Performance Enhancement of Shunt APFs Using Various Topologies, Control Schemes and Optimization Techniques

Following the advent of solid-state power electronics technology, extensive usage of nonlinear loads has lead to severe disturbances like harmonics, unbalanced currents, excessive neutral current and reactive power burden in three-phase power systems. Harmonics lower down the efficiency and power factor, increase losses, and result in electromagnetic interference with neighbouring communication lines and other harmful consequences. Over the years, active power filter (APF) has been proven to be a brilliant solution among researchers and application engineers dealing with power quality issues. Selection of proper reference compensation current extraction scheme plays the most crucial role in APF performance. This thesis describes three time-domain schemes viz. Instantaneous active and reactive power (p-q), modified p-q, and Instantaneous active and reactive current component (i_d-i_q) schemes. The objective is to bring down the source current THD below 5%, to satisfy the IEEE-519 Standard recommendations on harmonic limits. Comparative evaluation shows that, i_d-i_q is the best APF control scheme irrespective of supply and load conditions. Results are validated with simulations, followed by real-time analysis in RT-Lab.In view of the fact that APFs are generally comprised of voltage source inverter (VSI) based on PWM, undesirable power loss takes place inside it due to the inductors and switching devices. This is effectively minimized with inverter DC-link voltage regulation using PI controller. The controller gains are determined using optimization technique, as the conventional linearized tuning of PI controller yield inadequate results for a range of operating conditions due to the complex, nonlinear and time-varying nature of power system networks. Developed by hybridization of Particle swarm optimization (PSO) and Bacterial foraging optimization (BFO), an Enhanced BFO technique is proposed here so as to overcome the drawbacks of both PSO and BFO, and accelerate the convergence of optimization problem. Extensive simulation studies and RT-Lab real-time investigations are performed for comparative assessment of proposed implementation of PSO, BFO and Enhanced BFO on APF. This validates that, the APF employing Enhanced BFO offers superior harmonic compensation compared to other alternatives, by lowering down the source current THD to drastically small values.Another indispensable aspect of APF is its topology, which plays an essential role in meeting harmonic current requirement of nonlinear loads. APFs are generally developed with current-source or voltage-source inverters. The latter is more convenient as it is lighter, cheaper, and expandable to multilevel and multistep versions for improved performance at high power ratings with lower switching frequencies. There can be different topologies of VSI depending on the type of supply system. With each topology, constraints related to DC-link voltage regulation change. For effective compensation, irrespective of the number and rating of DC-link capacitors used in any particular topology, voltages across them must be maintained constant with optimal regulation of DC-link voltage. Various topologies for three-phase three-wire systems (conventional two-level and multilevel VSIs) and four-wire systems (split-capacitor (2C), four-leg (4L), three H-bridges (3HB) and three-level H-bridge (3L-HB) VSIs) are analyzed and compared based on component requirements, effectiveness in harmonic compensation, cost and area of application

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Identification of Alphanumeric Pattern Using Android

The “Identification of Alphanumeric pattern using Android” is a smart phone apps using Android platform and combines the functionality of Optical Character Recognition and identification of alphanumeric pattern and after processing, data is stored in server. This paper present, to design an apps using the Android SDK that will enable the Identification of Alphanumeric pattern using optical character reader technique for the Android based smart phone application. Camera, captures the document image and then the OCR is convert that image in to text (Binarization of captured data) according to the Alphanumeric (alphabetic and numeric characters) database and data stored in server. DOI: 10.17762/ijritcc2321-8169.160414

Brief Review on Identification, Categorization and Elimination of Power Quality Issues in a Microgrid Using Artificial Intelligent Techniques

Power quality is the manifestation of a disruption in the supply voltage, current or frequency that damages the utility equipment and has become an important issue with the introduction of more sophisticated and sensitive devices. So, the supply power quality issue still remains a major challenge as its degradation can cause huge destabilization of electrical networks. As renewable energy sources have irregular nature, a microgrid essentially needs energy storage system containing advanced power electronic converters which is the root cause of majority of power quality disturbances. Also, the integration of non-linear and unbalanced loads into the grid adds to its power quality problems. This article gives a compact overview on the identification, categorization and mitigation of these power quality events in a microgrid by using various Artificial Intelligence-based techniques like Optimization techniques, Adaptive Learning techniques, Signal Processing and Pattern Recognition, Neural Networks and Fuzzy Logic

A Comprehensive Survey on Different Control Strategies and Applications of Active Power Filters for Power Quality Improvement

