The growing complexity of spacecraft constellations, communication relay offerings, and mission architectures drives the need for the development of autonomous communication systems. The National Aeronautics and Space Administration (NASA) has traditionally launched single spacecraft missions that are served by the Space Communication and Navigation (SCaN) program. Operations on SCaN networks are typically scheduled weeks in advance, and often each asset serves a single user spacecraft at a time. Recent movement towards swarm missions could make the current approach unsustainable. Additionally, the integration of commercial communication service providers will substantially increase the data transfer options available to new missions. NASA science missions have found benefit in launching swarms of space-craft, allowing coordinated simultaneous observations from different perspectives. Inter-spacecraft communication (mesh networking) is an enabler for this architecture, as are CubeSats that allow cost-effective provisioning of distributed mission assets. As more complex swarm missions launch, one challenge is coordinating communication within the swarm and choosing the appropriate mechanism for telemetry, tracking, control, and data services to and from Earth. Cognitive communications research conducted by SCaN aims to mitigate the increasing communication complexity for mission users by increasing the autonomy of links, networks, and service scheduling. By considering automation techniques including recent advances in artificial intelligence and machine learning, cognitive algorithms and related approaches enable increased mission science return, improved resource utilization for service provider networks, and resiliency in unpredictable or unplanned environments. The Cognitive Communications Project at the NASA Glenn Research Center develops applications of data-driven, nondeterministic methods to improve the autonomy of space communication. The project emphasizes the development of decentralized space networks with artificial intelligence agents optimizing communication link throughput, data routing, and system-wide asset management. This chapter discusses the objectives, approaches, and opportunities of the research to address growing needs of the space communications community.

A novel islanding detection technique by hybridising empirical mode decomposition (EMD) and multi-scale mathematical morphology (MMM) is proposed to detect the islanding condition in a distributed generation system to ensure personnel and equipment safety. The proposed method first uses EMD to efficiently separate the collected raw signal into the number of intrinsic mode functions (IMFs) with different frequency scales and the signal is reconstructed considering important IMFs which carry transient features for further analysis using their correlation coefficients. MMM is used for determining a ratio index named as MMMRI, the threshold value of the proposed MMMRI decides islanding and other power quality (PQ) disturbances. The proposed hybrid method name coined as EMD-MMMRI. The main motivation behind hybridising two signal processing techniques is to reduce detection time and improve accuracy. The efficacy of the method is demonstrated for different PQ disturbances and islanding events simulated on a grid-connected, heavily wind energy penetrated distributed generation system using MATLAB/Simulink environment. The test bench validation of the proposed technique is obtained through TMS 320C6713 Starter Kit (DSK) in digital signal processor platform. The efficacy of proposed work is demonstrated with large number of simulation studies.

Secondary direct-current loss within a substation is a severe event. In China, each line protection device is generally powered by one set of secondary direct current (DC) power system within a substation of 110 kV voltage level. In this case, the line protection devices will be immediately taken out of service when the secondary DC is lost, and the lines protected by them will lose their corresponding protections. Once a fault occurs on the line, only the remote back-up protection can be relied on to isolate the fault, which expands the fault influence and increases its clearance time. To handle the above problem, a novel wide-area protection algorithm based on compensation voltage moduli comparison is proposed, which is independent of multi-terminal data synchronism. Then, a comprehensive protection strategy composed of the proposed protection algorithm and the original local distances protection is proposed. The results from a power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC)-based simulation verify that the proposed protection algorithm has higher sensitivity. The comprehensive protection strategy could cope with most line faults effectively, even if only partial data are available, the selectivity and speed of which are better than that of distance protections.

A microgrid (MG) is a cyber-physical system that facilitates integration of several distributed renewable energy resources. In the last decade, several efforts were made to standardise the framework of a cyber-physical MG network and its control structure. In this perspective, various studies discussing the different control techniques are reported in the literature. However, a comprehensive and systematic review of a cyber-physical MG is discussed rarely. In this study, a comprehensive review of a MG architecture and hierarchical control structure in both islanded and grid-connected modes are presented. The hierarchical control of the MG includes primary, secondary, and tertiary control. Recent studies provide significant opportunities in dividing the control task among various layers resulting in a distributed framework. This study analyses the cyber and physical networks separately while discussing the primary, centralised, and distributed secondary control levels with their merits, demerits, and typical applications. A Venn diagram analysis is also presented that clearly distinguishes the primary control scheme in different research sub-areas. Furthermore, the MG communication structure, protocols, design, constraints, and cyber security are also reviewed systematically. Finally, future trends are summarised based on the state-of-the-art MG research.

Penetrating distributed generation units into the radial distribution networks has been making the protection schemes become ever so complex. The microgrid modes of operation have also increased this complexity. Moreover, the ‘loop-based microgrid’ concept has made it extremely difficult or even impossible to utilise the available radial protection schemes. Although redesigning a new protection structure solves the problem, there should be an affordable way for surviving today's protection systems; accordingly, this study equips the ordinary overcurrent relays with intelligent electronic devices to act as an agent within a multi-agent framework. Given these devices are equipped with an efficient communication infrastructure for industrial applications, named IEC 61850 standard, the proposed approach uses a communication-based strategy, named token, to securely protect the system without any conflict in relays’ operation. The simulation results prove the effectiveness of the proposed method for any DG's penetration, network configuration and fault location. This kind of flexible strategies can affordably differ the expensive protection system replacement in the future revolutionary network upgrades.

Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open-source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short-term memory (LSTM) for short-term wind power forecasting based on the publicly accessible ERA5-Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3-hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air-to-ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal-to-noise-ratio, throughput values, the categories of line-of-sight, and non-line-of-sight links. Furthermore, K-means used as a clustering method for class identification, long short-term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi-layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks.

With the real-time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

To solve the problem of manpower and time consumption caused by power flow state adjustment in a large-scale power grid, a power system operation state adjustment method considering the static stability constraint based on parallel deep reinforcement learning is proposed. By introducing the process of adjusting the power flow state that satisfies static stability, the Markov decision-making process of adjusting power flow is constructed. Then, based on the positioning of the adjustment target, the selection of actionable devices and the calculation of the amount of action, a power flow state adjustment strategy is developed. The adjustment process is accelerated through sensitivity, transfer ratio and load margin. Then, a parallel deep reinforcement learning model is established, and it maps actions to power flow adjustment to form a pair of generator actions and realises parallel adjustment of multi-sectional objectives. In addition, the reinforcement learning strategy and the deep learning network are improved to promote learning efficiency. Finally, the New England 39-bus standard system and actual power grid are used to verify the effectiveness of the method.

Considering both environmental and commercial aspects, mixed three-terminal transmission lines are attractive. Mixed three-terminal lines consist of two sections of the overhead line and one section of underground cable. In this work, a selective auto-reclosing, fault location, and parameter estimation technique for mixed three-terminal lines using synchronised data from all three terminals are proposed. The proposed method uses the pre and during-fault data recorded at the three ends of the line. Using the recorded sampled data, pre-fault and during-fault phasors are estimated. Four functions are formulated from the pre-fault equivalent network of the system. Additionally, three sets of four functions each are formulated after assuming the fault to be on each of the three sections. Subsequently, the faulted section, fault location, and three sets of line parameters are simultaneously calculated by solving each set of equations. Finally, the correct faulted section, location, and parameters are identified based on the characteristics of obtained line parameters and fault location. Furthermore, the identified faulted section information is applied to enable or block the auto-reclosing function. The simulations and analysis are carried out using PSCAD/EMTDC and MATLAB scripts, respectively. The accuracy, capabilities, applications, and limitations are discussed for different fault scenarios.