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In the distribution system, voltage sags and swell have adverse effects on sensitive and critical loads. Dynamic Voltage Restorer (DVR) play a vital role to mitigate voltage sags and swell through its organized control when connected in series with the system. Various conventional control schemes have been reported for the control of DVR. The performance of DVR with these schemes has been successful in the mitigation of voltage sags/swell, but drawbacks, such as undershoot, and overshoot during sag dynamics are observed in the regulated load terminal voltage. These drawbacks may cause malfunctioning of the equipment connected and hence their elimination is indispensable. With this inspiration, a novel Adaline filter-based adaptive control scheme using learning-rate parameter-based Least-Mean-Square algorithm has been proposed. This scheme has the capability to overcome the drawbacks with the conventional control schemes thus improving the performance of DVR. In this paper, the performance of DVR using proposed algorithm is compared with that using synchronous reference frame (SRF)-based conventional control scheme. The performance is studied and validated through simulation results under different voltage sag and swell conditions in MATLAB software using Sim Power System toolboxes. [ABSTRACT FROM AUTHOR]

Backward/forward load flow algorithm is commonly used in the power system, particularly in radial distribution networks. However, the rapid development in the power electronics technologies makes power systems more flexible with the integration of Renewable Energy Resources (RESs) and compensation devices. Consequently, this paper presents simple models for wind turbine generators based on Squirrel Cage Induction Generator (SCIG) and Distribution Static Synchronous Compensator (DSTATCOM) into a backward/forward load flow algorithm. In the wind generator model, the mechanical powers are collected from the manufactures data where the electrical power is calculated through the SCIG equivalent circuit and injected as negative loads at the connected buses. The second model in this paper is the DSTATCOM devices which are utilized to recover the SCIG reactive power. In the proposed model, the connected DSTATCOM bus is converted to a voltage-controlled bus (PV-bus type) with zero active power. Moreover, the reactive power of the DSTATCOM is updated during the iterative process until the load flow convergence. The simplified models for both wind generators and DSTATCOM are validated based on standard IEEE 33-bus and 85-bus radial distribution systems. The obtained results show the feasibility and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

This paper introduces a modified slime mould algorithm to enhance the exploitation and exploration of the algorithm. As stated in no free lunch theorem no single algorithm can proficiently solve the optimization problems at hand. Hence, hybridization of more than one heuristic algorithm is employed to tackle the situation. The proposed strategy employs opposition-based learning and wavelet mutation-based strategy to the basic slime mould algorithm for exploring the search space while avoiding local optima. These methods are tested on standard benchmark functions, CEC-14 functions and different single and multiobjective thermal load dispatch problems including small, medium and large-scale problems while satisfying non-differentiable constraints namely valve point loading, prohibited operating zone and ramp rate limit which make objective function non-convex and discontinuous. The pareto dominance technique is employed to achieve the optimal solution in multiobjective problems. This paper also compares the performance of the proposed technique with other methods available in the literature for different economic load dispatch problems and emission dispatch problems. The Wilcoxon signed-rank test performed to confirm the competitiveness of the proposed method for independent samples. [ABSTRACT FROM AUTHOR]

Three-phase Pulse-Width Modulated (PWM) rectifiers used between the power grid and the load in applications requiring DC voltage have features such as high efficiency, high power factor, and low harmonics. This paper proposes a hybrid control approach to improve the dynamic performance of three-phase PWM rectifiers under different operating conditions. Operating conditions are considered as step response, internal disturbance, and regenerative operation. First, Interval Type-2 Fuzzy Neural Network (IT2FNN) is designed and then antecedent and consequent parameters of IT2FNN are optimized with Modified Golden Sine Algorithm (GoldSA-II). The dynamic performance of the hybrid controller, named GoldSA-II-IT2FNN, is analyzed for all operating conditions in Matlab/Simulink environment. The simulation studies are realized to evaluate the performance of the proposed controller. In the simulations, settling times of proposed controller are observed as 27.2 ms, and 10.8 ms for step response, respectively. Moreover, recovery times are calculated as being 12 ms to 5.5 ms for internal disturbance, and 7.2 ms to 19 ms for regenerative operation, respectively. The obtained results demonstrate that the proposed controller not only provides better dynamic performance but also improves the stability of PWM rectifier. [ABSTRACT FROM AUTHOR]

Due to the increase in environmental concerns, it becomes mandatory now to focus on energy management and the planning of resources. An integration of distributed generation resources with conventional sources is gaining popularity. The hybrid modeling of the power system poses a complex and challenging optimization problem without getting trapped in a local optimum. A novel Hybrid Statistical Multiswarm Particle Swarm Optimizer-Sine Cosine Algorithm (HSMPSO-SCA) is developed in this paper, and its performance is evaluated on various test benchmark functions. The real operating conditions such as valve point loading, prohibited zones, ramp rate limits and uncertainties in solar and wind power generation for the IEEE 30 bus system are considered for combined emission economic load dispatch (CEELD). The performance of the HSMPSO-SCA is also analyzed for the solution of CEELD for various standard test systems with interaction of solar and wind plants. The efficacy of the proposed HSMPSO-SCA is found to be much better than various metaheuristics, hybrid and multi-agent algorithms in minimizing cost along with emissions optimally and achieves better accuracy and a faster convergence rate. [ABSTRACT FROM AUTHOR]

