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Interconnection of distributed generation (DG) in distribution system will result in formation of islands in the event of loss of main supply. This scenario is harmful to the power system, hence quick detection is critical to halt the formation of islands. Among the common passive and active detection methods available, the hybrid detection method is identified as the most reliable method. This paper proposes a new hybrid method using the combination of passive and active technique which is the rate of change of frequency (ROCOF) and load impedance, respectively. The passive method works when the value of ROCOF exceeds the threshold value which is set at 0.3Hz/s. The active method works when it detects low value of ROCOF and immediately inject a pre-specified load into the system to increase the ROCOF value up to its threshold value. Simulation study on different case studies is carried out on distribution test system to evaluate the performance of the proposed method. Results show that this method is effective in detecting any events that could result in islanding.

Reducing the self-power consumption of an off-grid solar home system is an economic model in which consumer employs photovoltaic (PV) system for its own electrical requirements. The latch-based clock gating approach has been employed in existing solar charge controllers to reduce integrated circuit (IC) power being used in the low-powered intended mode, although the reducing power is limited. This paper presents a self-power reduction technique based on wake-up power and latch-hold time; which minimize power supply during idle time for a solar home system. Wake-up power introduces a push-switch mechanism using typical transistor technology. Latch-hold time function is designed using an operational amplifier and negative-positive-negative (NPN) transistor. A technique with dynamic self-supply mechanism is also introduced for decreasing self-power consumption. The self-power consumption is identified via simulation studies where the result shows that the power usage is 70% lower than traditional approaches. This is determined using a simulated wave-shape analysis.

Fault is a common problem in power system and classifying the cause would be helpful to minimize the risk of permanent damage to the equipment and increase the quality of the power supply. Transient in power system is one of the causes of fault because it takes time to be discovered and the impact is clear when the severity is at the worst such as the equipment is totally damaged and cannot be fixed anymore. Lightning strikes is a natural phenomenon that can produce transient in power system and eventually produce fault. Most of lightning cases are direct strike to equipment that result to exceeding the threshold equipment limit. However, faults due to lightning are easily mistakenly classified since the strikes could be indirect and the time taken to identify the fault could be affected by other factors such as external contact by animals. This paper investigates type of faults due to transient overvoltage source and fault classification method by using Artificial Neural Network. Initially, the data from EPRI’s website is extracted and analysed before it can be initialized as input data in MATLAB. Since faults due to lightning can lead to equipment damage, this paper classifies faults based on lightning and non-lightning. The results obtained have shown that the developed method is able to classify fault types to either lightning or non-lightning faults.

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this research. This research will identify acceptable parameters and the best AI technique to use in predicting the STS performance. Previous research on STS demonstrates that the efficiency of STS has been estimated using different input parameters. The results show that the prediction of the LM training algorithm is the best for STS performance.

Electrical energy source in Malaysia is commonly from by fossil fuel such as coal, petroleum, LNG, LPG, kerosene, etc. However, these unrenewable sources of energy are neither economical nor favorable to the environment. Even though Malaysia produces enough electricity to meet current consumption levels, the reduction of electrical usage should be taken for us as a main consumer in this country in order to reduce energy consumption. Towards this end, solar energy seems to be a singularly legitimate and feasible source of alternative energy for heating water. Solar energy is permanently renewable, environmentally beneficial, and convertible to various energy forms. A solar water heating system is used to convert solar energy to thermal energy to heat water. This research investigates the application of solar energy to meet water heating needs especially in Malaysia and models the water heating system. Through modelling in the simulation, the average system efficiency for solar collector and thermal storage are determined as the total costs on a daily basis or monthly average.

Bioelectricity is one of the high potential alternatives for sustainable energy production. Bioelectricity generated from living organisms including plants and bacteria has been discussed and explored by researchers in the past decades. Several methods of electrical energy harvesting had been researched and developed with living organisms as the sources of energy including plant microbial fuel cell (PMFC) and plant-based cell (PBC). It was recognized that electrical energy can be harvested from living organisms and has great potential. Aquaponics on the other hands is known as the combination food production system involving aquatic species, plants, and bacteria, where it has the elements needed for bioelectricity generation. This paper will review the potential of harvesting electrical energy from living organisms at the aquaponics system and recommendations for future works.

