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• Frequency adaptive: multioscillatory control and repetitive control are studied. • Methods are applied for a grid-tie converter. • Memory and computational burden analysis of both methods are presented. • A linear prewarping for frequency adaptive multioscillatory control is proposed. • Practical implementation hints are provided. The paper describes two main approaches of periodic control under variable frequency conditions: frequency adaptive multioscillatory control (FAMOSC) and fractional delay repetitive control (FDRC). The methods are applied in the control system of a grid-tie converter to increase the quality of phase currents in case of distorted grid voltages. The paper compares structures, performance and implementation of FAMOSC, FDRC and the conventional approach of repetitive and multioscillatory control. Performances of the control systems are illustrated with experiments on a laboratory test bench. The article describes tuning process of FDRC and FAMOSC with the usage of Particle Swarm Optimization algorithm. A detailed analyse and research of the techniques in terms of computational complexity is presented. Practical hints for implementation and resource optimization of both techniques on DSP are included. For FAMOSC a fast implementation of prewarping is proposed. [ABSTRACT FROM AUTHOR]

• To propose sooty-tern optimization (STO) for designing PSS parameters of multi-machine power system. • The performance of STO is evaluated using eigenvalue analysis, simulation results and performance indices. • The results are matched with other powerful optimization techniques. • Robustness is verified on hidden operating cases also under critical disturbances. This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO. [ABSTRACT FROM AUTHOR]

• A systematic planning method to allocate dynamic VAR sources is proposed. • The concept of voltage control area is proposed to partition a bulk power system. • A zone-based voltage supporting index is defined in the site selection process. • A zone-based distributed optimization framework is put forward. In this paper, a practical planning method to allocate dynamic VAR sources (DVSs) using the concept of voltage control areas (VCAs) is proposed to enhance short-term voltage stability of power systems. Firstly, based on the defined transient distance index, a spectral clustering method is employed to partition a system into VCAs. Then, candidate DVS sites in each zone are determined by the proposed voltage supporting index. In each VCA, the capacity of candidate sites is preliminarily estimated by use of the dichotomy method to decide the most effective control bus and the initial capacity guess, based on which further system-wide optimization is implemented using particle swarm optimization (PSO) to obtain the final allocation scheme. Case study on the New England 39-bus and 68-bus test systems along with the comparisons with the existing methods confirm the effectiveness and advantages of the proposed methodology. [ABSTRACT FROM AUTHOR]

• A hybrid prediction method is designed to improve the prediction accuracy of data. • An energy management strategy based on demand-side response is proposed. • A neighbor prediction method is designed to solve data interruption problem. • An improved particle swarm optimization algorithm is proposed to solve the optimization model. Data interruption may cause the centralized energy management system of a microgrid (MG) to collapse. To solve this problem, a hybrid prediction-based energy management strategy is proposed in this paper to predict interrupted data for the centralized dispatching process of an MG. This hybrid prediction method is designed following a combination of model predictive control and extreme learning machine techniques. Based on the predicted data of distributed energy resources and three types of loads from demand-side response, an optimization model is formulated to minimize the operational cost. If some predicted data are interrupted during transmission, then a prospect vulnerability assessment method is applied to select a neighboring device to predict the interrupted data. In the end, an improved particle swarm optimization algorithm is proposed with the help of genetic algorithms to accelerate convergence to global optimal solutions for the proposed MG energy management problem. The effectiveness of the proposed models and solution methods is also verified by a case study. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization [ABSTRACT FROM AUTHOR]

It is often necessary to investigate the output power against load demand in a system having distributed generation (DG) resources connected to the existing conventional power system. In this paper, the load frequency control (LFC) problem is presented using different optimization algorithms for two types of power system configurations: (i) hybrid configuration of thermal power system (TPS) integrated with DG, comprising wind turbine generators (WTGs), diesel engine generators (DEGs), fuel cells (FCs), aqua-electrolyzer (AE) and battery energy storage system (BESS); (ii) two area interconnected power system with DG connected in area-1. The inclusion of wind energy system in DG, having high variability in its output power, results into a challenging task for the realization of an effective controller design. This difficulty is further enhanced with random variation of load demand. The control scheme proposed in this paper is based on linear matrix inequalities (LMI) with its parameters tuned by particle swarm optimization (PSO), as a new contribution to earlier studies. The robustness of this controller is thoroughly demonstrated in the above hybrid power systems with different conditions of load disturbances, wind power and parameter variations. [ABSTRACT FROM AUTHOR]

