Power Control Variables Research Articles

Battery-Friendly DC–DC Converter With Ripple-Free Current Modulation Strategy

In this article, a battery-friendly dc–dc converter with a ripple-free current technique is proposed interfaced battery energy storage system (BESS). Two-phase interleaved circuits are adopted to cancel the current ripple on the BESS side. On the high voltage (HV) side, the circuit combined with a half-bridge circuit and full-bridge circuit is employed to achieve voltage-second balance control. With the associated control strategy, the power control variables can be decoupled. The voltage-second balance control on two ends of the leakage inductor is realized by the adjustment of the duty cycle. After that, the transferred power only changes monotonously along with the change of phase shift angle. The zero-voltage switching (ZVS) conditions are analyzed in detail to ensure that the ZVS of all switches can be achieved through parameter design. Moreover, a comparison is conducted to illustrate the merits of the proposed converter. The theoretical analysis has been fully validated experimentally by an experimental prototype of 40–60 V to 400 V/1 kW operating at 50 kHz. The accordance between the theoretical analysis and experimental verification further testifies to the effectiveness.

Power Flow Study of MT-HVDC Grid Compensated by Multiport Interline DC Power Flow Controller

DC power flow controllers (DCPFCs) are emerging devices to control power flow in voltage source converter (VSC)-based multi-terminal HVDC (MT-HVDC) grids. In this paper, a novel Newton-Raphson (NR)-based DC power flow solver (DCPFS) is proposed to solve the DC power flow problem (DCPFP) by using a novel multiport interline DC power flow controller (MIDCPFC), where physical and control state variables of the whole system (MIDCPFC and MT-HVDC grid) are modified simultaneously to achieve predetermined control objectives. The static model (SM) and the power injection model (PIM) of the considered MIDCPFC have been derived and their equations are embedded within the proposed DCPFS. Since there are no fictitious buses in the proposed DCPFS, the original conductance matrix of the system and its symmetry are preserved, and only minor modifications are needed on the original system's Jacobin matrix. It is very straightforward to implement the proposed DCPFS as the voltage of intermediate capacitor of the MIDCPFC is treated as an independent variable, and thus, an external process to control its value is not needed. In this study, comprehensive models have been proposed to model losses of the MIDCPFC and VSCs for the first time; and the shunt conductance of HVDC lines have also been considered. Finally, a modified 15-bus MT-HVDC grid is proposed for verification purposes. The obtained results verify the accuracy and efficacy of the proposed concepts, models, and formulations of this study.

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

Well-structured reactive power policies and dispatch are major concerns of operation and control technicians of any power system. Obtaining a suitable reactive power dispatch for any given load condition of the system is a prime duty of the system operator. It reduces loss of active power occurring during transmission by regulating reactive power control variables, thus boosting the voltage profile, enhancing the system security and power transfer capability, thereby attaining an improvement in overall system operation. The reactive power dispatch (RPD) problem being a mixed-integer discrete continuous (MIDC) problem demands the solution to contain all these variable types. This paper proposes a methodology to achieve an optimal and practically feasible solution to the RPD problem through the diversity-enhanced particle swarm optimization (DEPSO) technique. The suggested method is characterized by the calculation of the diversity of each particle from its mean position after every iteration. The movement of the particles is decided based on the calculated diversity, thereby preventing both local optima stagnation and haphazard unguided wandering. DEPSO accounts for the accuracy of the variables used in the RPD problem by providing discrete values and integer values compared to other algorithms, which provide all continuous values. The competency of the proposed method is tested on IEEE 14-, 30-, and 118-bus test systems. Simulation outcomes show that the proposed approach is feasible and efficient in attaining minimum active power losses and minimum voltage deviation from the reference. The results are compared to conventional particle swarm optimization (PSO) and JAYA algorithms.

MINIMIZE POWER LOSS USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

This paper presents the solution of optimal power flow (OPF) using particle swarm optimization (PSO). The objective function is minimize power loss by adjustment the power control variables and at the same time satisfying the equality and inequality constraints. The proposed particle swarm optimization (PSO) method is compared with newton’s Raphson method (conventional method) approach on the standard IEEE 14 bus system. The analysis indicated that Particle Swarm Optimization (PSO) method was the most efficient method in terms of minimizing the power loss. This can be concluded that the Artificial Intelligence (AI) method such as Particle Swarm Optimization (PSO) is the most suitable and efficient method for analysing the Optimal Power Flow (OPF) problem in terms of minimizing power loss.

