Top PDF Optimal power flow solution with uncertain RES using augmented grey wolf optimization

Optimal power flow solution with uncertain RES using augmented grey wolf optimization

Optimal power flow solution with uncertain RES using augmented grey wolf optimization

Abstract—This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hy- dropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Log- normal and Gumbel probability density functions. The system- wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm’s exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE- 30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.
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Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm

established the supremacy of bat algorithm over simulated anneal- ing in tuning PI-controller using different performance indices. Guha in his most recent endeavor has explained the solution of LFC problem using BSA [36]. Shabani et al. [37] have employed an imperialist competitive algorithm (ICA) to optimize the PID- controller gains in a multi-area multi-unit power system. Simu- lated annealing based optimal controller for the control of system frequency and terminal voltage of an interconnected multi-area multi-source power system is discussed in [38]. In [39], the use and effectiveness of interline power flow controller (IPFC) in LFC area have been investigated. Although the aforementioned meth- ods give an optimal solution of LFC problem leaving behind some deficiencies which are further corrected by the researchers. The main drawback is the slow convergence towards the optimal solu- tion and more or less all aforesaid techniques depends on the proper initialization of input parameters. Additionally, the algo- rithms demand proper tuning of some of their own input control parameters. For example, GA involves the determination of algorithm-specific parameters such as crossover rate and mutation rate. PSO has its own parameters like inertia weight, social and cognitive parameters. If the parameters are not properly defined, the algorithm may easily trap into local optimum solution. Thus exploring new optimization technique is still prevailing to enhance the relative stability of power system, especially, by the design of an optimal controller.
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Improved optimal power flow for a power system incorporating wind power generation by using Grey Wolf Optimizer algorithm

Improved optimal power flow for a power system incorporating wind power generation by using Grey Wolf Optimizer algorithm

A common drawback to meta-heuristic methods is that, in general, the optimization performance is highly dependent on fine parameter tuning. However, the pro- posed approach outperforms these methods in term of convergence speed to the best solution. Moreover, the use of OPF is extended to include the study of renew- able energy systems like wind power, which becomes more and more useful in recent power networks, and many studies are made to integrate this natural power efficiently to a power system. Ranjit and Jadhav in [19], as well as Maskar et al. in [20], presented a study of OPF problem in a system incorporating wind power sources, using modified ABC algorithm named Gbest guided ABC algorithm; the method showed good re- sults for fuel cost optimization case, and voltage profile enhancement, then under wind condition the total op- erating cost is optimized efficiently, compared to other methods. The method presented some benefits con- cerning reserve coefficient adjustment when consider- ing imbalance cost of wind power. Meanwhile, Shanhe et al. [21] presented a new economic dispatch technique based on PSO-GSA algorithm for a power system in- cluding two wind power sources; the method was tested on a six generators’ system connected with two stochas- tic wind power sources. The test yielded good results compared with other results found in the literature with different methods especially for cost and emission reduction. Panda and Tripathy [22], and Mishra and Vignesh [23] introduced another OPF algorithm based on security constrained OPF solution of wind-thermal generation system using modified bacteria foraging al- gorithm. The method was tested on the same system stated in [18], in which the wind power variability was modelled incorporating conventional thermal generat- ing system. Recent works in [19], [24] and [25] pre- sented better results and faster convergence character- istics using Grey Wolf Optimizer algorithm. Grey Wolf Optimizer (GWO) algorithm mimics the behaviour of grey wolves in nature by simulating their leadership hierarchy, through haunting, searching for, encircling, and attacking the prey [26].
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Biobjective Optimal Reactive Power Flow using Chemical Reaction based Optimization

Biobjective Optimal Reactive Power Flow using Chemical Reaction based Optimization

More recently, a Chemical Reaction Optimization (CRO), a population based stochastic optimization technique inspired from the process of chemical reactions, has been suggested for solving combinatorial optimization problems in discrete domains by Lam et al. (2010). A modified version of CRO, named as real coded CRO, to handle problems in both continuous and discrete domains, has been outlined in (Lam Albert, Y.S., et al., 2012). The CRO has been applied to a variety of optimization problems (Kuntal Bhattacharjee, et al., 2014; Xu, J., et al., 2011; Yu, J.J.Q., et al., 2011) that includes economic emission load dispatch (Kuntal Bhattacharjee, et al., 2014) and found to yield satisfactory results.
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Comparative Analysis of Optimization Algorithms for Solving Optimal Power Flow using IPFC

