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The mechanical power of the wind turbine has a cubic relationship with wind speed. Therefore, during high wind speed, the output power of wind turbine driven permanent magnet synchronous generator (PMSG) needs to be maintained at rated value effectively. This can be achieved by the pitch angle control of the wind turbine. In this paper, a new application of the fractional‐order nonlinear proportional‐integral‐derivative (FONPID) controller is proposed to control the pitch angle of the wind turbine. The FONPID controller is derived from the fundamental structure of the proportional‐integral‐derivative (PID) controller, wherein the fractional operators are implemented to integral and derivative gains; also the integrator value is changed with respect to the magnitude of the error over the simulation period. Moreover, the gains of the proposed FONPID pitch angle controller are well‐optimized using parameter‐less teaching‐learning based optimization (TLBO) algorithm. The effectiveness of the proposed FONPID pitch angle controller is evaluated with regard to minimization of percentage overshoot, rise time, settling time, and also oscillation around the average output power. Furthermore, the performance of the TLBO tuned FONPID pitch angle controller is compared with the performance of TLBO tuned gain‐scheduled PID (GSPID), fractional‐order PID (FOPID), and nonlinear PID (NPID) based pitch angle controllers. [ABSTRACT FROM AUTHOR]

Background: Positive feedback (PFB) based anti‐islanding schemes have gained wide popularity in islanding detection. However, the disturbance injected by the PFB methods can easily cause the destabilization of the grid‐connected distributed generation (DG) system. Therefore, how to effectively balance the normal operation of the grid‐connected system and the effectiveness of the islanding detection is essential. Purpose: In this study, the trade‐off design of the PFB control gain is given, where the requirements of grid‐tied control and islanding detection are simultaneously considered. Design/Method: Firstly, the small‐signal models are established to investigate the stability of a DG system in both islanded and grid‐connected mode. Meanwhile, the minimum and maximum PFB gains are obtained by root loci method, thus, the PFB gain can be accurately determined without trial and error. Additionally, the Sensitivity analysis is conducted for the DG system. Factors such as grid impedance, phase‐locked loop (PLL) bandwidth, proportional‐integral (PI) controller gains, inverter output current rating, and load quality factor are thoroughly taken in account, which provides the guideline of the PFB gain design. Finally, simulations and experiment tests are performed to confirm the theoretical analysis. Results: This paper presents a trade‐off design of the PFB control gain for a DG system from the essence of the PFB algorithm. The essence of PFB as well as the cause of the interconnected system instability are revealed. In addition, the minimum and maximum PFB gains are obtained by the stability analysis. Conclusions: A high PFB gain will contribute to the islanding detection but deteriorate the grid‐connected system stability. Further, it is found that 1) the maximum PFB gain decreases with the grid impedance increasing, 2) the increase in PLL bandwidth and the integral gain in current loop lead to a decrease in the maximum PFB gain, 3) the minimum PFB gain which reflects the islanding detection capability of the VPF scheme is not affected by the load quality factor, 4) when the inverter output current is large, a reasonable PFB gain may not be found as the islanding detection and the normal operation of the grid‐connected system cannot be balanced. [ABSTRACT FROM AUTHOR]

Modeling high‐dimension dependence is a challenging problem since it involves too many parameters. In this paper, aquasi‐Monte Carlo (QMC) method based probabilistic load flow computation algorithm, which uses truncated regular vine copula and considers high‐dimension dependence of wind powers, is proposed. Firstly, the regular vine copulas, which use bivariate copulas as building blocks, are used to construct the primary high dimensional dependence. Then, truncation technology is adopted to reduce the computation burden and the memory consumption caused by the rapidly increased parameters number of input variables. Meanwhile, the nonparametric kernel estimation is used to estimate the wind speed marginal distributions and the bandwidth of kernel function is obtained by the direct plug‐in method. Further, QMC method is integrated into the probabilistic power flow computation for obtaining the sampled data of input variables. By the numerical simulation experiments on the modified IEEE 118‐bus power system, the superiority of the proposed probabilistic load flow computation method is verified. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new population‐based meta‐heuristic algorithm called Social Optimization Algorithm (SOA) is proposed. This algorithm has been inspired by the social behavior of human‐beings, and mimics an ideal justly‐established society where the two principles of justice, that is, equality of opportunity and community principles are meticulously followed. Reaching goals in such a society is optimally achieved. Although a real fully‐just society does not exist in the real world, we implement the principles in such ideal societies through simulations to introduce our new optimization algorithm. The advantage of the proposed method is that it does not have any algorithm‐specified parameters to be set. This is a great merit compared to similar meta‐heuristics. The algorithm has been simulated on 23 well‐known test functions, and the results have been compared with those produced by Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Differential Evolution (DE). In 57% of the 23 cases SOA yields superior results. The algorithm has also been tested on five other benchmark functions to be compared with Teaching‐Learning‐Based Optimization (TLBO) and Artificial Bee Colony (ABC).The results provide evidence for efficient performance with superior solution quality of the proposed algorithm in solving benchmark problems. To further illustrate the effectiveness of the proposed algorithm, it has been tested on the Economic Dispatch problem as a real‐world constrained optimization problem. The results have been compared with Biogeography‐Based Optimizer (BBO), Harmony Search (HS) and PSO. The results show that the SOA is able to produce very competitive results compared to other well‐known meta‐heuristic algorithms. Meta‐heuristic, SOA, Economic Dispatch. [ABSTRACT FROM AUTHOR]