A scenario-based voltage stability constrained planning model for integration of large-scale wind farms

Recently, penetration of intermittent power sources has been increased in power systems due to an international drive for clean and sustainable energies; but these alternative sources could encounter power systems with some problems, which need planning and prevention. This paper proposes a two-stage scenario-based planning model for large-scale wind farms development, based on a project management approach. Considering a 10-year project of large wind farms development, the annual installation capacities of wind turbines are first optimally planned in order to minimize the Levelized Cost of Energy (LCOE) of wind farms. Second, as wind power penetration is consistently increasing in the grid, some stability concerns will come up such as voltage instability. To remedy this condition, the optimum dispatch of grid’s conventional power sources and control variables is determined in such a way that not only in any state of operation (taking into account wind and load uncertainties) but also in post-contingency conditions, the prescribed security margin is ensured at the lowest possible cost. This study has been conducted on an actual power system of Iran’s southeast grid, as well as IEEE 118-bus standard test system. Also, modified crow search algorithm (MCSA) is utilized to solve the developed optimization model. The numerical studies substantiate the effectiveness of the proposed method for long-term planning of wind farms.

Hybrid-Bridge-Based DAB Converter With Voltage Match Control for Wide Voltage Conversion Gain Application

This paper proposes a voltage match (VM) control for hybrid-bridge-based dual active bridge (DAB) converter in wide voltage conversion gain applications. With the addition of an auxiliary half-bridge circuit, the topology becomes an integration of a half-bridge and a full-bridge DAB converter. Unlike other pulse width modulation generation method for DAB converters, this converter utilizes four-level voltage at one port of the transformer to obtain matched voltage waveforms within the range of twice the minimum conversion gain. Wide conversion gain, decoupling of the two power control variables and wide zero-voltage switching (ZVS) ranges can be achieved with the proposed VM control. Full load ranges of ZVS for the six main power switches can be achieved and the two auxiliary switches can also operate in a wide ZVS range. In addition, the power control is done only using two control variables and its implementation is very simple, only needing a divider and a conventional voltage regulator. These characteristics and benefits of the proposed control are verified by experimental results from a 1-kw converter prototype.

Unified Boundary Trapezoidal Modulation Control Utilizing Fixed Duty Cycle Compensation and Magnetizing Current Design for Dual Active Bridge DC–DC Converter

The unified boundary trapezoidal modulation (TZM) control utilizing fixed duty cycle compensation and magnetizing current design for dual active bridge dc-dc converter is proposed in this paper. The fixed duty cycle compensation and magnetizing current design are first introduced to achieve the zero voltage switching (ZVS) of the power switches, which cannot be ensured with the conventional TZM control. As a result, all the power switches of dual active dc-dc converter can achieve ZVS and four switches can be turned off with very low current. Besides, based on the revealed power transfer characteristic, the power control variables including the duty cycles and phase-shift ratio can be unified without lookup tables or operation region division. With the proposed boundary TZM control, circulating current losses can be reduced and nonactive power is significantly suppressed according to the mathematic analysis, resulting in decrease of the conduction loss. A 1.6-kW laboratory prototype is built to verify the theoretical analysis and effectiveness of the proposed control.

Distributed Energy Spectral Efficiency Optimization for Partial/Full Interference Alignment in Multi-user Multi-relay Multi-cell MIMO Systems

The energy spectral efficiency maximization (ESEM) problem of a multi-user, multi-relay, multi-cell system is considered, where all the network nodes are equipped with multi-antenna transceivers. To deal with the potentially excessive interference originating from a plethora of geographically distributed transmission sources, a pair of transmission protocols based on interference alignment (IA) are conceived. The first, termed the full-IA, avoids all intra-cell interference (ICI) and other-cell interference by finding the perfect interference-nulling receive beamforming matrices (RxBFMs). The second protocol, termed partial-IA, only attempts to null the ICI. Employing the RxBFMs computed by either of these protocols mathematically decomposes the channel into a multiplicity of non-interfering multiple-input--single-output channels, which we term as spatial multiplexing components (SMCs). The problem of finding the optimal SMCs as well as their power control variables for the ESEM problem considered is formally defined and converted into a convex optimization form with carefully selected variable relaxations and transformations. Thus, the optimal SMCs and power control variables can be distributively computed using both the classic dual decomposition and subgradient methods. Our results indicate that indeed, the ESEM algorithm performs better than the baseline equal power allocation algorithm in terms of its ESE. Furthermore, surprisingly the partial-IA outperforms the full-IA in all cases considered, which is because the partial-IA is less restrictive in terms of the number of available transmit dimensions at the transmitters. Given the typical cell sizes considered in this paper, the path-loss sufficiently attenuates the majority of the interference, and thus the full-IA over-compensates, when trying to avoid all possible sources of interference.