The grid-connected inverter is the key component for the reliable and safe operation of grid-interconnected renewable energy systems. In order to ensure that the inverter output current is in-phase synchronized with the grid voltage, a highly efficient and fast control strategy is required. This paper proposes an algorithmically driven approach for designing the adaptive controller for a grid-connected DC/AC inverter. The controller consists of two adaptive IIR filters based on the LMS algorithm and has two modes of operation. During the learning mode, the controller uses the filters to estimate the coefficients of the inverse transfer function of the PWM-driven inverter at several grid frequencies within a specific range. During the online mode, the controller generates the driving signal for the inverter block using a set of learned coefficients corresponding to the current grid frequency. By constantly monitoring the grid voltage and reconfiguring the controller accordingly, a fast adaptation of the inverter output current to grid frequency variations is achieved. For grid frequency variations in the range of 48 to 52 Hz, the MATLAB simulation results show that the phase difference between the grid voltage and inverter output current waveforms becomes less than 1 degree after at most four periods. [ABSTRACT FROM AUTHOR]

This paper presents the successful application of direct power control (DPC) based algorithm in the DC–AC conversion of a standalone system. This algorithm is successful at the smooth variation of active power in the standalone mode of operation of renewable power generating system. Thus, it can supply the variable nature of power demand. The algorithm also successfully rejects the variation of DC voltage at the input of the inverter. The power output of the inverter in this standalone mode is successfully maintained during this condition. So, it can be said that the DPC in the standalone mode, is flexible enough to adopt the fluctuation of the DC voltage. It has also been seen that the inverter operating with DPC-based control can deliver constant power at the load end even under fluctuating load impedance. The claims stated, in theory, are successfully justified in both simulation and real-time environment. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid (MFO-SCA) method based on Moth Flame Optimization (MFO) algorithm and Sine Cosine Algorithm (SCA). MFO is utilized for the exploitation phase while SCA is used for the exploration phase. In addition, the hybrid MFO-SCA method is used with combined power loss sensitivity (CPLS) to determine the optimal allocation of single and multiple of distributed generations (DGs) and capacitors in distribution system. In this paper, the total active power loss is used as a single objective function for incorporating DGs and capacitors in RDS. Also, this paper studies the system performance when integrating DGs or capacitors alone and DGs with capacitors simultaneously in RDS. The proposed method is validated using the standard IEEE 69-bus and IEEE 33-bus radial distribution system (RDS). From comparative study, the novel hybrid MFO-SCA proved its effectiveness in obtaining the best global optimum results of the objective function compared with other recent optimization techniques, especially SCA and MFO algorithms. [ABSTRACT FROM AUTHOR]

Frequency, amplitude, and phase information of the grid voltage are the main constraints for constructing a robust controller algorithm for grid connected applications under unbalanced and distorted voltage conditions. This paper narrates a simple, robust, straight forward method to estimate the instantaneous positive and negative sequence voltage components under unbalanced and distorted voltage circumstances. A second order generalized integrator (SOGI) is encapsulated to filter out the distorted voltage as well as to generate orthogonal voltage components for the three phases of AC grid. Furthermore, these filtered and orthogonal components are accounted for the calculation of instantaneous symmetrical components. Developed technique is more frequency adaptive compared to conventional phase locked loop (PLL) techniques. A set of test outcome results are provided in this paper based on MATLAB/Simulink simulations with real grid data captured from an industrial plant. Moreover, SOGI based estimator is digitally implemented by using dSPACE ds1103 digital controller to validate the numerical simulation results in accordance with the developed theoretical prediction. [ABSTRACT FROM AUTHOR]

This paper deals with the load frequency control (LFC) of a three unequal area system with multiple energy sources such as thermal-geothermal, thermal-wind energy system, and thermal-hydro system. An attempt has been made for the first time to utilize cascade integral double derivative and proportional-integral-derivative (IDD-PID) controller for LFC application. A new and powerful evolutionary algorithm called as exchange market algorithm is applied for optimizing the controller gains using an integral square error technique. The investigation is carried out on the dynamic performances of the proposed cascade IDD-PID controller against those of controllers such as PI and PID for a step load perturbation of 1% in area 1. Analysis of results depicts the better response of the proposed cascade IDD-PID controller compared to PI and PID controller responses. In addition, the cascade IDD-PID controller also provides superior dynamic performance even for the application of random load pattern in area 1 which ascertains the robustness of the proposed cascade controllers in containing the area frequency oscillations and tie line power deviations. An unequal three area system with multiple-energy sources is developed for LFC operation. A new cascade IDD-PID controller is introduced for LFC operation. Exchange market algorithm is applied for the first time for tuning the proposed cascade controllers. Cascade IDD-PID controller results in better dynamic performance compared to PI and PID controllers. [ABSTRACT FROM AUTHOR]

