Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others is usually beyond human control. Consequently, there is the need to set up models capable of prompt identification and classification of these faults for immediate action. This paper, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11 kV distribution network of University of Lagos. The ANN is applied because it offers high speed, higher efficiency and requires less human intervention. Datasets of the case study obtained were sectioned proportionately for training, testing, and validation. The mathematical formulations for the method are presented with python used as the programming tools for the analysis. The results obtained from this study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms and discussed. The results showed the effectiveness of the ANN in fault identification and classification in a distribution network as the model yielded satisfactory results for the available limited datasets used. The information obtained from this study could be helpful to the system operators in faults identification and classification for making informed decisions regarding power system design and reliability

Probabilistics Risk Assessment of Power Quality Variations and Events Under Temporal and Spatial Characteristic of Increased PV Integration in Low Voltage Distribution Networks

Integration of PVDG reduces the voltage unbalance as compared with no or low PVDG penetration. There is a higher probability of observing deep sag at the site as PVDG integration increases. This probabilistic approach can be used as a tool to assess the likely impacts due to PVDG integration against the worst-case scenarios

Probabilistic Risk Assessment of Power Quality Variations and Events under Temporal and Spatial Characteristic of Increased PV Integration in Low-Voltage Distribution Networks

The aim of this paper is to perform a probabilistic risk assessment of power quality variations and events that may arise due to high photovoltaic distributed generation (PVDG) integration in a low-voltage distribution network (LVDN). Due to the spatial and temporal behavior of PV generation and load demand, such an assessment is vital before integrating PVDG at the existing load buses. Two power quality (PQ) variations such as voltage magnitude variation and phase unbalance together with one PQ abnormal event are considered as the PQ impact metrics. These PQ impact metrics are assessed in terms of two PQ indices, namely site and system indices. A Monte Carlo based simulation is applied for the probabilistic risk assessment. From the results, site overvoltage shows a likely impact to observe as the PVDG integration increases. The probability of 20% of customers violating 1.1 p.u. at 100% penetration level is 0.5. Integration of PVDG reduces the voltage unbalance as compared with no or low PVDG penetration. There is a higher probability of observing deep sag at the site as PVDG integration increases. This probabilistic approach can be used as a tool to assess the likely impacts due to PVDG integration against the worst-case scenarios

Fault Classification in a DG Connected Power System using Artificial Neural Network

Distributed generation is playing an important role in power system to meet the increased load demand. Integration of Distributed Generator (DG) to grid leads to various issues of   protection and control of power system structure.  The effect of the distributed generators to the grid is changes the fault current level, which makes the fault analysis more complex. From the different fault issues occurs in a distributed generator integrated power system, classification of fault remains as one of the most vital issue even after years of in-depth research. This paper emphasis on the classification of faults in DG penetrated power system using Artificial Neural Network (ANN). Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells, and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purpose.  Artificial neural network technique is utilized for the classification of fault in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration

Power quality analysis for renewable power generation in household

Power quality is becoming essential part of Power Industry. The introduction of smarter and more sensitive equipment at both grid and residential level has created performance issues that need investigation. The cost of the power losses is rising due to power quality problems. The other significant factor that is proving vital is the customer dissatisfaction. The introduction of Renewable Energy (RE) into modern grids has also created Power Quality (PQ) problems. A study is required to narrow down the factors that can cause these PQ issues. The power companies are buying electricity back from the consumer produced by these RE sources. The power produced by RE sources coming into the electrical grid needs to be monitored. The research will focus on the factors that impact PQ especially the Total Harmonic Distortion in a electrical grid powered by renewable sources. The factors impacting power quality will be studied in detail by using an simulation approach aided by an experimental set up. The simulation approach will be used to test the hypothesis that total harmonic distortion increases by changing the nature and size of the load in the electrical system. The load type used for the research will be linear and nonlinear loads. The simulation will use single and three phase electrical system. The simulation results will be analysed and discussed. The experimental setup will be used to verify the simulation result. The experiment will be conducted on different set of load to observe the impact on the total harmonic distortion in particular. The experimental result will be collected over period of time enabling the researcher to study in detail the impact of weather, temperature, and inclination of solar panels. These factors will impact the research result. The collected data will be presented for discussion

Power quality analysis for renewable power generation in household

Power quality is becoming essential part of Power Industry. The introduction of smarter and more sensitive equipment at both grid and residential level has created performance issues that need investigation. The cost of the power losses is rising due to power quality problems. The other significant factor that is proving vital is the customer dissatisfaction. The introduction of Renewable Energy (RE) into modern grids has also created Power Quality (PQ) problems. A study is required to narrow down the factors that can cause these PQ issues. The power companies are buying electricity back from the consumer produced by these RE sources. The power produced by RE sources coming into the electrical grid needs to be monitored. The research will focus on the factors that impact PQ especially the Total Harmonic Distortion in a electrical grid powered by renewable sources. The factors impacting power quality will be studied in detail by using an simulation approach aided by an experimental set up. The simulation approach will be used to test the hypothesis that total harmonic distortion increases by changing the nature and size of the load in the electrical system. The load type used for the research will be linear and nonlinear loads. The simulation will use single and three phase electrical system. The simulation results will be analysed and discussed. The experimental setup will be used to verify the simulation result. The experiment will be conducted on different set of load to observe the impact on the total harmonic distortion in particular. The experimental result will be collected over period of time enabling the researcher to study in detail the impact of weather, temperature, and inclination of solar panels. These factors will impact the research result. The collected data will be presented for discussion

Improved finite control set model predictive control for distributed energy resource in islanded microgrid with fault-tolerance capability

In this paper, improved finite control set model predictive voltage control (FCS-MPVC) is proposed for the distributed energy resource (DER) in AC islanded microgrid (MG). Typically, AC MGs have two or more power electronic-based DERs, which have the ability to maintain a constant voltage at the point of common coupling (PCC) as well as perform power sharing among the DERs. Though linear controllers can achieve above-mentioned tasks, they have several restrictions such as slow transient response, poor disturbance rejection capability etc. The proposed control approach uses mathematical model of power converter to anticipate the voltage response for possible switching states in every sampling period. The proposed dual-objective cost function is designed to regulate the output voltage as well as load current under fault condition. Two-step horizon prediction technique reduces the switching frequency and computational burden of the designed algorithm. Performance of the proposed control technique is demonstrated through MATLAB/Simulink simulations for single distributed generator (DG) and AC MG under linear and non-linear loading conditions. The investigated work presents an excellent steady state performance, low computational overhead, better transient performance and robustness against parametric variations in contrast to classical controllers. Total harmonic distortion (THD) for linear and non-linear load is 0.89% and 1.4% respectively as illustrated in simulation results. Additionally, the three-phase symmetrical fault current has been successfully limited to the acceptable range.©2020 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed