Power System Controlled Islanding using Modified Discrete Optimization Techniques

The energy storage system (ESS) is developing into a very important element for the stable operation of power systems. An ESS is characterized by rapid control, free charging, and discharging. Because of these characteristics, it can efficiently respond to sudden events that affect the power system and can help to resolve congested lines caused by the excessive output of distributed generators (DGs) using renewable energy sources (RESs). In order to efficiently and economically install new ESSs in the power system, the following two factors must be considered: the optimal installation placements and the optimal sizes of ESSs. Many studies have explored the optimal installation placement and the sizing of ESSs by using analytical approaches, mathematical optimization techniques, and artificial intelligence. This paper presents an algorithm to determine the optimal installation placement and sizing of ESSs for a virtual multi-slack (VMS) operation based on a power sensitivity analysis in a stand-alone microgrid. Through the proposed algorithm, the optimal installation placement can be determined by a simple calculation based on a power sensitivity matrix, and the optimal sizing of the ESS for the determined placement can be obtained at the same time. The algorithm is verified through several case studies in a stand-alone microgrid based on practical power system data. The results of the proposed algorithm show that installing ESSs in the optimal placement could improve the voltage stability of the microgrid. The sizing of the newly installed ESS was also properly determined.

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Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

Sustainability ◽

10.3390/su13126644 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6644

Author(s):

Ali Selim ◽

Salah Kamel ◽

Amal A. Mohamed ◽

Ehab E. Elattar

Keyword(s):

Power System ◽

Radial Distribution ◽

Distribution Systems ◽

Optimal Allocation ◽

Optimization Technique ◽

Mathematical Formulation ◽

Optimization Techniques ◽

Sensitivity Factor ◽

Hybrid Technique ◽

Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

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Central Tunicate Swarm NFOPID-Based Load Frequency Control of the Egyptian Power System Considering New Uncontrolled Wind and Photovoltaic Farms

Energies ◽

10.3390/en14123604 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3604

Author(s):

Hady H. Fayek ◽

Panos Kotsampopoulos

Keyword(s):

Power System ◽

Fractional Order ◽

Frequency Control ◽

Optimization Techniques ◽

Load Frequency Control ◽

The Novel ◽

Operating Condition ◽

Load Frequency ◽

Non Linear ◽

Fractional Order Pid

This paper presents load frequency control of the 2021 Egyptian power system, which consists of multi-source electrical power generation, namely, a gas and steam combined cycle, and hydro, wind and photovoltaic power stations. The simulation model includes five generating units considering physical constraints such as generation rate constraints (GRC) and the speed governor dead band. It is assumed that a centralized controller is located at the national control center to regulate the frequency of the grid. Four controllers are applied in this research: PID, fractional-order PID (FOPID), non-linear PID (NPID) and non-linear fractional-order PID (NFOPID), to control the system frequency. The design of each controller is conducted based on the novel tunicate swarm algorithm at each operating condition. The novel method is compared to other widely used optimization techniques. The results show that the tunicate swarm NFOPID controller leads the Egyptian power system to a better performance than the other control schemes. This research also presents a comparison between four methods to self-tune the NFOPID controller at each operating condition.

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Application of chaos-based chaotic invasive weed optimization techniques for environmental OPF problems in the power system

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2014.10.007 ◽

2014 ◽

Vol 69 ◽

pp. 271-284 ◽

Cited By ~ 25

Author(s):

Mojtaba Ghasemi ◽

Sahand Ghavidel ◽

Jamshid Aghaei ◽

Mohsen Gitizadeh ◽

Hasan Falah

Keyword(s):

Power System ◽

Optimization Techniques ◽

Invasive Weed Optimization ◽

Invasive Weed

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A Three-Stage Procedure for Controlled Islanding to Prevent Wide-Area Blackouts

Energies ◽

10.3390/en11113066 ◽

2018 ◽

Vol 11 (11) ◽

pp. 3066 ◽

Cited By ~ 1

Author(s):

Hongbo Shao ◽

Yubin Mao ◽

Yongmin Liu ◽

Wanxun Liu ◽

Sipei Sun ◽

...

