With the gradual increase of wind power penetration in the power system, the impact of wind farm on system stability is becoming more significant. This study designs the power system stabilizer (PSS) based on the model reference adaptive control (MRAC) method and virtual impedance (VI) control strategy. Then, the active power difference of the doubly fed induction generator (DFIG) is selected as input signal of MRAC-PSS-VI. The small-signal stability of the power system with the DFIG is enhanced by installing MRAC-PSS-VI on the rotor side control (RSC) control link. The controller model is built in DigSILENT/PowerFactory. The improvement effect of the controller on the low-frequency oscillation (LFO) characteristics of the power system is verified by using eigenvalue analysis and the time domain simulation method under different transmission powers of the tie line and different installed positions of the DFIG.

Ping He

Mingming Zheng

Haoran Jin

Zhijie Gong

Jie Dong

Introducing MRAC-PSS-VI to Increase Small-Signal Stability of the Power System after Wind Power Integration

<i>Summary</i>. When the microgrid topology changes, the power output of the inverter cannot be adaptively adjusted by traditional droop control, and the dynamic performance and steady-state accuracy of the inverter are affected. To solve this problem, a three-partition multistrategy adaptive fruit fly optimization algorithm (MSAD-FOA) is proposed, which performs a real-time optimization of the PI parameters to realize microgrid droop control. The fruit fly population is divided into three regions according to the ranking of the fitness values of the algorithm. Next, the multistrategy model is automatically updated according to the difference in the fruit fly performance in each region. The local fine search in zone I ensures that the population does not degenerate. Zone II pertains to the adaptive adjustment to ensure the diversity and convergence of the algorithm. Zone III guides the fruit flies to accelerate convergence. The effectiveness of the algorithm and feasibility of the proposed control strategy are verified through a theoretical simulation and microgrid droop control simulation. The comparison with other algorithms demonstrates the superiority of the development and exploration ability of the proposed algorithm. The response speed of the inverter is 40 times higher when the proposed control strategy is used, and the steady-state error is reduced by 4.3&#x0025;.

Xinkun Tao

Li Zhang

Fuzhong Wang

Guangqiang Tian

Hongwei Zhang

Three-Partition Multistrategy Adaptive Fruit Fly Optimization Algorithm for Microgrid Droop Control

The modular multilevel converter (MMC) topology is gaining more interest because of its modular design, high efficiency, and scalable voltage levels in medium- and high-power industrial applications, where the nearest level modulation (NLM) method is frequently preferred. In this paper, a novel NLM method is proposed with increased output voltage quality for MMC topology. In conventional NLM (C-NLM), output voltage is obtained as <i>N</i>&#x2009;&#x2b;&#x2009;1 levels, where <i>N</i> is the number of submodules (SMs) per arm. The output voltage is increased to 2N&#x2009;&#x2b;&#x2009;1 levels by the proposed NLM method without using any additional SMs. The proposed NLM method is based on the offset term injection, which is optimally determined in terms of the best output performance of MMC. Also, trapezoidal reference signal is used instead of sinusoidal reference, which provides better output voltage quality and controls the modulation process. The proposed NLM method presents simple implementation as in the C-NLM, and it is implemented to the upper and lower arms of the MMC; then, arm voltages are successfully controlled. Furthermore, output voltage returns the value of zero in the C-NLM process for low-modulation-ratio applications in relatively small amount of SM usage of MMC design. However, the proposed NLM method gives promising results instead of zero voltage. In order to validate the superior performance of the proposed NLM method, a comparative study is presented with C-NLM and third-harmonic injected NLM method in terms of total harmonic distortion (THD) and magnitude of the output voltage and current. THD of output waveforms of MMC is significantly reduced, and DC voltage utilization is remarkably increased, thanks to the proposed NLM method. In addition, capacitor voltage balancing for the proposed NLM method is accomplished to keep the capacitor voltage of each SM of MMC constant. Simulation results are presented to verify the effectiveness of the proposed NLM method under various case studies. Finally, experimental validation is carried out using a field programmable gate array (FPGA)-based hardware implementation on the laboratory prototype to show the applicability of the proposed NLM method.

Mehmet Kurto&#x11f;lu

Ahmet Mete Vural

A Novel Nearest Level Modulation Method with Increased Output Voltage Quality for Modular Multilevel Converter Topology

The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (<i>P</i><sub>max</sub>), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98&#x0025;, which is much better than that of other optimization techniques.