The transmission line fault location algorithms are affected in presence of FACTS device like unified power flow controller (UPFC) due to changed voltage and currents signals at the relaying point after occurrence of fault. Additionally, these algorithms involve high computation burden associated with signal processing techniques and are proven to be in‐efficient during severe power system conditions. To eliminate the limitations stated above, a novel travelling wave aided intrinsic time‐decomposition (ITD) based fault location algorithm is presented in this paper for estimation of fault location in UPFC employed transmission lines. ITD offers several advantages over other time‐frequency‐energy (TFE) analysis tools such as presenting precise temporal information and involving less computation burden. ITD decomposes a given signal into a sum of proper rotation components (PRCs) followed by a monotonic trend. The proposed ITD based fault location algorithm estimates the arrival times of travelling waves from first PRC of modal voltage signals of the terminals to which the transmission line is connected. From the estimated arrival times, the fault location is calculated. For simulation, a 100 MVA UPFC controlling the power flows in a 500 kV transmission system is used. Different cases of faults with diverse power system conditions are applied to examine the robustness and efficacy of ITD based fault location algorithm. Further, ITD base method is compared to existing methods available in literature and found to be a better performer in the context of fault localization. [ABSTRACT FROM AUTHOR]

Summary: Objective: Online estimation of voltage stability margin (VSM) is critical to the long‐term safety and stable operation of power system. In this paper, a deep neural network (DNN)‐based model that incorporates the intrinsic grid topology information is proposed to achieve accurate VSM estimation. Methods: The node embedding information together with the electrical parameter measurements are combined together into an array of local structures as the input feature vector. A local‐global deep neural network (LGDNN) model is proposed to extract and integrate the local information into high‐level global representation for VSM estimation. The Node2vec algorithm is employed to obtain embedded node vectors and a NodeRank algorithm is designed to form local structures, which are composed of the measurements of a fixed number of similar nodes according to the similarity among their embedded vectors. A sequential DNN with cascade‐connected depthwise separable 1D convolutional layer and fully connected layer is trained to estimate the current VSM. Results: The model performance is validated on the IEEE‐39 and IEEE‐118 benchmark systems under normal and post‐contingency situations. Experiment results indicate that the proposed model can achieve VSM estimation with high precision that surpasses seven mainstream approaches in multiple estimation error indices. Conclusion: Through the integration of local structures, the proposed LGDNN increases the estimation accuracy with relatively few parameters than various DNN‐based models. Thus, it is an practical model for online power grid voltage stability monitoring. [ABSTRACT FROM AUTHOR]

This paper presents a novel data‐oriented unsupervised machine learning‐based theft detection approach for efficiently identifying the fraudster consumers. It accomplishes the above‐mentioned objective by exploiting the intelligence of the robust principal component analysis (ROBPCA) algorithm in conjunction with the outlier removal clustering (ORC) algorithm. To avoid the irregularities in acquired consumers' data from a power utility, the statistical features are extracted from each consumer's consumption patterns using an anomalous time series extension. Based on the extracted features, the consumers with most similar features are initially grouped into two categories using the ROBPCA algorithm. In order to evade any overlapping between the two newly formed groups, the ORC algorithm is utilized to categorize the consumers distinctly as "suspicious" and "non‐suspicious". Finally, a very selective onsite inspection is proposed, thus, saving the considerable time, resources, and overall cost of the utilities. The effectiveness of the proposed theft detection method is validated by comparing its performance with nine most widely used outlier detection methods on the basis of seven of the most prominent performance metrics. The accuracy and detection rate of the proposed technique are found as 94.34% and 92.52%, respectively, which is significantly higher than that of other studied conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to solve line outage detection (LOD) problem utilizing current phasors obtained from phasor measurement units (PMUs). A new term that is, objective coefficient value (OCV), is introduced utilizing the rank correlation coefficient (RCC) and line outage detection is carried out based on the OCV. Moreover, the effect of bad data (BD) and measurement noises are also considered in the developed mathematical model. Furthermore, it has been proved that the suggested scheme can identify outage of parallel lines (PLs), whereas the existing voltage phasor‐based algorithms fail to do so. Moreover, a new logical index that is, performance improvement w.r.t detection accuracy (PIDA) is introduced to quantify the superiority of the proposed scheme over some existing techniques. The developed algorithm is implemented on IEEE 5‐bus, 14‐bus, 57‐bus, 118‐bus systems, and large‐scale Polish 2383‐bus system. The comparison of test results with some existing methods proves the applicability and brevity of the developed algorithm. [ABSTRACT FROM AUTHOR]

Distortion of fault monitoring information is an important factor that affects the accuracy of faulted line section location in low‐voltage active distribution networks (ADNs). This paper proposes a faulted line section location method that considers the layered solution for monitoring information distortion, to improve the accuracy and efficiency of fault location in low‐voltage ADNs. A multi‐target fault section location optimization model that considers information distortion, multipoint faults, and multiple information distortion types is formulated. A combination of information distortion factors is introduced to represent various types of information distortion and to transform the original optimization model into an unconstrained 0‐1 programming problem. A hierarchical fault location algorithm for segmenting the distribution network is designed to solve the low accuracy and efficiency of direct solutions for fault location models resulting from a large number of active distribution line sections. Simulation results show that the fault location method considering information distortion effectively guarantees the accuracy and solution efficiency of faulted line section location of low‐voltage ADN. [ABSTRACT FROM AUTHOR]

Summary: Background: In this paper, a new hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. It is a combination of modified particle swarm optimization (PSO) and perturb and observe (P&O) methods. The proposed GMPPT technique, adaptive butterfly PSO (ABF‐PSO) uses butterfly swarm intelligence for modifying the conventional PSO algorithm with parameter tuning to avoid premature convergence. Aims: Hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. Further, a new reinitialization of particles for any irradiance change is proposed to get faster tracking. Materials & Methods: In the proposed hybrid GMPPT technique, first GP region is easily identified with adaptive sensitivity parameter of the ABF‐PSO algorithm and in the region identified, GMPP tracking is continued with P&O algorithm with variable length perturbations to avoid the unnecessary exploration of search space even after reaching global peak (GP) region. Results: The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady‐state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady‐state. Discussion: Boost DC‐DC converter is used as an MPPT controller to test the performance of the proposed algorithm under different irradiance patterns by using MATLAB/Simulink model and hardware prototype developed. Conclusion: The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady‐state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady‐state. [ABSTRACT FROM AUTHOR]

