David C. Yu

Weather sensitive short-term load forecasting using nonfully connected artificial neural network

The authors present an artificial neural network (ANN) model for forecasting weather-sensitive loads. The proposed model is capable of forecasting the hourly loads for an entire week. The model is not fully connected; hence, it has a shorter training time than the fully connected ANN. The proposed model can differentiate between the weekday loads and the weekend loads. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data. The average percentage peak error for the test cases was 1.12%. >

Microgrid Generation Capacity Design With Renewables and Energy Storage Addressing Power Quality and Surety

Microgrids are receiving attention due to the increasing need to integrate distributed generations and to insure power quality and to provide energy surety to critical loads. Since renewables need to be in the mix for energy surety, a high renewable-energy penetrated microgrid is analyzed in this paper. The standard IEEE 34 bus distribution feeder is adapted and managed as a microgrid by adding distributed generation and load profiles. The 25 kV system parameters are scaled down to 12 kV and renewable sources including solar PV and wind turbines, an energy storage system, and a diesel generator for islanded mode have been added to the 34-bus system. The distribution generations (DG) and renewables are modeled in detail using PSCAD software and practical constraints of the components are considered. The monitoring of the microgrid for measuring power quality and control requirements for these DGs and storage are modeled to maintain the power quality of the system when loads are varied. Renewable sources are modeled with seasonal variation at different locations. The microgrid is monitored at number of buses and the power quality issues are measured and indexes are calculated. This paper proposes a generalized approach to design (determine the capacity requirements) and demonstrates the management of microgrids with metrics to meet the power quality indexes.

Correction of current transformer distorted secondary currents due to saturation using artificial neural networks

Current transformer saturation can cause protective relay misoperation or even prevent tripping. This paper presents the use of artificial neural networks (ANN) to correct current transformer (CT) secondary waveform distortions. The ANN is trained to achieve the inverse transfer function of iron-core toroidal CTs which are widely used in protective systems. The ANN provides a good estimate of the true (primary) current of a saturated transformer. The neural network is developed using MATLAB and trained using data from EMTP simulations and data generated from actual CTs. In order to handle large dynamic ranges of fault currents, a technique of employing two sets of network coefficients is used. Different sets of network coefficients deal with different fault current ranges. The algorithm for running the network was implemented on an Analog Devices ADSP3101 digital signal processor. The calculating speed and accuracy proved to be satisfactory in real-time application.

Bayesian network model for reliability assessment of power systems

This paper presents an application of Bayesian networks (BN) to the problem of reliability assessment of power systems. Bayesian networks provide a flexible means of representing and reasoning with probabilistic information. Uncertainty and dependencies are easily incorporated in the analysis. Efficient probabilistic inference algorithms in Bayesian networks permit not only computation of the loss of load probability but also answering various probabilistic queries about the system. The advantages of BN models for power system reliability evaluation are demonstrated through examples. Results of a reliability case study of a multi-area test system are also reported.

Economic Allocation for Energy Storage System Considering Wind Power Distribution

Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.

An adaptive high and low impedance fault detection method

An integrated high impedance fault (HIF) and low impedance fault (LIF) detection method is proposed in his paper. For a HIF detection, the proposed technique is based on a number of characteristics of the HIF current. These characteristics are: fault current magnitude; magnitude of the 3rd harmonic current; magnitude of the 5th harmonic current; the angle of the third harmonic current; the angle difference between the third harmonics current and the fundamental voltage; and the negative sequence current of HIF. These characteristics are identified by modeling the distribution feeders in EMTP. Apart from these characteristics, the above ambient (average) negative sequence current is also considered. An adjustable block out region around the average load current is provided. The average load current is calculated at every 18000 cycles (5 minutes) interval. This adaptive feature will not only make the proposed scheme more sensitive to low fault current, but it will also prevent the relay from tripping during the normal load current. In this paper, the logic circuit required for implementing the proposed HIF detection method is also included. With minimal modifications, the logic developed for the HIF detection can be applied to low impedance fault detection. A complete logic circuit which detects both the HIF and LIF is proposed. Using this combined logic, the need of installing separate devices for HIF and LIF detection can be eliminated. >

A multiple-objective optimization model of transmission enhancement planning for independent transmission company (ITC)

This paper proposes a multiple-objective fuzzy optimization model for an independent transmission company (ITC) to plan its transmission enhancements. There are several objectives to be considered, such as investment cost objective, power transfer efficiency objective, revenue objective, network congestion mitigation objective and environmental concern objective. The uncertain capital investment are also taken into account in the proposed model. By applying fuzzy set theory, the above multi-objective optimization model has been converted into a regular single-objective optimization problem to be solved. The numerical examples are given to demonstrate the effectiveness of the proposed model.

A practical approach to the conductor size selection in planning radial distribution systems

This paper presents a new approach to the optimization problem of conductor size selection in planning radial distribution systems. In this study, a multisection, branching feeder model with nonuniform load distribution has been chosen to best approximate actual conditions found in most distribution systems. Since the optimization problem is formulated as an integer programming problem, it is difficult to solve such a large-scale problem accurately. Therefore, in order to find an approximately optimal solution quickly, the authors have developed a simple, practical approach, which relies on the combined usage of an economical current density based method and a heuristic index directed method. This approach needs no sophisticated optimization technique and, thus, is easy to implement. Because of the practicality of its features, the proposed approach can be a useful tool for distribution engineers. It is presently in use at several electric power bureaus, such as the Chongqing Electric Power Bureau in China. The detailed results of the designs of some typical radial feeders using the proposed approach are also presented and discussed.

Transition Management of Microgrids With High Penetration of Renewable Energy

Microgrids are receiving attention due to the increasing need to integrate distributed generations and to ensure power quality and to provide energy surety to critical loads. Some of the main topics concerning microgrids are transients and stability concerns during transitions including intentional and unintentional islanding and reconnection. In this paper, the standard IEEE 34 bus distribution feeder is adapted and managed as a microgrid by adding distributed generations and load profiles. Supervisory power managements have been defined to manage the transitions and to minimize the transients on voltage and frequency. Detailed analyses for islanding, reconnection, and black start are presented for various conditions. The proposed control techniques accept inputs from local measurements and supervisory controls in order to manage the system voltage and frequency. An experimental system has been built which includes three 250 kW inverters emulating natural gas generator, energy storage, and renewable source. The simulation and experimental results are provided which verifies the analytical presentation of the hardware and control algorithms.

Dynamic Modeling and Interaction of Hybrid Natural Gas and Electricity Supply System in Microgrid

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks.