In this paper describes the operation of power system networks to be nearest to stability rated values limits. State estimation for monitoring and protection power system is very important because it provides a real-time (RT) Phase angle of different nodes of accuracy and then analysis and decided to choose control way (methods). In order to detect the exact situation (instant state) for power system networks parameters. In this paper proposes a new monitoring and analysis system state estimation method integrating with MATLAB environment ability, by using phasor measurement units (PMU's) technology, by this system the estimation problem, iterations numbers, and processing time will reduce. The measurements of phasors value of voltage signal and current estimated and analyzed. Mat lab/PSAT package use as a tool to design and simulate four electrical power systems networks such as INSG 24 buses, IEEE14 bus, Diyala city 10buses (IRAQ), and IEEE6 bus and then installed and applied PMU’s devices to each system. Simulation results show that the PMU's performances effectiveness appear clearly. All results show the validation of PMU’s devices as an estimator to power system networks states and a significant improvement in the accuracy of the calculation of network status. All results achieved and discussed through this paper setting up mathematical models with Graph Theoretic Procedure algorithm.
Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3- machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.
This work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the "idealistic" full state optimal control. The LQG damping controller's regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.
This paper investigates Load Frequency Control of multi area inter connected power system having different turbines with PID controller. The gain values of controller are optimized using different Metaheuristic Algorithms. The performance and validity of designed controllers were checked on multi area interconnected power system with various Step Load Perturbations. Finally, the performance of proposed controllers was compared with conventional controller and from the result it was proved that the proposed controller exhibits superior performance than conventional controller for various Step Load Perturbations.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.
Load Frequency Control (LFC) is a basic control strategy for proper operation of the power system. It ensures the ability of each generator in regulating its output power in such way to maintain system frequency and tie-line power of the interconnected system at prescribed levels. This article introduces comprehensive comparative study between Chaos Optimization Algorithm (COA) and optimal control approaches, such as Linear Quadratic Regulator (LQR), and Optimal Pole Shifting (OPS) regarding the tuning of LFC controller. The comparison is extended to the control approaches that result in zero steady-state frequency error such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Ziegler-Nicholas method is widely adopted for tuning such controllers. The article then compares between PI and PID controllers tuned via Ziegler-Nicholas and COA. The optimal control approaches as LQR and OPS have the characteristic of steady-state error. Moreover, they require the access for full state variables. This limits their applicability. Whereas, Ziegler-Nicholas PI and PID controllers have relatively long settling time and high overshoot. The controllers tuned via COA remedy the defects of optimal and zero steady-state controllers. The performance adequacy of the proposed controllers is assessed for different operating scenarios. Matlab and its dynamic platform, Simulink, are used for stimulating the system under concern and the investigated control techniques. The simulation results revealed that COA results in the smallest settling time and overshoot compared with traditional controllers and zero steady-state error controllers. In the overshoot, COA produces around 80% less than LQR and 98.5% less than OPS, while in the settling time, COA produces around 81% less than LQR and 95% less than OPS. Moreover, COA produces the lowest steady-state frequency error. For Ziegler-Nicholas controllers, COA produces around 53% less in the overshoot and 42% less in the settling time.
This paper presents a case study on transmission pricing practices. Now a days, restructuring of the power system market through deregulation is gaining attention as technical and economical benefits at generator and user. In India, after the Electricity Act 2003, restructuring has been introduced in the Indian power system market. Researchers are continuously working towards improvement of deregulation based on restructuring to improve transmission pricing practices and calculations in a better way. Therefore, this paper presents an overview of MW- Mile and postage stamp methods to estimate the transmission cost. Further, the North Indian practical power system of 37 bus test system has been analyzed by reverse, absolute and dominant Mw-Mile methods. The results obtained to be expected for deregulated power market.
In this paper, the power system stabilizer (PSS) and Thyristor controlled phase shifter(TCPS) interaction is investigated . The objective of this work is to study and design a controller capable of doing the task of damping in less economical control effort, and to globally link all controllers of national network in an optimal manner , toward smarter grids . This can be well done if a specific coordination between PSS and FACTS devices , is accomplished . Firstly, A genetic algorithm-based controller is used. Genetic Algorithm (GA) is utilized to search for optimum controller parameter settings that optimize a given eigenvalue based objective function. Secondly, an optimal pole shifting, based on modern control theory for multi-input multi-output systems, is used. It requires solving first order or second order linear matrix Lyapunov equation for shifting dominant poles to much better location that guaranteed less overshoot and less settling time of system transient response following a disturbance.
Since recent societies become more hooked into electricity, a higher level of power supply continuity is required from power systems. The expansion of those systems makes them liable to electrical faults and several failures are raised due to totally different causes, like the lightning strike, power system element failure caused by mechanical aging as well as human mistakes. These conditions impact the stability of the power as well as lead to costly maintenance and loss of output. This article examines the latest technologies and strategies to determine the location of faults in medium voltage distribution systems. The aim is to classify and assess different strategies in order to determine the best recommended models in practice or for further improvement. Several ways to locate failures in distribution networks have therefore been established. Because faults are unpredictable, quick fault location as well as isolating are necessary to reduce the impact of faults in distribution networks as well as removing the emergency condition from the entire system. This study also includes a comprehensive evaluation of several defect location methods depending on the algorithm employed, the input, the test system, the characteristics retrieved, and the degree of complexity. In order to gain further insight into the strengths and limitations of each method and also comparative analysis is carried out. Then the main problems of the fault location methods in distribution network are briefly expounded.
The continuously ever-growing demand for the electrical power causing the continuous expansion and complexity of power systems, environmental and economic factors forcing the system to work near the critical limits of stability, so research's stability have become research areas worthy of attention in the resent day. The present work includes two phases: The first one is to determine the Voltage Stability Index for the more insensitive load bus to the voltage collapse in an interconnected power system using fast analyzed method based on separate voltage and current for PQ buses from these of PV buses, while the second phase is to suggested a simulated optimization technique for optimal voltage stability profile all around the power system. The optimization technique is used to adjust the control variables elements: Generator voltage magnitude, active power of PV buses, VAR of shunt capacitor banks and the position of transformers tap with satisfied the limit of the state variables (load voltages, generator reactive power and the active power of the slack bus). These control variables are main effect on the voltage stability profile to reach the peak prospect voltage stable loading with acceptable voltage profile. An optimized voltage collapse based on Particle Swarm Optimization has been tested on both of the IEEE 6 bus system and the Iraqi Extra High Voltage 400 kV Grid 28 bus . To ensure the effectiveness of the optimization technique a comparison between the stability indexes for load buses before and after technical application are presented. Simulation results have been executed using Matlab software). Keyword: Voltage Stability Indicator; voltage collapse; Stability of Extra High Voltage Grid; PSO optimization technique.
Accurate long-term load forecasting (LTLF) is crucial for smart grid operations, but existing CNN-based methods face challenges in extracting essential featuresfrom electricity load data, resulting in diminished forecasting performance. To overcome this limitation, we propose a novel ensemble model that integratesa feature extraction module, densely connected residual block (DCRB), longshort-term memory layer (LSTM), and ensemble thinking. The feature extraction module captures the randomness and trends in climate data, enhancing the accuracy of load data analysis. Leveraging the DCRB, our model demonstrates superior performance by extracting features from multi-scale input data, surpassing conventional CNN-based models. We evaluate our model using hourly load data from Odisha and day-wise data from Delhi, and the experimental results exhibit low root mean square error (RMSE) values of 0.952 and 0.864 for Odisha and Delhi, respectively. This research contributes to a comparative long-term electricity forecasting analysis, showcasing the efficiency of our proposed model in power system management. Moreover, the model holds the potential to sup-port decisionmaking processes, making it a valuable tool for stakeholders in the electricity sector.
Large disturbances in an induction generator-based wind system necessitate rapid compensation for the reactive power. This article addresses the application of Static Synchronous Compensator (STATCOM) in optimizing the performance of grid connected wind power system. The functionality of the static synchronous compensator in maintaining system stability and reliability during/post diverse severe disturbances is thoroughly investigated. A design procedure for STATCOM, particularly the capacitor in the DC side was advised.
The increasing demand for electricity due to population expansion has led to frequent interruptions in electrical power, so there are backup power lines everywhere, especially in the sectors of education, health, banking, transportation and communications. DC sources are beginning to become widely spread in terms of low maintenance requirements, no need for refueling, and no pollutant emission in these institutions. The problems of DC systems are; losses in DC system components, and change in output voltage as loads change. This research presents a power system that generates 1760W AC power from batteries bank, the system consists of a twin inverter to reduce losses in switches and filters, and thus improving the efficiency and the power factor of the system, and fuzzy logic controllers to regulate the output voltage of the converter and inverter. Modeling and simulation in MATLAB / Simulink showed obtaining a constant load voltage with acceptable values of total harmonics distortion (THD) under different conditions of loads and batteries.
This review article puts forward the phenomena of chaotic oscillation in electrical power systems. The aim is to present some short summaries written by distinguished researchers in the field of chaotic oscillation in power systems. The reviewed papers are classified according to the phenomena that cause the chaotic oscillations in electrical power systems. Modern electrical power systems are evolving day by day from small networks toward large-scale grids. Electrical power systems are constituted of multiple inter-linked together elements, such as synchronous generators, transformers, transmission lines, linear and nonlinear loads, and many other devices. Most of these components are inherently nonlinear in nature rendering the whole electrical power system as a complex nonlinear network. Nonlinear systems can evolve very complex dynamics such as static and dynamic bifurcations and may also behave chaotically. Chaos in electrical power systems is very unwanted as it can drive system bus voltage to instability and can lead to voltage collapse and ultimately cause a general blackout.