Summary: Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis‐based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision‐making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. [ABSTRACT FROM AUTHOR]

This paper presents a novel heuristic optimization approach using population variant differential evolution algorithm for solving the multiobjective economic emission load dispatch (EELD) problem. The EELD problem simultaneously takes into consideration the effect of gaseous pollutants like NOx, SOx, etc, including the cost of the fossil fuel used in the thermal power plants. A population refreshment mechanism, based on the concept of interquartile range, has been applied to the classical differential evolution method to compute the EELD problem successfully. The algorithm has been tested on IEEE-30 bus 6-generator test system and a standard 40-generator test system, taking valve point loading effects into consideration for effective solutions. The results have been compared with the standard existing techniques in the literature such as linear programming, multiobjective stochastic search technique, nondominated sorting genetic algorithm, fuzzy clustering particle swarm optimization, and modified bacterial foraging algorithm, which authenticate the capability of the proposed algorithm. A novel concept of applying the proposed methodology to a deregulated power market has also been presented in this paper, where successful results have been obtained. This method appears as an efficient and robust optimization algorithm in terms of minimizing the total cost, emission, and computational time. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a comprehensive analytical study of conventional proportional‐integral‐derivative (PID) controller and fuzzy logic controller in a hybrid micro‐grid system (HMGS) for frequency stabilization. Conventional PID and fuzzy logic controllers are tuned by the competition over resources (COR) algorithm. The performance of proposed COR based fuzzy tilt‐integral‐derivative with filter (FTIDF) method is demonstrated on an interconnected HMGS. HMGS completes with variety of energy storage systems and different renewable generation sources. The system analysis is performed under number of scenarios. The scenarios include load fluctuations, variations in wind speed, and sun irradiance. The supremacy of FTIDF controller is established by comparing the results with CRO tuned conventional PID with filter (PIDF), fuzzy PIDF and fuzzy TIDF controllers. The effectiveness of the proposed system is investigated using simulation studies, which are performed using MATLAB/SIMULINK software. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the operation of various optimised AC‐coupled hybrid power systems for an isolated community in South Australia. In addition to diesel generators, various combinations of distributed generators and storage technologies are considered to form different hybrid power systems. Optimal design of the hybrid power systems is implemented using particle swarm optimisation (PSO) algorithm. The performance of the PSO in optimizing the above hybrid systems is discussed in detail. Whilst meeting the power system operational constraints, the minimum levelised cost of electricity (LCOE) of all systems, with various combinations of system components, are calculated. Simulation results obtained for each optimized system are carefully described and analysed. The annual operations of various components in each system are then compared and discussed in detail. A sensitivity analysis by varying the cost of wind turbines and the photovoltaic is also conducted for all hybrid systems. It has been found that the inclusion of energy storage systems (battery and flywheel) can reduce the LCOE and CO2 emission significantly compared to the inclusion of renewable generations (solar and wind) alone. The diesel‐wind‐solar‐battery system is the eco‐friendliest with an annual CO2 emission of 451 t compared to the reference diesel system (1064 t). In addition, the hybrid system consisting of diesel, wind, solar, battery and flywheel is found to be the most economic with a LCOE of 58.94 ¢/kWh compared to the diesel system with a LCOE of 67.17 ¢/kWh. [ABSTRACT FROM AUTHOR]

Summary: Conventionally, voltage profile are regulated by controllers that are not operational flexible to the system dynamism and challenges offered by the integration of renewable energy sources. Consequently, there is a need for the adaptive coordinating mechanism as an interface between the passive network and the new active network. This will provide a platform for the smooth integration of distributed energy resources in most developing countries where traditional network is still operational. This paper proposes an algorithm for real‐time multiperiod adaptive compensation of network reactive power support based on hierarchical structure and decentralized layered multiagent system voltage coordinating scheme. A multiperiod voltage control algorithm replicates the daily load flow evaluation aiming at ameliorating voltage fluctuation over a 24‐hour simulation period. The algorithm applies the decentralized voltage control mechanism to monitor the network parameters and dynamically evaluate the capacity of the capacitor banks (CBs) to be injected for voltage compensation. This mitigates the challenges associated with under and over reactive power compensation experienced in the day‐ahead programming and manual switching operation. The algorithm works in conjunction with an on‐load tap changing transformer (OLTC) and energy storage system (ESS). The ESS complements the OLTC during switching delay, dip in its output root mean square and voltage fluctuation as network loading condition varies. The effectiveness of the proposed method is verified on standard IEEE 33 and 118‐bus distribution systems. The results show an improved voltage performance and reduction in stress/complexity associated with load and renewable generation forecast in manual switching operations. [ABSTRACT FROM AUTHOR]

Summary: Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi‐objective method. The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multi‐objective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33‐bus and IEEE 123‐bus radial network. [ABSTRACT FROM AUTHOR]

Summary: For proper performance of protection and control systems in microgrids, an islanding event must be detected as soon as possible. In this paper, a novel algorithm is proposed to detect the microgrid operation mode using only the voltage and current samples at the relay location. This approach can be used in all microgrids with any type of source such as synchronous or inverter‐based distributed generators. An optimal method is proposed to determine the input parameters of a classification method by using local information. The goal of the optimization is to minimize the time and maximize the accuracy of microgrid connection state detection. Moreover, in the classification method, three states, that is, islanding, reconnection to the utility grid and other events are considered. The objective function of this problem is defined as the summation of the weighted time factor and detection error. The inputs of the islanding detection problem can be a large number of network parameters, such as frequency, voltage, current, power, their sequence components or rates of changes. Consequently, the variables of the classification problem are the number and the type of input parameters of the classification problem. The optimization problem is solved by using a genetic algorithm‐based, and a support vector machine is used for the classification process. In order to evaluate the validity of the proposed method, different situations are considered for the operation of the grid, and in each case, different events are simulated. The results are compared with similar methods, and the advantage of the proposed method is shown. [ABSTRACT FROM AUTHOR]

