Inertia Weight Approach Research Articles

Operation Scheduling of Distribution Network with Photovoltaic/Wind/Battery Multi-Microgrids and Reconfiguration considering Reliability and Self-Healing

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network’s optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value.

A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management.

Drilling boreholes for the exploration of groundwater incurs high cost with potential risk of failures. However, borehole drilling should only be done in regions with a high probability of faster and easier access to water-bearing strata, so that groundwater resources can be effectively managed. However, regional strati-graphic uncertainties drive the decision of the optimal drilling location search. Unfortunately, due to the unavailability of a robust solution, most contemporary solutions rely on physical testing methods that are resource intensive. In this regard, a pilot study is conducted to determine the optimal borehole drilling location using a predictive optimization technique that takes strati-graphic uncertainties into account. The study is conducted in a localized region of the Republic of Korea using a real borehole data set. In this study we proposed an enhanced Firefly optimization algorithm based on an inertia weight approach to find an optimal location. The results of the classification and prediction model serve as an input to the optimization model to implement a well-crafted objective function. For predictive modeling a deep learning based chained multioutput prediction model is developed to predict groundwater-level and drilling depth. For classification of soil color and land-layer a weighted voting ensemble classification model based on Support Vector Machines, Gaussian Naïve Bayes, Random Forest, and Gradient Boosted Machine is developed. For weighted voting, an optimal set of weights is determined using a novel hybrid optimization algorithm. Experimental results validate the effectiveness of the proposed strategy. The proposed classification model achieved an accuracy of 93.45% and 95.34% for soil-color and land-layer, respectively. While the mean absolute error achieved by proposed prediction model for groundwater level and drilling depth is 2.89% and 3.11%, respectively. It is found that the proposed predictive optimization framework can adaptively determine the optimal borehole drilling locations for high strati-graphic uncertainty regions. The findings of the proposed study provide an opportunity to the drilling industry and groundwater boards to achieve sustainable resource management and optimal drilling performance.

An OCF-IoTivity enabled smart-home optimal indoor environment control system for energy and comfort optimization

The growing energy demand with diminishing energy resources calls for development of optimal indoor control system through accurate energy and occupant comfort modeling in a smart-home. While some major obstacles faced by smart-home IoT network are heterogeneity and disparate IP frameworks resulting in a connectivity and interoperability issues. In this paper we developed a new Open Connectivity Foundation (OCF) based prediction assisted optimal control framework for optimizing energy consumption and maximizing occupant comfort in smart-home. To the best of our knowledge, we are the first to design a scalable, secure, and inter-operable optimal control solution for smart-home IoT networks. The developed framework offloads machine learning models on IoT device for accurate energy and thermal comfort modeling to enable prediction assisted optimization. The system enables edge analytics using deep learning based inference models for proactive response. For optimization standard Firefly algorithm is modified based on inertia weight approach to achieve improved performance. OCF based optimal actuator control test-bed deployment and real-time experimentation is conducted to evaluate the performance of the system. Empirical investigation verified the effectiveness of proposed framework with energy saving scope of 36.82%, 5 s average response time and 3.36 MS Round Trip Time.

An Improved Particle Swarm Optimization Algorithm for Data Classification

Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.

A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

FIR FILTER DESIGN WITH INERTIA WEIGHT AND COMPRESSION FACTOR APPROACH USING ADVANCED OPTIMIZATION TECHNIQUES

This paper includes the design of Finite impulse response filter with inertia weight and compression factor approach. The objective function of filter design involves accurate control of various parameters of frequency spectrum and is thus highly non-uniform, non-linear, non-differentiable and multimodal in nature. Classical optimization methods cannot converge to solutions. Because they have disadvantages such as highly sensitive to starting points, frequent convergence to local optimum solution or divergence or revisiting the same solution, requirement of continuous and differentiable objective cost function, requirement of the piecewise linear cost approximation and problem of convergence and algorithm. Evolutionary optimization methods for the design of optimal digital filters have better control of parameters and the highest stop band attenuation.

A linear antenna array failure correction using improved bat algorithm

The failure of antenna array elements causes disturbance in the sidelobe power level. In this article, an improved flexible approach that use bat algorithm is proposed and applied to solve the problem of antenna array failure by controlling only the amplitude excitation of array elements. An adaptive inertia weight approach is applied to the standard bat algorithm to improve the quality of the solution and the speed of convergence. The effectiveness of the proposed improved bat algorithm (IBA) is verified on different standard test functions. Numerical examples of element failure correction are presented to show the capability of this flexible approach in antenna array failure correction.

