Voltage Instability Analysis Of Electric Power Systems Using Artificial Neural Network Based Approach

Voltage instability analysis in electric power system is one of the most important factors in order to maintain the equilibrium of the electric power system. A power system is said to be experiencing voltage instability whenever the system is not able to maintain the voltage at all buses in the system remain the same after the system is being subjected to a disturbance. Voltage instability can lead to total system blackout. Therefore, it is important to implement voltage instability analysis in order to make sure that the voltage level at all buses is at stable state. Even though the research regarding voltage instability analysis has been carried out for decades, there is still room for improvement especially in terms of accuracy and time execution. The research work presented in this thesis is about the analysis of voltage instability of electric power system by using reactive power-voltage (QV) and real power-voltage (PV) curves. PV and QV curves are very important for calculating voltage instability indices. These voltage instability indices are voltage stability margin (VSM) and load power margin (LPM). VSM can be divided into two indices which are VSM for real and reactive power of load, VSM (P) and VSM (Q). Similarly, there are two categories of LPM which are LPM of real power and reactive power of load, LPM (P) and LPM (Q). Besides that, modal analysis technique is used in this research for determining the weakest load buses in the electrical power system. This research will explore the implementation of real power (P) modal analysis technique in addition to the reactive power (Q) modal analysis technique. It was found that reactive power (Q) of load gives more effects towards the stability of the system voltages than real power (P) of load. Subsequently, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for providing the target values of VSM (P), VSM (Q), LPM (P) and LPM (Q). The accuracy and computing time of both ANN and ANFIS are being recorded and compared. The research showed that the accuracy and computing time of ANFIS are better than ANN’s. Finally, Probabilistic Neural Network is applied for classifying the voltage instability indices. IEEE 14, 30 and 39-Bus Test Power System were selected as the reference power systems in this research. The load flow analyses were simulated by using Power World Simulator software version 16. Both Q and P modal analysis were done by using MATLAB application software

Analysis load forecasting of power system using fuzzy logic and artificial neural network

Load forecasting is a vital element in the energy management of function and execution purpose throughout the energy power system. Power systems problems are complicated to solve because power systems are huge complex graphically widely distributed and are influenced by many unexpected events. This paper presents the analysis of load forecasting using fuzzy logic (FL), artificial neural network (ANN) and ANFIS. These techniques are utilized for both short term and long-term load forecasting. ANN and ANFIS are used to improve the results obtained through the FL. It also studied the effects of humidity, temperature and previous load on Load Forecasting. The simulation is done by the Simulink environment of MATLAB software

Voltage stability assessment of power system network using QV and PV modal analysis

The analysis of voltage instability in electric power system is very crucial in order to maintain the equilibrium of the power system. This paper presents the analysis of voltage instability of electric power system by using reactive power-voltage (QV) and real power-voltage (PV) modal analysis. This research focuses on the voltage instability analysis by implementing the QV and PV modal analysis for mesh-type power system. IEEE 14-bus system has been chosen as the power system. Both QV and PV modal analysis will be run by using MATLAB application software

Identification Of Weak Buses In Electrical Power System Based On Modal Analysis And Load Power Margin

This paper presents the identification of weak buses in electrical power system with the use of modal analysis technique and load power margin values. A weak bus can be defined as a load bus that has high tendency towards experiencing voltage instability. This type of bus cannot afford high value of load incremental values. The modal analysis technique will show the list of weak buses in the power system. Meanwhile load power margin is very useful for showing how much the load at the bus can be increased before experiencing voltage instability. Both modal analysis technique and load power margin values are applied upon the IEEE 39-bus test power system. From there, five weak buses in the test power system are selected and compared. The results proved that weak buses determined by modal analysis technique have low load power margin values

Evaluating the Performance of a Photovoltaic System Using Acceptance Ration (AR)