Summary: This paper elaborates a novel optimum programming‐based algorithm that embeds the stochastic multiobjective particle swarm optimization (PSO) method and the deterministic interior point method to calculate the maximum wind power penetration level. With the optimization target of promoting the wind generation capacity, the three‐stage optimization strategy is established to contemplate the transient stability constraint (TSC) as well as the uncertainty factors in the high wind penetrated system. To address the uncertainty factors in system, the chance‐constrained optimization approach is practiced to figure out the initial optimal operating point in the first stage. On the ground of which, the TSC is reinforced in the second stage to delineate the dynamic feasible region. Among the obtained security domain, the ultimate operation solution is calculated in the last stage and provides operators with specific operating scheme. The framework is capable of supporting alternative optimal methods and can be extended to more complex system modeling. The feasibility of the algorithm framework has been demonstrated by simulations on two benchmark systems. And it has prosperous application prospects for optimization problems that need to consider system dynamic security and uncertainty simultaneously. [ABSTRACT FROM AUTHOR]

Summary: This study addressed the problem of multi‐area state estimation in a clustered distribution system. Distribution networks are inherently expansive and comprise a multitude of nodes. This issue increases the state estimation computation time and makes it inapplicable for control of sophisticated distribution networks. Multi‐area state estimation is a technique to reduce computation time while concerning computation accuracy. Many efforts are required to reach a perfect algorithm, followed by the optimization of different parameters of the proposed algorithms. This paper performed a precise mathematical analysis of the impact made by the common (shared) node exchanged information between the areas in the multi‐area state estimation algorithm. Furthermore, a new iterative multi‐area state estimation algorithm equipped with machine learning tools was designed based on analytical detections for enhancing the convergence speed and accuracy of the estimation results. The improvement was evaluated in two clustered networks with 356 and 711 nodes. The results indicated the benefits provided by the proposed modification in terms of convergence speed and accuracy with minimum data exchanges in an iterative multi‐area distribution network. [ABSTRACT FROM AUTHOR]

Summary: In the electrical grids, the main target of the controller design is represented in the decreasing of the settling time and the overshoot of frequency deviations. The optimization of the controller gains represents the main challenge that affects the controller performance. Many intelligence techniques are used for the tuning of the controller gains such as the genetic algorithm (GA) and the bacterial foraging optimization algorithm (BFOA). These algorithms may have some drawbacks when the complexity of the system increases. These drawbacks may be due to the design method of the gain selection of the proportional‐integral (PI) controller. The design method may characterize the PI controller to reduce the maximum overshoot of the system response but the settling time increases. On the contrary, another different design method may characterize the PI controller to reduce the settling time of the system response but the maximum overshoot increases. This paper suggests a new variable structure gain scheduling (VSGS) for frequency regulation. The VSGS can take advantage of the BFOA‐based PI controller, which is the minimization of settling time, and the advantage of the GA‐based PI controller, which is the minimization of overshoot. The performance of the proposed method is compared with the performance of the GA‐based PI controller and the BFOA‐based PI controller. [ABSTRACT FROM AUTHOR]

Summary: A higher accurate load forecasts can significantly reduce the potential cost for power system operation and power market decision. There have been extensive load forecasting methods proposed by now. Ensemble learning is an effective way to integrate various forecasts and further improve the forecasting accuracy. Traditional ensemble learning algorithms static. However, how to select a suitable forecasting method from a large number of load forecasting methods under the complex and varying environment conditions is still challenging. In this context, this paper proposes a dynamic integration approach for multiple load forecasts based on reinforcement learning. Compared with the traditional integration methods, this method adopts Q‐learning algorithm to realize the adaptive selection of the forecasting methods and dynamic weight calculation for different and varying environment. The basic principles and implementation processes of Q‐learning algorithm is firstly introduced. Then, the implementation methodology of dynamic integration approach of multiple load forecasts is proposed. On this basis, the environment state, action space and return function are designed according to the requirements of Q‐learning algorithm. Finally, a case study based on the real‐world data in Guangzhou shows that the average forecasting accuracy could be improved by almost 3% by adopting the dynamic integration approach with the traditional ones. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a protection method for microgrids by data mining of voltage disturbances. The occurrence of a fault on the system is associated with a sudden voltage depression, which is fast detected by the adaptive cumulative sum (ACUSUM) algorithm. There are other operational (no‐fault) events causing voltage depression such as motor starting, transformer energizing, and capacitor or heavy load switching. In order to discriminate between the fault and no‐fault events, one cycle of the voltage waveform is preprocessed by the short‐time Fourier transform (STFT) to extract and construct effective features of the disturbance. The features are then used in the decision trees (DTs) for the discrimination. The proposed protection method is tested for fault or no‐fault conditions of grid‐connected or islanded mode of the microgrid operation, as well as radial or meshed topology. The proposed method also identifies the fault type and faulted phase(s) for selective phase tripping. The immunity of the method against different noise levels is investigated. It is shown by the simulation study that by using only two features for symmetrical events and six features for asymmetrical events, any fault can be detected accurately. [ABSTRACT FROM AUTHOR]