• Gauss-Hermite integral based multi-point estimation method is proposed to calculate the probabilistic transient stability criterion derived through the PDSR. • To guarantee the calculation accuracy and speed at the same time, the optimal number for the sampling points is determined based on PDSR hyperplanes and improved particle swarm optimization algorithm (IPSO). • Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. • Calculation accuracy of the proposed method is higher than other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. With the increasing penetration of wind power, probabilistic transient stability assessment becomes critical to deal with the uncertainties and correlations of wind power, thus improving the reliability of power system. This paper proposes an improved multi-point estimation based probabilistic transient stability assessment method. Extreme learning machine (ELM) is introduced to describe the uncertainty of practical wind power output. Gauss-Hermite integral based multi-point estimation method is proposed to calculate transient stability criterion derived through practical dynamic security region (PDSR). Optimal number of the sampling points is determined based on PDSR hyperplanes and an improved particle swarm optimization algorithm (IPSO). Test results on the New England 10-generators 39-buses system and a practical provincial power system in China show that the calculation accuracy of the proposed method is higher than that of other commonly-used point-estimation methods without much loss of computational efficiency under different wind power penetration and correlation levels. [ABSTRACT FROM AUTHOR]

• A strategy is proposed to increase the PFC's reliability in co-phase TPSS. • Switching frequency conversion and modulation changes is combined in this paper. • The reliability assessment process is established to calculate failure rate of PFC. • This strategy can reduce the failure rate of IGBT module in PFC by 39.01%. Co-phase traction power supply system (TPSS) is proposed to solve the problems of neutral section and power quality in electrical railways. As the key component of co-phase TPSS, the lifetime of the power flow controller (PFC) largely relies on the power module (PM)'s reliability. The PMs' lifetime is affected by junction temperature, including average temperature and temperature fluctuation. In this paper, a lifetime extension strategy is presented for junction temperature management, which combines switching frequency conversion with modulation changes. Firstly, in the low-load area, switching frequency conversion is utilized to minimize temperature fluctuation; in the high-load area, an optimized single-phase space vector pulse width modulation (SVPWM) is introduced to reduce the average temperature. Secondly, the particle swarm optimization with constriction factor is applied to solve the load partition point, as well as optimize switching frequency for PFC. Finally, the validity of the proposed lifetime extension strategy is verified by case studies, the result is shown that the failure rate of IGBT module can be reduced by 39.01% and the mean time to failure (MTTF) of the PFC is prolonged by 16.28%. The optimization algorithm can effectively improve the reliability of PFC. [ABSTRACT FROM AUTHOR]

This paper presents a newly developed hybrid optimization algorithm for solving the problem of Economic Dispatch (ED) for a multi-generator system. The hybrid algorithm named ACO–ABC–HS combines the framework of Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Harmonic Search (HS) algorithms to find the optimized solution for the system. The ACO algorithm is used to find the initial solution set, the ABC algorithm is employed to test and improve each of the probable solutions provided by the ACO module, while the HS module is used to discard the inferior solutions from the solution set and replace them with better ones. The performance of this hybrid algorithm is compared with those of conventional ED solving techniques like Gradient Search as well as other evolutionary algorithms namely ABC, ACO, HS and Particle Swarm Optimization (PSO). Valve point loading, environmental emissions, line losses and ramping rate constraints have been included in the ED analysis to provide more practical results. The algorithm’s performance is also tested for Multi-Area Economic Dispatch (MAED) with tie-line constraints. The results obtained clearly point out the superiority of the hybrid algorithm in finding out the optimum results, while satisfying the constraints of minimizing the generation costs, reducing the emissions as well as tie-line costs and transmission losses. [ABSTRACT FROM AUTHOR]

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts. [ABSTRACT FROM AUTHOR]

These smaller consumers usually prefer to purchase at a constant price that is adjusted at most a few times per year. Electricity retailers are in business to bridge the gap between the wholesale market and these smaller consumers. This paper proposes a hybrid approach based on binary imperialist competitive algorithm (BICA) and binary particle swarm optimization (BPSO) to find the optimal energy procurement for electricity retailer with multiple procurement options such as self-generating facility, bilateral contracts and pool market purchase in restructured electricity markets. The solutions of these problems provide adequate information to obtain an energy procurement strategy for retailers. Also, the results of proposed hybrid approach are compared with conventional ICA and PSO methods. Test results show that the proposed hybrid approach is more effective and has higher capability in finding optimum solutions in comparison to ICA and PSO methods. A case study is used to illustrate the efficiency of the proposed hybrid approach. [ABSTRACT FROM AUTHOR]