S. Senthilkumar

V. Mohan

S. P. Mangaiyarkarasi

M. Karthikeyan

Analysis of Single-Diode PV Model and Optimized MPPT Model for Different Environmental Conditions

Five-phase induction machines (5pIMs) are a viable alternative for a wide range of industrial applications. The penetration of drives with 5pIM into industrial applications is slow because of the complexity of the proposed control algorithms. In standard <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="14.58pt" style="vertical-align:-3.42947pt" id="M2" height="12.7178pt" version="1.1" viewBox="-0.0498162 -9.28833 14.58 12.7178"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-87"/></g><g transform="matrix(.013,0,0,-0.013,9.224,0)"><use xlink:href="#g113-48"/></g></svg><span class="isep"/><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="8.52pt" style="vertical-align:-3.42947pt" height="12.7178pt" version="1.1" viewBox="14.5351838 -9.28833 8.52 12.7178"><g transform="matrix(.013,0,0,-0.013,14.585,0)"><use xlink:href="#g113-103"/></g></svg></span> control of induction machines, the stator current is not controlled directly and options for current limitation are restricted. This paper hence addresses a novel current limitation technique for the <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="14.58pt" style="vertical-align:-3.42947pt" id="M3" height="12.7178pt" version="1.1" viewBox="-0.0498162 -9.28833 14.58 12.7178"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-87"/></g><g transform="matrix(.013,0,0,-0.013,9.224,0)"><use xlink:href="#g113-48"/></g></svg><span class="isep"/><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="8.52pt" style="vertical-align:-3.42947pt" height="12.7178pt" version="1.1" viewBox="14.5351838 -9.28833 8.52 12.7178"><g transform="matrix(.013,0,0,-0.013,14.585,0)"><use xlink:href="#g113-103"/></g></svg></span> control algorithm of 5pIM. In addition, a simple speed sensorless control algorithm based on the slip compensation is introduced. A combination of both algorithms is suitable for low-cost medium-precision drive applications. Experimental results, realized on Texas Instruments F28335 DSP, illustrating the performance of novel current limitation technique and sensorless control, are included.

Karol Kyslan

Milan Lacko

&#x17d;elm&#xed;ra Ferkov&#xe1;

Viktor Petro

Sanjeevikumar Padmanaban

Daniela Perdukov&#xe1;

Current Limitation Method for <svg  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"   style="vertical-align:-3.42947pt" height="12.7178pt" version="1.1" viewBox="-0.0498162 -9.28833 23.1046 12.7178" width="23.1046pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-87" d="M697 650H468L461 623L492 619C539 613 547 605 518 546C481 471 367 264 278 116H276C239 278 197 500 186 567C180 604 185 613 226 619L252 623L260 650H24L17 623C78 617 92 613 108 533L216 -11H247C365 200 515 462 560 529C616 612 624 615 689 623L697 650Z" /></g><g transform="matrix(.013,0,0,-0.013,9.224,0)"><path id="g113-48" d="M368 703H309L44 -163H104L368 703Z" /></g><g transform="matrix(.013,0,0,-0.013,14.585,0)"><path id="g113-103" d="M619 670C619 686 593 712 555 712S459 686 410 634S335 504 320 430H250L219 400L222 388H312L258 73C223 -133 201 -166 187 -180C175 -191 158 -199 140 -199C123 -199 88 -188 74 -172C68 -166 63 -164 54 -171C38 -185 23 -201 23 -215C23 -236 60 -261 93 -261C122 -261 161 -247 207 -200C268 -138 300 -71 337 94C365 220 376 277 394 387L501 399L521 430H401C432 623 464 665 501 665C524 665 544 651 567 627C577 617 583 618 592 625C601 631 619 651 619 670Z" /></g></svg> Control of Five-Phase Induction Machines

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers&#x2019; parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods.

Ali Toolabi Moghadam

Morteza Aghahadi

Mahdiyeh Eslami

Shima Rashidi

Behdad Arandian

Srete Nikolovski

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Electricity price risk assessment (EPRA) is essential for spot market analysis and operation. The statistical moments (i.e., the mean and standard deviation) of the price need to be assessed to support market risk control. This paper proposes a data-driven approach for EPRA based on the Gaussian process (GP) framework. Compared with the deep learning algorithms, GP has two merits: (1) the scale of training sample required is small and (2) the time-consuming hyperparameter tuning process is avoided. However, the direct application of GP for EPRA is not tractable due to the complicated discrete relationship between the system operating status and the electricity price. To deal with that, a data-driven EPRA framework is developed that contains a GP surrogate model for the direct current optimal power flow (DC-OPF) problem and a hybrid model-data-based hybrid electricity price calculation method. To guarantee the accuracy of EPRA, an adaptability criterion and a second verification process based on the Karush&#x2013;Kuhn&#x2013;Tucker (KKT) condition are developed to distinguish the samples with GP learning errors. Numerical results carried out on IEEE benchmark systems demonstrate that the proposed method can achieve exactly the same EPRA results as Monte Carlo (MC) simulation, which significantly improved the computational efficiency.

En Lu

Ning Wang

Wei Zheng

Xuanding Wang

Xingyu Lei

Zhengchun Zhu

Zhaoyu Gong

Data-Driven Electricity Price Risk Assessment for Spot Market

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