Unsupervised anomaly detection in streaming time series is challenging due to evolving data distributions and the lack of labeled anomalies, where concept drift can significantly degrade detection accuracy during long-term operation. Most existing adaptive methods rely on uniform update strategies or continuous incremental learning. Such designs can be slow to react to abrupt distribution shifts and are prone to overwriting previously learned regimes when patterns recur. This paper proposes ADAPTS (Adaptive Drift-Aware Pool-based framework for Time Series), an unsupervised and drift-aware framework for adaptive anomaly detection in non-stationary data streams. ADAPTS integrates statistical drift detection, explicit drift-type classification, drift-specific model adaptation, and a bounded model pool for knowledge reuse within a unified system. The framework distinguishes sudden, incremental, and recurrent concept drift and aligns adaptation strategies accordingly through retraining, controlled fine-tuning, or selective model reuse. Experiments on benchmark datasets show that ADAPTS can keep stable and reliable detection results under different types of drift. Experimental results show that ADAPTS achieves competitive performance across multiple datasets, with performance improvements observed under several drift scenarios, including up to 0.11 absolute gain in AUC on challenging scenarios such as the NAB dataset, while maintaining improved long-term stability under diverse drift conditions.

In the DC fault event, DC link capacitors immediately discharge a colossal current of high frequency. These large currents of high frequencies can instantly damage the components attached to the DC transmission system. Therefore, a rapid and robust DC fault detection topology is necessary, facilitating protection coordination in forthcoming Multi-Terminal Direct Current (MTDC) systems. This study introduces a novel fault detection technique using the time derivative of the High Pass Filter (HPF) response to identify fault currents in just a few microseconds (µs). This topology enables protection coordination for modern MTDC systems. Furthermore, this technique can identify different types of DC faults and remains unaffected by AC faults and load variation.

The DC link capacitor discharge in the event of a DC fault is rapid and contains high frequencies. This rapid discharge of high current and frequency interface with DC bus, Voltage Source Converter (VSC), and AC source. This interface damages the equipment and possibly living beings in proximity. Therefore, it is necessary to develop a topology to detect and identify fault types promptly, along with the isolation and restoration of the system. This study introduces an improved novel fault detection technique using the Highpass Chebyshev type 2 filter due to its flatter pass-band response. Further, the polarities of peaks obtained from the fault detection calculation are used again for another proposed novel fault location method. It identifies fault types to locate and isolate the faulty system using only polarities of amplitude response peaks. In the simulation, both methods were fast and accurate in detecting, locating, and identifying appropriate fault types only by High Pass Filter (HPF) amplitude response peaks and their polarities.

The article proposes an extended Morlet wavelet-based FIR (et-MW-FIR) which can be piled on top of enhanced MW-FIR (e-MW-FIR) to further improve the overall estimation accuracy. Moreover, by integrating the extended algorithm block, the wavelet parameter selection range is enlarged. In this case, as one application of the proposed method, the article also introduces a fully compliant P-class estimator by varying the parameters rather than using a threshold parameter which may lead to nonconvergence in practice. The new estimators with two sets of parameters are compared with the conventional and enhanced estimators under IEC/IEEE 60255-118-1. Additionally, some extensive simulations, field data, and hardware implementation are also conducted to evaluate the performance of the proposed block and estimator.

This article introduces a way to optimize the power output of a photovoltaic (PV) system by implementing a maximum power point tracking (MPPT) technique. The algorithm is based on adaptive particle swarm optimization (APSO) under partial shading conditions (PSC). APSO aims to solve the issue that conventional MPPT algorithms cannot track the optimal global solution when the photovoltaic array's power-voltage (P-V) curve shows multiple peaks under PSC. In APSO, the algorithm adaptively adjusts the learning factor and inertia weight to optimize convergence speed and precision. The simulation results prove that APSO outperforms the conventional particle swarm optimization (PSO) algorithm by rapidly and precisely tracking the maximum power point (MPP) under uniform illumination and static or dynamic PSC. Moreover, the APSO exhibits fewer power fluctuations during the tracking process.

