Distributed Generation & Alternative Energy Journal最新文献

{"title":"Priority Based Critical Load Selection Algorithm for Grid Integrated PV Powered EV Charging System with Optimal DC Link Control","authors":"M. Aijaz, Ikhlaq Hussain, S. A. Lone","doi":"10.13052/dgaej2156-3306.38113","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.38113","url":null,"abstract":"This article presents a single phase double stage photovoltaic (PV) array powered grid connected residential premise integrated with electric vehicle (EV) charging functionality. Taking criticality of the loads into consideration, a unique multi-modal control is developed which ensures incessant power supply to the loads via EVs in case of common occurrences of power interruption thereby enhancing the power security of the system. Favourable regulation of DC link voltage is achieved via proportional integral (PI) controller (DCVPI). Comparison between genetic algorithm (GA) and modified particle swarm optimisation (PSO) based tuning proves modified PSO tuned DCVPI achieves faster convergence and better fitness function evaluation. The system is subjected to various dynamic conditions during which modified PSO tuned DCVPI stabilises to the reference voltage faster and results in 1.38% reduction in overshoots opposed to the manual tuning. The proposed system is designed to work both in grid connected mode as well as islanded mode of operation. Moreover, a resynchronisation control is developed to achieve a seamless transition from islanded mode to grid connected mode post the mitigation of power failure. The proposed system achieves unity power factor and complies with the IEEE -519 power quality standard","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85217112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Neural Network Based Algorithm for Fault Detection in a Ring DC Microgrid Under Diverse Fault Conditions","authors":"Shankarshan Prasad Tiwari","doi":"10.13052/dgaej2156-3306.3812","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3812","url":null,"abstract":"The DC microgrid has become a greater power system in modern power technology due to its wider acceptance as compared to the AC-based traditional power distribution network. Nevertheless, protection of the DC microgrid is a difficult and complicated task due to numerous types of fault scenarios such as pole-to-ground and pole-to-pole faults, variation in fault current magnitude during grid connected and islanded mode, as well as bidirectional behaviour of the converters. In addition to the abovementioned challenges, fault detection during varying fault resistance and intermittency is also a crucial and tricky task because the level of the fault current can vary due to the distinct value of the fault resistance. Therefore, in this manuscript, an ANN-based protection scheme is proposed to detect the fault under varying fault conditions. Furthermore, to investigate the appropriateness of the protection scheme, DT and kNN-based techniques have also been considered for analysis purpose. In the proposed protection scheme, the tasks of mode identification, fault detection/classification, as well as section identification, have been proposed. The results in Section 5 indicate that the protection scheme is capable and accurate for fault detection in any type of faulty condition.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90271381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Intelligent Fault Diagnosis of Wind Power Generation System Based on Data Fusion","authors":"Yuhang Tan, Kangyou Liang, Zhentao Zhang","doi":"10.13052/dgaej2156-3306.3766","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3766","url":null,"abstract":"With the consume of traditional petrifaction energy origin such as coal, matelote and physical gas and the increasingly serious question of entire warming, the penetration ratio of wind power in the energy economy continues to enhance. Wind farms are generally built-in areas with strong winds, tough working environments and a high probability of equipment failure. Faults on large grid-connected wind turbines will seriously influence the safety and stability of conventional strength grids. In addition, unplanned maintenance after a breakdown of wind turbines needs a lot of manpower and corporeal resources, which greatly decrease the efficiency of wind strength production and enhance production costs. Therefore, the key to solving the above problems is to quickly and efficiently identify fan faults, which in turn enables accurate troubleshooting. In the article, the malfunction diagnosis of intelligent wind power system based on data fusion is discussed, and it is found that the GBoost algorithm has high accuracy in detecting sensor gain error, sensor offset error and sensor standard error when the Gaussian white-to-noise ratio exceeds 45 dB. In addition, DBN has different diagnostic effects for different faults with different Gaussian noises, at 45 dB and 35 dB, each type of error varies slightly, and the dotted line varies; at 25 dB, each type of error has a large difference. The difference is large, indicating that at 25 dB, this type of error is more sensitive; comparing the state estimation effect makes DLSTM have good adaptability to time series, and also shows that DLSTM considers the system to be reliable enough, and can be obtained by data fusion of the parameters of each system. What is the state of its system, and then take corresponding measures.