The islanding operation of a distribution network connected with distributed generation may be a useful way to improve its power reliability. However, it requires a good control strategy for ensuring a safe operation of islanded system controlling voltage and frequency of the system within their permissible range. This paper proposed a centralized controller to coordinate the islanding operation with multiple islanding configurations for rotating type DGs. Multiple islanding configurations are introduced to maximize the benefit of islanded operation. The advantage of the proposed strategy is that DG will be able to operate in different islanding areas instead of energizing only one islanding area. The proposed controller regularly communicates with the governor and excitation controllers of the DG units with the utility grid to carry out action in case of an islanding event. The performance of the proposed controller is investigated for various simulations carried out on an existing Malaysia distribution network. The simulation results show that the islanding operation for each configuration is dynamically stable.

This paper presents a new technique namely Ant Lion Optimizer (ALO) to determine optimal unit commitment. The proposed technique is simulated on IEEE 39-bus test system which consists of 10-unit generators with consideration of system constraints in unit commitment such as power balance, system reserve requirement, generation limit of generators, and minimum up and down time constraints. ALO is inspired based on hunting behavior of ant lion. There are five main steps, which include random walk of ants, trapping of ants in antlions' trip, building trap, sliding of ants towards antlion, catching prey and rebuilding the pit. The proposed ALO algorithm is able to identify the global optimum solution since the intensity of ants' movement is adaptively decreased as the number of iterations increase. In addition, the exploration of search space is guaranteed within the limitation of set-up boundaries. This behavior will enhance the optimization towards the optimal and global solution. The performance of the proposed algorithm is compared with the performance of Dynamic Programming (DP) technique in terms of generation scheduling, total operating cost (TOC) and computation time. From the results obtained, ALO provides better generation scheduling with lower TOC, as compared to DP technique. The cost saving per year performed by ALO technique as compared to DP is $236,520. Based on the results, ALO provides better solution as compared to DP in terms of providing better generation scheduling, and significant reduction of TOC and with lower computation time.

This paper proposes a systematic approach to calculate the customer interruption cost due to the effect of hidden failure in a power system. The distribution of electrical energy from the generation to the consumer might experience interruption due to some specific reasons. One of the reasons that could give catastrophic impact to the system is due to cascading collapse that might happen in the system. In this paper, the reliability cost from the customer's perspective being conducted in order to analyse the interruption cost based on cascading collapse. The analysis has been carried out from some sectors of the building that use the source of energy. Analysis has been made by using IEEE Reliability Test System 1979 (IEEE RTS-79) in order to verify the proposed technique is accurate. The total loading condition of the system have been calculated for this analysis in order to investigate the reliability cost towards the customers based on the interruption cost. The average probability of system cascading collapse and interruption cost of the failure shows a great impact to the consumer if the system is collapsed.

Cost/benefit analysis of power system is one of the most challenging task due to the impact of electric utility deregulation and market competition. However, it becomes an essential factor in order to determine the system reinforcement and expansion projects. A major role in the application reliability concepts and accomplishment of an acceptable level of reliability is economic. Therefore, it is important to determine the optimal reliability level at which the reliability investment achieves the best results in reducing the customer damage costs due to power supply interruption. This paper presents an assessment of reliability cost/benefit index of expected customer interruption cost (ECOST) by considering power system contingency which are load adjustment and transmission line tripping by applying the sensitivity analysis. ECOST index quantifies the reliability of power system in terms of monetary. The value of ECOST is evaluated based on load increment/decrement and transmission line tripping. In order to calculate ECOST, load curtailment is performed. IEEE Reliability Test System 1979 (IEEE RTS- 79) is used to validate the assessment of ECOST due to load adjustment and line tripping. The findings have proved that the ECOST increases with the increased of the total loading condition and line tripping. Therefore, this method could be applied in order to assist the power system utility in monitoring the contingency in the system.

An improved hybrid bidirectional DC-DC converter is proposed in this paper which is suitable to be deployed in energy storage applications interfacing the DC bus of a microgrid. The converter utilizes voltage boosting techniques such as a switched-capacitor network and coupled inductor to achieve a large voltage conversion ratio. Furthermore, the converter requires a small number of components and a simple PWM controller. The steady-state analysis of the converter is presented and derived. The performance of the converter is also compared with other similar converters. Simulation is carried out using Matlab Simulink which verifies the theoretical analysis of the converter.