Hybrid biogeography-based optimisation for optimal power flow incorporating FACTS devices

This paper presents hybrid biogeography–based optimisation (BBO) technique to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). The proposed hybrid method combines the BBO algorithm with the differential evolution (DE) to improve the performance of the conventional BBO and DE algorithms. Here, BBO is the main optimiser, while the DE is used to fine tune the solution of the BBO algorithm. Two types of FACTS devices namely, thyristor–controlled series capacitor (TCSC) and thyristor–controlled phase shifter (TCPS) are considered in this paper. The proposed method is implemented with MATLAB and tested on modified IEEE 30–bus system in five different cases. The simulation results show that the proposed HDE–BBO algorithm is effective, fast and accurate in finding the optimal parameter settings for FACTS devices to solve OPF problems. The proposed HDE–BBO method gives better solution quality compared to particle swarm optimisation with inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), differential evolution (DE) and conventional BBO. The simulation study also shows that FACTS devices are capable of providing an economically attractive solution to power system problems.

Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability

The interconnected systems is continually increasing in size and extending over whole geographical regions, it is becoming increasingly more difficult to maintain synchronism between various parts of the power system. This paper work presents an advanced adaptive Particle swarm optimization technique to optimize the SVC controller parameters for enhancement of the steady state stability & overcoming the premature convergence & stagnation problems as in basic PSO algorithm & Particle swarm optimization with shrinkage factor & inertia weight approach (PSO-SFIWA). In this paper SMIB system along with PID damped SVC controller is considered for study. The generator speed deviation is used as an auxiliary signal to SVC, to generate the desired damping. This controller improves the dynamic performance of power system by reducing the steady-state error. The controller parameters are optimized using basic PSO, PSO-SFIWA & Advanced Adaptive PSO. Computational results show that Advanced Adaptive based SVC controller is able to find better quality solution as compare to conventional PSO & PSO-SFIWA Techniques.

Optimal power flow using krill herd algorithm

In this article, an efficient optimization procedure based on herding behavior of krill individuals, krill herd algorithm (KHA), for the solution of multi-objective optimal power flow (OPF) with the objective of fuel cost minimization, voltage deviation minimization and voltage stability improvement is proposed. This algorithm is based on the effect of the influence of a teacher on the output of learners in a class. The proposed KHA approach is carried out on the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems to solve different single and multi-objective OPF problems. Simulation results of the proposed approach are compared to differential evolution (DE), modified DE (MDE), multi-objective DE (MODE), evolving ant direction DE (EADDE), particle swarm optimization (PSO), improved genetic algorithm (IGA), gradient method, biogeography-based optimization (BBO), PSO with inertia weight approach (PSOIWA), PSO with constriction factor approach (PSOCFA), real-coded GA (RGA), artificial bee colony (ABC), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA), interior point method (IPM) and quantum-inspired evolutionary algorithm (QEA). The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve multi-objective OPF problems. Copyright © 2014 John Wiley & Sons, Ltd.

Artificial Bee Colony Optimization for Optimal Reactive Power Dispatch Incorporating FACTS Devices

This paper illustrates, for the first time, the use of artificial bee colony optimization (ABC) technique to study optimal reactive power dispatch (ORPD) in power system with the use of flexible AC transmission systems (FACTS). FACTS controller cannot only increase the power transmission capacity without installing new transmission lines, but they can also enhance voltage profile and reduce transmission loss in power system. Two types of FACTS devices namely, thyristor-controlled series capacitor (TCSC) and thyristor-controlled phase shifter (TCPS) are considered in this paper. A standard IEEE 30-bus test system with multiple TCSC and TCPS devices is used for two different objective functions to validate the performance of the proposed method. The simulation results demonstrates the ability of the ABC to produce better optimal solutions compared to particle swarm optimization with inertia weight approach (PSOIWA) and real coded genetic algorithm (RGA).

Optimal linear phase FIR filter design using particle swarm optimization with constriction factor and inertia weight approach with wavelet mutation

In this paper, Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach is hybridized with Wavelet Mutation (PSOCFIWA-WM) strategy for the optimal design of linear phase FIR filters. Real coded genetic algorithm (RGA), particl

Shipboard Power System Restoration Using S-Curve Inertia Weight Particle Swarm Optimization

The shipboard power system (SPS) supplies energy to sophisticated systems for weapons, communications, navigation and operation. It is critical for the system to be reconfigurable for the purpose of survivability and reliability. The present paper proposes a new variation of PSO model for restoration of shipboard power system. A new inertia weight of S-Curve Decreasing variation is introduced. Particle Swarm Optimization with S-Curve Decreasing Inertia Weight (SDW-PSO) approach can improve the speed of convergence as well as fine tune the search. The proposed Particle Swarm Optimization algorithm enables to find the optimal combination of loads that can be supplied after the occurrence of the fault, in which the priorities of the loads and the constraint of balance between the total load and total generation are considered.

RISK-CONSTRAINED OPTIMAL BIDDING STRATEGY FOR A GENERATION COMPANY USING SELF-ORGANIZING HIERARCHICAL PARTICLE SWARM OPTIMIZATION

This article proposes optimal bidding strategies for a generation company (GenCo) considering risk of profit variation by self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients (SPSO-TVAC). Based on a trade-off technique, the expected profit maximization and risk minimization are achieved. Nonconvex operating cost functions of thermal generation units and minimum up/down time constraints are cooperated to provide the optimal bid prices in a day-ahead uniform price spot market. The rivals’ bidding behavior is estimated by Monte Carlo simulation. Test results indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimization (IWAPSO) and genetic algorithm (GA) in searching the optimal bidding strategy solutions.