Comparative Analysis of Optimization Algorithms for Solving Optimal Power Flow using IPFC

Interline power flow controller plays a vital role in power system control as they have multi functionality and dynamic responsibility. It can manage power between two or more interconnected transmission lines. PSO, Cuckoo search, Bat Algorithm, Firefly optimization and plant pollination Algorithms have been analyzed and the best one is utilized along with IPFC for solving transmission system problems. However optimal power flow is a major problem in power system. This paper aims to reduce the power loss and balance the power flow of electrical energy in the multi transmission line can be controlled by using optimization algorithms incorporating with IPFC. This paper provides overview of all the above algorithms.
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Allocation of SSSC FACTS Device for Optimal Power Flow Solution Using DE Approach

Allocation of SSSC FACTS Device for Optimal Power Flow Solution Using DE Approach

In today’s highly complex and interconnected power systems, there is a great need to improve electric power utilization while still maintaining reliability and security. While power flows in some of the transmission lines are well below their normal limits, other lines are overloaded, which has an overall effect on deteriorating voltage profiles and decreasing system stability and security. Because of all that, it becomes more important to control the power flow along the transmission lines to meet the needs of power transfer. On the other hand, the fast development of solid-state technology has introduced a series of power electronic devices that made FACTS a promising pattern of future power systems. Power flow is a function of transmission line impedance, the magnitude of the sending end and receiving end voltages and the phase angle between voltages. By controlling one or a combination of the power flow arrangements, it is possible to control the active as well as the reactive power flow in the transmission line [1]. With FACTS technology [2], such as Static Var Compensators (SVCs), Static Synchronous Compensators (STATCOMs), Static Synchronous Series Compensators (SSSCs) and Unified Power Flow Controller (UPFC) etc., bus voltages, line impedances and phase angles in the power system can be regulated rapidly and flexibly. Thus, FACTS can facilitate the power flow control, enhance the power transfer capability, decrease the generation cost, and improve the security and stability of the power system.
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Enhancing Social based Routing Approach using Grey Wolf Optimization in Vehicular ADHOC Networks

Enhancing Social based Routing Approach using Grey Wolf Optimization in Vehicular ADHOC Networks

VANET (Vehicular ad-hoc network) is a formation of intelligent vehicles with the plan to defeat the transportation issue and consequently diminishes the accident proportion. Since VANET is in like way a sort of MANET enclosed by human-driven center points, the social idea can be utilized to comprehend the routing decisions in VANET. This propels investigators to procure the possibility of Social Network Analysis (SNA) to develop routing plans. The serious issue in any system is the drop of parcel. Social characteristics based framework similarly face such issues which results in wasteful message conveyance. In this paper, grey wolf optimization is applied on social based routing scheme for fixed line VANET with the end goal to diminish the drop of parcel and enhance the throughput of the network. This is made possible with system centrality analysis by shapely value which enhances the choice by wolf optimization approach. Other than this, comparison is done between the social based fixed line routing scheme and the proposed work with GWO. The conduct of the utilizing nodes availability by its centrality calculation will then be observed. The parameters utilized in this paper to quantify the viability of the optimized routing scheme are Throughput, Delay, PDR, Latency, Buffer Time and Hop count.
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Combined Economic Emission Dispatch Problem of Thermal Generating Units Using Grey Wolf Optimization

Combined Economic Emission Dispatch Problem of Thermal Generating Units Using Grey Wolf Optimization

Optimization of the modern power system plays a major role in thermal power plants energy production. The challenges of the engineers are to optimize the real power of the generating units and to minimize the fuel cost of the power plant. Economic dispatch (ED) is one of the most fundamental issues in operation and control of power systems to allocate generations among the committed units. The main goal of the ED problem is to determine the amount of real power contributed by online thermal generators satisfying load demand at any time subject to unit and system constraints so as the total generation cost is minimized. Therefore, it is very important to solve the problem as quickly and precisely as possible [1, 2]. Therefore, recently most of the researchers made studies for finding the most suitable power values produced by the generators depending on fuel costs. In these studies, they produced successful results by using various optimization algorithms [3-5]. Despite the fact that the traditional ED can optimize generator fuel costs, it still can not produce a solution for environmental pollution due to the excessive emission of fossil fuels.
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Particle Swarm Optimization (PSO) Inspired Grey Wolf Optimization (GWO) Algorithm

