Wind Farm Coordinated Control and Optimisation

This thesis develops and implements computationally efficient and accurate wind farm coordinated control strategies increasing energy per area by mitigating wake losses. Simulations with data from the Brazos, Le Sole de Moulin Vieux (SMV) and Lillgrund wind farms show an increase of up to 8% in farm production and up to 6% in efficiency. A live field implementation of coordinated control strategies show that curtailing upstream turbine by up to 17% in full or near-full wake conditions can increase downstream turbine’s production by up to 11%. To the best knowledge of the author, this is the first practical implementation of Light Detection And Ranging (LiDAR) based coordinated control strategies in an operating wind farm. With coordinated control, upstream turbines are curtailed using coefficient of power or yaw offsets in such a way that the decrease in upstream turbines’ production is less than the increase in downstream turbines’ production resulting in net gain. This optimum curtailment is achieved with on-line coordinated control which requires an accurate and fast processing wind deficit model and an optimiser which achieves the desired results with high processing speed using minimum overheads. Performance evaluation of carefully selected optimisers was undertaken using an objective function developed for increasing farm production based on coordinated control. This evaluation concluded that Particle Swarm Optimisation (PSO) is the most suitable optimiser for on-line coordinated control due to its high processing speed, computational efficiency and solution quality. The standard Jensen model was used as a starting point for developing a fast processing and accurate wind deficit model referred to as the Turbulence Intensity based Jensen Model (TI-JM), taking wake added turbulence intensity and deep array effect into consideration. The TI-JM uses free-stream and wake-added turbulence intensities for predicting effective values of wake decay coefficients deep inside the farm. This model is validated using WindPRO and data from three wind farms case studies as benchmarks. A methodology for assessing the impact of wakes on farm production is developed. This methodology visualises wake effects (in 360°) by calculating power production using data from the wind farms (case-studies). The wake affected wind conditions are further analysed by calculating relative efficiency. The innovative coordinated control strategies are evaluated using data from the wind farms case studies and WindPRO as benchmarks. A live field implementation of coordinated control strategies demonstrated that the production of downstream turbines can be increased by curtailing upstream turbines. This field setup consisted of two operating wind turbines equipped with modern LiDAR. Analyses of the high frequency real time data were performed comparing field results with simulations. It was found that simulations are in good agreement (within a range of 1.5%) with field results

Burning of Crop Residue and its Potential for Electricity Generation

This paper identified the factors influencing the rice crop residue burning decision of the farmers and the potential of the burnt residue to generate electricity. For this study, data were collected from 400 farmers in the rice-wheat cropping system. Effects of different variables on the burning decision of rice residue are investigated through logit model. A number of factors had significant effects on the burning decision of crop residue. These included farming experience of the farmer, Rajput caste, farm size, owner operated farm, owner-cum-tenants operated farm, silty loam soil type, livestock strength, total cost associated with the handling of residue and preparation of wheat field after rice, availability of farm machinery for incorporation, use of residue as feed for animals, use of residue as fuel, intention of the respondent to reduce turnaround time between harvesting of rice and sowing of wheat, convenience in use of farm machinery after burning of residue and the geographic location of farm. The overall quantity of rice straw burnt is estimated to be 1704.91 thousand tonnes in the rice-wheat cropping areas with a potential to generate electric power of 162.51 MW. This power generation from crop residues would be a source of income for the farmers along with generation of additional employment opportunities and economic activities on sustainable basis. In order to minimise the cost of haulage of rice straw, installation of decentralised power plants at village level would be a good option. Further, use of rice crop residue as an energy source can help in reducing foreign exchange requirements for import of furnace oil. JEL Classification: O44, Q12, Q16, Q42, Q48 Keywords: Bioenergy, Crop Residue, Electricity, Energy, Growth, Ric

JMI at SemEval 2024 Task 3: Two-step approach for multimodal ECAC using in-context learning with GPT and instruction-tuned Llama models

This paper presents our system development for SemEval-2024 Task 3: "The Competition of Multimodal Emotion Cause Analysis in Conversations". Effectively capturing emotions in human conversations requires integrating multiple modalities such as text, audio, and video. However, the complexities of these diverse modalities pose challenges for developing an efficient multimodal emotion cause analysis (ECA) system. Our proposed approach addresses these challenges by a two-step framework. We adopt two different approaches in our implementation. In Approach 1, we employ instruction-tuning with two separate Llama 2 models for emotion and cause prediction. In Approach 2, we use GPT-4V for conversation-level video description and employ in-context learning with annotated conversation using GPT 3.5. Our system wins rank 4, and system ablation experiments demonstrate that our proposed solutions achieve significant performance gains. All the experimental codes are available on Github.Comment: Paper Accepted at SemEval 202