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Passive anti-islanding protections rely on measurements of voltage, frequency, and rate of change of frequency at the point of common coupling of the distributed generators, and their performance is impacted by the dynamic behavior of distributed generators operating under specific control modes. In this context, this paper proposes a novel heuristic-based method for setting the excitation system parameters of synchronous distributed generators using the differential evolution algorithm to enhance the performance of anti-islanding protection. The results demonstrate that the heuristic-based method achieves improved islanding detection performance compared to traditional values used to specify excitation system parameters. Furthermore, the optimized excitation system parameters do not adversely affect the transient response of the generator during fault events. • A novel heuristic method for setting the excitation system of synchronous generators. • The method aims to minimize the actuation time of passive anti-islanding protection. • The heuristic reduced the non-detection zone of passive techniques. • The method does not affect the dependability of passive anti-islanding protection. • The overall performance of the synchronous generator is not affected. [ABSTRACT FROM AUTHOR]

• Total harmonic distortion (THD) and power loss are minimized in this study by effectively allocating tie switches and soft open points (SOPs). • The obtained results are also examined under the uncertainties in load demands through Monte Carlo Simulation (MCSs). • To size and site, the required equipment, an optimization framework based on the fuzzy-integrated differential evolution algorithm (FDEA) is constructed. The growing power quality issues stemming from the higher presence of converter-based appliances in the distribution network increases the harmonic risks. Given the importance of tackling these issues to deliver high-quality power to customers, this investigation provides a promising approach focused on effectively designating soft open points (SOPs) and tie switches in the distribution networks. The objectives of this investigation include the reduction of total harmonic distortion (THD), power loss per various harmonic frequencies and the number of SOPs. To evaluate the performance of the proposed approach, the study is carried out on two well-known networks, 33-bus, and 69-bus. Through these analyses, it was shown how effectively THD, power loss, and voltage profiles are improved when SOPs and tie switches are allocated together. The outcomes of thorough analyses provide credence to the assertion that the suggested approach is very promising for mitigating power quality issues by handling uncertainties in loads by Monte Carlo Simulation (MCS). In more detail, the obtained solutions assure that the suggested method has superior power quality across the networks by maintaining the THD levels within the standard margins. Additionally, the developed model offers advantages including reduced power loss and enhanced voltage profiles, which increase the overall network efficiency, while reducing the total investment cost by reducing the number of SOPs and power loss in power distribution networks. [ABSTRACT FROM AUTHOR]

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow problem, called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution operator in a single algorithmic framework. Additionally, an adaptive method is proposed to update the number of solutions evolved by each DE operator based on both the diversity of the population and the quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%. • Development of an adaptive method (AM) for optimizing diversity and solution quality. • Innovative constraint handling approach, progressively adding constraints for improved performance. • Incorporation of intermittent renewable energy models for realistic problem solving. • Extensive validation on IEEE 30-bus and IEEE 118-bus networks, outperforming state-of-the-art algorithms in cost, loss, and environmental impact reduction. [ABSTRACT FROM AUTHOR]

• The output power volatility of wind-photovoltaic and the operating characteristics of the electrolyzer is considered. • A capacity optimization method for off-grid RES-H2 production systems considering the load-follow-source mechanism is proposed. • A multi-objective optimization model with minimum cost, minimum energy storage capacity and optimal power supply efficiency was constructed. • An improved optimization algorithm based on two common algorithms is designed. Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Unbalanced current of non-fault pole introduced by the coupling effect is analyzed. • Differential protection using the mode current at compensation point is proposed. • Calculation of transient current is studied based on distributed-parameter model. • Fault pole can be detected using the compensated current in differential mode. Abstract The current differential protection presently being used for high-voltage direct-current (HVDC) transmission lines requires increases in time delay and threshold to avoid misoperation, which is induced from a distributed capacitance current along the transmission line, leading to low operation speed and disabling the device from distinguishing between internal and external faults. The unbalanced currents of the non-fault pole are studied in two cases: under an external fault as induced by the distributed characteristics and under a single-pole-to-ground fault as induced by the coupling characteristics of the transmission line parameter. To overcome its defects, this paper develops a new differential protection algorithm based on the distributed-parameter transmission-line model. The fault identification criterion using the compensated current of the common mode and fault pole selection criterion using the compensated current of the differential mode are introduced. Unaffected by the distributed capacitance current, this differential protection correctly operates with high sensitivity through the entire process during the fault. The feasibility and validity of the differential protection algorithm are verified. [ABSTRACT FROM AUTHOR]

