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• In this paper, the optimized GWO-MCSVM assessment model is constructed step by step, and the nonlinear convergence factor is used to replace the conventional convergence factor in the Grey Wolf algorithm and the thought of parameter initialization by tent chaotic mapping, which makes it more suitable for the data analysis of transformers. The improved GWO-MCSVM model ensures the rapidity and reliability of assessment. • The initialization of model parameters based on Tent chaotic map completes the preprocessing of model input data, determines the initial parameter selection of GWO parameter optimization process, and improves the optimization speed of the algorithm. • The parameters of GWO-MCSVM model are optimized through training samples of transformer operation parameters in practical projects, and the GWO-MCSVM model is tested through groups of test samples. Compared with the existing GA-SVM model and PSO-SVM model, it is verified that the model has high accuracy (accuracy rate is more than 90%) in transformer condition assessment, is superior to the existing assessment model, and has bright scalability. The continuous and reliable operation of the transformer is the basis to ensure the normal operation of the power system. Relevant departments collect multi-dimensional and multi-source heterogeneous parameter data during the operation, maintenance and repair of transformers. The effective information contained in the parameter data can directly reflect the current operating status of the transformer. On the basis of support vector machine and grey wolf algorithm, an improved GWO-MCSVM algorithm based on nonlinear convergence factor and Tent chaotic mapping is proposed. The algorithm parameters are optimized through training samples, and the results are evaluated and verified in the algorithm itself, so as to improve the accuracy of the status assessment results. Finally, the accuracy of assessment results of the algorithm proposed in this paper, existing genetic algorithms and particle swarm optimization algorithms are compared by evaluating multiple sets of measured samples. By comparison, the effectiveness of the algorithm proposed in this paper for transformer condition assessment has been verified. [ABSTRACT FROM AUTHOR]

This paper presents the design of proportional integral derivative (PID) based load frequency control (LFC) scheme effectively optimized through novel combination of bacteria foraging oriented particle swarm optimization (BFO-PSO) technique for a hydro dominating energy system model. The design control are implemented for 1% load disturbance in area-1 and compared with classical PID, Pessen integral rule, some overshoot, no overshoot as well as with recently published bacteria foraging optimization algorithm (BFOA) in terms of computed gains and inverse time multiplied absolute error (ITAE). The system performance for hydro energy system is sluggish and oscillatory. Hence, the further enhancement with proposed design are observed by considering the combination of unified power flow control (UPFC) in series with the tie-line and hydrogen aqua electrolyzer units installed at terminal of area-2. The system performance of the designed control is evaluated under various system investigations considering the dead-band and generation rate constraint (GRC) non-linearity and the applications results are presented to show the superiority of the proposed work. [ABSTRACT FROM AUTHOR]

• Improve the fitness function of PSO. • Improve the iterative of PSO. • DF combined with improved PSO. • A non-intrusive load disaggregation method based on multi-model combination is introduced. Non-Intrusive Load Monitoring (NILM) technology is used to obtain the detail of household electricity consumption by analyzing total electricity consumption data without installation of any sensors. It is of great significance to power demand-side management. A new load disaggregation method based on combinatorial optimization is proposed in this paper. Firstly, the particle swarm optimization (PSO) algorithm is improved by applying time probability to the fitness function. Meanwhile, the iterative process of PSO is constrained to avoid falling into local optimal solution. The improvement is verified by experiments on the Reference Energy Disaggregation Data Set (REDD). And then, on the basis of a single model and with the idea of ensemble learning, this paper studies the multi-model combination load disaggregation method that combines the improved PSO mentioned above, integer programming (IP) algorithm and delta feature (DF) model in parallel, and the experimental group is designed through the control variate method. Experimental results show that the multi-model combination method has a good performance with a high average accuracy of 87% for load disaggregation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a novel time varying acceleration coefficients particle swarm optimization (TVAC-PSO) algorithm is implemented to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is a challenging non-convex and non-linear optimization problem. The acceleration coefficients in PSO algorithm are varied adaptively during iterations to improve solution quality of original PSO and avoid premature convergence. The effect of valve-point in cost function considered with adding an absolute sinusoidal term to conventional polynomial cost function. The proposed method is applied to five test cases with different characteristics. Also, a new large scale test case considering valve-points effects is proposed in this paper. The obtained results demonstrate the superiority of the proposed method in solving non-convex and constrained CHPED problem. [Copyright &y& Elsevier]

