Atif Maqsood

Seamless recovery and sustained power to critical infrastructures (CIs), after grid failure, is a crucial need arising in disaster scenarios that are increasingly becoming more frequent. Accreditation standards recommend CIs to have emergency power supply system (EPSS) in order to form a local microgrid network with backup resources (generation units/renewable resources) in case of sudden power blackouts of main grid supply. The standards also recommend enhancements such as seamless transition capability during on-grid to off-grid jump and vice versa, efficient and optimal operation while maintaining the standard regulation and power quality limits during power contingencies, and operation with sustenance of reliable power for long periods of time. This article is proposing a comprehensive design of the EPSS for uninterrupted operation of CIs by employing novel techniques, such as 1) mode-dependent droop controlled grid-forming inverters for seamless transition capability; 2) fast-acting optimal net-load management engine for efficient and optimal operations maintaining regulation and power quality limits; 3) optimization-based horizon of viability engine for longer, sustained and viable operation in the aftermath of grid failure as recommended in standards. Two stage validations are conducted using defined test cases while considering various types of contingencies on a 3-ϕ, 60 Hz, 480 V, 450 kVA test system based on a realistic electrical network of a commercial scale medical center. The comprehensive controller-hardware-in-loop and power-hardware-in-the-loop based experiments corroborate the efficacy and robustness of the proposed strategy for EPSS employed in the CI. The mode-dependent droop controller enables seamless transition of the critical infrastructure between the off-grid and the on-grid modes. Inverters operate in the gird-forming mode when the CI is off-grid and also when the CI is on-grid while keeping frequency and voltage regulated between ±0.5 Hz and ±10% of the nominal respectively. The fast-acting net-load management engine maintains the balance in total available generation and the power demand by determining the optimal decisions within 100 ms during various contingencies while regulating the voltage, frequency, and total harmonic distortion (THD) of the system under the strict limit recommended by the grid codes. The horizon of viability optimization engine showcases advantages over conventional scheduling engines with higher utilization of critical loads and maintaining higher energy at the end of an 1-hour experiment.

Seamless recovery of power to critical infrastructures, after grid failure, is a crucial need arising in scenarios that are increasingly becoming more frequent. In this article, we propose a seamless transition strategy using a single and unified mode-dependent droop-controlled grid-forming inverters. The control strategy achieves the following objectives: First, regulates the output active and reactive power by the droop-controlled inverters to a desired value while operating in on-grid mode; second, seamless transition and recovery of power injections into the load after grid failure by inverters that operates in grid-forming mode all the time; and third, requires only a single bit of information on the grid/network status for the mode transition. A framework for assessing the stability of the system and to guide the choice of parameters for controllers is developed using control-oriented modeling. A controller hardware-in-the-loop-based real-time simulation study on a test system based on the realistic electrical network of a commercial-scale medical center is conducted for initial prototyping of the control strategy. A hardware experiment is conducted with two $\mathbf {3}$ - $\boldsymbol{\phi }$ , $\mathbf {480}$ -V, $\mathbf {125}$ -kVA grid-forming inverters, a $\mathbf {3}$ - $\boldsymbol{\phi }$ , $\mathbf {480}$ -V, $\mathbf {270}$ -kVA grid simulator, a physical grid switch, and a physical load bank. The experimental data establish the effectiveness of the always grid-forming operation and control of inverters in meeting power delivery objectives when on-grid and off-grid under various kinds of loads and scenarios while minimizing transients during transitions. Furthermore, performance comparison with the existing strategies showcases the advantage of the proposed strategy.

All-electric warships require high power-dense distribution system to power-advanced weapon loads. Medium-voltage dc (MVdc) power distribution is well suited to fulfill this requirement if certain risks are addressed. A particularly emerging problem is that the advanced pulsating loads draw large currents in extremely short periods of time and behave similar to the shunt fault. The nature of the load and the operating cycle determines the unique structure of the pulse in time and frequency domains. If the load operating cycle consists of a finite number of transitions, then the corresponding frequency content of the current profile can be used to identify these transients. The wavelet transform is used to extract this useful frequency domain information from the sampled current data. A proposed computationally light data-driven machine learning-based fault detection and load monitoring solution extracts the frequency-domain features of the observed transient and compares that to a database of stored features to identify the observed transient and then to further identify faults that may create an abnormal disturbance in the load current profile such as arcing and shunt faults. It can be further applied to any load profile with prerequisite of a finite number of repetitive transients during normal condition. This article focuses on the fault detection only and not for fault isolation while it can achieve isolation capability once the fault was diagnosed. In final real-time implementation, the recursive Haar stationary wavelet transform (SWT)-fed computationally light machine learning is employed to validate the proposed scheme in a single-core Texas Instrument (TI) digital signal processer (DSP) TMS320F28335.

Extensive deployment of power electronics loads in naval ship power systems indicate the ship electrification is inevitable in future trends. Next generation warships require high power density weapons drawing pulse power from dc grid. A particularly concerning issue is that these pulse loads draw large currents in short periods of time similar to fault behavior; and may be indiscernible from a fault. This paper proposed a novel machine learning based algorithm – long short-term memory (LSTM) recurrent neural network (RNN) based autoencoder (AE) networks to detect dc faults and monitor load conditions applied to naval pulse loads. The novel load monitoring solution presented herein can be applied to any load profile that exhibits repetitive transients during normal operation. The frequency-domain features of the load current are extracted under wavelet transform for the network training to set the network weights and biases. Once the network training is completed, the LSTM RNN based AE will produce both signal classification and signal reconstruction of the pulse load based on wavelet features of input current. Any faults should yield large reconstruction errors for protective action. Finally the method is demonstrated in experimental results.

Current trends in Naval shipboard power system architecture indicate that the electrification of future warships is inevitable and it will be equipped with loads that draw periodic pulsed currents from the dc microgrid or have large transients while switching state. In order to monitor the operation of those loads, solely time-based features are not enough as they do not provide sufficiently unique information to differentiate various transient stages of the load profile. The focus of more recent research has been on extracting time-frequency features. However, no comprehensive solution exists that could work for any general load profile. The proposed load monitoring and fault detection method presented in this paper outlines a data clustering based approach to extract unique feature vectors from short-time Fourier transform analysis for any pulsed load. These features can then be used to identify various events in the load transient as well as shunt faults and series arcing faults. Implementation and performance of the scheme for several load profiles and fault scenarios is included.

Since the great debate between Thomas Edison and Nikola Tesla, our nation's power system has operated on alternating current (ac). This was chosen over direct current (dc) because of the need to increase voltage with ac transformers to a high value using transformers for long-distance power transmission. The system has served its purpose well, but now, many energy sources, such as solar panels, fuel cells, and batteries, supply dc voltage. Also, dc/dc power converters are commonly used to transform voltage and to interface these dc sources with a larger system. Because of this, local dc power systems (or microgrids) have become popular topics in research literature. It also turns out that interfacing a wind power generator to a dc system is simpler than interfacing it to an ac system because ac/dc conversion is needed for the former and ac/dc/ac conversion is needed for the latter. Although energy sources and power conversion are readily available for dc power systems, some highperformance applications require fast-acting dc circuit breakers, which are currently in the experimental phase. This article discusses options for high-performance dc circuit breakers and specifically details the coupled-inductor dc breaker. This breaker is demonstrated for fault protection in a notional dc microgrid.