This paper uses the neural network to realize a photovoltaic (PV) system's maximum power point tracking. After Bayesian optimization of hyperparameters, the model can accurately determine the maximum power point voltage according to solar irradiance and temperature. The optimized PV system not only adapts to different operating conditions but also achieves the optimal power output under uniform irradiance, static irradiance shading, and dynamic shading, ensuring the maximum efficiency of the PV system. Moreover, the optimized model is superior to the unoptimized model and the traditional perturbation and observation method in terms of accuracy. The improved model enhances the practicality and reliability of the PV system, helping to improve PV efficiency and reduce energy loss.

The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.

Net-zero carbon emissions goals are rapidly organising every energy requirement around renewable energy sources that produce electricity. There is a push for rapid electrification of the economy to meet this demand. Dynamic Energy Resource Management Systems (DERMs) are going to operate in a federated structure to build a DERM of DERMs, and dock with the distribution management system (DMS) of the Distribution System Operators (DSO). The ability to monitor the network using real time data for protection and control purposes will be prime. This paper focuses on laying the groundwork for it. A Phasor Measurement Unit (PMU) is a device for estimating the phase angle and magnitude of an electrical phasor quantity in the electricity grid using a common time source for synchronisation. Real-time streaming, however, has not been attempted and applications of PMU data in under-frequency load shedding (UFLS) events have been scarce. This paper focuses on ‘dShed’ - our cloud and distributed streaming application while proposing a rate of change of frequency (ROCOF) method to shed load smartly during a UFLS event. Following the implementation of this method, a case study involving the 2011 UFLS event caused by the loss of generation at the Huntly (NZ) Power Station is used to test the method. A comparison between the existing load shedding scheme and the new proposed scheme has identified a 10% reduction in the amount of load to be shed.

Security and stability of the power supply are the primary concerns of the transmission system operators. Any deviation in the power system characteristics like the frequency results in power interruptions or even blackouts. Several events can cause changes in the system frequency like short circuits which leads to outage of critical components of power grid like transmission lines/substation, loss of generation, sudden loss of load etc. The system frequency drops whenever the load exceeds the generation at a given time. Automatic Under-Frequency Load Shedding (AUFLS) is a scheme adopted by the system operators to prevent large scale blackouts due to drop in system frequency below certain range. New Zealand's system operator is currently revamping its AUFLS scheme to transit towards efficient procurement of extended reserves. This paper systematically reviews the existing and the new AUFLS scheme in New Zealand. Bench-testing of New Zealand standard 4777.2 (2015) compliant inverters are conducted to analyze the response of the DER to such under-frequency events. The test conducted will assist system operator to set under frequency ride through criteria for the DERs to minimize the impact of high penetration of Distributed Energy Resources (DER) on the proposed AUFLS scheme. This bench testing of the DER will provides better insight to the system operator as the fine-tuned DER models can be aggregated in the network modelling for planning and operation purposes.

High impact low probability (HILP) weather events are known to have the capability to damage the power system network and lead to a complete outage. Such HILP events encourage the system to have continuous improvement for the resilience of the network. Enhancing the power system resilience has always been challenging for system operators to implement resilient strategies and techniques for pre-, during, and post-weather events. This paper introduces an approach to visualize the effect of wind gusts and the possibility of an outage from HILP, considering the contingencies of the power system network to analyze the risks of failure on the system. Furthermore, this paper also discussed the resilient strategy of utilizing battery storage to reduce the risk on the other components due to the overloading of critical components. Weather data analysis was conducted and incorporated with the power system network to visualize the weatherization effect on the Auckland Network, assuming the contingencies of the two busbars.

