Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception

Multiple partial discharge source discrimination with multiclass support vector machines

The costs of decommissioning high-voltage equipment due to insulation breakdown are associated to the substitution of the asset and to the interruption of service. They can reach millions of dollars in new equipment purchases, fines and civil lawsuits, aggravated by the negative perception of the grid utility. Thus, condition based maintenance techniques are widely applied to have information about the status of the machine or power cable readily available. Partial discharge (PD) measurements are an important tool in the diagnosis of power systems equipment. The presence of PD can accelerate the local degradation of insulation systems and generate premature failures. Conventionally, PD classification is carried out using the phase resolved partial discharge (PRPD) pattern of pulses

Time domain analysis of switching transient fields in high voltage substations

Switching operations of circuit breakers and disconnect switches generate transient currents propagating along the substation busbars. At the moment of switching, the busbars temporarily acts as antennae radiating transient electromagnetic fields within the substations. The radiated fields may interfere and disrupt normal operations of electronic equipment used within the substation for measurement, control and communication purposes. Hence there is the need to fully characterise the substation electromagnetic environment as early as the design stage of substation planning and operation to ensure safe operations of the electronic equipment. This paper deals with the computation of transient electromagnetic fields due to switching within a high voltage air-insulated substation (AIS) using the finite difference time domain (FDTD) metho

Display Enhanced Testing For Concussions And Mild Traumatic Brain Injury

Cognitive assessment systems and methods that provide an integrated solution for evaluating the presence or absence of cognitive impairment. The present invention is used to test cognitive functions of an individual including information processing speed, working memory, work list learning and recall, along with variations of these tasks. Immersive and non-immersive systems and methods are disclosed. Testing and results feedback using the present invention may be completed in real time, typically in less than 15 minutes.Emory UniversityGeorgia Tech Research Corporatio

Usage of antenna for detection of tree falls on overhead lines with covered conductors

The direct contact of a tree or a branch of tree with Covered Conductors (CC) overhead lines causes Partial Discharges (PD) inside the insulation. The presence of PD degrades the insulation systems and eventually destroys insulation, which may lead to power delivery interruption. The detection and diagnosis of PD is an important tool to address the problem of tree caused faults in forested terrains. The PD occurs in the impulse component of the signal, which is usually measured by Rogowski coil (current signal) or single layer inductors (voltage signal). In this paper, we introduce a possibility to detect the tree caused faults with the usage of whip antenna. The advantage of the antenna is a very low price and the possibility to install antenna under voltage. The disadvantages are sensitivity to ferromagnetic materials and impossibility to distinguish affected phase. The measurements were carried out in the real environment in forested terrain in Jeseniky Mountains. The real environment is different from a laboratory conditions due to heavy noise (e.g. corona, radio emissions). This paper provides an examination of the background noise from the antenna signal. The experimental results indicate that the antenna may be successfully used instead of the current approach

Characteristics of partial discharge under high voltage AC & DC conditions

High voltage AC has been the technology of choice for electricity transmission since the creation of electric grids throughout the world. However due to a wide range of factors, including the availability of new AC-DC and DC-AC converter designs, an increasing requirement for subsea interconnections for off-shore wind and continental super-grids, a greater emphasis on efficient operation for cost and environmental reasons, and a desire for ever-increasing transmission distances, high voltage DC is increasingly seen as an attractive and viable choice. While HVDC technologies have existed for as long as HVAC, their lack of significant use until recently mean that several gaps in knowledge exist, including in the area of condition monitoring of assets. One such condition monitoring technology is partial discharge monitoring, which, while it is a mature technology that is frequently used in AC systems, it has had limited deployment under DC conditions. As such it still lacks the field experience, well-developed standards, technical expertise, and overall knowledge base for DC that exists for AC. This thesis presents research into the characteristics of partial discharge under both AC and DC conditions. A review of relevant literature is presented, followed by the research methodology, including the use of thin film polymer samples, and a 'coring' method for introducing an artificial void into a cable sample. Data and analysis are then presented which compare the presentation of PD in different materials, the impact of multiple void configurations on PD, and statistical analysis of the PD pulses themselves. These analyses demonstrate clear differences between the behaviour of PD, and the characteristics of the PD pulses themselves, under the different voltage types, void configurations, and materials investigated, with potential reasons for these differences discussed, and suggestions made for how knowledge of these differences should affect the detection of PD, and, therefore improve condition monitoring of both AC and DC equipment.High voltage AC has been the technology of choice for electricity transmission since the creation of electric grids throughout the world. However due to a wide range of factors, including the availability of new AC-DC and DC-AC converter designs, an increasing requirement for subsea interconnections for off-shore wind and continental super-grids, a greater emphasis on efficient operation for cost and environmental reasons, and a desire for ever-increasing transmission distances, high voltage DC is increasingly seen as an attractive and viable choice. While HVDC technologies have existed for as long as HVAC, their lack of significant use until recently mean that several gaps in knowledge exist, including in the area of condition monitoring of assets. One such condition monitoring technology is partial discharge monitoring, which, while it is a mature technology that is frequently used in AC systems, it has had limited deployment under DC conditions. As such it still lacks the field experience, well-developed standards, technical expertise, and overall knowledge base for DC that exists for AC. This thesis presents research into the characteristics of partial discharge under both AC and DC conditions. A review of relevant literature is presented, followed by the research methodology, including the use of thin film polymer samples, and a 'coring' method for introducing an artificial void into a cable sample. Data and analysis are then presented which compare the presentation of PD in different materials, the impact of multiple void configurations on PD, and statistical analysis of the PD pulses themselves. These analyses demonstrate clear differences between the behaviour of PD, and the characteristics of the PD pulses themselves, under the different voltage types, void configurations, and materials investigated, with potential reasons for these differences discussed, and suggestions made for how knowledge of these differences should affect the detection of PD, and, therefore improve condition monitoring of both AC and DC equipment

Distribution Network Fault Prediction Utilising Protection Relay Disturbance Recordings And Machine Learning

As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learning based fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages

Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS

Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit failure and/or battery degradation. The proposed autonomy is modeled as a Markov Decision Process (MDP) whose solution is a contingency management policy that appropriately executes emergency landing, flight termination or continuation of planned flight actions. Motivated by the potential for errors in fault/failure indicators, partial observability of the MDP state space is investigated. The performance of optimal policies is analyzed over varying observability conditions in a high-fidelity simulator. Results indicate that both partially observable MDP (POMDP) and maximum a posteriori MDP policies performed similarly over different state observability criteria, given the nearly deterministic state transition model

Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future