• Optimize the electricity prices based on the DR characteristics of multi-type users. • Construct a flexible power retail portfolio optimization model. • Propose a flexible electricity retail strategy with integrated DR. Since the electricity market is opening up in China, it is necessary for power retailers to participate in the market and find a way to gain benefits. In this context, this paper constructs a two-stage flexible power sales optimization model with multi-type demand response strategies. First, the demand response strategies of different types of users are analyzed. Second, two-stage optimization is conducted: In the first stage, the fuzzy K-means clustering method is used to divide a day into peaks, flats, and valleys, and then, a multi-objective time-of-use pricing model is constructed with the aims of minimizing the peak–valley load ratio and maximizing the response revenue rate of users. A flexible power retailing portfolio optimization model is built considering multiple types of users in the second stage and the model is solved by the chaotic particle swarm optimization algorithm. Finally, a case study with four scenarios is conducted, where the demand response is taken as a changeable factor. The results show that (1) the price-based demand response can shave peaks and fill valleys, whilst also reducing the electricity cost, and optimization for industrial users has the optimal effect, followed by that of commercial users and agricultural users; (2) different types of demand responses have different emphases on peak shaving and valley filling, and a combination can achieve the best effect; (3) with the demand response, an electricity retailer can effectively reduce the deviation assessment cost, and increase the revenue by flexibly allocating electricity sales, without resulting in unsatisfied users. The conclusions verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algorithm inspired by brainstorming process in human beings. In this paper, BSOA is employed to find optimal location and setting of FACTS devices. Static var compensators (SVC’s) and thyristor controlled series compensators (TCSC’s) are used as FACTS devices. FACTS allocation problem is formulated as a multi-objective problem whose objectives are voltage profile enhancement, overload minimisation and loss minimisation. The results of applying BSOA to FACTS allocation problem in IEEE 57 bus system demonstrate its high efficacy in solving this problem both with TCSC and SVC units. BSOA leads to better voltage profile and lower losses than particle swarm optimisation (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), hybrid of genetic algorithm and pattern search (GA–PS), backtracking search algorithm (BSA), gravitational search algorithm (GSA) and asexual reproduction optimisation (ARO). The findings of this research can be used by power system decision makers in order to establish a better voltage profile and lower voltage deviations during contingencies. [ABSTRACT FROM AUTHOR]

This paper presents a load shedding algorithm for alleviating line overloads employing Teaching learning based optimization (TLBO). The buses are selected for load shed based on the sensitivity of severity index with respect to load shed. Load shed is based on the next interval predicted load which could cause emergency situation from thermal limit consideration. Line flow constraints have not only considered for next predicted interval but in present base case loading conditions also. Optimum load shed at the selected buses have been obtained for 30-bus, 39-bus standard test systems. Further another technique modified version of bare bones particle swarm optimization known as (BBExp) has been used to validate the algorithm for load shedding. [ABSTRACT FROM AUTHOR]

To reduce the influence of wind power output uncertainty on power system stable operation, demand response (DRPs) and energy storage system (ESSs) are introduced while solving scheduling optimization problems of system with wind power. To simulate wind power scenarios, this paper used interval method to generate the initial scenario set, and construct scenario reduction strategy based on Kantorovich distance. Then, DRPs and ESSs are respectively introduced in the demand-side and generation side, taking wind power day-ahead forecasting and ultra-short-term forecasting as a random variable and its implementation, a two-stage scheduling optimization model for wind energy storage systems is construct combined with two stage optimization theory. To solve the proposed model, the ergodic of chaos search is applied to improve the inadequate that binary particle swarm algorithm may fall into local optimum, chaotic binary particle swarm optimization algorithm is proposed. Finally, example simulation is made in the IEEE36 node 10 machine systems to analyze the influence of energy storage system and demand response on system's wind power consumptive capacity. The result shows chaotic binary particle swarm algorithm can get a global optimal solution, applicable to solve wind power energy storage systems two-stage model. The synergies of DRPs and ESSs can be used to suppress wind power uncertainty, improve the utilization efficiency of wind power, and reduce coal consumption level with significantly overall efficiency. [ABSTRACT FROM AUTHOR]

