Fuzzy based load shedding strategies for avoiding voltage collapse

Sasikala, J.; Ramaswamy, M.

doi:10.1016/j.asoc.2010.12.020

Cited by 39 publications

(29 citation statements)

References 19 publications

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“…Therefore, more attention has been paid recently to solve this problem; in this regard, intelligent techniques have been used, such as expert systems, artificial neural networks (ANNs) [3,[11][12][13], fuzzy logic [14], neurofuzzy [15], genetic algorithm (GA) [16], particle swarm optimization (PSO) [17], and bacterial foraging oriented by the PSO algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

A New Optimal Under-frequency Load-shedding Method Using Hybrid Culture–Particle Swarm Optimization–Co-evolutionary Algorithm and Artificial Neural Networks

Moazzami

Khodabakhshian

Hooshmand

2014

Electric Power Components and Systems

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Section: Introductionmentioning

confidence: 99%

A New Optimal Under-frequency Load-shedding Method Using Hybrid Culture–Particle Swarm Optimization–Co-evolutionary Algorithm and Artificial Neural Networks

Moazzami

Khodabakhshian

Hooshmand

2014

Electric Power Components and Systems

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“…The number of interval in the output membership functions are specified by N mem.o . 6) Forming the fuzzy rules based according to the user's settings: in this section with respect to the importance percentage of each parameters in operator's view, the fuzzy rules are calculated from Equation (14). is the selected output membership function (FCF membership function).…”

Section: Fuzzy Selection Algorithm (Fsa)mentioning

confidence: 99%

“…A framework is presented to determine a load shedding strategy for the restoration of power flow solvability and improvement of voltage stability margin. Two fuzzy based load shedding algorithms that use a voltage stability indicator for averting voltage collapse are thus proposed in [14]. The first method identifies the most appropriate locations and uses an analytical procedure to compute the disconnected load, while the second directly predicts the amount of load to be shed at the critical buses.…”

Section: Introductionmentioning

confidence: 99%

Multi Objective Load Shedding Framework

Delavari¹,

Nemati²,

Moradi³

2015

SGRE

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In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems; the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator's opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator; in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator's opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator's opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.

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“…For this reason, the adaptive UFLS technique comes with advantage of using swing equation to calculate the imbalance of power. Further efforts to accurately estimate the power imbalance were the application of computational intelligence based techniques such as artificial neural network (ANN) [4][5][6], fuzzy logic control [7][8][9], particle swarm optimization technique (PSO) [10,11], and genetic algorithm (GA) [12,13] an UFLS scheme called fixed and random load shed priority (FRLSP) is proposed to shed the optimal combination of loads [14]. However, study proposed in [14] takes quite a while, since it goes through all the possible combination of loads.…”

Section: Introductionmentioning

confidence: 99%

A New Under-Frequency Load Shedding Scheme Based on Adaptive Neuro-Fuzzy Inference System and Evolutionary Programming Shedding Priority

Dridy

Mokhlis

Mekhilef

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract.Frequency stability of islanded distribution system is a topic of interest, due to the significant penetration of distributed generation (DG). This paper proposes a new under frequency load shedding technique, which address the over-shedding issue caused by fixed priority. The proposed scheme consists of two units; first, the power imbalance estimation unit (PIEU), which use adaptive neuro-fuzzy inference system (ANFIS) to estimates the power imbalance. Second, the load shed unit (LSU), which is based on binary evolutionary programming (BEP) technique to shed the optimal loads. To validate the performance of the proposed UFLS scheme, different simulation studies have been conducted using PSCAD software. Moreover, the response of the proposed scheme is compared with another UFLS scheme having fixed priority loads. From the simulation results, the proposed scheme can shed the optimal loads, which is not achieved in the existing load shedding schemes. IntroductionRecently, controlling the frequency within the permissible limits during islanding operation is the most important technical challenge being investigated worldwide. Hence, alternative techniques are required to enhance the reliability of today's complex distribution network. This can be mitigated by using accurate load shedding (UFLS) scheme, which balances the system. Accurate load shedding depends upon two main factors; accurate estimation of power imbalance and accurate amount of load to be shed. In the literature, several load shedding schemes have been proposed: conventional, adaptive, and intelligent. The conventional UFLS is the most applicable in this case [1][2][3]. In this technique, a pre-determined load values will be shed based on frequency value. Although this technique is very simple, it is suffering from over-shedding issue. For this reason, the adaptive UFLS technique comes with advantage of using swing equation to calculate the imbalance of power. Further efforts to accurately estimate the power imbalance were the application of computational intelligence based techniques such as artificial neural network (ANN) [4][5][6], fuzzy logic control [7-9], particle swarm optimization technique (PSO) [10,11], and genetic algorithm (GA) [12,13]. From reviewing different load shedding techniques, there is inaccurate load shedding issue since every technique is bounded by fixed priorities shedding load. Fixed load shedding priority is linked to the loads that are shed sequentially based on lookup table which consist of critical and non-critical loads. For this reason,

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Fuzzy based load shedding strategies for avoiding voltage collapse

Cited by 39 publications

References 19 publications

A New Optimal Under-frequency Load-shedding Method Using Hybrid Culture–Particle Swarm Optimization–Co-evolutionary Algorithm and Artificial Neural Networks

A New Optimal Under-frequency Load-shedding Method Using Hybrid Culture–Particle Swarm Optimization–Co-evolutionary Algorithm and Artificial Neural Networks

Multi Objective Load Shedding Framework

A New Under-Frequency Load Shedding Scheme Based on Adaptive Neuro-Fuzzy Inference System and Evolutionary Programming Shedding Priority

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