Doctor of Philosophy (Ph.D.)

Permanent URI for this collectionhttp://192.248.9.226/handle/123/13707

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item: Thesis-Full-text
Evaluation and enhancement of solar PV hosting capacity for management of voltage rise in LV networks
(2022) Chathurangi WLDM; Perera S; Jayatunga JVUP; Agalgaonkar AP; Siyambalapitiya T
Proliferation of solar photovoltaics (PV) in low-voltage (LV) distribution networks is inciting technical challenges in network design and operation with regard to the quality of power. Violations of operational performance limits are increasingly evident at higher solar PV penetration levels, in particular, local voltage rise has become the major issue of concern in LV distribution networks. As the solar PV industry continues to grow, emerging challenges need to be addressed by adopting best policies and practices at a utility level. Thus, to comply with stipulated network operational limits, distribution network operators (DNOs) are compelled to develop comprehensive techniques to determine acceptable levels of solar PV hosting capacity (HC) and explore HC enhancement options. Complexity of modelling distribution networks is a barrier for DNOs to decide levels of maximum solar PV penetration using stochastic approaches. Thus, there is a necessity to develop a systematic approach to assess solar PV HC, considering factors such as geographic layout of networks and their electrical characteristics. This thesis extends the knowledge of managing of solar PV integration in LV networks by developing systematic approaches to evaluate solar PV HC subjected to over voltage curtailment. In this regard, a novel feeder based solar PV HC evaluation approach was developed to address the diverse network characteristics of multi feeder systems in LV distribution networks. To assess the voltage violations and critical factors a ecting the solar PV HC, a comprehensive analysis of potential power quality issues was conducted on a practical LV distribution network in Sri Lanka. The approach proposed in this thesis establishes a safe limit for solar PV HC for a given distribution feeder based on the locational and operational aspects of the solar PV units deployed on a LV network. The safe limit for HC was developed employing a number of sensitivity analyses considering factors in uencing solar PV HC. Further, the proposed feeder based solar PV HC approach was extended to develop a generic method to quantify solar PV HC under di erent operating con- ditions of PV inverters. Thus, it can be used as an approximate guide or a rule of thumb to evaluate solar PV HC at a given point on an LV feeder without using complex stochastic techniques. With the increasing demand for solar PV systems, development of both solar PV HC assessment and enhancement techniques is essential in managing network voltage. DNOs have recognised the need for smart PV inverter technologies to maintain acceptable voltage levels in distribution networks. Smart PV inverters possess fast and exible active and reactive power control functions such as; Volt-VAr and Volt-Watt control modes which can be used to manage over-voltage conditions that often limit the solar PV HC. At present, most of solar PV connection standards provide a set of rules and guidelines to mitigate voltage violations by enabling VoltVAr and Volt-Watt control modes of the inverters. In particular, it is imperative to analyse the impact of such solar PV connection standards on HC assessment and its potential to enhance solar PV HC. Thus, a detailed analysis of smart solar PV inverter capabilities and a comparative evaluation of solar PV HC enhancement facilitated by di erent connection standards are presented in this thesis. Electricity utilities around the world seek to develop strategies to increase solar PV integration while maintaining acceptable network performance. Hence, more generalised and straightforward tools are required to rapidly assess solar PV HC without complex and extensive network modelling and simulations. Extending the deterministic outcomes, a nomogram based solar PV HC assessment approach is proposed in this thesis to determine HC values speci c to any location of a given conductor. Further, solar PV connection criteria is proposed which permits the electric utilities to approve new solar PV connections which facilitates reasonable modelling insights to assess HC. The proposed nomogram based solar PV HC assessment approach and solar PV connection criteria cover technical and regulatory aspects to manage PV integration in LV distribution networks. For the continued development of solar PV as a distributed generation, accuracy of PV connection approval process is crucial to correctly and easily allow PV connections that will not cause issues. Therefore, grid codes, distribution codes or guidelines on interconnection of solar PV require to be re ned or re-written in relation to solar PV HC assessment/enhancement and approval criteria for new PV connections in LV distribution networks. The solar PV HC assessment/enhancement and solar PV connection criteria proposed in this thesis shall be a contribution to further improvement of the available guidelines/standards on solar PV installation in LV networks. All network modeling and simulations presented in this thesis were carried out in DIgSILENT PowerFactory platform.
item: Thesis-Full-text
Framework for adaptive human - robot interaction initiation for domestic environments
(2020) Sirithunge, HPC; Jayasekara, AGBP
Intelligent robot companions contribute signi cantly in improving living standards in the modern society. Therefore human-like decision making skills and perception are sought after during the design of such robots. On the one hand, such features enable a robot to be easily handled by a non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the its behavior. Mimicking human emotional intelligence is one of the best and reasonable ways of laying the foundation for such an emotional intelligence in robots. As robots are widely deployed in social environments today, perception of the situation or the intentions of a user prior to an interaction is required in order to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. This thesis addresses the problem of perceiving nonverbals in human behavior and fusing human-environment semantic representations with a robot's cognition before interacting with humans. The novelty lies in laying the background to relate nonverbal human behavior and the features of the environment to generate context-aware interactive responses during robot-initiated interaction. That informs the robot about