Topology combined machine learning for consonant recognition

In artificial-intelligence-aided signal processing, existing deep learning models often exhibit a black-box structure, and their validity and comprehensibility remain elusive. The integration of topological methods, despite its relatively nascent application, serves a dual purpose of making models more interpretable as well as extracting structural information from time-dependent data for smarter learning. Here, we provide a transparent and broadly applicable methodology, TopCap, to capture the most salient topological features inherent in time series for machine learning. Rooted in high-dimensional ambient spaces, TopCap is capable of capturing features rarely detected in datasets with low intrinsic dimensionality. Applying time-delay embedding and persistent homology, we obtain descriptors which encapsulate information such as the vibration of a time series, in terms of its variability of frequency, amplitude, and average line, demonstrated with simulated data. This information is then vectorised and fed into multiple machine learning algorithms such as k-nearest neighbours and support vector machine. Notably, in classifying voiced and voiceless consonants, TopCap achieves an accuracy exceeding 96% and is geared towards designing topological convolutional layers for deep learning of speech and audio signals

Digital twins of distributed energy resources for real-time monitoring : data reporting rate considerations

This paper analyzes the requirements for the reporting rate of the live data source to support the operation of Digital Twins (DTs) of Distributed Energy Resources (DERs) for real-time power systems monitoring applications. The visibility of distribution networks is currently limited due to the lack of sufficient measurement and communication infrastructures. With the rapid increase of DERs, it becomes increasingly important to improve the visibility of the distribution networks to ensure the critical system operating constrains are continuously met. DTs are virtual replicas of physical systems, and with certain live measurement data, they can be used to accurately represent the real-time dynamics of the physical entities. The features of DTs could therefore be applied to increase the visibility of network and potentially support real-time decisions making. This paper presents the investigation of the impact of data reporting rate on DT accuracy, based on which, the paper presents a method that could be used to quantify the minimum requirements for data reporting rate to adequately support the DT operation, which provides valuable learning for specifying measurement devices and communication networks to enable DTs-based solutions

High-fidelity validation with smart grid modelling complexity : considerations on emerging solutions

The continued integration of increased volumes of distributed energy resources and flexibility services into power networks across the world is introducing increasing complexity into system operations. With the growing number and dimensions of complexity, modelling of smart grids for simulation is becoming more demanding. In particular, achieving high-fidelity validation of such complex cyber-physical systems is growing in importance and in scale of challenge. Coordinated real-time simulation across multiple platforms, termed geographically distributed simulations (GDS), paves a new pathway for high-fidelity validation of large-scale smart grids. Furthermore, the integration of cloud solutions enables efficient initialization of simulations and ensures secure data communications among GDS participants. This paper provides a comprehensive overview of different types of real-time simulation concepts and explains how they can best be utilized to realize GDS with enhanced computational capability. Subsequently, this paper summarizes the applicability of GDS, specifically emphasizing on cloud-based GDS, to facilitate high-fidelity validation of complex smart grids

ANN driven FOSMC based adaptive droop control for enhanced DC microgrid resilience

Parallel operation of power converters in islanded DC microgrids exhibits significant trade-off in voltage regulation and current sharing with conventional droop control. The converters exhibit inaccuracies in proportionate sharing of current when subject to heavy and transient loading while sharing a common bus. Moreover, the inaccuracies further persist due to unmodeled dynamics, parametric uncertainties, disturbance in the system and communication reliability. Therefore, the resilient parallel operation of power converters in DC microgrids requires a robust and fast control strategy that can mitigate the effect of disturbances and maintain regulated bus voltage with proportional current sharing amongst the power converters. Consequently, this work proposes a novel ANN driven droop control for a DC microgrid to enhance the transient response and mitigate disturbance in finite time. Two controllers based on adaptive droop strategy are proposed; the primary controller is a generalized Hebb's learning law-based PI integrated controller that can adjust the gains in real time for finite-time disturbance compensation in the networks and the secondary control regulates the bus voltage using fractional order sliding mode control. The effectiveness of the proposed method is evaluated by simulation and experiment and compared with the conventional and distributed droop control methods, proving its robust and adaptive performance for resilient DC microgrid applications

Adaptive Smith predictor for enhanced stability of power hardware-in-the-loop setups