Abstract: This paper presents a multiyear dynamic transmission expansion planning, TEP, model aiming at minimizing operation and investment costs along the entire planning horizon while ensuring an adequate quality of service and enforcing constraints modeling the operation of the network along the planning horizon. The developed model profits from the experience of planners when preparing a list of possible branch (lines and transformers) additions each of them associated to the corresponding investment cost. The objective of solving a TEP problem is to select a number of elements of this list and provide its scheduling along the planning horizon such that one is facing a mixed integer optimization problem. In this case, this problem was solved using a discrete evolutionary particle swarm optimization algorithm, DEPSO, based on already reported EPSO approaches but particularly suited to treat discrete problems. Apart from detailing the developed DEPSO, this paper describes the mathematical formulation of the TEP problem and the adopted solution algorithm. It also includes results of the application of the DEPSO to the TEP problem using two test networks widely used by other researchers on this area. [Copyright &y& Elsevier]

Abstract: A bi-directional dc/dc converter model is investigated for a notional Medium Voltage DC (MVDC) shipboard power system to improve energy flexibility and deal with peak energy demand in shipboard power system. Surplus energy in the MVDC system during light load condition can be captured by energy storages distributed in local load zones through the bi-directional dc/dc converters and then can be used during heavy load condition or black starting of the MVDC system. In this paper, the derivation process of the small-signal average models of the isolated-type bi-directional dc/dc converter is presented for controller design. This paper also presents the controller optimization process using intelligent optimal searching algorithm, Particle Swarm Optimization, for optimizing dynamic and steady-state control performance of a bi-directional dc/dc converter. The control performance of the proposed controller is evaluated using frequency-domain analysis and time-domain simulation of the large-scale notional MVDC shipboard power system using the Real-Time Digital Simulator. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE. [Copyright &y& Elsevier]

The aim of this paper is to explore the use of the proposed hybrid Particle Swarm Optimization-Simplex Method (PSO-SM) algorithm to optimize the design of PM couplings subject to several key design constraints. The proposed hybrid optimization algorithm is constructed based on combining two well-known optimization techniques: Particle Swarm Optimization (PSO) and Simplex Method (SM). The PSO has obvious capabilities in global search while the SM has exceptional advantages in local search. As a hybrid algorithm, the PSO-SM has the outstanding feature of combining the ability of global searching and local canvassing. On the other hand, Permanent Magnet (PM) drive couplings are used in power transmission in a wide range of industrial applications. A standard coupling design is used as a good starting point for the conventional Simplex Method and to define the performance constraints for the proposed hybrid optimization algorithm. New coupling designs are developed and optimized to demonstrate the superior capabilities of PSO-SM algorithm as a global optimization technique. [ABSTRACT FROM AUTHOR]

• In this paper, an adaptive differential protection scheme is proposed for wind farm integrated power network. • A particle swarm optimization (PSO) based differential relay algorithm is proposed in this paper. • The positive sequence current differential principle is used. • For different operating conditions, fault conditions and configurations of the system, the threshold value can be calculated online. • The threshold setting is designed considering the fault location and fault inception angle. • The method is tested using a standard power system model integrated with wind farm and simulated using MATLAB R2016a software. In this paper, an adaptive differential protection scheme is proposed for wind farm integrated power network. Any fixed threshold setting based protection scheme is not always suitable for the different operating conditions of power system. Considering the possible modulations in the fault signal, a particle swarm optimization (PSO) based differential relay algorithm is proposed in this paper. The positive sequence current differential principle is used. For different operating conditions, fault conditions and configurations of the system, the threshold value can be calculated online. So, practically the method can be applied to any power system model and the impact of dynamic operation of wind farm on the protection function is negligible. The threshold setting is designed considering the fault location and fault inception angle. Initially, the most effective faults among all the possible faults are identified with a single variable (fault location) objective function. Later, only single-phase-to-ground fault is considered for threshold setting with all possible parameters. The method is tested using a standard power system model integrated with wind farm and simulated using MATLAB R2016a software. Results prove the efficacy of the method. [ABSTRACT FROM AUTHOR]