Decline of Real Power Loss and Preservation of Voltage Stability by using Chirping Algorithm

In this paper, a new Chirping Algorithm (CA) is developed based on the chirping behaviour of crickets for solving the reactive power optimization problem. It is based on the chirping behaviour of crickets (insect) found almost everywhere around the world. The proposed Chirping Algorithm (CA) has been tested in standard IEEE 30, 57,118 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss and control variables are well within the limits.

Using Artificial Neural Network Algorithm for Voltage Stability Improvement

This paper presents an application of Artificial Neural Network (ANN) algorithm for improving power system voltage stability. The training data is obtained by solving several normal and abnormal conditions using the Linear Programming technique. The selected objective function gives minimum deviation of the reactive power control variables, which leads to the maximization of minimum Eigen value of load flow Jacobian. The considered reactive power control variables are switchable VAR compensators, OLTC transformers and excitation of generators. The method has been implemented on a modified IEEE 30-bus test system. The results obtain from the test clearly show that the trained neural network is capable of improving the voltage stability in power system with a high level of precision and speed. Keywords—Artificial Neural Network (ANN), Load Flow, Voltage Stability, Power Systems.

Comparison of Advanced Control Techniques for Grid Side Converter of Doubly-Fed Induction Generator Back-to-Back Converters to Improve Power Quality Performance During Unbalanced Voltage Dips

A doubly-fed induction generator grid side converter is studied under voltage disturbances. During voltage dips, conventional current control techniques involving the decoupling of d – q axis in the synchronous reference frame (SRF) exhibits oscillations in the stiff dc link voltage, as well as in the active and reactive power outputs of the voltage source converter (VSC). To mitigate the oscillations, an advanced control technique called the sequence domain control (SDC) is evaluated. This approach implements individual controllers in the positive and negative sequence domains and demonstrates stabilization of the dc link voltage to a greater extent during a disturbance, but is more sluggish than the conventional control. An innovative control technique called direct power control (DPC) is also investigated. This control achieves active and reactive power stability with simple active and reactive power control variables replacing the traditional current control loops. The DPC technique is verified using a prototype in the laboratory. A modified DPC algorithm combining the benefits of DPC and SDC to eliminate the current harmonics created by DPC during system disturbances is introduced. The benefits of the proposed controllers are compared using a simulation of voltage dip on a VSC based on the IEC-61400-21 standard.

Spectral and Energy Spectral Efficiency Optimization of Joint Transmit and Receive Beamforming Based Multi-Relay MIMO-OFDMA Cellular Networks

We first conceive a novel transmission protocol for a multi-relay multiple-input--multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. We then address the associated network-wide spectral efficiency (SE) and energy spectral efficiency (ESE) optimization problems. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input--single-output (MISO) channels, which are essentially spatially multiplexed for transmission. Hence, these effective MISO channels are referred to as spatial multiplexing components (SMCs). For the sake of improving the SE/ESE performance attained, the SMCs are grouped using a pair of proposed grouping algorithms. The first is optimal in the sense that it exhaustively evaluates all the possible combinations of SMCs satisfying both the semi-orthogonality criterion and other relevant system constraints, whereas the second is a lower-complexity alternative. Corresponding to each of the two grouping algorithms, the pair of SE and ESE maximization problems are formulated, thus the optimal SMC groups and optimal power control variables can be obtained for each subcarrier block. These optimization problems are proven to be concave, and the dual decomposition approach is employed for obtaining their solutions. Relying on these optimization solutions, the impact of various system parameters on both the attainable SE and ESE is characterized. In particular, we demonstrate that under certain conditions the lower-complexity SMC grouping algorithm achieves 90% of the SE/ESE attained by the exhaustive-search based optimal grouping algorithm, while imposing as little as 3.5% of the latter scheme's computational complexity.

Optimal reactive power rescheduling based on EPSDE algorithm to enhance static voltage stability

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal reactive power dispatch (ORPD) problem. Optimal reactive power dispatch is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed approach employs Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE) algorithm for optimizing a set of reactive power control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation which maximizes reactive reserve available at generating buses. Voltage dependent reactive power limits have been accounted. Developed algorithm accounts inequality constraints not only in present operating conditions (after reactive power rescheduling) but also for predicted next interval load (with reactive power rescheduling). Proposed methodology has been implemented on IEEE 14-bus and 25-bus test systems. Performance of the methodology has been compared with Selfadaptive Differential Evolution (SaDE), Teaching Learning Based Optimization (TLBO) and Genetic Algorithm (GA) techniques based on statistical inference.

An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow

Optimal reactive power flow (OPRF) reduces power system losses and provides better system voltage control by adjusting the reactive power control variables. It has significant influence on economic and secure operation of power systems. In this article, an improved biogeography-based optimization algorithm based on local search strategy (ILSBBO) is presented for solving optimal reactive power flow. The proposed method integrates local search strategy and selection operation of differential evolution (DE) with migration operator in original BBO to improve the efficiency of migration and overcome the premature convergence in BBO algorithm. It has been applied to standard IEEE 30-bus and IEEE 118-bus test systems, and the comparison results show that the proposed approach is feasible and efficient.