In this paper, a single layer Recurrent Neural Network (RNN) algorithm has been proposed for the control of single-stage three-phase grid integrated solar photovoltaic (PV) system. Voltage Source converter (VSC) has been controlled for overcoming power quality problems in distribution systems viz. harmonic mitigation, active and reactive power compensation, load balancing etc. in three-phase grid connected PV systems. The PV array has been designed such that it delivers maximum power to the grid via VSC. The proposed algorithm has been implemented in MATLAB/Simulink and tested on hardware. Simulation and experimental results have been shown for the verification of the proposed algorithm under steady-state and dynamic conditions. These results conform to standard IEEE-1547 and the harmonics in grid currents are reduced to < 5% for the grid-connected PV system. [ABSTRACT FROM AUTHOR]

The Phasor Measurement Unit (PMU) is a modern metering device installed to monitor and control the power system network. However, the number of PMUs and their placements ought to be optimized to satisfy the techno-economic demand. In this paper, the PMU placement problem has been expressed as a multi-objective problem. Modified whale optimization algorithm (MWOA) is proposed to solve Optimal Placement of PMUs (OPP) problem formulation aiming at minimizing the required number of PMU installation subjected to complete network observability and maximizing the reliability for better operation of power systems. Here, the simultaneous optimization of the two conflicting objectives is performed using a fuzzy-based modified whale optimization algorithm (FMWOA). Besides, single objective optimization problems with and without considering the zero-injection bus constraint and multi-objective optimization problem with zero injection bus constraint have been investigated. The proposed method is validated on small and larger test systems, namely IEEE 14, 30, 57, 118 bus systems, and the polish 2383-bus system. Also, the proposed algorithm results are compared with those of previously published for better clarity. [ABSTRACT FROM AUTHOR]

No single metaheuristic search algorithm can be adjudged universally best general-purpose optimizer. The performance of search algorithms mainly depends upon the weightage assigned to global and local search strategies. This paper proposed an improved directional bat optimizer to minimize the operating cost of the electric power dispatch (EPD) problem that establishes a balance between global and local search strategies. Improved directional bat algorithm exploits directional echolocation bat behavior, directional exploration, neighborhood search and opposition based learning for generation jumping. The directional bat algorithm acts as a global search tool whereas exploration in each direction and neighborhood search performs local search. Opposition learning improves convergence with diversity. An effect of valve-point loading introduces a discontinuity in cost characteristics. The EPD problem addresses energy balance, generator capacity, ramp-rate limits and prohibited operating zones (POZ) avoidance constraints. An iterative technique handles energy balance constraint. The generation is adjusted to avoid the violation of generation capacity, ramp-rate limit and POZ constraints. The proposed algorithm is verified on various electric power systems. The results verify that the proposed algorithm is a potential algorithm to solve EPD problems as it competes with recent existing algorithms undertaken for comparison [ABSTRACT FROM AUTHOR]

—This paper provides a computational methodology based on monarch butterfly optimization (MBO) to find a solution to the problem of cost-based unit commitment (CBUC). The binary variables of unit commitment problems are handled by modifying the continuous-time nature of the monarch butterfly algorithm. Thermal unit generation, uptime, downtime, ramp rate limits as well as system reserve are considered in the test systems. The computational approach has many parts that not only minimize the cost function but also handle the mixed constraints of the commitment problem. The effect of thermal turbine valve-point loading is also taken into consideration. The computational technique has been used to solve a ten-unit original system and five scaled-up adaptations obtained from this base system. The results obtained are in agreement with the recent results available in the literature. Comparative analysis shows the effectiveness of the proposed MBO-based solution methodology in terms of operating costs and execution time in relation to other techniques. [ABSTRACT FROM AUTHOR]

Aiming at the problem of optimal power flow in photovoltaic (PV) Inverters, this paper proposes a real-time algorithm for optimal power flow (OPF) in distributed PV inverters with sophisticated communication technologies and measurement. A higher accuracy and computation speed are obtained by proposing a linear, time-varying optimization method, and the objective function and constraints were linearized using the Taylor expansion under the premise of small variable changes during the short control period. By comparing with the constant linearization method, the proposed time-varying linear approximation method is more accurate and efficient. The problems faced by the centralized calculation is overcome by proposing a distributed control method which utilizes the alternating direction method of multipliers. The proposed distributed control method enables the distributed PV inverters to optimize their own power setpoints with only a little information required from neighbor nodes. Case study results demonstrate that the proposed algorithm makes the distributed PV Inverters more efficient and economical than the other methods. [ABSTRACT FROM AUTHOR]