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77208093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of Power Supply Stability Control Model for Wind Connected Power Grid","authors":"Xiao Xue, Yangbin Zheng, Chao Lu","doi":"10.13052/dgaej2156-3306.3767","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3767","url":null,"abstract":"There are many problems in the power supply stability control of wind power generation system, such as large fluctuations, poor control effect and so on. Therefore, a new stability control model of wind power grid connected is designed. Determine the DC grid connection mode when the wind farm is connected, convert the DC power into AC power through the converter station, and transmit it to the final AC system to realize the grid connection of wind power and power grid; According to the determined wind power access mode, calculate the mechanical operation power, mechanical torque and wind energy utilization coefficient collected by the wind turbine, complete the best collection of wind energy, and determine the shafting according to the mass block model of the wind turbine and generator, so as to realize the research on the mathematical model of wind power generation. By analyzing the power flow direction of the stator and rotor of the wind turbine generator set, the unstable state of the power supply voltage of the wind turbine generator set after grid connection is determined. The PV curve method is used to calculate the steady-state voltage stability of grid connected wind turbines, and a power supply stability control model based on the voltage stability of grid connected wind turbines is established. The nonlinear objective function method is used to optimize the critical point of power supply stability, calculate the maximum load and maximum power of the system, establish the static power supply and transient power supply stability model after wind power grid connection, and realize the power supply stability control research of grid connected wind power through the analysis of power supply characteristics. The experimental results show that the model is closer to the stability of the actual power supply in the test of improving the stability of the power supply, ensuring the quality of power supply, while the test results of the other two methods have large fluctuations. In the analysis of the change of power supply after grid connection, the experimental results obtained by the model are very close to the actual data values. Therefore, this method can effectively improve the performance of power system.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77469547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fault and Islanding Detection Scheme using Differential Positive Sequence Power Angle for a Microgrid","authors":"Salauddin Ansari, O. Gupta","doi":"10.13052/dgaej2156-3306.3765","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3765","url":null,"abstract":"Implementation of distributed generation (DG) fault and islanding detection in a microgrid are two difficult jobs to complete. Efforts by many researchers to develop solutions to these a kind of challenges are ongoing. Still, there is hardly any scheme that can detect and distinguish both the fault and islanding events. To detect and differentiate between fault and islanding events, this article presents a Differential Positive Sequence Power Angle (DPSPA)-based protection technique. The scheme is widely examined considering different working conditions of a microgrid such as DG disconnection, DG penetration, different fault parameters like fault type, fault resistance, fault location, fault inception angle, fault during single-pole tripping (STP), simultaneous faults, and evolving faults. Tests were also performed for non-fault cases like load switching, capacitor switching, sectional cut-off, DG disconnection, and impact of noise and sampling frequency. Furthermore, the scheme’s outcomes have been compared to that of recent protection schemes. Finally, using the OP4510 real-time simulator, the proposed approach is validated in an online environment. The results show that the proposed DPSPA-based scheme can be a notable scheme to protect a microgrid in a wide variety of situations.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80530044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sizing of Rooftop PV Array and Community-Run Battery Storage for an Energy Cooperative in Prosumer Cluster","authors":"M. Sujikannan, A. R. Kumar, S. A. Daniel","doi":"10.13052/dgaej2156-3306.3764","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3764","url":null,"abstract":"A standalone system can address the problem of uncovered electricity from the grid. The cost of energy storage for installing renewable energy systems is one of the issues of such a system. This paper introduces and investigates the optimal capacity of a novel energy cooperative system with prosumer clusters and a community battery bank as typical energy storage. The system’s function is formulated to minimize the investor’s annual expenditure. The proposed energy cooperative system uses actual annual solar insolation data and the electric load demand of houses in the optimization process. The model, as mentioned above, is applied to two system configurations – energy cooperative without and with Prosumer to Prosumer (P2P) energy sharing. The reliability factor Loss of Power Supply Probability (LPSP) from the Cooperative Energy Sharing algorithm is taken as a constraint in the formulation. The comparison of the two configurations brings out the importance of P2P energy sharing in a standalone Energy Cooperative system. Particle Swarm Optimization (PSO) algorithm is used to achieve this optimization. The PSO results show that the proposed Energy Cooperative configurations are promising to facilitate the system’s reliability.