This paper reports on optimal space heating set-point allocation methodology to help optimize the energy demand for university building facilities while maintaining the level of occupancy comfort. The paper uses degree-days energy signature and implementing electricity price variables into the heating system to minimize the energy cost over a 24-hour duration. It is an extension of the work reported earlier, which uses historical energy consumption data of the University of Auckland buildings and historical outdoor temperatures for the Auckland region in New Zealand. The current work uses statistics to obtain average base temperatures for the building and the minimum electricity price threshold. Two scenarios are simulated in Matlab Simulink, the first being the heating system price responsive behaviour and the second for heating system in operation with the 30 minute locational marginal trading price as an input variable. The results show a reduction in the daily energy cost for the building fleet under consideration supported through simulation result. There are a few caveats that are noted for future improvements to this approach of using degree-day energy signature for buildings.

Matauranga Māori is the knowledge of Māori in New Zealand. It can influence engineering by providing an alternative viewpoint and value system for consideration during initial planning and design such that projects are in line with the local sustainability goals. The goal of this research is to investigate and current and past works on Mātauranga Māori and how it can influence / interface with power engineering project design towards a carbon neutral and sustainable future. Currently there is no clear guidelines and interface, making conflict resolution between power companies and iwi challenging if a situation arises. If Māori ideas of spirituality and environmentalism can be quantified, communication between engineers / academics and iwi can be refined thereby making the design process more environmentally sustainable.

The concept of resilience in power systems has gained focused attention due to increasing outages, including large-scale blackouts, attributed directly or indirectly due to extreme weather and natural geo-physical triggered events. The resilience of electricity infrastructure ground assets and power system equipment can be improved by strengthening the foundations or re-engineering the vulnerable impact spots of equipment. However, understanding the criticality and thereby assessing resilience of underground cables is challenging due to the underground dynamics and the infrastructure spanning across a large geographical area. The cables go through different terrains and depths and the effects of earthquakes vary on these cables depending upon the magnitude. To address this problem, this piece of research explores the potential use of geospatial information software's to map underground cables to study the performance of underground cables by evaluating the repair rates and developing fragility curves for underground cables damaged based on utility data during an earthquake event. Based on the observed case-study, a resilience-based criticality framework is proposed for underground cables. This helps provide actionable and accepted metrics, particularly for short term and long-term resilience of underground cables in a distribution network.

This paper proposes solutions for distributed protection applications, to improve current and design of future protection schemes. This solution utilises advantages of fast and reliable peer-to-peer communication of IEC 61850 and interoperability and configurability of IEC 61499. The integration of the two standards improves protection schemes by decreasing fault clearing times and minimizes the effect of short circuit faults on sensitive loads. Additional advantages include the reductions of the number of hard-wired connections, especially in a large substation where all protection IEDs has a significant number of binary inputs and relay outputs. IEC 61499 is an open standard for designing distributed control systems to promote portability, interoperability and configurability, i.e. vendor independent, flexible and robust solutions. IEC 61499 can be used to represent programmable logic of power system devices, in a standard form and in a vendor independent way. The proposed distributed protection system is implemented and simulated using the developed co-simulation environment.

This paper presents a new approach to power system automation, based on distributed intelligence rather than traditional centralized control. The paper investigates the interplay between two international standards, IEC 61850 and IEC 61499, and proposes a way of combining of the application functions of IEC 61850-compliant devices with IEC 61499-compliant “glue logic,” using the communication services of IEC 61850-7-2. The resulting ability to customize control and automation logic will greatly enhance the flexibility and adaptability of automation systems, speeding progress toward the realization of the smart grid concept.

This paper presents new approach to power system automation, based on distributed intelligence rather than traditional centralised control. The paper investigates the interplay between two international standards, IEC 61850 and IEC 61499, and proposes a way of combining of the application functions of IEC 61850-compliant devices with IEC 61499-compliant ldquoglue logic,rdquo using the communication services of IEC 61850-7-2. The resulting ability to customise control and automation logic will greatly enhance the flexibility and adaptability of automation systems, speeding progress toward the realisation of the smart grid concept.