Owing to the superior transient and steady-state performance of the fractional-order proportional-integral-derivative (FOPID) controller over its conventional counterpart, this paper exploited its application in an automatic voltage regulator (AVR) system. Since the FOPID controller contains two more control parameters (µ and $\lambda$ ) as compared to the conventional PID controller, its tuning process was comparatively more complex. Thus, the intelligence of one of the most recently developed metaheuristic algorithms, called the salp swarm optimization algorithm (SSA), was utilized to select the optimized parameters of the FOPID controller in order to achieve the optimal dynamic response and enhanced stability of the studied AVR system. To validate the effectiveness of the proposed method, its performance was compared with that of the recently used tuning methods for the same system configuration and operating conditions. Furthermore, a stability analysis was carried out using pole-zero and bode stability criteria. Finally, in order to check the robustness of the developed system against the system parameter variations, a robustness analysis of the developed system was undertaken. The results show that the proposed SSA-based FOPID tuning method for the AVR system outperformed its conventional counterparts in terms of dynamic response and stability measures.

The increasing penetration of Microgrids (MGs) into existing power systems and “plug and play” capability of Distributed Generators (DGs) causes large overshoots and settling times along with various power quality issues such as voltage and frequency flickers, current harmonics and short current transients. In this context, over the past few years, considerable research has been undertaken to investigate and address the mentioned issues using different control schemes in conjunction with soft computational techniques. The recent trends and advancements in the field of Artificial Intelligence (AI) have led the development of Swarm Intelligence (SI) based optimized controllers for smooth Renewable Energy Sources (RES) penetration and optimal voltage, frequency, and power-sharing regulation. Moreover, the recent studies have proved that the SI-based controllers provide enhanced dynamic response, optimized power quality and improved the dynamic stability of the MG systems as compared to the conventional control methods. Their importance in modern AC MG architectures can be judged from the growing number of publications in the recent past. However, literature, pertaining to SI applications to AC MG, is scattered with no comprehensive review on this significant development. As such, this study provides an overview of 15 different SI optimization techniques as applied to AC MG controls from 43 research publications including a detailed review of one of the elementary and most widely used SI based metaheuristic optimization algorithms called Particle Swarm Optimization (PSO) algorithm. This comprehensive review provides a valuable one-stop source of knowledge for the researchers and experts working on SI controller’s applications for AC MG dynamic response and power quality improvements.

Testing and verification of digital circuits is of vital importance in electronics industry. Moreover, key designs require preservation of their intellectual property that might restrict access to the internal structure of circuit under test. Random testing is a classical solution to black box testing as it generates test patterns without using the structural implementation of the circuit under test. However, random testing ignores the importance of previously applied test patterns while generating subsequent test patterns. An improvement to random testing is Antirandom that diversifies every subsequent test pattern in the test sequence. Whereas, computational intensive process of distance calculation restricts its scalability for large input circuit under test. Fixed sized candidate set adaptive random testing uses predetermined number of patterns for distance calculations to avoid computational complexity. A combination of max-min distance with previously executed patterns is carried out for each test pattern candidate. However, the reduction in computational complexity reduces the effectiveness of test set in terms of fault coverage. This paper uses a total cartesian distance based approach on fixed sized candidate set to enhance diversity in test sequence. The proposed approach has a two way effect on the test pattern generation as it lowers the computational intensity along with enhancement in the fault coverage. Fault simulation results on ISCAS’85 and ISCAS’89 benchmark circuits show that fault coverage of the proposed method increases up to 20.22% compared to previous method.

Among the black-box approaches to digital circuit testing, Random testing is popular due to its simplicity and cost effectiveness. Unfortunately, available evidences suggest that Random testing is equipped with a number of redundant patterns that increase test length without significantly raising the fault coverage. An extension to Random testing is Antirandom that removes redundancy by introducing a divergent pattern with every subsequent test pattern selection. A divergent pattern is induced by maximizing the Hamming distance and Cartesian distance of every subsequent test pattern from the set of previously applied test patterns. However, an enumeration of input combinations is required for the selection of a divergent pattern. Therefore, selection of a divergent pattern from all input combinations restricts the scalability of an Antirandom test pattern generation. One of the recently considered approaches is the stacking of locally optimized short sequences to generate a complete test sequence. Locally optimized short sequences originate from randomly chosen patterns instead of divergent patterns to avoid enumeration of input space. Seeding of random patterns for short sequences affects global diversity of the generated test sequence and hence, fault coverage is compromised. Therefore, this paper firstly proposes a tree traversal search based selection of divergent patterns that eliminates the search space. Ease in divergent pattern selection is used to generate optimal short sequences for divergent patterns instead of random patterns. Consequently, Multiple Controlled Antirandom Tests (MCATs) are generated that maximize distance between locally optimal short sequences to elevate the fault coverage. Fault simulation results on both ISCAS’85 and ISCAS’89 benchmark circuits prove the scalability and effectiveness of the proposed approach. Moreover, the comparison shows that up to 12% of fault coverage is improved as a result of proposed MCAT test pattern generation.