The power distribution network design problem has a growing impact on secure and economical operation of distribution power system. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In this study, a new approach using seeker optimization algorithm (SOA) is proposed for distribution system planning problem with simultaneous placement of automatic reclosers (RAs), considering total system economic cost, overall system reliability, system power losses and voltage deviations as an objective functions. Normally, conventional power distribution systems (CPDS) are radial in nature and momentary fault in the system causes large area of the grid to be blacked out, which leads to huge load interruptions. Reliable distribution system minimize this effect by allowing faults to clear themselves by protection device operations such as automatic reclosers (RAs) and quickly restores the power through system reconfiguration by minimizing the loads affected. Thus, in order to prevent the system from momentary faults, simultaneous placement of RAs has been also done which leads to a design of an advanced power distribution system (APDS). Thus, the reliability issues are of major importance for effective planning of an APDS. For evaluation of reliability measure, the contingency-load-loss index (CLLI) is assessed in this paper, which is independent of the failure rate and fault repair duration of the feeder branches. The performance of the proposed algorithm is extensively assessed and comparisons are made with Particle Swarm Optimization (PSOs), Genetic Algorithm (GA) and graph theoretical approach (GTA) applied on the 54 and 100-bus primary power distribution systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the optimal design of an advanced power distribution system. [ABSTRACT FROM AUTHOR]

Economic load dispatch (ELD) is one of the important optimization problems in the operation of power systems which can be helpful to create effective generating management plans. In this paper, the ELD problem is discussed considering transmission network losses and practical constraints of plants such as generation ramp rate, prohibited operation zones, and valve-point effects. A new method called Species-based Quantum Particle Swarm Optimization (SQPSO), which is based on the Quantum Particle Swarm Optimization (QPSO), is proposed and applied in order to solve this problem possessing non-smooth and smooth cost functions. In the proposed intelligent technique, the particles are classified according to the output results and they explore the answers in each iteration in-group form. The new method has just one control parameter. The simulation results and the numerical investigations of various examples are compared with other conventional techniques to assess the efficiency of the proposed method. The results of the numerical investigations well depict that in spite of its simplicity, the proposed method is superior over the other techniques from standpoint of optimum answer exploitation as well as possessing appropriate and acceptable speed and stability. [ABSTRACT FROM AUTHOR]

• New algorithm particle swarm optimization with oscillatory inertia factor with exponential decay. • Simultaneous adjustment of the AVR and PSS parameters through the PSO-OED optimization technique. • Better adjustment of the AVR and PSS providing a better dynamic performance and transient system response. • New objective function capable of analyzing the performance of several generators simultaneously. This paper presents a new algorithm for simultaneous tuning of the optimal parameters of Automatic Voltage Regulator (AVR) and the Power System Stabilizer (PSS) using a new technique of Particle Swarm Optimization with Oscillating Exponential Decay (PSO-OED). The proposed algorithm was applied both the Single-Machine Infinite-Bus (SMIB) Power System and the 9-Bus Multi-Machine Power System. The results of the simulation show that the proposed method is efficient and guarantees an improvement in the dynamic stability and convergence rate of the simulations. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents artificial bee colony optimization for solving multi-area economic dispatch (MAED) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system. [Copyright &y& Elsevier]

Abstract: This paper presents an artificial bee colony algorithm to enhance the fault section estimation performance in power systems. Through mimicking the foraging behaviors of honeybee swarms, the algorithm owns exploitation and exploration procedures to constitute an effective near-optimal search mechanism. This proposed method excels at saving decision on external parameters such as crossover and mutation rates, facilitating the improvement of computation performance. Meanwhile, the method has added a random selection scheme to look for a new source, by which the probability of being trapped into local minimum can be largely reduced, hence serving as beneficial aids of grasping the faulted section more effectively. Through this proposed approach, it benefits the engineers to find the accurate fault section among voluminous alarms, and reduces the possibility of inaccurate diagnosis. To validate the effectiveness of the method, it has been tested on practical power systems with comparisons to published techniques. [Copyright &y& Elsevier]