Geomagnetic induced current (GIC) caused by geomagnetic disturbance (GMD) can be a serious threat to a stable electrical power grid operation. GIC that is induced in the earth magnetic field flows through the neutrals of grounded-wye transformers may lead to increasing reactive power consumption which can result to voltage collapse. Therefore, it is important to study the evaluation of this event on the electrical power grid. This paper presents the analysis of GIC modelling and calculation in a 4 bus test case system using Power World Simulator. The value of calculated GIC using Power World are validated and compared with theoretical using nodal analysis method. Based on the study conducted, it is found that the geomagnetic storm magnitude and direction in degrees can affect the GIC values.

Transient stability in power system is vital to be addressed due to large disturbances that could damage the system such as load changes and voltage increases. This paper presents a multi-machine transient stability using the Static Synchronous Series Compensator (SSSC). SSSC is a device that is connected in series with the power transmission line and produces controllable voltage which contribute to a better performance in the power system stability. As a result, this research has observed a comparison of the synchronization of a three-phase system during single-phase faults before and after installing the SSSC device. In addition, this research investigates the ability of three different types of controllers i.e. Proportional Integral (PI), Proportional Integral Derivation (PID), and Generic controllers to be added to the SSSC improve the transient stability as it cannot operate by itself. This is because the improvement is too small and not able to achieve the desired output. The task presented is to improve the synchronization of the system and time taken for the voltage to stabilize due to the fault. The simulation result shows that the SSSC with an additional controller can improve the stability of a multi-machine power system in a single phase fault.

A sensorless control of permanent magnet synchronous motor (PMSM) by using model reference adaptive system (MRAS) and artificial neural network is presents in this paper. The objective for this study is to improve the speed and position estimation of the PMSM rotor which used the MRAS with the conventional PI controller. In this study, the multilayer perceptron (MLP) network and hybrid multilayer perceptron (HMLP) network from family of artificial neural network (ANN) were evaluated. Before the controller can be used, the value of weight between the network layers need to be optimised. To train the controller weight, particle swarm optimization (PSO) is used. Finally, the proposed method is evaluated by comparing with the proposed controller in controlling the speed and position of PMSM. Simulation results under various speed and load conditions indicated that the PSO-HMLP network controller achieved well results than the compared controller in terms transient response and overall system analysis.

Sequence of failures that aggravates catastrophic events of a power system has attracted a great number of researchers’ attention in exploring and analyzing its enormous losses to the society and economy of a country. Power system cascading collapse is an event in which failure of an electrical component such as transmission line or generator leads to sequence of failures in other equipment. This catastrophic event could lead to major electrical energy failure. Therefore, it is imperative to study the effect of power system cascading collapse in assessing reliability cost/worth. This research introduces the assessments of risk and reliability cost/worth-based Customer Interruption Cost (CIC) in relation to dynamic system cascading collapse. The results obtained from the analysis performed have proven that a large cost of CIC is resulted due to dynamic electrical energy failure. This connotes that uncertain disconnection of the exposed transmission lines together with the exposed generator which occurred in the power system failure, ultimately will impose a significant impact on the customer interruption cost. On top of that, the results of customer interruption cost also have proven that the uncertainty of dynamic power electrical power and energy failure should not be neglected. Therefore, the proposed technique is reliable and confers promising results in determining risk and reliability cost/worth of the system.

This paper presents the transient stability analysis (TSA) in a power system based on one machine infinite bus (OMIB) and equal area criterion (EAC) method. A typical power system network consists of numerous components such as generators, transformers, interconnected lines and loads. Due to the enormous number of electrical components in a power system, it is difficult to perform the TSA for the entire network. Therefore, this research proposes a TSA by using one machine infinite bus (OMIB) technique. The main purpose to use OMIB is due to the fact that it could minimize the multi-machine in the network by facilitate the original large-scale system to dynamic equivalent model. The propose technique classify the multi-machine system into two groups which are: critical machines and non-critical machines, which is then reduced to only one machine infinite bus system (OMIB). This paper also presents the analysis of critical clearing angle (CCA) where it is the maximum change in the load angle curve before clearing the fault without loss of generator synchronism. Three phase fault conditions which are; pre-fault, during fault and post-fault conditions are used in the transmission line via the equal area criterion (EAC) method. This method could identify whether the condition may cause to system instability if there is a sudden load increase to the system. The main objective to perform CCA analysis is to identify the value of rotor angle that is important and could be set as a benchmark to the protection relay in order to maintain the transient stability during fault condition. This paper also proposes a monitoring mechanism by using graphical user interface (GUI) to monitor the CCA at different system loading condition. The IEEE RTS-79 is used to validate the robustness of the proposed methodology in determining the CCA.