Particle Swarm Optimization (PSO) Inspired Grey Wolf Optimization (GWO) Algorithm

The meta-heuristic algorithms are not only simple but also have many interesting characteristics such as problem independency, adaptivenessand learning capabilities [2]. Most of the meta-heuristic algorithms uses natural (either physical or bio-intelligence) phenomena’s to find the solutions. Examples of the bio-intelligence inspired optimization algorithms are genetic algorithm, ant colony optimization, bee colony optimization, while the physical phenomenon inspired algorithms are water filling algorithm, particle swarm optimization, gravitational search algorithms etc. Although the meta-heuristic algorithms have several advantages but they also have some limitations as solution is not always guaranteed to be optimum the improper initialization could cause completely irrelative solution etc. hence for any meta-heuristic optimization algorithm these problems must be dealt properly. As sated above a number of meta-heuristic algorithms are already available but everyone has its own advantages and limitations which provide space for development of new algorithms one of such algorithm is Grey Wolf Optimization (GWO).
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A Novel Adaptive Grey Wolf Optimizer for Global Numerical Optimization

A Novel Adaptive Grey Wolf Optimizer for Global Numerical Optimization

Where components of a are linearly decreased from 2 to 0 over the course of iterations and r 1 , r 2 are random vectors in [0, 1]. 3) Grey wolf hunting process is calculated as following equations ⃗ = ⃒ ⃗ . ⃗ − ⃒⃗ (5)

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Grey Wolf Optimization for Multi Input Multi Output System

Grey Wolf Optimization for Multi Input Multi Output System

Abstract Grey wolf optimizer (GWO) is a new technique, which can be applied successfully for solving optimized problems. The GWO indeed simulates the leadership hierarchy and hunting mechanism of grey wolves. There are four types of grey wolves which are alpha, beta, delta and omega. Those four types can be used for simulating the leadership hierarchy. In order to complete the process of GWO a three main steps of hunting, searching for prey, encircling prey and attacking prey are implemented. This work describes a novel meta-heuristic based on grey wolf optimization for optimum allocation of STATCOM devices on power system grid to minimized load buses voltage deviations and system power losses. Bus voltages have been solved by controlling the reactive power of shunt compensator. The Contingency management problem (such as system over-loading and a single line outages) by optimum installation of STATCOM devices, has been presented. Simulations are performed on IEEE 30-bus power system indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using traditional algorithms.
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Formulation of Improved Grey Wolf Optimization Methodology for EELD Problem

Formulation of Improved Grey Wolf Optimization Methodology for EELD Problem

A latest meta-heuristic technique IGWO (Improved Grey Wolf Optimization) is being formulated to solve Economic Emission Load Dispatch Problem in this paper. And the results have been compared with various techniques like GSA, MODE, PDE, SPEA, NSGA for six-unit system and for ten-unit system. Furthermore, the proposed algorithm obtains competitive and satisfying results compared to other proposed techniques.

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Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

During the past two decades, several artificial intelligent models were utilized for hydrologic model prediction [9] and hydropower stream flow forecasting [10]. Among them, the ensemble models [11–13] and hybrid models [14] have recently become very popular. Recently, to produce novel hybrid models, different optimization algorithms were coupled with these models to improve their performance [15–20]. Among the optimization algorithms, Grey wolf optimization (GWO) has shown promising results in a wide range of application when coupled with machine learning algorithms [21]. Consequently, in this study, to reduce the source of uncertainty, an artificial intelligent model was used for hydropower generation forecasting. For this purpose, the adaptive neuro-fuzzy inference system (ANFIS) was coupled with GWO to forecast the monthly hydropower generation directly based on the precipitation over the basin, the inflow to the dam and the hydropower generation in previous months. This method is capable to facilitate the hydropower generation forecasting. The rest of this chapter is organized as follows. In Section 2, the coupled model of ANFIS and GWO and study area are presented. Section 3 involves the results of hydropower forecasting and its reliability. Finally, Section 4 includes the conclusion of the study.
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Solution of Optimal Power Flow Problem Incorporating Various FACTS Devices