The Transmission Network Expansion Planning problem (TNEP) can be modeled either as a static, a pseudo-dynamic, or a dynamic problem. Most of the existing formulations do not include reactive power planning within the TNEP problem, leading to sub-optimal designs leading to higher system costs in reality. This paper proposes a dynamic (multi-stage) non-convex formulation that optimizes the addition of transmission circuits and reactive power compensation devices, accounting for operational costs including losses. The planning is done considering (N − 1) security constraints using an AC model. As there are no similar research works to benchmark the outcomes, the results were compared with those obtained from the static and pseudo-dynamic approaches, showing that the proposed approach provides more economical solutions. It is also shown that better solutions are obtained when Reactive Power Planning (RPP) is considered in the problem formulation. An improved Differential Evolution (DE) and Continuous Population Based Incremental Learning (PBILc) hybrid solution method (IDE-PBILc) is proposed which drastically improves calculation time and robustness. Comparisons with two different state-of-the-art metaheuristics are performed for validation. The results were obtained for the Garver 6-bus, IEEE 24-bus, and the IEEE 118-bus systems. Even though in this work uncertainties are not considered, the proposed approach could be of particular use when studying systems with high renewable energy penetration scenarios, due to its computational efficiency. • Dynamic AC transmission expansion planning. • Full AC non-convex modeling. • Reactive power planning. • (N − 1) Security constraints and operational costs. • Improved DE-PBILc solution method. [ABSTRACT FROM AUTHOR]

Three-phase induction motors are extensively used in the industry due to their robustness characteristics, low cost and easy maintenance. Usually, it is necessary to implement drive and control systems for such motors, which requires the knowledge of their mechanical and electrical parameters. However, in some cases, these data are not immediately available, or the values of the parameters may change due to the wear of motor components. Such problems can be circumvented if an efficient parameter estimation technique is available. In order to automatically estimate the parameters efficiently, the present work proposes a method, based on the differential evolution algorithm, aimed at the estimation of the electrical and mechanical parameters of three-phase induction motors. Such algorithm is capable of estimating the parameters of the equivalent electrical circuit, such as stator and rotor resistances and leakage inductances, the magnetizing inductance, and also mechanical parameters, such as moment of inertia and the friction coefficient. The performance of the proposed parameter estimation technique is evaluated for three different input signals: (i) current signal of a phase associated with the speed measured from a tachogenerator, (ii) current signal of a phase associated with the speed acquired from a torquemeter, and (iii) only the current signal of one phase. Finally, a series of simulated and experimental results are presented to validate the proposed technique, and the results show the good performance of the proposed strategies. [ABSTRACT FROM AUTHOR]

This work describes the application of a soft computing technique—known as the differential evolution (DE) to implement the harmonics elimination pulse-width modulation (HEPWM) for the direct control of distribution static synchronous compensator (D-STATCOM). The main contribution of the work is the application of DE to solve eleven HEPWM switching angles to synthesize a 23-level cascaded multilevel voltage source inverter (MVSI). The key feature of this algorithm is the wide modulation index ( M I ) range that can be produced (i.e. 5.40–8.15 p.u) with very small step size (0.01), while maintaining the total harmonic distortion (THD) of the MVSI output voltage below 5%. Furthermore, the utilization of high levels MVSI allows for the output voltage of the D-STATCOM to be sufficiently high, thus avoiding the use of step-up transformer. The correctness of the computed HEPWM angles is validated using single phase 23-level cascaded MVSI experimental rig. Then, the proposed method is compared to the phase-shifted PWM (PS-PWM) using a ±6.5 MVAr/11 kV D-STATCOM modelled in MATLAB-Simulink. For the same switching frequency, the HEPWM switching results in a superior harmonics spectra and lower switching losses. Furthermore, if the switching frequency is fixed to a particular value, the size of the series coupling inductor can be reduced to at least half. Dynamically, the steady state value of the reactive current ( i Cq ) is reached in less than one mains cycle when a transition from the full inductive to full capacitive modes is imposed. [ABSTRACT FROM AUTHOR]

• A new improved grey wolf optimization algorithm is proposed, which integrates a non-linear convergence factor and a location mutation strategy, to solve the problem of complex non-linear components. • The effectiveness of the developed PM-GWO was verified in a hybrid renewable energy system. • Compared with the existing popular methods, the advantages in improving the utilization of new energy sources and reducing the operating costs of hybrid renewable energy systems are highlighted. Sustainable new energies that are green and pollution-free are effective in decreasing carbon emissions and the cost of electricity consumption, so many new energy systems have been connected to the power grid. However, if a large amount of new energy is connected to a power grid, the system will be unable to efficiently distribute and utilize electricity. As a solution, this paper proposes an optimal operating strategy for hybrid renewable energy systems (HRESs) based on position-mutation gray wolf optimization (PM-GWO), with the aim of minimizing the operating costs of HRESs. To avoid falling into the local optimum, a nonlinear convergent factor and a position-mutation strategy were integrated into GWO to solve the problem of complex and nonlinear components. Then, the optimal operating strategy for each component in the HRES was analyzed using the proposed PM-GWO. Consequently, the components of the HRES were found to operate in coordination. Based on simulation cases, it was also demonstrated that the optimized operating strategy for HRESs utilizes the rate of new energy and decreases electricity costs. The HRES system was also verified to possess high-cost effectiveness and stability. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced differential evolution algorithm (DE) for solving the directional overcurrent relay (DOCR) coordination problem. Comparisons among different versions of DE for coordination of DOCRs in larger and interconnected power systems are presented. The evaluation criteria consist of fitness value, number of violation and standard deviation. The outstanding DE algorithm has been enhanced in four aspects: dial reduction scheme, elitism and mediocrity scheme, mutation index renew scheme, and population reduction scheme. The enhanced-DE (eDE) shown best result in terms of execution time, result quality, robustness and convergence ability compared to the others analyzed in this research. The IEEE 14 and 57 bus systems are used to validate the proposed algorithm. A significant comparison result between eDE and DE on the IEEE 57 bus network are: execution time in seconds 364 vs. 2040, fitness value 1.933 vs. 7.91, and standard deviation of fitness value 0.018 vs. 5.52. Results are averaged values in 50 runs. [ABSTRACT FROM AUTHOR]