This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.

Transmission line act as a medium of transportation for electrical energy from a power station to the consumer. There are many factors that could cause the cascading collapse such as instability of voltage and frequency, the change of environment and weather, the software and operator error and also the failure in protection system. Protection system plays an important function in maintaining the stability and reliability of the power grid. Hidden failures in relay protection systems are the primary factors for triggering the cascading collapse. This paper presents an Artificial Neural Network (ANN) model for prediction of cascading collapse occurrence due to the effect of hidden failure of protection system. The ANN model has been developed through the normalized training and testing data process with optimum number of hidden layer, the momentum rate and the learning rate. The ANN model employs probability of hidden failure, random number of line limit power flow and exposed line as its input while trip index of cascading collapse occurrence as its output. IEEE 14 bus system is used in this study to illustrate the proposed approach. The performance of the results is analysed in terms of its Mean Square Error (MSE) and Correlation Coefficient (R). The results show the ANN model produce reliable prediction of cascading collapse occurrence.

This paper depicts a new technique for prediction of the total harmonic distortion (THD) in Grid-Connected Photovoltaic System. Global environmental awareness, increasing demand for energy and down price tendency has led to new opportunities for utilization of renewable energy resources such as photovoltaic (PV) system. The integration of PV system to the grid must comply with the relevant standards given by the utility company. However, the output of PV somehow causes a harmonic distortion as the installation of inverter. The output of PV mainly depends on solar irradiation. Therefore, solar irradiation is selected as one of the input to the prediction model. The hybridize method of heuristic-algorithm namely Grey Wolf Optimizer-Least Square Support Vector (GWO-LSSVM) is introduced in order to improve the prediction accuracy. GWO is inspired by the leadership hierarchy and hunting mechanism of grey wolf in nature. The top hierarchy of grey wolf that considered as the fittest solution is alpha, followed by beta, delta and omega. The optimization process implementing three main steps such as hunting, searching for prey, encircling prey and attacking prey. GWO is utilized to optimize the parameters in LS-SVM model. The results showed that GWO-LSSVM predict more accurate than PSO-LSSVM and LSVM.

This paper presents a new technique namely Grey Wolf Optimizer- Artificial Neural Network (GWO-ANN) as a technique to forecast electrical load. GWO is a meta heuristic technique inspired by the hierarchy of leadership of the grey wolf hunting mechanism in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling are also imitated in the algorithm. GWO is utilized to determine the optimal momentum rate and learning rate of ANN for accurate prediction. In the ANN configuration, the temperature, humidity, wind speed, maximum power, and average power were used as the input data. While total power was used as the output data. ANN is trained by adjusting the parameters of momentum rate and learning rate until the output data matches the actual data. The performance of GWO-ANN was compared to the performance of ANN and Particle Swarm Optimization - Artificial Neural Network (PSO-ANN). The results showed GWO-ANN provide better result in terms of the Mean Absolute Percentage Error (MAPE) and coefficients of determination (R2) as compared to other methods.

Voltage instability in power distribution systems can result in voltage collapse throughout the grid. Today, with the advanced of power generation technology from renewable sources, concerns of utility companies are much being focused on the stability of the grid when there is an integration of distributed generation (DG) in the system. This paper presents a study on DG units placement and sizing in a radial distribution network by using a pre-developed index called Voltage Stability Condition Index (VSCI). In this paper, VSCI is used to determine DG placement candidates, while the value of power losses is used to identify the best DG placement. The proposed method is tested on a standard 33-bus radial distribution network and compared with existing Ettehadi and Aman methods. The effectiveness of the method is presented in terms of reduction in power system losses, maximization of system loadability and voltage quality improvement. Results show that VSCI can be utilized as the voltage stability indicator for DG placement in radial distribution power system. The integration of DG is found to improve voltage stability by increasing the system loadability and reducing the power losses of the network.