its environment. Toward this end, we introduce novel methods of perceiving human nonverbals and spatial factors in the environment which make up a context in which we integrated that knowledge to determine appropriate responses from a robot to assist its user. Visual information acquired by a vision sensor was analyzed, and the level of emotional engagement demanded by the user's nonverbals was evaluated, before a robot initiates an interaction. After such an analysis, a robot's conversational and proxemic behavior was adjusted to maintain an empathetic relationship between the user and the robot. Our algorithms e ciently sustained the empathy between user and robot so that the interaction resembles human-human interaction to a larger extent. To assist the main problem, we formulated novel methods to recognize human nonverbals such as postures, gestures, hand poses, psychophysiological behavior of humans and human activities, and decode the emotional hints displayed to the outside world. In support of this work, we conducted a series of human studies to explore the patterns in human behavior which could be perceived by a proactive robot using its cognitive capabilities. We introduce separate systems which can decode the sentiments of humans using observable cues based on accepted social norms. We detail the meanings of human nonverbals by observing human behavior over time and evaluating the context for any patterns in behavior. Ambiguities in human behavior and random, meaningless behaviors could be omitted through this approach. This approach further omits the negative e ect of human responses that can be faked, such as facial expressions and words. Finally we introduce an adaptive approach towards robot-initiated human-robot interaction by letting a robot observe a context and generate responses while changing its responses continuously as human behavior changes. We rst developed algorithms based on a limited number of observable human cues and decoded their sentiments based on modern psycho-physiological interpretations of human behavior. Next, we expanded such approaches towards multiple observable human cues. Finally we integrated observations from the human and the environment which create the context during HRI (Human-Robot Interaction). Hence we integrated all the recognition iii approaches to perceive a complete scenario which comprises the user, robot and the environment. Upon unimodal systems to identify these features independently, we propose a multi modal approach to evaluate above features together to understand a scenario. Through this approach, we took an e ort to make proactive behavior of a social robot more instinctive, ethical and socially acceptable or simply, humanlike. We evaluate this approach by means of physical experiments in simulated social and domestic environments and demonstrate its performance in appropriate occasions as determined by a robot according to the formulated criteria of perceiving a context.
item: Thesis-Full-text
Enhancing interpretation of uncertain information in Navigational commands for service robots using neuro-fuzzy approach
Muthugala, MAVJ; jayasekara, B
An intelligent service robot is a machine that is able to gather information from the environment and use its knowledge to operate safely in a meaningful and purposive manner. Intelligent service robots are currently being developed to cater to demands in emerging areas of robotic applications such as caretaking and assistance, healthcare and edutainment. These service robots are intended to be operated by nonexpert users. Hence, they should have the ability to interact with humans in a human-friendly manner. Humans prefer to use voice instructions, responses, and suggestions in their daily interactions. Such voice instructions and responses often include uncertain information such as “little” and “far” rather than precise quantitative values. The uncertain information such as “little” and “far” have no deﬁnitive meanings and depend heavily on factors such as environment, context, user and experience. Therefore, the ability of robots to understand uncertain information is a crucial factor in the implementation of human-friendly interactive features in robots. This research has been conducted with the intention of developing eﬀective methodologies for interpreting uncertain notions such as “little”, “near” and “far” in navigational user commands in order to enhance human-robot interaction. The natural tendencies of humans have been considered for the development of the methodologies since ability of the robot in replicating the natural behavior of humans vastly enhances the rapport between the robot and the user. The methodologies have been developed using fuzzy logic and fuzzy neural networks that are capable of adapting the perception of uncertain information according to the environment, experience and user. User studies have been conducted in artiﬁcially created domestic environments to experimentally validate the performance of the proposed methods. An intelligent service robot named as Moratuwa Intelligent Robot (MIRob), which has been developed as a part of the research, has been used for the experiments. The robot’s perception of distance and direction related uncertain information in navigation commands is adapted according to the environment. According to the experimental results, a service robot can eﬀectively cope with distance-related uncertain information when the robot’s perception of distance-related uncertain information is adapted to the environment. The eﬀectiveness can be further improved by perceiving the environment in a human-like manner. The adaptation of the directional perception in accordance to the environment remarkably improves the overall interpretation ability of uncertain notions. User feedback is used to adapt the perception toward the user while adapting to the environment and this adaptation vastly improves user satisfaction. Methods have also been proposed to interpret the uncertain information in relation to relative references and the methods are capable of replicating human-like behavior. Furthermore, the information conveyed though pointing gestures that accompany voice instructions is fused to further enhance the understanding of the user instructions. This fusion signiﬁcantly reduces the errors in interpreting the uncertain information. Furthermore, it reduces the number of steps required to navigate a robot toward a goal. A vast research gap is still remaining in this particular research niche for future developments and hence possible future improvements are also synthesized.

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