This paper proposes a novel control scheme for Interline dynamic voltage restorer (IDVR). The optimized fractional order PID (FOPID) controller is used in this work for power quality enhancement such as voltage regulation, harmonics distortion reduction, voltage sag and swells compensation, fault compensation etc. The gain parameters of FOPID controller are tuned by gravitational search algorithm (GSA). The performance of the suggested controller is evaluated by contrasting it with other optimization technique such as particle swarm optimization and cuckoo-search optimization algorithm. These simulation results represents that FOPID with GSA works better than the other techniques in terms of power quality enhancement.

A recent case study conducted at one of the thermal power plants shows that the CO2 emission which is the main reason for global warming is high compared to maximum limits. Plug-in hybrid electric vehicles (PHEVs) and renewable energy sources, in particular wind energy have recently been getting more interest because of various environmental and economic considerations Hence a solution is proposed to reduce emission level as well as cost of operation by incorporating PHEVs and renewable energy. Combined environmental economic dispatch (CEED) problem proposed in several literatures shows the problem is highly nonlinear. Hence, a powerful optimization tool is required to solve such a problem and because of that NSGA-II algorithm is proposed in this paper. In this paper, the impact of PHEVs and renewable energy integration into the electric grid is investigated. A case study in ten unit system is presented to verify the success of this proposed algorithm.

This paper introduces new algorithm to improve the performance of a Shunt Active Filter (SAF) by tuning the PI controller gain in order to maintain constant dc link voltage for non-linear balanced and unbalanced loads. The planned PI controller utilize a new artificial intelligent technique methods called grey wolf Optimization (GWO) for tuning the gain parameters of PI controller to attain optimality for SAF dc link voltage . For testing the heftiness of the controller unbalanced resistive loads that fed from uncontrolled three-phase bridge rectifier, proposed system uses balanced are taken as a non-linear loads. the hysteresis current control method and the instantaneous PQ theory is used to make the reference current is used in this strategy to distinguish the the actual currents and extracting reference in order to make the switching pulse for the Shunt Active Power Filter method. Determined Simulation results are attain with (Matlab/Simulink) it demonstrates so as to the GWO based PI controller tuning method is effective for maintaining the constant dc link voltage in minimum time period to improve the performance of the active filter.

The quasi-impedance source inverters/quasi-Z source inverters (Q-ZSIs) have shown improvement to overwhelmed shortcomings of regular voltage-source inverters (VSIs) and current-source inverters (CSIs) in terms of efficiency and buck-boost type operations. The Q-ZSIs encapsulated several significant merits against conventional ZSIs, i.e., realized buck/boost, inversion and power conditioning in a single power stage with improved reliability. The conventional inverters have two major problems; voltage harmonics and boosting capability, which make it impossible to prefer for renewable generation and general-purpose applications such as drive acceleration. This work has proposed a Q-ZSI with five-level six switches coupled inverter. The proposed Q-ZSI has the merits of operation, reduced passive components, higher voltage boosting capability and high efficiency. The modified space vector pulse width modulation (PWM) developed to achieve the desired control on the impedance network and inverter switching states. The proposed PWM integrates the boosting and regular inverter switching state within one sampling period. The PWM has merits such as reduction of coupled inductor size, total harmonic reduction with enhancing of the fundamental voltage profile. In comparison with other multilevel inverters (MLI), it utilizes only half of the power switch and a lower modulation index to attain higher voltage gain. The proposed inverter dealt with photovoltaic (PV) system for the stand-alone load. The proposed boost inverter topology, operating performance and control algorithm is theoretically investigated and validated through MATLAB/Simulink software and experimental upshots. The proposed topology is an attractive solution for the stand-alone and grid-connected system.

In recent years for reactive power compensation, inverter based conditioners have been used, due to their faster responses. To improve the quality of power in a distribution system, Distribution Static Synchronous Compensator (DSTATCOM), which is an inverter based device, has been used broadly. To control these kinds of devices, Proportional-Integral (PI) controller is used with certain pre-specified fixed parameters. Nowadays, the performances of these kind of controllers are not up to the expectation due to the nonlinearity of the system .In this paper , the Fuzzy Logic controller is described with the use of Fuzzy C-Means clustering (FCM) algorithm to design fuzzy rules and membership functions for the control of direct and quadrature axes currents of DSTATCOMs. Simulations on wide range of processes are carried out by using MATLAB/Simulink software and the responses are observed by changing the reference reactive current. The results are compared between the controllers in terms of several performance measures and in which, the DSTATCOM improves the damping of a power system by the proposed schemes.