This paper presents a novel design of an energy optimization approach for Induction Motor (IM) drives. Differently from former optimal control studies, the proposed control strategy is based on a designed cost function given as a weighting sum of power and energy models. The problem to be considered is to achieve the loss minimization of IM drives for any possible constraints imposed by the machine power quantity, such as ramp inputs in accelerate and decelerate modes and during different torque levels. The suggested energy optimization strategy, using the concept of Rotor Field Oriented Control (RFOC), allows a low-order current-fed IM model by means of a high-gain current control loop, which considers the stator currents as new inputs to the model and the rotor flux as a main state variable. An optimal rotor flux trajectory is determined by an off-line algorithm based on an optimal closed-loop control problem resulting in the Hamilton Jacobi Bellman equation. The resolution leads to an analytical solution which is implemented to RFOC and performs loss energy minimization during transient speed. Aiming to check the validity of the proposed strategy, a comparison study is conducted between RFOC operating with the optimal rotor flux and RFOC using a rated flux norm. The simulation and experimental results obtained for a 1.5 KW laboratory IM demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The traditional service restoration methods are experiencing a significant challenge because the performance of such methods is often dominated by very large processing time. Therefore, it is important to develop a fast and an accurate method to solve a complex non-linear optimization problem representing the service restoration problem in a distribution system. In this paper, due to a binary nature of the service restoration problem, the Binary Water Cycle Algorithm (BWCA) is proposed to reduce out-of-service loads and system losses considering operational constraints, the radial topology of the distribution systems, and the influence of the presence of distributed generations (DGs) on the service restoration problem. The proposed method is a modified form of the Water Cycle Algorithm (WCA) that is mainly used for discrete and continuous optimization problems. The effectiveness of the proposed method is validated by comparing with other well-known meta-heuristic algorithms using a modified IEEE 33-node radial distribution system. The simulation results demonstrate the robustness of this method to provide a precise decision with short convergence time for solving service restoration problems. [ABSTRACT FROM AUTHOR]

The requirement of a robust and adaptive control algorithm in active filters is inevitable nowadays. In this paper, an ADAptive LINEar neuron (ADALINE) based Least Mean Square (LMS) control algorithm is proposed for a three-phase-four-wire (3P4W) distribution power system under unbalanced loading conditions. The proposed controller has a unique inbuilt characteristic to extract the fundamental active positive sequence component from an unbalanced load current. Particularly, the control algorithm is intended to calculate an adaptive and suitable amplitude for the three-phase reference source currents which are used for the switching pulse generation in the subsequent steps. Moreover, the Distribution STATic COMpensator (DSTATCOM) is performed to eliminate harmonic contents of supply current and harmful excessive neutral current in the system. Further, the DC-link voltage is regulated in order to reduce the power loss across the DSTATCOM, and to attain a balanced, high-quality power availability at the Point of Common Coupling (PCC). The proposed system is accomplished in the MATLAB2014a/Simulink, and a low power rated 3P4W DSTATCOM prototype incorporating with the dSPACE1104 real-time environment to verify the proposed control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm Optimization and Gravitational Search Algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results. [ABSTRACT FROM AUTHOR]

In this article, a new selection technique based on Enhanced Nature-Inspired Meta-Heuristic (ENIMH) optimization algorithm is presented to improve the Microgrid (MG) dynamic performance. Interconnected microgrids have the ability to provide a clean and sustainable energy during normal and emergency operating conditions. The concerned microgrid includes hybrid renewable energy sources (RES) and energy storages systems (ESS). MG achieves a reduced dependency on the electric grid and provides flexible and adaptive energy supply. This paper develops a new selection technique based on ENIMH optimization that distinguishes the degrees of resemblance between the best individual and other individuals of current population. This technique proposes a binary coding of individuals, and is compared to conventional techniques; it allows each individual to occupy a section of the modified roulette wheel selection for the calculated degree of resemblance. This enhanced optimization technique tunes the dynamic PID parameters of microgrid closed loop system. The designed strategy is dependably to locate the arrangement of enhanced parameters to minimize the system frequency fluctuations in the microgrid and to provide the improved dynamic performance by being sensitive to variations for closed loop response under various power and load conditions. The proposed technique has been demonstrated using Matlab/Simulink simulation on the underlined microgrid, where the achieved results confirm the effectiveness of proposed selection method for the reproduction of best individuals to show the improved performance. The proposed technique achieved satisfactory performance for PID-controllers, and provided a good closed loop performance, minimum overshoot and minimum fitness index, in comparison with other well-established methods. The results emphasize that ENIMH optimization algorithm has the exploration and exploitation capability of population best individuals to accomplish the best solutions. [ABSTRACT FROM AUTHOR]

In order to analyze the randomness of wind power in dynamic economic dispatch (DED) with wind power, based on non-parametric kernel density estimation (KDE) technology, the probability distribution of wind power output and wind power forecast error is accurately modeled. A segmented statistical method on wind power forecast data is adopted to construct the confidence interval of the wind power output, the upper and lower bounds of the forecast errors. According to the established wind power output probability model, forecast confidence interval and forecast error upper and lower bounds, a DED model with wind power is formulated in this paper. A hybrid algorithm combining the evolutionary advantages of bat algorithm (BA) and particle swarm optimization (PSO) algorithm is designed to solve the proposed model. A crossover mechanism, which can solve the problem of falling into local optimum easily existed in BA and PSO, is introduced in the evolution of the algorithm. Finally, the effectiveness of the proposed model and algorithm is verified by simulation examples. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced control algorithm in permanent magnet synchronous machines (PMSMs) with machine current sensor fault detection and isolation (FDI) and current management for hardware protection. Different from existing work on machine current sensor FDI, the proposed method addresses the sensor FDI problem under co-existence of PMSM current sensor and non-sensor imbalance faults, and thereby help prevent the controller performance degradation caused by a faulty machine current sensor signal. Meanwhile, hardware protection is achieved through the proposed algorithm which has a unique feature of providing online overcurrent regulation and source power management at the same time to enhance protection of power inverter and power source. Benchmark comparison of the proposed sensor FDI method to state-of-the-art techniques is carried out to validate the good performance of the proposed FDI method in terms of fault detection time, false detection rate and missed detection rate. Sensitivity analysis of the proposed current management algorithm with overcurrent regulation and source power management is performed under changing rotor flux linkage and machine stator d-axis self-inductance, as an example to demonstrate the robustness of the proposed algorithm for hardware protection against system parameter variations. The effectiveness of the proposed algorithm is confirmed by simulation results in MATLAB for a PMSM. [ABSTRACT FROM AUTHOR]

