS) on an electric vehicle drive system. Real-time comparisons with a real, small-scale electric vehicle drive validate the fidelity of the real-time simulation under various operating and fault conditions. Test case simulations demonstrate the fidelity and prototyping capability of the hardware-in-the-loop platform when used for electric vehicle drive testing applications. Additionally, real-time simulation and test results demonstrate the ability of the HIL platform to accurately encapsulate electric vehicle dynamics with time constants that span more than five orders of magnitude.