The conventional electric power grid is presently evolving into smart grid by providing new services based mainly on information and communication technology. Due to the new facilities and services, the strength of a smart grid communication network against data attack is one of the ultimate problems that affect the entire system. In this work, some of the most vulnerable data attacks such as random fault attack, denial of service (DoS) attack and false data injection attacks against the state estimation in electric power system are discussed. This article is dedicated to study these cyber security issues in smart grid enabled power system using Unscented Kalman filter (UKF) along with χ 2 -detector and Euclidean detector. To obtain a reliable estimate of the system’s state, the noise covariance of the Kalman filter has to be tuned before the operation. Therefore, tuning of the UKF using particle swarm optimization (PSO) technique for minimizing estimation error is presented in this work. The simulation results show the benefits of the PSO tuned UKF for solving the proposed problem.

This paper proposes a novel control strategy based interline dynamic voltage restorer to improve the power quality between the two adjacent feeders in a distribution system. The interline dynamic voltage restorer is an interconnected form of multi dynamic voltage restorer with a common share of DC link. The artificial intelligence based control strategy is proposed here to achieve the faster rectification of the power quality issues such as voltage sag, voltage swell and harmonics. The whale optimization algorithm is proposed as the control strategy to accelerate the performance of the compensating unit towards the issues rectification. The architecture, control strategy and the performance are discussed in the paper. The effectiveness of the proposed whale optimized interline dynamic voltage restorer system is verified through the comparative analysis with the conventional optimization algorithms such as Ant Lion Optimizer and particle Swarm Optimization. The Fast Fourier transformation analysis for the harmonics rectification was discussed in the resultant part of the paper. The unabridged working of the proposed technique had implemented in Matlab/Simulink and verified.

DC to AC inverters are the well-known and improved in various kinds photovoltaic (PV) and gird tied systems. However, these inverters are require interfacing transformers to be synchronized with the grid-connected system. Therefore, the system is bulky and not economy. The transformerless inverter (TLI) topologies and its grid interface techniques are increasingly engrossed for the benefit of high efficiency, reliability, and low cost. The main concern in the TL inverters is common mode voltage (CMV), which causes the switching-frequency leakage current, grid interface concerns and exaggerates the EMI problems. The single-phase inverter two-level topologies are well developed with additional switches and components for eliminating the CMV. Multilevel inverters (MLIs) based grid connected transformerless inverter topology is being researched to avail additional benefits from MLI, even through that are trust topologies presented in the literature. With the above aim, this paper has proposed three -phase three-level T type NP-MLI (TNP-MLI) topology with transformerless PV grid connected proficiency. The CM leakage current should handle over mitigating CMV through removing unwanted switching events in the inverter pulse width modulation (PWM). This paper is proposes PV connected T type NP-MLI interface with three-phase grid connected system with the help of improved space vector modulation (SVM) technique to mitigate the CM leakage current to overcome the above said requests on the PV tied TL grid connected system. This proposed the SVM technique to mitigate the CM leakage current by selecting only mediums, and zero vectors with suitable current control method in order to maintain the inverter current and grid interface requirements. The proposed PV tied TNP-MLI offering higher efficiency, lower breakdown voltage on the devices, smaller THD of output voltage, good reliability, and long life span. The paper also investigated the CM leakage currents envisage and behavior for the three-phase MLI through the inverter switching function, which is not discussed before. The proposed SVM on TL-TNP-MLI offers the reliable PV grid interface with very low switching-frequency leakage current (200mA) for all the PV and inverter operation conditions. The feasibility and effectiveness of the TLI and its control strategy is confirmed through the MATLAB/Simulink simulation model directly as compared with 2kW roof top PV plant connected TL-TNP-MLI experimentation, showing good accordance with theoretical investigation. The simulation and experimental results are demonstrated and presented in the good stability of steady state and dynamics performances. The proposed inverter reduces the cost of grid interface transformer, harmonics filter, and CMV suppressions choke.