A hybrid algorithm, biogeography based optimization with simulated annealing (SABBO), is proposed in this paper by integrating the SA strategy into a basic BBO algorithm to solve combined heat and power unit economic dispatch (CHPED) problem. In this modified algorithm, a simulated annealing operator is used to randomly perturb the best individual retains after migration and mutation operating in the basic BBO. Elitism solution is further optimized to enhance the algorithm convergence capacity, and to prevent the algorithm from getting trapped into local optimum. Several benchmark functions are then taken to test the proposed method's optimal capacity, and the results show that SABBO could significantly outperform the basic BBO algorithm and mostly as the best solution listed BBO algorithms with different versions. Then, various systems are taken to tested SABBO algorithm, compared with the basic BBO and algorithms from references. Test results indicate that the SABBO is better than others to solve CHPED problems including large-scale ones. Further, the energy saving potential is calculated based on the whole plant's feasible operating region to work as guidance for economic operation. [ABSTRACT FROM AUTHOR]

In this paper is proposed a reconfiguration methodology with the presence of Distributed Generation (DG), aimed at achieving the minimum power loss, minimum number of switching operation and minimum deviation of bus voltage while satisfying all constraints using improved shuffled frog leaping algorithm (ISFLA).The performance of the proposed method is examined on 33 and 69 bus IEEE test distribution systems. The ISFLA performance is evaluated with the well-known algorithm including of harmony search algorithm (HSA), refined genetic algorithm (GRA), particle swarm optimization (PSO), differential evolutionary (DE) and conventional SFLA. Simulation results showed that the total power loss and voltage bus minimum in primary distribution network can be reduced significantly. Also the results in different scenarios are showed that the simultaneous reconfiguration and DG placement method is better in less losses and also in more minimum voltage. Moreover, the ISFLA superiority is proved in comparison with the HAS, GRA, PSO, DE, and SFLA in view of more convergence speed and accuracy and also converges in less number iteration. Also, the performance of the proposed method is favorable compared to previous studies. [ABSTRACT FROM AUTHOR]

This paper presents the optimal step least mean square algorithm for controlling three-phase distributed static compensator (DSTATCOM). The presented algorithm provides harmonic compensation in addition to reactive power compensation in three-phase connected linear and non-linear loads. In order to provide desired compensation by DSTATCOM system, the proposed control algorithm adaptively estimates an optimal value of a step-size parameter for active and reactive weight calculation. These calculated weights are used for deriving the reference current and triggering pulses for the IGBTs of the voltage source converter. The test result shows that the performance of DSTATCOM system is satisfactory, as the three-phase source current remains balanced and sinusoidal with total harmonic distortion within a 5% level set by an International Standard IEEE 519-1992. [ABSTRACT FROM AUTHOR]

Demand response is the reduction in power consumption of electric utility customers from their normal consumption to better match the demand for power with the supply. It comprises changes in consumption when the power system operators need it in order to balance the electrical grid. These changes are obtained by offering price incentives or any other way to encourage consumers to shift their demand peaks to higher energy production hours. The reduction in electric energy due to large consumers' response can be considered as virtual power plants which can be added to the system capacity as a demand resource. In this paper, demand response is included in the optimal economic load dispatch problem as a demand resource. The demand response cost function is proposed in a similar way as the cost functions of conventional generation units. A new nature-inspired optimization algorithm called ant lion optimizer is used to calculate the optimum combination between both generations; conventional generators and demand response as a demand resource. The proposed approach takes into account two objectives; minimization of generation cost, and the emissions from the thermal power plants. The ant lion optimizer algorithm is coded and implemented in MATLAB environment. The proposed method is validated by applying it to two test systems; 6-bus system and IEEE-30 bus test system. The obtained results prove that the proposed method can significantly minimize both the operation and emissions costs of power systems while satisfying the specified constraints. [ABSTRACT FROM AUTHOR]

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly Algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a developed predictor formulation for performing the time-series power-flow simulation of balanced and unbalanced distribution systems. The developed technique uses the changes in reactive and active powers of loads during the 24 hr in order to predict the updated voltage magnitudes and phase angles, respectively. The Lagrange linear interpolation is used as a predictor approach in the developed formulation. The developed technique is compared with the traditional technique that uses the previous solution as predictor values for the next solution. The conventional and developed techniques are implemented into forward/backward sweep for the three-phase radial distribution power-flow algorithm. The iterations number and computation time of the power-flow algorithm are significantly reduced in the case of using the developed technique compared with the previous solution technique. To prove its effectiveness, the developed technique is adopted for IEEE test systems, such as the radial balanced 69-node and unbalanced IEEE 123-node feeder. All calculations are performed using C++ programming. [ABSTRACT FROM AUTHOR]