In light of the widespread usage of power electronics devices, power quality (PQ) has become an increasingly essential factor. Due to nonlinear characteristics, the power electronic devices produce harmonics and consume lag current from the utility. The UPQC is a device that compensates for harmonics and reactive power while also reducing problems related to voltage and current. In this work, a three-phase, three-wire UPQC is suggested to reduce voltage-sag, voltage-swell, voltage and current harmonics. The UPQC is composed of shunt and series Active Power Filters (APFs) that are controlled utilizing the Unit Vector Template Generation (UVTG) technique. Under nonlinear loads, the suggested UPQC system can be improved PQ at the point of common coupling (PCC) in power distribution networks. The simulation results show that UPQC reduces the effect of supply voltage changes and harmonic currents on the power line under nonlinear loads, where the Total Harmonic Distortion (THD) of load voltages and source currents obtained are less than 5%, according to the IEEE-519 standard.
This paper presents a novel approach, Equal Incremental fuel cost (λ-Concept) approach, for solving Multi Area Economic Dispatch (MAED) problem. It is a simple approach and developed from the basic observation of incremental fuel cost of an area. The proposed approach has been tested on 4-area system with four generators in each area and a large 2-area system consists of 120 generators. The suggested algorithm has been tested extensively by considering the different tie line power transfer limits and useful recommendations are provided. Further, the impact of tie line power transfer limits on total fuel cost is also discussed. It is found from the test cases that the proposed method is shown to be robust, very fast and extensible to include a large class of problems. The simulation results of the proposed method have been compared with the existing methods.
The reliability of power system under fault susceptible environment has become major challenge for the power sector units. The injection of renewable power source has increased the complexity for distribution system and to deal with massive network, evolution of smart-grid has been enforced, which works in an automated fashion to improve overall reliability, efficiency and quality of the system. Proactive Self-healing is a critical feature of smart-grid. This paper tries to explain the concept sensing the occurrence of fault beforehand and providing possible solution for self-healing in smart grid. The fundamental base for incorporating afore discussed technology viz. understanding nature of fault, sources of fault and implementation of effective measuring techniques are enumerated in paper briefly. Support required in terms of technology is reviewed towards the end followed by a case study of practical implementation of self-healing control in a distribution system.
Recently, numerous researches have emphasized the importance of professional inspection and repair in case of suspected faults in Photovoltaic (PV) systems. By leveraging electrical and environmental features, many machine learning models can provide valuable insights into the operational status of PV systems. In this study, different machine learning models for PV fault detection using a simulated 0.25MW PV power system were developed and evaluated. The training and testing datasets encompassed normal operation and various fault scenarios, including string-to-string, on-string, and string-to-ground faults. Multiple electrical and environmental variables were measured and exploited as features, such as current, voltage, power, temperature, and irradiance. Four algorithms (Tree, LDA, SVM, and ANN) were tested using 5-fold cross-validation to identify errors in the PV system. The performance evaluation of the models revealed promising results, with all algorithms demonstrating high accuracy. The Tree and LDA algorithms exhibited the best performance, achieving accuracies of 99.544% on the training data and 98.058% on the testing data. LDA achieved perfect accuracy (100%) on the testing data, while SVM and ANN achieved 95.145% and 89.320% accuracy, respectively. These findings underscore the potential of machine learning algorithms in accurately detecting and classifying various types of PV faults. .
In this paper describe to mathematical analysis for a three-phase, two level inverter designs. As we know the power electronic devices (inverter) to convert the DC power to AC power (controller on output voltage and frequency level). In Industrial applications, the inverters are used for adjustable speed (AC Drives). In this paper, the mathematical analyses for inverter design are done by using Software packages C++ Builder and visual C++ Language. For non- linear distortions described by the load power factor in power system networks. The P.F is reverse proportional with the harmonics distortion. Small P.F means much more of harmonic distortion, and lower power quality for consumers. to improve the P.F, and power quality in this paper the small capacitor installed as part of the rectified the load current has power (30 KW with P.F load 0.8), the fluctuations of the rectified voltage must not greater than +/- 10%.The power factor proportion of the load power, with Modulation coefficient p.u approximately unity. The calculation is achieved with different integrations steps with load power 30KW, 0.8 P.F. all results done Based on model and experimental data..
In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.
