Based on the background of Huainan–Wannan UHV AC double circuit transmission line, the mechanism of the transient recovery voltage (TRV) is analyzed in the case of a wire of split conductor failure. The simulation model is established to simulate the TRV of the circuit breaker when the multiple split conductors are in different positions, and the influence of the induced voltage on the TRV is also considered. The simulation results show that the more the number of fault split conductors is, the more serious the TRV is, and the peak and rate of rise of TRV are increased due to the induced voltage. Therefore, it is very important to consider the coupling effect on the transient characteristics of circuit breakers in practical engineering.
The foremost problem facing by the photovoltaic (PV) system is to identify the faults and partial shade conditions. Further, the power loss can be avoided by knowing the number of faulty modules and strings. Hence, to attend these problems, a new method is proposed to differentiate the faults and partially shaded conditions along with the number of mismatch modules and strings for a dynamic change in irradiation. The proposed method has developed in two main steps based on a simple observation from the Current versus Voltage (I-V) characteristic curve of PV array at Line-Line (LL) fault. First, the type of fault is detected using defined variables, which are continuously updated from PV array voltage, current, and irradiation. Second, it gives the number of mismatch modules (or short-circuited bypass diodes) and mismatch strings (or open-circuited blocking diodes) by comparing with the theoretical predictions from the I-V characteristic curve of PV array. The proposed algorithm has been validated both on experimentation using small scale grid-connected PV array developed in the laboratory as well as MATLAB/Simulink simulations. Further, the comparative assessment with existing methods is presented with various performance indices to show the effectiveness of the proposed algorithm.
A new principle of UHVDC Line pilot protection based on internal parameters of the trigger angle of converter is proposed, in order to improve the ability of the protection to withstand transition resistance. Through the analysis of UHVDC control system, it is found that, due to the regulation of control system, in terms of trigger delay angle of rectifier side and trigger leading angle of inverter side, the change tendency in internal fault and external fault is different. Thus, the protection criterion is constructed. Compared with the traditional protection principle using voltage and current to establish protection criteria, this principle uses internal parameters as protection quantity. The simulation based on PSCAD/EMTDC verifies the effectiveness of the protection principle.
This paper proposes a new maximum power point tracking (MPPT) technique of photovoltaic system based on Kalman filter (KF) and associate to Artificial Neural Networks (ANN). The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models. Furthermore, the use of a neural model especially for accuracy improvement of the electrical equivalent circuit parameters, where the analytic equation of the model cannot be easily expressed, because the relationship between parameters is nonlinear. The proposed neural network is trained once by using some measured I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. For that reason, to get the fast tracking performance on this noisy conditions, and to maximize the power of photovoltaic system a KF method have been used. The performance analysis of perturb and observe (P&O) and KF MPPT techniques has been simulated in MATLAB/Simulink software and their model and control schemes has been analyzed and validated.
An accumulator or battery is an energy storage cramped in an adaptable stockade. Lithium-ion batteries are commonly used in hybrid electric vehicles (HEV) and battery operated electric vehicles (BOEV) due to its eco-friendliness and increased efficiency. To maintain lithium batteries in the safe operating region and also to perform tasks like cell balancing, preventing thermal runaway, maintain the state of health, an effective battery management system (BMS) is required. The BMS should also communicate effectively between host devices and battery packs. This paper proposes a reliable, modular and cost-efficient BMS, which will emanate an alert when a fault occurs and thus preventing the battery from damage. An efficient control strategy has been proposed for charging and discharging of the battery pack. The thermal analysis of the lithium-ion battery used in this work is simulated using battery design studio (BDS) with the inclusion of a self-discharging effect. The proposed hardware setup also provides a provision for on-board diagnosis (OBD) and logging in the accumulator management system (AMS) to constantly monitor the cell parameters like voltage, current, and temperature. The live data display of AMS working is also shown during abnormal and normal conditions. Also, an attempt is made to use the design of proposed AMS for HEV.
The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.
Controlled islanding is an effective way of preventing the system from catastrophic blackouts. This is generally solved either as a constrained combinatorial optimization problem or a slow coherency based linearized approach. The combinatorial explosion of the solution space of an extensive power network increases the complexity of solving, while the linearized slow coherency approach cannot track the varying coherent generator groups with a change in system operating conditions. Offline coherent studies are utilized in wide area measurement system (WAMS) data-based approaches to determine islanding boundaries. So, the present study proposes a novel coherency based controlled islanding technique that clusters generators and load buses simultaneously from the measured signals, ensuring generator coherency. Therefore, identification of inter-area modes from bus voltage angle signals is necessary to determine coherent bus groups. So, Zolotarev polynomial based filter bank (ZPBFB) is adopted in the present work to determine inter-area modes. The dimensional reduction techniques are used to cluster the coherent buses. The bus clusters thus obtained with the proposed method are compared with bus clusters determined from small signal stability analysis. The proposed method is demonstrated on IEEE 39-bus, 68-bus and 118-bus test systems and compared with graph spectra based controlled islanding.
Modern power systems are increasingly becoming more complex and thus become vulnerable to voltage collapse due to constant increase in load demand and introduction of new operation enhancement technologies. In this study, an approach which is based on network structural properties of a power system is proposed for the identification of critical nodes that are liable to voltage instability. The proposed Network Structurally Based Closeness Centrality (NSBCC) is formulated based on the admittance matrix between the interconnection of load to load nodes in a power system. The vertex (node) that has the highest value of NSBCC is taken as the critical node of the system. To demonstrate the significance of the concept formulated, the comparative analysis of the proposed NSBCC with the conventional techniques such as Electrical Closeness Centrality (ECC), Closeness Voltage Centrality (CVC) and Modal Analysis is performed. The effectiveness of all the approaches presented is tested on both IEEE 30 bus and the Southern Indian 10-bus power systems. Results of simulation obtained show that the proposed NSBCC could serve as valuable tool for rapid real time voltage stability assessment in a power system.