This paper suggests a new self-adaptive modification method using firefly algorithm (FA) to investigate the multi-objective probabilistic distribution feeder reconfiguration problem. In this regard, the idea of phase angle vector is employed to replace the traditional Cartesian framework in the FA and thus called θ-FA. Also, a new modification method based on an adaptive mechanism is suggested that will allow each firefly to choose the appropriate modification technique during the optimization suitably. As regards the objective functions, the main focus of this paper is to assess the effect of the reconfiguration on the reliability indices including active power losses, voltage deviation, and system average interruption frequency index. In order to handle the uncertainty effects, a sufficient framework based on 2 m + 1 point estimate method is proposed too. The satisfying performance of the proposed method is checked using IEEE 32-bus radial distribution system. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper investigates bidding strategy problems of generation companies in electricity markets. A new bidding model is set up, and a solution approach is presented. Compared with existing models, the model proposed in this paper has some different characteristics. First, the model adopts a bilevel optimization framework where the upper-level optimization is to maximize the profit of generation companies, and the aim of the lower-level optimization is the maximal social welfare and the security of the operation system considered by the Independent System Operator. This model combines practical situations faced by market agents in real-life operations and the requirement of both the system security and the social welfare. Second, the popular risk measure, called conditional value at risk, is used in the bidding model, which can quantify the market risk and describe the competitive mechanism effectively. For solving the complicated bilevel optimization model, a hybrid particle swarm optimization algorithm is presented. Numerical examples are used to validate the model and the algorithm. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Power transformer differential relays may incorrectly operate due to inrush condition or transformer external faults lead to current transformer (CT) saturation. In this paper, a new technique is presented to distinguish internal fault current from magnetizing inrush current in power transformers. The proposed technique applies two moving windows which both of them estimate magnitude of transformer differential current using different methods. The first moving window method is based on full-cycle Fourier algorithm (long window) which uses one cycle samples of the current and provides precise estimation. The second one is originated from least error square (LES) method that has five sample point input data (short window). LES method has fast response, but its estimation might not be quite accurate. The proposed technique (which is a combination of two mentioned methods) presents a new criterion for discrimination between internal fault currents and inrush currents using the differences between output values of these two estimators. Meanwhile, obtained results demonstrate precise operation of the proposed algorithm for different conditions such as CT saturation and over-flux condition; therefore, it can be a multi-objective technique for transformer protection. Also, a general scheme is presented in order to improve differential protection. In this paper, a real part of 400/230 kV Iranian national grid is simulated using PSCAD software for evaluating the performance of the proposed algorithm. Also, additional validation of the proposed technique is tested offline using data collected from a three-phase, 5 kVA, 60 Hz, laboratory prototype transformer. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is concerned with determining the compensation capability of an interline dynamic voltage restorer (IDVR) under various balanced and unbalanced voltage sag scenarios. An IDVR consists of two or more DVRs with a common dc link which enables active power exchange among DVRs. This feature increases the compensation range of an IDVR as compared with separate but otherwise similar DVRs with no energy storage. The steady-state modeling and analysis of an IDVR is briefly explained in this paper. The expressions governing power requirement for compensating various voltage sags are derived. Consequently, the constraints associated with an IDVR operation are obtained and an algorithm is developed to calculate the compensation range of an IDVR and two similar storage-less DVRs. Illustrative graphs demonstrate the operating characteristics of an IDVR and compare these characteristics with those of two similar storage-less DVRs. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Control of grid tied inverter attached to the distribution level single phase grid needs to be robust in order to be resilient to grid disturbances and dynamics, without dissension of output power quality. This calls for an improvement of existing synchronization techniques whose bedrock is the phase locked loop algorithm. The paper proposes a phase and frequency estimation technique which shows excellent performance to dynamic changes and has high distortion rejection capability. The technique used interpolated FFT which estimates the frequency followed by sample value adjustment which gives highly accurate phase estimate by transforming off-nominal frequency samples into nominal data points. The technique being a discrete algorithm, system identification is performed to estimate the transfer function model upon which stability and dynamic analysis are performed. A comparative analysis with SOGI PLL, PARK PLL, TPFA PLL, and Fourier PLL shows the superiority of the proposed technique in terms of accurate steady and dynamic state estimation capacity during grid disturbance and faults. The study of the computational complexity and applicability in FPGA controller reveals the feasibility of implementation of the technique in low level FPGA controllers. The proposed algorithm has been implemented in NI cRIO FPGA controller to tap real-time signals and control the inverter. Thus, this PLL is suitable for fault-ride-through operation of the inverter in feeding power to the distribution grid. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach of Inherent Structural Characteristics Theory (ISCT), for solving loss allocation to network participants as well as generation-to-load matching problems in a deregulated environment. The mathematical formulations of ISCT, based on the fundamental circuit theory laws, are revisited. A Generation-to-Load Allocation Coefficient (GLAC) matrix for solving generation-to-load allocation and network loss allocation to load problems, for efficient transmission pricing, is formulated. The allocation of real and reactive power contributions, by individual generator, required to serve the network demands is also determined on the basis of the GLAC matrix. Total network losses are determined and allocated to individual network loads based on GLAC matrix. The approach is demonstrated using the standard IEEE 30 bus network. The results obtained are compared with that obtained using graph theory approach based on the solved power flow. The comparison of the results shows that the ISCT approach is reasonable and it is a good signal, which could be useful for pricing of electricity by the market regulators. [ABSTRACT FROM AUTHOR]