Power system security requires distance relay settings to work properly for the maximum number of possible operational scenarios. This work presents a new hybrid intelligent system model to automatically determine and verify distance relay settings to achieve high performance using exhaustive verification through simulation of faults. A performance engineering approach has been considered to propose a hybrid model based on three concepts. Automatic topology recognition based on constructive methods. Expert rules are used to define the ranges of the settings, initial solutions, and intelligent fault verification selection with fuzzy logic. A modified differential evolution algorithm is used to optimize initial conventional solutions, considering a new performance evaluation as an objective function. The objective function evaluates operation times according to expected zone time ranges and fault relevance. DIgSILENT Power Factory software was used to perform fault simulations and to program the model with DIgSILENT Programming Language. Two real-world cases of the application of the model are presented with successful results. [Display omitted] • New approach for evaluating distance relay performance using performance engineering. • Optimization of expert relay settings by a differential evolution modified algorithm. • A hybrid intelligent system was formulated, implemented, and tested in the real field. [ABSTRACT FROM AUTHOR]

The main aim of the static Transmission Network Expansion Planning (TNEP) is to determine which and where new transmission equipment must be installed. The complexity added by the non-linearities leads to simplifications, which include the DC model. However, most non-linearities are solvable nowadays. Thus, the new scenario of large non-dispatchable power sources penetration and the several developments in technologies, e.g, Flexible AC Transmission Systems (FACTS) devices and High Voltage Direct Current (HVDC) interconnections, motivate the use of the AC model with its non-linearities. Some research works address the use of some of those technologies for TNEP in an independent fashion, which can lead to sub-optimal solutions. In this work, Voltage Source Controlled-Multiterminal HVDC (VSC-MTDC) systems, FACTS devices, and Reactive Power Planning (RPP) are integrated into the same planning optimization process, so that a unified AC TNEP formulation is proposed. A non-linear mathematical programming technique and a differential evolution based metaheuristics are chosen to achieve an optimal transmission configuration. To evaluate the benefits of the proposed approach, two IEEE modified test systems (9 and 118 buses) are used. Results suggest that more economical solutions can be obtained if different types of reinforcement strategies are taken into account in a unified approach. • TNEP problem with a unified formulation for several transmission equipment. • The model and approach allow greater flexibility in the TNEP process. • Different types of reinforcements in the same expansion process. [ABSTRACT FROM AUTHOR]

This article proposes a method based on the reinforcement learning (RL) for preventing cascading failure (CF) and blackout in smart grids by acting on the output power of the generators in real-time. The proposed research work utilizes the Q-learning algorithm to train the system for the optimal action selection strategy during the state-action learning process by updating the action values based on the obtained rewards. The trained system then is able to relieve congestion of transmission lines in real-time by adjusting the output power of the generators (actions) to prevent consecutive line outages and blackout after N-1 and N-1-1 contingency conditions. The proposed RL-based control is validated through experimental implementation as well as simulation studies on the IEEE 118-bus test system for different contingency case studies. The results obtained from the experimental and simulation studies show the accuracy and robustness of the proposed approach in preventing cascading failure and blackout. [ABSTRACT FROM AUTHOR]

Planning of protection systems plays a significant role to achieve an efficient behavior (or performance) of distribution systems. Feeders that offer high robustness and reliability are essential to ensure continuous energy delivering to all customer loads. Since distributed generation (DG) has increased its penetration in distribution systems, it has become necessary to place protective devices (such as reclosers) in order to allow DG islanded operation, which decreases the energy not supplied. This paper addresses efficient placement of protective switches, which mainly comprises circuit breakers and reclosers. Two optimization approaches are proposed to enhance reliability indices. First, a single objective optimization problem is defined to maximize the profit of the distribution system operator. Such problem is solved through implementing the differential evolution algorithm (DE). Second, a multi-objective optimization problem is defined to reduce the system average interruption duration index (SAIDI), the system average interruption frequency index (SAIFI), and system costs. This approach yields optimal planning of switches by applying the non-dominated sorting differential evolution algorithm (NSDE). The heuristic algorithms are implemented in Matlab, while the power system is modeled in DIgSILENT in order to use a co-simulation environment between these two tools. Simulations are developed on a real test feeder for two cases: (i) without DG; and (ii) with 6 MW penetration of distributed sources. Results show the importance of both DG and protective devices to enhance reliability, and the effect of enabling intentional islanded operation. [ABSTRACT FROM AUTHOR]