An Adaptive Neuro Fuzzy Inference System (ANFIS) based Extreme Learning Machine (ELM) theory is utilised in this research work In particular, the proposed algorithm is applied for designing a controller for electric vehicle to grid (V2G) integration in smart grid scenario. Initially, learning speed and accuracy of this proposed approach are continuously monitored and then, the performance of ELM-ANFIS (e-ANFIS) based controller is examined for its transient response. The proposed new learning technique overcomes the slow learning speed of the conventional ANFIS algorithm without sacrificing the generalization capability. Hence, a control practice for their charge and discharge patterns can be easily calculated even with the presence of large numbers of Plug-in Hybrid Electric Vehicles (PHEV). To examine the computational performance and transient response of the e-ANFIS based controller, it is evaluated with the usual ANFIS supported controller. The IEEE 33 bus radial distribution system based approach is implemented to ensure the sturdiness of this prescribed approach.

The alarming rate at which the global energy reserves are depleting is a major worldwide concern at both economic and environmental levels. Hence, the usage of renewable energy and plug-in hybrid electric vehicles (PHEVs) are recommended all over the world to minimize the effect of global warming and environmental pollution. Based on the case study conducted at thermal power plants in the state of Tamil Nadu, India, this article attempts to solve combined environmental/economic dispatch (CEED) scenario of thermal power plants by incorporating wind energy and plug-in hybrid electric vehicles. Particularly, the impact of PHEVs and renewable energy integration into the electric grid is analyzed for reduced CO2 emission and cost of operation.

In this paper, a DC-link voltage tuning algorithm is introduced to control the shunt active filter (SAF) with sinusoidal and trapezoidal power supplies. The purpose of the proposed optimization algorithm is for tuning the PI controller and reducing the harmonics level. Artificial bee colony (ABC) algorithm is introduced for tuning the gain of the controller and the voltage variation of power converter by using PWM pulses. It regulates the DC-link voltage as per the signal harmonics and the active power loss of the system is reduced. Therefore, the accurate compensation current is injected by the SAF devices. The proposed ABC-PI controller-based harmonic compensation method is implemented in MATLAB/Simulink platform. Then, the Total Harmonic Distortion (THD) and the power factor are evaluated. The results of the proposed method are compared with PI controller and PSO-PI controller. The proposed method has fast DC-link voltage response, low THD and good power factor.

Distributed Generation (DG) units are also called Dispersed Generation, Decentralized Generation and Embedded Generation. They are normally small generating plants, connected directly to either distribution side or customer side. The installation of inverter-based distributed generation (DG) has increased rapidly in recent years. This higher penetration level may result in the increased level of harmonics, which could exceed the permissible harmonic distortion level. The penetration level of DG is restricted by harmonic distortion, because of the nonlinear current injected by inverter based DG units. In this work, the maximum DG penetration level is determined, by considering the harmonic limits. The harmonics are determined by using the Decoupled Harmonic Power Flow (DHPF) approach. The constraints of this proposed problem include power balance equations, bus voltage limits, total and individual harmonic distortion limits specified by IEEE-519 standard. The problem is solved by using Particle Swarm Optimization (PSO) algorithm based optimization technique. Simulation results are obtained by MATPOWER/MATLAB in IEEE 30 and IEEE 57 bus test systems and the results prove the effectiveness of this proposed approach.

Energy technologies and their efficient use plays a vital role in socio-economic development of any country. In the recent years, the restructuring of electricity market evolves some major improvements in the technologies of energy production and thus, it has paved the way for increasing the applications of Distributed Generation (DG) with renewable energy sources. In this research, the optimal placement and sizing of multiple DGs are achieved by a novel indicator United Bus and Line Voltage Firmness Factor (UBL_VFF). The objectives of this work are the minimization of system losses and maximization of voltage stability and they are achieved by identifying the weakest voltage bus due to the weakest link in the system. Particle Swarm Optimization (PSO) is used for solving this optimization problem. The effectiveness of this proposed approach is tested in 33 and 69 bus radial distribution test systems. The results of this proposed method is compared with the results reported in the contemporary literature. The results have proved to be robust in terms of reduction in system losses and maximization of bus and line voltage stability.

