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Voltage regulator, which operates within preset current limits, acts as a circuit breaker to prevent overload failure, and automatically resets when the overload is removed. The power dissipated in the series transistor of the circuit is constant from normal load to short circuit condition.
Network reconfiguration has long been employed as a strategic approach to minimize power distribution system losses and effectively regulate voltage levels. Tap-changing voltage regulators are also critical for controlling bus voltages, especially in accommodating the increasing integration of distributed energy resources (DERs) with intermittent outputs. This paper introduces novel methodologies to address the challenges of dynamic reconfiguration and optimal tap setting in unbalanced three-phase distribution systems. We propose an approximated mixed-integer quadratically constrained program (MIQCP) to model dynamic reconfiguration, along with a pioneering formulation for voltage regulator (VR) tap-setting based on Special Ordered Set type 1 (SOS1). To mitigate computational complexity, we propose a physics-informed spatial-temporal graph convolutional network (STGCN) with an integrated link classifier. The proposed approach enables efficient solution generation by fixing specific variables in the MIQCP instance and solving the simplified sub-MIP using an MIP solver. Numerical studies demonstrate the superior prediction accuracy of our STGCN model compared to baseline neural network models, resulting in reduced DER curtailment and voltage deviation with shorter computation time.
Ripple voltage in output of switching voltage regulator reduced substantially by simple additional circuitry adding little to overall weight and size of regulator. Heretofore, additional filtering circuitry needed to obtain comparable reductions in ripple typically as large and heavy as original regulator. Current opposing ripple current injected into filter capacitor.
Voltage regulator alternately connects an inductor in parallel and in series with the input voltage source. This flyback voltage regulator provides a regulated dc voltage to varying loads from a varying dc supply and gives fast response to load and supply changes.
Circuit provides voltage regulation through a wide range of operating frequencies without intervals of high power dissipation.
Current regulating voltage divider design with load current shunting
Shunt voltage regulators for solar panels uses internal resistance of panel to control its output voltage. Regulator can be scaled up for high-current solar panels by adding more transistors.
Voltage regulator provides improved voltage-regulating system in which power dissipation and consequent heat generation are minimized. Each power source section is controlled sequentially so that only one operates in a linear range at a time.
Smart inverters (SIs) have demonstrated their potential to provide grid services for both transmission and distribution systems. One of these grid services, distribution network voltage regulation by SIs, has the potential to improve network voltage regulation through controlling the reactive and active power output of the SIs. Voltage regulation by SIs will be distributed and might be better suited to controlling local conditions to complement traditional voltage-regulating assets, e.g., tap-changing transformers, capacitor banks, and line voltage regulators. There is a gap in the literature on comparing the SI response characteristics when the SIs are controlled by a local controller or external control signals. This paper presents an experimental study to characterize SI reactive power regulation responses to two different control methods: autonomous control and remote dispatch. We found that SI reactive power regulation responses exhibit important differences between these methods in terms of delays and ramp rate. Finally, power-hardware-in-the-loop (PHIL) tests were conducted to evaluate the performance of these two methods. The PHIL test results show that the SI response characteristics for autonomous control and remote dispatch need to be considered when planning for distribution network voltage regulation using SIs.
Two simple circuits developed to provide voltage regulation for highvoltage (i.e., is greater than 75 volts) and low-voltage (i.e., is less than 36 volts) photovoltaic/battery power systems. Use of these circuits results in voltage regulator small, low-cost, and reliable, with very low power dissipation. Simple oscillator circuit controls photovoltaic-array current to regulate system voltage and control battery charging. Circuit senses battery (and system) voltage and adjusts array current to keep battery voltage from exceeding maximum voltage.
Ninety-six parallel MOSFET devices with two-stage feedback circuit form a high-current dc voltage regulator that also acts as fully-on solid-state switch when fuel-cell out-put falls below regulated voltage. Ripple voltage is less than 20 mV, transient recovery time is less than 50 ms. Parallel MOSFET's act as high-current dc regulator and switch. Regulator can be used wherever large direct currents must be controlled. Can be applied to inverters, industrial furnaces photovoltaic solar generators, dc motors, and electric autos.
Hydropower units often rely on legacy excitation systems with fixed automatic voltage regulator (AVR) settings, but parameter drift, changing operating conditions, and evolving grid-service demands can degrade terminal-voltage regulation over time. This problem should be addressed because poor voltage regulation affects reactive-power support, system stability, and reliable plant operation, and replacing installed AVR and limiter logic is costly and disruptive in practice. To address this issue, this paper proposes a retrofit-friendly enhancement for hydropower generator excitation systems that combines adaptive tuning of the AVR middle lead–lag branch with a small proportional-integral correction for removing steady bias and slow drift. The adaptive laws are normalized, projection-bounded, and equipped with leakage, while adaptation is enabled only in the middle branch. Thus, the intent of the underexcitation and overexcitation limiters, as well as legacy current and rate limits, remains unchanged. Stability is analyzed under incremental performance assumptions, yielding boundedness and asymptotic tracking in the absence of saturation. The method is evaluated using real operational data from a utility-scale hydropower unit at Rocky Reach Dam, Unit C-8. Compared with the fixed AVR baseline, the proposed method reduces terminal-voltage mean squared error by about 70%, removes long periods of steady-state bias, and improves transient tracking while coexisting with limiter actions. These results show that substantial voltage-control improvement can be achieved on existing hydropower units with minimal integration effort and without replacing the installed AVR structure.
Balanced positive and negative voltage output circuit, in which error voltage for control is developed from difference in absolute value of positive and negative voltages referenced to a common point, regulates voltage for use with inertial reference unit. Fast-acting, temperature-compensated, high-gain operational amplifier circuits maintain common point.
This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.
Regulator requires minimum amount of power for voltage division and it is not required continuously. The only power loss, except for regulating purposes, is that needed to provide for imbalances in load current requirements. For balanced loads, only leakage current flows through regulating transistors.
This viewgraph presentation reviews the use of Low Voltage Dropout (LVDO) Voltage Regulators in environments where heavy ion induced Single Event Transients are a concern to the designers.Included in the presentation are results of tests of voltage regulators.
Improvements in the efficiency and size of DC-DC converters have resulted from advances in components, primarily semiconductors, and improved topologies. One topology, which has shown very high potential in limited applications, is the Series Connected Boost Unit (SCBU), wherein a small DC-DC converter output is connected in series with the input bus to provide an output voltage equal to or greater than the input voltage. Since the DC-DC converter switches only a fraction of the power throughput, the overall system efficiency is very high. But this technique is limited to applications where the output is always greater than the input. The Series Connected Buck Boost Regulator (SCBBR) concept extends partial power processing technique used in the SCBU to operation when the desired output voltage is higher or lower than the input voltage, and the implementation described can even operate as a conventional buck converter to operate at very low output to input voltage ratios. This paper describes the operation and performance of an SCBBR configured as a bus voltage regulator providing 50 percent voltage regulation range, bus switching, and overload limiting, operating above 98 percent efficiency. The technique does not provide input-output isolation.
Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.