• Analyze the correlation between power load data and relevant search engine indices. • Take search engine indices as input variables to forecast electricity load. • Use Hodrick-Prescott filter to decompose the time series of search engine indices. • Propose a LSTM model based on quantum-behaved particle swarm optimization algorithm. Accurate electricity load forecast plays an important role in the operation of a power system. Many factors influence the electricity load data such as air temperature, humidity and holidays, and they are taken as the explanatory variables in load forecasting cases traditionally. Search engine indices, a variable may be related to load data which has been never considered before in load forecasting, is discussed and utilized to increase the accuracy of power load prediction in this paper. Spearman's correlation coefficients and Granger test results verify the correlation between Google Trends (GT) and electricity load data. A methodology for processing GT time series with Hodrick-Prescott filter is proposed. To forecast electricity load with an adaptive network model in such a novel situation, we propose a long short-term memory neural network model based on quantum particle swarm algorithm. The performance of load forecast for Long Island region taking GT and weather data as input variables is compared with that taking only weather data as input variables, which shows that the introduction of GT improves short-term forecasting effectiveness significantly. [ABSTRACT FROM AUTHOR]

Abstract: Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the EED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of EED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically. [Copyright &y& Elsevier]

Abstract: Detailed R-C-L-M models of power transformers, which are based on lumped parameters, are used extensively not only for transient analysis of power transformers to determine electrical stresses in windings, but also for studying transients in power systems. Models with few elements are generally more practicable for power system studies but at the expense of accuracy. The use of artificial methods to reduce an R-C-L-M model is the main contribution of this paper. Advantages of the suggested method include: (1) a reduced loss of accuracy compared with the original model and (2) the flexibility to choose the number of model elements to achieve the desired model depending on size and accuracy. The ability of three different artificial methods, genetic algorithm, particle swarm optimization, and bacterial foraging algorithm, to model reduction is evaluated using measurements on an actual 400kV test object and the results are compared with those obtained by common analytical formulae. [Copyright &y& Elsevier]

Abstract: We propose a novel decision-support tool that aims to optimize the provision of residential energy services from the perspective of the end-user. The tool is composed of a novel energy service model and a novel distributed energy resources scheduling algorithm. The proposed model takes into account the time-varying demand and benefit that end-users derive from different services, and assigns the benefit to the energy that realizes the service. The scheduling algorithm determines how distributed energy resources available to the end-users and under their control should be operated so that the net benefit of energy services is maximized based on the energy service models, and their technical characteristics and capabilities. The scheduling is a challenging optimization problem; hence, a heuristic simulation-based approach based around cooperative particle swarm optimization is used. The paper presents a case study where this decision-support tool is used to optimize the provision of desired energy services in a ‘smart’ home that includes a number of controllable loads, energy storage and photovoltaic generation. [Copyright &y& Elsevier]

Abstract: In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces an indirect adaptive fuzzy controller as a power system stabilizer used to damp inter-area modes of oscillation following disturbances in power systems. Compared to the IEEE standard multi-band power system stabilizer (MB-PSS), indirect adaptive fuzzy-based stabilizers are more efficient because they can cope with oscillations at different operating points. A nominal model of the power system is identified on-line using a variable structure identifier. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature. [Copyright &y& Elsevier]

Abstract: In recent years, the number of small size wind farm made up with doubly fed induction generators (DFIG) located within the distribution system is rapidly increasing. DFIG can be utilized as the continuous reactive power source to support system voltage control by taking advantage of their reactive power control capability. In this paper, considering both reactive power control and distribution network reconfiguration can be used to reduce power losses and improve voltage profile, a joint optimization algorithm of combining reactive power control of wind farm and network reconfiguration is proposed to obtain the optimal reactive power output of wind farm and network structure simultaneously. The proposed algorithm has been successfully implemented on the 16 bus distribution network and the results obtained demonstrate the efficiency of the algorithm. [Copyright &y& Elsevier]