The distributed static synchronous compensator (DSTATCOM) has emerged as an indispensable compensator element in the management of reactive power demand in distributed power management systems as well as grid connected systems. The DSTATCOM maintains the voltage at the point of common coupling (PCC) and at the DC link voltage across the DC link capacitor. For these two purposes, there are two independent controllers and these controllers are predominantly of PI types. Tuning the PI controller is a challenging exercise. In this paper, a novel method for tuning the PI controllers using particle swarm optimization is presented. By MATLAB SIMULINK simulation it is shown that the particle swarm optimization-tuned PI controller performs better than the traditional Ziegler-Nichols technique-tuned PI controller. In order to validate the proposed idea, an experimental setup has also been constructed with a PIC16F877A micro controller as the central control element and as a scale down physical model of the DSTATCOM.

In the current electricity paradigm, the rapid elevation of demands in industrial sector and the process of restructuring are the main causes for the overuse of transmission systems. Hence, the evolution of novel technology is the ultimate need to avoid the damages in the available transmission systems. An appreciable volume of renewable energy sources is used to produce electric power, after the implementation of deregulation in power system. Even though, they are intended to improve the reliability of power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand and the sudden failures of vital equipment cause transmission congestion in one or some transmission lines. Generation rescheduling and load shedding can be used to alleviate congestion, but some cases require quite few improved methods. With the extensive application of Distributed Generation (DG), congestion management is also performed by the optimal placement of DGs. Therefore, this research employs a Line Flow Sensitivity Factor (LFSF) and Particle Swarm Optimization (PSO) for the determination of optimal location and size of multiple DG units, respectively. This proposed problem is formulated to minimize the total system losses and real power flow performance index. This approach is experimented in modified IEEE-30 bus test system. The results of N-1 contingency analysis with DG units prove the competence of this proposed approach, since the total numbers of congested lines get reduced from 15 to 2. Hence, the results show that the proposed approach is robust and simple in alleviating transmission congestion by the optimal placement and sizing of multiple DG units.

This paper proposes exact radial basis function neural network (ERBFN) to identify the harmonic sources and their contributions at the point of common coupling without disconnecting the load from the network. The main advantage of the method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. Comparisons are made with the different types of neural networks such as feed forward back propagation neural network (FFBPN), cascade feed forward back propagation network (CFBPN) and radial basis function neural network (RBFNN) to verify the accuracy of the proposed method in harmonic source identification. Results proved that the identification of harmonic source at the point of common coupling using proposed method is more accurate with less computational time when compared to the other neural network structures.

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

This paper presents a new approach to detect and classify power quality disturbances in the power system using fuzzy logic (FL) and radial basis function neural networks (RBFNN). Feature extracted through the wavelet is used for training; after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification, five types of disturbance are taken into account. The classification performance of FL is compared with RBFNN. The classification accuracy of FL is improved with the help of cognitive as well as the social behavior of particles along with fitness value using particle swarm optimization, just by determining the ranges of the feature of the membership function for each rules to identify each disturbance specifically. The simulation result using FL possesses significant improvements and gives classification results in less than a cycle when compared over other considered approach.

The increasing wind power integration with power grid has forced the situation to improve the reliability of wind generators for stable operation. One important problem with induction generator based wind farm is its low ride through capability to the grid voltage disturbance. Any disturbance such as voltage dip may cause wind farm outages. Since wind power contribution is in predominant percentage, such outages may lead to stability problem. The proposed strategy is to use dynamic voltage controller (DVR) to compensate the voltage disturbance. The DVR provides the wind generator the ability to remain connected in grid and improve the reliability. Extensive simulation results are included to illustrate the control and operation of DVR.

The recent advancement in electric energy storage technologies provides an opportunity of using energy storage systems to address the issues of grid-integrated wind energy conversion systems. This paper proposes a novel configuration of a unified power quality conditioner (UPQC) with a supercapacitor-based short-term energy storage system for managing wind power intermittency during grid faults. The STATCOM-like compensation device can compensate only current related issues. The dynamic voltage restorer can compensate voltage-related issues but it can contribute only 50{\%} voltage due to converter rating limitations. Moreover, real power handling capabilities of these devices are very poor. This new UPQC scheme can compensate voltage sag, negative sequence current, and real and reactive powers from 0.1 pu to 0.9 pu. The proposed system improves fault ride-through capability of the wind turbine generators and satisfies the grid code requirement. A synchronous reference frame-based control method is employed for the UPQC. The energy storage system is controlled using a two-quadrant DC/DC converter. The proposed model was developed and tested in the MATLAB/SIMULINK environment.