Voltage stability evaluation of power system with FACTS devices using fuzzy neural network

Modi, P. K.; Singh, Swapnil; Sharma, Jaydev

doi:10.1016/j.engappai.2006.08.003

Cited by 16 publications

(3 citation statements)

References 18 publications

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“…It includes Linear Programming (LP) and Mixed Integer LP [5]. With the development of artificial intelligent techniques and Evolutionary algorithms, researchers have used Particle Swarm Optimization [6], Genetic Algorithm [7], Tabu Search [8] and Neural Networks [9] to place the FACTS devices at the desired locations in transmission line. Later the researchers used sensitivity factors of the transmission system such as Line Voltage Sensitivity Index, Bus Voltage Sensitivity Index and Maximum Power Stability Index to locate these FACTS devices.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms

Karuppiah¹,

Muthubalaji²,

Ravivarman³

et al. 2018

IJET

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Flexible Alternating Current Transmission System devices have numerous applications in electrical transmission lines like improvement of voltage stability, reactive power compensation, congestion management, Available Transfer Capacity enhancement, real power loss reduction, voltage profile improvement and much more. The effectiveness of these FACTS devices is enhanced by the placement of these devices in the transmission lines. The placement is based on transmission line sensitivity factors such as Bus voltage stability index and line voltage stability index. This research article focuses on optimizing the location, number and ratings of FACTS devices using Evolutionary Algorithms like Bacterial Foraging Algorithm and Gravitational search algorithm. FACTS devices such as Static Var Compensator, Thyristor Controlled Series Capacitor and Unified Power Flow Controller are placed on IEEE 14 bus and IEEE 30 bus systems for reducing the real power loss in the transmission system. The results show that the performance of the transmission lines is e nhanced more using Bacterial Foraging Algorithm than Gravitational Search Algorithm.

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Section: Introductionmentioning

confidence: 99%

Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms

Karuppiah¹,

Muthubalaji²,

Ravivarman³

et al. 2018

IJET

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“…It has also been introduced in to the optimization techniques, such as spatial load forecasting [21], power scheduling and transactions [22], and industrial power system planning [23]. Furthermore, it is applied in combination with the artificial neural networks and probabilistic reasoning in the concept of soft computing [24]. This concept can be found in many power system operation areas such as analysis and modeling, system control, fault detection and diagnosis, and power market issues [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

The uniform fuzzy index of power system security

Halilčević

Gubina

2009

Int Trans Elec Energy Syst

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SUMMARYThe paper presents a new approach to assessment of the power system security. Using fuzzy membership functions of power system elements, it continuously quantifies the security state of a power system, as opposed to conventional methods which use off-line screening for the most vulnerable system states. It introduces global fuzzy security index (GFSI) of the power system using system's variables characterized by fuzzy sets of a trapezoidal form. GFSI is calculated by multiplying the security membership grade (SMG) values of the power system's segments that exhibit the lowest SMG values. The power system security levels are traced continually through time using real data of power flow and state estimation calculations as well as measurements of variables inaccessible by the load flow analysis. The GFSI enables the system operator to identify the insecure state and then continuously monitor the distance of the power system's actual state and the insecure one. Application of the algorithm is illustrated using the data of Bosnian power system.

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“…Authors in [21] used a subtractive clustering (SC) method and ANFIS to predict the Voltage Stability Margin (VSM), where different voltage stability indices are used as input variables. The ANFIS model has been also adapted to predict the loadability margin of the power system incorporated STATCOM and SVC, the real and reactive powers at all buses are used as the input variables [22,23]. However, for large power systems, training ANFIS model with large input features consumes large training time.…”

mentioning

confidence: 99%

On-Line Voltage Stability Evaluation Using Neuro-Fuzzy Inference System and Moth-Flame Optimization Algorithm

Bourzami

Amroune

Bouktir

2019

Elektroteh. elektromeh.

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Purpose. In recent years, the problem of voltage instability has received special attention from many utilities and researchers. The present paper deals with the on-line evaluation of voltage stability in power system using Adaptive Neuro-Fuzzy Inference System (ANFIS). The developed ANFIS model takes the voltage magnitudes and their phases obtained from the weak buses in the system as input variables. The weak buses identification is formulated as an optimization problem considering the operating cost, the real power losses and the voltage stability index. The recently developed Moth-Flame Optimization (MFO) algorithm was adapted to solve this optimization problem. The validation of the proposed on-line voltage stability assessment approach was carried out on IEEE 30bus and IEEE 118-bus test systems. The obtained results show that the proposed approach can achieve a higher accuracy compared to the Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks.References 37, tables 3, figures 10. Key words: voltage stability, line voltage stability index, Moth-Flame optimization, adaptive neuro-fuzzy inference system. В последние годы проблема нестабильности напряжения привлекла особое внимание многих служб эксплуатации и исследователей. Настоящая статья посвящена оценке в режиме онлайн стабильности напряжения в энергосистеме с использованием адаптивной нейро-нечеткой системы вывода (ANFIS). Разработанная модель ANFIS принимает в качестве входных переменных величины напряжения и их фазы, полученные от шин в системе. Идентификация шин сформулирована как задача оптимизации, учитывающая эксплуатационные расходы, реальные потери мощности и показатель стабильности напряжения. Недавно разработанный алгоритм оптимизации методом мотылька и пламени (MFO) адаптирован для решения данной задачи оптимизации. Проверка предложенного подхода к онлайн оценке стабильности напряжения в сети проводилась на тестовых системах IEEE с 30 шинами и IEEE со 118 шинами. Полученные результаты показывают, что предлагаемый подход может обеспечить более высокую точность по сравнению с многоуровневыми нейронными сетями (MLP) и нейронными сетями с радиальными базисными функциями (RBF).Библ. 37, табл. 3, рис. 10. Ключевые слова: стабильность напряжения, показатель стабильности напряжения сети, оптимизация методом мотылька и пламени, адаптивная нейро-нечеткая система вывода.

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Voltage stability evaluation of power system with FACTS devices using fuzzy neural network

Cited by 16 publications

References 18 publications

Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms

Enhancing the performance of Transmission Lines by FACTS Devices using GSA and BFOA Algorithms

The uniform fuzzy index of power system security

On-Line Voltage Stability Evaluation Using Neuro-Fuzzy Inference System and Moth-Flame Optimization Algorithm

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