Abstract: In this paper, a Biogeography Based Optimization (BBO) technique is introduced to solve multi-constrained optimal reactive power flow (ORPF) problem in power system. ORPF is a multi-objective nonlinear optimization problem that minimizes the bus voltage deviation and real power loss. The feasibility of the proposed algorithm is demonstrated for IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results reveals optimization efficacy of the proposed scheme over other well established population based optimization techniques like conventional particle swarm optimization (PSO), general passive congregation PSO (GPAC), local passive congregation PSO (LPAC), coordinated aggregation (CA) and interior point based OPF (IP-OPF). [Copyright &y& Elsevier]

Abstract: Rephasing strategy is one of the main methods used for phase balancing and neutral current reduction in electrical distribution networks and the reconfiguration technique is an effective method for network loss reduction. In this paper, a new method for the simultaneous implementation of reconfiguration and phase balancing strategies is presented as a combinational strategy. In order to solve the proposed optimization problem, Nelder Mead algorithm combined with a bacterial foraging algorithm (BF–NM) is used based on a fuzzy multi-objective function. The proposed method allows for the simultaneous execution of reconfiguration and phase balancing while minimizing the interruption cost of rephasing in addition to eliminating network unbalancing and reducing neutral current and network losses. To demonstrate the efficiency of the BF–NM algorithm, its performance is compared with bacterial foraging (BF), particle swarm optimization (PSO), genetic and immune algorithms (GA and IA). The proposed method is applied to the IEEE 123-bus test network for evaluation. The simulation results confirm the efficiency of the method in reducing the system costs and network phase balancing. [Copyright &y& Elsevier]

Abstract: This paper presents a heuristic optimization methodology, namely, Bacterial foraging PSO-DE (BPSO-DE) algorithm by integrating Bacterial Foraging Optimization Algorithm (BFOA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving non-smooth non-convex Dynamic Economic Dispatch (DED) problem. The DED problem exhibits non-smooth, non-convex nature due to valve-point loading effects, ramp rate limits, spinning reserve capacity, prohibited operating zones and security constraints. The proposed hybrid method eliminates the problem of stagnation of solution with the incorporated PSO and DE operators in original bacterial foraging algorithm. It achieves global cost by selecting the bacterium with good foraging strategies. The bacteria with good foraging strategies are obtained in the updating process of every chemo-tactic step by the PSO operator. The DE operator fine tunes the solution obtained through bacterial foraging and PSO operator. A 3- and 7-unit systems for static economic dispatch, a 26-bus, 6-generator test system and an IEEE 39-bus, 10-unit New England test systems are considered to show the effectiveness of the proposed method over other methods reported in the literature. [Copyright &y& Elsevier]

Abstract: This paper develops a novel algorithm for simultaneous coordinated designing of power system stabilizers (PSSs) and thyristor controlled series capacitor (TCSC) in a multimachine power system. The coordinated design problem of PSS and TCSC over a wide range of loading conditions is formulated as an optimization problem. The bacterial swarm optimization (BSO) algorithm is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and TCSC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the bacterial swarm based coordinated controllers gives robust damping performance over wide range of operating conditions, and different disturbances in compare to optimized PSS controller based on BSO (BSOPSS) and optimized TCSC controller based on BSO (BSOTCSC). Moreover, the results are compared to the results obtained using the bacteria foraging (BF) and particle swarm optimization (PSO) to show the effectiveness of using BSO to attain a global optimal solution of the proposed coordinated design problem. [Copyright &y& Elsevier]

Abstract: This paper presents a multiple tabu search (MTS) algorithm to solve the economic dispatch (ED) problem by taking valve-point effects into consideration. The practical ED problem with valve-point effects is represented as a non-smooth optimization problem with equality and inequality constraints that make the problem of finding the global or near global optimum difficult. The proposed MTS algorithm is the sequential execution of individual tabu search (TS) algorithm simultaneously by only one personal microcomputer. The MTS algorithm introduces additional techniques for improvement of search process, such as initialization, adaptive searches, multiple searches, replacing and restarting process. To show its effectiveness, the MTS is applied to test two studied systems consisting of 13 and 40 power generating units with valve-point effects. The optimized results by MTS are compared with those of conventional approaches, such as simulated annealing (SA), genetic algorithm (GA), TS algorithm and particle swarm optimization (PSO). Studied results confirm that the proposed MTS approach is capable of obtaining higher quality solution efficiently and lowest computational time. [Copyright &y& Elsevier]

Abstract: In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables. [Copyright &y& Elsevier]