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Power quality (PQ) has become an important topic in today’s power system scenario. PQ issues are raised not only in normal three-phase systems but also with the incorporation of different distributed generations (DGs), including renewable energy sources, storage systems, and other systems like diesel generators, fuel cells, etc. The prevalence of these issues comes from the non-linear features and rapid changing of power electronics devices, such as switch-mode converters for adjustable speed drives and diode or thyristor rectifiers. The wide use of these fast switching devices in the utility system leads to an increase in disturbances associated with harmonics and reactive power. The occurrence of PQ disturbances in turn creates several unwanted effects on the utility system. Therefore, many researchers are working on the enhancement of PQ using different custom power devices (CPDs). In this work, the authors highlight the significance of the PQ in the utility network, its effect, and its solution, using different CPDs, such as passive, active, and hybrid filters. Further, the authors point out several compensation strategies, including reference signal generation and gating signal strategies. In addition, this paper also presents the role of the active power filter (APF) in different DG systems. Some technical and economic considerations and future developments are also discussed in this literature. For easy reference, a volume of journals of more than 140 publications on this particular subject is reported. The effectiveness of this research work will boost researchers’ ability to select proper control methodology and compensation strategy for various applications of APFs for improving PQ.publishedVersio

On Applications of New Soft and Evolutionary Computing Techniques to Direct and Inverse Modeling Problems

Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. If the plants or systems are nonlinear, dynamic, Hammerstein and multiple-input and multiple-output (MIMO) types, the identification task becomes very difficult. Further, the existing conventional methods like the least mean square (LMS) and recursive least square (RLS) algorithms do not provide satisfactory training to develop accurate direct and inverse models. Very often these (LMS and RLS) derivative based algorithms do not lead to optimal solutions in pole-zero and Hammerstein type system identification problem as they have tendency to be trapped by local minima. In many practical situations the output data are contaminated with impulsive type outliers in addition to measurement noise. The density of the outliers may be up to 50%, which means that about 50% of the available data are affected by outliers. The strength of these outliers may be two to five times the maximum amplitude of the signal. Under such adverse conditions the available learning algorithms are not effective in imparting satisfactory training to update the weights of the adaptive models. As a result the resultant direct and inverse models become inaccurate and improper. Hence there are three important issues which need attention to be resolved. These are : (i) Development of accurate direct and inverse models of complex plants using some novel architecture and new learning techniques. (ii) Development of new training rules which alleviates local minima problem during training and thus help in generating improved adaptive models. (iii) Development of robust training strategy which is less sensitive to outliers in training and thus to create identification and equalization models which are robust against outliers. These issues are addressed in this thesis and corresponding contribution are outlined in seven Chapters. In addition, one Chapter on introduction, another on required architectures and algorithms and last Chapter on conclusion and scope for further research work are embodied in the thesis. A new cascaded low complexity functional link artificial neural network (FLANN) structure is proposed and the corresponding learning algorithm is derived and used to identify nonlinear dynamic plants. In terms of identification performance this model is shown to outperform the multilayer perceptron and FLANN model. A novel method of identification of IIR plants is proposed using comprehensive learning particle swarm optimization (CLPSO) algorithm. It is shown that the new approach is more accurate in identification and takes less CPU time compared to those obtained by existing recursive LMS (RLMS), genetic algorithm (GA) and PSO based approaches. The bacterial foraging optimization (BFO) and PSO are used to develop efficient learning algorithms to train models to identify nonlinear dynamic and MIMO plants. The new scheme takes less computational effort, more accurate and consumes less input samples for training. Robust identification and equalization of complex plants have been carried out using outliers in training sets through minimization of robust norms using PSO and BFO based methods. This method yields robust performance both in equalization and identification tasks. Identification of Hammerstein plants has been achieved successfully using PSO, new clonal PSO (CPSO) and immunized PSO (IPSO) algorithms. Finally the thesis proposes a distributed approach to identification of plants by developing two distributed learning algorithms : incremental PSO and diffusion PSO. It is shown that the new approach is more efficient in terms of accuracy and training time compared to centralized PSO based approach. In addition a robust distributed approach for identification is proposed and its performance has been evaluated. In essence the thesis proposed many new and efficient algorithms and structure for identification and equalization task such as distributed algorithms, robust algorithms, algorithms for ploe-zero identification and Hammerstein models. All these new methods are shown to be better in terms of performance, speed of computation or accuracy of results

Situational Intelligence for Improving Power System Operations Under High Penetration of Photovoltaics