SUMMARY The work carried out in this paper is to deal with a proposed conductor temperature rise computation algorithm. This simple algorithm gives more accurate calculation, making use of the load current and the weather data parameters, particularly the amount of solar incidental radiation upon the conductor and the conductor material properties. The calculation is based on the heat transfer mechanisms occurring in the stranded exposed overhead transmission lines (OHTL) conductors in a natural environment. In this paper, the mathematical models for the use of the natural and forced convections are presented by using Numbers of Grashof and Reynolds, respectively, in the determination of the temperature rise of the OHTLs. Skin effect and iron losses are also taken into account in order to determine the alternating current resistance of an Aluminium Conductor Steel Reinforced in the process of iteration's heat balance equation. The surface area factor of the stranded conductors that increases the loss by radiation from the conductor surface is also used to calculate the temperature rise of the stranded OHTL. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach based on modified binary artificial bee colony (MBABC) algorithm and dynamic economic dispatch (DED) method for unit commitment problem (UCP). MBABC algorithm is used for committing/decommitting the thermal units, while optimum dispatch solution is determined using DED method. Proposed MBABC algorithm utilizes a new mechanism based on the measure of dissimilarity between binary strings for generating the new binary solutions for UCP. Moreover, in MBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The solution quality achieved by MBABC is enhanced by hybridizing the genetic crossover (GC) that provides the diversified search space. Performance of the proposed MBABC-GC algorithm is tested up to 300 thermal units over 24-hour time interval. The comparison of obtained results with other methods in the literature has confirmed the superiority of the MBABC-GC algorithm in terms of production cost and robustness. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The choice and design of a high efficient maximum power point tracking (MPPT) algorithm is a necessity in the PV system design. Many approaches have been proposed in literature, among them, the methods that are based on perturb and observe (P&O), widely used in commercial products due their simplicity and ease of implementation. In this paper, a new modified P&O (MPPT) method with adaptive duty cycle step size using fuzzy logic controller is proposed. Both, simulation and experimental design are provided in several aspects. The proposed and classical methods are developed and tested successfully using a CUKDC-DC converter, which is connected to a SunTech STP085B model. The proposed method is able to improve the dynamic response and steady-state performance of the PV systems simultaneously and effectively. In addition, analysis and comparison with the conventional fixed step size P&O have been presented. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The recent trends toward enhancing distribution system automation and the development of microgrids are making the perspective of applying intraday reconfigurations more and more appealing. This paper formulates and applies a comprehensive rule-based approach to determine the configuration with minimum losses during time for a distribution system with time-varying load and local generation patterns. The results of the optimization carried out for successive periods are used to develop an original procedure for determining the timings of the intraday reconfiguration by taking into account the ranking of the best configurations and the persistence in time of these configurations. The objective function used depends on a parameter called cost ratio, that is, the ratio between the cost of the energy losses kilowatthour and the cost of performing a switching operation. A specific indicator (called objective assessment ratio) is introduced in order to quantify the convenience of performing intraday reconfiguration with respect to maintaining during the day the same configuration leading to the lowest daily energy losses. The results obtained on two classical test systems and on a real distribution network are included, comparing the outcomes of the proposed approach with the results obtained from other methods. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY The significance and influence of bilateral component on optimal dispatch of mixed pool-bilateral electricity market is the major issue to be examined for trade-off between pool and pre-specified bilateral transactions. This paper proposes single part optimal dispatch model that dispatches pool in coordination with privately negotiated bilateral transactions while minimizing cost of generation, accounting power balance equality constraints and inequality constraints. The critically loaded transmission line is identified, and when Unified Power Flow Controller (UPFC) is placed in it, optimal dispatch solution gets modified because of incorporation of UPFC parameters in the developed formulation. The methodology of multilayer perceptron network is adopted in this work with inputs to the neural network as percentage of bilateral component and status of UPFC for online evaluation of generation cost and incremental cost. The network is trained with back propagation training algorithm, and for IEEE 30 and IEEE 118 bus systems, it is established that Levenberg-Marquardt algorithm outperforms other algorithms in terms of accuracy. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for the determination of minimal cut sets in substation reliability evaluation. The proposed technique detects events including the malfunction of circuit breakers referred to as stuck-breaker conditions. These selections are important steps in substation reliability evaluation. The effectiveness of the proposed technique is examined by it being applied to breaker-and-a-half, ring bus and radial configurations. The proposed technique can be easily used in the expansion of computer programs in order to obtain the substation reliability indices. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Passive and Resonant Harmonic Filters (RHF) are the effective devices installed in distribution systems for reducing distortions caused by nonlinear loads by means of creating a low impedance path. Achieving both harmonic reduction and filter performance is complicated and contradictory. To attain optimum filter performance, a compromise is to be made between these two conflicting goals. Optimal selection of filter parameters reduces supply current harmonics and improves filter performance. This paper examines Passive Harmonic Filter and RHF for three-phase rectifier system acting as a nonlinear load. It seeks to find optimal values of filter branch parameters to diminish harmonics using Evolutionary Algorithms such as Particle Swarm Optimization and Differential Evolution. Also, it improves system power factor. The detailed simulation results using MATLAB are presented to support the performance of the proposed optimized filters. The actual measurement of the harmonic levels from the hardware implementation validates the superiority of the optimized filter over conventional filter. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a new fast discrete S-transform (ST)-based time-frequency signal analyzer has been proposed for the detection, classification, and monitoring of power quality (PQ) disturbances varying in an electric power system. The proposed algorithm is based on the generalized Fourier algorithm that is used to obtain the time-localized spectral characteristics of the time-varying voltage and current signals belonging to PQ events. The fast ST algorithm is realized with different types of frequency scaling, band pass filtering, and interpolation techniques based on Heisenberg's uncertainty principle resulting in a reduced computation cost. In the conventional ST, the window width decreases at higher frequencies with a reduction in frequency resolution and conversely at low frequencies with wider windows. Therefore, the time-varying PQ disturbance signal is down sampled at low frequencies and cropped at high frequencies resulting in the evaluation of a fewer samples. From the time-frequency matrix output, important features are extracted and used with a binary decision tree for an accurate classification of single and simultaneous PQ events. Further, a unified approach is presented to track the time-varying PQ disturbance waveforms like voltage sag, swell, harmonics, and oscillatory transients and produce estimation of their amplitudes and phase angles. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Multi-objective evolutionary algorithm (MOEA) based on Pareto optimality has emerged as an important approach for optimal reactive power dispatch (ORPD) problem. However, because of the deficiency of adopting evolution operators with single search characteristics, existing MOEA for multi-objective ORPD (MORPD) often fails to maintain universal and robust performance in different phases of optimization process. On the basis of running multiple algorithms simultaneously and adaptive selection strategy, this paper proposes a new optimal method for MORPD. Firstly, an algorithm candidate pool containing four different algorithms is constructed through the analysis of characteristics of state-of-the-art MOEA while considering the rules of consistency and complementarity. Then, during the optimizing search, the quantity of offspring individuals generated by each candidate algorithm at different stages of optimization process is determined adaptively by learning from its previous experience in generating promising solutions. The proposed method is tested on the IEEE 30-bus system; its computing performance is compared with existing popular MOEAs from the point of view of Pareto fronts, outer solutions and C measure. Experimental results show that the new method can obtain better performance of convergence during the entire optimization process. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we propose an optimal bidding strategy for industrial combined heat and power (CHP) installations selling their power on multiple markets. This corresponds to the typical situation where power is traded on a day-ahead market (DAM) and a continuous intraday market (CIM). Each market has its own trading rules. The considered installations consist of a CHP, a conventional heating installation and a heat buffer. Each device has its own constraints, such as maximum and minimum deliverable heat and electrical power, and minimum and maximum buffer capacity. The objective is to determine the bidding strategy that will maximise the expected profit, while the future time evolution of both heat demand and market prices are unknown. To tackle this problem, we assume that the probability density functions (PDFs) of these variables are known or can be extracted from historical data. Then, by applying a tailored stochastic programming algorithm, the optimal bidding strategy can be constructed based on these PDFs and includes the different market rules and constraints on the installation. For a DAM, the bidding functions must be estimated in advance, which is a typical open-loop problem. On the other hand, the bidding functions for a CIM may be estimated almost in real time. This new scheme is exemplified for the Belgian market. Combining both markets can increase the expected profits significantly because risks due to uncertainties in heat demand are better controlled. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Reactive power optimization plays an important role in safe and economic operation of power systems. However, with multiple mixed variables, this issue is well known as complex, nonlinear and multi-constrained. In this paper, combined particle swarm optimization (PSO) algorithm with chaos and multi-agent system, a novel algorithm (MACPSO) is developed and applied to reactive power optimization of power system. It synthesizes advantages of the swarm search of PSO and the intelligent search of agents. Seen as a particle of PSO during the search process, each agent competes and cooperates with the neighboring agents so as to obtain solutions of high quality swiftly. To strike a balance between intensification and diversification strategy and to reduce the probability of falling into local optimum, chaos optimization algorithm is adopted in the proposed algorithm. In order to verify the effectiveness of the proposed algorithm, the performance of MACPSO is evaluated on four classical testing functions and reactive power optimization. Experiment results show that MACPSO has excellent search ability and highly accurate convergence. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Distributed generation (DG) systems are small-scale power generation technologies that offer better reliability of supply, reduced transmission, and distribution losses and cost. The benefits of DG greatly depend on their placement and size. Most of the existing analytical methods developed so far are applicable for single DG installation only. Further, in meta-heuristic-based methods, location and sizing problem of DG were dealt separately in which optimal locations are identified with sensitivity-based methods, and optimal sizing is obtained using meta-heuristic algorithms which may lead to incorrect location and suboptimal sizing. In this paper, a novel method that employs harmony search algorithm with differential operator is proposed to install multiple DG units optimally in distribution system with an objective of minimizing active power loss and improving voltage profile. The proposed method is tested on 33-bus and 69-bus radial distribution systems to demonstrate its performance and efficacy. Results show that the proposed method is superior to existing methods in quality of solution. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, the application of the hyperbolic S-transform is proposed for detecting internal incipient faults of transformers during the impulse test. This test is performed on the under consideration transformer and during this test, the input current and applied voltage are recorded. The S-transform is applied to the recorded input current. An index is proposed and computed considering absolute deviations of the S-matrix. Faulty and non-faulty windings are discriminated by using the suggested index. The data obtained from field tests of a 66 kV/25MVA interleaved transformer winding and computer simulations, are recorded. Then, these data are fed to the proposed method, and faulty and normal conditions are discriminated, accordingly. Also, a wavelet-based method is implemented in MATLAB environment and the proposed method is compared with the wavelet-based method. According to simulations and experimental results, the proposed method is superior to wavelet transform-based method. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Current differential protection is widely used in transmission line due to its inherent simplicity and good performance. In order to strive to increase sensitivity of protection without compromising stability on external faults, phase correlation current differential protection is presented in this paper. Current phase difference is introduced to the differential current principle so as to improve the sensitivity and reliability of differential protection simultaneously. Comprehensive analysis and comparisons of performance characteristics in Alpha Plane demonstrate that phase correlation current differential protection has preferable performance than that of conventional current differential protection. As far as CT saturation is concerned, the proposed protection scheme has flexible adaptability to ensure significant improvement in sensitivity and dependability for internal faults while still improving stability for external faults. The performance of the proposed protection is investigated for various faults of transmission line simulated by Electro-Magnetic Transients Program including DC and record fault data from real power system. The test results show that the proposed protection has satisfactory operation performance. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: This paper describes about implementation of phase‐locked loop (PLL)–based control algorithms for distribution static compensator (DSTATCOM) used to mitigate power quality issues due to unbalanced and non‐linear load. Three control algorithms based on moving average filter PLL (MAF‐PLL), double second order generalized integrator PLL (DSOGI‐PLL), and adaptive filter with frequency estimation loop PLL (AFFEL‐PLL) are used in this paper. These are more suitable to mitigate the power quality problems created by non‐linear and unbalanced loads in distribution system. The reason behind selection in this application is explained as follows. The MAF‐PLL has great filtering quality with less computational burden on processor which is verified using total harmonic distortion (THD) performance. AFFEL‐PLL has variable integral gain that prevents large phase jump during frequency drift and load transient condition, this gives fast response. DSOGI‐PLL uses both orthogonal components and prevents oscillatory error and gives more stable operation. The MAF‐PLL and AFFEL‐PLL uses only quadrature axis "q" component for reduction of phase error and detection of theta. To mitigate power quality issues, continuous monitoring of AC grid system is required, which provide information in terms of frequency, phase, and fundamental voltage magnitude. Out of all, the phase detecting methods using PLL is standard method to extract such information. All the PLLs mentioned here extract the required grid information. Further, DSOGI‐PLL converts the distorted three‐phase load current into respective orthogonal components. All the control algorithms mentioned are implemented using MATLAB and performance of most effect algorithm is recorded in laboratory environments. [ABSTRACT FROM AUTHOR]