Keyword(s):

Power System ◽

Dynamic Stability ◽

Power Flow ◽

Transient Stability ◽

Load Shedding ◽

Area Measurement ◽

Wide Area ◽

Measurement Systems ◽

Controlled Islanding ◽

Three Stages

Controlled islanding has been proposed as a last resort action to stop blackouts from happening when all standard methods have failed. Successful controlled islanding has to deal with three important issues: when, and where to island, and the evaluation of the dynamic stability in each island after islanding. This paper provides a framework for preventing wide-area blackouts using wide area measurement systems (WAMS), which consists of three stages to execute a successful islanding strategy. Normally, power system collapses and blackouts occur shortly after a cascading outage stage. Using such circumstances, an adapted single machine equivalent (SIME) method was used online to determine transient stability before blackout was imminent, and was then employed to determine when to island based on transient instability. In addition, SIME was adopted to assess the dynamic stability in each island after islanding, and to confirm that the chosen candidate island cutsets were stable before controlled islanding was undertaken. To decide where to island, all possible islanding cutsets were provided using the power flow (PF) tracing method. SIME helped to find the best candidate islanding cutset with the minimal PF imbalance, which is also a transiently stable islanding strategy. In case no possible island cutset existed, corresponding corrective actions such as load shedding and critical generator tripping, were performed in each formed island. Finally, an IEEE 39-bus power system with 10 units was employed to test this framework for a three-stage controlled islanding strategy to prevent imminent blackouts.

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Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i5.pp2349-2356 ◽

2017 ◽

Vol 7 (5) ◽

pp. 2349 ◽

Cited By ~ 7

Author(s):

Surender Reddy Salkuti

Keyword(s):

Power System ◽

Reactive Power ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Optimization Techniques ◽

Mixed Integer ◽

Integer Programming Problem ◽

Scheduling Problem ◽

Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

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Load shedding scheme based metaheuristic technique for power system controlled islanding

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i3.pp1306-1314 ◽

2021 ◽

Vol 23 (3) ◽

pp. 1306

Author(s):

N. Z. Saharuddin ◽

I. Zainal Abidin ◽

H. Mokhlis ◽

E. F. Shair

Keyword(s):

Power System ◽

Evolutionary Programming ◽

Exhaustive Search ◽

Load Shedding ◽

Computational Time ◽

Power Balance ◽

Optimal Amount ◽

Search Techniques ◽

Controlled Islanding ◽

Mitigation Techniques

<p>Power system-controlled islanding is one of the mitigation techniques taken to prevent blackouts during severe outage. The implementation of controlled islanding will lead to the formation of few islands, that can operate as a stand-alone island. However, some of these islands may not be balanced in terms of generation and load after the islanding execution. Therefore, a good load shedding scheme is required to meet the power balance criterion so that it can operate as a balanced stand-alone island. Thus, this paper developed a load shedding scheme-based metaheuristics technique namely modified discrete evolutionary programming (MDEP) technique to determine the optimal amount of load to be shed in order to produce balanced stand-alone islands. The developed load shedding scheme is evaluated and validated with two other load shedding techniques which are conventional EP and exhaustive search techniques. The IEEE 30-bus and 39-bus test systems were utilized for this purpose. The results proves that the load shedding based MDEP technique produces the optimal amount of loads to be shed with shortest computational time as compared with the conventional EP and exhaustive search techniques.</p>

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PMU Measurement-Based Intelligent Strategy for Power System Controlled Islanding

Energies ◽

10.3390/en11010143 ◽

2018 ◽

Vol 11 (1) ◽

pp. 143 ◽

Cited By ~ 3

Author(s):

Yi Tang ◽

Feng Li ◽

Chenyi Zheng ◽

Qi Wang ◽

Yingjun Wu

Keyword(s):

Power System ◽

Controlled Islanding

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A Simplified Approach to Controlled Islanding of Power System

2019 8th International Conference on Power Systems (ICPS) ◽

10.1109/icps48983.2019.9067725 ◽

2019 ◽

Author(s):

Naga Chaitanya Munukutla ◽

Venkata Siva Krishna Rao Gadi ◽

Ramamoorty Mylavarapu

Keyword(s):

Power System ◽

Simplified Approach ◽

Controlled Islanding

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Artificial Intelligence Based Technique for the Classification of Incipient Faults in Power Transformer

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.785.29 ◽

2015 ◽

Vol 785 ◽

pp. 29-33

Author(s):

Fathiah Zakaria ◽

Dalina Johari ◽

Ismail Musirin

Keyword(s):

Power System ◽

Power Transformer ◽

Optimization Technique ◽

Gas Analysis ◽

Optimization Techniques ◽

Computational Technique ◽

Ann Model ◽

Great Ability ◽

Incipient Faults

Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This paper presents the development of an Evolutionary Programming (EP) – Taguchi Method (TM) – Artificial Neural Network (ANN) based technique for the classification of incipient faults in power transformer using Dissolved Gas Analysis (DGA) method based on historical industrial data. It involved the development of ANN model and embedding TM and EP as the optimization techniques in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn from experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP were employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proved the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.

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