Competitive trend towards restructuring and unbundling of transmission services has resulted in the need renteto discover the impact of a particular generator to load. This paper initially presents the analysis of three diff reactive power valuation methods namely, Modified Y bus , Virtual flow approach and Modified Power flow tracing to compute the reactive power output from a particular generator to particular load. Among these methods, the modified power flow electricity tracing method is identified as the best because of its various features. Secondly, based on this Method, the opportunity cost for practical system is determined. Hence, this method can be useful in providing additional insight into power system operation and can be used to modify existing tariffs of charging for reactive power transmission loss and reactive power transmission services. Simulation and comparison results are shown by taking IEEE 30 bus system as test system.
Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.
Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.
This paper principally advises a simple and reliable control for Static Synchronous Compensator (STATCOM) in a stand-alone wind driven self-excited induction generator power system. The control was proposed based on instantaneous P-Q theory. The advised control enjoys the merits of robustness, reliability and simplicity. The paper also proposes a dimensioning procedure for the STATCOM that involves advising an annotative analytical expression for sizing the DC-link capacitor. This procedure has the advantages of applicability for different reactive power compensators that depend on a separate DC-link in its operation. Comprehensive simulation results in Matlab environment were illustrated for corroborating the performance of the advised control under rigorous operating scenarios. The results show the feasibility, reliability and practicability of the proposed controller.
This paper presents a novel approach, Equal Incremental fuel cost (λ-Concept) approach, for solving Multi Area Economic Dispatch (MAED) problem. It is a simple approach and developed from the basic observation of incremental fuel cost of an area. The proposed approach has been tested on 4-area system with four generators in each area and a large 2-area system consists of 120 generators. The suggested algorithm has been tested extensively by considering the different tie line power transfer limits and useful recommendations are provided. Further, the impact of tie line power transfer limits on total fuel cost is also discussed. It is found from the test cases that the proposed method is shown to be robust, very fast and extensible to include a large class of problems. The simulation results of the proposed method have been compared with the existing methods.
In order to mitigate the effect of voltage sag on sensitive loads, a dynamic voltage restorer (DVR) should be used for this purpose. The DVR should be accompanied with a fast and accurate sag detection circuit or algorithm to determine the sag information as quickly as possible with an acceptable precision. This paper presents the numerical matrix method as a distinctive candidate for voltage sag detection. The design steps of this method are demonstrated in detail in this work. The simulation results exhibit the superiority of this technique over the other detection techniques in term of the speed and accuracy of detection, simplicity in implementation, and the memory size. The results also accentuate the recognition capability of the proposed method in distinguishing different types of voltage sag by testing three different voltage sag scenarios.
Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA). Key words: Distributed generation, distribution network planning, multi-objective optimization, and Imperialist competitive algorithm.
The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.
This paper describes the capability of artificial neural network for predicting the critical clearing time of power system. It combines the advantages of time domain integration schemes with artificial neural network for real time transient stability assessment. The training of ANN is done using selected features as input and critical fault clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulation. Multi layer feed forward neural network trained with Levenberg Marquardt (LM) back propagation algorithm is used to provide the estimated CCT. The effectiveness of ANN, the method is demonstrated on single machine infinite bus system (SMIB). The simulation shows that ANN can provide fast and accurate mapping which makes it applicable to real time scenario.
Power transformer protective relay should block the tripping during magnetizing inrush and rapidly operate the tripping during internal faults. Recently, the frequency environment of power system has been made more complicated and the quantity of 2nd frequency component in inrush state has been decreased because of the improvement of core steel. And then, traditional approaches will likely be maloperated in the case of magnetizing inrush with low second harmonic component and internal faults with high second harmonic component. This paper proposes a new relaying algorithm to enhance the fault detection sensitivities of conventional techniques by using a fuzzy logic approach. The proposed fuzzy-based relaying algorithm consists of flux-differential current derivative curve, harmonic restraint, and percentage differential characteristic curve. The proposed relaying was tested with MATLAB simulation software and showed a fast and accurate trip operation.
The hybrid AC/DC microgrid is considered to be more and more popular in power systems as increasing loads. In this study, it is presented that the hybrid AC/DC microgrid is modeled with some renewable energy sources (e.g. solar energy, wind energy) in the residential of the consumer in order to meet the demand. The power generation and consumption are undergoing a major transformation. One of the tendencies is to integrate microgrids into the distribution network with high penetration of renewable energy resources. In this paper, a new distributed coordinated control is proposed for hybrid microgrid, which could apply to both grid-connected mode and islanded mode with hybrid energy resources and variable loads. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. Also, the maximum power point tracking technique is applied to both photovoltaic stations and wind turbines to extract the maximum power from the hybrid power system during the variation of the environmental conditions. Finally, a simulation model is built with a photovoltaic, wind turbine, hybrid microgrid as the paradigm, which can be applied to different scenarios, such as small-sized commercial and residential buildings. The simulation results have verified the effectiveness and feasibility of the introduced strategy for a hybrid microgrid operating in different modes