Abstract: This paper presents a harmony search algorithm for optimal reactive power dispatch (ORPD) problem. Optimal reactive power dispatch is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices to optimize a certain object. The objects are power transmission loss, voltage stability and voltage profile which are optimized separately. In the presented method, the inequality constraints are handled by penalty coefficients. The study is implemented on IEEE 30 and 57-bus systems and the results are compared with other evolutionary programs such as simple genetic algorithm (SGA) and particle swarm optimization (PSO) which have been used in the last decade and also other algorithms that have been developed in the recent years. [Copyright &y& Elsevier]

Abstract: This paper describes an algorithm for rescheduling reactive power control variables so as to have an adequate loadability margin for current operating point. Co-ordinated aggregation based PSO (CAPSO) has been used as optimization technique. Two objective functions have been selected for load margin enhancement. One is minimization of total reactive power loss and other considers minimization of incremental reactive power loss. The developed algorithm has been implemented on 14-bus and 57-bus standard test systems. Results obtained have been compared with those obtained using Davidon–Fletcher–Powell’s (DFP) optimization technique. CAPSO performance is better over DFP method. It has been observed that incremental loss minimization formulation does not require additional voltage stability related constraints hence computational time required is less as compared to reactive power loss minimization. Reactive power loss minimization formulation is of significance when limited reactive power source is available. [ABSTRACT FROM AUTHOR]

Abstract: Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of complex engineering optimization problems which traditional derivative-based optimization techniques cannot handle. The most attractive features of PSO are its algorithmic simplicity and fast convergence. However, PSO tends to suffer from premature convergence when applied to strongly multi-modal optimization problems. This paper proposes a method of incorporating a real-valued mutation (RVM) operator into the PSO algorithms, aimed at enhancing global search capability. Three variants of PSO algorithms are considered. The resultant hybrid PSO-RVM algorithms are experimentally investigated along with the PSO variants and an existing PSO with Gaussian mutation using six typical benchmark functions. It is interesting to see that the effectiveness of RVM varies for different PSO variants as well as different kinds of functions. It has been found that one of the hybrid algorithms, CBPSO-RVM, which is an integration of the PSO with the constriction factor and inertia weight (CBPSO) and the RVM operator, exhibits significantly better performance in most of the test cases compared to the other algorithms under consideration. Furthermore, this algorithm is superior to most of the existing algorithms used in this study when applied to two practical ED problems with non-smooth cost function considering the multiple fuel type and/or valve-point loading effects. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a newly developed optimization approach involving a modified bacterial foraging algorithm (MBFA) applied for the solution of the economic and emission load dispatch (EELD) problem. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies in the fitness function. The bacterial foraging algorithm (BFA) appears to be a robust and reliable optimization algorithm for the solution of the EELD problems. To obtain the best compromising solution a fuzzy decision making approach using MBFA is applied to the standard IEEE 30-bus six generator test system and a Taiwan power system of 40 generating units with valve point loading effects. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, linear programming (LP), multi-objective stochastic search technique (MOSST), differential evolution (DE), non-dominated sorting genetic algorithm (NSGA), niched pareto genetic algorithm (NPGA), strength pareto evolutionary algorithm (SPEA) and fuzzy clustering based particle swarm optimization (FCPSO) performed in different central load dispatch centers to solve EELD problems. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained. [Copyright &y& Elsevier]

Abstract: This paper presents a new formulation of multi-objective reactive power and voltage control for power system. The objectives are active power loss, voltage deviation and the voltage stability index of the system. The load constrains and operational constrains are also taken into consideration. The multi-objective formulation of the problem requires a global performance index of the problem. A pseudogoal function derived on the basis of the fuzzy sets theory gives a unique expression for the global objective function, eliminating the use of weighing coefficients or penalty terms. Both objective functions and constraints are evaluated by membership functions. The inequality constrains are embedded into the fitness function by pseudogoal function which guarantees that the searched optimal solution is feasible. Moreover, a new type of evolutionary algorithm, particle swarm optimization (PSO), has been adopted and improved for this problem. To improve the performance of PSO, a fuzzy adaptive PSO (FAPSO) is proposed. A fuzzy system is employed to adaptively adjust the parameters of PSO, such as the inertia weight and learning factors, during the evolutionary process. The proposed approach has been examined and tested with promising numerical results of the IEEE 30-bus and IEEE 118-bus power systems. [Copyright &y& Elsevier]