For maximizing the customer satisfaction and retention, improvement of service reliability is a major concern for electric utilities. Service reliability can be improved by placing switches and reclosers at appropriate locations in the distribution system so that supply from the main substation to the healthy load points can be maintained after isolating the faulted section. This paper presents a formulation for an optimal placement of switches and reclosers in a distribution system for maximizing system reliability while minimizing the associated investment and outage costs considering uncertainties in load data, system failure and repair rates. The uncertainties have been incorporated in the formulation using point estimate method (PEM). The proposed formulation has been tested on 13, 58 and IEEE 123-bus test systems using differential evolution (DE) and mixed integer nonlinear programming (MINLP) method. The obtained results establish the effectiveness of the consideration of data uncertainties in improving the distribution system reliability and maximizing utilities’ profits. [ABSTRACT FROM AUTHOR]

• The majority of academics focused more on Distributed Generations (DG) ideal siting and size with less research being done on economic studies. • The economic analysis of the ideal sizing and placement of distributed generation is also viewed as a contribution to this work. In addition to technical analysis of the placement and sizing of DGs, economic analysis is performed as an added advantage. • Additionally, the majority of the research was concentrated on placing a single DG; however, in this work simultaneous installation of DGs at various sites was carried out and its techno-economic analysis was examined. • Furthermore, simultaneous multi-objective formulation tasks have included assessing annual economic losses by positioning DGs optimally and minimizing real power loss. In radial distribution networks, the appropriate placement of properly sized Distributed Generation (DG) units can significantly improve the performance of the system. The biggest techno-economic benefits can be obtained by reducing annual economic losses that include the expenses of deployment, operation and maintenance along with voltage variations and power loss across the buses. The current problem is examined in light of various multi-objective frameworks as well as the optimum compromise solution also termed the Pareto-optimal solution is presented. When dealing with a multi-objective optimization problem, certain constraints on equality and inequality are also examined. The focus of this paper is on a one-of-a-kind multi-objective whale optimization (MOWOA) algorithm for multi-objective problem-solving. To test its effectiveness, the method that was suggested is implemented on radial bus distribution systems IEEE-33 and IEEE-69. This paper also includes a comparison with other recent multi-objective algorithms such as opposition-based chaotic differential evolution (OCDE), Krill herd algorithm (KHA) and Power Loss Sensitivity Factor and Simulated Annealing (LSFSA). It has been discovered that the method proposed may improve power loss, annual economic loss mitigation and voltage profile improvement. [ABSTRACT FROM AUTHOR]

Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models. • An advanced deep neuroevolution model is proposed for probabilistic load forecasting. • The proposed algorithm automatically optimizes parameters of the deep neural network. • Mean scaled interval score is used as fitness function for the evolutionary algorithm. • The proposed model has been tested on a load dataset, being easily applied to others. [ABSTRACT FROM AUTHOR]

• Unbalanced distribution system optimization method considering 24-hour load curves. • Definition and creation of Optimal Allocation Zones to reduce computational time. • Capacitor banks and distributed photovoltaic generation are considered. • Advantage of the probabilistic nature of the GA to perform a sensitivity analysis. • The computational time can be reduced up to 60% in large scale systems. In the last years, many authors have dedicated themselves to solving the problem of distributed resources allocation in Distribution Systems (DS). The optimal siting and sizing of such equipment can improve the efficiency of the provided service, promoting technical and economic gains. Several papers use artificial intelligence algorithms and methods based on population evolutions to achieve this goal, due to the increasing complexity of the simulated systems and the highly combinatorial characteristics of this problem. Reducing the search space and/or setting the initial population are critical factors for improving the performance of population-based optimization methods. In the literature, the adjustment of the initial population does not receive the attention it should, especially when dealing with three-phase large-scale unbalanced systems – when computational time becomes primary adversity. The objective of this paper is to propose a novel approach for DS optimization, using Genetic Algorithms (GA) to map which areas are more sensitive to equipment allocation to reduce losses. In this work, capacitor banks and distributed photovoltaic (PV) generation are considered. The method adopted to carry out the allocation considers proper modeling of unbalanced DS through multiphase Optimal Power Flow frameworks. Its development takes advantage of the probabilistic nature of the GA to perform a sensitivity analysis to determine the positions that may reduce the losses in a daily planning horizon. For this, the execution of GA is done sequentially, under different conditions of generation and load, selected at random, setting in optimization a 24-hour load and PV generation curves that take into consideration the uncertainties, representing each hour of the day individually for better detail of the planning horizon. It was seen that the best positions are always concentrated in specific regions, called Optimal Allocation Zones (OAZ), which may favor the planning of Utilities. Furthermore, the OAZ method can be used to define an initial population of evolutionary algorithms, thus achieving a reduction in computational time and a better quality of solutions, which is a major contribution to literature. Through this approach, the computational time can be reduced up to 60% in large-scale systems. Simulations were carried out on four unbalanced distribution test-feeders: IEEE 4, IEEE 13, IEEE 37, and IEEE 123 Node Test Feeders. [ABSTRACT FROM AUTHOR]