SUMMARY: Measurement algorithm is the important element of Intelligent Electronic Devices (IEDs) used in protection and monitoring of transmission and distribution networks. The function of the measurement algorithm is to estimate fundamental frequency component (phasor) of current and voltage signals recorded during network faults. The estimated fundamental component is used by variety of protection algorithms for the successful operation of the IEDs. The most popular measurement algorithm is the Discrete Fourier Transform (DFT). However, the estimation of the fundamental frequency component by the DFT algorithm will produce error if input signal contains nuisance components such as a decaying DC offset. This paper evaluates the performance of DFT measurement algorithm when its input signal is influenced by variety of the nuisance components. Various random factors will impact the amount of nuisance components present in a measured signal. Therefore, we propose a novel global uncertainty and sensitivity analysis technique to determine in a systematic way the impact of nuisance components on the performance of measurement algorithms. In addition to the full‐cycle DFT, we analyze performance of half‐cycle DFT and full‐cycle cosine filter algorithms. Impact of nuisance components due to current transformer (CT) saturation and without saturation was studied. Steady‐state and transient performance indices are defined to evaluate the performance of the algorithms. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Power demand control is required at steelmaking plants and other energy intensive industrial plants where the electrical bills include high demand charges and are subject to demand limit contracts. This paper introduces a novel and simplified algorithm for the implementation of a power demand controller. The presented algorithm does not require a model of the electrical load since it is based on a geometrical approach for the estimation of the disconnection time of the controllable load and only requires the nominal power values and the load factors of both the total (full) load and the noncontrollable (base) load as parameters. The power demand control method presented is verified with a computational simulation, and precise control of the demand limit is achieved under random variations of the base and full loads. The proposed control algorithm is simpler than commercial methods and achieves a competitive performance. Simulation results show a better performance by calculating the disconnection times based on load parameters specified as mean values between the average and nominal powers of the full and base loads. Some considerations to tune‐up the load factor parameters are also discussed. The presented control method has been successfully implemented on industrial controllers in two steelmaking facilities in Mexico, having an electric arc furnace as controllable load in both cases. This novel demand control is adequate for industrial plants with electrical loads that can be disconnected without affecting the quality of the product and with minor impact on productivity. [ABSTRACT FROM AUTHOR]