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89846026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network","authors":"Zhang Yiming, Cheng Li","doi":"10.13052/dgaej2156-3306.3761","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3761","url":null,"abstract":"At present, wind power has become the most promising energy supply. However, the intermittent and fluctuating wind power also poses a huge challenge to accurately adjust the electrical load. In order to find a method capable of forecasting wind power generation in a short period of time, we propose a short-term wind power generation forecasting method based on an optimized deep belief network approach. Based on GEFCom2012 competition dataset, by continuously tuning the parameters of the deep belief network for 15 sets of experiments, we obtained three optimal laboratory combinations: Experiment 4, Experiment 10, and Experiment 12. The results show that the R-squared values of Experiment 4, Experiment 10 and Experiment 12 are the highest, which are 0.955, 0.93 and 0.98, respectively. The average R-squared value of these three tuned experiments is 0.2342 higher than the average of the other 12 experiments. At the same time, it is concluded that when the learning frequency is low, the linear function can learn the most obvious features more directly; When the learning frequency is high, the nonlinear function can learn the internal latent features more directly.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86558711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning-based Fractional Order PID Controller for Load Frequency Control of Distributed Energy Resources Including PV and Wind Turbine Generator","authors":"Mohsen Babaei, Mohsen Hadian","doi":"10.13052/dgaej2156-3306.3762","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3762","url":null,"abstract":"Due to the ever-increasing penetration of renewable resources, Frequency control of microgrids has recently been received special consideration from researchers. The continual supply of load consumption is the major issue of standalone microgrids due to the high penetration of renewable resources. Furthermore, microgrids suffer from low inertia against load changes due to their small size and unpredictable load interruption. In addition to the above-mentioned issues, the uncertain and intermittent behaviors of renewable resources cause problems to keep the balance between load and generation sides. Hence, it is very important to consider novel control methods for keeping balance and consequently control of frequency deviation. In this research, a novel learning-based fractional-order controller is proposed to control the frequency of microgrids including micro-turbines, photovoltaic panels, and wind turbines in order to increase system stability and reduce frequency fluctuation time. The efficiency of this controller has been compared with conventional methods in the simulation and result section.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"213 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74621151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maximum Energy Extraction in Partially Shaded PV Systems Using Skewed Genetic Algorithm: Computer Simulations, Experimentation and Evaluation on a 30 kW PV Power Plant","authors":"Gireesh V. Puthusserry, K. Sundareswaran, S. P. Simon, G. Krishnan","doi":"10.13052/dgaej2156-3306.3763","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3763","url":null,"abstract":"This paper presents an improved Genetic algorithm (GA) for Maximum Power Point Tracking (MPPT) in shaded Photovoltaic (PV) power generation systems. The proposed GA uses shrinking population wherein fitter chromosomes are retained for next generations while lesser-performing chromosomes are removed from the population sequentially. This methodology reduces convergence time while retains major advantages of GA. The method is explained lucidly and then computer simulations and experimental results on a prototype fabricated in the laboratory are presented. The practical feasibility of the new method is then showcased by applying the new theory on a 30-kW Photovoltaic (PV) power plant established in an educational institution premise. The PV plant undergoes partial shading conditions (PSC) during morning and afternoon hours due to branches of tall trees grown around the school building. The MPPT algorithm employed in the PV plant is Perturb and Observe (P&O) which fails to track global power peak at several shading conditions leading to loss of energy. The realistic shading patterns occurring on the PV plant were recorded and the new method is shown to exhibit enhanced energy yield.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76669636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Optimal Economic Dispatch of a Fuel Cell and Combined Heat and Power Based Renewable Integrated Grid Tied Micro-grid Using Whale Optimization Algorithm","authors":"S. Prakash, N. Kumarappan","doi":"10.13052/dgaej2156-3306.3757","DOIUrl":"https://doi.org/10.13052/dgaej2156-3306.3757","url":null,"abstract":"Micro-grids are practical solution for combining distributed energy resources and combined heat and power units in order to satisfy the system power and heat demands. Nowadays, in order to integrate both renewable and non-renewable energy resources like photovoltaic, wind turbine, combined heat and power systems and fuel-cell unit; micro-grid seems to be a good idea. The aim of this paper is to obtain optimal scheduling of proposed generating units and to reduce the total operational cost and net emission of the system through economic/environmental power dispatch, while considering the impact of grid tied and autonomous mode of operation and satisfying the operational constraints. In this paper, a novel whale optimization algorithm is employed to solve this multi-objective problem. The obtained optimal results through this proposed whale optimization algorithm represents the efficiency, feasibility and capability of handling non-linear optimization problems in an efficient way compared to other optimization techniques. The proposed system is studied in a 24-h time horizon. The results obtained from this proposed technique are compared with other techniques which are recently employed.","PeriodicalId":11205,"journal":{"name":"Distributed Generation & Alternative Energy Journal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75117543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}