A Hybrid PSO with Dynamic Inertia Weight and GA Approach for Discovering Classification Rule in Data Mining

Mining is the efficient knowledge discovery form database. It is also form of knowledge discovery essential for solving problem in specific domain like health care, business and other field. The proposed system is based on population based on heuristic search technique, which can used to solve combinatorial optimization problem. Our research focus on studying the hybrid algorithm that result in performance and enhancement in classification rule discovery task. In standard Particle Swarm Optimization (PSO) the non oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solution that is not even guaranteed to local optimal solution. In this paper we have present novel hybrid algorithm, PSO with Dynamic Inertia Weight and Genetic Algorithm (GA) approach for classification rule. The selection of inertia weight was very important to ensure the convergent behavior of particle In this hybrid algorithm approach incorporates a dynamic inertia weight in order to help the algorithm to find global and overcome the problem convergence to local optima, essentially GA can perform a global search over the entire search space with faster convergence speed. Thus the hybrid algorithm is easily implemented because of use of simple classifier it has, its computational complexity is low, are the special characteristics for the use of this hybrid algorithm. KeywordsAlgorithm; Particle Swarm Optimization; Classification;

Swarm Intelligence based Optimal Linear Phase FIR High Pass Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach

Swarm Intelligence based Optimal Linear Phase FIR High Pass Filter Design using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach

Optimized radii and excitations with concentric circular antenna array for maximum sidelobe level reduction using wavelet mutation based particle swarm optimization techniques

In this paper, a Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach with Wavelet Mutation (PSOCFIWA-WM) is applied to the process of synthesizing three-ring Concentric Circular Antenna Arrays (CCAA) without and with central element feeding, focused on maximum sidelobe level reductions. Sidelobe level (SLL) is a critical radiation pattern parameter in the task of reducing background noise and interference in the most recent wireless communication systems. To improve the radiation pattern with maximum SLL reduction, an optimum set of antenna element parameters as excitation weights and radii of the rings are to be developed. The extensive computational results show that the method of PSOCFIWA-WM provides a maximum sidelobe level reduction of 96.06% with respect to the uniformly excited case for the particular CCAA containing 4, 6, and 8 numbers of elements in the three successive rings along with central element feeding.

Optimal Reactive Power Dispatch Considering Flexible AC Transmission System Devices Using Biogeography-based Optimization

This study presents biogeography-based optimization to solve the optimal reactive power dispatch problem incorporating a flexible AC transmission system. The purpose of optimal reactive power dispatch is to provide a solution that improves voltage profile and reduces transmission loss for every practical power system. The proposed biogeography-based optimization algorithm is implemented and tested on the IEEE 30-bus system with multiple flexible AC transmission system devices, such as a thyristor control series compensator and a thyristor control phase shifter. The proposed approach results have been compared to those of particle swarm optimization with inertia weight approach, real-coded genetic algorithm, and differential evolution. The comparison of the results with other methods shows the superiority of the proposed biogeography-based optimization.

A novel particle swarm optimization algorithm with adaptive inertia weight

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of some animals. The most important advantages of the PSO are that PSO is easy to implement and there are few parameters to adjust. The inertia weight ( w) is one of PSO's parameters originally proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. Since the introduction of this parameter, there have been a number of proposals of different strategies for determining the value of inertia weight during a course of run. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature. These approaches are classified and discussed in three main groups: constant, time-varying and adaptive inertia weights. A new adaptive inertia weight approach is also proposed which uses the success rate of the swarm as its feedback parameter to ascertain the particles’ situation in the search space. The empirical studies on fifteen static test problems, a dynamic function and a real world engineering problem show that the proposed particle swarm optimization model is quite effective in adapting the value of w in the dynamic and static environments.

Optimal trading strategy for GenCo in LMP-based and bilateral markets using self-organising hierarchical PSO

This paper proposes an optimal trading strategy for a generation company (GenCo) in multi-market environment including day-ahead spot and long term bilateral contract markets using self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO-TVAC). The proposed trading strategy is formulated as a two-stage optimisation problem. Firstly, the GenCo’s objective model which is to maximise expected profit and to minimise risk of profit variation is solved by SPSO-TVAC. Secondly, the market clearing model which is to minimise system cost of locational marginal price (LMP) based market is solved by DC optimal power flow (DCOPF). The Monte Carlo method is employed to simulate other bidders’ behaviour in competitive environment. Test results on the PJM 5-bus system indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimisation (IWAPSO) and genetic algorithm (GA) in searching the optimal trading solution. In addition, different bilateral contract prices and spot demand significantly impact GenCos’ trading behaviour. Accordingly, the proposed approach could be a beneficial decision-making tool for a GenCo in energy trading. Keywords: Optimal bidding strategy, energy trading, locational marginal price, bilateral contract market, particle swarm optimisation, Monte Carlo simulation.