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Optimal SVC allocation via symbiotic organisms search for voltage security improvement Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Sharifah Azma Syed Mustaffa; Saiful Izwan Suliman
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.9905

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.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

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.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

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.

Load Management for Voltage Control Study Using Parallel Immunized-computational Intelligence Technique Amirul Izzat Abu Bakar; Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Nor Azura Md Ghani
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

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.

Chaos Embedded Symbiotic Organisms Search Technique for Optimal FACTS Device Allocation for Voltage Profile and Security Improvement Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Saiful Izwan Suliman; Tarek Bouktir
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 1: October 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i1.pp146-153

Due to the ever-increasing energy demand, power system operators have attempted to cope with these demands while keeping the power system remain operable. Economic constraints have forced the power system operator to abandon their effort in expanding the power system. The increased load demand can cause the power system to suffer from voltage instability and voltage collapse, especially during contingency condition. Hence, a strategy is required to maintain the steady state operation of a power system. Various research has been conducted to tackle this problem. Therefore, this paper presents the implementation of Chaos Embedded Symbiotic Organisms Search technique to solve optimal FACTS device allocation problem in power transmission system. Various practical constraints are also considered in the optimisation process to emulate the real-life constraints in power system. The optimisation process is conducted on a 26-bus IEEE RTS has validated that the results obtained has not violated the power system stability. The results provided by the proposed optimisation technique has successfully improved the voltage profile and voltage security in the system. Comparative studies are also conducted involving Particle Swarm Optimization and Evolutionary Programming technique resulting good results agreement and superiority of the proposed technique. Results obtained from this study would be beneficial to the power system operators regarding optimisation in power system operation for the implementation in real power transmission network.

Chaotic Local Search Based Algorithm for Optimal DGPV Allocation Sharifah Azma Syed Mustaffa; Ismail Musirin; Mohd. Murtadha Othman; Mohamad Khairuzzaman Mohamad Zamani; Akhtar Kalam
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp113-120

The advent of advanced technology has led to the increase of electricity demand in most countries in the world. This phenomenon has made the power system network operate close to the stability limit. Therefore, the power utilities are looking forward to the solution to increase the loadability of the existing infrastructure. Integration of renewable energy into the grid such as Distributed Generation Photovoltaic (DGPV) can be one of the possible solutions. In this paper, Chaotic Mutation Immune Evolutionary Programming (CMIEP) algorithm is used as the optimization method while the chaotic mapping was employed in the local search for optimal location and sizing of DGPV. The chaotic local search has the capability of finding the best solution by increasing the possibility of exploring the global minima. The proposed technique was applied to the IEEE 30 Bus RTS with variation of load. The simulation results are compared with Evolutionary Programming (EP) and it is found that CMIEP performed better in most of the cases.

Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Halim Hassan; Sharifah Azwa Shaaya; Shahril Irwan Sulaiman; Nor Azura Md. Ghani; Saiful Izwan Suliman
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp365-372

This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss and voltage profile of the system resulted from the optimization, where the FVSI value at the observed line, minimum voltage of the system and loss were monitored during the load increment. Results obtained from the study are convincing in addressing the scheduling of power in wind generator. Implementation of FA approach to solve power scheduling revealed its flexibility and feasible for solving larger system within different objective functions.

Gravitational Search Algorithm Based Technique for Voltage Stability Improvement Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Mohamad Sabri Omar; Saiful Izwan Suliman; Nor Azura Md. Ghani; Nur Azwan Mohamed Kamari
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp123-130

Voltage instability problem has been known as a significant threat to power system operation since its occurrence can lead to power interruption. This phenomenon can be due to uncontrollable load increment, line and generator outage contingencies or unplanned load curtailment. Optimal reactive power dispatch involving reactive power support can be one of the options for improving voltage stability of a power system, which also requires optimization process. Optimal sizing and location can of reactive power support can avoid the system from experiencing over-compensated or under-compensated phenomena. The presence of optimization techniques has helped solving non-optimal phenomenon, nevertheless some setbacks have also been experienced in terms of inaccuracy and stuck in local optima. This paper presents the application of Gravitational Search Algorithm (GSA) technique in attempt to solve optimal reactive power dispatch problem in terms of reactive power support for voltage stability improvement. Optimization process tested on IEEE 14-bus Reliability Test System (RTS) has revealed its superiority with significant promising results in terms of voltage stability improvement in the test system.

Symbiotic Organisms Search Technique for SVC Installation in Voltage Control Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Saiful Izwan Suliman
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp318-329

Increasing demand experienced by electric utilities in many parts of the world involving developing country is a normal phenomenon. This can be due to the urbanization process of a system network, which may lead to possible voltage decay at the receiving buses if no proper offline study is conducted. Unplanned load increment can push the system to operate closes to its instability point. Various compensation schemes have been popularly invented and proposed in power system operation and planning. This would require offline studies, prior to real system implementation. This paper presents the implementation of Symbiotic Organisms Search (SOS) algorithm for solving optimal static VAr compensator (SVC) installation problem in power transmission systems. In this study, SOS was employed to perform voltage control study in a transmission system under several scenarios via the SVC installation scheme. This realizes the feasibility of SOS applications in addressing the compensating scheme for the voltage control study. Minimum and maximum bound of the voltage at all buses have been considered as the inequality constraints as one of the aspects. A validation process conducted on IEEE 26-Bus RTS realizes the feasibility of SOS in performing compensation scheme without violating system stability. Results obtained from the optimization process demonstrated that the proposed SOS optimization algorithm has successfully reduced the total voltage deviation index and improve the voltage profile in the test system. Comparative studies have been performed with respect to the established evolutionary programming (EP) and artificial immune system (AIS) algorithms, resulting in good agreement and has demonstrated its superiority. Results from this study could be beneficial to the power system community in the planning and operation departments in terms of giving offline information prior to real system implementation of the corresponding power system utility.

Chaotic immune symbiotic organisms search for SVC installation in voltage security control Mohamad Khairuzzaman Mohamad Zamani; Ismail Musirin; Saiful Izwan Suliman; Muhammad Murtadha Othman
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp623-630

Parallel with the urbanization of the world, energy demand in the world also increased. The increase in energy demand will require a power system to be operated near its stability limit. To mitigate the problem, Flexible Alternating Current Transmission System (FACTS) devices can be installed as a compensation scheme to improve voltage security in a power system. For an effective compensation, FACTS devices should be optimally allocated in a power system. Although optimization techniques can be implemented to optimally allocate these devices, problems have been reported which would affect the performance of the optimization techniques in terms of producing high quality solutions. This paper presents the implementation of Chaotic Immune Symbiotic Organisms Search for solving optimal Static VAr Compensator (SVC) allocation problem for voltage security control. The optimization is validated in IEEE 26-Bus Reliability Test System (RTS) realizes the capability of CISOS in solving the optimization problem. Comparative studies with respect to Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) resulting in good agreement on the results and demonstrated superior performance of CISOS. Results of the study can be beneficial to power system community in terms of compensation planning prior to real world implementation.

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