Comprehensive Review of Distributed FACTS Control Algorithms for Power Quality Enhancement in Utility Grid With Renewable Energy Penetration

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Abstract: The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

Optimal sizing and placement of energy storage system in power grids: A state-of-the-art one-stop handbook

Abstract: Energy storage system (ESS) has been expected to be a viable solution which can provide diverse benefits to different power system stakeholders, including generation side, transmission network (TN), distribution network (DN) and off-grid microgrid. Prudent ESS allocation in power grids determines satisfactory performance of ESS applications. Optimal sizing and placement of ESS are crucial for power quality improvement of DN and transmission system protection setting. To solve this issue, considerable researches have been done either in modelling or algorithms. However, various options and complex characteristics in different sub-systems make it difficult to appraise a specific method for an ESS application, while the existing reviews only focus on ESS applications in DN. This paper provides a critical one-stop handbook related to one hundred and four methods in six categories covering all kinds of networks and tailored applications. Meanwhile, a systematic methodology is presented with an extensive latest literature review, including ESS selection, evaluation criteria, modelling and solution methods. Particularly, different technical requirements and modelling methods of different sub-systems are outlined. Besides, pros and cons of optimization methods are thoroughly analysed and compared to reveal state-of-the-art studies. Finally, key points in optimal ESS sizing and placement are concluded along with recommendations for future research.

Incremental Conductance Based Particle Swarm Optimization Algorithm for Global Maximum Power Tracking of Solar-PV under Nonuniform Operating Conditions

Abstract: In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.

Dynamic Voltage Restorer (DVR): A Comprehensive Review of Topologies, Power Converters, Control Methods, and Modified Configurations

Abstract: Power quality is a pressing concern and of the utmost importance for advanced and high-tech equipment in particular, whose performance relies heavily on the supply’s quality. Power quality issues like voltage sags/swells, harmonics, interruptions, etc. are defined as any deviations in current, voltage, or frequency that result in end-use equipment damage or failure. Sensitive loads like medical equipment in hospitals and health clinics, schools, prisons, etc. malfunction for the outages and interruptions, thereby causing substantial economic losses. For enhancing power quality, custom power devices (CPDs) are recommended, among which the Dynamic Voltage Restorer (DVR) is considered as the best and cost-effective solution. DVR is a power electronic-based solution to mitigate and compensate voltage sags. This paper provides a thorough discussion and comprehensive review of DVR topologies based on operations, power converters, control methods, and applications. The review compares the state-of-the-art in works of literature, and comparative study on power quality issues, the DVR principle along with its operation modes, the DVR components, the DVR topologies based on energy storage, the DVR topologies based on single-/three-phase power converters, and the DVR topologies based on control units that have different control processing stages. Furthermore, modified and improved configurations of the DVR, as well as its integration with distributed generations, are described. This work serves as a comprehensive and useful reference for those who have an interest in researching DVRs.

A Hybrid Algorithm for Recognition of Power Quality Disturbances

Abstract: An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.

Adaptive Filter Theory

Abstract: Background and Overview. 1. Stochastic Processes and Models. 2. Wiener Filters. 3. Linear Prediction. 4. Method of Steepest Descent. 5. Least-Mean-Square Adaptive Filters. 6. Normalized Least-Mean-Square Adaptive Filters. 7. Transform-Domain and Sub-Band Adaptive Filters. 8. Method of Least Squares. 9. Recursive Least-Square Adaptive Filters. 10. Kalman Filters as the Unifying Bases for RLS Filters. 11. Square-Root Adaptive Filters. 12. Order-Recursive Adaptive Filters. 13. Finite-Precision Effects. 14. Tracking of Time-Varying Systems. 15. Adaptive Filters Using Infinite-Duration Impulse Response Structures. 16. Blind Deconvolution. 17. Back-Propagation Learning. Epilogue. Appendix A. Complex Variables. Appendix B. Differentiation with Respect to a Vector. Appendix C. Method of Lagrange Multipliers. Appendix D. Estimation Theory. Appendix E. Eigenanalysis. Appendix F. Rotations and Reflections. Appendix G. Complex Wishart Distribution. Glossary. Abbreviations. Principal Symbols. Bibliography. Index.

Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey

Abstract: The use of distributed energy resources is increasingly being pursued as a supplement and an alternative to large conventional central power stations. The specification of a power-electronic interface is subject to requirements related not only to the renewable energy source itself but also to its effects on the power-system operation, especially where the intermittent energy source constitutes a significant part of the total system capacity. In this paper, new trends in power electronics for the integration of wind and photovoltaic (PV) power generators are presented. A review of the appropriate storage-system technology used for the integration of intermittent renewable energy sources is also introduced. Discussions about common and future trends in renewable energy systems based on reliability and maturity of each technology are presented

A review of active filters for power quality improvement

Abstract: Active filtering of electric power has now become a mature technology for harmonic and reactive power compensation in two-wire (single phase), three-wire (three phase without neutral), and four-wire (three phase with neutral) AC power networks with nonlinear loads. This paper presents a comprehensive review of active filter (AF) configurations, control strategies, selection of components, other related economic and technical considerations, and their selection for specific applications. It is aimed at providing a broad perspective on the status of AF technology to researchers and application engineers dealing with power quality issues. A list of more than 200 research publications on the subject is also appended for a quick reference.

Stationary and nonstationary learning characteristics of the LMS adaptive filter

Abstract: This paper describes the performance characteristics of the LMS adaptive filter, a digital filter composed of a tapped delay line and adjustable weights, whose impulse response is controlled by an adaptive algorithm. For stationary stochastic inputs, the mean-square error, the difference between the filter output and an externally supplied input called the "desired response," is a quadratic function of the weights, a paraboloid with a single fixed minimum point that can be sought by gradient techniques. The gradient estimation process is shown to introduce noise into the weight vector that is proportional to the speed of adaptation and number of weights. The effect of this noise is expressed in terms of a dimensionless quantity "misadjustment" that is a measure of the deviation from optimal Wiener performance. Analysis of a simple nonstationary case, in which the minimum point of the error surface is moving according to an assumed first-order Markov process, shows that an additional contribution to misadjustment arises from "lag" of the adaptive process in tracking the moving minimum point. This contribution, which is additive, is proportional to the number of weights but inversely proportional to the speed of adaptation. The sum of the misadjustments can be minimized by choosing the speed of adaptation to make equal the two contributions. It is further shown, in Appendix A, that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value. When the eigenvalues are highly disparate (λ max /λ min > 10), an algorithm similar to LMS but based on Newton's method would approach this theoretical limit very closely.