Solution of Optimal Power Flow Problem Incorporating Various FACTS Devices

The possibility of operating power systems at the lower cost, while satisfying the given transmission and security constraints is one of the main current issues in elongating the transmission capacity through the use of FACTS devices. FACTS devices can direct the active and reactive power control and flexible to voltage-magnitude control simultaneously, because of their adaptability and fast control characteristics. With the aid of FACTS technology, namely SVC, STATCOM, SSSC and UPFC etc., the bus voltages, line impedances and phase angles in the power system can be controlled quickly and flexibly.
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Optimal Power Flow with Power Flow Routers

Optimal Power Flow with Power Flow Routers

In this research, we only consider the basic and continuous scenarios of the AC OPF problem, and do not include con- tingencies and discrete variables. With such simplifications, this paper aims to address the non-convexity introduced by Kirchhoff’s laws as well as PFCs and PFRs, and pursues global optimality of the PFR-OPF problem through convex relaxation. However, it is worth pointing out that the compu- tational challenges induced by the security constraints and the discrete variables can be formidable as both the numbers of contingencies and discrete variables can be huge, thousands or more in the real-world industrial OPF problems. Therefore, in the industrial practice, given the stringent time constraint to provide a solution, achieving global optimality of the OPF solution is often not the primary concern since it can be too time-consuming [14]. Although the solution method for the OPF problem proposed in this paper is not designed to address the large-scale SCOPF problem, we expect that the proposed PFR-OPF framework is extensible to incorporate the security constraints. In fact, due to their fast-response capability, PFCs and PFRs can be very powerful resources to perform post-contingency corrective control [5]. The flexible SCOPF framework proposed in [5] incorporates PFCs into the corrective SCOPF problem, while the ability to handle a large contingency set is not discussed. When the PFCs and PFRs are treated as re-dispatchable resources in the corrective SCOPF problem, the coupling effect among their post-contingency decision variables would be a new challenge due to the nonlinear control regions of PFCs and PFRs, especially for the phase shifting effect. Hence, existing methods for the large-scale SCOPF problem, such as contingency selection and decomposition [12], [14], may not be applicable directly. It is an important problem and will be our future work.
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Incorporating Of Grey Wolf Optimization To The Bees Algorithm For Continuous Optimization In Welded-Beam Design Problem

Incorporating Of Grey Wolf Optimization To The Bees Algorithm For Continuous Optimization In Welded-Beam Design Problem

LIST OF TABLES 2.1 List of previous researches on bee-inspired algorithm and related applications 33 2.2 Bees algorithm parameters 40 2.3 Classification of I 0 benchmark test functions 4[r]

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Probit  Based Grey Wolf Optimal Route Path Discovery In Internet Of Vehicles

Probit Based Grey Wolf Optimal Route Path Discovery In Internet Of Vehicles

between the network An efficient order-aware hybrid genetic algorithm was proposed [13] for solving the vehicle routing problems. But the performance of the routing metrics such as delivery ratio remained unaddressed. Modified Ant Colony Optimizer was developed [14] for efficient communication with high delivery ratio and minimum delay. The designed technique failed to consider the multiple objective functions to solve the optimization problem. A weighted and undirected graph method[15] was introduced for IoV sensing networks to improve the efficiency of traffic information collection. The method failed to achieve improved transmission performance. A content accessibility preference (CAP) method [16] was proposed for vehicle selection to perform data communication with higher throughput and minimum delay. The performance of packet loss was not minimized. A Group Acknowledgment Strategy (GAS)[17] algorithm was designed for increasing the transmission efficiency with minimum delay and packet loss. But reliable data transmission was not performed. Bayesian nonparametric learning method was proposed [18] for information exchanging and content sharing between the vehicles. The method failed to perform the multi- hop vehicle to vehicle communicationA data content based vehicle clustering technique was designed[19] for enhancing vehicle communication. Though the designed technique minimizes the delay and packet loss, the throughput was not improved. A link reliability-based clustering algorithm (LRCA) was prposed [20] to provide efficient and reliable data communication by choosing stable neighbor vehicles. The designed algorithm failed to minimize the end-to-end delay. The major issues identified from the existing literature are overcome by introducing a new technique called PRCBGWO. The process of PRCBGWO technique is presented in the next section.
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An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

