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Company profile

Shanghai Yingtong(YT) Electric Co., Ltd is a pioneer and leader in power quality solutions, and specialize in R&D, production and sale of Active Power Filter, Static Var Generator, Active Load Balancer, Hybrid Reactive Power Compensation and Energy Storage System.

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Hybrid Reactive Power compensation HPFC

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a hybrid dynamic reactive power compensation system was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

Hybrid Real Time PFC bank with SVG

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a hybrid Real Time PFC with SVG was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

United system of TSC and SVG

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a United system of TSC and SVG was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

Hybrid Static Var Generator

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a hybrid Static Var Generator was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

Hybrid Power Factor Correction

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a hybrid Power Factor Correction was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

SVG Hybrid Active Power Factor Correction

To meet the requirement of continuous reactive power compensation with low cost, high reliability and large capacity, a hybrid Active Power Factor Correction was proposed based on TSC (thyristor switched capacitor in parallel) and SVG (static var compensator). This system consisting of discrete subsystem TSC and continuous subsystem SVG, coordinated the capacitor switching rating of TSC with dynamic compensation of SVG by using two hybrid control laws based on expert decision making, made full use of their respective advantages. The machine learning approach was applied to avoid repeated movements of capacitors and extend equipment life.

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