Comparison of SVC and TCSC installation in transmission line with loss minimization and cost of installation via particle swarm optimization

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that PSO a feasible to achieve the objective functio

Application of artificial neural network for automatic contingency analysis in power security assessment / Ismail Musirin and Titik Khawa Abdul Rahman

Several incidents that occurred around the world involving power failure caused by unscheduled line outages were identified as one of the main contributors to power failure and cascading blackout in electric power environment. With the advancement of computer technologies, artificial intelligence (AI) has been widely accepted as one method that can be applied to predict the occurrence of unscheduled disturbance. This paper presents the development of automatic contingency analysis and ranking algorithm for the application in the Artificial Neural Network (ANN). The ANN is developed in order to predict the post-outage severity index from a set of preoutage data set. Data were generated using the newly developed automatic contingency analysis and ranking (ACAR) algorithm. Tests were conducted on the 24-bus IEEE Reliability Test Systems. Results showed that the developed technique is feasible to be implemented practically and an agreement was achieved in the results obtained from the tests. The developed ACAR can be utilised for further testing and implementation in other IEEE RTS test systems particularly in the system, which required fast computation time. On the other hand, the developed ANN can be used for predicting the post-outage severity index and hence system stability can be evaluated

Results of Fitted Neural Network Models on Malaysian Aggregate Dataset

This result-based paper presents the best results of both fitted BPNN-NAR and BPNN-NARMA on MCCI Aggregate dataset with respect to different error measures.  This section discusses on the results in terms of the performance of the fitted forecasting models by each set of input lags and error lags used, the performance of the fitted forecasting models by the different hidden nodes used, the performance of the fitted forecasting models when combining both inputs and hidden nodes, the consistency of error measures used for the fitted forecasting models, as well as the overall best fitted forecasting models for Malaysian aggregate cost indices dataset

Hybrid Stand-alone Photovoltaic Systems Sizing Optimization Based on Load Profile

This paper presents a sizing optimization technique for Hybrid Stand-Alone Photovoltaic (HSAPV). In this research, three optimization techniques have been developed, namely Dolphin Echolocation Algorithm (DEA), Fast Evolutionary Programming (FEP), and Classical Evolutionary Programming (CEP). These techniques have been incorporated into the sizing process to maximize the technical performance of the SAPV system. The components of PV modules, charge controllers, inverters, and batteries are used to determine the optimum value. These components are used as the control parameters to maximize the expected performance ratio (PR) of the SAPV system. The Iterative Sizing Algorithm (ISA) is the benchmarking technique to conduct the optimization technique achieving maximum PR value and minimal computation time. Results obtained from the research show that DE overcomes FEP and CEP. In addition, the optimization techniques also demonstrated comparatively fast with respect to ISA as the benchmark technique.

Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

Recently, renewable energy (RE) has become a trend in power generation. It is slowly evolving from an alternative energy source into the main energy source. The technology is currently working as an auxiliary to the existing generators. Demands for electricity is expanding rapidly nowadays, which require generators to run near its operation limit. This activity put grieve risk to the generators. Nonetheless, the extensive analysis should be conducted upon RE integration into the existing power system. This paper assesses its economic impact on the power system. Setting up RE technology such as photovoltaic and wind turbine are costly, yet may reduce generator’s fuel cost in the long run. Thus, economic load dispatch (ELD) is conducted to compute the operating cost of power system with the integration of RE system. In this study, the operating cost represents the fuel cost of conventional fossil-fuel generators. Furthermore, a novel optimization technique namely Differential Evolution Immunized Ant Colony Optimization is proposed as the optimization engine. Comparative studies are conducted to assess the performance of the proposed approach

Optimal SVC allocation via symbiotic organisms search for voltage security improvement

It is desirable that a power system operation is in a normal operating condition. However, the increase of load demand in a power system has forced the system to operate near to its stability limit whereby an increase in load poses a threat to the power system security. In solving this issue, optimal reactive power support via SVC allocation in a power system has been proposed. In this paper, Symbiotic Organisms Search (SOS) algorithm is implemented to solve for optimal allocation of SVC in the power system. IEEE 26 Bus Reliability Test System is used as the test system. Comparative studies are also conducted concerning Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) techniques based on several case studies. Based on the result, SOS has proven its superiority by producing higher quality solutions compared to PSO and EP. The results of this study can benefit the power system operators in planning for optimal power system operations

Reliability-based phasor measurement unit with outage of transmission lines

This paper discussed on the Monte-Carlo simulation technique to determine the optimal placement of Phasor Measurement Unit (PMU) in power system whilst ensuring the observability of the system. In addition, the information on Force Outage Rate (FOR) of the system can be calculated using Markov Chain technique. The FOR represents the level of risk security for the transmission line that happened because of unscheduled and unexpected failure or repair in the system. Subsequently, the reliability model of the transmission line can be developed. Using IEEE 57-bus system, the results obtained from Monte-Carlo simulation technique demonstrate the optimal PMU placement, the desired reliability of the Wide Area Monitoring System (WAMS) as well as the number and location of covered contingencies of the system

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method

Sizing Optimization of Hybrid Stand Alone Photovoltaic System

This paper presents a sizing optimization technique for Hybrid Stand-Alone Photovoltaic (HSAPV). In this study, two optimization techniques based on the mechanics of Evolutionary Programming (EP) have been developed, namely Fast Evolutionary Programming (FEP) and Classical Evolutionary Programming (CEP). These techniques have been integrated into the sizing process to maximize the technical performance of the HSAPV system. It is used to determine the optimum PV modules, charge controllers, inverters, battery and diesel generator. These variables are used as the control parameters to maximize the expected performance ratio (PR) of the HSAPV system. Comparative studies with respect to a benchmarking technique namely the Iterative Sizing Algorithm (ISA) were conducted in order to reveal their merit in terms of achieving maxima PR value and minimal computation time. Results obtained from the study exhibited that FEP outperformed CEP. The developed FEP and CEP also demonstrated comparatively fast with respect to ISA as the benchmark technique