The use of fossil fuels for energy production has several negative impacts on the environment and human health. Therefore, renewable energy is needed as an environmentally friendly source. Solar energy is one of the renewable energy produced from solar radiation using photovoltaic (PV). The explanations given by science about how the sun works are in line with what is stated in the Qur'an. One verse in the Quran speaks of Allah's Greatness and His Mercy, and it is found in: “And there He created a shining moon and made the sun a (brilliant) lamp. (Q.S. Nuh: 16)”. However, one of the problems in using PV systems is that the energy produced is unstable because it is influenced by the environment. Therefore, it is necessary to evaluate the performance of the PV system from time to time. In this research, the performance of the PV system is being done by determining the Acceptance Ratio (AR). AR is used to define the ratio of actual AC power to expected AC power. The largest expected output power value is 436.4788 W, on May 26. The acceptance ratio (AR) obtained ranges from 0.921 to 0.982, which should be worth approximately 0.9. This is influenced by the output power generated and the expected AC power generation data obtained from the calculation. The expected AC power generation value is different for each data depending on the weather conditions whether it is cloudy or sunny

PV and QV curves approach for voltage instability analysis on mesh-type electrical power networks using digsilent

The analysis of voltage instability in electric power system is very crucial in order to maintain the equilibrium of the system. This paper presents the analysis of voltage instability of electric power system by using power-voltage (PV) curve and reactive power-voltage (QV) curve. This research focuses on the voltage instability analysis using PV and QV curves for mesh-type power networks. The power flow analysis for mesh-type power network will be done by using DIgSILENT and the plotting of PV and QV curve will be done by using Microsoft Excel

Power Monitoring System using the Internet of Things for Photovoltaic Powered Fertigation System

This research paper explains the concept of photovoltaic (PV) systems. Among renewable energy technologies, PV systems rank third after hydropower and wind in terms of usage. They find significant application in agricultural activities, contributing to income generation for daily livelihoods. However, certain plant species demand meticulous attention to achieve optimal efficiency, necessitating a larger workforce for their care. By incorporating the Internet of Things (IoT), users gain the ability to remotely monitor PV system performance through a dedicated application. The central goal of this project involves crafting a simulation of an IoT system. This system is intended for monitoring the functionality of PV systems and devising an IoT-based model for overseeing the power aspects of PV systems in conjunction with fertigation systems (watering plants with nutrient solutions). A key feature of this system is its capacity to gauge the IoT model's effectiveness by contrasting outcomes derived from hands-on observation and simulated scenarios. The outcomes of this study reveal that the developed IoT system effectively tracks real-time parameters like current, voltage, generated power, and solar PV temperature. It successfully establishes a link between users and the IoT infrastructure for system monitoring. Looking ahead, the gathered PV output data will be instantly accessible on mobile devices. This empowers users to assess the performance of their PV systems concerning fertigation

A GUI based teaching and learning software for system sizing of a stand alone hybrid solar electricity system

This research focuses on the development of Graphical User Interface (GUI) based software that can be used to size a stand-alone hybrid solar system for teaching and learning application. The software is able to calculate the load requirements and then determine the suitable size of the stand-alone solar system’s components such as inverter, PV array, solar charge controller, and battery-bank so that the system is able to produce sufficient energy to meet the load requirements. Sizing is very important because if the solar system is not being sized carefully, it might result in producing more energy or less energy than is needed. Diesel generator is chosen to be combined with the solar system to form a hybrid solar system. GUI is used to present all of the procedures system sizing procedures to make it much easier to understand by the users. The GUI will be done by using Visual Basic 2010. The software is targeted for engineering students and practicing engineers

Long –term load forecasting of power systems using Artificial Neural Network and ANFIS

Load forecasting is very important for planning and operation in power system energy management. It reinforces the energy efficiency and reliability of power systems. Problems of power systems are tough to solve because power systems are huge complex graphically, widely distributed and influenced by many unexpected events. It has taken into consideration the various demographic factors like weather, climate, and variation of load demands. In this paper, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used to analyse data collection obtained from the Metrological Department of Malaysia. The data sets cover a seven-year period (2009- 2016) on monthly basis. The ANN and ANFIS were used for long-term load forecasting. The performance evaluations of both models that were executed by showing that the results for ANFIS produced much more accurate results compared to ANN model. It also studied the effects of weather variables such as temperature, humidity, wind speed, rainfall, actual load and previous load on load forecasting. The simulation was carried out in the environment of MATLAB software

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB