Impedance-based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model-based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model-based approach efficiently to estimate the fault location and fault resistance using artificial neural networks-based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.

In the past few decades, the arcing-high-impedance fault (arcing-HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing-HIF detection in distribution network-based AI. First, characteristics and models of arcing-HIF are analysed, the arcing-HIF database construction method is also explained; this part is a foundation work for arcing-HIF detection. Next, arcing-HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing-HIF detection algorithm. Finally, the future trends and challenges to arcing-HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing-HIF detection-based AI.

This study presents a hybrid data-driven physics model-based framework for real-time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real-time monitoring becomes more vulnerable to cyber-attacks like false data injections (FDIs). Although smart grids cyber-physical security has an extensive scope, this study focuses on FDI attacks, which are modelled as bad data. State-of-the-art strategies for FDI detection in real-time monitoring rely on physics model-based weighted least-squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modelling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this study presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118-bus system.

The increasing coupling between the physical and communication layers in the cyber-physical system (CPS) brings up new challenges in system monitoring and control. Smart power grids with the integration of information and communication technologies are one of the most important types of CPS. Proper monitoring and control of the smart grid are highly dependent on the transient stability assessment (TSA). Effective TSA can provide system operators with insightful information on stability statuses and causes under various contingencies and cyber-attacks. In this study, a real-time stability condition predictor based on a feedforward neural network is proposed. The conjugate gradient backpropagation algorithm and Fletcher–Reeves updates are used for training, and the Kohonen learning algorithm is utilised to improve the learning process. By real-time assessment of the network features based on the minimum redundancy maximum relevancy algorithm, the proposed method can successfully predict transient stability and out of step conditions for the network and generators, respectively. Simulation results on the IEEE 39-bus test system indicate the superiority of the proposed method in terms of accuracy, precision, false positive rate, and true positive rate.

Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error.

This study develops a machine learning-based method for a fast estimation of the dynamic voltage security margin (DVSM). The DVSM can incorporate the dynamic system response following a disturbance and it generally provides a better measure of security than the more commonly used static voltage security margin (VSM). Using the concept of transient P - V curves, this study first establishes and visualises the circumstances when the DVSM is to prefer the static VSM. To overcome the computational difficulties in estimating the DVSM, this study proposes a method based on training two separate neural networks on a data set composed of combinations of different operating conditions and contingency scenarios generated using time-domain simulations. The trained neural networks are used to improve the search algorithm and significantly increase the computational efficiency in estimating the DVSM. The machine learning-based approach is thus applied to support the estimation of the DVSM, while the actual margin is validated using time-domain simulations. The proposed method was tested on the Nordic32 test system and the number of time-domain simulations was possible to reduce with ∼70%, allowing system operators to perform the estimations in near real-time.

Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

Individual microgrids can improve the reliability of power systems during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations can be subject to large transients due to switching and end-use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid-following inverter-connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study investigates the use of a reinforcement-learning-based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional–integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and conditions.

Big data standards and capability maturity models (CMMs) help developers build applications with reduced coupling and increased breadth of deployment. In smart grids, stakeholders currently work with data management techniques that are unique and customised to their own goals, thereby posing challenges for grid-wide integration and deployment. Although big data standards and CMMs exist for other domains, no work in the literature considers adapting them to smart grids, which will benefit from both. Further, existing smart grid standards and CMMs do not fully account for big data challenges. This study bridges the gap by analysing the role of big data in smart grids, and explores if and how big data standards and CMMs can be adapted specifically to ten distributed generation (DG) use-cases that use big data. In doing so, this work provides a useful starting point for researchers and industry members developing standards and CMM assessments for smart grid DG.

Battery energy storage systems (BESSs) offer many desirable services from peak demand lopping/valley filling too fast power response services. These services can be scheduled so they enhance each other; in this manner, the inverter size is effectively leveraged with battery capacity. A variable cost for under-frequency service provision will be required to incentivise this type of operation, and various options are explored in this study. It is demonstrated that BESSs may struggle for profitability under certain service payments; however, returns can be maximised through revenue stacking. In this study, enhanced service provision results in increased power system service provision and returns from energy arbitrage. A 10 and 2 years datasets, containing information on the Irish power system, are used to investigate potential per MW revenue from a BESS with a 1.5 and 2.5 h batteries. Three statistically derived, engineering-based, power system service scalar multipliers are investigated and compared with the scalar being introduced on the Irish system. It is demonstrated that flexible service payments can be increased by more than 10% while receiving arbitrage payments. The seasonal variation in BESS revenue is investigated and observed to be mitigated or exacerbated by service scalars.

It can be foreseen that the modern electric power system tends to have an increasing penetration of renewable energy sources (RESs) and controllable loads under ancillary services programmes, which will cause the power system inertia reduction. For the thermostatically controlled loads (TCLs) (e.g. air conditioners) have large potentials for providing ancillary services for power systems and are more flexible and cost-efficient than traditional energy storage system, in this study, an approach of modelling power system virtual inertia (VI) by TCLs is proposed, while maintaining customers’ comfort level. Considering the TCLs’ heterogeneous characteristic and response uncertainty, a load tracking control strategy based on proportional–integral controller is proposed to preserve the performance of VI control strategy. Besides, the coefficient is put forward for the first time to quantify and analyse the conversion efficiency between kinetic energy and thermal energy. Then, the method to design based on the energy analysis is presented. Finally, some simulation results are provided to verify the effectiveness of the control scheme.

The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size.