A drastic change has occurred over the last few years in the electrical power business, where a significant worldwide revolution has taken place and changing the design of electrical powers. It has become imperative for the utility to handle the electrical power reliably as poor performance can lead to electrical power outages which will inevitably have an adverse impact on a nation’s economy and society. Hence, the power company should consider devising suitable pre-emptive measures to avoid the possibility of electricity supply disruptions. These disruptions may have a potentially catastrophic effect on the running of the system, which may even include consumers having to bear monetary costs. This research looks at the valuations of risk and reliability cost/worth-based customer interruption cost due to static system sequential tripping. The performance of the suggested techniques are evaluated through comparing risk-based valuations and reliability cost/worth valuations corresponding to the transfer capability results which are correlated to the static system sequential tripping. The results have demonstrated that the unpredictable sequential tripping must not be ignored from the transmission reliability margin determination. Hence, the proposed techniques are likely to play an important role for energy sustainability of the future.

This paper aims to analyze the impacts associated with the incorporation of EV charging stations into the distribution network. Using NEPLAN software, a typical distribution network was simulated and analyzed under normal conditions and with integrated coordinated and uncoordinated EV charging stations. A comparative study was then conducted on the main parameters of the distribution system: voltage profiles, transformer loading, as well as power demand and losses. Based on the simulation results, it can be argued that the worst adverse effects are associated with an uncoordinated charging strategy, while the use of a coordinated charging strategy demonstrates significant improvements. Although the distribution network components or requirements of either strategy have not been changed, it is evident, however, that shifting and splitting EV loads has resulted in a coordinated approach that eliminates all adverse effects. Implementing these strategies helps to overcome the challenges of incorporating charging stations into the distribution network, such as equipment overheating, rapid aging issues, as well as the need for additional investment in distribution infrastructure and capacity expansion.

Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer – Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods.

Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system.

Environmental and energy audit of industrial building is important as it may help in providing a comfortable workplace for the staff as well as to observe the company’s environmental practices during operational hours while managing the energy usage in an efficient manner. It is also important in ensuring the company comply with applicable laws and regulations This paper discusses on the environmental and energy audit to identify the performance of environmental and energy efficiency in two manufacturing industrial buildings with heavy machineries. Analyses on the energy performance, visual comfort and thermal improvement including recommended solutions for optimization of energy efficiency and environmental aspects of the two buildings are presented. Energy efficiency and environmental aspects covered in this audit study are the illuminance of the building, indoor building temperature, relative humidity and carbon dioxide (CO2) percentage in indoor air quality. Jabatan Kerja Raya (JKR) standards is used as a benchmark to observe the illuminance of the buildings, Malaysian Standard 1525 (MS1525) is used as a quality control for temperature and relative humidity, while Department of Occupational Safety and Health, 2005 (DOSH 2005) is used as a quality control for CO2 percentage in air. Based on the results from the audit, the performance for both industrial building is compared. It was found that the energy efficiency and environmental performance for Factory A is better than Factory B.

The increasing penetration of electric vehicle (EV) at distribution system is expected in the near future leading to rising demand for power consumption. Large scale uncoordinated charging demand of EVs will eventually threatens the safety operation of the distribution network. Therefore, a charging strategy is needed to reduce the impact of charging. This paper proposes an optimal centralized charging schedule coordination of EV to minimize active power losses while maintaining the voltage profile at the demand side. The performance of the schedule algorithm developed using particle swarm optimization (PSO) technique is evaluated at the IEEE-33 Bus radial distribution system in a set time frame of charging period. Coordinated and uncoordinated charging schedule is then compared in terms of active power losses and voltage profile at different level of EV penetration considering 24 hours of load demand profile. Results show that the proposed coordinated charging schedule is able to achieve minimum total active power losses compared to the uncoordinated charging.

This paper presents a computationally accurate technique used to determine the critical clearing time using the one machine infinite bus equivalent system based on the equal area criterion. The critical clearing time is the maximum time interval by which the fault must be cleared in order to preserve the system stability. The computation of critical clearing time involves an intrinsic mathematical formulation derived from the pre-fault, during fault and post-fault conditions. The value of critical clearing time becomes significantly less when transient instability is induced by a three phase fault occurred at the bus bar closest to the substation connected with a sensitive generator. By setting the protection relay with the obtained value of critical clearing time, it is adequate to sustain the transient stability even though fault happened at the other locations. During the occurrence of fault, a circuit breaker which is operating earlier than the smallest critical clearing time will not agitate to a transient instability. The IEEE Reliability Test System 1979 (RTS-79) is used to verify the robustness of the methodology in a determining the critical clearing time.

