D.G. circuit analysis

a.c. circuit analysis

Preface. 1. The Physics of Electricity. 1.1 Basic Quantities. 1.1.1 Introduction. 1.1.2 Charge. 1.1.3 Potential or Voltage. 1.1.4 Ground. 1.1.5 Conductivity. 1.1.6 Current. 1.2 Ohm's law. 1.2.1 Resistance. 1.2.2 Conductance. 1.2.3 Insulation. 1.3 Circuit Fundamentals. 1.3.1 Static Charge. 1.3.2 Electric Circuits. 1.3.3 Voltage Drop. 1.3.4 Electric Shock. 1.4 Resistive Heating. 1.4.1 Calculating Resistive Heating. 1.4.2 Transmission Voltage and Resistive Losses. 1.5 Electric and Magnetic Fields. 1.5.1 The Field as a Concept. 1.5.2 Electric Fields. 1.5.3 Magnetic Fields. 1.5.4 Electromagnetic Induction. 1.5.5 Electromagnetic Fields and Health Effects. 1.5.6 Electromagnetic Radiation. 2. Basic Circuit Analysis. 2.1 Modeling Circuits. 2.2 Series and Parallel Circuits. 2.2.1 Resistance in Series. 2.2.2 Resistance in Parallel. 2.2.3 Network Reduction. 2.2.4 Practical Aspects. 2.3 Kirchhoff's Laws. 2.3.1 Kirchhoff's Voltage Law. 2.3.2 Kirchhoff's Current Law. 2.3.3 Application to Simple Circuits. 2.3.4 The Superposition Principle. 2.4 Magnetic Circuits. 3. AC Power. 3.1 Alternating Current and Voltage. 3.1.1 Historical Notes. 3.1.2 Mathematical Description. 3.1.3 The rms Value. 3.2 Reactance. 3.2.1 Inductance. 3.2.2 Capacitance. 3.2.3 Impedance. 3.2.4 Admittance. 3.3 Power. 3.3.1 Definition of Electric Power. 3.3.2 Complex Power. 3.3.3 The Significance of Reactive Power. 3.4 Phasor Notation. 3.4.1 Phasors as Graphics. 3.4.2 Phasors as Exponentials. 3.4.3 Operations with Phasors. 4. Generators. 4.1 The Simple Generator. 4.2 The Synchronous Generator. 4.2.1 Basic Components and Functioning. 4.2.2 Other Design Aspects. 4.3 Operational Control of Synchronous Generators. 4.3.1 Single Generator: Real Power. 4.3.2 Single Generator: Reactive Power. 4.3.3 Multiple Generators: Real Power. 4.3.4 Multiple Generators: Reactive Power. 4.4 Operating Limits. 4.5 The Induction Generator. 4.5.1 General Characteristics. 4.5.2 Electromagnetic Characteristics. 4.6 Inverters. 5. Loads. 5.1 Resistive Loads. 5.2 Motors. 5.3 Electronic Devices. 5.4 Load from the System Perspective. 5.4.1 Coincident and Noncoincident Demand. 5.4.2 Load Profiles and Load Duration Curve. 5.5 Single- and Multiphase Connections. 6. Transmission and Distribution. 6.1 System Structure. 6.1.1 Historical Notes. 6.1.2 Structural Features. 6.1.3 Sample Diagram. 6.1.4 Topology. 6.1.5 Loop Flow. 6.1.6 Stations and Substations. 6.1.7 Reconfiguring the System. 6.2 Three-Phase Transmission. 6.2.1 Rationale for Three Phases. 6.2.2 Balancing Loads. 6.2.3 Delta and Wye Connections. 6.2.4 Per-Phase Analysis. 6.2.5 Three-Phase Power. 6.2.6 D.C. Transmission. 6.3 Transformers. 6.3.1 General Properties. 6.3.2 Transformer Heating. 6.3.3 Delta and Wye Transformers. 6.4 Characteristics of Power Lines. 6.4.1 Conductors. 6.4.2 Towers, Insulators, and Other Components. 6.5 Loading. 6.5.1 Thermal Limits. 6.5.2 Stability Limit. 6.6 Voltage Control. 6.7 Protection. 6.7.1 Basics of Protection and Protective Devices. 6.7.2 Protection Coordination. 7. Power Flow Analysis. 7.1 Introduction. 7.2 The Power Flow Problem. 7.2.1 Network Representation. 7.2.2 Choice of Variables. 7.2.3 Types of Buses. 7.2.4 Variables for Balancing Real Power. 7.2.5 Variables for Balancing Reactive Power. 7.2.6 The Slack Bus. 7.2.7 Summary of Variables. 7.3 Example with Interpretation of Results. 7.3.1 Six-Bus Example. 7.3.2 Tweaking the Case. 7.3.3 Conceptualizing Power Flow. 7.4 Power Flow Equations and Solution Methods. 7.4.1 Derivation of Power Flow Equations. 7.4.2 Solution Methods. 7.4.3 Decoupled Power Flow. 7.5 Applications and Optimal Power Flow. 8. System Performance. 8.1 Reliability. 8.1.1 Measures of Reliability. 8.1.2 Valuation of Reliability. 8.2 Security. 8.3 Stability. 8.3.1 The Concept of Stability. 8.3.2 Steady-State Stability. 8.3.3 Dynamic Stability. 8.3.4 Voltage Stability. 8.4 Power Quality. 8.4.1 Voltage. 8.4.2 Frequency. 8.4.3 Waveform. 9. System Operation, Management, and New Technology. 9.1 Operation and Control on Different Time Scales. 9.1.1 The Scale of a Cycle. 9.1.2 The Scale of Real-Time Operation. 9.1.3 The Scale of Scheduling. 9.1.4 The Planning Scale. 9.2 New Technology. 9.2.1 Storage. 9.2.2 Distributed Generation. 9.2.3 Automation. 9.2.4 FACTS. 9.3 Human Factors. 9.3.1 Operators and Engineers. 9.3.2 Cognitive Representations of Power Systems. 9.3.3 Operational Criteria. 9.3.4 Implications for Technological Innovation. 9.4 Implications for Restructuring. Appendix: Symbols, Units, Abbreviations, and Acronyms. Index.