Both the lack of the downward regulation capacity and the transmission capacity limit have led to considerable wind power curtailments in China. The battery energy storage system (BESS) provides a new solution to reduce the wind power curtailments due to its relatively high energy density and flexible installed location. In this paper, a new bi-level programming model is proposed to optimize the size and location of BESS for reducing the wind curtailments. The revenues of BESS, when it is located either at the wind farm or the load center, are considered comprehensively. Moreover, a numerical optimization algorithm is developed to solve the proposed model. Simulation results demonstrate the validity of the proposed model and developed algorithm. They also reveal that the BESS located at the load center offers more net profit compared to the one located at the wind farm. Subsequently, sensitivity analysis is performed to assess the effect of key parameters on the optimal values of BESS. [ABSTRACT FROM AUTHOR]

The Multilevel inverter topologies (MLIs) are increasingly being used, which is the preferred choice in many industrial Medium-voltage (MV) applications. The inherent low switching frequency in medium voltage neutral point clamped MLI (NPC-MLI) fed industrial drives represent the power quality issues and filter-design challenges. In this paper, two multilevel pulse width modulation (PWM) methods; multi-carrier sine PWM (MCSPWM) and space vector PWM (SVPWM) has been considered for NPC-MLI. Both the methods are compared and analyzed based on different constraints such as voltage profile, total harmonic distortion (THD), common mode voltage (CMV) and neutral point fluctuation (NPF). The results convey that the SVPWM method is superior when compared with the MCSPWM algorithms. Analytical and simulation results achieved by using the MATLAB/Simulink for control systems are experimentally verified using FPGA-SPARTAN - III generation - 3AN -XC3S400 with a 2KW NPC-MLI fed adjustable speed - drive system. [ABSTRACT FROM AUTHOR]

Uncontrolled network separation (system island formation) is one of the most critical contingencies in power systems. The integrity of the whole power transmission network is a prerequisite for reliable system operation. Therefore, in order to safeguard the system operation and control, it is imperative to quickly identify the topological changes, especially the formation of system islands. In this paper, we developed a spectral clustering-based approach to efficiently detect the existing or the potential network islands. The core of this approach is a graph-algebraic model, which combines the real-power deliverability and topological information of a power transmission network. Based on an improved spectral clustering algorithm, the proposed approach can efficiently identify the critical situations of the network splitting under multiple line outages. This approach has been successfully tested by using the New England 39-bus and 118-bus systems under different scenarios. [ABSTRACT FROM AUTHOR]

This paper focuses on implementation of differential equation of the single-phase source-load system based control algorithm for three-phase distribution static compensator (DSTATCOM) in a power distribution system with a stiff source. The DSTATCOM has been adopted to alleviate the power quality issues such as load unbalancing, reactive power, and current harmonics, which are caused by solid-state equipment and devices. The proposed control algorithm is employed for extraction of fundamental active and reactive current components of polluted load currents, which are used for estimation of reference source currents. The control algorithm carries out computations by using a sliding sampling window that has a width of half of the fundamental cycle. The primary advantages of this control algorithm are immune to noise, not influenced from DC component that may exist in load current, and being less affected from frequency deviations. A prototype of voltage source converter (VSC)-based DSTATCOM is developed using a field programmable gate array (FPGA) processor to validate the proposed control algorithm and its performance is analyzed under steady-state and dynamic conditions. Experimental results demonstrate satisfactory performance of the DSTATCOM with the proposed control algorithm under linear and non-linear loads. [ABSTRACT FROM AUTHOR]

This paper presents a method for determining the optimum number, impedance, and locations of fault current limiter (FCL) to reduce the negative impact of distributed generations presence in the power systems such as increasing the short circuit current levels and improve the system reliability. In the considered method, we have modeled the FCL placement as an optimization problem while the objectives are; bus fault current difference, reliability, the number and impedance of FCLs. Moreover, to solve the proposed problem, a new multi-objective optimization algorithm based on firework algorithm namely multi-objective firework algorithm has been implemented. In the new algorithm, several iterations have been considered, and the non-dominated solutions are extracted and stored in an external repository in the iterations. Finally, a fuzzy clustering technique is used to control the size of the repository during the algorithm evolution. The proposed approach is tested on two different systems, namely, Roy Billinton Test System (RBTS) 2 and RBTS 4 test system. The obtained results demonstrate the effectiveness and feasibility of the new method. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach to the unit commitment problem using the absolutely stochastic simulated annealing method. In each iteration, a solution is taken with a certain probability. Typically, in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. That may lead to the near optimization slowly. However in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities. Besides, the number of bits flipping is decided by an appropriate distribution. Excess units with system-dependent probability distribution handle constraints efficiently. To reduce the economic load dispatch (ELD) overhead recalculations, a sign bit vector is introduced as well. The proposed method is tested using the reported problem data sets. Simulation results for the systems up to 100 units are compared to the previous reported results. Numerical results show an improvement in solution cost and time over to the results obtained from recent powerful algorithms. [ABSTRACT FROM AUTHOR]