• This paper presents Hybridization of Multiverse Optimizer with Sequential Quadratic Programming (SQP) to form Hybrid Multiverse Optimizer (HMVO) • HMVO has been applied on non-convex economic dispatch problem • The effectiveness of HMVO has been verified on four different ED problems. • It is found that HMVO based approach can provide better solution of ED problem. Economic Dispatch (ED) is one of the important problems among numerous vital operational problems of power economics. ED is a non-convex, non-drivable, and non-linear optimization issue along with diverse constraints. In this research work, a neoteric natural influential optimization tool Multi-verse Optimizer (MVO) hybridized with sequential quadratic programming (SQP) termed as Hybrid MVO (MVO-SQP) is proposed for the solution of multi-constrained ED problem. This contemporary effort contemplates on ED problem in the milieu of convergence features, numerical proficiency, and stoutness.The non-convex ED problem is encrypted at Hybrid Multi-verse Optimizer (HMVO) framework and employed in MATLAB for 30 trials, 300 iterations, and 30 exploration universes. The presented mechanism is verified on typical standard test systems comprising of 6, 13, 15, and 40 unit test systems, and experimental outcomes are juxtaposed with the reported approaches in the literature. The statistical analysis shows that the proposed approach saves 2.927 $/h for 6 units test system, 9.0618 $/h for 13 units test system, 1.831 $/h for 15 units test system, and 113.515 $/hr for 40 units test system with losses. It is envisioned that this work will equip the researchers with an improved tool to solve ED problem. [ABSTRACT FROM AUTHOR]

• In addition to typical PD data, various types of interference data are collected. • An image feature extraction method suitable for GIS partial discharge PRPS patterns is reported. • A DEABFO-SVM classification model is proposed to improve the accuracy of fault diagnosis. Feature extraction and classifier optimization are important parts in the diagnosis of partial discharge (PD). Traditional feature extraction methods generally target not the images but structured data. Traditional classifier optimization methods have many disadvantages such as the many parameters that need to be adjusted, difficulty in determining the best parameters, and the ease in falling into local optimal solutions. To eliminate these defects, a PD diagnosis method of gas-insulated switchgear (GIS) based on the Zernike moment and improved support vector machine (SVM) is proposed. Firstly, five typical PD models of corona discharge, metal particle discharge, floating electrode discharge, void discharge, and surface discharge were designed. Then, the UHF sensor was used to collect discharge signals of different defects and different types of interference signals to build a PD phase-resolved pulse sequence (PRPS) patterns library. After that, the Zernike moment of the PRPS images were extracted as the PD features. Finally, the SVM model based on the differential evolution adaptive bacterial foraging optimization (DEABFO) algorithm was used to fulfill the GIS PD diagnosis. The results show that the Zernike moment feature extraction method is suitable for PD PRPS images, and DEABFO improves the convergence speed of the SVM parameter optimization process and can effectively avoid the probability of falling into the local optimum. Compared with other image feature extraction methods and other SVM optimization algorithms, this method has high accuracy in GIS PD fault diagnosis, with the recognition rate reaching 91.23%. [ABSTRACT FROM AUTHOR]

This research has been developed with the aim of analyzing of the reported results in ‘Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems’ by K.K. Mandal, N. Chakraborty Electric Power Systems Research 78 (2008) 1972–1979. Indeed, that optimization problem did not take into account the storage capacity of the reservoirs constraints and the positivity of the power generated by each hydro unit over the scheduling horizon. [ABSTRACT FROM AUTHOR]

• The deficiency of the "decomposition-prediction" combination model is improved. • The model is constructed by introducing the ensemble learning. • The model is applied to the multi-step-ahead research. Research on forecasting electricity prices is of great significance to market participants. It is very difficult, however, to forecast the electricity price series because of its nonlinearity and high volatility. Considering that the existing studies directly ignore the important information contained in the residual term (Res.) after variational modal decomposition (VMD), this paper introduces a two-layer decomposition technique based on the combination of VMD technology and ensemble empirical modal decomposition (EEMD), carrying out EEMD decomposition on the residual term after VMD decomposition to improve the overall prediction accuracy of the model. At the same time, in order to address the defects of the existing hybrid model prediction methods—which use equal weights to reconstruct the prediction results—this paper draws on the idea of ensemble learning, combining the extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm, introducing the DE-ELM meta-learner to optimize the reconstruction weights of the prediction components, and constructing a new hybrid model VMD-Res.-EEMD-DE-ELM-DE-ELM to obtain better prediction results. In order to verify the model's prediction performance, this paper uses electricity prices from Australian and Spanish electricity markets for empirical analysis. The results show that the hybrid model proposed in this paper has significant forecasting advantages. [ABSTRACT FROM AUTHOR]

