On-line partial discharge(PD)diagnostics data are corrupted by various noise sources and this makes it more challenging to extract the PD signal contained in the raw data.Though the noise sources can be filtered out u...On-line partial discharge(PD)diagnostics data are corrupted by various noise sources and this makes it more challenging to extract the PD signal contained in the raw data.Though the noise sources can be filtered out using signal processing techniques,PDs from neighboring cables and other high voltage equipment make the de-noising process more difficult due to the similar features of these signals with the PD signal of interest.Proposed in this paper is a double-ended partial discharge diagnostic system with dual sensors at each end which uses wireless time triggering using global time reference with the aid of global positioning system(GPS).Using the time of arrival method based on the velocity of propagation on the data,PD pulses originating from other sources can be discarded which reduces the volume of data to be stored and would eventually also reduce the hardware and software requirements of the denoising process thereby improving de-noising efficiency.System design,laboratory tests and on-site measurements are discussed.展开更多
The variable and unpredictable nature of renewable energy generation(REG)presents challenges to its large-scale integration and the efficient and economic operation of the electricity network,particularly at the distr...The variable and unpredictable nature of renewable energy generation(REG)presents challenges to its large-scale integration and the efficient and economic operation of the electricity network,particularly at the distribution level.In this paper,an operational coordination optimization method is proposed for the electricity and natural gas networks,aiming to overcome the identified negative impacts.The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants.A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption.This method allows for the full consideration of unbalanced operational scenarios.Meanwhile,the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines.The sequential symmetrical second-order cone programming(SS-SOCP)method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process.The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network.The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost,the curtailment of installed REG,the voltage imbalance of three-phase power system,and the overall carbon emissions.展开更多
Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting met...Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.展开更多
基金the funding and technical support provided by Scottish Power Energy Networks (SPEN) for the research
文摘On-line partial discharge(PD)diagnostics data are corrupted by various noise sources and this makes it more challenging to extract the PD signal contained in the raw data.Though the noise sources can be filtered out using signal processing techniques,PDs from neighboring cables and other high voltage equipment make the de-noising process more difficult due to the similar features of these signals with the PD signal of interest.Proposed in this paper is a double-ended partial discharge diagnostic system with dual sensors at each end which uses wireless time triggering using global time reference with the aid of global positioning system(GPS).Using the time of arrival method based on the velocity of propagation on the data,PD pulses originating from other sources can be discarded which reduces the volume of data to be stored and would eventually also reduce the hardware and software requirements of the denoising process thereby improving de-noising efficiency.System design,laboratory tests and on-site measurements are discussed.
基金supported by the Engineering and Physical Sciences Research Council(EPSRC,UK)in project“Street2Grid–an electricity blockchain platform for P2P energy trading”(No.EP/S001778/2).
文摘The variable and unpredictable nature of renewable energy generation(REG)presents challenges to its large-scale integration and the efficient and economic operation of the electricity network,particularly at the distribution level.In this paper,an operational coordination optimization method is proposed for the electricity and natural gas networks,aiming to overcome the identified negative impacts.The method involves the implementation of bi-directional energy flows through power-to-gas units and gas-fired power plants.A detailed model of the three-phase power distribution system up to each phase is employed to improve the representation of multi-energy systems to consider real-world end-user consumption.This method allows for the full consideration of unbalanced operational scenarios.Meanwhile,the natural gas network is modelled and analyzed with steady-state gas flows and the dynamics of the line pack in pipelines.The sequential symmetrical second-order cone programming(SS-SOCP)method is employed to facilitate the simultaneous analysis of three-phase imbalance and line pack while accelerating the solution process.The efficacy of the operational coordination optimization method is demonstrated in case studies comprising a modified IEEE 123-node power distribution system with a 20-node natural gas network.The studies show that the operational coordination optimization method can simultaneously minimize the total operational cost,the curtailment of installed REG,the voltage imbalance of three-phase power system,and the overall carbon emissions.
基金the financial support provided by EPSRC(EP/T022701/1,EP/V042033/1,EP/V030515/1,EP/W027593/1)in the UK.
文摘Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.