Nowadays, power grid operators are experiencing challenges and pressures to balance the interconnected grid frequency with rapidly increasing photovoltaic (PV) power penetration levels. PV sources are variable and intermittent. To mitigate the effect of this intermittency, power system frequency is regulated towards its security limits. Under aforementioned stressed regimes, frequency oscillations are inevitable, especially during disturbances and may lead to costly consequences as brownout or blackout. Hence, the power system operations need to be improved to make the appropriate decision in time. Specifically, concurrent or beforehand power system precise frequencies simplified straightforward-to-comprehend power system visualizations and cooperated well-performed automatic generation controls (AGC) for multiple areas are needed for operation centers to enhance. The first study in this dissertation focuses on developing frequency prediction general structures for PV and phasor measurement units integrated electric grids to improve the situational awareness (SA) of the power system operation center in making normal and emergency decisions ahead of time. Thus, in this dissertation, a frequency situational intelligence (FSI) methodology capable of multi-bus type and multi-timescale prediction is presented based on the cellular computational network (CCN) structure with a multi-layer proception (MLP) and a generalized neuron (GN) algorithms. The results present that both CCMLPN and CCGNN can provide precise multi-timescale frequency predictions. Moreover, the CCGNN has a superior performance than the CCMLPN. The second study of this dissertation is to improve the SA of the operation centers by developing the online visualization tool based on the synchronous generator vulnerability index (GVI) and the corresponding power system vulnerability index (SVI) considering dynamic PV penetration. The GVI and SVI are developed by the coherency grouping results of synchronous generator using K-Harmonic Means Clustering (KHMC) algorithm. Furthermore, the CCGNN based FSI method has been implemented for the online coherency grouping procedure to achieve a faster-than-real-time grouping performance. Last but not the least, the multi-area AGCs under different PV integrated power system operating conditions are investigated on the multi-area multi-source interconnected testbed, especially with severe load disturbances. Furthermore, an onward asynchronous tuning method and a two-step (synchronous) tuning method utilizing particle swarm optimization algorithm are developed to refine the multi-area AGCs, which provide more opportunities for power system balancing authorities to interconnect freely and to utilize more PV power. In summary, a number of methods for improving the interconnected power system situational intelligence for a high level of PV power penetration have been presented in this dissertation

Performance Analysis of Photovoltaic Fed Distributed Static Compensator for Power Quality Improvement

Owing to rising demand for electricity, shortage of fossil fuels, reliability issues, high transmission and distribution losses, presently many countries are looking forward to integrate the renewable energy sources into existing electricity grid. This kind of distributed generation provides power at a location close to the residential or commercial consumers with low transmission and distribution costs. Among other micro sources, solar photovoltaic (PV) systems are penetrating rapidly due to its ability to provide necessary dc voltage and decreasing capital cost. On the other hand, the distribution systems are confronting serious power quality issues because of various nonlinear loads and impromptu expansion. The power quality issues incorporate harmonic currents, high reactive power burden, and load unbalance and so on. The custom power device widely used to improve these power quality issues is the distributed static compensator (DSTATCOM). For continuous and effective compensation of power quality issues in a grid connected solar photovoltaic distribution system, the solar inverters are designed to operate as a DSTATCOM thus by increasing the efficiency and reducing the cost of the system. The solar inverters are interfaced with grid through an L-type or LCL-type ac passive filters. Due to the voltage drop across these passive filters a high amount of voltage is maintained across the dc-link of the solar inverter so that the power can flow from PV source to grid and an effective compensation can be achieved. So in the thesis a new topology has been proposed for PV-DSTATCOM to reduce the dc-link voltage which inherently reduces the cost and rating of the solar inverter. The new LCLC-type PV-DSTATCOM is implemented both in simulation and hardware for extensive study. From the obtained results, the LCLC-type PV-DSTATCOM found to be more effective than L-type and LCL-type PV-DSTATCOM. Selection of proper reference compensation current extraction scheme plays the most crucial role in DSTATCOM performance. This thesis describes three time-domain schemes viz. Instantaneous active and reactive power (p-q), modified p-q, and IcosΦ schemes. The objective is to bring down the source current THD below 5%, to satisfy the IEEE-519 Standard recommendations on harmonic limits. Comparative evaluation shows that, IcosΦ scheme is the best PV-DSTATCOM control scheme irrespective of supply and load conditions. In the view of the fact that the filtering parameters of the PV-DSTATCOM and gains of the PI controller are designed using a linearized mathematical model of the system. Such a design may not yield satisfactory results under changing operating conditions due to the complex, nonlinear and time-varying nature of power system networks. To overcome this, evolutionary algorithms have been adopted and an algorithm-specific control parameter independent optimization tool (JAYA) is proposed. The JAYA optimization algorithm overcomes the drawbacks of both grenade explosion method (GEM) and teaching learning based optimization (TLBO), and accelerate the convergence of optimization problem. Extensive simulation studies and real-time investigations are performed for comparative assessment of proposed implementation of GEM, TLBO and JAYA optimization on PV-DSTATCOM. This validates that, the PV-DSTATCOM employing JAYA offers superior harmonic compensation compared to other alternatives, by lowering down the source current THD to drastically small values. Another indispensable aspect of PV-DSTATCOM is that due to parameter variation and nonlinearity present in the system, the reference current generated by the reference compensation current extraction scheme get altered for a changing operating conditions. So a sliding mode controller (SMC) based p-q theory is proposed in the dissertation to reduce these effects. To validate the efficacy of the implemented sliding mode controller for the power quality improvement, the performance of the proposed system with both linear and non-linear controller are observed and compared by taking total harmonic distortion as performance index. From the obtained simulation and experimentation results it is concluded that the SMC based LCLC-type PV-DSTATCOM performs better in all critical operating conditions