Abstract: A new multiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and emission objectives. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run. The comparison with the different reported techniques demonstrates the superiority of the proposed MOPSO in terms of the diversity of the Pareto-optimal solutions obtained. In addition, a quality measure to Pareto-optimal solutions has been implemented where the results confirm the potential of the proposed MOPSO technique to solve the multiobjective EED problem and produce high quality nondominated solutions. [Copyright &y& Elsevier]

Abstract: Conventional economic load dispatch problem uses deterministic models, which are however not able to reflect some real situations in practical applications since certain inaccurate and uncertain factors are normally involved in system operations. Stochastic models are more suited to be used for investigating some of the power dispatch problems. In this paper, both deterministic and stochastic models are first formulated, and then an improved particle swarm optimization (PSO) method is developed to deal with the economic load dispatch while simultaneously considering the environmental impact. Comparative studies are carried out to examine the effectiveness of the proposed approach. First, a comparison is made between the proposed PSO approach and other approaches including weighted aggregation and evolutionary optimization. Then, based on the proposed PSO, the impacts of different problem formulations including stochastic and deterministic models on power dispatch results are investigated and analyzed. [Copyright &y& Elsevier]

• The power distribution system patrol procedure has been formulated in a multi-objective context. • A MODPSO algorithm has been proposed with a Local Search based on a 3-Opt algorithm and an initialization heuristics with a RNN algorithm. • Determine an initialization heuristic which emulates the decision-making process of determining the patrol sequences by repair crews in real-world situations. • Analyzing how initialization heuristics affects the final set of patrol sequences. During outages, Distribution Operation Centers establish a restoration plan to isolate the area that is probably faulty and reconnect all functioning components of a feeder. Despite all technological advances made in the last few decades, the final step to locate the faulty point is taken visually by repair field crews based on a patrol sequence. There is a lack of procedures to determine patrol sequences in literature, which is usually performed by the repair crew itself. Therefore, this paper offers an introduction to define patrol sequences in a multi-objective context. Furthermore, the Multi-Objective Discrete Particle Swarm Optimization has been proposed by using the 3-Opt algorithm and initialization heuristics based on an RNN algorithm - MODPSO+RNN+LS(3-Opt)-, which emulates the decisions made by the repair crew in the field. Lastly, the MODPSO+RNN+LS(3-Opt) was evaluated based on the IEEE 37-node and 123-node test feeders. The obtained results cast light upon how the initialization heuristics and local search method improve the final set of patrol sequence. In addition, it is also possible to find how the multi-objective context of the problem increases its complexity and how likely decisions that are typically made by the field repair crew could be improved by an optimization technique. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new problem formulation is proposed to calculate the optimal relay settings of directional overcurrent relays in power systems. The proposed coordination problem is formulated as a mixed integer nonlinear problem to take into account the discrete values for the pickup current settings. A modified particle swarm optimization (PSO) algorithm is proposed to calculate the optimal relay settings. A comparison between the original particle swarm optimization based method, the proposed PSO algorithm and the GAMS solver is presented. [Copyright &y& Elsevier]

Abstract: This paper presents a deterministically guided particle swarm optimization (DGPSO) algorithm to solve the dynamic economic dispatch problem (DEDP) of generating units considering the valve-point effects. The cost function of the generating units exhibits the non-convex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components in the cost function. The DGPSO method is a two-phase optimizer: in the first phase the PSO technique will explore the solution space freely. In the second phase, SQP (sequential quadratic programming) will be called only when there is an improvement of solution (a feasible solution) in the PSO run. Thus, SQP (deterministically) guides the PSO algorithm for better performance in the complex solution space. To validate the feasibility of the DGPSO method a 10-unit system is considered and studied under three different cases. The effectiveness of the presented method over EP (evolutionary programming) and EP-SQP methods is shown in general. [Copyright &y& Elsevier]