• The parameter reverse identification method applies the connected relationship between T-connection nodes and other nodes with measurement data, and employs the differential evolution algorithm (DE) to realize accurate identification under the conditions of lacking many original parameters and all measurement data of T-connection nodes. • This method utilizes several sets of mixed measurement data and identifies line parameters in a noise environment which is close to the actual power grid. • 3.Mixed and partial measurement data is taken to identify the line parameters in distribution network with multi-T nodes. • The identification results show that the proposed method is less affected by measurement errors and can be applied to distribution network in multi-voltage level. Reverse identification of line parameters plays an important role in the calculation and analysis of the power grid, However, based on the absence of many original line parameters and much measurement data, the line parameter estimation that employs the common methods (such as the least squares method) may be unsatisfactory or even unachievable. Hence, this paper proposes a reverse identification method of line parameters in the distribution network with multi-T nodes based on partial measurement data of the SCADA and the μPMU. Without many original values of line parameters, a multi-time reverse identification model of line parameters is established. The model is solved by the differential evolution algorithm, which is employed to realize accurate identification. The proposed method is tested based on 110 kV, 35 kV, and 10 kV distribution network cases, and the results show that the proposed method can realize precise identification under the conditions of lacking many original parameters and all measurement data of T-connection nodes. [ABSTRACT FROM AUTHOR]

• A new topological method to screen critical transmission lines and buses under multicontingency of transmission lines. • Current flow betweenness centrality for structural analysis of the system, requiring no knowledge of its electrical measures, applied as pre-analysis tool. • Local analysis identifies how transmission line outages affect each bus. • Global analysis identifies the impact of multi-contingencies, detecting potential overloaded buses and transmission lines that can trigger large blackouts. • Optimized search approach for large power systems based on the Differential Evolution algorithm. Security assessment considering multi-contingency is necessary since the complexity of the power system is constantly increasing. This paper proposes a topological method to screen critical transmission lines and buses under multi-contingency of transmission lines. It is based on a topological measure called current flow betweenness centrality, and provides a structural analysis of the system, requiring no knowledge of its electrical measures. The proposed method is applicable to the local and global contexts. The local analysis identifies how transmission line outages affect each bus, whereas the global analysis identifies the impact of multi-contingencies, detecting potential overloaded buses and transmission lines that can trigger large blackouts. An exhaustive search formulation of the method is presented, followed by an optimized search approach for large power systems based on the Differential Evolution algorithm. Numerical analyses are performed for ITAIPU 11 bus, IEEE 39 bus, IEEE 57 bus, IEEE 118 bus, and Polish 2383 bus transmission systems. This work reports promising results with the utilization of current flow betweenness to identify critical lines and buses when compared to the literature. Since our method works even when traditional measures of the system are unavailable, it reaches the needs of complex grids and can be applied as a pre-analysis tool. [ABSTRACT FROM AUTHOR]

Highlights: [•] The dynamic economic emission dispatch (DEED) model is presented. [•] In the DEED the total fuel cost and emissions are simultaneously minimized. [•] Hybrid DE-SQP and PSO-SQP methods have been proposed for solving the DEED problem. [•] The periodic implementation of the DEED solution is presented. [•] Results are compared to those published in the recent state-of-the art literatures. [Copyright &y& Elsevier]

Abstract: In this paper, a novel metaheuristic optimization methodology is proposed to solve large scale nonconvex economic dispatch problem. The proposed approach is based on a hybrid shuffled differential evolution (SDE) algorithm which combines the benefits of shuffled frog leaping algorithm and differential evolution. The proposed algorithm integrates a novel differential mutation operator specifically designed to effectively address the problem under study. In order to validate the SDE methodology, detailed simulation results obtained on three standard test systems 13, 40, and 140-unit test system are presented and discussed. Transmission losses are considered along with valve point loading effects for 13 and 40-unit test systems and calculated using B-coefficient matrix. A comparative analysis with other settled nature-inspired solution algorithms demonstrates the superior performance of the proposed methodology in terms of both solution accuracy and convergence performances. [Copyright &y& Elsevier]

Abstract: The implementation of Emission Trading Schemes (ETS) in the electricity supply industry has driven generation companies (GENCOs) to put efforts to reduce their produced emissions. Under a multimarket environment, a GENCO generates electricity subject to physical, fuel and environmental constraints. Separate and evolving research efforts are currently shaping electricity market, fuel market and carbon market without paying adequate attentions to how each market affects the others, though the markets have overlapping goals with respect to the global economic and environmental benefits. Under this background, this paper investigates the impacts of carbon policies on a GENCO''s decision making under multimarket environment. A dynamic decision making model is proposed to deal with the multimarket trading problem for a GENCO during each trading period. Differential Evolution (DE) algorithm is employed to solve the multi-period optimization problem for each time interval. Comparisons between different scenarios demonstrate the economic and environmental influences of different policies on a GENCO. With the proposed model, a GENCO can make a rational tradeoff between profit making and emission reduction under the three interactive markets environment. Policies defining the three interactive markets can accurately reflect the intended goals such as reducing emissions, promoting renewable and keeping electricity cost at a reasonable level. [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]