Load-frequency control (LFC) is one of the major problems when dealing with the design and/or operation of electric power systems. Moreover, due to practical and physical considerations, bounds are often imposed on the level of control signals as well as the maximum frequency deviation. Control strategies that satisfy systems constraints, and, at the same time, guarantee optimal systems performance, are often more difficult to achieve. In this article, a technique is developed for the solution of LFC problems with state and/or control constraints via the solution of constrained linear quadratic problem (LQP). After formulating the problem and deriving the necessary conditions of optimality, an iterative algorithm is proposed that leads to the optimal control strategy while satisfying systems constraints. In order to illustrate the effectiveness of this technique, one- and two-area constrained LFC problems are solved, and the results are demonstrated at the end of the paper. [ABSTRACT FROM AUTHOR]

Several methods exist for the determination of the efficiency of induction motors. Many of them require a no-load test, which is not possible for in-situ determination. Calculation of motor efficiency on the basis of a motor's nameplate or a manufacturer's data is also practicable, but these methods do not provide a correct measure of the efficiency of an induction motor in the plant. This paper discusses the application of evolutionary programming (EP) to predetermine induction motor efficiency. The validity of the proposed algorithm is tested with the help of a sample motor, and the results are found to be satisfactory compared to torque gauge results. [ABSTRACT FROM AUTHOR]

This paper presents a new technique for the analysis of a three-phase self-excited induction generator feeding a three-phase balanced unity power factor load. A genetic algorithm has been proposed to find the generated frequency and magnetizing reactance simultaneously. This leads to a complete analysis of the self-excited induction generator. Computed results have been compared with experimental results. The comparison confirms the validity and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an improved genetic algorithm with multiplier updating method (IGAMUM) to solve the nonconvex power economic dispatch problems (NPEDPs) constrained by reserve and prohibited operating zones (POZ). A genetic algorithm (GA) equipped with the improved evolutionary direction operator (IEDO) and migration called the improved genetic algorithm (IGA) is proposed, which can efficiently search and explore solutions. The multiplier updating method (MUM) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed method combining with the IGA and the MUM can use a wide range of penalty parameters and a small population size in evolutionary computation. Three examples are investigated, and the computational results of the proposed method are compared with that of the previous methods. [ABSTRACT FROM AUTHOR]

In this paper, a Galerkin-based method is applied to a single machine infinite busbar (SMIB) power system. The method computes the separatrix cycle and the exact values for the input mechanical power at which a limit cycle bifurcates from the separatrix cycle. The first part of the algorithm approximates the limit cycle by a truncated Fourier series which employs N harmonics, where N is a user-specified. The second part of the algorithm finds the exact value of the input mechanical power by utilizing the fact that the separatrix cycle must approach the saddle point. The algorithm is applied to simple example with N = 3. Data is given which relates the damping coefficient to the input mechanical power. [ABSTRACT FROM AUTHOR]

Although spot price theory has been successfully practiced in some power markets worldwide, it has always been criticized for its lack of transparency. A competitive open power market requires transparent price signals, and each market participant has the right to query what has been charged and why. This paper presents a practical algorithm to give the detailed analysis of spot prices. By avoiding complicated sensitivity analysis of nonlinear programming, the implementation of the proposed algorithm is fast and highly efficient. Although the spot price can only be approximately decomposed by this algorithm, the accuracy of the result is still high due to its fully making use of the power system characteristics. Most important of all, the individual market participant can apply the proposed algorithm to evaluate the rationality of the price that he has to pay, while the complete sensitivity analysis of nonlinear programming cannot, since not all of the information necessary for the sensitivity analysis is available among the market participants. A well-known three-bus system and the IEEE 30-bus system are studied to illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for solution of the stochastic optimal power flow (OPF) problem. The stochastic OPF problem minimizes the total cost of a base-case operating state plus the expected cost of recovery from contingencies such as line or generator outages. The algorithm presented is based on a cutting plane method that decomposes the OPF problem into smaller subproblems, which can be solved quickly and efficiently. The solutions of the individual subproblems are coordinated by the addition of linear constraints to a master problem. Typical results show a slight increase in the cost of the base-case operating state but a significant reduction in the expected cost of contingency recovery. [ABSTRACT FROM AUTHOR]

In a self-tuning adaptive control algorithm, parameters of a model of the plant to be controlled are identified online, and the control is calculated based on the identified parameters. In practice, duplicate controllers, one active and one standby, are often employed. In the case of a digital adaptive controller, because the standby unit is not tracking the system operating conditions, a sudden changeover from the active unit to the standby unit can cause deterioration in control. Performance of a power system with a duplicate adaptive power system stabilizer and the actions required for a smooth changeover from one controller to the other are studied in this paper. [ABSTRACT FROM AUTHOR]