Summary: Due to high importance of islanding detection as an essential protection requirement, especially for the distributed generation (DG), a new approach to islanding detection is developed in the present paper. The method is based on a mixture of both active and passive algorithms, which uses voltage phase angle (VPA) and voltage unbalance (VU). The suggested method presents a combination of benefits of each individual passive/active method, and therefore it is useful to appropriately detect islanding situation in a wide range of scenarios. The use of the suggested method results in smaller nondetection zone (NDZ), faster response, and improved power quality. In order to illustrate the effectiveness of this approach, simulation results are provided under different conditions, showing that the suggested algorithm can reach the required islanding protection requirements. In addition, the performance of the proposed method and some other methods are compared by the presence of a variety of transient faults and insignificant power mismatch. [ABSTRACT FROM AUTHOR]

Summary: This paper describes an empirical mode decomposition control algorithm to extract the fundamental component and set the reference for harmonic extraction in a cascaded multilevel inverter–based shunt active filter. The proposed technique is fully reliant on the data samples that are decomposed instantaneously and does not require any weighing factor. The fundamental component of load current is found by applying a novel empirical mode decomposition algorithm from the nonlinear load current samples. Spline interpolation is used to extract the intrinsic mode function. For online implementation, the maxima and minima points of load current are stored using a modified field programming gate array (FPGA)‐based circular buffer circuit. Finally, the proposed cascaded multilevel inverter–based shunt active filter is investigated both by simulation and hardware implementation. The results obtained validates the performance of the proposed control technique under balanced and unbalanced loading conditions. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an energy management strategy of direct current (DC) microgrids (MGs) is presented. The proposed MG consists of fuel cells, batteries, and supercapacitors, along with associated DC/DC and DC/AC converters. Coordination between different sources of MG is implemented using a multi‐agent system. Multi‐agent algorithm is implemented using an open source agent building toolkit, Java Agent Development framework (JADE). The energy source components are modeled and implemented using MatLab/Simulink (power system library). The proposed energy management strategy is designed using proportional‐integral (PI) controllers, which is implemented using the JADE. Interface between MatLab/Simulink and multi‐agent system (JADE) is done with the help of MACSimJX. The proposed multi‐agent framework is presented and the interface between the JADE and MatLab/Simulink is described in details. Then, the design and implementation steps of the PI controller using JADE are presented. Simulation work is carried out, and the results show that the proposed multi‐agent‐based controller effectively coordinated with variable loads in MG. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes an improved moth flame optimization (IMFO) algorithm to effectively solve the optimal power flow (OPF) problems. The concept of moth flame optimization (MFO) is inspired from the movement of moth towards the moon direction. IMFO is mainly based on the concept of MFO with modifying the path of moths in new spirals around the flame. Standard IEEE 30‐bus, IEEE 57‐bus and IEEE 118‐bus test systems are used to validate and prove the efficiency and robustness of IMFO algorithm. The validation of the proposed algorithm is based on 15 case studies in terms of different single and multi‐objective functions: fuel cost minimization, gas emission reduction, active power loss minimization, voltage profile improvement, and voltage stability enhancement. The simulation results of the proposed algorithm are compared with those obtained by other well‐known optimization techniques. The obtained results demonstrate the capability and robustness of IMFO algorithm to solve OPF problems. The results reveal that IMFO algorithm is capable of finding precise and better OPF solutions compared with the other techniques. A comparison among the convergence characteristics of IMFO technique and the other techniques proves the prevalence of IMFO to attain the optimal power flow solution with fast convergence. [ABSTRACT FROM AUTHOR]