Wind DGs Research Articles

A Coordination Scheme for a Combined Protection System Considering Dynamic Behavior and Wind DGs Fault Ride-Through Constraints

• A proposed design of a hierarchical protection scheme is presented. • Protective devices are optimally coordinated based on fault current transients. • Hybrid superconducting fault current limiters are optimally allocated to keep coordination under synchronous and inverter-based wind DGs. • Ride-through capability of wind DGs is integrated into the fault current limiter sizing problem. • Changes of network topology and variation of detected fault current are considered. A combined protection system has multiple types of devices such as directional overcurrent relays, reclosers, and fuses to protect distinct areas in a DG-incorporated network. An approach for setting all protective devices to realize complete coordination of adjacent ones taking transient responses into account is presented. The participation of synchronous generator-based and/or inverter-based wind DGs can degrade protection coordination. So, a scheme for sustaining coordination by optimal hybrid superconducting fault current limiters is proposed. The scheme can cope with variation of network topology; and handles the change of fault current level perceived by the protection device under a given topology. Moreover, instead of the conventional steady-state models, more realistic dynamic models of relays, reclosers, DGs, and fault current limiter are used. As a serious operation problem, undesired tripping of wind DGs under fault condition can occur due to the sag of terminal voltage below certain limits defined by national and international standards. Therefore, the proposed scheme prevents this undesired tripping of wind DGs by integrating its ride-through capability constraints into the fault current limiter allocation problem. The proposed approach is applied to the IEEE 9-bus and 30-bus systems. Results confirm the efficacy of the proposed method.

Distributed wind and solar power for grid sustainability and emission reduction

AbstractThis article intends to maximize the amount of solar and wind power produced while minimizing the tested network's annual energy loss. It also wants to improve the voltage profile and reduce the dependence on the upstream network. Two types of distributed generation sources, that is, wind DGs and solar DGs, are investigated. In this article, the network loads vary according to the local consumption pattern. This research shows that the optimal utilizing of solar and wind distributed generation sources causes energy loss to decrease by about 43% and 94%, respectively. Besides, the grid will receive 1109.49 GWh of energy from the upstream power grid without distributed generation sources. In contrast, solar and wind generation sources receive only 801.47 and 125.84 GWh, respectively. Additionally, in solar and wind scenarios, micro‐grid sends 71.62 and 102.42 GWh energy to the upstream network. Also, by calculating the emission reduction, it was observed that by installing 465.45 MW of the wind turbine units in the area under consideration, the country's CO2 equivalent is reduced by 754,586 tons, and the country's primary energy is reduced by 263,916 TOE.

Effect of Penetration of Wind DGs on Transient Stability of Captive Power Generation Units

This paper presents the analysis of transient stability for industrial generators and the effect of distributed generator units on transient stability has been studied. Here we considered IEEE 39 busses system having one utility bus and 9 captive generation units. It is observed that the voltages at all the buses, decreased when Wind Power DGs are connected. Voltages at all the buses in the system are reduced because of reactive power demand by Induction Generators. The total load of the system is 121.57 MW + j 56.6 MVAr. Critical Clearing Times (CCTs) are obtained without introducing any DG and later, CCTs are obtained with four Wind Power DGs. Their capacities are 7.0 MW, 8.0 MW, 9.0 MW and 5.0 MW with 0.8, 0.85, 0.9 and 0.95 power factors respectively. The Induction Generators used in Wind DGs inject active power and consume reactive power. It is observed that the voltages at all the buses, decreased when Wind Power DGs are connected. Voltages at all the buses in the system are reduced because of reactive power demand by Induction Generators. LLLG faults at seven load buses and nine Generator buses and are considered for obtaining Critical Clearing Times (CCT). It is observed that transient stability of system is improved by placing DGs. DGs used in this system improves the critical clearing time (CCT) for faults at load buses by 30% and faults at generator buses by 15.5%. The detailed results are tabulated in this paper.

Islanding Detection of Microgrid Incorporating Inverter Based DGs Using Long Short-Term Memory Network

This paper proposes a new approach for rapid detection of islanding events in a microgrid (MG). The proposed approach is a two-step procedure in which the first step is to extract some valuable features from the voltage and current signals. Such signals are analyzed for finding the second harmonic by the discrete Fourier transform (DFT). Then, the symmetrical components of this second harmonic are calculated for voltage and current, resulting in six features; positive, negative and zero sequence components. In the second step, a novel deep learning classifier based on long short-term memory (LSTM) network to identify the islanding decision is applied. The LSTM is a new artificial intelligence technique which is a distinctive pattern of recurrent neural networks. To evaluate the performance of the proposed approach, simulated and practical voltage and current signals are used. The simulated signals are generated by simulating a MG consisting of inverter based wind DGs using Matlab Simulink, while the practical data are collected from an experimental model consisting of wind and PV DGs. Different intentional and unintentional islanding events are conducted and processed using the proposed approach. The results show that in comparison with other artificial intelligence algorithms such as decision tree (DT), support vector machine (SVM) and artificial neural network (ANN), the proposed approach is efficient and reliable in detecting the islanding with high accuracy, high dependability and small detection time.

