Advanced Grid-Tied Photovoltaic Micro-Inverter

Along with the damaged environment and the emerging energy crisis, many problems have been caused by utilizing fossil fuels. Green energy, also known as renewable energy, has been trusted as a good alternative for the conventional energy resources and effort has been contributed in the development of modern green energy. Solar energy is one of the renewable energy resources. Owing to its advantages of being nearly unlimited, pollution free, noise free and relatively easy to maintain, photovoltaic (PV) systems have experienced a significant increase in the past few decades. In this thesis, a grid-tied solar micro inverter has been presented and several key technology issues on this PV system are investigated: 1. Maximum power point tracking (MPPT) strategies. Under changing atmospheric conditions, intensity of the sunlight irradiation and shading problems, the output of a solar panel varies nonlinearly. MPPT techniques are designed to enable PV panels always operate at the optimal power point and produce maximum power. In this paper, different MPPT strategies are compared and analysed. An improved variable step-size P&O MPPT strategy is also proposed to compensate those drawbacks from conventional MPPT techniques. Simulation results are also given. 2. Control strategies of a single-phase grid-tied inverter. A deadbeat controller, named the OSAP control, is proposed for the inverter. This inverter is analysed into two states: standalone inverter and grid-tied inverter. In each state, the OSAP controller is applied to control the inverter. Some disadvantages are also shown for the OSAP controllers. An improved OSAP controller is then introduced to compensate these drawbacks. Simulation results are given to support the theory. 3. Experiment of this solar inverter. An interleaved boost converter is shown to implement the MPPT techniques. Experiments of the stand-alone inverter and grid-tied inverter are also conducted with the OSAP control strategies. The experiment of this PV system under some environmental changes are also conducted and the transient response is given. Chapter 1 deals with the background introduction and literature review. A model of solar cell is introduced in Chapter 2 and the simulation model is also built to analysis the characteristics of solar panel output power. Several Maximum Power Point Tracking (MPPT) techniques are evaluated and an improved variable step-size MPPT technique is proposed to overcome the disadvantages. In Chapter 3, a control strategy is developed for a grid-tied PV micro-inverter, which is called one-sampling-ahead-preview (OSAP) control. Firstly a full-bridge inverter is analysed. Two states of this inverter are introduced, one is the stand-alone inverter and the other is the grid-tied inverter. Mathematical and simulation model have been built for each inverter. Then an OSAP voltage controller is proposed for the stand-alone inverter and an OSAP current controller is proposed the grid-tied inverter. However, since OSAP controllers belong to the deadbeat control category, these exists a deadbeat response in the output. And another problem is that OSAP controllers highly depend on the inverters have accurate parameters for the components, which is not practical in real life. So an improved OSAP controller is introduced to solve these problems, which is the OSAP with a resonant controller. Simulation results are also given to support the theory. In Chapter 4, the experiment of this system has been shown and experimental results have been provided. Chapter 5 explains the conclusions and some developments need to be done in the future work

Graph Neural Network for Customer Engagement Prediction on Social Media Platforms

Social media platforms such as Twitter and Facebook play a pivotal role in companies’ strategy of engaging customers. How to target potential customers on social media effectively and efficiently is an important yet unsolved question. Predicting customer engagement on social media platforms is facing several challenges that cannot be solved by traditional methods. In this work, we design a framework that leverages individual behavior on Facebook together with network contextual information to predict customer engagement (like/comment/share) of a brand’s posts. We first build a meta-path based Heterogeneous Information Network (HIN) to exploit large-scale content consumption information. We then design a Graph Neural Network (GNN) model combined with attention mechanism to learn structural feature representations of users to make the customer-brand engagement prediction. The proposed model is examined using a large-scale Facebook dataset and the result shows significant performance improvement compared with state-of-the-art baselines. Besides, the effectiveness of attention mechanism reveals the potential interpretability of the proposed model for the prediction results

Content Creator versus Brand Advertiser? The Effect of Inserting Advertisements in Videos on Influencers Engagement

Influencer advertising has become an indispensable component of online marketing due to the exponential growth of social influencers and their influence. Whereas the effectiveness of using influencer endorsements is well studied from the brand or company perspective, how the commercial endorsements affect influencers themselves is an important yet unrevealed question. We empirically examine the instantaneous (measured using live comment sentiment) and longer-term (measured using video feedback and follower number change) influence of inserting advertisements in videos on influencers’ reputation. We further investigate how this effect can be moderated when influencers demonstrate stronger endorsement by showing their faces during advertisements. Our result suggests that inserting advertisements have a negative impact on both instantaneous and longer-term viewer engagement; advertisements with influencers’ face showing moderate the negative effect of advertisements on viewers’ instantaneous response, while the different impact between advertisements with/out influencers showing their faces is not significant in the longer term