• Pareto-Based Multi-Objective Optimization of Multi-Area Power Generation • Nonlinear Threshold Accepting Based Heuristics • Multi-Area Power Generation Systems Control • Optimization of Coupled Automatic Voltage Regulation- and Load Frequency Schemes The AVR and LFC schemes are essential power system tools, which are used to support the operation, stability, and security of the generation systems in the main power networks. These control units are designed using numerical as well as analytical approaches. The numerical solutions that are based on heuristics for instance, vary in quality and procedure of the underlying optimization processes leading to the desired outcomes. These processes are mostly used to optimize a single objective criterion that compromises between different performance characteristics like, steady state and transient responses. This work introduces a nonlinear threshold accepting heuristic with an innovative multi-objective optimization architecture. This method is employed to select the control gains of a coupled AVR-LFC scheme for a two-area power system to ensure stable and fast AC generator bus voltage-frequency regulation in react to dynamic disturbances. This approach decides a set of control laws that guarantee fast dynamic response while optimizing conflicting objective criteria. It is validated against other standard heuristics, namely artificial bee colony, differential evolution, and particle swarm optimization in addition to other analytical approaches like internal model-control schemes. [ABSTRACT FROM AUTHOR]

Abstract: In this work, differential evolution (DE) algorithm was studied for solving economic load dispatch (ELD) problems in power systems. DE has proven to be effective in solving many real world constrained optimization problems in different domains. ELD problems are complex and nonlinear in nature with equality and inequality constraints and here special measures were taken to satisfy those. Five ELD problems of different characteristics were used to investigate the effectiveness of the proposal. Comparing with the other existing techniques, the current proposal was found better than, or at least comparable to, them considering the quality of the solution obtained. [Copyright &y& Elsevier]

Abstract: This paper presents a differential evolution (DE) based optimal power flow (OPF) for reactive power dispatch in power system planning studies. DE is a simple population-based search algorithm for global optimization and has a minimum number of control parameters. The problem is formulated as a mixed integer non-linear optimization problem taking into account both continuous and discrete control variables. The proposed method determines control variable settings such as generator voltages (continuous), tap positions (discrete) and the number of shunt reactive compensation devices to be switched (discrete) for real power loss minimization in the transmission system using DE algorithm. Most of the evolutionary algorithm applications to optimization problems apply penalty function approach to handle the inequality constraints, involving penalty coefficients. The correct combination of these coefficients can be determined only by a trial and error basis. In the proposed approach, the inequality constraints are handled by penalty parameterless scheme. Voltage security margin was evaluated using continuation power flow (CPF), to ensure the feasibility of the optimal control variable setting. The suitability of the method was tested on IEEE 14 and IEEE RTS 24-bus systems and results compared with sequential quadratic programming (SQP) method. The DE provides near global solutions comparable to that obtained using SQP. [Copyright &y& Elsevier]

Abstract: This paper presents a variable scaling hybrid differential evolution (VSHDE) for solving the economic dispatch (ED) problem in large-scale systems. Different from the hybrid differential evolution (HDE), the concept of a variable scaling factor is used in the VSHDE method. The variable scaling factor based on 1/5 success rule of evolution strategies (ESs) is used to overcome the drawback of the fixed and random scaling factor used in the HDE method. One 15-unit system from the literature and one practical economic dispatch system of Taiwan power company (TPC) are used to compare the performance of the proposed method with HDE, genetic algorithms (GAs) and simulated annealing (SA). Numerical results show that the performance of the proposed method is better than the other methods. [Copyright &y& Elsevier]

This paper presents the application of particle swarm optimization (PSO) technique and its variants to least-cost generation expansion planning (GEP) problem. The GEP problem is a highly constrained, combinatorial optimization problem that can be solved by complete enumeration. PSO is one of the swarm intelligence (SI) techniques, which use the group intelligence behavior along with individual intelligence to solve the combinatorial optimization problem. A novel ‘virtual mapping procedure’ (VMP) is introduced to enhance the effectiveness of the PSO approaches. Penalty function approach (PFA) is used to reduce the number of infeasible solutions in the subsequent iterations. In addition to simple PSO, many variants such as constriction factor approach (CFA), Lbest model, hybrid PSO (HPSO), stretched PSO (SPSO) and composite PSO (C-PSO) are also applied to test systems. The differential evolution (DE) technique is used for parameter setting of C-PSO. The PSO and its variants are applied to a synthetic test system of five types of candidate units with 6- and 14-year planning horizon. The results obtained are compared with dynamic programming (DP) in terms of speed and efficiency. [Copyright &y& Elsevier]

