Band 23, Heft 1

The short-circuit withstand capability test of distribution transformer has the characteristics of long test period, high cost, strong destructiveness, low qualification rate, and can not be verified by simulation or calculation. It has become the bottleneck restricting the engineering and industrialization development of transformer industry. Based on experimental big data, this paper uses the expert diagnosis method of fuzzy mathematics to establish a superior evaluation model. The typical design process of distribution transformers is scored by experts, and the short-circuit passing rate is calculated according to the real short-circuit test data. Linear regression analysis is used to establish the regression equation, and a pre-diagnosis technique for short-circuit withstand capability of distribution transformers is proposed. At the same time, the validity of the model is verified by the actual case, and the accurate pre-diagnosis of the resistance to short-circuit capability is realized. It breaks through the common key technologies of the transformer industry and provides an early warning for the quality control of power grid pro-ducts and the elimination of hidden danger products. It has important theoretical guiding significance and engineering application value.

Converter transformers are an interface point of HVDC frameworks and are basic hardware from activity and reliability view point. Converter transformers are turned off intentionally under planned support and un-deliberately during fault conditions. Random energization of HVDC Converter transformer delivers high inrush currents due to core saturation asymmetries and endanger equipment health. In this paper, charging approach has been proposed. By utilizing point on wave exchanging for moderation of charging drifters during charging of 888 MVA 400/325 kV Converter Transformer of ±800 kV HVDC Sub-station. The proposed procedure has been applied on HVAC electrical Circuit Breaker furnished with Pre-Insertion Resistor considering diverse degree of residual flux. Performance of proposed philosophy has been affirmed by field tests and reproduction in PSCAD programming package and is found very viable. Near investigation is done in this paper and shows that with the use of proposed system, the switching transient during stimulation and field tests of coupled three phase converter transformers is decreased to worthy level.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.

Insulation based on epoxy resin (ER) is widely used for high voltage and high fault current rated electrical substation equipment. This paper details the formulation of epoxy based Nano-composites and influence of the Nano filler material on the electrical and thermal properties like breakdown voltage (BDV), relative permittivity, tan δ and thermal conductivity of the epoxy resin composites (ERC). The nano-filler materials such as alumina (Al 2 O 3 ), silica (SiO 2 ) and titania (TiO 2 ) are considered for the study. Diglycidyl ether of biphenyl-A (DGEBA) type epoxy resin CY5950 and acid anhydride hardener HY5951 are used for development of samples/insulated spacers. Scanning electron microscopy (SEM) images have been established for various ER composites to understand how the dispersion of nano-filler material influences the performance parameters of the samples. In the present study, the sample preparation with metallic inserts has shown additional advantages compared to existing practice of purely insulated samples. Developed samples help to estimate electrical and mechanical properties more accurately. Most appropriate nano filler is identified based on the test results obtained on samples developed in the study. Finally, by using the identified epoxy–filler composite, insulated spacers for 145 kV GIS have been developed and evaluated for electro-mechanical parameters. Necessary dielectric tests have been carried out on developed insulated spacer as per IEC.

Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

This paper presents a method for allocating active power losses in electric power networks to generators. A technique that uses current distribution factor is used to allocate losses to generator nodes. The core of the allocation scheme is based on graph theory and flows distribution in a network. Losses are only allocated based on the segment of a network that is used for power evacuation. Models of IEEE 14, 39, 57 and 118 test systems in PYPOWER 5.12 were used to test the scheme. It was observed that although the total network losses is minimised when optimal power flow is used for scheduling generation, however that does not translate to minimisation of loss allocation to some generators. The results obtained show that, the scheme can be used to allocate transmission network losses to generation nodes in electric power networks in a fair manner.

This article presents optimized gains for regulation of frequency and terminal voltage irrespective of the varying wind speeds in an autonomous wind power generation feeding linear and non-linear loads. Icos φ control algorithm is used to calculate and estimate reference source currents in a remote area wind energy conversion system (WECS) using an Induction Generator (IG). The Icos φ control algorithm do not have any phase locked loop or any conversions from one reference frame to other, which improves the dynamics and power system quality issues. The heart of the control algorithm is how quickly it estimates the reference source currents; this in turn depends on values of proportional and integral controller gains in the control algorithm. Here we are applying three optimization techniques to find the optimal proportional-integral (PI) controller output gains, the best convergence values are taken from optimization technique and applied for WECS. Battery energy storage system (BESS) connected to the direct current (DC) link of voltage source converter (VSC) manages the power of WECS. When load useful power level is less than the generated power level, the excess will be diverted and stored in the battery. But when generated power level is less than the load applied on WECS then the excess power requirement of the load is met by the battery, thus regulating the frequency under varying wind speeds. An isolated zigzag transformer is connected between point of common coupling and controller for neutral line current compensation. The controller is used for load balancing, current harmonic suppression, voltage and frequency regulation.

In this paper, a coordinated power-sharing strategy for interconnected DC-microgrid (DC-MG) is proposed. The DC-MG consists of two subgrids with an interlinking bidirectional DC/DC converter (IBDDC). Each subgrid has a secondary-1 controller based on a state of charge (SoC) balancing based droop control strategy of the battery unit (BU). The proposed droop strategy regulates the DC bus voltage according to the SoC of BU. With the SoC balancing based droop method, BU with higher SoC supplies more power to the microgrid (MG) as compared to low SoC BU. The SoC information of batteries in all subgrids is communicated through low bandwidth communication (LBC). In case of failure of LBC, a secondary-2 controller is implemented for the battery controller to regulate the DC bus voltage considering the SoC of BU. Secondary-2 does not depend on the communication line. Considering the levels of DC bus voltages, a secondary power regulating controller is introduced for IBDDC. Further, a coordinated power control strategy is proposed for distributed generation to avoid overcharging of batteries. The whole system operates in a distributed way without a central controller. The proposed strategy has been verified in MATLAB/Simulink.

Nonlinearity is a major constraint in analysing and controlling power systems. The behaviour of the nonlinear systems will vary drastically with changing operating conditions. Hence a detailed study of the response of the power system with nonlinearities is necessary especially at frequencies closer to natural resonant frequencies of machines where the system may jump into the chaos. This paper attempt such a study of a single machine to infinite bus power system by modelling it as a Duffing equation with softening spring. Using the method of multiple scales, an approximate analytical expression which describes the variation of load angle is derived. The phase portraits generated from the slow flow equations, closer to the jump, display two stable equilibria (centers) and an unstable fixed point (saddle). From the analysis, it is observed that even for a combination of parameters for which the system exhibits jump resonance, the system will remain stable if the variation of load angle is within a bounded region.