Introduction to Circuit Analysis and Design

Chapter 3 - Antenna Fundamentals II

In this paper, it is demonstrated that the photothermal beam deflection technique can be used for measuring the series resistance, optimum load resistance, and conversion efficiency of thin-film solar cells. This technique is also used for determining the carrier transport properties of an absorber and window layer of \(\hbox {CuInS}_{2}/\hbox {In}_{2}\hbox {S}_{3}/\hbox {Ag}\)-based solar cells during different stages of cell fabrication. Transport properties such as the carrier mobility, lifetime, and surface recombination velocity of the individual absorber and window layer are shown to influence the open-circuit voltage and short-circuit current of the final photovoltaic device. The cell parameters measured using the photothermal technique agree well with the electrical measurements. The principle of the technique is explained on the basis of the “mirage effect” and maximum power transfer theorem.

Most Maximum Power Point Tracking (MPPT) techniques for Photovoltaic (PV) systems require one or more of the following sensors: voltage sensor, current sensor, temperature sensor, solar irradiance sensor. This paper introduces a new MPPT control algorithm called Input Characteristic Impedance (ICI) method which uses just a single voltage sensor. The method is based on the maximum power transfer theorem, where the Input Characteristic Impedance of the DC-DC converter is modified to reach the Maximum Power Point (MPP). This technique can be applied to any DC-DC converter like Buck, Boost, Buck-Boost, Cúk, SEPIC and Zeta, and to validate the new proposal the last was chosen.

Measurement of gap junction conductance (gj) with patch-clamp amplifiers can, due to series resistance problems, be subject to considerable errors when large currents are measured. Formulas developed to correct for these errors unfortunately depend on exact estimates of series resistance, which are not always easy to obtain. Discontinuous single-electrode voltage-clamp amplifiers (DSEVCs) were shown to overcome series resistance problems in single whole cell recording. With the use of two synchronized DSEVCs, the simulated gj in a model circuit can be measured with a maximum error of <5% in all recording situations investigated (series resistance, 5-47 MOmega; membrane resistance, 20-1,000 MOmega; gj, 1-100 nS). At a very low gj of 100 pS, the error sometimes exceeded 5% (maximum of 15%), but the error was always <5% when membrane resistance was >100 MOmega. The precision of the measurements is independent of series resistance, membrane resistance, and gj. Consequently, it is possible to calculate gj directly from Ohm's law, i.e., without using correction formulas. Our results suggest that DSEVCs should be used to measure gj if large currents must be recorded, i.e., if cells are well coupled or if membrane resistance is low.

The steady-state kinetics of parallel reaction networks

Whole cell patch clamp has provided much insight into the function of voltage-gated ion channels in central neurons. However, voltage errors caused by the resistance of the recording electrode [series resistance (Rs)] limit its application to relatively small ionic currents. Ohm's law is often applied to estimate and correct the membrane potential for these voltage errors. We tested this assumption in brainstem motoneurons of adult frogs with dual patch-clamp recordings, one performing whole cell voltage clamp of K+ currents and the other directly recording the membrane potential. We hypothesized that Ohm's law-based correction would approximate the measured voltage error. We found that voltage errors averaged <5 mV for currents considered to be large for patch clamp (∼7-13 nA) and <10 mV for massive currents thought to be experimentally intractable (25-30 nA), each error falling within commonly accepted inclusion limits. In most cases Ohm's law-based correction overpredicted these measured voltage errors by roughly 2.5-fold. Consequently, the use of Ohm's law to correct for voltage errors led to erroneous current-voltage (I-V) relationships, showing the greatest distortion for inactivating currents. Finally, recordings with low electrode Rs compensated moderately by the amplifier circuitry appeared to have smaller voltage errors than those with larger Rs and high compensation despite the same "effective Rs" and current magnitude. Therefore, when Rs is low, large currents may be studied with better-than-expected voltage control. These results suggest that patch-clamp may be used to study ionic currents often interpreted to be off limits because of size.NEW & NOTEWORTHY Voltage errors occur in whole cell voltage clamp. We make, to our knowledge, the first direct measurements of these errors and find that voltage errors are far smaller than standard calculations would predict. Since voltage errors were often minimal during the measurement of large ion channel currents, this technique may be applied to large neurons of adults to gain insight into ion channel function across the life span and progression of disease.

This paper presents a discussion about a methodology to obtain the main parameters of solar panels and power converters in photovoltaic systems. This study is essential to obtain the maximum power transfer from the PV source to the mains grid or a load. It is also presented a formal mathematical model based on the experimental data acquisition of the key parameters of a photovoltaic cell which are: shunt and series resistances, photo generated current, the ideality factor and the diode reverse saturation current.

This study presents a structural analysis of NiO and the fabrication of Al/NiO/p-Si isotype heterojunction photodiode (PD) as well as an investigation of the photo-response and photovoltaic (PV) properties of the PD in the various illumination intensities. The series resistance (RS) value of the Al/NiO/p-Si was calculated to be 0.11 kΩ using Ohm's Law at 2 V. This result corroborates the assertion that RS is the pivotal parameter for attaining optimal performance in a PD. The NiO has made a notable improvement to the electrical parameters such as ideality factor, barrier height, and series resistance. The spectral responsivity R value of the PD was calculated as 1.59 A/W at -2 V. The Al/NiO/p-Si exhibited a short-circuit current density (JSC) of 60.38 mA.cm-2, an open-circuit voltage (VOC) of 149.0 mV, a fill factor (FF) of 27.67%, and a power conversion efficiency (PCE) of 2.49%. In view of the experimental results, it can be concluded that NiO represents a promising material for the optimization of heterojunction photodiode applications.

Circuit theory is one of the most important tools of the electrical engineer, and it can be derived with suitable approximations from Maxwell's equations. Despite this, university courses treat electromagnetism and circuit theory as two separate subjects and at advanced level, students can lack a basic understanding of the classical electromagnetism applied in the context of electric circuits to fully appreciate and apply circuit theory and understand its limitations. Here the authors build on their graduate teaching experiences and lectures to treat these topics as a single subject and derive and present the important results from circuit analyses, such as Kirchhoff's laws and Ohm's law, using the ideas of the classical electromagnetism

In this paper is presented new method of an integral transformation of alternating periodic functions for the determination of circuit parameters of an electromagnetic phenomenon. This integral transformation is based on the electromagnetic physical laws: Ohm's law, induction law, equation of continuity, Kirchhoff's laws and the mathematical definition of the scalar (inner) product time varying quantities. The new method is an effective tool for the production of the circuit models of electromagnetic phenomena.

Control networks are critical offices that have to be exceptionally solid and productive. Finding issues in these frameworks is exceptionally imperative for keeping them steady, keeping gear from breaking, and maintaining a strategic distance from long power blackouts. It has gotten to be clear that scientific modeling may be a awesome way to think about and discover issues in control frameworks. Taking after this strategy requires making a scientific show of the control framework, which incorporates motors, transformers, transmission lines, and loads. A few physical laws, like Ohm's Law and Kirchhoff's laws, are frequently utilized within the models to accurately show how electrical circuits work when they are working legitimately and when they aren't. On the off chance that you need to discover issues, you'll utilize numerical models to form flaws like brief circuits, open circuits, and ground deficiencies appear genuine. Through examining how the framework responds to these fake issues, engineers can make programs that can discover genuine flaws and tell where they are when they happen. These calculations frequently utilize strategies like evaluating the state, evaluating the parameters, and recognizing designs. Utilizing real-time information to figure the current state of the framework is what state estimation is all approximately. On the other hand, parameter estimation is all almost finding changes in framework parameters that might cruel there is a problem. Design acknowledgment employments past information and machine learning to discover peculiarities and sort diverse sorts of flaws. Differential conditions, framework polynomial math, and numerical strategies are fair some of the progressed science instruments that are frequently utilized to unravel the complicated conditions that depict how the power framework works. Putting these models beside unused computer innovations like high-performance computing and real-time simulations makes issue location strategies more exact and quicker. As green vitality sources and shrewd network advances are included to control frameworks, they will continue to alter. Mathematical modeling will become more and more important to make sure they work safely and efficiently. It talks about how to mathematically model electrical power systems to find faults, what they can be used for, and what the future holds for this field.

Circuit theory is one of the most important tools of the electrical engineer, and it can be derived with suitable approximations from Maxwell's equations. Despite this, university courses treat electromagnetism and circuit theory as two separate subjects and at advanced level, students can lack a basic understanding of the classical electromagnetism applied in the context of electric circuits to fully appreciate and apply circuit theory and understand its limitations. Here the authors build on their graduate teaching experiences and lectures to treat these topics as a single subject and derive and present the important results from circuit analyses, such as Kirchhoff's laws and Ohm's law, using the ideas of the classical electromagnetism