The stability and accuracy of power hardware-in-the-loop (PHIL) setups are sensitive to and deteriorated by the dynamics and non-ideal characteristics of their power interfaces, such as time delay, noise perturbation, and signal distortion. In this paper, a compensation scheme comprising a Smith predictor compensator is proposed to mitigate the impact of time delay on PHIL stability. Furthermore, an online system impedance identification technique is leveraged to enhance the robustness of the compensator and facilitate the compensation scheme with adaptivity to system impedance variation. Analytical assessment, simulation results, and PHIL experimental results are presented to verify the proposed compensation scheme. This scheme enables robust and stable testing of novel power technologies under varying impedance ratios representative of the complex scenarios emerging within the power sector

Challenges, solutions and lessons learnt from testing power system performance with a general power theory-controlled converter

A novel control approach for power-electronic converters has been shown to reduce the losses in delivery systems to below the levels possible with conventional methods. In this research, an 80 kW converter was retrofitted to operate using the General Power Theory (GPT). The effect of compensation using the GPT in a three-bus test network was studied by Simulink simulation and in the physical power system infrastructure of the Power Networks Demonstration Centre. The simulation results demonstrated that the converter did not need the concept of reactive power for control and could improve the system power factor. The experimental measurements were used for comparison with the simulation results. Challenges faced during experimental testing are discussed. Solutions are proposed to resolve some of the measurement problems that hindered the full experimental validation at this stage. The practical lessons learnt are helpful for future tests and identified real-world issues that may be encountered during deployment

Design and implementation of a real-time hardware-in-the-loop platform for prototyping and testing digital twins of distributed energy resources

Power systems worldwide are experiencing rapid evolvements with a massive increase of renewable generation in order to meet the ambitious decarbonization targets. A significant amount of renewable generation is from Distributed Energy Resources (DERs), upon which the system operators often have limited visibility. This can bring significant challenges as the increasing DERs' can lead to network constraints being violated, presenting critical risks for network security. Enhancing the visibility of DERs can be achieved via the provision of communication links, but this can be costly, particularly for real time applications. Digital Twin (DT) is an emerging technology that is considered as a promising solution for enhancing the visibility of a physical system, where only a limited set of data is required to be transmitted with the rest data of interest can be estimated via the DT. The development and demonstration of DTs requires realistic testing and validation environment in order to accelerate its adoption in the industry. This paper presents a real time simulation and hardware-in-the-loop (HiL) testing platform, specifically designed for prototyping, demonstrating and testing DTs of DERs. Within the proposed platform, a software-based communication emulator is developed, which allows the investigation of the impact of communication latency and jitter on the performance of DTs of the DERs. Case studies are presented to demonstrate the application of the developed DT prototyping process and testing platform to enable frequency control using the DTs, which provide valuable learnings and tools for enabling future DTs-based solutions

Virtual shifting impedance method for extended range high-fidelity PHIL testing

A novel power hardware-in-the-loop interface algorithm, the Virtual Shifting Impedance, is developed, validated and demonstrated in this paper. Building on existing interface algorithms, this method involves shifting a part of the software impedance to the hardware side to improve the stability and accuracy of power hardware-in-the-loop setups. However, compared to existing approaches, this impedance shifting is realized by modifying the command signals of the power amplifier controller, thus avoiding the requirement for hardware passive components. The mathematical derivation of the Virtual Shifting Impedance interface algorithm is realized step-by-step, while its stability and accuracy properties are thoroughly examined. Finally, the applicability of the proposed method is verified through power hardware-in-the-loop simulation results

Interface compensation for more accurate power transfer and signal synchronization within power hardware-in-the-loop simulation

Power hardware-in-the-loop (PHIL) simulation leverages the real-time emulation of a large-scale complex power system, while also enabling the in-depth investigation of novel actual power components and their interactions with the emulated power grid. The dynamics and non-ideal characteristics (e.g., time delay, non-unity gain, and limited bandwidth) of the power interface result in stability and accuracy issues within the PHIL closed-loop simulations. In this paper, a compensation method is proposed to compensate for the non-ideal power interface by maximizing its bandwidth, maintaining its unity-gain characteristic, and compensating for its phase-shift over the frequencies of interest. The accuracy of power signals synchronization and the transparency of power transfer within the PHIL configuration are assessed by employing the error metrics. In conjunction with the frequency-domain stability analysis and the time-domain simulations, a case study is made to validate the proposed compensation method