An energy-efficient cluster head selection in wireless sensor network using grey wolf optimization algorithm

Since diverse algorithms catering to different needs are there, low-energy adaptive clustering (LEACH) [20] is of the predominant clustering algorithm which elects the cluster head with some feasibility. It provides aggregation of the crammed data thus reducing the unwanted traffic and energy consumption of the network [21], thereby increasing the longevity of the network. However, it does not provide any adequate information about the number of cluster heads in a network. Sometimes it may opt a node with low energy as a cluster head thereby shortening the lifetime of the network. Other most popular algorithms include power-efficient gathering in sensor information systems (PEGASIS) and hybrid energy-efficient distributed (HEED). PEGASIS [22] is an addendum to that of LEACH protocol. It is more advantageous in the sense because it aggregates all the data and sends it to the central processing unit. However, it introduces an additional lag if nodes are distant. It is unsuitable for large scale WSNs which involves multi-hop communication. HEED [23] is also an extension of the LEACH; it suffers from serious communication overhead between a cluster head and a base station. In the case of E-LEACH [14], the cluster head communication between different clusters is highly efficient, but in the case of larger networks, it fails to select the nodes with low energy. TL-LEACH [24] increases the lifetime of the network, but it wastes the energy while performing communication between cluster heads and the other nodes. M-LEACH carries an advantage by considering mobility in a routing protocol. It assumes that all the nodes are congruent, and it does not care about the formation of the cluster while clustering. B-LEACH [25] is another extension where the communication is entirely depending upon the position of the cluster heads which needs no information about all the other nodes
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Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

An existing ML based disease risk prediction system includes several methods suchas Logistic Regression (LR), Convolutional Neural Network (CNN),Support Vector Machine (SVM) and etc.[6], [7]. In testing process, patient’s data are classified into the group of either normal or abnormal. Moreover,these plans have often accompanied with less attributes and imperfections. The information collection is regularly small, for patients and infections with particular conditions [8], the attributes are chosen through involvement. In any case, these pre-chosen quality features are not possibly fulfilling the changes in the disease and its influence factors. With the advancement of big data analytics innovation, more consideration has been paid to ailment forecast from the point of huge data investigation, different explores have been directed by choosing the attributes from an extensive number of information to enhance the risk classification accuracy [9], [10], instead of the other qualities.Better frameworks created by machine learning methods can be utilized to help doctors in diagnosing and forecasting diseases. To predict the disease dynamics, a few examinations have been directed to create techniques for their classification. Moreover, there is a huge difference between diseases in various regions, because of variations in the atmosphere and living propensities in the region. In this manner, risk classification based on big data analysis, makes a superior model by considering missing information for forecasting the disease dynamic. In this paper, an efficient medical disease prediction model named as GWO+RNNis proposed. The GWO algorithm is used for feature selection which removes the unrelated attributes and redundant attributes. It’s majorly improves the performance of prediction. After feature selection, Auto Encoder (AE) based RNN method avoids the feature dimensionality problems. Also, predict the different kinds of diseases significantly using the UCI database.
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Optimum Network Reconfiguration using Grey Wolf Optimizer

Optimum Network Reconfiguration using Grey Wolf Optimizer

Distribution system Reconfiguration is the process of changing the topology of the distribution network by opening and closing switches to satisfy a specific objective. It is a complex, combinatorial optimization problem involving a nonlinear objective function and constraints. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic search algorithm inspired by the leadership hierarchy and hunting strategy of grey wolves in nature. The objective of this paper is to determine an optimal network reconfiguration that presents the minimum power losses, considering network constraints, and using GWO algorithm. The proposed algorithm was tested using some standard networks (33 bus, 69 bus, 84 bus and 118 bus), and the obtained results reveal the efficiency and effectiveness of the proposed approach.
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