This paper presents the application of Park's transformation for identifying and measuring power system harmonics. One of the advantages of the proposed technique is that it does not need a model for the harmonics components or a prior knowledge of the number of harmonics expected to be in the voltage or current signal. The proposed algorithm uses the digitized samples of the three phases of voltage or current to identify and measure the harmonics content in these signals. Sampling frequency is the only parameter tied to the harmonic order in question to verify the sampling theorem. The identification process is simple and applicable. Results for simulated data generated from EMTP for an actual system are reported in the text. [ABSTRACT FROM AUTHOR]

Excitation controllers such as automatic voltage regulators (AVRs) and power system stabilizers (PSSs) are normally installed on synchronous generators for improving electric power systems' transient stability. The PSS optimized by the genetic algorithm (GA) has a certain robustness. However, since the power system is nonlinear, drastic changes in the system caused by faults and circuit switching may cause control performance to become unsatisfactory. Then a method using a nonlinear neural network can be used to tune the control systems. This method of using neural networks has been reported in recent years. This paper presents a recurrent neural network (RNN) stabilization controller to improve the transient stability of power systems. The proposed controller is constructed by a three-layer (8-9-1) RNN, of which inputs are ΔP e and Δω. The weights of the proposed controller are adjusted online to maintain electrical output power deviation equal to zero. By applying the proposed controller, good damping characteristics over a wide range of operating conditions can be realized. The ability of the proposed controller has been investigated in a single-machine infinite-bus system. [ABSTRACT FROM AUTHOR]

This paper presents an impedance-based technique for the discrimination between inrush current and short-circuit current in single-phase transformers. The method is based on the simulation of the behavior of the impedance relay. A Hilbert transform is adapted for the numerical calculation of the impedance at the input terminals of the transformer. First, the inrush current is computed numerically by approximating the magnetizing curve. The nonlinearity as well as the saturated portions are taken into account. Second, the transformer input impedance is calculated using the Hilbert transform in a time domain. The input impedance in the short-circuit case is calculated similarly. A comparison between the two input impedances is necessary for protective relay to discriminate between transformer switching transients and short circuits. The method is applied to a i kVA, 220/110 V single-phase transformer. The obtained results can be used to design an impedance relay to block the transformer differential protection during the inrush current. The proposed method is simple and computationally efficient. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology using evolutionary computation techniques based on system measurements to construct power system load models and achieve distribution network reduction. An equivalent area load model is proposed to represent the loads in an area of the power system. A population diversity-based genetic algorithm is used to obtain the structure and parameters of the load model. Simulation results on a 5-bus power system and an IEEE 30-bus power system are given to show the potential of this novel methodology of power system load modeling. [ABSTRACT FROM AUTHOR]

Online implementations of unified Optimal Power Flow (OPF) algorithms pose several challenges. One such challenge is to maximize the voltage stability margin (VSM), which arises because of the associated computational complexity. In this paper a new artificial neural network (ANN) memory model-based algorithm for unified OPF is proposed. The proposed algorithm maximizes VSM while minimizing two other system-level objectives of generation cost and transmission loss. The proposed algorithm uses a slower conventional algorithm like that found in [17] to schedule several load patterns and obtains their associated optimal schedules. The ANN memory model stores these load patterns and their associated optimal schedules. Whenever the ANN memory model is given a load pattern, it finds out the closest stored load pattern and its associated optimal schedule. Thereafter power flow equations are solved using the present load pattern with the recalled optimal setting vector. Load bus voltage violations, if any, are removed using a fuzzy expert system corrective control algorithm (FECCA). The proposed algorithm was implemented and tested on the IEEE 30-bus system. Its execution time was one-sixteenth of that of the conventional algorithm like that in [17]. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for the parallel solution of the security constrained optimal power flow (SCOPF) problem in a decentralized framework, consisting of regions, using a price-based mechanism that models each region as an economic unit. We first solve the distributed optimal power flow (OPF) problem to determine the maximum secure simultaneous transfer capability of each tie-line between adjacent regions by taking into account only the security constraints imposed on the tie-lines. In this article, the line outage distribution factors (LODF) calculated at the current state are used to formulate the appended constraints. Once the secure transfer capability of each tie-line is determined, the intraregional SCOPF is performed using the conventional linear programming (LP) approach. A description of the inclusion of security constraints with the distributed optimal power flow algorithm will be given, followed by the case studies for Korea Electric Power System. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a nonlinear modeling of multiphase switched reluctance motors (SRMs) in which both the self- and mutual-couplings among all phases are described in terms of self-inductance, mutual-inductance and coupling coefficients. Moreover, a novel iteration algorithm to solve the initial value problems of the modeling is proposed. Expressions for computing the instantaneous torque from both the self- and mutual-coupling are derived, too. The simulation and experimental results are compared as a validation of the proposed methods and iteration algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an efficient and reliable tabu search (TS)-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs TS algorithm for optimal settings of the control variables of the OPF problem. Incorporation of TS as a derivative-free optimization technique in solving OPF problem significantly reduces the computational burden. One of the main advantages of TS algorithm is its robustness to its own parameter settings as well as the initial solution. In addition, TS is characterized by its ability to avoid entrapment in local optimal solution and prevent cycling by using flexible memory of search history. The proposed approach has been examined on the standard IEEE 30-bus test system with different objectives and generator cost curves. The results are promising and show the effectiveness and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]