• Two voltage symbolic functions are defined to determine the fault segment. • Fault location equation is established based on the characteristic component. • Two-population Differential Evolution algorithm can accurately solve the equation. • ACD-IGLFD adaptively extracts the characteristic component and its appearing moment. Because the traditional fault location method cannot be applied to overhead and cable hybrid transmission line in the VSC-HVDC system, this paper proposes a novel fault location method, which primarily uses the principle of first determining the fault segment and then locating the fault point in the fault segment. Two voltage symbolic functions are defined, and the fault segment is determined according to the different numerical combinations of the two voltage symbolic functions. Through combining adaptive characteristic scale decomposition with improved general local frequency decomposition to adaptively extract the fault characteristic component, the intra-segment location equation is established based on the characteristic component to locate the fault point. The simulation results verify that the proposed method can accurately locate the fault point for the overhead and cable hybrid transmission line in the VSC-HVDC system. [ABSTRACT FROM AUTHOR]

• A two stage optimization APS to consider DG, FCL and DOCR. • Considering the scenario of over limitation of faults caused by FCL. • Formulating FCL sizing and location optimization using DEMO. • Formulating DOCR coordination optimization using DEMO. • DEMO avoids the time-consuming objective function parameter tuning. This paper presents a two stage optimization approach for solving the excessive network fault currents and resetting of overcurrent relays based on Adaptive Protection Scheme (APS) concept. The first stage consists of Fault Current Limiter (FCL) optimization to restore fault currents within Circuit Breaker (CB) thermal capacity whether caused by Distributed Generation (DG) or not. The second stage consists of Directional Overcurrent Relay (DOCR) coordination based on the adaptive protection concept to reset DOCR in order to function properly in presence of FCL and DG. This is of great importance since FCL may cause over limitation of fault current during minimum load operation which may degrade DOCR performance. The optimization process has been achieved by formulating both the FCL sizing and location problem and DOCR coordination problem using Differential Evolution Multi-Objective (DEMO) algorithm. The proposal has effectively re-coordinated the DOCRs contemplating the effects of FCLs and DGs which mitigated the faults surpassing CB thermal limit, DOCR miscoordination and degraded performance caused by FCL over limitation. [ABSTRACT FROM AUTHOR]

• With DC transients based differential protection, internal & external faults can be easily identified along with the phases involved in the fault based on sign of DC transients. • The proposed method does not require any special compensation for line charging currents. • Faults involving very high resistance and CT saturation can also be identified. Current differential protection is simple in principle and is immune to power swings, load encroachment and mutual coupling problems. It provides simultaneous tripping at both ends of the transmission line and multi phase auto reclosing can be easily enabled because of its accuracy in determining the faulted phase and its communication channels. Conventional differential protection has certain issues to be dealt with before using it as a best alternative to distance protection for power transmission lines. Many researchers strive to improve the sensitivity of the current differential protection. Sensitivity and safety are opposing elements and increased sensitivity may sacrifice safety. In this paper, a new differential protection algorithm is proposed using the polarity of the terminal current DC transients. Different cases are studied in order to illustrate the applicability of the proposed differential approach. The proposed algorithm is able to detect high resistance faults and is not affected by line charging current, CT saturation. It improves the sensitivity without compromising the safety of the system. Simulations are carried out on 10 Bus system using PSCAD® software and obtained results are used for illustrating the efficiency of the proposed differential protection. [ABSTRACT FROM AUTHOR]

• Looped structure is good for distribution smart grids. • Centralized fault management system with real-time Ethernet can provide ultra-fast speed for fault localization over a large geographical area. • Communication cycle time is derived for the design of fault management system. • Instantaneous values of sample points can be used in the percentage differential protection algorithm. • A test-bed is built with real-time digital system and a computer network simulator. The distribution smart grids in future are likely to have looped structures with a variety of new electronic devices to integrate renewable energy. However, tremendous challenges are brought to the fault management system (FMS), particularly the ultra-fast speed of fault localization with data acquisition at many different locations over a large geographical area. In this work, we propose a centralized fault management system (CFMS) with operation resilience based on real-time Ethernet (rtEthernet) protocols for the protection of smart grids. First, we propose a model to estimate the communication cycle time and the mis-synchronization error based on rtEthernet protocols. The model can be used to design CFMS over a large geographical area with a large number of power system devices. Second, we propose to use the instantaneous values of sample points in the percentage differential protection algorithm, instead of the value of root mean square (RMS), so that ultra-fast speed for data sampling and fault localization can be achieved. Third, we have established a comprehensive test-bed for the CFMS by using our large-scale real-time digital system (RTDS) to simulate the power system. Our extensive experimental studies have demonstrated that the proposed CFMS achieves ultra-fast speed within a sub-cycle and protects the smart grids successfully. [ABSTRACT FROM AUTHOR]