• Detection and classification of systemic events based on the wavelet transform using low-voltage data. • Filtering and cross-checking mechanisms to reject local events and classify systemic events. • Parameter optimization using a Particle Swarm Optimization-based approach. • Validations with data from the Brazilian Interconnected System and Chilean Power System. This paper presents an approach to automatically analyse events on electric power systems using data from phasor measurement units installed at low voltage level. The proposed approach is based on the application of multiresolution analysis through the wavelet transform, decomposing signals into functions of both time and frequency domains, aiming to detect and characterize systemic events. Detection and classification algorithms are designed to recognize systemic events according to the energy extracted from wavelet detail coefficients, taking into account decomposition levels, thresholds and decision variables which are set using a particle swarm optimization algorithm. Numerical results, validated with low voltage phasor measurements of the Brazilian and Chilean power systems, show that the approach can be used effectively for event detection and classification. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an algorithm for computing shortest distance to voltage collapse or determination of CSNBP using PSO technique. A direction along CSNBP gives conservative results from voltage security view point. This information is useful to the operator to steer the system away from this point by taking corrective actions. The distance to a closest bifurcation is a minimum of the loadability given a slack bus or participation factors for increasing generation as the load increases. CSNBP determination has been formulated as an optimization problem to be used in PSO technique. PSO is a new evolutionary algorithm (EA) which is population based inspired by the social behavior of animals such as fish schooling and birds flocking. It can handle optimization problems with any complexity since mechanization is simple with few parameters to be tuned. The developed algorithm has been implemented on two standard test systems. [Copyright &y& Elsevier]

• Proposed WEC system is modeled using the wave turbine, DC generator, inverter, and BESS. • The proposed WEC system can be operated in islanded and grid-connected modes. • WOA is implemented for optimum parameters to maximize Savonius turbine output power. • Islanded control is based on three levels droop controller, inner voltage, and current controllers, • Grid-connected control method is based on the P/Q controller. In this paper, a Wave Energy Conversion (WEC) model consisting of a Savonius type wave turbine plus DC generator, Battery Energy Storage System (BESS), inverter, and Inductance-Capacitance-Inductance (LCL) filter is designed. In this study, the optimization techniques are used to maximize Savonius type wave turbine self-efficiency considering different factors such as the height, depth, length, and time period of the wave. Four meta‐heuristic optimizations are competing in this article for obtaining the optimum parameter design based on the output electrical power of the wave. These are Whale Optimization Algorithm (WOA), Artificial Immune System (AIS), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) algorithm. The comparative results verify the reliability of WOA in maximizing the overall electrical output power from the wave turbine while confining with the constraints. Moreover, an efficient control system is designed to operate in Islanded Mode (IM) and Grid-Connected (GC) operations according to the matching between the maximum estimated output power from the WEC system and the system load. A proposed control scheme is performed for adjusting the system frequency and voltage in islanded mode operation and ensuring active power-sharing in grid-connected mode. The islanded control scheme consists of three levels; droop control, inner voltage, and current control, outer control based on model predictive control (MPC) to maintain the system frequency and voltage to their nominal values. WOA is also proposed to improve the performance index by optimally evaluate the control signal and tracking error weighted parameters. The GC control scheme is based on the Active/Reactive Power (P/Q) controller to ensure active power-sharing. The effectiveness of the proposed controller has been verified by considering system parameters uncertainties. [ABSTRACT FROM AUTHOR]

• Proposing a novel straightforward algorithm for solving various OPF problems. • Introducing an efficient fuzzy strategy to accommodate the inertia coefficient of heuristic algorithm. • Conducting accuracy tests via ten different benchmark problems. • Applying the proposed algorithm on small- and large-scale OPF problems. The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems. [ABSTRACT FROM AUTHOR]

Inspired by the powerful feature extraction and the data reconstruction ability of autoencoder, a stacked sparse denoising autoencoder is developed for electricity theft detection in this paper. The technical route is to employ the electricity data from honest users as the training samples, and the autoencoder can learn the effective features from the data and then reconstruct the inputs as much as possible. For the anomalous behavior, since it contributes little to the autoencoder, the detector returns to a comparatively higher reconstruction error; hence the theft users can be recognized by setting an appropriate error threshold. To improve the feature extraction ability and the robustness, the sparsity and noise are introduced into the autoencoder, and the particle swarm optimization algorithm is applied to optimize these hyper-parameters. Moreover, the receiver operating characteristic curve is put forward to estimate the optimal error threshold. Finally, the proposed approach is evaluated and verified using the electricity dataset in Fujian, China. [ABSTRACT FROM AUTHOR]