Integration of Solar and Wind Based DGs with DSTATCOM in Distribution Systems using Modified Bat Algorithm

Nowadays, renewable energy sources based distributed generations (REDGs) interconnection with distribution system is gaining more attention by the power system engineers since it meets increasing load demand and cancel out environmental threats. Suitable incorporation of REDGs in radial distribution system (RDS) is vital in ensuring qualitative network operational profits. In this present approach a modified bat algorithm (MBA) based optimization method is employed to allocate solar and wind DGs along with DSTATCOM in RDS. The proposed new multi objective function to mitigate power loss, bus voltage development stability betterment and Total Operating Cost (TOC) minimization. Proper modelling of solar irradiance and wind speed have been done in the RDS. The present approach is tested to Indian countrified system structure and standard IEEE 69-bus system, and received satisfactory outputs.

Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO 2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multi-objective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

Unified Planning of Wind Generators and Switched Capacitor Banks: A Multiagent Clustering-Based Distributed Approach

In this paper, a multiagent clustering-based distributed approach for the optimal planning of wind-distributed generators (DGs) and switched capacitor banks (SCBs) is proposed. First, electrical distance matrices for the power systems are constructed. Additionally, a constrained optimization problem, which includes several indices and a few constraints, for the optimal clustering of distribution networks is formulated and solved. After obtaining optimal clusters, agents are assigned to the clusters, and a second multiobjective optimization problem (MOOP) for the distributed planning of wind DGs and SCBs is formulated and assigned to a head agent. The number of objective functions in the MOOP is equal to the number of agents. The objective function of an agent consists of three indices: annual energy losses, investment costs, and voltage enhancement. Moreover, a deep neural network architecture is designed, and four independent networks are trained with six years of wind speed data for the seasonal wind speed forecasting. Two IEEE unbalanced test feeders, one with 37 nodes and the other with 123 nodes, and eight test cases are considered for simulations. The results show that losses and costs are optimized, and the voltage unbalance of the system is reduced.

Comparison of Optimum Wind–Solar DG, Statcom and Capacitor Placement and Sizing Based on Voltage Stability Margin Enhancement in Microgrid with Three Different Evolutionary Algorithms

This study evaluates the most effective method to maximize the voltage stability margin in two simulated microgrids under seven different scenarios, 33 and 69 IEEE RTS models. Three different evolution algorithms are used to improve the voltage stability margin. Moreover, other power system indices such as voltage profile improvement and reducing the power losses are considered in the presented simulations. Power system limitations such as power flow restriction, line heat capacity, power transmission capacity through the line as well as voltage variation constraint are viewed to make the simulation more realistic. This paper presents solar–wind DGs, Dstatcom and capacitor placement and sizing separately as well as simultaneously. The main objective of this study is to achieve the maximum voltage stability margin in microgrids and its comparison by three different evolutionary algorithms, particle swarm optimization (PSO), genetic algorithm (GA) and honey bees mating optimization (HBMO). In the first stage, GA is applied on all seven scenarios. After selecting the best combination of the devices which creates the maximum voltage stability margin, the combination is also studied using two other evolutionary algorithms, PSO and HBMO.

PSO based placement of multiple wind DGs and capacitors utilizing probabilistic load flow model

Wind Power Distributed Generators (WPDGs) are being increasingly placed in the power system due to their several technical and environmental benefits. In this paper, a Modified Particle Swarm Optimizer (MPSO) based method is proposed for placement of multiple WPDGs and capacitors. Monte Carlo Simulation (MCS) based probabilistic load flow, considering uncertainty in load demand and wind generation, is developed. It is used to modify the WPDGs׳ and capacitors׳ sizes utilizing a sensitivity based approach, which maintains branch currents and bus voltages within their prescribed limits. The proposed method is simple, accurate and generic, and it can provide multiple choices to the utilities to place capacitors and WPDGs under various system constraints. Results on three distribution networks demonstrate the effectiveness of the proposed method. The impact of the DG placement on the system voltage profile, line loss, environment, and cost of generation has also been investigated on three distribution systems.