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The extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability.

In the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability.

The proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping.

This paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.

The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system.

In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor.

The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range.

In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.

The power systems behavior is nonlinear, and this is reflected in that the measurement signals are composed by multi-components. Thus, this paper aims to present a method for analyzing multi-component signals that allow calculating signal parameters such as frequency, damping constant, amplitude and phase for each component, as well as determining the direct current component.

The method proposed is based in the Z-transform of a damped sinusoidal signal with direct current. Only the Z-transform poles are used to form equation systems which are used to obtain frequency and damping. Then, the amplitude, the phase and the direct current component are determined by the above results.

The method is able to determine frequencies, damping constants, phases and amplitudes of the different modal components of a signal using only a few measurements. Moreover, the method does not require filter banks tuned with some previous knowledge of signal’s characteristics. The presented test cases of field measured signals show the good performance of the proposed method, which is able to obtain the parameters of interest with a very short observation window.

One quality of this method is that it has a very short delay to reach the first solution and from there you get one result each sample; the delay time is equivalent to 2 + 4C samples where C is the number of components in the signal. Finally, it is concluded that because of the small number of samples that are needed and the low algorithmic complexity of the methodology, the method is apt to make applications in real time.

Modern power systems suffer from a well‐known problem of low‐frequency oscillations. Flexible AC transmission systems devices are used to overcome this problem. The aim of this paper is to develop a particle swarm optimization (PSO) based supplementary damping control system design for thyristor control series compensator (TCSC).

The problem is formulated as an optimization problem with an eigenvalue‐based multi‐objective function. PSO is then used to find optimal set of controller parameters by minimizing the objective function.

The performance and robustness of the proposed approach is validated through small signal and large signal for different loading conditions of a multi‐machine power system.

The paper presents a novel PSO‐based control system design that exhibits robustness and excellent damping performance.

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

Power‐electronic systems have been playing a significant role in the integration of large‐scale wind turbines into power systems due to the fact that during the past three decades power‐electronic technology has experienced a dramatic evolution. This second part of the paper aims to focus on a comprehensive survey of power converters and their associated control systems for high‐power wind energy generation applications.

Advanced control strategies, i.e. field‐oriented vector control and direct power control, are initially reviewed for wind‐turbine driven doubly fed induction generator (DFIG) systems. Various topologies of power converters, comprising back‐to‐back (BTB) connected two‐ and multi‐level voltage source converters (VSCs), BTB current source converters (CSCs) and matrix converters, are identified for high‐power wind‐turbine driven PMSG systems, with their respective features and challenges outlined. Finally, several control issues, viz., basic control targets, active damping control and sensorless control schemes, are elaborated for the machine‐ and grid‐side converters of PMSG wind generation systems.

For high‐power PMSG‐based wind turbines ranging from 3 MW to 5 MW, parallel‐connected 2‐level LV BTB VSCs are the most cost‐effective converter topology with mature commercial products, particularly for dual 3‐phase stator‐winding PMSG generation systems. For higher‐capacity wind‐turbine driven PMSGs rated from 5 MW to 10 MW, medium voltage multi‐level converters, such as 5‐level regenerative CHB, 3‐ and 4‐level FC BTB VSC, and 3‐level BTB VSC, are preferred. Among them, 3‐level BTB NPC topology is the favorite with well‐proven technology and industrial applications, which can also be extensively applicable with open‐end winding and dual stator‐winding PMSGs so as to create even higher voltage/power wind generation systems. Sensorless control algorithms based on fundamental voltages/currents are suggested to be employed in the basic VC/DPC schemes for enhancing the robustness in the entire PMSG‐based wind power generation system, due to that the problems related with electromagnetic interferences in the position signals and the failures in the mechanical encoders can be avoided.

This second part of the paper for the first time systematically reviews the latest state of arts with regard to power converters and their associated advanced control strategies for high‐power wind energy generation applications. It summarizes a variety of converter topologies with pros and cons highlighted for different power ratings of wind turbines.

Static VAR compensators (SVC) have been recognized to be one of the most important flexible AC transmission systems devices used for mitigating the low-frequency electrochemical oscillations occurring in the system and for reactive power compensation, thereby improving the overall dynamic stability and efficiency of the system. The purpose of this paper is to optimize and dynamically tune the control parameters of the classical proportional integral and derivative (PID) controller of the SVC for a two-machine system by designing a new robust optimization technique.

The angular speed deviation between the two machines is used as an auxiliary signal to SVC for generation of the required damping output. To justify the efficacy of the system undertaken, a light load fault at time t = 1 s is projected to the system. The simulation is carried out in MATLAB/Simulink architecture.

The proposed technique helps in the enhancement of system efficiency, reliability and controllability and by effectively responding to the non-linearities taking place in a power grid network. The results obtained are indicative of the fact that the proposed modified brain storming optimization (MBSO) technique reduces system disturbances very quickly, increases the system response in terms of better rise time, settling time and peak overshoot and improves the efficiency of the system.

A detailed comparison of the MBSO technique is compared with the conventional brain storming optimization (BSO) and PID technique. Total harmonic distortion through fast Fourier transform is also compiled to prove that the values of the proposed MBSO method found out to be confined well within the prescribed IEEE-514 boundaries.

This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.

Conventional FACTS based stabilizers are decentralized controllers that adopt local measurements and operate in closed loop. To improve overall system dynamic performance, a coordinating application of FACTS based stabilizer is essential. Although, numerous researches have indicated the effectiveness and superiority of fuzzy logic controllers in comparison with the conventional linear controllers in power system application but researchers have not adequately investigated coordination of multi fuzzy controllers in multi‐machine power systems to provide optimal performance. Genetic algorithm is used to determine optimum values of controllers' parameters.

The search space of the optimisation procedure is decreased to a smaller one, design and computation time can be reduced significantly and the design process becomes more systematic.

A systematic method is introduced to coordinate multi fuzzy FACTS based stabilizers in multi‐machine power systems.

Large optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.

The ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.

Experimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.

The ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.