Unbalanced loads create unacceptable voltage unbalance (VU) in low voltage (LV) three-phase four-wire distribution networks with unbalanced rooftop photovoltaic (PV) systems. This paper proposes a mitigation strategy for restricting the VU depending on the distributed batteries that included in grid interfaced rooftop PV systems. Moreover, it introduces a dynamic control model based on two PI-controllers for determining the required batteries' compensating currents and ensuring equality resultant stress current on each bus. It overcomes three existing challenges; the inequality stresses currents on the distributed batteries along the feeder, the insufficient size or non-existing of the distributed batteries, and satisfies the same stress among batteries on the same bus. For investigating the influence of equal phase magnitude batteries' compensating current at the same bus on the whole distribution network (DN). Particle Swarm Optimization (PSO) and Bat optimization techniques are used to determine the optimum self and mutual sharing co-efficients of each battery. The limits of the distributed batteries' size are taken into consideration in the optimization process. The proposed dynamic model is implemented using MATLAB software. The simulation results show the effectiveness of the proposed control scheme for mitigating the VU on the distribution feeder's buses at three scenarios; different loads, PVs, and states of charge of batteries. Moreover, the batteries' compensating currents are presented to examine the implementation feasibility. [ABSTRACT FROM AUTHOR]

• Swarm and evolutionary algorithms are used to improve DC-electrified railway systems. • The algorithms find the optimum installation of ESSs (location and power). • The algorithms are combined with a very realistic railway simulator. • The performance of Genetic, Particle Swarm and Fireworks Algorithms is compared. • Energy savings and considerable installation's profitability are obtained. The installation of wayside Energy Storage Systems (ESSs) in DC-electrified railway systems is one of the main measures to improve their energy efficiency. They store the excess of regenerated energy produced by the trains during the braking phases and give it back to the system when necessary. Nevertheless, the big cost of the associated installation can make railway operators hesitate about the convenience of the investment. Additionally, the decisions about the configuration of the installation (locations and sizes for the ESSs) are usually based on the previous experience of the railway operators or, at best, in assessments made with simulation tools with low accuracy. This paper proposes a model to optimize the profitability of the investment. Nature-inspired optimization algorithms are applied in combination with a very realistic railway simulator. The flexibility of the nature-inspired optimization algorithms, together with their ability to successfully deal with the computationally-intensive and highly non-linear and non-convex problem posed by the realistic railway simulator, makes them the perfect choice. Three different nature-inspired optimization algorithms have been selected and compared: the Genetic Algorithm (GA) as the main exponent of the evolutionary algorithms, the Particle Swarm Optimization algorithm (PSO) as the main exponent of the swarm algorithms and the Fireworks Algorithm (FA) as another variant of the swarm algorithms. The algorithms have shown an excellent behavior, providing solutions that combine the increase of energy efficiency with a very good profitability of the installation required to obtain that increase. [ABSTRACT FROM AUTHOR]

• A protection coordination is proposed based on dual setting directional overcurrent relays. • A new dynamic objective function is devised to direct the relays settings toward optimal solutions. • A user-defined strategy optimizes the relays inverse curve, intuitively. • An efficient optimization model considers the overall existing constraints. • The overall operation time of relay is cut down. • Miscoordinations among primary and backup relays are eliminated. Considering different configurations of a protected network in relays coordination offers higher reliability, security, and sustainability. However, overcurrent relaying in N-1 contingency states stands as a complicated problem especially in interconnected sub-transmissions systems with distance relays. To overcome this challenge, the current paper proposes a coordination scheme in which dual setting directional overcurrent relays (D-DOCRs) and low bandwidth communication links are jointly deployed instead of conventional DOCRs. By doing so, more flexibility and extensibility is provided in the coordination process. Moreover, in order to direct the relays settings toward optimal solutions, a new dynamic objective function is devised. This approach strengthens the controllability of relay settings in different conditions by pulling the variables towards the feasible region. The proposed formulation considers both standard and non-standard relay characteristics and demonstrates a nonlinear programming fashion which is solved by chaotic particle swarm optimization algorithm. Results approve that D-DOCRs in conjunction with distance relays can successfully maintain a proper coordination in N-1 contingency states, supported by the proposed dynamic approach. [ABSTRACT FROM AUTHOR]