Modified Differential Evolution algorithm for multi-objective VAR management

Reactive power or VAR management is one of the most crucial tasks required for proper operation and control of a power system. Reactive power management is carried out to reduce losses and to improve voltage profile in a power system, by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. VAR management provides better system security, improved power transfer capability and overall system operation. VAR management is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the VAR management problem is formulated as a nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of real power losses and voltage deviation simultaneously. This multi-objective problem is solved using Differential Evolution (DE), which is a population based search algorithm. For avoiding the time and the effort in tuning the parameters of DE algorithm, a modified DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-objective VAR management problem. Weighing factor method has been employed for finding Pareto optimal set for VAR management problem. Fuzzy membership function is used to obtain the best compromise solution out of the available Pareto-optimal solutions. Effectiveness of the proposed modified DE algorithm based approach has been demonstrated on IEEE 30-bus system and is found to be superior to classical DE and its variants Self-adaptive Differential Evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE) in terms of convergence behavior and solution quality.

Fuzzy-TLBO optimal reactive power control variables planning for energy loss minimization

This paper offers a new approach to the problem of optimal reactive power control variables planning (ORPVCP). The basic idea is division of Load Duration Curve (LDC) into several time intervals with constant active power demand in each interval and then solving the energy loss minimization (ELM) problem to obtain an optimal initial set of control variables of the system so that is valid for all time intervals and can be used as an initial operating condition of the system. In this paper, the ELM problem has been solved by the linear programming (LP) and fuzzy linear programming (Fuzzy-LP) and evolutionary algorithms i.e. MHBMO and TLBO and the results are compared with the proposed Fuzzy-TLBO method. In the proposed method both objective function and constraints are evaluated by membership functions. The inequality constraints are embedded into the fitness function by the membership function of the fuzzy decision and the problem is modeled by fuzzy set theory. The proposed Fuzzy-TLBO method is performed on the IEEE 30 bus test system by considering two different LDC; and it is shown that using this method has further minimized objective function than original TLBO and other optimization techniques and confirms its potential to solve the ORPCVP problem with considering ELM as the objective function.

A novel methodology for maximization of Reactive Reserve using Teaching-Learning-Based Algorithm

This paper presents a new algorithm to optimize the reactive reserve. As the amount of reactive reserves at generating station is a measure of voltage stability. A new approach for scheduling of reactive power control variables for voltage stability enhancement using teaching-learning-based optimization (TLBO) has been developed. An objective function selected for maximization of reactive reserve for maintaining the voltage stability. A Sensitivity inequality relation analysis based proximity indicator has been selected for obtaining desired stability margin whose value along with reactive power reserve maximization assures desired static voltage stability margin. TLBO has been selected because this is an efficient optimization method for large scale non linear optimization problems for finding global solutions. It is basically based on the influence of a teacher on an output of learners in a class. Developed algorithm has been implemented on 6-bus standard test systems. The problem of reactive power optimization has played an important role in the reliable and optimum operation of power system. Reactive power reserve optimization (RPRO) has complex and non-linear characteristic with large number of equality and inequality constraints (1). The reactive power optimization problem is a nonlinear combinational and computational optimization problem and during last two decades there are many efforts has been devoted to the development are done by using different mathematical methods known as optimization techniques for solving the reactive power optimization problems. Primarily the conventional optimization techniques such as linear programming and non linear programming are in practice with the advantage of computational speed and convergence with the objective function of continuous and differentiable value. These are so named as conventional optimization techniques because they cannot handle the large and discrete-continuous problems such as reactive power optimization. So recently, computational intelligence based optimization techniques are in practice and have been proposed in the application of reactive power optimization such as genetic algorithm (GA), Tabu search, simulation annealing, particle swarm optimization(PSO), differential evolution(DE) and most recent one is teaching- learning-based-optimization (TLBO).these all are consider as most practical, user friendly and powerful scheme to obtain the global optimum solution for different optimization problems(2).

Anticipatory reactive power reserve maximization using differential evolution

This paper presents an algorithm for anticipatory control of load bus voltages. The algorithm optimizes a set of reactive power control variables and maximizes reactive reserve available at generating buses. Voltage dependent reactive power limits have been accounted. The optimal settings of reactive power control variables have been obtained for next interval predicted loading condition. These optimized settings satisfy the operating inequality constraints in predicted load condition as well as in present base case loading conditions. A population based differential evolution strategy has been used for optimization. Results obtained have been compared with those obtained using another population based technique known as PSO.