This paper presents a computationally accurate technique used to determine the estimated average probability of a system cascading collapse in association with the effect of hidden failure on a protection system. This includes an accurate calculation of the probability of hidden failure as it will provide considerable effect on the results of the estimated average probability of system cascading collapse. The estimated average probability of a system cascading collapse is then used to determine the sensitive transmission contributing to a higher risk of a system cascading collapse. This information is important because it will assist the utility to determine the critical transmission lines before the occurrence of critical power system cascading collapse. Comparative study has been done with other techniques to verify the effectiveness of the proposed method used in the determination of sensitive transmission lines.

This paper presents two dual-mode rectangular ring resonators, designed at RF frequency above 20 GHz for bandpass filter applications. The first resonator is designed at 20 GHz using single layer microstrip technology, on Rogers Duroid TMM10 substrate with the following characteristics; relative dielectric constant (εr) = 9.2, substrate thickness (h) = 1.270 mm, dielectric loss tangent (tan δ) = 0. The second resonator is built using multilayer CMOS technology at 75 GHz. The resonator is simulated using fluorinated silicon glass (FSG) and silicone rich oxide (SRO) with relative dielectric constant (εr) equals to 3.7 and 4.2 respectively. Both filter designs are built using full-wave electromagnetic simulation tool. For filter design using microstrip technology, the return lossis found at 9.999 dB and the insertion lossis at 3.108 dB while for filter design using CMOS technology, the return loss is found at 11.299 dB and the insertion lossat 0.335 dB. Both results had shown good passband performance with high rejection level at the out-of band.

This research paper presents an application of Fast Voltage Stability Index (FVSI) power flow based technique and Evolutionary Programming (EP) technique to determine the placement and sizing of Static VAR Compensator (SVC) for installation at transmission system. SVC which is one of the Flexible AC Transmission System (FACTS) available in the market is used in this research for reducing the power losses in the system as well as monitoring the minimum voltage. The system will be stressed out by increasing the loadability of certain buses until it is near to its maximum loadability. The determination of the placement of Static VAR Compensator (SVC) was determined by using power flow based approach of Fast Voltage Stability Index (FVSI) to detect the weakest line which consequently will lead to the weakest bus. The Evolutionary Programming (EP) technique is used to look for the best sizing of Static VAR Compensator (SVC). The Evolutionary Programming (EP) technique and Fast Voltage Stability Index (FVSI) is tested with IEEE 14 bus test system to prove the effectiveness of the proposed technique. Further, the comparison between installation of SVC and transformer tap setting in order to identify which one is more effective in minimizing the power losses.

In many countries around the world, the electric power systems are operating in a deregulated environment where the generation, transmission and distribution systems owned by different companies will enhance intense competition in the electricity market among them in order to provide more reliable, cost effective and efficient electricity to the consumers. For that reason, the power system dynamic security assessment and control is becoming more challenging task in the transfer capability determination due to several new constraints, obligations and challenges that need to be encountered by the utility. Dynamic response of a system plays an imperative role in the analysis of system stability and blackout events that usually caused by the impact of enormous and unforeseen occurrence of fault, unexpected generation and load outages, and abrupt disengagement of loads. This paper presents a monitoring tool by using the Graphical User Interface (GUI) in order to monitor the frequency response of a power system due to the effect of contingency event. The proposed technique has been validated by using the IEEE Reliability Test System. This technique can assist the utility in order to monitor the stability of a power system in the event of contingency.

Power system is heavily loaded with increase in electricity demand. Frequency instability during islanding scenarios is an important issue to be addressed. Frequency instability happens when the power balance between generation and load demand is not met. During islanding, Under-Frequency Load Shedding (UFLS) technique is implemented to stabilize the system frequency and to ensure uninterrupted power supply to the customers. This paper proposes an optimal load shedding approach using Particle Swarm Optimization (PSO) technique for islanded mode of operation. The technique determines the optimal amount of load that needs to be shed considering power imbalance. The proposed load shedding approach is developed using MATLAB and is validated on distribution system modeled using PSCAD software. Simulation results show that the proposed PSO based approach was able to stabilize the system frequency by shedding the load optimally.